diff --git a/gee_catalog.json b/gee_catalog.json
index 07184d8..876853a 100644
--- a/gee_catalog.json
+++ b/gee_catalog.json
@@ -762,7 +762,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S1_GRD')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2014-10-03",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "backscatter, copernicus, esa, eu, polarization, radar, sar, sentinel",
@@ -780,7 +780,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S2')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2015-06-27",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -56, 180, 83",
"deprecated": true,
"keywords": "copernicus, esa, eu, msi, radiance, sentinel",
@@ -798,7 +798,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY')",
"provider": "European Union/ESA/Copernicus/SentinelHub",
"state_date": "2015-06-27",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -56, 180, 83",
"deprecated": false,
"keywords": "cloud, copernicus, esa, eu, msi, radiance, sentinel, sentinelhub",
@@ -816,7 +816,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S2_HARMONIZED')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2015-06-27",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -56, 180, 83",
"deprecated": false,
"keywords": "copernicus, esa, eu, msi, radiance, sentinel",
@@ -834,7 +834,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S2_SR')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2017-03-28",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -56, 180, 83",
"deprecated": true,
"keywords": "copernicus, esa, eu, msi, reflectance, sentinel, sr",
@@ -852,7 +852,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2017-03-28",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -56, 180, 83",
"deprecated": false,
"keywords": "copernicus, esa, eu, msi, reflectance, sentinel, sr",
@@ -870,7 +870,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S3/OLCI')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2016-10-18",
- "end_date": "2025-01-08",
+ "end_date": "2025-01-09",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "copernicus, esa, eu, olci, radiance, sentinel, toa",
@@ -888,7 +888,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_AI')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-10",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai",
@@ -906,7 +906,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_LH')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-10",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai",
@@ -924,7 +924,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CLOUD')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-05",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi",
@@ -942,7 +942,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-11-22",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi",
@@ -960,7 +960,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_HCHO')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-10-02",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi",
@@ -978,7 +978,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-10",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi",
@@ -996,7 +996,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_O3')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-10",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi",
@@ -1014,7 +1014,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_SO2')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-10",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi",
@@ -1032,7 +1032,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-04",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai",
@@ -1050,7 +1050,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_LH')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-04",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai",
@@ -1068,7 +1068,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CH4')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2019-02-08",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, copernicus, esa, eu, knmi, methane, s5p, sentinel, sron, tropomi",
@@ -1086,7 +1086,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CLOUD')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-07-04",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi",
@@ -1104,7 +1104,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CO')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-06-28",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi",
@@ -1122,7 +1122,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_HCHO')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-12-05",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi",
@@ -1140,7 +1140,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-06-28",
- "end_date": "2024-12-31",
+ "end_date": "2025-01-01",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi",
@@ -1158,7 +1158,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-09-08",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi",
@@ -1176,7 +1176,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3_TCL')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-04-30",
- "end_date": "2024-12-25",
+ "end_date": "2024-12-26",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi",
@@ -1194,7 +1194,7 @@
"snippet": "ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_SO2')",
"provider": "European Union/ESA/Copernicus",
"state_date": "2018-12-05",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi",
@@ -1662,7 +1662,7 @@
"snippet": "ee.ImageCollection('ECMWF/ERA5_LAND/DAILY_AGGR')",
"provider": "Daily Aggregates: Google and Copernicus Climate Data Store",
"state_date": "1950-01-02",
- "end_date": "2025-01-02",
+ "end_date": "2025-01-03",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind",
@@ -1680,7 +1680,7 @@
"snippet": "ee.ImageCollection('ECMWF/ERA5_LAND/HOURLY')",
"provider": "Copernicus Climate Data Store",
"state_date": "1950-01-01",
- "end_date": "2025-01-03",
+ "end_date": "2025-01-04",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind",
@@ -2436,7 +2436,7 @@
"snippet": "ee.ImageCollection('FIRMS')",
"provider": "NASA / LANCE / EOSDIS",
"state_date": "2000-11-01",
- "end_date": "2025-01-08",
+ "end_date": "2025-01-09",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal",
@@ -2724,7 +2724,7 @@
"snippet": "ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED')",
"provider": "Google Earth Engine",
"state_date": "2015-06-27",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "google, cloud, sentinel2_derived",
@@ -2742,7 +2742,7 @@
"snippet": "ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1')",
"provider": "World Resources Institute",
"state_date": "2015-06-27",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "global, google, landcover, landuse, nrt, sentinel2_derived",
@@ -3030,7 +3030,7 @@
"snippet": "ee.ImageCollection('IDAHO_EPSCOR/GRIDMET')",
"provider": "University of California Merced",
"state_date": "1979-01-01",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-124.9, 24.9, -66.8, 49.6",
"deprecated": false,
"keywords": "climate, fireburning, gridmet, humidity, merced, metdata, nfdrs, precipitation, radiation, temperature, wind",
@@ -3786,7 +3786,7 @@
"snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V3')",
"provider": "Global Change Observation Mission (GCOM)",
"state_date": "2021-11-29",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index",
@@ -3840,7 +3840,7 @@
"snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V3')",
"provider": "Global Change Observation Mission (GCOM)",
"state_date": "2021-11-29",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst",
@@ -3894,7 +3894,7 @@
"snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V3')",
"provider": "Global Change Observation Mission (GCOM)",
"state_date": "2021-11-29",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color",
@@ -3948,7 +3948,7 @@
"snippet": "ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V3')",
"provider": "Global Change Observation Mission (GCOM)",
"state_date": "2018-01-22",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst",
@@ -3966,7 +3966,7 @@
"snippet": "ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/operational')",
"provider": "JAXA Earth Observation Research Center",
"state_date": "2014-03-01",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -60, 180, 60",
"deprecated": false,
"keywords": "climate, geophysical, gpm, hourly, jaxa, precipitation, weather",
@@ -4002,7 +4002,7 @@
"snippet": "ee.ImageCollection('JAXA/GPM_L3/GSMaP/v7/operational')",
"provider": "JAXA Earth Observation Research Center",
"state_date": "2014-03-01",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -60, 180, 60",
"deprecated": false,
"keywords": "climate, geophysical, gpm, hourly, jaxa, precipitation, weather",
@@ -4020,7 +4020,7 @@
"snippet": "ee.ImageCollection('JAXA/GPM_L3/GSMaP/v8/operational')",
"provider": "JAXA Earth Observation Research Center",
"state_date": "1998-01-01",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -60, 180, 60",
"deprecated": false,
"keywords": "climate, geophysical, gpm, hourly, jaxa, precipitation, weather",
@@ -5550,7 +5550,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC08/C02/T1_RT')",
"provider": "USGS",
"state_date": "2013-03-18",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "c2, global, l8, landsat, lc8, nrt, oli_tirs, radiance, rt, t1, tier1, usgs",
@@ -5568,7 +5568,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC08/C02/T1_RT_TOA')",
"provider": "USGS/Google",
"state_date": "2013-03-18",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "c2, global, l8, landsat, lc8, toa, usgs",
@@ -5658,7 +5658,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T1')",
"provider": "USGS",
"state_date": "2021-10-31",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "c2, global, l9, landsat, lc9, oli_tirs, radiance, t1, tier1, usgs",
@@ -5676,7 +5676,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T1_L2')",
"provider": "USGS",
"state_date": "2021-10-31",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs",
@@ -5694,7 +5694,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA')",
"provider": "USGS/Google",
"state_date": "2021-10-31",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "c2, global, landsat, toa, usgs",
@@ -5712,7 +5712,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T2')",
"provider": "USGS",
"state_date": "2021-11-02",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "c2, global, l9, landsat, lc9, oli_tirs, radiance, t2, tier2, usgs",
@@ -5730,7 +5730,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T2_L2')",
"provider": "USGS",
"state_date": "2021-10-31",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs",
@@ -5748,7 +5748,7 @@
"snippet": "ee.ImageCollection('LANDSAT/LC09/C02/T2_TOA')",
"provider": "USGS/Google",
"state_date": "2021-11-02",
- "end_date": "2025-01-08",
+ "end_date": "2025-01-09",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "c2, global, l9, landsat, lc9, toa, usgs",
@@ -7710,7 +7710,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MCD19A1_GRANULES')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-12-21",
- "end_date": "2025-01-06",
+ "end_date": "2025-01-07",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aerosol, aod, aqua, daily, global, maiac, modis, nasa, terra, usgs",
@@ -7728,7 +7728,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MCD19A2_GRANULES')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2025-01-06",
+ "end_date": "2025-01-07",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aerosol, aod, aqua, daily, global, maiac, mcd19a2, modis, nasa, terra, usgs",
@@ -7746,7 +7746,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MCD43A1')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2024-12-28",
+ "end_date": "2024-12-31",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "albedo, brdf, daily, global, mcd43a1, modis, nasa, reflectance, usgs",
@@ -7764,7 +7764,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MCD43A2')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2024-12-28",
+ "end_date": "2024-12-31",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "albedo, brdf, daily, global, modis, nasa, quality, reflectance, usgs",
@@ -7782,7 +7782,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MCD43A3')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2024-12-28",
+ "end_date": "2024-12-31",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "albedo, black_sky, daily, global, modis, nasa, usgs, white_sky",
@@ -7800,7 +7800,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MCD43A4')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2024-12-28",
+ "end_date": "2024-12-31",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "albedo, brdf, daily, global, modis, nasa, reflectance, usgs",
@@ -7818,7 +7818,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MCD43C3')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2024-12-28",
+ "end_date": "2024-12-31",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "albedo, black_sky, brdf, daily, global, modis, nasa, usgs, white_sky",
@@ -7890,7 +7890,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MOD09CMG')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2025-01-05",
+ "end_date": "2025-01-07",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "brightness_temperature, ozone, surface_reflectance, terra",
@@ -7908,7 +7908,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MOD09GA')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2025-01-05",
+ "end_date": "2025-01-07",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "daily, global, mod09ga, modis, nasa, sr, surface_reflectance, terra, usgs",
@@ -7926,7 +7926,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MOD09GQ')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2025-01-05",
+ "end_date": "2025-01-07",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "daily, global, mod09gq, modis, nasa, sr, surface_reflectance, terra, usgs",
@@ -7962,7 +7962,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MOD10A1')",
"provider": "NASA NSIDC DAAC at CIRES",
"state_date": "2000-02-24",
- "end_date": "2025-01-04",
+ "end_date": "2025-01-07",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "albedo, daily, geophysical, global, mod10a1, modis, nasa, nsidc, snow, terra",
@@ -7980,7 +7980,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MOD11A1')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2025-01-05",
+ "end_date": "2025-01-07",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "daily, emissivity, global, lst, mod11a1, modis, nasa, surface_temperature, terra, usgs",
@@ -8160,7 +8160,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MOD16A2')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2001-01-01",
- "end_date": "2024-12-18",
+ "end_date": "2024-12-26",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "8_day, evapotranspiration, global, mod16a2, modis, nasa",
@@ -8214,7 +8214,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MOD17A2HGF')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2021-01-01",
- "end_date": "2024-01-09",
+ "end_date": "2024-09-05",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "8_day, global, gpp, modis, nasa, photosynthesis, productivity, psn, terra, usgs",
@@ -8250,7 +8250,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MOD21A1D')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2025-01-05",
+ "end_date": "2025-01-07",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs",
@@ -8268,7 +8268,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MOD21A1N')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2025-01-06",
+ "end_date": "2025-01-07",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs",
@@ -8286,7 +8286,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MOD21C1')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2025-01-06",
+ "end_date": "2025-01-07",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs",
@@ -8376,7 +8376,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MYD09CMG')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2002-07-04",
- "end_date": "2025-01-06",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "brightness_temperature, ozone, surface_reflectance, aqua",
@@ -8394,7 +8394,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MYD09GA')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2002-07-04",
- "end_date": "2025-01-06",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aqua, daily, global, modis, myd09ga, nasa, sr, surface_reflectance, usgs",
@@ -8412,7 +8412,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MYD09GQ')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2002-07-04",
- "end_date": "2025-01-06",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aqua, daily, global, modis, myd09gq, nasa, sr, surface_reflectance, usgs",
@@ -8448,7 +8448,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MYD10A1')",
"provider": "NASA NSIDC DAAC at CIRES",
"state_date": "2002-07-04",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "albedo, aqua, daily, geophysical, global, modis, myd10a1, nasa, nsidc, snow",
@@ -8466,7 +8466,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MYD11A1')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2002-07-04",
- "end_date": "2025-01-05",
+ "end_date": "2025-01-07",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aqua, daily, emissivity, global, lst, modis, myd11a1, nasa, surface_temperature, usgs",
@@ -8682,7 +8682,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MYD21A1D')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2025-01-06",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs",
@@ -8700,7 +8700,7 @@
"snippet": "ee.ImageCollection('MODIS/061/MYD21A1N')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2000-02-24",
- "end_date": "2025-01-05",
+ "end_date": "2025-01-07",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs",
@@ -9852,7 +9852,7 @@
"snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/fcst/htf')",
"provider": "NASA / GMAO",
"state_date": "2022-10-01",
- "end_date": "2025-01-08",
+ "end_date": "2025-01-09",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "composition, forecast, geos, gmao, nasa",
@@ -9870,7 +9870,7 @@
"snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/fcst/tavg1hr')",
"provider": "NASA / GMAO",
"state_date": "2022-10-01",
- "end_date": "2025-01-08",
+ "end_date": "2025-01-09",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "composition, forecast, geos, gmao, nasa",
@@ -9888,7 +9888,7 @@
"snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/rpl/htf')",
"provider": "NASA / GMAO",
"state_date": "2018-01-01",
- "end_date": "2025-01-08",
+ "end_date": "2025-01-09",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "composition, forecast, geos, gmao, nasa",
@@ -9906,7 +9906,7 @@
"snippet": "ee.ImageCollection('NASA/GEOS-CF/v1/rpl/tavg1hr')",
"provider": "NASA / GMAO",
"state_date": "2018-01-01",
- "end_date": "2025-01-08",
+ "end_date": "2025-01-09",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "composition, forecast, geos, gmao, nasa",
@@ -10014,7 +10014,7 @@
"snippet": "ee.ImageCollection('NASA/GPM_L3/IMERG_MONTHLY_V07')",
"provider": "NASA GES DISC at NASA Goddard Space Flight Center",
"state_date": "2000-06-01",
- "end_date": "2024-06-01",
+ "end_date": "2024-07-01",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, geophysical, gpm, imerg, jaxa, monthly, nasa, precipitation, weather",
@@ -10392,7 +10392,7 @@
"snippet": "ee.ImageCollection('NASA/LANCE/NOAA20_VIIRS/C2')",
"provider": "NASA / LANCE / NOAA20_VIIRS",
"state_date": "2023-10-08",
- "end_date": "2025-01-08",
+ "end_date": "2025-01-09",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs",
@@ -10410,7 +10410,7 @@
"snippet": "ee.ImageCollection('NASA/LANCE/SNPP_VIIRS/C2')",
"provider": "NASA / LANCE / SNPP_VIIRS",
"state_date": "2023-09-03",
- "end_date": "2025-01-08",
+ "end_date": "2025-01-09",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs",
@@ -10518,7 +10518,7 @@
"snippet": "ee.ImageCollection('NASA/NLDAS/FORA0125_H002')",
"provider": "NASA GES DISC at NASA Goddard Space Flight Center",
"state_date": "1979-01-01",
- "end_date": "2025-01-05",
+ "end_date": "2025-01-06",
"bbox": "-125.15, 24.85, -66.85, 53.28",
"deprecated": false,
"keywords": "climate, evaporation, forcing, geophysical, hourly, humidity, ldas, nasa, nldas, precipitation, pressure, radiation, temperature, wind",
@@ -10698,7 +10698,7 @@
"snippet": "ee.ImageCollection('NASA/SMAP/SPL4SMGP/007')",
"provider": "Google and NSIDC",
"state_date": "2015-03-31",
- "end_date": "2025-01-06",
+ "end_date": "2025-01-07",
"bbox": "-180, -84, 180, 84",
"deprecated": false,
"keywords": "drought, nasa, smap, soil_moisture, surface, weather",
@@ -10716,7 +10716,7 @@
"snippet": "ee.ImageCollection('NASA/VIIRS/002/VNP09GA')",
"provider": "NASA Land SIPS",
"state_date": "2012-01-19",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "daily, nasa, noaa, npp, reflectance, sr, viirs, vnp09ga",
@@ -10770,7 +10770,7 @@
"snippet": "ee.ImageCollection('NASA/VIIRS/002/VNP14A1')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2012-01-19",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "fire, land, nasa, noaa, surface, viirs",
@@ -10806,7 +10806,7 @@
"snippet": "ee.ImageCollection('NASA/VIIRS/002/VNP21A1D')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2012-01-19",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "daily, day, land, nasa, noaa, surface, temperature, viirs",
@@ -10824,7 +10824,7 @@
"snippet": "ee.ImageCollection('NASA/VIIRS/002/VNP21A1N')",
"provider": "NASA LP DAAC at the USGS EROS Center",
"state_date": "2012-01-19",
- "end_date": "2025-01-06",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "daily, land, nasa, night, noaa, surface, temperature, viirs",
@@ -10896,7 +10896,7 @@
"snippet": "ee.ImageCollection('NCEP_RE/sea_level_pressure')",
"provider": "NCEP",
"state_date": "1948-01-01",
- "end_date": "2025-01-06",
+ "end_date": "2025-01-07",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "atmosphere, climate, geophysical, ncep, noaa, pressure, reanalysis",
@@ -10914,7 +10914,7 @@
"snippet": "ee.ImageCollection('NCEP_RE/surface_temp')",
"provider": "NCEP",
"state_date": "1948-01-01",
- "end_date": "2025-01-06",
+ "end_date": "2025-01-07",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "atmosphere, climate, geophysical, ncep, noaa, reanalysis, temperature",
@@ -10932,7 +10932,7 @@
"snippet": "ee.ImageCollection('NCEP_RE/surface_wv')",
"provider": "NCEP",
"state_date": "1948-01-01",
- "end_date": "2025-01-06",
+ "end_date": "2025-01-07",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "atmosphere, climate, geophysical, ncep, noaa, precipitable, reanalysis, vapor",
@@ -11166,7 +11166,7 @@
"snippet": "ee.ImageCollection('NOAA/CDR/OISST/V2_1')",
"provider": "NOAA",
"state_date": "1981-09-01",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature",
@@ -11238,7 +11238,7 @@
"snippet": "ee.ImageCollection('NOAA/CFSR')",
"provider": "NOAA NWS National Centers for Environmental Prediction (NCEP)",
"state_date": "2018-12-13",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather",
@@ -11256,7 +11256,7 @@
"snippet": "ee.ImageCollection('NOAA/CFSV2/FOR6H')",
"provider": "NOAA NWS National Centers for Environmental Prediction (NCEP)",
"state_date": "1979-01-01",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather",
@@ -11274,7 +11274,7 @@
"snippet": "ee.ImageCollection('NOAA/CPC/Precipitation')",
"provider": "NOAA Physical Sciences Laboratory",
"state_date": "2006-01-01",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "daily, noaa, precipitation, weather",
@@ -11292,7 +11292,7 @@
"snippet": "ee.ImageCollection('NOAA/CPC/Temperature')",
"provider": "NOAA Physical Sciences Laboratory",
"state_date": "1979-01-01",
- "end_date": "2025-01-08",
+ "end_date": "2025-01-09",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "daily, noaa, precipitation, weather",
@@ -11346,7 +11346,7 @@
"snippet": "ee.ImageCollection('NOAA/GFS0P25')",
"provider": "NOAA/NCEP/EMC",
"state_date": "2015-07-01",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "climate, cloud, emc, flux, forecast, geophysical, gfs, humidity, ncep, noaa, precipitation, radiation, temperature, vapor, weather, wind",
@@ -11364,7 +11364,7 @@
"snippet": "ee.ImageCollection('NOAA/GOES/16/FDCC')",
"provider": "NOAA",
"state_date": "2017-05-24",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-152.11, 14, -49.18, 56.77",
"deprecated": false,
"keywords": "abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire",
@@ -11382,7 +11382,7 @@
"snippet": "ee.ImageCollection('NOAA/GOES/16/FDCF')",
"provider": "NOAA",
"state_date": "2017-05-24",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire",
@@ -11400,7 +11400,7 @@
"snippet": "ee.ImageCollection('NOAA/GOES/16/MCMIPC')",
"provider": "NOAA",
"state_date": "2017-07-10",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-152.11, 14, -49.18, 56.77",
"deprecated": false,
"keywords": "abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather",
@@ -11418,7 +11418,7 @@
"snippet": "ee.ImageCollection('NOAA/GOES/16/MCMIPF')",
"provider": "NOAA",
"state_date": "2017-07-10",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather",
@@ -11436,7 +11436,7 @@
"snippet": "ee.ImageCollection('NOAA/GOES/16/MCMIPM')",
"provider": "NOAA",
"state_date": "2017-07-10",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather",
@@ -11544,7 +11544,7 @@
"snippet": "ee.ImageCollection('NOAA/GOES/18/FDCC')",
"provider": "NOAA",
"state_date": "2022-10-13",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, 14.57, 180, 53.51",
"deprecated": false,
"keywords": "abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire",
@@ -11562,7 +11562,7 @@
"snippet": "ee.ImageCollection('NOAA/GOES/18/FDCF')",
"provider": "NOAA",
"state_date": "2022-10-13",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire",
@@ -11580,7 +11580,7 @@
"snippet": "ee.ImageCollection('NOAA/GOES/18/MCMIPC')",
"provider": "NOAA",
"state_date": "2018-12-04",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, 14.57, 180, 53.51",
"deprecated": false,
"keywords": "abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather",
@@ -11598,7 +11598,7 @@
"snippet": "ee.ImageCollection('NOAA/GOES/18/MCMIPF')",
"provider": "NOAA",
"state_date": "2018-12-04",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather",
@@ -11616,7 +11616,7 @@
"snippet": "ee.ImageCollection('NOAA/GOES/18/MCMIPM')",
"provider": "NOAA",
"state_date": "2018-12-04",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather",
@@ -11724,7 +11724,7 @@
"snippet": "ee.ImageCollection('NOAA/NWS/RTMA')",
"provider": "NOAA/NWS",
"state_date": "2011-01-01",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-130.17, 20.15, -60.81, 52.91",
"deprecated": false,
"keywords": "climate, cloud, geophysical, humidity, noaa, nws, precipitation, pressure, rtma, surface, temperature, visibility, weather, wind",
@@ -12102,7 +12102,7 @@
"snippet": "ee.ImageCollection('OREGONSTATE/PRISM/AN81d')",
"provider": "PRISM / OREGONSTATE",
"state_date": "1981-01-01",
- "end_date": "2025-01-06",
+ "end_date": "2025-01-07",
"bbox": "-125, 24, -66, 50",
"deprecated": false,
"keywords": "climate, daily, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather",
@@ -13236,7 +13236,7 @@
"snippet": "ee.ImageCollection('TOMS/MERGED')",
"provider": "NASA / GES DISC",
"state_date": "1978-11-01",
- "end_date": "2025-01-07",
+ "end_date": "2025-01-08",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "atmosphere, aura, climate, geophysical, ges_disc, goddard, nasa, omi, ozone, toms",
@@ -14838,7 +14838,7 @@
"snippet": "ee.ImageCollection('UTOKYO/WTLAB/KBDI/v1')",
"provider": "Institute of Industrial Science, The University of Tokyo, Japan",
"state_date": "2007-01-01",
- "end_date": "2025-01-08",
+ "end_date": "2025-01-09",
"bbox": "60, -60, 180, 60",
"deprecated": false,
"keywords": "drought, kbdi, lst_derived, rainfall, utokyo, wtlab",
@@ -16224,7 +16224,7 @@
"snippet": "ee.ImageCollection('projects/gcp-public-data-weathernext/assets/59572747_4_0')",
"provider": "Google",
"state_date": "2020-01-01",
- "end_date": "2025-01-09",
+ "end_date": "2025-01-10",
"bbox": "-180, -90, 180, 90",
"deprecated": false,
"keywords": "weather, weathernext, forecast, temperature, precipitation, wind",
diff --git a/gee_catalog.tsv b/gee_catalog.tsv
index 89bd4f1..8db3cb3 100644
--- a/gee_catalog.tsv
+++ b/gee_catalog.tsv
@@ -41,31 +41,31 @@ COPERNICUS/CORINE/V20/100m Copernicus CORINE Land Cover image_collection ee.Imag
COPERNICUS/DEM/GLO30 Copernicus DEM GLO-30: Global 30m Digital Elevation Model image_collection ee.ImageCollection('COPERNICUS/DEM/GLO30') Copernicus 2010-12-01 2015-01-31 -180, -90, 180, 90 False copernicus, dem, elevation, geophysical https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_DEM_GLO30.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_DEM_GLO30 proprietary
COPERNICUS/Landcover/100m/Proba-V-C3/Global Copernicus Global Land Cover Layers: CGLS-LC100 Collection 3 image_collection ee.ImageCollection('COPERNICUS/Landcover/100m/Proba-V-C3/Global') Copernicus 2015-01-01 2019-12-31 -180, -90, 180, 90 False copernicus, eea, esa, eu, landcover, proba, probav, vito https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_Landcover_100m_Proba-V-C3_Global.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global proprietary
COPERNICUS/Landcover/100m/Proba-V/Global Copernicus Global Land Cover Layers: CGLS-LC100 Collection 2 [deprecated] image_collection ee.ImageCollection('COPERNICUS/Landcover/100m/Proba-V/Global') Copernicus 2015-01-01 2015-01-01 -180, -90, 180, 90 True copernicus, eea, esa, eu, landcover, proba, probav, vito https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_Landcover_100m_Proba-V_Global.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V_Global proprietary
-COPERNICUS/S1_GRD Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected, log scaling image_collection ee.ImageCollection('COPERNICUS/S1_GRD') European Union/ESA/Copernicus 2014-10-03 2025-01-09 -180, -90, 180, 90 False backscatter, copernicus, esa, eu, polarization, radar, sar, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S1_GRD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD proprietary
-COPERNICUS/S2 Sentinel-2 MSI: MultiSpectral Instrument, Level-1C [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2') European Union/ESA/Copernicus 2015-06-27 2025-01-09 -180, -56, 180, 83 True copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 proprietary
-COPERNICUS/S2_CLOUD_PROBABILITY Sentinel-2: Cloud Probability image_collection ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY') European Union/ESA/Copernicus/SentinelHub 2015-06-27 2025-01-09 -180, -56, 180, 83 False cloud, copernicus, esa, eu, msi, radiance, sentinel, sentinelhub https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_CLOUD_PROBABILITY.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY proprietary
-COPERNICUS/S2_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-1C image_collection ee.ImageCollection('COPERNICUS/S2_HARMONIZED') European Union/ESA/Copernicus 2015-06-27 2025-01-09 -180, -56, 180, 83 False copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED proprietary
-COPERNICUS/S2_SR Sentinel-2 MSI: MultiSpectral Instrument, Level-2A [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2_SR') European Union/ESA/Copernicus 2017-03-28 2025-01-09 -180, -56, 180, 83 True copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR proprietary
-COPERNICUS/S2_SR_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-2A image_collection ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') European Union/ESA/Copernicus 2017-03-28 2025-01-09 -180, -56, 180, 83 False copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED proprietary
-COPERNICUS/S3/OLCI Sentinel-3 OLCI EFR: Ocean and Land Color Instrument Earth Observation Full Resolution image_collection ee.ImageCollection('COPERNICUS/S3/OLCI') European Union/ESA/Copernicus 2016-10-18 2025-01-08 -180, -90, 180, 90 False copernicus, esa, eu, olci, radiance, sentinel, toa https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S3_OLCI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S3_OLCI proprietary
-COPERNICUS/S5P/NRTI/L3_AER_AI Sentinel-5P NRTI AER AI: Near Real-Time UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_AI') European Union/ESA/Copernicus 2018-07-10 2025-01-09 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_AI proprietary
-COPERNICUS/S5P/NRTI/L3_AER_LH Sentinel-5P NRTI AER LH: Near Real-Time UV Aerosol Layer Height image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_LH') European Union/ESA/Copernicus 2018-07-10 2025-01-09 -180, -90, 180, 90 False aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_LH.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_LH proprietary
-COPERNICUS/S5P/NRTI/L3_CLOUD Sentinel-5P NRTI CLOUD: Near Real-Time Cloud image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CLOUD') European Union/ESA/Copernicus 2018-07-05 2025-01-09 -180, -90, 180, 90 False climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CLOUD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CLOUD proprietary
-COPERNICUS/S5P/NRTI/L3_CO Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO') European Union/ESA/Copernicus 2018-11-22 2025-01-09 -180, -90, 180, 90 False air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CO proprietary
-COPERNICUS/S5P/NRTI/L3_HCHO Sentinel-5P NRTI HCHO: Near Real-Time Formaldehyde image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_HCHO') European Union/ESA/Copernicus 2018-10-02 2025-01-09 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_HCHO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_HCHO proprietary
-COPERNICUS/S5P/NRTI/L3_NO2 Sentinel-5P NRTI NO2: Near Real-Time Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2') European Union/ESA/Copernicus 2018-07-10 2025-01-09 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_NO2 proprietary
-COPERNICUS/S5P/NRTI/L3_O3 Sentinel-5P NRTI O3: Near Real-Time Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_O3') European Union/ESA/Copernicus 2018-07-10 2025-01-09 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_O3.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_O3 proprietary
-COPERNICUS/S5P/NRTI/L3_SO2 Sentinel-5P NRTI SO2: Near Real-Time Sulfur Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_SO2') European Union/ESA/Copernicus 2018-07-10 2025-01-09 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_SO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_SO2 proprietary
-COPERNICUS/S5P/OFFL/L3_AER_AI Sentinel-5P OFFL AER AI: Offline UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI') European Union/ESA/Copernicus 2018-07-04 2025-01-07 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_AI proprietary
-COPERNICUS/S5P/OFFL/L3_AER_LH Sentinel-5P OFFL AER LH: Offline UV Aerosol Layer Height image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_LH') European Union/ESA/Copernicus 2018-07-04 2025-01-07 -180, -90, 180, 90 False aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_LH.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_LH proprietary
-COPERNICUS/S5P/OFFL/L3_CH4 Sentinel-5P OFFL CH4: Offline Methane image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CH4') European Union/ESA/Copernicus 2019-02-08 2025-01-07 -180, -90, 180, 90 False climate, copernicus, esa, eu, knmi, methane, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CH4.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CH4 proprietary
-COPERNICUS/S5P/OFFL/L3_CLOUD Sentinel-5P OFFL CLOUD: Near Real-Time Cloud image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CLOUD') European Union/ESA/Copernicus 2018-07-04 2025-01-07 -180, -90, 180, 90 False climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CLOUD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CLOUD proprietary
-COPERNICUS/S5P/OFFL/L3_CO Sentinel-5P OFFL CO: Offline Carbon Monoxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CO') European Union/ESA/Copernicus 2018-06-28 2025-01-07 -180, -90, 180, 90 False air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CO proprietary
-COPERNICUS/S5P/OFFL/L3_HCHO Sentinel-5P OFFL HCHO: Offline Formaldehyde image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_HCHO') European Union/ESA/Copernicus 2018-12-05 2025-01-07 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_HCHO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO proprietary
-COPERNICUS/S5P/OFFL/L3_NO2 Sentinel-5P OFFL NO2: Offline Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2') European Union/ESA/Copernicus 2018-06-28 2024-12-31 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2 proprietary
-COPERNICUS/S5P/OFFL/L3_O3 Sentinel-5P OFFL O3: Offline Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3') European Union/ESA/Copernicus 2018-09-08 2025-01-07 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3 proprietary
-COPERNICUS/S5P/OFFL/L3_O3_TCL Sentinel-5P OFFL O3 TCL: Offline Tropospheric Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3_TCL') European Union/ESA/Copernicus 2018-04-30 2024-12-25 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3_TCL.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3_TCL proprietary
-COPERNICUS/S5P/OFFL/L3_SO2 Sentinel-5P OFFL SO2: Offline Sulfur Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_SO2') European Union/ESA/Copernicus 2018-12-05 2025-01-07 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_SO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2 proprietary
+COPERNICUS/S1_GRD Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected, log scaling image_collection ee.ImageCollection('COPERNICUS/S1_GRD') European Union/ESA/Copernicus 2014-10-03 2025-01-10 -180, -90, 180, 90 False backscatter, copernicus, esa, eu, polarization, radar, sar, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S1_GRD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S1_GRD proprietary
+COPERNICUS/S2 Sentinel-2 MSI: MultiSpectral Instrument, Level-1C [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2') European Union/ESA/Copernicus 2015-06-27 2025-01-10 -180, -56, 180, 83 True copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 proprietary
+COPERNICUS/S2_CLOUD_PROBABILITY Sentinel-2: Cloud Probability image_collection ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY') European Union/ESA/Copernicus/SentinelHub 2015-06-27 2025-01-10 -180, -56, 180, 83 False cloud, copernicus, esa, eu, msi, radiance, sentinel, sentinelhub https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_CLOUD_PROBABILITY.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_CLOUD_PROBABILITY proprietary
+COPERNICUS/S2_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-1C image_collection ee.ImageCollection('COPERNICUS/S2_HARMONIZED') European Union/ESA/Copernicus 2015-06-27 2025-01-10 -180, -56, 180, 83 False copernicus, esa, eu, msi, radiance, sentinel https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED proprietary
+COPERNICUS/S2_SR Sentinel-2 MSI: MultiSpectral Instrument, Level-2A [deprecated] image_collection ee.ImageCollection('COPERNICUS/S2_SR') European Union/ESA/Copernicus 2017-03-28 2025-01-10 -180, -56, 180, 83 True copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR proprietary
+COPERNICUS/S2_SR_HARMONIZED Harmonized Sentinel-2 MSI: MultiSpectral Instrument, Level-2A image_collection ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED') European Union/ESA/Copernicus 2017-03-28 2025-01-10 -180, -56, 180, 83 False copernicus, esa, eu, msi, reflectance, sentinel, sr https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S2_SR_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED proprietary
+COPERNICUS/S3/OLCI Sentinel-3 OLCI EFR: Ocean and Land Color Instrument Earth Observation Full Resolution image_collection ee.ImageCollection('COPERNICUS/S3/OLCI') European Union/ESA/Copernicus 2016-10-18 2025-01-09 -180, -90, 180, 90 False copernicus, esa, eu, olci, radiance, sentinel, toa https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S3_OLCI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S3_OLCI proprietary
+COPERNICUS/S5P/NRTI/L3_AER_AI Sentinel-5P NRTI AER AI: Near Real-Time UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_AI') European Union/ESA/Copernicus 2018-07-10 2025-01-10 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_AI proprietary
+COPERNICUS/S5P/NRTI/L3_AER_LH Sentinel-5P NRTI AER LH: Near Real-Time UV Aerosol Layer Height image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_AER_LH') European Union/ESA/Copernicus 2018-07-10 2025-01-10 -180, -90, 180, 90 False aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_AER_LH.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_AER_LH proprietary
+COPERNICUS/S5P/NRTI/L3_CLOUD Sentinel-5P NRTI CLOUD: Near Real-Time Cloud image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CLOUD') European Union/ESA/Copernicus 2018-07-05 2025-01-10 -180, -90, 180, 90 False climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CLOUD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CLOUD proprietary
+COPERNICUS/S5P/NRTI/L3_CO Sentinel-5P NRTI CO: Near Real-Time Carbon Monoxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_CO') European Union/ESA/Copernicus 2018-11-22 2025-01-10 -180, -90, 180, 90 False air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_CO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_CO proprietary
+COPERNICUS/S5P/NRTI/L3_HCHO Sentinel-5P NRTI HCHO: Near Real-Time Formaldehyde image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_HCHO') European Union/ESA/Copernicus 2018-10-02 2025-01-10 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_HCHO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_HCHO proprietary
+COPERNICUS/S5P/NRTI/L3_NO2 Sentinel-5P NRTI NO2: Near Real-Time Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_NO2') European Union/ESA/Copernicus 2018-07-10 2025-01-10 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_NO2 proprietary
+COPERNICUS/S5P/NRTI/L3_O3 Sentinel-5P NRTI O3: Near Real-Time Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_O3') European Union/ESA/Copernicus 2018-07-10 2025-01-10 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_O3.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_O3 proprietary
+COPERNICUS/S5P/NRTI/L3_SO2 Sentinel-5P NRTI SO2: Near Real-Time Sulfur Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/NRTI/L3_SO2') European Union/ESA/Copernicus 2018-07-10 2025-01-10 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_NRTI_L3_SO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_NRTI_L3_SO2 proprietary
+COPERNICUS/S5P/OFFL/L3_AER_AI Sentinel-5P OFFL AER AI: Offline UV Aerosol Index image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_AI') European Union/ESA/Copernicus 2018-07-04 2025-01-08 -180, -90, 180, 90 False aai, aerosol, air_quality, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_AI.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_AI proprietary
+COPERNICUS/S5P/OFFL/L3_AER_LH Sentinel-5P OFFL AER LH: Offline UV Aerosol Layer Height image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_AER_LH') European Union/ESA/Copernicus 2018-07-04 2025-01-08 -180, -90, 180, 90 False aerosol, air_quality, alh, copernicus, esa, eu, knmi, pollution, s5p, sentinel, tropomi, uvai https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_AER_LH.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_AER_LH proprietary
+COPERNICUS/S5P/OFFL/L3_CH4 Sentinel-5P OFFL CH4: Offline Methane image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CH4') European Union/ESA/Copernicus 2019-02-08 2025-01-08 -180, -90, 180, 90 False climate, copernicus, esa, eu, knmi, methane, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CH4.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CH4 proprietary
+COPERNICUS/S5P/OFFL/L3_CLOUD Sentinel-5P OFFL CLOUD: Near Real-Time Cloud image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CLOUD') European Union/ESA/Copernicus 2018-07-04 2025-01-08 -180, -90, 180, 90 False climate, cloud, copernicus, dlr, esa, eu, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CLOUD.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CLOUD proprietary
+COPERNICUS/S5P/OFFL/L3_CO Sentinel-5P OFFL CO: Offline Carbon Monoxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_CO') European Union/ESA/Copernicus 2018-06-28 2025-01-08 -180, -90, 180, 90 False air_quality, carbon_monoxide, copernicus, esa, eu, knmi, pollution, s5p, sentinel, sron, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_CO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CO proprietary
+COPERNICUS/S5P/OFFL/L3_HCHO Sentinel-5P OFFL HCHO: Offline Formaldehyde image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_HCHO') European Union/ESA/Copernicus 2018-12-05 2025-01-08 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, formaldehyde, hcho, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_HCHO.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO proprietary
+COPERNICUS/S5P/OFFL/L3_NO2 Sentinel-5P OFFL NO2: Offline Nitrogen Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_NO2') European Union/ESA/Copernicus 2018-06-28 2025-01-01 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, knmi, nitrogen_dioxide, no2, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_NO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2 proprietary
+COPERNICUS/S5P/OFFL/L3_O3 Sentinel-5P OFFL O3: Offline Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3') European Union/ESA/Copernicus 2018-09-08 2025-01-08 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3 proprietary
+COPERNICUS/S5P/OFFL/L3_O3_TCL Sentinel-5P OFFL O3 TCL: Offline Tropospheric Ozone image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_O3_TCL') European Union/ESA/Copernicus 2018-04-30 2024-12-26 -180, -90, 180, 90 False air_quality, copernicus, esa, eu, o3, ozone, pollution, s5p, sentinel, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_O3_TCL.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3_TCL proprietary
+COPERNICUS/S5P/OFFL/L3_SO2 Sentinel-5P OFFL SO2: Offline Sulfur Dioxide image_collection ee.ImageCollection('COPERNICUS/S5P/OFFL/L3_SO2') European Union/ESA/Copernicus 2018-12-05 2025-01-08 -180, -90, 180, 90 False air_quality, bira, copernicus, dlr, esa, eu, pollution, s5p, sentinel, so2, sulfur_dioxide, tropomi https://storage.googleapis.com/earthengine-stac/catalog/COPERNICUS/COPERNICUS_S5P_OFFL_L3_SO2.json https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2 proprietary
CPOM/CryoSat2/ANTARCTICA_DEM CryoSat-2 Antarctica 1km DEM image ee.Image('CPOM/CryoSat2/ANTARCTICA_DEM') CPOM 2010-07-01 2016-07-01 -180, -88, 180, -60 False antarctica, cpom, cryosat_2, dem, elevation, polar https://storage.googleapis.com/earthengine-stac/catalog/CPOM/CPOM_CryoSat2_ANTARCTICA_DEM.json https://developers.google.com/earth-engine/datasets/catalog/CPOM_CryoSat2_ANTARCTICA_DEM proprietary
CSIC/SPEI/2_8 SPEIbase: Standardised Precipitation-Evapotranspiration Index database, Version 2.8 [deprecated] image_collection ee.ImageCollection('CSIC/SPEI/2_8') Spanish National Research Council (CSIC) 1901-01-01 2021-01-01 -180, -90, 180, 90 True climate, climate_change, drought, evapotranspiration, global, monthly, palmer, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_8.json https://developers.google.com/earth-engine/datasets/catalog/CSIC_SPEI_2_8 CC-BY-4.0
CSIC/SPEI/2_9 SPEIbase: Standardised Precipitation-Evapotranspiration Index database, Version 2.9 image_collection ee.ImageCollection('CSIC/SPEI/2_9') Spanish National Research Council (CSIC) 1901-01-01 2023-01-01 -180, -90, 180, 90 False climate, climate_change, drought, evapotranspiration, global, monthly, palmer, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/CSIC/CSIC_SPEI_2_9.json https://developers.google.com/earth-engine/datasets/catalog/CSIC_SPEI_2_9 CC-BY-4.0
@@ -91,8 +91,8 @@ DOE/ORNL/LandScan_HD/Ukraine_202201 LandScan High Definition Data for Ukraine, J
ECMWF/CAMS/NRT Copernicus Atmosphere Monitoring Service (CAMS) Global Near-Real-Time image_collection ee.ImageCollection('ECMWF/CAMS/NRT') European Centre for Medium-Range Weather Forecasts (ECMWF) 2016-06-22 2024-12-03 -180, -90, 180, 90 False aerosol, atmosphere, climate, copernicus, ecmwf, forecast, particulate_matter https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_CAMS_NRT.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_CAMS_NRT proprietary
ECMWF/ERA5/DAILY ERA5 Daily Aggregates - Latest Climate Reanalysis Produced by ECMWF / Copernicus Climate Change Service image_collection ee.ImageCollection('ECMWF/ERA5/DAILY') ECMWF / Copernicus Climate Change Service 1979-01-02 2020-07-09 -180, -90, 180, 90 False climate, copernicus, dewpoint, ecmwf, era5, precipitation, pressure, reanalysis, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_DAILY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_DAILY proprietary
ECMWF/ERA5/MONTHLY ERA5 Monthly Aggregates - Latest Climate Reanalysis Produced by ECMWF / Copernicus Climate Change Service image_collection ee.ImageCollection('ECMWF/ERA5/MONTHLY') ECMWF / Copernicus Climate Change Service 1979-01-01 2020-06-01 -180, -90, 180, 90 False climate, copernicus, dewpoint, ecmwf, era5, precipitation, pressure, reanalysis, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_MONTHLY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_MONTHLY proprietary
-ECMWF/ERA5_LAND/DAILY_AGGR ERA5-Land Daily Aggregated - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/DAILY_AGGR') Daily Aggregates: Google and Copernicus Climate Data Store 1950-01-02 2025-01-02 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_DAILY_AGGR.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_DAILY_AGGR proprietary
-ECMWF/ERA5_LAND/HOURLY ERA5-Land Hourly - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/HOURLY') Copernicus Climate Data Store 1950-01-01 2025-01-03 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_HOURLY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY proprietary
+ECMWF/ERA5_LAND/DAILY_AGGR ERA5-Land Daily Aggregated - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/DAILY_AGGR') Daily Aggregates: Google and Copernicus Climate Data Store 1950-01-02 2025-01-03 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_DAILY_AGGR.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_DAILY_AGGR proprietary
+ECMWF/ERA5_LAND/HOURLY ERA5-Land Hourly - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/HOURLY') Copernicus Climate Data Store 1950-01-01 2025-01-04 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_HOURLY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_HOURLY proprietary
ECMWF/ERA5_LAND/MONTHLY ERA5-Land Monthly Averaged - ECMWF Climate Reanalysis [deprecated] image_collection ee.ImageCollection('ECMWF/ERA5_LAND/MONTHLY') Copernicus Climate Data Store 1950-02-01 2023-04-01 -180, -90, 180, 90 True cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_MONTHLY.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY proprietary
ECMWF/ERA5_LAND/MONTHLY_AGGR ERA5-Land Monthly Aggregated - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/MONTHLY_AGGR') Monthly Aggregates: Google and Copernicus Climate Data Store 1950-02-01 2024-12-01 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_MONTHLY_AGGR.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_AGGR proprietary
ECMWF/ERA5_LAND/MONTHLY_BY_HOUR ERA5-Land Monthly Averaged by Hour of Day - ECMWF Climate Reanalysis image_collection ee.ImageCollection('ECMWF/ERA5_LAND/MONTHLY_BY_HOUR') Climate Data Store 1950-01-01 2024-12-01 -180, -90, 180, 90 False cds, climate, copernicus, ecmwf, era5_land, evaporation, heat, lakes, precipitation, pressure, radiation, reanalysis, runoff, snow, soil_water, temperature, vegetation, wind https://storage.googleapis.com/earthengine-stac/catalog/ECMWF/ECMWF_ERA5_LAND_MONTHLY_BY_HOUR.json https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_BY_HOUR proprietary
@@ -134,7 +134,7 @@ FAO/WAPOR/2/L1_NPP_D WAPOR Dekadal Net Primary Production 2.0 image_collection e
FAO/WAPOR/2/L1_RET_D WAPOR Dekadal Reference Evapotranspiration 2.0 image_collection ee.ImageCollection('FAO/WAPOR/2/L1_RET_D') FAO UN 2009-01-01 2023-03-11 -30.15, -39.9953437, 65.13, 40.0044643 False agriculture, fao, wapor, water https://storage.googleapis.com/earthengine-stac/catalog/FAO/FAO_WAPOR_2_L1_RET_D.json https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_RET_D proprietary
FAO/WAPOR/2/L1_RET_E WAPOR Daily Reference Evapotranspiration 2.0 image_collection ee.ImageCollection('FAO/WAPOR/2/L1_RET_E') FAO UN 2009-01-01 2023-03-20 -30.15, -39.9953437, 65.13, 40.0044643 False agriculture, fao, wapor, water https://storage.googleapis.com/earthengine-stac/catalog/FAO/FAO_WAPOR_2_L1_RET_E.json https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_RET_E proprietary
FAO/WAPOR/2/L1_T_D WAPOR Dekadal Transpiration 2.0 image_collection ee.ImageCollection('FAO/WAPOR/2/L1_T_D') FAO UN 2009-01-01 2023-03-01 -30.0044643, -40.0044644, 65.0044644, 40.0044643 False agriculture, fao, wapor, water https://storage.googleapis.com/earthengine-stac/catalog/FAO/FAO_WAPOR_2_L1_T_D.json https://developers.google.com/earth-engine/datasets/catalog/FAO_WAPOR_2_L1_T_D proprietary
-FIRMS FIRMS: Fire Information for Resource Management System image_collection ee.ImageCollection('FIRMS') NASA / LANCE / EOSDIS 2000-11-01 2025-01-08 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal https://storage.googleapis.com/earthengine-stac/catalog/FIRMS/FIRMS.json https://developers.google.com/earth-engine/datasets/catalog/FIRMS proprietary
+FIRMS FIRMS: Fire Information for Resource Management System image_collection ee.ImageCollection('FIRMS') NASA / LANCE / EOSDIS 2000-11-01 2025-01-09 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal https://storage.googleapis.com/earthengine-stac/catalog/FIRMS/FIRMS.json https://developers.google.com/earth-engine/datasets/catalog/FIRMS proprietary
FORMA/FORMA_500m FORMA Global Forest Watch Deforestation Alerts, 500m [deprecated] image ee.Image('FORMA/FORMA_500m') Global Forest Watch, World Resources Institute 2006-01-01 2015-06-10 -180, -90, 180, 90 True alerts, deforestation, forest, forma, geophysical, gfw, modis, nasa, wri https://storage.googleapis.com/earthengine-stac/catalog/FORMA/FORMA_FORMA_500m.json https://developers.google.com/earth-engine/datasets/catalog/FORMA_FORMA_500m proprietary
Finland/MAVI/VV/50cm Finland NRG NLS orthophotos 50 cm by Mavi image_collection ee.ImageCollection('Finland/MAVI/VV/50cm') NLS orthophotos 2015-01-01 2018-01-01 18, 59, 29.2, 69.4 False falsecolor, finland, mavi, nrg, orthophoto https://storage.googleapis.com/earthengine-stac/catalog/Finland/Finland_MAVI_VV_50cm.json https://developers.google.com/earth-engine/datasets/catalog/Finland_MAVI_VV_50cm CC-BY-4.0
Finland/SMK/V/50cm Finland RGB NLS orthophotos 50 cm by SMK image_collection ee.ImageCollection('Finland/SMK/V/50cm') NLS orthophotos 2015-01-01 2023-01-01 18, 59, 29.2, 69.4 False finland, orthophoto, rgb, smk https://storage.googleapis.com/earthengine-stac/catalog/Finland/Finland_SMK_V_50cm.json https://developers.google.com/earth-engine/datasets/catalog/Finland_SMK_V_50cm proprietary
@@ -150,8 +150,8 @@ GLIMS/20230607 GLIMS 2023: Global Land Ice Measurements From Space table ee.Feat
GLIMS/current GLIMS Current: Global Land Ice Measurements From Space table ee.FeatureCollection('GLIMS/current') National Snow and Ice Data Center (NSDIC) 1750-01-01 2023-06-07 -180, -90, 180, 90 False glacier, glims, ice, landcover, nasa, nsidc, snow https://storage.googleapis.com/earthengine-stac/catalog/GLIMS/GLIMS_current.json https://developers.google.com/earth-engine/datasets/catalog/GLIMS_current proprietary
GLOBAL_FLOOD_DB/MODIS_EVENTS/V1 Global Flood Database v1 (2000-2018) image_collection ee.ImageCollection('GLOBAL_FLOOD_DB/MODIS_EVENTS/V1') Cloud to Street (C2S) / Dartmouth Flood Observatory (DFO) 2000-02-17 2018-12-10 -180, -90, 180, 90 False c2s, cloudtostreet, dartmouth, dfo, flood, gfd, inundation, surface, water https://storage.googleapis.com/earthengine-stac/catalog/GLOBAL_FLOOD_DB/GLOBAL_FLOOD_DB_MODIS_EVENTS_V1.json https://developers.google.com/earth-engine/datasets/catalog/GLOBAL_FLOOD_DB_MODIS_EVENTS_V1 CC-BY-NC-4.0
GOOGLE/AirView/California_Unified_2015_2019 Google Street View Air Quality: High Resolution Air Pollution Mapping in California table ee.FeatureCollection('GOOGLE/AirView/California_Unified_2015_2019') Google / Aclima 2015-05-28 2019-06-07 -180, -90, 180, 90 False air_quality, nitrogen_dioxide, pollution https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_AirView_California_Unified_2015_2019.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_AirView_California_Unified_2015_2019 CC-BY-NC-4.0
-GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED Cloud Score+ S2_HARMONIZED V1 image_collection ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED') Google Earth Engine 2015-06-27 2025-01-09 -180, -90, 180, 90 False google, cloud, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED CC-BY-4.0
-GOOGLE/DYNAMICWORLD/V1 Dynamic World V1 image_collection ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1') World Resources Institute 2015-06-27 2025-01-09 -180, -90, 180, 90 False global, google, landcover, landuse, nrt, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_DYNAMICWORLD_V1.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1 CC-BY-4.0
+GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED Cloud Score+ S2_HARMONIZED V1 image_collection ee.ImageCollection('GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED') Google Earth Engine 2015-06-27 2025-01-10 -180, -90, 180, 90 False google, cloud, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_CLOUD_SCORE_PLUS_V1_S2_HARMONIZED CC-BY-4.0
+GOOGLE/DYNAMICWORLD/V1 Dynamic World V1 image_collection ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1') World Resources Institute 2015-06-27 2025-01-10 -180, -90, 180, 90 False global, google, landcover, landuse, nrt, sentinel2_derived https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_DYNAMICWORLD_V1.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1 CC-BY-4.0
GOOGLE/GLOBAL_CCDC/V1 Google Global Landsat-based CCDC Segments (1999-2019) image_collection ee.ImageCollection('GOOGLE/GLOBAL_CCDC/V1') Google 1999-01-01 2020-01-01 -180, -60, 180, 72 False change_detection, google, landcover, landsat_derived, landuse https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_GLOBAL_CCDC_V1.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_GLOBAL_CCDC_V1 CC-BY-4.0
GOOGLE/Research/open-buildings-temporal/v1 Open Buildings Temporal V1 image_collection ee.ImageCollection('GOOGLE/Research/open-buildings-temporal/v1') Google Research - Open Buildings 2016-06-30 2023-06-30 -180, -90, 180, 90 False building_height, height, annual, built_up, open_buildings, africa, asia, south_asia, southeast_asia, high_resolution https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_Research_open-buildings-temporal_v1.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_Research_open-buildings-temporal_v1 CC-BY-4.0
GOOGLE/Research/open-buildings/v1/polygons Open Buildings V1 Polygons [deprecated] table ee.FeatureCollection('GOOGLE/Research/open-buildings/v1/polygons') Google Research - Open Buildings 2021-04-30 2021-04-30 -180, -90, 180, 90 True africa, building, built_up, open_buildings, structure https://storage.googleapis.com/earthengine-stac/catalog/GOOGLE/GOOGLE_Research_open-buildings_v1_polygons.json https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_Research_open-buildings_v1_polygons CC-BY-4.0
@@ -167,7 +167,7 @@ HYCOM/GLBu0_08/sea_water_velocity HYCOM: Hybrid Coordinate Ocean Model, Water Ve
HYCOM/sea_surface_elevation HYCOM: Hybrid Coordinate Ocean Model, Sea Surface Elevation image_collection ee.ImageCollection('HYCOM/sea_surface_elevation') NOPP 1992-10-02 2024-09-05 -180, -80.48, 180, 80.48 False elevation, hycom, nopp, ocean, ssh, water https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_surface_elevation.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_surface_elevation proprietary
HYCOM/sea_temp_salinity HYCOM: Hybrid Coordinate Ocean Model, Water Temperature and Salinity image_collection ee.ImageCollection('HYCOM/sea_temp_salinity') NOPP 1992-10-02 2024-09-05 -180, -80.48, 180, 80.48 False hycom, nopp, ocean, salinity, sst, water, water_temp https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_temp_salinity.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_temp_salinity proprietary
HYCOM/sea_water_velocity HYCOM: Hybrid Coordinate Ocean Model, Water Velocity image_collection ee.ImageCollection('HYCOM/sea_water_velocity') NOPP 1992-10-02 2024-09-05 -180, -80.48, 180, 80.48 False hycom, nopp, ocean, velocity, water https://storage.googleapis.com/earthengine-stac/catalog/HYCOM/HYCOM_sea_water_velocity.json https://developers.google.com/earth-engine/datasets/catalog/HYCOM_sea_water_velocity proprietary
-IDAHO_EPSCOR/GRIDMET GRIDMET: University of Idaho Gridded Surface Meteorological Dataset image_collection ee.ImageCollection('IDAHO_EPSCOR/GRIDMET') University of California Merced 1979-01-01 2025-01-07 -124.9, 24.9, -66.8, 49.6 False climate, fireburning, gridmet, humidity, merced, metdata, nfdrs, precipitation, radiation, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_GRIDMET.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_GRIDMET proprietary
+IDAHO_EPSCOR/GRIDMET GRIDMET: University of Idaho Gridded Surface Meteorological Dataset image_collection ee.ImageCollection('IDAHO_EPSCOR/GRIDMET') University of California Merced 1979-01-01 2025-01-08 -124.9, 24.9, -66.8, 49.6 False climate, fireburning, gridmet, humidity, merced, metdata, nfdrs, precipitation, radiation, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_GRIDMET.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_GRIDMET proprietary
IDAHO_EPSCOR/MACAv2_METDATA MACAv2-METDATA: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models image_collection ee.ImageCollection('IDAHO_EPSCOR/MACAv2_METDATA') University of California Merced 1900-01-01 2100-12-31 -124.9, 24.9, -67, 49.6 False climate, conus, geophysical, idaho, maca, monthly https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_MACAv2_METDATA.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA CC0-1.0
IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY MACAv2-METDATA Monthly Summaries: University of Idaho, Multivariate Adaptive Constructed Analogs Applied to Global Climate Models image_collection ee.ImageCollection('IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY') University of California Merced 1900-01-01 2099-12-31 -124.9, 24.9, -67, 49.6 False climate, conus, geophysical, idaho, maca, monthly https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_MACAv2_METDATA_MONTHLY.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_MACAv2_METDATA_MONTHLY CC0-1.0
IDAHO_EPSCOR/PDSI PDSI: University of Idaho Palmer Drought Severity Index [deprecated] image_collection ee.ImageCollection('IDAHO_EPSCOR/PDSI') University of California Merced 1979-03-01 2020-06-20 -124.9, 24.9, -66.8, 49.6 True climate, conus, crop, drought, geophysical, merced, palmer, pdsi https://storage.googleapis.com/earthengine-stac/catalog/IDAHO_EPSCOR/IDAHO_EPSCOR_PDSI.json https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_PDSI proprietary
@@ -209,20 +209,20 @@ JAXA/ALOS/PALSAR/YEARLY/SAR Global PALSAR-2/PALSAR Yearly Mosaic, version 1 imag
JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH Global PALSAR-2/PALSAR Yearly Mosaic, version 2 image_collection ee.ImageCollection('JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH') JAXA EORC 2015-01-01 2023-01-01 -180, -90, 180, 90 False alos, alos2, eroc, jaxa, palsar, palsar2, sar https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_ALOS_PALSAR_YEARLY_SAR_EPOCH.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_PALSAR_YEARLY_SAR_EPOCH proprietary
JAXA/GCOM-C/L3/LAND/LAI/V1 GCOM-C/SGLI L3 Leaf Area Index (V1) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V1') Global Change Observation Mission (GCOM) 2018-01-01 2020-06-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V1.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LAI_V1 proprietary
JAXA/GCOM-C/L3/LAND/LAI/V2 GCOM-C/SGLI L3 Leaf Area Index (V2) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V2') Global Change Observation Mission (GCOM) 2018-01-01 2021-11-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V2.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LAI_V2 proprietary
-JAXA/GCOM-C/L3/LAND/LAI/V3 GCOM-C/SGLI L3 Leaf Area Index (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V3') Global Change Observation Mission (GCOM) 2021-11-29 2025-01-07 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LAI_V3 proprietary
+JAXA/GCOM-C/L3/LAND/LAI/V3 GCOM-C/SGLI L3 Leaf Area Index (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LAI/V3') Global Change Observation Mission (GCOM) 2021-11-29 2025-01-08 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, lai, land, leaf_area_index https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LAI_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LAI_V3 proprietary
JAXA/GCOM-C/L3/LAND/LST/V1 GCOM-C/SGLI L3 Land Surface Temperature (V1) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V1') Global Change Observation Mission (GCOM) 2018-01-01 2020-06-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LST_V1.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LST_V1 proprietary
JAXA/GCOM-C/L3/LAND/LST/V2 GCOM-C/SGLI L3 Land Surface Temperature (V2) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V2') Global Change Observation Mission (GCOM) 2018-01-01 2021-11-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LST_V2.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LST_V2 proprietary
-JAXA/GCOM-C/L3/LAND/LST/V3 GCOM-C/SGLI L3 Land Surface Temperature (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V3') Global Change Observation Mission (GCOM) 2021-11-29 2025-01-07 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LST_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LST_V3 proprietary
+JAXA/GCOM-C/L3/LAND/LST/V3 GCOM-C/SGLI L3 Land Surface Temperature (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/LAND/LST/V3') Global Change Observation Mission (GCOM) 2021-11-29 2025-01-08 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, land, land_surface_temperature, lst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_LAND_LST_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_LAND_LST_V3 proprietary
JAXA/GCOM-C/L3/OCEAN/CHLA/V1 GCOM-C/SGLI L3 Chlorophyll-a Concentration (V1) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V1') Global Change Observation Mission (GCOM) 2018-01-01 2020-06-28 -180, -90, 180, 90 False chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V1.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_CHLA_V1 proprietary
JAXA/GCOM-C/L3/OCEAN/CHLA/V2 GCOM-C/SGLI L3 Chlorophyll-a Concentration (V2) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V2') Global Change Observation Mission (GCOM) 2018-01-01 2021-11-28 -180, -90, 180, 90 False chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V2.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_CHLA_V2 proprietary
-JAXA/GCOM-C/L3/OCEAN/CHLA/V3 GCOM-C/SGLI L3 Chlorophyll-a Concentration (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V3') Global Change Observation Mission (GCOM) 2021-11-29 2025-01-07 -180, -90, 180, 90 False chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_CHLA_V3 proprietary
+JAXA/GCOM-C/L3/OCEAN/CHLA/V3 GCOM-C/SGLI L3 Chlorophyll-a Concentration (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/CHLA/V3') Global Change Observation Mission (GCOM) 2021-11-29 2025-01-08 -180, -90, 180, 90 False chla, chlorophyll_a, climate, g_portal, gcom, gcom_c, jaxa, ocean, ocean_color https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_CHLA_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_CHLA_V3 proprietary
JAXA/GCOM-C/L3/OCEAN/SST/V1 GCOM-C/SGLI L3 Sea Surface Temperature (V1) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V1') Global Change Observation Mission (GCOM) 2018-01-01 2020-06-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V1.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V1 proprietary
JAXA/GCOM-C/L3/OCEAN/SST/V2 GCOM-C/SGLI L3 Sea Surface Temperature (V2) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V2') Global Change Observation Mission (GCOM) 2018-01-01 2021-11-28 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V2.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V2 proprietary
-JAXA/GCOM-C/L3/OCEAN/SST/V3 GCOM-C/SGLI L3 Sea Surface Temperature (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V3') Global Change Observation Mission (GCOM) 2018-01-22 2025-01-07 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V3 proprietary
-JAXA/GPM_L3/GSMaP/v6/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V6 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/operational') JAXA Earth Observation Research Center 2014-03-01 2025-01-09 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v6_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_operational proprietary
+JAXA/GCOM-C/L3/OCEAN/SST/V3 GCOM-C/SGLI L3 Sea Surface Temperature (V3) image_collection ee.ImageCollection('JAXA/GCOM-C/L3/OCEAN/SST/V3') Global Change Observation Mission (GCOM) 2018-01-22 2025-01-08 -180, -90, 180, 90 False climate, g_portal, gcom, gcom_c, jaxa, ocean, sea_surface_temperature, sst https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GCOM-C_L3_OCEAN_SST_V3.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GCOM-C_L3_OCEAN_SST_V3 proprietary
+JAXA/GPM_L3/GSMaP/v6/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V6 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/operational') JAXA Earth Observation Research Center 2014-03-01 2025-01-10 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v6_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_operational proprietary
JAXA/GPM_L3/GSMaP/v6/reanalysis GSMaP Reanalysis: Global Satellite Mapping of Precipitation image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v6/reanalysis') JAXA Earth Observation Research Center 2000-03-01 2014-03-12 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v6_reanalysis.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v6_reanalysis proprietary
-JAXA/GPM_L3/GSMaP/v7/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V7 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v7/operational') JAXA Earth Observation Research Center 2014-03-01 2025-01-09 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v7_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v7_operational proprietary
-JAXA/GPM_L3/GSMaP/v8/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V8 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v8/operational') JAXA Earth Observation Research Center 1998-01-01 2025-01-09 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v8_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v8_operational proprietary
+JAXA/GPM_L3/GSMaP/v7/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V7 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v7/operational') JAXA Earth Observation Research Center 2014-03-01 2025-01-10 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v7_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v7_operational proprietary
+JAXA/GPM_L3/GSMaP/v8/operational GSMaP Operational: Global Satellite Mapping of Precipitation - V8 image_collection ee.ImageCollection('JAXA/GPM_L3/GSMaP/v8/operational') JAXA Earth Observation Research Center 1998-01-01 2025-01-10 -180, -60, 180, 60 False climate, geophysical, gpm, hourly, jaxa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/JAXA/JAXA_GPM_L3_GSMaP_v8_operational.json https://developers.google.com/earth-engine/datasets/catalog/JAXA_GPM_L3_GSMaP_v8_operational proprietary
JCU/Murray/GIC/global_tidal_wetland_change/2019 Murray Global Tidal Wetland Change v1.0 (1999-2019) image ee.Image('JCU/Murray/GIC/global_tidal_wetland_change/2019') Murray/JCU 1999-01-01 2019-12-31 -180, -90, 180, 90 False coastal, ecosystem, intertidal, landsat_derived, mangrove, murray, saltmarsh, tidal_flat, tidal_marsh https://storage.googleapis.com/earthengine-stac/catalog/JCU/JCU_Murray_GIC_global_tidal_wetland_change_2019.json https://developers.google.com/earth-engine/datasets/catalog/JCU_Murray_GIC_global_tidal_wetland_change_2019 CC-BY-4.0
JRC/CEMS_GLOFAS/FloodHazard/v1 JRC Global River Flood Hazard Maps Version 1 image_collection ee.ImageCollection('JRC/CEMS_GLOFAS/FloodHazard/v1') Joint Research Centre 2024-03-16 2024-03-16 -180, -90, 180, 90 False flood, monitoring, wri https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_CEMS_GLOFAS_FloodHazard_v1.json https://developers.google.com/earth-engine/datasets/catalog/JRC_CEMS_GLOFAS_FloodHazard_v1 CC-BY-4.0
JRC/D5/EUCROPMAP/V1 EUCROPMAP image_collection ee.ImageCollection('JRC/D5/EUCROPMAP/V1') Joint Research Center (JRC) 2018-01-01 2022-01-01 -16.171875, 34.313433, 36.386719, 72.182526 False crop, eu, jrc, lucas, sentinel1_derived https://storage.googleapis.com/earthengine-stac/catalog/JRC/JRC_D5_EUCROPMAP_V1.json https://developers.google.com/earth-engine/datasets/catalog/JRC_D5_EUCROPMAP_V1 CC-BY-4.0
@@ -307,18 +307,18 @@ LANDSAT/GLS2005_L5 Landsat Global Land Survey 2005, Landsat 5 scenes image_colle
LANDSAT/GLS2005_L7 Landsat Global Land Survey 2005, Landsat 7 scenes image_collection ee.ImageCollection('LANDSAT/GLS2005_L7') USGS 2003-07-29 2008-07-29 -180, -90, 180, 90 False etm, gls, l7, landsat, radiance, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_GLS2005_L7.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_GLS2005_L7 PDDL-1.0
LANDSAT/LC08/C02/T1 USGS Landsat 8 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1') USGS 2013-03-18 2025-01-07 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, oli_tirs, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1 PDDL-1.0
LANDSAT/LC08/C02/T1_L2 USGS Landsat 8 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') USGS 2013-03-18 2024-12-30 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l8sr, landsat, lasrc, lc08, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_L2 proprietary
-LANDSAT/LC08/C02/T1_RT USGS Landsat 8 Collection 2 Tier 1 and Real-Time data Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT') USGS 2013-03-18 2025-01-09 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, nrt, oli_tirs, radiance, rt, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT PDDL-1.0
-LANDSAT/LC08/C02/T1_RT_TOA USGS Landsat 8 Collection 2 Tier 1 and Real-Time data TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT_TOA') USGS/Google 2013-03-18 2025-01-09 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT_TOA PDDL-1.0
+LANDSAT/LC08/C02/T1_RT USGS Landsat 8 Collection 2 Tier 1 and Real-Time data Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT') USGS 2013-03-18 2025-01-10 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, nrt, oli_tirs, radiance, rt, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT PDDL-1.0
+LANDSAT/LC08/C02/T1_RT_TOA USGS Landsat 8 Collection 2 Tier 1 and Real-Time data TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_RT_TOA') USGS/Google 2013-03-18 2025-01-10 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_RT_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_RT_TOA PDDL-1.0
LANDSAT/LC08/C02/T1_TOA USGS Landsat 8 Collection 2 Tier 1 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T1_TOA') USGS/Google 2013-03-18 2025-01-07 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T1_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T1_TOA PDDL-1.0
LANDSAT/LC08/C02/T2 USGS Landsat 8 Collection 2 Tier 2 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC08/C02/T2') USGS 2021-10-28 2025-01-07 -180, -90, 180, 90 False c2, global, l8, landsat, lc8, oli_tirs, radiance, t2, tier2, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2 PDDL-1.0
LANDSAT/LC08/C02/T2_L2 USGS Landsat 8 Level 2, Collection 2, Tier 2 image_collection ee.ImageCollection('LANDSAT/LC08/C02/T2_L2') USGS 2013-03-18 2024-12-30 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l8sr, landsat, lasrc, lc08, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T2_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2_L2 proprietary
LANDSAT/LC08/C02/T2_TOA USGS Landsat 8 Collection 2 Tier 2 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC08/C02/T2_TOA') USGS/Google 2021-10-28 2025-01-07 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC08_C02_T2_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C02_T2_TOA PDDL-1.0
-LANDSAT/LC09/C02/T1 USGS Landsat 9 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1') USGS 2021-10-31 2025-01-09 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1 PDDL-1.0
-LANDSAT/LC09/C02/T1_L2 USGS Landsat 9 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1_L2') USGS 2021-10-31 2025-01-07 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_L2 proprietary
-LANDSAT/LC09/C02/T1_TOA USGS Landsat 9 Collection 2 Tier 1 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA') USGS/Google 2021-10-31 2025-01-09 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_TOA PDDL-1.0
-LANDSAT/LC09/C02/T2 USGS Landsat 9 Collection 2 Tier 2 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2') USGS 2021-11-02 2025-01-09 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t2, tier2, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2 PDDL-1.0
-LANDSAT/LC09/C02/T2_L2 USGS Landsat 9 Level 2, Collection 2, Tier 2 image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2_L2') USGS 2021-10-31 2025-01-07 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_L2 proprietary
-LANDSAT/LC09/C02/T2_TOA USGS Landsat 9 Collection 2 Tier 2 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2_TOA') USGS/Google 2021-11-02 2025-01-08 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_TOA PDDL-1.0
+LANDSAT/LC09/C02/T1 USGS Landsat 9 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1') USGS 2021-10-31 2025-01-10 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1 PDDL-1.0
+LANDSAT/LC09/C02/T1_L2 USGS Landsat 9 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1_L2') USGS 2021-10-31 2025-01-08 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_L2 proprietary
+LANDSAT/LC09/C02/T1_TOA USGS Landsat 9 Collection 2 Tier 1 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T1_TOA') USGS/Google 2021-10-31 2025-01-10 -180, -90, 180, 90 False c2, global, landsat, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T1_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T1_TOA PDDL-1.0
+LANDSAT/LC09/C02/T2 USGS Landsat 9 Collection 2 Tier 2 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2') USGS 2021-11-02 2025-01-10 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, oli_tirs, radiance, t2, tier2, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2 PDDL-1.0
+LANDSAT/LC09/C02/T2_L2 USGS Landsat 9 Level 2, Collection 2, Tier 2 image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2_L2') USGS 2021-10-31 2025-01-08 -180, -90, 180, 90 False cfmask, cloud, fmask, global, l9sr, landsat, lasrc, lc09, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_L2 proprietary
+LANDSAT/LC09/C02/T2_TOA USGS Landsat 9 Collection 2 Tier 2 TOA Reflectance image_collection ee.ImageCollection('LANDSAT/LC09/C02/T2_TOA') USGS/Google 2021-11-02 2025-01-09 -180, -90, 180, 90 False c2, global, l9, landsat, lc9, toa, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LC09_C02_T2_TOA.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC09_C02_T2_TOA PDDL-1.0
LANDSAT/LE07/C02/T1 USGS Landsat 7 Collection 2 Tier 1 Raw Scenes image_collection ee.ImageCollection('LANDSAT/LE07/C02/T1') USGS 1999-05-28 2024-01-19 -180, -90, 180, 90 False c2, etm, global, l7, landsat, le7, radiance, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LE07_C02_T1.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1 PDDL-1.0
LANDSAT/LE07/C02/T1_L2 USGS Landsat 7 Level 2, Collection 2, Tier 1 image_collection ee.ImageCollection('LANDSAT/LE07/C02/T1_L2') USGS 1999-05-28 2024-01-19 -180, -90, 180, 90 False cfmask, cloud, etm, fmask, global, landsat, lasrc, le07, lst, reflectance, sr, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LE07_C02_T1_L2.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_L2 proprietary
LANDSAT/LE07/C02/T1_RT USGS Landsat 7 Collection 2 Tier 1 and Real-Time data Raw Scenes image_collection ee.ImageCollection('LANDSAT/LE07/C02/T1_RT') USGS 1999-05-28 2024-01-19 -180, -90, 180, 90 False c2, etm, global, l7, landsat, le7, nrt, radiance, rt, t1, tier1, usgs https://storage.googleapis.com/earthengine-stac/catalog/LANDSAT/LANDSAT_LE07_C02_T1_RT.json https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_RT PDDL-1.0
@@ -427,22 +427,22 @@ MODIS/061/MCD12Q2 MCD12Q2.006 Land Cover Dynamics Yearly Global 500m image_colle
MODIS/061/MCD15A3H MCD15A3H.061 MODIS Leaf Area Index/FPAR 4-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MCD15A3H') NASA LP DAAC at the USGS EROS Center 2002-07-04 2025-01-01 -180, -90, 180, 90 False 4_day, fpar, global, lai, mcd15a3h, modis, nasa, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD15A3H.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD15A3H proprietary
MODIS/061/MCD18A1 MCD18A1.061 Surface Radiation Daily/3-Hour image_collection ee.ImageCollection('MODIS/061/MCD18A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-06-01 -180, -90, 180, 90 False par, radiation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD18A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD18A1 proprietary
MODIS/061/MCD18C2 MCD18C2.061 Photosynthetically Active Radiation Daily 3-Hour image_collection ee.ImageCollection('MODIS/061/MCD18C2') NASA LP DAAC at the USGS EROS Center 2002-02-24 2024-06-01 -180, -90, 180, 90 False par, radiation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD18C2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD18C2 proprietary
-MODIS/061/MCD19A1_GRANULES MCD19A1.061: Land Surface BRF Daily L2G Global 500m and 1km image_collection ee.ImageCollection('MODIS/061/MCD19A1_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-12-21 2025-01-06 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A1_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A1_GRANULES proprietary
-MODIS/061/MCD19A2_GRANULES MCD19A2.061: Terra & Aqua MAIAC Land Aerosol Optical Depth Daily 1km image_collection ee.ImageCollection('MODIS/061/MCD19A2_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-06 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, mcd19a2, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A2_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A2_GRANULES proprietary
-MODIS/061/MCD43A1 MCD43A1.061 MODIS BRDF-Albedo Model Parameters Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-28 -180, -90, 180, 90 False albedo, brdf, daily, global, mcd43a1, modis, nasa, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A1 proprietary
-MODIS/061/MCD43A2 MCD43A2.061 MODIS BRDF-Albedo Quality Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A2') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-28 -180, -90, 180, 90 False albedo, brdf, daily, global, modis, nasa, quality, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A2 proprietary
-MODIS/061/MCD43A3 MCD43A3.061 MODIS Albedo Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-28 -180, -90, 180, 90 False albedo, black_sky, daily, global, modis, nasa, usgs, white_sky https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A3 proprietary
-MODIS/061/MCD43A4 MCD43A4.061 MODIS Nadir BRDF-Adjusted Reflectance Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A4') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-28 -180, -90, 180, 90 False albedo, brdf, daily, global, modis, nasa, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A4.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A4 proprietary
-MODIS/061/MCD43C3 MCD43C3.061 BRDF/Albedo Daily L3 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MCD43C3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-28 -180, -90, 180, 90 False albedo, black_sky, brdf, daily, global, modis, nasa, usgs, white_sky https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43C3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43C3 proprietary
+MODIS/061/MCD19A1_GRANULES MCD19A1.061: Land Surface BRF Daily L2G Global 500m and 1km image_collection ee.ImageCollection('MODIS/061/MCD19A1_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-12-21 2025-01-07 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A1_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A1_GRANULES proprietary
+MODIS/061/MCD19A2_GRANULES MCD19A2.061: Terra & Aqua MAIAC Land Aerosol Optical Depth Daily 1km image_collection ee.ImageCollection('MODIS/061/MCD19A2_GRANULES') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-07 -180, -90, 180, 90 False aerosol, aod, aqua, daily, global, maiac, mcd19a2, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD19A2_GRANULES.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD19A2_GRANULES proprietary
+MODIS/061/MCD43A1 MCD43A1.061 MODIS BRDF-Albedo Model Parameters Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-31 -180, -90, 180, 90 False albedo, brdf, daily, global, mcd43a1, modis, nasa, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A1 proprietary
+MODIS/061/MCD43A2 MCD43A2.061 MODIS BRDF-Albedo Quality Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A2') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-31 -180, -90, 180, 90 False albedo, brdf, daily, global, modis, nasa, quality, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A2 proprietary
+MODIS/061/MCD43A3 MCD43A3.061 MODIS Albedo Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-31 -180, -90, 180, 90 False albedo, black_sky, daily, global, modis, nasa, usgs, white_sky https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A3 proprietary
+MODIS/061/MCD43A4 MCD43A4.061 MODIS Nadir BRDF-Adjusted Reflectance Daily 500m image_collection ee.ImageCollection('MODIS/061/MCD43A4') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-31 -180, -90, 180, 90 False albedo, brdf, daily, global, modis, nasa, reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43A4.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43A4 proprietary
+MODIS/061/MCD43C3 MCD43C3.061 BRDF/Albedo Daily L3 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MCD43C3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-31 -180, -90, 180, 90 False albedo, black_sky, brdf, daily, global, modis, nasa, usgs, white_sky https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD43C3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD43C3 proprietary
MODIS/061/MCD64A1 MCD64A1.061 MODIS Burned Area Monthly Global 500m image_collection ee.ImageCollection('MODIS/061/MCD64A1') NASA LP DAAC at the USGS EROS Center 2000-11-01 2024-11-01 -180, -90, 180, 90 False burn, change_detection, fire, geophysical, global, mcd64a1, modis, monthly, nasa, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MCD64A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MCD64A1 proprietary
MODIS/061/MOD08_M3 MOD08_M3.061 Terra Atmosphere Monthly Global Product image_collection ee.ImageCollection('MODIS/061/MOD08_M3') NASA LAADS DAAC at NASA Goddard Space Flight Center 2000-02-01 2024-12-01 -180, -90, 180, 90 False atmosphere, geophysical, global, mod08, mod08_m3, modis, monthly, nasa, temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD08_M3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD08_M3 proprietary
MODIS/061/MOD09A1 MOD09A1.061 Terra Surface Reflectance 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD09A1') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-12-26 -180, -90, 180, 90 False 8_day, global, mod09a1, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09A1 proprietary
-MODIS/061/MOD09CMG MOD09CMG.061 Terra Surface Reflectance Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD09CMG') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-05 -180, -90, 180, 90 False brightness_temperature, ozone, surface_reflectance, terra https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09CMG.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09CMG proprietary
-MODIS/061/MOD09GA MOD09GA.061 Terra Surface Reflectance Daily Global 1km and 500m image_collection ee.ImageCollection('MODIS/061/MOD09GA') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-05 -180, -90, 180, 90 False daily, global, mod09ga, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GA.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09GA proprietary
-MODIS/061/MOD09GQ MOD09GQ.061 Terra Surface Reflectance Daily Global 250m image_collection ee.ImageCollection('MODIS/061/MOD09GQ') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-05 -180, -90, 180, 90 False daily, global, mod09gq, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GQ.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09GQ proprietary
+MODIS/061/MOD09CMG MOD09CMG.061 Terra Surface Reflectance Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD09CMG') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-07 -180, -90, 180, 90 False brightness_temperature, ozone, surface_reflectance, terra https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09CMG.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09CMG proprietary
+MODIS/061/MOD09GA MOD09GA.061 Terra Surface Reflectance Daily Global 1km and 500m image_collection ee.ImageCollection('MODIS/061/MOD09GA') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-07 -180, -90, 180, 90 False daily, global, mod09ga, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GA.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09GA proprietary
+MODIS/061/MOD09GQ MOD09GQ.061 Terra Surface Reflectance Daily Global 250m image_collection ee.ImageCollection('MODIS/061/MOD09GQ') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-07 -180, -90, 180, 90 False daily, global, mod09gq, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09GQ.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09GQ proprietary
MODIS/061/MOD09Q1 MOD09Q1.061 Terra Surface Reflectance 8-Day Global 250m image_collection ee.ImageCollection('MODIS/061/MOD09Q1') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-12-26 -180, -90, 180, 90 False 8_day, global, mod09q1, modis, nasa, sr, surface_reflectance, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD09Q1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD09Q1 proprietary
-MODIS/061/MOD10A1 MOD10A1.061 Terra Snow Cover Daily Global 500m image_collection ee.ImageCollection('MODIS/061/MOD10A1') NASA NSIDC DAAC at CIRES 2000-02-24 2025-01-04 -180, -90, 180, 90 False albedo, daily, geophysical, global, mod10a1, modis, nasa, nsidc, snow, terra https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD10A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD10A1 proprietary
-MODIS/061/MOD11A1 MOD11A1.061 Terra Land Surface Temperature and Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD11A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-05 -180, -90, 180, 90 False daily, emissivity, global, lst, mod11a1, modis, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A1 proprietary
+MODIS/061/MOD10A1 MOD10A1.061 Terra Snow Cover Daily Global 500m image_collection ee.ImageCollection('MODIS/061/MOD10A1') NASA NSIDC DAAC at CIRES 2000-02-24 2025-01-07 -180, -90, 180, 90 False albedo, daily, geophysical, global, mod10a1, modis, nasa, nsidc, snow, terra https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD10A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD10A1 proprietary
+MODIS/061/MOD11A1 MOD11A1.061 Terra Land Surface Temperature and Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD11A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-07 -180, -90, 180, 90 False daily, emissivity, global, lst, mod11a1, modis, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A1 proprietary
MODIS/061/MOD11A2 MOD11A2.061 Terra Land Surface Temperature and Emissivity 8-Day Global 1km image_collection ee.ImageCollection('MODIS/061/MOD11A2') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-12-26 -180, -90, 180, 90 False 8_day, emissivity, global, lst, mod11a2, modis, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD11A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A2 proprietary
MODIS/061/MOD13A1 MOD13A1.061 Terra Vegetation Indices 16-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD13A1') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-12-18 -180, -90, 180, 90 False 16_day, evi, global, mod13a1, modis, nasa, ndvi, terra, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13A1 proprietary
MODIS/061/MOD13A2 MOD13A2.061 Terra Vegetation Indices 16-Day Global 1km image_collection ee.ImageCollection('MODIS/061/MOD13A2') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-12-18 -180, -90, 180, 90 False 16_day, evi, global, mod13a2, modis, nasa, ndvi, terra, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD13A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13A2 proprietary
@@ -452,24 +452,24 @@ MODIS/061/MOD13Q1 MOD13Q1.061 Terra Vegetation Indices 16-Day Global 250m image_
MODIS/061/MOD14A1 MOD14A1.061: Terra Thermal Anomalies & Fire Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD14A1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-02 -180, -90, 180, 90 False daily, fire, global, mod14a1, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD14A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD14A1 proprietary
MODIS/061/MOD14A2 MOD14A2.061: Terra Thermal Anomalies & Fire 8-Day Global 1km image_collection ee.ImageCollection('MODIS/061/MOD14A2') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-12-26 -180, -90, 180, 90 False 8_day, fire, global, mod14a2, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD14A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD14A2 proprietary
MODIS/061/MOD15A2H MOD15A2H.061: Terra Leaf Area Index/FPAR 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD15A2H') NASA LP DAAC at the USGS EROS Center 2000-02-18 2024-12-26 -180, -90, 180, 90 False 8_day, fpar, global, lai, mod15a2h, modis, nasa, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD15A2H.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD15A2H proprietary
-MODIS/061/MOD16A2 MOD16A2.061: Terra Net Evapotranspiration 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD16A2') NASA LP DAAC at the USGS EROS Center 2001-01-01 2024-12-18 -180, -90, 180, 90 False 8_day, evapotranspiration, global, mod16a2, modis, nasa https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD16A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD16A2 proprietary
+MODIS/061/MOD16A2 MOD16A2.061: Terra Net Evapotranspiration 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD16A2') NASA LP DAAC at the USGS EROS Center 2001-01-01 2024-12-26 -180, -90, 180, 90 False 8_day, evapotranspiration, global, mod16a2, modis, nasa https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD16A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD16A2 proprietary
MODIS/061/MOD16A2GF MOD16A2GF.061: Terra Net Evapotranspiration Gap-Filled 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD16A2GF') NASA LP DAAC at the USGS EROS Center 2000-01-01 2023-12-27 -180, -90, 180, 90 False 8_day, evapotranspiration, global, modis, nasa https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD16A2GF.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD16A2GF proprietary
MODIS/061/MOD17A2H MOD17A2H.061: Terra Gross Primary Productivity 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD17A2H') NASA LP DAAC at the USGS EROS Center 2021-01-01 2024-12-26 -180, -90, 180, 90 False 8_day, global, gpp, mod17a2, modis, nasa, photosynthesis, productivity, psn, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A2H.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD17A2H proprietary
-MODIS/061/MOD17A2HGF MOD17A2HGF.061: Terra Gross Primary Productivity 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD17A2HGF') NASA LP DAAC at the USGS EROS Center 2021-01-01 2024-01-09 -180, -90, 180, 90 False 8_day, global, gpp, modis, nasa, photosynthesis, productivity, psn, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A2HGF.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD17A2HGF proprietary
+MODIS/061/MOD17A2HGF MOD17A2HGF.061: Terra Gross Primary Productivity 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MOD17A2HGF') NASA LP DAAC at the USGS EROS Center 2021-01-01 2024-09-05 -180, -90, 180, 90 False 8_day, global, gpp, modis, nasa, photosynthesis, productivity, psn, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A2HGF.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD17A2HGF proprietary
MODIS/061/MOD17A3HGF MOD17A3HGF.061: Terra Net Primary Production Gap-Filled Yearly Global 500m image_collection ee.ImageCollection('MODIS/061/MOD17A3HGF') NASA LP DAAC at the USGS EROS Center 2001-01-01 2023-01-01 -180, -90, 180, 90 False global, gpp, nasa, npp, photosynthesis, productivity, psn, terra, usgs, yearly https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD17A3HGF.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD17A3HGF proprietary
-MODIS/061/MOD21A1D MOD21A1D.061 Terra Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD21A1D') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-05 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21A1D.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21A1D proprietary
-MODIS/061/MOD21A1N MOD21A1N.061 Terra Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD21A1N') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-06 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21A1N.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21A1N proprietary
-MODIS/061/MOD21C1 MOD21C1.061 Terra Land Surface Temperature and 3-Band Emissivity Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD21C1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-06 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21C1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21C1 proprietary
+MODIS/061/MOD21A1D MOD21A1D.061 Terra Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD21A1D') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-07 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21A1D.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21A1D proprietary
+MODIS/061/MOD21A1N MOD21A1N.061 Terra Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MOD21A1N') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-07 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21A1N.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21A1N proprietary
+MODIS/061/MOD21C1 MOD21C1.061 Terra Land Surface Temperature and 3-Band Emissivity Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD21C1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-07 -180, -90, 180, 90 False daily, emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21C1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21C1 proprietary
MODIS/061/MOD21C2 MOD21C2.061 Terra Land Surface Temperature and 3-Band Emissivity 8-Day L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD21C2') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-18 -180, -90, 180, 90 False emissivity, global, lst, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21C2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21C2 proprietary
MODIS/061/MOD21C3 MOD21C3.061 Terra Land Surface Temperature and 3-Band Emissivity Monthly L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MOD21C3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-01 -180, -90, 180, 90 False emissivity, global, lst, monthly, nasa, surface_temperature, terra, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MOD21C3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD21C3 proprietary
MODIS/061/MYD08_M3 MYD08_M3.061 Aqua Atmosphere Monthly Global Product image_collection ee.ImageCollection('MODIS/061/MYD08_M3') NASA LAADS DAAC at NASA Goddard Space Flight Center 2002-07-01 2024-12-01 -180, -90, 180, 90 False aqua, atmosphere, geophysical, global, modis, monthly, myd08, myd08_m3, nasa, temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD08_M3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD08_M3 proprietary
MODIS/061/MYD09A1 MYD09A1.061 Aqua Surface Reflectance 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MYD09A1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-12-26 -180, -90, 180, 90 False 8_day, aqua, global, modis, myd09a1, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09A1 proprietary
-MODIS/061/MYD09CMG MYD09CMG.061 Aqua Surface Reflectance Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MYD09CMG') NASA LP DAAC at the USGS EROS Center 2002-07-04 2025-01-06 -180, -90, 180, 90 False brightness_temperature, ozone, surface_reflectance, aqua https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09CMG.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09CMG proprietary
-MODIS/061/MYD09GA MYD09GA.061 Aqua Surface Reflectance Daily Global 1km and 500m image_collection ee.ImageCollection('MODIS/061/MYD09GA') NASA LP DAAC at the USGS EROS Center 2002-07-04 2025-01-06 -180, -90, 180, 90 False aqua, daily, global, modis, myd09ga, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GA.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GA proprietary
-MODIS/061/MYD09GQ MYD09GQ.061 Aqua Surface Reflectance Daily Global 250m image_collection ee.ImageCollection('MODIS/061/MYD09GQ') NASA LP DAAC at the USGS EROS Center 2002-07-04 2025-01-06 -180, -90, 180, 90 False aqua, daily, global, modis, myd09gq, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GQ.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GQ proprietary
+MODIS/061/MYD09CMG MYD09CMG.061 Aqua Surface Reflectance Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MYD09CMG') NASA LP DAAC at the USGS EROS Center 2002-07-04 2025-01-08 -180, -90, 180, 90 False brightness_temperature, ozone, surface_reflectance, aqua https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09CMG.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09CMG proprietary
+MODIS/061/MYD09GA MYD09GA.061 Aqua Surface Reflectance Daily Global 1km and 500m image_collection ee.ImageCollection('MODIS/061/MYD09GA') NASA LP DAAC at the USGS EROS Center 2002-07-04 2025-01-08 -180, -90, 180, 90 False aqua, daily, global, modis, myd09ga, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GA.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GA proprietary
+MODIS/061/MYD09GQ MYD09GQ.061 Aqua Surface Reflectance Daily Global 250m image_collection ee.ImageCollection('MODIS/061/MYD09GQ') NASA LP DAAC at the USGS EROS Center 2002-07-04 2025-01-08 -180, -90, 180, 90 False aqua, daily, global, modis, myd09gq, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09GQ.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09GQ proprietary
MODIS/061/MYD09Q1 MYD09Q1.061 Aqua Surface Reflectance 8-Day Global 250m image_collection ee.ImageCollection('MODIS/061/MYD09Q1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-12-26 -180, -90, 180, 90 False 8_day, aqua, global, modis, myd09q1, nasa, sr, surface_reflectance, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD09Q1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD09Q1 proprietary
-MODIS/061/MYD10A1 MYD10A1.061 Aqua Snow Cover Daily Global 500m image_collection ee.ImageCollection('MODIS/061/MYD10A1') NASA NSIDC DAAC at CIRES 2002-07-04 2025-01-07 -180, -90, 180, 90 False albedo, aqua, daily, geophysical, global, modis, myd10a1, nasa, nsidc, snow https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD10A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD10A1 proprietary
-MODIS/061/MYD11A1 MYD11A1.061 Aqua Land Surface Temperature and Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD11A1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2025-01-05 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, modis, myd11a1, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD11A1 proprietary
+MODIS/061/MYD10A1 MYD10A1.061 Aqua Snow Cover Daily Global 500m image_collection ee.ImageCollection('MODIS/061/MYD10A1') NASA NSIDC DAAC at CIRES 2002-07-04 2025-01-08 -180, -90, 180, 90 False albedo, aqua, daily, geophysical, global, modis, myd10a1, nasa, nsidc, snow https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD10A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD10A1 proprietary
+MODIS/061/MYD11A1 MYD11A1.061 Aqua Land Surface Temperature and Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD11A1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2025-01-07 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, modis, myd11a1, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD11A1 proprietary
MODIS/061/MYD11A2 MYD11A2.061 Aqua Land Surface Temperature and Emissivity 8-Day Global 1km image_collection ee.ImageCollection('MODIS/061/MYD11A2') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-12-26 -180, -90, 180, 90 False 8_day, aqua, emissivity, global, lst, modis, myd11a2, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD11A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD11A2 proprietary
MODIS/061/MYD13A1 MYD13A1.061 Aqua Vegetation Indices 16-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MYD13A1') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-12-10 -180, -90, 180, 90 False 16_day, aqua, evi, global, modis, myd13a1, nasa, ndvi, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13A1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD13A1 proprietary
MODIS/061/MYD13A2 MYD13A2.061 Aqua Vegetation Indices 16-Day Global 1km image_collection ee.ImageCollection('MODIS/061/MYD13A2') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-12-10 -180, -90, 180, 90 False 16_day, aqua, evi, global, modis, myd13a2, nasa, ndvi, usgs, vegetation https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD13A2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD13A2 proprietary
@@ -481,8 +481,8 @@ MODIS/061/MYD14A2 MYD14A2.061: Aqua Thermal Anomalies & Fire 8-Day Global 1km im
MODIS/061/MYD15A2H MYD15A2H.061: Aqua Leaf Area Index/FPAR 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MYD15A2H') NASA LP DAAC at the USGS EROS Center 2002-07-04 2024-12-26 -180, -90, 180, 90 False 8_day, aqua, fpar, global, lai, modis, myd15a2h, nasa, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD15A2H.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD15A2H proprietary
MODIS/061/MYD17A2H MYD17A2H.061: Aqua Gross Primary Productivity 8-Day Global 500m image_collection ee.ImageCollection('MODIS/061/MYD17A2H') NASA LP DAAC at the USGS EROS Center 2021-01-01 2024-12-26 -180, -90, 180, 90 False 8_day, aqua, global, gpp, modis, myd17a2, nasa, photosynthesis, productivity, psn, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD17A2H.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD17A2H proprietary
MODIS/061/MYD17A3HGF MYD17A3HGF.061: Aqua Net Primary Production Gap-Filled Yearly Global 500m image_collection ee.ImageCollection('MODIS/061/MYD17A3HGF') NASA LP DAAC at the USGS EROS Center 2001-01-01 2023-01-01 -180, -90, 180, 90 False aqua, global, gpp, nasa, npp, photosynthesis, productivity, psn, usgs, yearly https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD17A3HGF.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD17A3HGF proprietary
-MODIS/061/MYD21A1D MYD21A1D.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD21A1D') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-06 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21A1D.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21A1D proprietary
-MODIS/061/MYD21A1N MYD21A1N.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD21A1N') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-05 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21A1N.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21A1N proprietary
+MODIS/061/MYD21A1D MYD21A1D.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD21A1D') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-08 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21A1D.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21A1D proprietary
+MODIS/061/MYD21A1N MYD21A1N.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily Global 1km image_collection ee.ImageCollection('MODIS/061/MYD21A1N') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-07 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21A1N.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21A1N proprietary
MODIS/061/MYD21C1 MYD21C1.061 Aqua Land Surface Temperature and 3-Band Emissivity Daily L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MYD21C1') NASA LP DAAC at the USGS EROS Center 2000-02-24 2025-01-07 -180, -90, 180, 90 False aqua, daily, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21C1.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21C1 proprietary
MODIS/061/MYD21C2 MYD21C2.061 Aqua Land Surface Temperature and 3-Band Emissivity 8-Day L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MYD21C2') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-18 -180, -90, 180, 90 False aqua, emissivity, global, lst, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21C2.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21C2 proprietary
MODIS/061/MYD21C3 MYD21C3.061 Aqua Land Surface Temperature and 3-Band Emissivity Monthly L3 Global 0.05 Deg CMG image_collection ee.ImageCollection('MODIS/061/MYD21C3') NASA LP DAAC at the USGS EROS Center 2000-02-24 2024-12-01 -180, -90, 180, 90 False aqua, emissivity, global, lst, monthly, nasa, surface_temperature, usgs https://storage.googleapis.com/earthengine-stac/catalog/MODIS/MODIS_061_MYD21C3.json https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MYD21C3 proprietary
@@ -546,16 +546,16 @@ NASA/EMIT/L2B/CH4ENH Earth Surface Mineral Dust Source Investigation- Methane En
NASA/EMIT/L2B/CH4PLM Earth Surface Mineral Dust Source Investigation- Methane Plume Complexes image_collection ee.ImageCollection('NASA/EMIT/L2B/CH4PLM') NASA Jet Propulsion Laboratory 2022-08-10 2024-10-26 -180, -90, 180, 90 False daily, emit, nasa, methane https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_EMIT_L2B_CH4PLM.json https://developers.google.com/earth-engine/datasets/catalog/NASA_EMIT_L2B_CH4PLM proprietary
NASA/FLDAS/NOAH01/C/GL/M/V001 FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System image_collection ee.ImageCollection('NASA/FLDAS/NOAH01/C/GL/M/V001') NASA GES DISC at NASA Goddard Space Flight Center 1982-01-01 2024-10-01 -180, -60, 180, 90 False climate, evapotranspiration, famine, fldas, humidity, ldas, monthly, nasa, runoff, snow, soil_moisture, soil_temperature, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_FLDAS_NOAH01_C_GL_M_V001.json https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001 proprietary
NASA/GDDP-CMIP6 NEX-GDDP-CMIP6: NASA Earth Exchange Global Daily Downscaled Climate Projections image_collection ee.ImageCollection('NASA/GDDP-CMIP6') NASA / Climate Analytics Group 1950-01-01 2100-12-31 -180, -90, 180, 90 False cag, climate, gddp, geophysical, ipcc, nasa, nex, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GDDP-CMIP6.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GDDP-CMIP6 various
-NASA/GEOS-CF/v1/fcst/htf GEOS-CF fcst htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/htf') NASA / GMAO 2022-10-01 2025-01-08 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_htf proprietary
-NASA/GEOS-CF/v1/fcst/tavg1hr GEOS-CF fcst tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/tavg1hr') NASA / GMAO 2022-10-01 2025-01-08 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_tavg1hr proprietary
-NASA/GEOS-CF/v1/rpl/htf GEOS-CF rpl htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/htf') NASA / GMAO 2018-01-01 2025-01-08 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_htf proprietary
-NASA/GEOS-CF/v1/rpl/tavg1hr GEOS-CF rpl tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/tavg1hr') NASA / GMAO 2018-01-01 2025-01-08 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_tavg1hr proprietary
+NASA/GEOS-CF/v1/fcst/htf GEOS-CF fcst htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/htf') NASA / GMAO 2022-10-01 2025-01-09 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_htf proprietary
+NASA/GEOS-CF/v1/fcst/tavg1hr GEOS-CF fcst tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/fcst/tavg1hr') NASA / GMAO 2022-10-01 2025-01-09 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_fcst_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_fcst_tavg1hr proprietary
+NASA/GEOS-CF/v1/rpl/htf GEOS-CF rpl htf v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/htf') NASA / GMAO 2018-01-01 2025-01-09 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_htf.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_htf proprietary
+NASA/GEOS-CF/v1/rpl/tavg1hr GEOS-CF rpl tavg1hr v1: Goddard Earth Observing System Composition Forecast image_collection ee.ImageCollection('NASA/GEOS-CF/v1/rpl/tavg1hr') NASA / GMAO 2018-01-01 2025-01-09 -180, -90, 180, 90 False composition, forecast, geos, gmao, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GEOS-CF_v1_rpl_tavg1hr.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GEOS-CF_v1_rpl_tavg1hr proprietary
NASA/GIMMS/3GV0 GIMMS NDVI From AVHRR Sensors (3rd Generation) image_collection ee.ImageCollection('NASA/GIMMS/3GV0') NASA/NOAA 1981-07-01 2013-12-16 -180, -90, 180, 90 False avhrr, gimms, nasa, ndvi, noaa, vegetation https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GIMMS_3GV0.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GIMMS_3GV0 proprietary
NASA/GLDAS/V021/NOAH/G025/T3H GLDAS-2.1: Global Land Data Assimilation System image_collection ee.ImageCollection('NASA/GLDAS/V021/NOAH/G025/T3H') NASA GES DISC at NASA Goddard Space Flight Center 2000-01-01 2024-12-15 -180, -90, 180, 90 False 3_hourly, climate, evaporation, forcing, geophysical, gldas, humidity, ldas, nasa, precipitation, pressure, radiation, soil, soil_moisture, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GLDAS_V021_NOAH_G025_T3H.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V021_NOAH_G025_T3H proprietary
NASA/GLDAS/V022/CLSM/G025/DA1D GLDAS-2.2: Global Land Data Assimilation System image_collection ee.ImageCollection('NASA/GLDAS/V022/CLSM/G025/DA1D') NASA GES DISC at NASA Goddard Earth Sciences Data and Information Services Center 2003-01-01 2024-09-30 -180, -90, 180, 90 False 3_hourly, climate, evaporation, forcing, geophysical, gldas, humidity, ldas, nasa, precipitation, pressure, radiation, soil, soil_moisture, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GLDAS_V022_CLSM_G025_DA1D.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V022_CLSM_G025_DA1D proprietary
NASA/GLDAS/V20/NOAH/G025/T3H Reprocessed GLDAS-2.0: Global Land Data Assimilation System image_collection ee.ImageCollection('NASA/GLDAS/V20/NOAH/G025/T3H') NASA GES DISC at NASA Goddard Space Flight Center 1948-01-01 2014-12-31 -180, -90, 180, 90 False 3_hourly, climate, evaporation, forcing, geophysical, gldas, humidity, ldas, nasa, precipitation, pressure, radiation, soil, soil_moisture, surface, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GLDAS_V20_NOAH_G025_T3H.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GLDAS_V20_NOAH_G025_T3H proprietary
NASA/GPM_L3/IMERG_MONTHLY_V06 GPM: Monthly Global Precipitation Measurement (GPM) v6 image_collection ee.ImageCollection('NASA/GPM_L3/IMERG_MONTHLY_V06') NASA GES DISC at NASA Goddard Space Flight Center 2000-06-01 2021-09-01 -180, -90, 180, 90 False climate, geophysical, gpm, imerg, jaxa, monthly, nasa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GPM_L3_IMERG_MONTHLY_V06.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_MONTHLY_V06 proprietary
-NASA/GPM_L3/IMERG_MONTHLY_V07 GPM: Monthly Global Precipitation Measurement (GPM) vRelease 07 image_collection ee.ImageCollection('NASA/GPM_L3/IMERG_MONTHLY_V07') NASA GES DISC at NASA Goddard Space Flight Center 2000-06-01 2024-06-01 -180, -90, 180, 90 False climate, geophysical, gpm, imerg, jaxa, monthly, nasa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GPM_L3_IMERG_MONTHLY_V07.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_MONTHLY_V07 proprietary
+NASA/GPM_L3/IMERG_MONTHLY_V07 GPM: Monthly Global Precipitation Measurement (GPM) vRelease 07 image_collection ee.ImageCollection('NASA/GPM_L3/IMERG_MONTHLY_V07') NASA GES DISC at NASA Goddard Space Flight Center 2000-06-01 2024-07-01 -180, -90, 180, 90 False climate, geophysical, gpm, imerg, jaxa, monthly, nasa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GPM_L3_IMERG_MONTHLY_V07.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_MONTHLY_V07 proprietary
NASA/GPM_L3/IMERG_V06 GPM: Global Precipitation Measurement (GPM) Release 06 [deprecated] image_collection ee.ImageCollection('NASA/GPM_L3/IMERG_V06') NASA GES DISC at NASA Goddard Space Flight Center 2000-06-01 2024-06-02 -180, -90, 180, 90 True climate, geophysical, gpm, half_hourly, imerg, jaxa, nasa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GPM_L3_IMERG_V06.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_V06 proprietary
NASA/GPM_L3/IMERG_V07 GPM: Global Precipitation Measurement (GPM) Release 07 image_collection ee.ImageCollection('NASA/GPM_L3/IMERG_V07') NASA GES DISC at NASA Goddard Space Flight Center 2000-06-01 2025-01-08 -180, -90, 180, 90 False climate, geophysical, gpm, half_hourly, imerg, jaxa, nasa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GPM_L3_IMERG_V07.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GPM_L3_IMERG_V07 proprietary
NASA/GRACE/MASS_GRIDS/LAND GRACE Monthly Mass Grids - Land [deprecated] image_collection ee.ImageCollection('NASA/GRACE/MASS_GRIDS/LAND') NASA Jet Propulsion Laboratory 2002-04-01 2017-01-07 -180, -90, 180, 90 True crs, gfz, grace, gravity, jpl, land, mass, nasa, tellus, water https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_GRACE_MASS_GRIDS_LAND.json https://developers.google.com/earth-engine/datasets/catalog/NASA_GRACE_MASS_GRIDS_LAND proprietary
@@ -576,14 +576,14 @@ NASA/GSFC/MERRA/slv/2 MERRA-2 M2T1NXSLV: Single-Level Diagnostics V5.12.4 image_
NASA/HLS/HLSL30/v002 HLSL30: HLS-2 Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m image_collection ee.ImageCollection('NASA/HLS/HLSL30/v002') NASA LP DAAC 2013-04-11 2025-01-07 -180, -90, 180, 90 False landsat, nasa, sentinel, usgs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_HLS_HLSL30_v002.json https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSL30_v002 proprietary
NASA/HLS/HLSS30/v002 HLSS30: HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m image_collection ee.ImageCollection('NASA/HLS/HLSS30/v002') NASA LP DAAC 2015-11-28 2024-10-08 -180, -90, 180, 90 False landsat, nasa, sentinel, usgs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_HLS_HLSS30_v002.json https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSS30_v002 proprietary
NASA/JPL/global_forest_canopy_height_2005 Global Forest Canopy Height, 2005 image ee.Image('NASA/JPL/global_forest_canopy_height_2005') NASA/JPL 2005-05-20 2005-06-23 -180, -90, 180, 90 False canopy, forest, geophysical, glas, jpl, nasa https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_JPL_global_forest_canopy_height_2005.json https://developers.google.com/earth-engine/datasets/catalog/NASA_JPL_global_forest_canopy_height_2005 proprietary
-NASA/LANCE/NOAA20_VIIRS/C2 VJ114IMGTDL_NRT Daily Raster: VIIRS (NOAA-20) Band 375m Active Fire image_collection ee.ImageCollection('NASA/LANCE/NOAA20_VIIRS/C2') NASA / LANCE / NOAA20_VIIRS 2023-10-08 2025-01-08 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_LANCE_NOAA20_VIIRS_C2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_NOAA20_VIIRS_C2 proprietary
-NASA/LANCE/SNPP_VIIRS/C2 VNP14IMGTDL_NRT Daily Raster: VIIRS (S-NPP) Band 375m Active Fire image_collection ee.ImageCollection('NASA/LANCE/SNPP_VIIRS/C2') NASA / LANCE / SNPP_VIIRS 2023-09-03 2025-01-08 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_LANCE_SNPP_VIIRS_C2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_SNPP_VIIRS_C2 proprietary
+NASA/LANCE/NOAA20_VIIRS/C2 VJ114IMGTDL_NRT Daily Raster: VIIRS (NOAA-20) Band 375m Active Fire image_collection ee.ImageCollection('NASA/LANCE/NOAA20_VIIRS/C2') NASA / LANCE / NOAA20_VIIRS 2023-10-08 2025-01-09 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_LANCE_NOAA20_VIIRS_C2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_NOAA20_VIIRS_C2 proprietary
+NASA/LANCE/SNPP_VIIRS/C2 VNP14IMGTDL_NRT Daily Raster: VIIRS (S-NPP) Band 375m Active Fire image_collection ee.ImageCollection('NASA/LANCE/SNPP_VIIRS/C2') NASA / LANCE / SNPP_VIIRS 2023-09-03 2025-01-09 -180, -90, 180, 90 False eosdis, fire, firms, geophysical, hotspot, lance, modis, nasa, thermal, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_LANCE_SNPP_VIIRS_C2.json https://developers.google.com/earth-engine/datasets/catalog/NASA_LANCE_SNPP_VIIRS_C2 proprietary
NASA/MEASURES/GFCC/TC/v3 Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30m image_collection ee.ImageCollection('NASA/MEASURES/GFCC/TC/v3') NASA LP DAAC at the USGS EROS Center 2000-01-01 2015-01-01 -180, -90, 180, 90 False forest, glcf, landsat_derived, nasa, umd https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_MEASURES_GFCC_TC_v3.json https://developers.google.com/earth-engine/datasets/catalog/NASA_MEASURES_GFCC_TC_v3 proprietary
NASA/NASADEM_HGT/001 NASADEM: NASA NASADEM Digital Elevation 30m image ee.Image('NASA/NASADEM_HGT/001') NASA / USGS / JPL-Caltech 2000-02-11 2000-02-22 -180, -56, 180, 60 False dem, elevation, geophysical, nasa, nasadem, srtm, topography, usgs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NASADEM_HGT_001.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NASADEM_HGT_001 proprietary
NASA/NEX-DCP30 NEX-DCP30: NASA Earth Exchange Downscaled Climate Projections image_collection ee.ImageCollection('NASA/NEX-DCP30') NASA / Climate Analytics Group 1950-01-01 2099-12-01 -125.03, 24.07, -66.47, 53.74 False cag, climate, cmip5, geophysical, ipcc, nasa, nex, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NEX-DCP30.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-DCP30 proprietary
NASA/NEX-DCP30_ENSEMBLE_STATS NEX-DCP30: Ensemble Stats for NASA Earth Exchange Downscaled Climate Projections image_collection ee.ImageCollection('NASA/NEX-DCP30_ENSEMBLE_STATS') NASA / Climate Analytics Group 1950-01-01 2099-12-01 -125.03, 24.07, -66.47, 49.93 False cag, climate, cmip5, geophysical, ipcc, nasa, nex, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NEX-DCP30_ENSEMBLE_STATS.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-DCP30_ENSEMBLE_STATS proprietary
NASA/NEX-GDDP NEX-GDDP: NASA Earth Exchange Global Daily Downscaled Climate Projections image_collection ee.ImageCollection('NASA/NEX-GDDP') NASA / Climate Analytics Group 1950-01-01 2100-12-31 -180, -90, 180, 90 False cag, climate, cmip5, gddp, geophysical, ipcc, nasa, nex, precipitation, temperature https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NEX-GDDP.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NEX-GDDP proprietary
-NASA/NLDAS/FORA0125_H002 NLDAS-2: North American Land Data Assimilation System Forcing Fields image_collection ee.ImageCollection('NASA/NLDAS/FORA0125_H002') NASA GES DISC at NASA Goddard Space Flight Center 1979-01-01 2025-01-05 -125.15, 24.85, -66.85, 53.28 False climate, evaporation, forcing, geophysical, hourly, humidity, ldas, nasa, nldas, precipitation, pressure, radiation, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NLDAS_FORA0125_H002.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NLDAS_FORA0125_H002 proprietary
+NASA/NLDAS/FORA0125_H002 NLDAS-2: North American Land Data Assimilation System Forcing Fields image_collection ee.ImageCollection('NASA/NLDAS/FORA0125_H002') NASA GES DISC at NASA Goddard Space Flight Center 1979-01-01 2025-01-06 -125.15, 24.85, -66.85, 53.28 False climate, evaporation, forcing, geophysical, hourly, humidity, ldas, nasa, nldas, precipitation, pressure, radiation, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_NLDAS_FORA0125_H002.json https://developers.google.com/earth-engine/datasets/catalog/NASA_NLDAS_FORA0125_H002 proprietary
NASA/OCEANDATA/MODIS-Aqua/L3SMI Ocean Color SMI: Standard Mapped Image MODIS Aqua Data image_collection ee.ImageCollection('NASA/OCEANDATA/MODIS-Aqua/L3SMI') NASA OB.DAAC at NASA Goddard Space Flight Center 2002-07-03 2022-02-28 -180, -90, 180, 90 False biology, chlorophyll, climate, modis, nasa, ocean, oceandata, reflectance, sst, temperature, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_OCEANDATA_MODIS-Aqua_L3SMI.json https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Aqua_L3SMI proprietary
NASA/OCEANDATA/MODIS-Terra/L3SMI Ocean Color SMI: Standard Mapped Image MODIS Terra Data image_collection ee.ImageCollection('NASA/OCEANDATA/MODIS-Terra/L3SMI') NASA OB.DAAC at NASA Goddard Space Flight Center 2000-02-24 2022-02-28 -180, -90, 180, 90 False biology, chlorophyll, climate, modis, nasa, ocean, oceandata, reflectance, sst, temperature, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_OCEANDATA_MODIS-Terra_L3SMI.json https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_MODIS-Terra_L3SMI proprietary
NASA/OCEANDATA/SeaWiFS/L3SMI Ocean Color SMI: Standard Mapped Image SeaWiFS Data image_collection ee.ImageCollection('NASA/OCEANDATA/SeaWiFS/L3SMI') NASA OB.DAAC at NASA Goddard Space Flight Center 1997-09-04 2010-12-10 -180, -90, 180, 90 False biology, chlorophyll, climate, nasa, ocean, oceandata, reflectance, seawifs, temperature, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_OCEANDATA_SeaWiFS_L3SMI.json https://developers.google.com/earth-engine/datasets/catalog/NASA_OCEANDATA_SeaWiFS_L3SMI proprietary
@@ -593,20 +593,20 @@ NASA/ORNL/biomass_carbon_density/v1 Global Aboveground and Belowground Biomass C
NASA/ORNL/global_forest_classification_2020/V1 Global 2020 Forest Classification for IPCC Aboveground Biomass Tier 1 Estimates, V1 image_collection ee.ImageCollection('NASA/ORNL/global_forest_classification_2020/V1') NASA ORNL DAAC at Oak Ridge National Laboratory 2020-01-01 2020-12-31 -180, -90, 180, 90 False aboveground, biomass, carbon, classification, forest, ipcc, nasa, primary_forest, secondary_forest https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_ORNL_global_forest_classification_2020_V1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_ORNL_global_forest_classification_2020_V1 proprietary
NASA/SMAP/SPL3SMP_E/005 SPL3SMP_E.005 SMAP L3 Radiometer Global Daily 9 km Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL3SMP_E/005') Google and NSIDC 2015-03-31 2023-12-03 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL3SMP_E_005.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL3SMP_E_005 proprietary
NASA/SMAP/SPL3SMP_E/006 SPL3SMP_E.006 SMAP L3 Radiometer Global Daily 9 km Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL3SMP_E/006') Google and NSIDC 2023-12-04 2025-01-07 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL3SMP_E_006.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL3SMP_E_006 proprietary
-NASA/SMAP/SPL4SMGP/007 SPL4SMGP.007 SMAP L4 Global 3-hourly 9-km Surface and Root Zone Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL4SMGP/007') Google and NSIDC 2015-03-31 2025-01-06 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_007.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL4SMGP_007 proprietary
-NASA/VIIRS/002/VNP09GA VNP09GA: VIIRS Surface Reflectance Daily 500m and 1km image_collection ee.ImageCollection('NASA/VIIRS/002/VNP09GA') NASA Land SIPS 2012-01-19 2025-01-07 -180, -90, 180, 90 False daily, nasa, noaa, npp, reflectance, sr, viirs, vnp09ga https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP09GA.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP09GA proprietary
+NASA/SMAP/SPL4SMGP/007 SPL4SMGP.007 SMAP L4 Global 3-hourly 9-km Surface and Root Zone Soil Moisture image_collection ee.ImageCollection('NASA/SMAP/SPL4SMGP/007') Google and NSIDC 2015-03-31 2025-01-07 -180, -84, 180, 84 False drought, nasa, smap, soil_moisture, surface, weather https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_SMAP_SPL4SMGP_007.json https://developers.google.com/earth-engine/datasets/catalog/NASA_SMAP_SPL4SMGP_007 proprietary
+NASA/VIIRS/002/VNP09GA VNP09GA: VIIRS Surface Reflectance Daily 500m and 1km image_collection ee.ImageCollection('NASA/VIIRS/002/VNP09GA') NASA Land SIPS 2012-01-19 2025-01-08 -180, -90, 180, 90 False daily, nasa, noaa, npp, reflectance, sr, viirs, vnp09ga https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP09GA.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP09GA proprietary
NASA/VIIRS/002/VNP09H1 VNP09H1: VIIRS Surface Reflectance 8-Day L3 Global 500m image_collection ee.ImageCollection('NASA/VIIRS/002/VNP09H1') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-12-26 -180, -90, 180, 90 False daily, nasa, noaa, npp, reflectance, sr, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP09H1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP09H1 proprietary
NASA/VIIRS/002/VNP13A1 VNP13A1.002: VIIRS Vegetation Indices 16-Day 500m image_collection ee.ImageCollection('NASA/VIIRS/002/VNP13A1') NASA LP DAAC at the USGS EROS Center 2012-01-17 2024-12-18 -180, -90, 180, 90 False 16_day, evi, nasa, ndvi, noaa, npp, vegetation, viirs, vnp13a1 https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP13A1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP13A1 proprietary
-NASA/VIIRS/002/VNP14A1 VNP14A1.002: Thermal Anomalies/Fire Daily L3 Global 1km SIN Grid image_collection ee.ImageCollection('NASA/VIIRS/002/VNP14A1') NASA LP DAAC at the USGS EROS Center 2012-01-19 2025-01-07 -180, -90, 180, 90 False fire, land, nasa, noaa, surface, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP14A1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP14A1 proprietary
+NASA/VIIRS/002/VNP14A1 VNP14A1.002: Thermal Anomalies/Fire Daily L3 Global 1km SIN Grid image_collection ee.ImageCollection('NASA/VIIRS/002/VNP14A1') NASA LP DAAC at the USGS EROS Center 2012-01-19 2025-01-08 -180, -90, 180, 90 False fire, land, nasa, noaa, surface, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP14A1.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP14A1 proprietary
NASA/VIIRS/002/VNP15A2H VNP15A2H: LAI/FPAR 8-Day L4 Global 500m SIN Grid image_collection ee.ImageCollection('NASA/VIIRS/002/VNP15A2H') NASA LP DAAC at the USGS EROS Center 2012-01-17 2024-12-26 -180, -90, 180, 90 False land, nasa, noaa, surface, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP15A2H.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP15A2H proprietary
-NASA/VIIRS/002/VNP21A1D VNP21A1D.002: Day Land Surface Temperature and Emissivity Daily 1km image_collection ee.ImageCollection('NASA/VIIRS/002/VNP21A1D') NASA LP DAAC at the USGS EROS Center 2012-01-19 2025-01-07 -180, -90, 180, 90 False daily, day, land, nasa, noaa, surface, temperature, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP21A1D.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP21A1D proprietary
-NASA/VIIRS/002/VNP21A1N VNP21A1N.002: Night Land Surface Temperature and Emissivity Daily 1km image_collection ee.ImageCollection('NASA/VIIRS/002/VNP21A1N') NASA LP DAAC at the USGS EROS Center 2012-01-19 2025-01-06 -180, -90, 180, 90 False daily, land, nasa, night, noaa, surface, temperature, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP21A1N.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP21A1N proprietary
+NASA/VIIRS/002/VNP21A1D VNP21A1D.002: Day Land Surface Temperature and Emissivity Daily 1km image_collection ee.ImageCollection('NASA/VIIRS/002/VNP21A1D') NASA LP DAAC at the USGS EROS Center 2012-01-19 2025-01-08 -180, -90, 180, 90 False daily, day, land, nasa, noaa, surface, temperature, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP21A1D.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP21A1D proprietary
+NASA/VIIRS/002/VNP21A1N VNP21A1N.002: Night Land Surface Temperature and Emissivity Daily 1km image_collection ee.ImageCollection('NASA/VIIRS/002/VNP21A1N') NASA LP DAAC at the USGS EROS Center 2012-01-19 2025-01-08 -180, -90, 180, 90 False daily, land, nasa, night, noaa, surface, temperature, viirs https://storage.googleapis.com/earthengine-stac/catalog/NASA/NASA_VIIRS_002_VNP21A1N.json https://developers.google.com/earth-engine/datasets/catalog/NASA_VIIRS_002_VNP21A1N proprietary
NASA_USDA/HSL/SMAP10KM_soil_moisture NASA-USDA Enhanced SMAP Global Soil Moisture Data [deprecated] image_collection ee.ImageCollection('NASA_USDA/HSL/SMAP10KM_soil_moisture') NASA GSFC 2015-04-02 2022-08-02 -180, -60, 180, 90 True geophysical, hsl, nasa, smap, soil, soil_moisture, usda https://storage.googleapis.com/earthengine-stac/catalog/NASA_USDA/NASA_USDA_HSL_SMAP10KM_soil_moisture.json https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP10KM_soil_moisture proprietary
NASA_USDA/HSL/SMAP_soil_moisture NASA-USDA SMAP Global Soil Moisture Data [deprecated] image_collection ee.ImageCollection('NASA_USDA/HSL/SMAP_soil_moisture') NASA GSFC 2015-04-02 2020-12-31 -180, -60, 180, 90 True geophysical, hsl, nasa, smap, soil, soil_moisture, usda https://storage.googleapis.com/earthengine-stac/catalog/NASA_USDA/NASA_USDA_HSL_SMAP_soil_moisture.json https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_SMAP_soil_moisture proprietary
NASA_USDA/HSL/soil_moisture NASA-USDA Global Soil Moisture Data [deprecated] image_collection ee.ImageCollection('NASA_USDA/HSL/soil_moisture') NASA GSFC 2010-01-13 2020-12-31 -180, -60, 180, 90 True geophysical, hsl, nasa, smos, soil, soil_moisture, usda https://storage.googleapis.com/earthengine-stac/catalog/NASA_USDA/NASA_USDA_HSL_soil_moisture.json https://developers.google.com/earth-engine/datasets/catalog/NASA_USDA_HSL_soil_moisture proprietary
-NCEP_RE/sea_level_pressure NCEP/NCAR Reanalysis Data, Sea-Level Pressure image_collection ee.ImageCollection('NCEP_RE/sea_level_pressure') NCEP 1948-01-01 2025-01-06 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, pressure, reanalysis https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_sea_level_pressure.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_sea_level_pressure proprietary
-NCEP_RE/surface_temp NCEP/NCAR Reanalysis Data, Surface Temperature image_collection ee.ImageCollection('NCEP_RE/surface_temp') NCEP 1948-01-01 2025-01-06 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, reanalysis, temperature https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_temp.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_temp proprietary
-NCEP_RE/surface_wv NCEP/NCAR Reanalysis Data, Water Vapor image_collection ee.ImageCollection('NCEP_RE/surface_wv') NCEP 1948-01-01 2025-01-06 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, precipitable, reanalysis, vapor https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_wv.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_wv proprietary
+NCEP_RE/sea_level_pressure NCEP/NCAR Reanalysis Data, Sea-Level Pressure image_collection ee.ImageCollection('NCEP_RE/sea_level_pressure') NCEP 1948-01-01 2025-01-07 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, pressure, reanalysis https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_sea_level_pressure.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_sea_level_pressure proprietary
+NCEP_RE/surface_temp NCEP/NCAR Reanalysis Data, Surface Temperature image_collection ee.ImageCollection('NCEP_RE/surface_temp') NCEP 1948-01-01 2025-01-07 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, reanalysis, temperature https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_temp.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_temp proprietary
+NCEP_RE/surface_wv NCEP/NCAR Reanalysis Data, Water Vapor image_collection ee.ImageCollection('NCEP_RE/surface_wv') NCEP 1948-01-01 2025-01-07 -180, -90, 180, 90 False atmosphere, climate, geophysical, ncep, noaa, precipitable, reanalysis, vapor https://storage.googleapis.com/earthengine-stac/catalog/NCEP_RE/NCEP_RE_surface_wv.json https://developers.google.com/earth-engine/datasets/catalog/NCEP_RE_surface_wv proprietary
NOAA/CDR/ATMOS_NEAR_SURFACE/V2 NOAA CDR: Ocean Near-Surface Atmospheric Properties, Version 2 image_collection ee.ImageCollection('NOAA/CDR/ATMOS_NEAR_SURFACE/V2') NOAA 1988-01-01 2021-08-31 -180, -90, 180, 90 False air_temperature, atmospheric, cdr, hourly, humidity, noaa, ocean, osb, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_ATMOS_NEAR_SURFACE_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_ATMOS_NEAR_SURFACE_V2 proprietary
NOAA/CDR/AVHRR/AOT/V3 NOAA CDR AVHRR AOT: Daily Aerosol Optical Thickness Over Global Oceans, v03 [deprecated] image_collection ee.ImageCollection('NOAA/CDR/AVHRR/AOT/V3') NOAA 1981-01-01 2022-03-31 -180, -90, 180, 90 True aerosol, aot, atmospheric, avhrr, cdr, daily, noaa, optical, pollution https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_AOT_V3.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_AOT_V3 proprietary
NOAA/CDR/AVHRR/AOT/V4 NOAA CDR AVHRR AOT: Daily Aerosol Optical Thickness Over Global Oceans, v04 image_collection ee.ImageCollection('NOAA/CDR/AVHRR/AOT/V4') NOAA 1981-01-01 2024-09-30 -180, -90, 180, 90 False aerosol, aot, atmospheric, avhrr, cdr, daily, noaa, optical, pollution https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_AVHRR_AOT_V4.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_AOT_V4 proprietary
@@ -619,38 +619,38 @@ NOAA/CDR/AVHRR/SR/V5 NOAA CDR AVHRR: Surface Reflectance, Version 5 image_collec
NOAA/CDR/GRIDSAT-B1/V2 NOAA CDR GRIDSAT-B1: Geostationary IR Channel Brightness Temperature image_collection ee.ImageCollection('NOAA/CDR/GRIDSAT-B1/V2') NOAA 1980-01-01 2024-03-31 -180, -90, 180, 90 False brightness, cdr, fundamental, geostationary, infrared, isccp, noaa, reflectance, sr https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_GRIDSAT-B1_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_GRIDSAT-B1_V2 proprietary
NOAA/CDR/HEAT_FLUXES/V2 NOAA CDR: Ocean Heat Fluxes, Version 2 image_collection ee.ImageCollection('NOAA/CDR/HEAT_FLUXES/V2') NOAA 1988-01-01 2021-08-31 -180, -90, 180, 90 False atmospheric, cdr, flux, heat, hourly, noaa, ocean, osb https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_HEAT_FLUXES_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_HEAT_FLUXES_V2 proprietary
NOAA/CDR/OISST/V2 NOAA CDR OISST v2: Optimum Interpolation Sea Surface Temperature [deprecated] image_collection ee.ImageCollection('NOAA/CDR/OISST/V2') NOAA 1981-09-01 2020-04-26 -180, -90, 180, 90 True avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2 proprietary
-NOAA/CDR/OISST/V2_1 NOAA CDR OISST v02r01: Optimum Interpolation Sea Surface Temperature image_collection ee.ImageCollection('NOAA/CDR/OISST/V2_1') NOAA 1981-09-01 2025-01-07 -180, -90, 180, 90 False avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2_1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2_1 proprietary
+NOAA/CDR/OISST/V2_1 NOAA CDR OISST v02r01: Optimum Interpolation Sea Surface Temperature image_collection ee.ImageCollection('NOAA/CDR/OISST/V2_1') NOAA 1981-09-01 2025-01-08 -180, -90, 180, 90 False avhrr, cdr, daily, ice, noaa, ocean, oisst, real_time, sst, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_OISST_V2_1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_OISST_V2_1 proprietary
NOAA/CDR/PATMOSX/V53 NOAA CDR PATMOSX: Cloud Properties, Reflectance, and Brightness Temperatures, Version 5.3 image_collection ee.ImageCollection('NOAA/CDR/PATMOSX/V53') NOAA 1979-01-01 2022-01-01 -180, -90, 180, 90 False atmospheric, avhrr, brightness, cdr, cloud, metop, noaa, optical, poes, reflectance, temperature https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_PATMOSX_V53.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_PATMOSX_V53 proprietary
NOAA/CDR/SST_PATHFINDER/V53 NOAA AVHRR Pathfinder Version 5.3 Collated Global 4km Sea Surface Temperature image_collection ee.ImageCollection('NOAA/CDR/SST_PATHFINDER/V53') NOAA 1981-08-24 2023-12-30 -180, -90, 180, 90 False avhrr, noaa, pathfinder, sea_ice, sst, temperature, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_SST_PATHFINDER_V53.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_SST_PATHFINDER_V53 proprietary
NOAA/CDR/SST_WHOI/V2 NOAA CDR WHOI: Sea Surface Temperature, Version 2 image_collection ee.ImageCollection('NOAA/CDR/SST_WHOI/V2') NOAA 1988-01-01 2021-08-31 -180, -90, 180, 90 False atmospheric, cdr, hourly, noaa, ocean, oisst, osb, sst, whoi https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CDR_SST_WHOI_V2.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_SST_WHOI_V2 proprietary
-NOAA/CFSR CFSR: Climate Forecast System Reanalysis image_collection ee.ImageCollection('NOAA/CFSR') NOAA NWS National Centers for Environmental Prediction (NCEP) 2018-12-13 2025-01-09 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSR.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSR proprietary
-NOAA/CFSV2/FOR6H CFSV2: NCEP Climate Forecast System Version 2, 6-Hourly Products image_collection ee.ImageCollection('NOAA/CFSV2/FOR6H') NOAA NWS National Centers for Environmental Prediction (NCEP) 1979-01-01 2025-01-09 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSV2_FOR6H.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSV2_FOR6H proprietary
-NOAA/CPC/Precipitation CPC Global Unified Gauge-Based Analysis of Daily Precipitation image_collection ee.ImageCollection('NOAA/CPC/Precipitation') NOAA Physical Sciences Laboratory 2006-01-01 2025-01-07 -180, -90, 180, 90 False daily, noaa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CPC_Precipitation.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CPC_Precipitation proprietary
-NOAA/CPC/Temperature CPC Global Unified Temperature image_collection ee.ImageCollection('NOAA/CPC/Temperature') NOAA Physical Sciences Laboratory 1979-01-01 2025-01-08 -180, -90, 180, 90 False daily, noaa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CPC_Temperature.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CPC_Temperature proprietary
+NOAA/CFSR CFSR: Climate Forecast System Reanalysis image_collection ee.ImageCollection('NOAA/CFSR') NOAA NWS National Centers for Environmental Prediction (NCEP) 2018-12-13 2025-01-10 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSR.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSR proprietary
+NOAA/CFSV2/FOR6H CFSV2: NCEP Climate Forecast System Version 2, 6-Hourly Products image_collection ee.ImageCollection('NOAA/CFSV2/FOR6H') NOAA NWS National Centers for Environmental Prediction (NCEP) 1979-01-01 2025-01-10 -180, -90, 180, 90 False climate, daylight, flux, forecast, geophysical, ncep, noaa, nws, precipitation, radiation, snow, temperature, vapor, water, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CFSV2_FOR6H.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CFSV2_FOR6H proprietary
+NOAA/CPC/Precipitation CPC Global Unified Gauge-Based Analysis of Daily Precipitation image_collection ee.ImageCollection('NOAA/CPC/Precipitation') NOAA Physical Sciences Laboratory 2006-01-01 2025-01-08 -180, -90, 180, 90 False daily, noaa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CPC_Precipitation.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CPC_Precipitation proprietary
+NOAA/CPC/Temperature CPC Global Unified Temperature image_collection ee.ImageCollection('NOAA/CPC/Temperature') NOAA Physical Sciences Laboratory 1979-01-01 2025-01-09 -180, -90, 180, 90 False daily, noaa, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_CPC_Temperature.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_CPC_Temperature proprietary
NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4 DMSP OLS: Global Radiance-Calibrated Nighttime Lights Version 4, Defense Meteorological Program Operational Linescan System image_collection ee.ImageCollection('NOAA/DMSP-OLS/CALIBRATED_LIGHTS_V4') Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines 1996-03-16 2011-07-31 -180, -65, 180, 75 False calibrated, dmsp, eog, imagery, lights, nighttime, ols, radiance, visible, yearly https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_DMSP-OLS_CALIBRATED_LIGHTS_V4.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_CALIBRATED_LIGHTS_V4 proprietary
NOAA/DMSP-OLS/NIGHTTIME_LIGHTS DMSP OLS: Nighttime Lights Time Series Version 4, Defense Meteorological Program Operational Linescan System image_collection ee.ImageCollection('NOAA/DMSP-OLS/NIGHTTIME_LIGHTS') Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines 1992-01-01 2014-01-01 -180, -65, 180, 75 False dmsp, eog, imagery, lights, nighttime, ols, visible, yearly https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_DMSP-OLS_NIGHTTIME_LIGHTS proprietary
-NOAA/GFS0P25 GFS: Global Forecast System 384-Hour Predicted Atmosphere Data image_collection ee.ImageCollection('NOAA/GFS0P25') NOAA/NCEP/EMC 2015-07-01 2025-01-09 -180, -90, 180, 90 False climate, cloud, emc, flux, forecast, geophysical, gfs, humidity, ncep, noaa, precipitation, radiation, temperature, vapor, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GFS0P25.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GFS0P25 proprietary
-NOAA/GOES/16/FDCC GOES-16 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/16/FDCC') NOAA 2017-05-24 2025-01-09 -152.11, 14, -49.18, 56.77 False abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCC proprietary
-NOAA/GOES/16/FDCF GOES-16 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/16/FDCF') NOAA 2017-05-24 2025-01-09 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCF proprietary
-NOAA/GOES/16/MCMIPC GOES-16 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPC') NOAA 2017-07-10 2025-01-09 -152.11, 14, -49.18, 56.77 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPC proprietary
-NOAA/GOES/16/MCMIPF GOES-16 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPF') NOAA 2017-07-10 2025-01-09 -180, -90, 180, 90 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPF proprietary
-NOAA/GOES/16/MCMIPM GOES-16 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Mesoscale image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPM') NOAA 2017-07-10 2025-01-09 -180, -90, 180, 90 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPM.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPM proprietary
+NOAA/GFS0P25 GFS: Global Forecast System 384-Hour Predicted Atmosphere Data image_collection ee.ImageCollection('NOAA/GFS0P25') NOAA/NCEP/EMC 2015-07-01 2025-01-10 -180, -90, 180, 90 False climate, cloud, emc, flux, forecast, geophysical, gfs, humidity, ncep, noaa, precipitation, radiation, temperature, vapor, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GFS0P25.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GFS0P25 proprietary
+NOAA/GOES/16/FDCC GOES-16 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/16/FDCC') NOAA 2017-05-24 2025-01-10 -152.11, 14, -49.18, 56.77 False abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCC proprietary
+NOAA/GOES/16/FDCF GOES-16 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/16/FDCF') NOAA 2017-05-24 2025-01-10 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_16, goes_east, goes_r, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_FDCF proprietary
+NOAA/GOES/16/MCMIPC GOES-16 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPC') NOAA 2017-07-10 2025-01-10 -152.11, 14, -49.18, 56.77 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPC proprietary
+NOAA/GOES/16/MCMIPF GOES-16 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPF') NOAA 2017-07-10 2025-01-10 -180, -90, 180, 90 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPF proprietary
+NOAA/GOES/16/MCMIPM GOES-16 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Mesoscale image_collection ee.ImageCollection('NOAA/GOES/16/MCMIPM') NOAA 2017-07-10 2025-01-10 -180, -90, 180, 90 False abi, climate, goes, goes_16, goes_east, goes_r, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_16_MCMIPM.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_16_MCMIPM proprietary
NOAA/GOES/17/FDCC GOES-17 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/17/FDCC') NOAA 2018-08-27 2023-01-10 -180, 14.57, 180, 53.51 False abi, climate, fdc, fire, goes, goes_17, goes_s, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_17_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_FDCC proprietary
NOAA/GOES/17/FDCF GOES-17 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/17/FDCF') NOAA 2018-08-27 2023-01-10 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_17, goes_s, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_17_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_FDCF proprietary
NOAA/GOES/17/MCMIPC GOES-17 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/17/MCMIPC') NOAA 2018-12-04 2023-01-10 -180, 14.57, 180, 53.51 False abi, climate, goes, goes_17, goes_s, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_17_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPC proprietary
NOAA/GOES/17/MCMIPF GOES-17 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/17/MCMIPF') NOAA 2018-12-04 2023-01-10 -180, -90, 180, 90 False abi, climate, goes, goes_17, goes_s, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_17_MCMIPF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPF proprietary
NOAA/GOES/17/MCMIPM GOES-17 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/17/MCMIPM') NOAA 2018-12-04 2023-01-10 -180, -90, 180, 90 False abi, climate, goes, goes_17, goes_s, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_17_MCMIPM.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_17_MCMIPM proprietary
-NOAA/GOES/18/FDCC GOES-18 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/18/FDCC') NOAA 2022-10-13 2025-01-09 -180, 14.57, 180, 53.51 False abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_FDCC proprietary
-NOAA/GOES/18/FDCF GOES-18 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/FDCF') NOAA 2022-10-13 2025-01-09 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_FDCF proprietary
-NOAA/GOES/18/MCMIPC GOES-18 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPC') NOAA 2018-12-04 2025-01-09 -180, 14.57, 180, 53.51 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPC proprietary
-NOAA/GOES/18/MCMIPF GOES-18 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPF') NOAA 2018-12-04 2025-01-09 -180, -90, 180, 90 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPF proprietary
-NOAA/GOES/18/MCMIPM GOES-18 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPM') NOAA 2018-12-04 2025-01-09 -180, -90, 180, 90 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPM.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPM proprietary
+NOAA/GOES/18/FDCC GOES-18 FDCC Series ABI Level 2 Fire/Hot Spot Characterization CONUS image_collection ee.ImageCollection('NOAA/GOES/18/FDCC') NOAA 2022-10-13 2025-01-10 -180, 14.57, 180, 53.51 False abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_FDCC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_FDCC proprietary
+NOAA/GOES/18/FDCF GOES-18 FDCF Series ABI Level 2 Fire/Hot Spot Characterization Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/FDCF') NOAA 2022-10-13 2025-01-10 -180, -90, 180, 90 False abi, climate, fdc, fire, goes, goes_18, goes_t, goes_west, hotspot, nesdis, noaa, ospo, wildfire https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_FDCF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_FDCF proprietary
+NOAA/GOES/18/MCMIPC GOES-18 MCMIPC Series ABI Level 2 Cloud and Moisture Imagery CONUS image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPC') NOAA 2018-12-04 2025-01-10 -180, 14.57, 180, 53.51 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPC.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPC proprietary
+NOAA/GOES/18/MCMIPF GOES-18 MCMIPF Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPF') NOAA 2018-12-04 2025-01-10 -180, -90, 180, 90 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPF.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPF proprietary
+NOAA/GOES/18/MCMIPM GOES-18 MCMIPM Series ABI Level 2 Cloud and Moisture Imagery Full Disk image_collection ee.ImageCollection('NOAA/GOES/18/MCMIPM') NOAA 2018-12-04 2025-01-10 -180, -90, 180, 90 False abi, climate, goes, goes_18, goes_t, goes_west, mcmip, nesdis, noaa, ospo, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_GOES_18_MCMIPM.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_GOES_18_MCMIPM proprietary
NOAA/IBTrACS/v4 International Best Track Archive for Climate Stewardship Project table ee.FeatureCollection('NOAA/IBTrACS/v4') NOAA NCEI 1842-10-25 2024-05-19 -180, 0.4, 180, 63.1 False hurricane, noaa, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_IBTrACS_v4.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_IBTrACS_v4 proprietary
NOAA/NCEP_DOE_RE2/total_cloud_coverage NCEP-DOE Reanalysis 2 (Gaussian Grid), Total Cloud Coverage image_collection ee.ImageCollection('NOAA/NCEP_DOE_RE2/total_cloud_coverage') NOAA 1979-01-01 2024-12-31 -180, -90, 180, 90 False atmosphere, climate, cloud, geophysical, ncep, noaa, reanalysis https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NCEP_DOE_RE2_total_cloud_coverage.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NCEP_DOE_RE2_total_cloud_coverage proprietary
NOAA/NGDC/ETOPO1 ETOPO1: Global 1 Arc-Minute Elevation image ee.Image('NOAA/NGDC/ETOPO1') NOAA 2008-08-01 2008-08-01 -180, -90, 180, 90 False bedrock, dem, elevation, geophysical, ice, noaa, topography https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NGDC_ETOPO1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NGDC_ETOPO1 proprietary
NOAA/NHC/HURDAT2/atlantic NOAA NHC HURDAT2 Atlantic Hurricane Catalog table ee.FeatureCollection('NOAA/NHC/HURDAT2/atlantic') NOAA NHC 1851-06-25 2018-11-04 -109.5, 7.2, 63, 81 False hurricane, nhc, noaa, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NHC_HURDAT2_atlantic.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NHC_HURDAT2_atlantic proprietary
NOAA/NHC/HURDAT2/pacific NOAA NHC HURDAT2 Pacific Hurricane Catalog table ee.FeatureCollection('NOAA/NHC/HURDAT2/pacific') NOAA NHC 1949-06-11 2018-11-09 -180, 0.4, 180, 63.1 False hurricane, nhc, noaa, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NHC_HURDAT2_pacific.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NHC_HURDAT2_pacific proprietary
-NOAA/NWS/RTMA RTMA: Real-Time Mesoscale Analysis image_collection ee.ImageCollection('NOAA/NWS/RTMA') NOAA/NWS 2011-01-01 2025-01-09 -130.17, 20.15, -60.81, 52.91 False climate, cloud, geophysical, humidity, noaa, nws, precipitation, pressure, rtma, surface, temperature, visibility, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NWS_RTMA.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NWS_RTMA proprietary
+NOAA/NWS/RTMA RTMA: Real-Time Mesoscale Analysis image_collection ee.ImageCollection('NOAA/NWS/RTMA') NOAA/NWS 2011-01-01 2025-01-10 -130.17, 20.15, -60.81, 52.91 False climate, cloud, geophysical, humidity, noaa, nws, precipitation, pressure, rtma, surface, temperature, visibility, weather, wind https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_NWS_RTMA.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_NWS_RTMA proprietary
NOAA/PERSIANN-CDR PERSIANN-CDR: Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record image_collection ee.ImageCollection('NOAA/PERSIANN-CDR') NOAA NCDC 1983-01-01 2024-03-31 -180, -60, 180, 60 False cdr, climate, geophysical, ncdc, noaa, persiann, precipitation, weather https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_PERSIANN-CDR.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_PERSIANN-CDR proprietary
NOAA/VIIRS/001/VNP09GA VNP09GA: VIIRS Surface Reflectance Daily 500m and 1km [deprecated] image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP09GA') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-06-16 -180, -90, 180, 90 True daily, nasa, noaa, npp, reflectance, sr, viirs, vnp09ga https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP09GA.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP09GA proprietary
NOAA/VIIRS/001/VNP09H1 VNP09H1: VIIRS Surface Reflectance 8-Day L3 Global 500m [deprecated] image_collection ee.ImageCollection('NOAA/VIIRS/001/VNP09H1') NASA LP DAAC at the USGS EROS Center 2012-01-19 2024-06-09 -180, -90, 180, 90 True daily, nasa, noaa, npp, reflectance, sr, viirs https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_001_VNP09H1.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_001_VNP09H1 proprietary
@@ -671,7 +671,7 @@ NOAA/VIIRS/DNB/MONTHLY_V1/VCMCFG VIIRS Nighttime Day/Night Band Composites Versi
NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG VIIRS Stray Light Corrected Nighttime Day/Night Band Composites Version 1 image_collection ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG') Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines 2014-01-01 2024-08-01 -180, -65, 180, 75 False dnb, eog, lights, monthly, nighttime, noaa, stray_light, viirs, visible https://storage.googleapis.com/earthengine-stac/catalog/NOAA/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG.json https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_MONTHLY_V1_VCMSLCFG proprietary
NRCan/CDEM Canadian Digital Elevation Model image_collection ee.ImageCollection('NRCan/CDEM') NRCan 1945-01-01 2011-01-01 -142, 41, -52, 84 False canada, cdem, dem, elevation, geophysical, nrcan, topography https://storage.googleapis.com/earthengine-stac/catalog/NRCan/NRCan_CDEM.json https://developers.google.com/earth-engine/datasets/catalog/NRCan_CDEM OGL-Canada-2.0
Netherlands/Beeldmateriaal/LUCHTFOTO_RGB Netherlands orthophotos image_collection ee.ImageCollection('Netherlands/Beeldmateriaal/LUCHTFOTO_RGB') Beeldmateriaal Nederland 2021-01-01 2022-12-31 3.2, 50.75, 7.22, 53.7 False orthophoto, rgb, netherlands https://storage.googleapis.com/earthengine-stac/catalog/Netherlands/Netherlands_Beeldmateriaal_LUCHTFOTO_RGB.json https://developers.google.com/earth-engine/datasets/catalog/Netherlands_Beeldmateriaal_LUCHTFOTO_RGB CC-BY-4.0
-OREGONSTATE/PRISM/AN81d PRISM Daily Spatial Climate Dataset AN81d image_collection ee.ImageCollection('OREGONSTATE/PRISM/AN81d') PRISM / OREGONSTATE 1981-01-01 2025-01-06 -125, 24, -66, 50 False climate, daily, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_AN81d.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81d proprietary
+OREGONSTATE/PRISM/AN81d PRISM Daily Spatial Climate Dataset AN81d image_collection ee.ImageCollection('OREGONSTATE/PRISM/AN81d') PRISM / OREGONSTATE 1981-01-01 2025-01-07 -125, 24, -66, 50 False climate, daily, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_AN81d.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81d proprietary
OREGONSTATE/PRISM/AN81m PRISM Monthly Spatial Climate Dataset AN81m image_collection ee.ImageCollection('OREGONSTATE/PRISM/AN81m') PRISM / OREGONSTATE 1895-01-01 2024-12-01 -125, 24, -66, 50 False climate, geophysical, monthly, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_AN81m.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_AN81m proprietary
OREGONSTATE/PRISM/Norm81m PRISM Long-Term Average Climate Dataset Norm81m [deprecated] image_collection ee.ImageCollection('OREGONSTATE/PRISM/Norm81m') PRISM / OREGONSTATE 1981-01-01 2010-12-31 -125, 24, -66, 50 True 30_year, climate, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_Norm81m.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_Norm81m proprietary
OREGONSTATE/PRISM/Norm91m PRISM Long-Term Average Climate Dataset Norm91m image_collection ee.ImageCollection('OREGONSTATE/PRISM/Norm91m') PRISM / OREGONSTATE 1991-01-01 2020-12-31 -125, 24, -66, 50 False 30_year, climate, geophysical, oregonstate, precipitation, pressure, prism, temperature, vapor, weather https://storage.googleapis.com/earthengine-stac/catalog/OREGONSTATE/OREGONSTATE_PRISM_Norm91m.json https://developers.google.com/earth-engine/datasets/catalog/OREGONSTATE_PRISM_Norm91m proprietary
@@ -734,7 +734,7 @@ TIGER/2018/States TIGER: US Census States 2018 table ee.FeatureCollection('TIGER
TIGER/2020/BG TIGER: US Census Block Groups (BG) 2020 table ee.FeatureCollection('TIGER/2020/BG') United States Census Bureau 2020-01-01 2020-01-02 -180, -14.69, -64.435, 71.567 False census, city, neighborhood, tiger, urban, us https://storage.googleapis.com/earthengine-stac/catalog/TIGER/TIGER_2020_BG.json https://developers.google.com/earth-engine/datasets/catalog/TIGER_2020_BG proprietary
TIGER/2020/TABBLOCK20 TIGER: 2020 Tabulation (Census) Block table ee.FeatureCollection('TIGER/2020/TABBLOCK20') United States Census Bureau 2020-01-01 2020-01-02 -180, -14.69, -64.435, 71.567 False census, city, neighborhood, tiger, urban, us https://storage.googleapis.com/earthengine-stac/catalog/TIGER/TIGER_2020_TABBLOCK20.json https://developers.google.com/earth-engine/datasets/catalog/TIGER_2020_TABBLOCK20 proprietary
TIGER/2020/TRACT TIGER: US Census Tracts table ee.FeatureCollection('TIGER/2020/TRACT') United States Census Bureau 2020-01-01 2020-01-02 -180, -14.69, -64.435, 71.567 False census, city, neighborhood, tiger, urban, us https://storage.googleapis.com/earthengine-stac/catalog/TIGER/TIGER_2020_TRACT.json https://developers.google.com/earth-engine/datasets/catalog/TIGER_2020_TRACT proprietary
-TOMS/MERGED TOMS and OMI Merged Ozone Data image_collection ee.ImageCollection('TOMS/MERGED') NASA / GES DISC 1978-11-01 2025-01-07 -180, -90, 180, 90 False atmosphere, aura, climate, geophysical, ges_disc, goddard, nasa, omi, ozone, toms https://storage.googleapis.com/earthengine-stac/catalog/TOMS/TOMS_MERGED.json https://developers.google.com/earth-engine/datasets/catalog/TOMS_MERGED proprietary
+TOMS/MERGED TOMS and OMI Merged Ozone Data image_collection ee.ImageCollection('TOMS/MERGED') NASA / GES DISC 1978-11-01 2025-01-08 -180, -90, 180, 90 False atmosphere, aura, climate, geophysical, ges_disc, goddard, nasa, omi, ozone, toms https://storage.googleapis.com/earthengine-stac/catalog/TOMS/TOMS_MERGED.json https://developers.google.com/earth-engine/datasets/catalog/TOMS_MERGED proprietary
TRMM/3B42 TRMM 3B42: 3-Hourly Precipitation Estimates image_collection ee.ImageCollection('TRMM/3B42') NASA GES DISC at NASA Goddard Space Flight Center 1998-01-01 2019-12-31 -180, -50, 180, 50 False 3_hourly, climate, geophysical, jaxa, nasa, precipitation, rainfall, trmm, weather https://storage.googleapis.com/earthengine-stac/catalog/TRMM/TRMM_3B42.json https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B42 proprietary
TRMM/3B43V7 TRMM 3B43: Monthly Precipitation Estimates image_collection ee.ImageCollection('TRMM/3B43V7') NASA GES DISC at NASA Goddard Space Flight Center 1998-01-01 2019-12-01 -180, -50, 180, 50 False climate, geophysical, jaxa, nasa, precipitation, rainfall, trmm, weather https://storage.googleapis.com/earthengine-stac/catalog/TRMM/TRMM_3B43V7.json https://developers.google.com/earth-engine/datasets/catalog/TRMM_3B43V7 proprietary
TUBerlin/BigEarthNet/v1 TUBerlin/BigEarthNet/v1 image_collection ee.ImageCollection('TUBerlin/BigEarthNet/v1') BigEarthNet 2017-06-01 2018-05-31 -9, 36.9, 31.6, 68.1 False chip, copernicus, corine_derived, label, ml, sentinel, tile https://storage.googleapis.com/earthengine-stac/catalog/TUBerlin/TUBerlin_BigEarthNet_v1.json https://developers.google.com/earth-engine/datasets/catalog/TUBerlin_BigEarthNet_v1 proprietary
@@ -823,7 +823,7 @@ USGS/WBD/2017/HUC06 HUC06: USGS Watershed Boundary Dataset of Basins table ee.Fe
USGS/WBD/2017/HUC08 HUC08: USGS Watershed Boundary Dataset of Subbasins table ee.FeatureCollection('USGS/WBD/2017/HUC08') United States Geological Survey 2017-04-22 2017-04-23 -180, -14.69, 180, 71.567 False hydrology, usgs, water, watershed, wbd https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_WBD_2017_HUC08.json https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC08 proprietary
USGS/WBD/2017/HUC10 HUC10: USGS Watershed Boundary Dataset of Watersheds table ee.FeatureCollection('USGS/WBD/2017/HUC10') United States Geological Survey 2017-04-22 2017-04-23 -180, -14.69, 180, 71.567 False hydrology, usgs, water, watershed, wbd https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_WBD_2017_HUC10.json https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC10 proprietary
USGS/WBD/2017/HUC12 HUC12: USGS Watershed Boundary Dataset of Subwatersheds table ee.FeatureCollection('USGS/WBD/2017/HUC12') United States Geological Survey 2017-04-22 2017-04-23 -180, -14.69, 180, 71.567 False hydrology, usgs, water, watershed, wbd https://storage.googleapis.com/earthengine-stac/catalog/USGS/USGS_WBD_2017_HUC12.json https://developers.google.com/earth-engine/datasets/catalog/USGS_WBD_2017_HUC12 proprietary
-UTOKYO/WTLAB/KBDI/v1 KBDI: Keetch-Byram Drought Index image_collection ee.ImageCollection('UTOKYO/WTLAB/KBDI/v1') Institute of Industrial Science, The University of Tokyo, Japan 2007-01-01 2025-01-08 60, -60, 180, 60 False drought, kbdi, lst_derived, rainfall, utokyo, wtlab https://storage.googleapis.com/earthengine-stac/catalog/UTOKYO/UTOKYO_WTLAB_KBDI_v1.json https://developers.google.com/earth-engine/datasets/catalog/UTOKYO_WTLAB_KBDI_v1 CC-BY-4.0
+UTOKYO/WTLAB/KBDI/v1 KBDI: Keetch-Byram Drought Index image_collection ee.ImageCollection('UTOKYO/WTLAB/KBDI/v1') Institute of Industrial Science, The University of Tokyo, Japan 2007-01-01 2025-01-09 60, -60, 180, 60 False drought, kbdi, lst_derived, rainfall, utokyo, wtlab https://storage.googleapis.com/earthengine-stac/catalog/UTOKYO/UTOKYO_WTLAB_KBDI_v1.json https://developers.google.com/earth-engine/datasets/catalog/UTOKYO_WTLAB_KBDI_v1 CC-BY-4.0
VITO/PROBAV/C1/S1_TOC_100M PROBA-V C1 Top Of Canopy Daily Synthesis 100m image_collection ee.ImageCollection('VITO/PROBAV/C1/S1_TOC_100M') Vito / ESA 2013-10-17 2021-10-31 -180, -90, 180, 90 False esa, multispectral, nir, proba, probav, swir, vito https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_C1_S1_TOC_100M.json https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_C1_S1_TOC_100M proprietary
VITO/PROBAV/C1/S1_TOC_333M PROBA-V C1 Top Of Canopy Daily Synthesis 333m image_collection ee.ImageCollection('VITO/PROBAV/C1/S1_TOC_333M') Vito / ESA 2013-10-17 2021-10-31 -180, -90, 180, 90 False esa, multispectral, nir, proba, probav, swir, vito https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_C1_S1_TOC_333M.json https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_C1_S1_TOC_333M proprietary
VITO/PROBAV/S1_TOC_100M PROBA-V C0 Top Of Canopy Daily Synthesis 100m [deprecated] image_collection ee.ImageCollection('VITO/PROBAV/S1_TOC_100M') Vito / ESA 2013-10-17 2016-12-14 -180, -90, 180, 90 True esa, multispectral, nir, proba, probav, swir, vito https://storage.googleapis.com/earthengine-stac/catalog/VITO/VITO_PROBAV_S1_TOC_100M.json https://developers.google.com/earth-engine/datasets/catalog/VITO_PROBAV_S1_TOC_100M proprietary
@@ -900,7 +900,7 @@ projects/forestdatapartnership/assets/community_forests/ForestPersistence_2020 F
projects/forestdatapartnership/assets/community_palm/20240312 Palm Probability v20240312 [deprecated] image_collection ee.ImageCollection('projects/forestdatapartnership/assets/community_palm/20240312') Produced by Google for the Forest Data Partnership 2020-01-01 2023-12-31 92.99, -11.94, 132.71, 11.71 True deforestation, eudr, biodiversity, conservation, crop, landuse, palm, plantation https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_community_palm_20240312.json https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_community_palm_20240312 CC-BY-4.0
projects/forestdatapartnership/assets/palm/model_2024a Palm Probability model 2024a image_collection ee.ImageCollection('projects/forestdatapartnership/assets/palm/model_2024a') Produced by Google for the Forest Data Partnership 2020-01-01 2023-12-31 -180, -90, 180, 90 False eudr, biodiversity, conservation, crop, landuse, palm, plantation https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_palm_model_2024a.json https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_palm_model_2024a CC-BY-NC-4.0
projects/forestdatapartnership/assets/rubber/model_2024a Rubber Tree Probability model 2024a image_collection ee.ImageCollection('projects/forestdatapartnership/assets/rubber/model_2024a') Produced by Google for the Forest Data Partnership 2020-01-01 2023-12-31 -180, -90, 180, 90 False eudr, biodiversity, conservation, crop, landuse, rubber, plantation, pre_review https://storage.googleapis.com/earthengine-stac/catalog/forestdatapartnership/projects_forestdatapartnership_assets_rubber_model_2024a.json https://developers.google.com/earth-engine/datasets/catalog/projects_forestdatapartnership_assets_rubber_model_2024a CC-BY-NC-4.0
-projects/gcp-public-data-weathernext/assets/59572747_4_0 WeatherNext Graph Forecasts image_collection ee.ImageCollection('projects/gcp-public-data-weathernext/assets/59572747_4_0') Google 2020-01-01 2025-01-09 -180, -90, 180, 90 False weather, weathernext, forecast, temperature, precipitation, wind https://storage.googleapis.com/earthengine-stac/catalog/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_59572747_4_0.json https://developers.google.com/earth-engine/datasets/catalog/projects_gcp-public-data-weathernext_assets_59572747_4_0 proprietary
+projects/gcp-public-data-weathernext/assets/59572747_4_0 WeatherNext Graph Forecasts image_collection ee.ImageCollection('projects/gcp-public-data-weathernext/assets/59572747_4_0') Google 2020-01-01 2025-01-10 -180, -90, 180, 90 False weather, weathernext, forecast, temperature, precipitation, wind https://storage.googleapis.com/earthengine-stac/catalog/gcp-public-data-weathernext/projects_gcp-public-data-weathernext_assets_59572747_4_0.json https://developers.google.com/earth-engine/datasets/catalog/projects_gcp-public-data-weathernext_assets_59572747_4_0 proprietary
projects/geoscience-aus-cat/assets/NIDEM National Intertidal Digital Elevation Model 25m 1.0.0 [deprecated] image ee.Image('projects/geoscience-aus-cat/assets/NIDEM') Geoscience Australia 1986-08-16 2017-07-31 108.81, -44.41, 157.82, -9.13 True australia, ga, dem, landsat_derived https://storage.googleapis.com/earthengine-stac/catalog/geoscience-aus-cat/projects_geoscience-aus-cat_assets_NIDEM.json https://developers.google.com/earth-engine/datasets/catalog/projects_geoscience-aus-cat_assets_NIDEM CC-BY-4.0
projects/geoscience-aus-cat/assets/ga_ls5t_nbart_gm_cyear_3 DEA Geometric Median and Median Absolute Deviation - Landsat 5 3.1.0 [deprecated] image_collection ee.ImageCollection('projects/geoscience-aus-cat/assets/ga_ls5t_nbart_gm_cyear_3') Geoscience Australia 1998-01-01 2012-01-01 108.81, -44.41, 157.82, -9.13 True australia, ga, landsat_derived https://storage.googleapis.com/earthengine-stac/catalog/geoscience-aus-cat/projects_geoscience-aus-cat_assets_ga_ls5t_nbart_gm_cyear_3.json https://developers.google.com/earth-engine/datasets/catalog/projects_geoscience-aus-cat_assets_ga_ls5t_nbart_gm_cyear_3 CC-BY-4.0
projects/geoscience-aus-cat/assets/ga_ls7e_nbart_gm_cyear_3 DEA Geometric Median and Median Absolute Deviation - Landsat 7 3.1.0 [deprecated] image_collection ee.ImageCollection('projects/geoscience-aus-cat/assets/ga_ls7e_nbart_gm_cyear_3') Geoscience Australia 2000-01-01 2021-01-01 108.81, -44.41, 157.82, -9.13 True australia, ga, landsat_derived https://storage.googleapis.com/earthengine-stac/catalog/geoscience-aus-cat/projects_geoscience-aus-cat_assets_ga_ls7e_nbart_gm_cyear_3.json https://developers.google.com/earth-engine/datasets/catalog/projects_geoscience-aus-cat_assets_ga_ls7e_nbart_gm_cyear_3 CC-BY-4.0
diff --git a/nasa_cmr_catalog.json b/nasa_cmr_catalog.json
index 274771a..c1d9239 100644
--- a/nasa_cmr_catalog.json
+++ b/nasa_cmr_catalog.json
@@ -106,7 +106,7 @@
{
"id": "0944645",
"title": "Age and Composition of the East Antarctic Shield",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -119,7 +119,7 @@
{
"id": "0944645",
"title": "Age and Composition of the East Antarctic Shield",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -496,7 +496,7 @@
{
"id": "10.25921/3edp-9d76_Not Applicable",
"title": "Alabama Near Coastal Meteorological & Hydrographic Continuous Data Sampling from 2003 to present",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-02-24",
"end_date": "",
"bbox": "-88.213, 30.09, -87.56, 30.66713",
@@ -509,7 +509,7 @@
{
"id": "10.25921/3edp-9d76_Not Applicable",
"title": "Alabama Near Coastal Meteorological & Hydrographic Continuous Data Sampling from 2003 to present",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2003-02-24",
"end_date": "",
"bbox": "-88.213, 30.09, -87.56, 30.66713",
@@ -574,7 +574,7 @@
{
"id": "10.25921/5p69-y471_Not Applicable",
"title": "A global monthly climatology of total alkalinity (AT): a neural network approach (NCEI Accession 0222470)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1957-01-01",
"end_date": "2018-12-31",
"bbox": "-179.5, -77.5, 179.5, 89.5",
@@ -587,7 +587,7 @@
{
"id": "10.25921/5p69-y471_Not Applicable",
"title": "A global monthly climatology of total alkalinity (AT): a neural network approach (NCEI Accession 0222470)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1957-01-01",
"end_date": "2018-12-31",
"bbox": "-179.5, -77.5, 179.5, 89.5",
@@ -600,7 +600,7 @@
{
"id": "10.25921/66nr-kv23_Not Applicable",
"title": "Adult Japanese eel, Anguilla japonica, by mid water trawl net, water temperature and salinity by CTD, and other parameters collected from the research vessel Kaiyo-maru, cruise KY1302, in the North Pacific from 2013-05-23 to 2013-07-16 (NCEI Accession 0224416)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2013-05-23",
"end_date": "2013-07-16",
"bbox": "140.35, 10.5, 143.55, 20",
@@ -613,7 +613,7 @@
{
"id": "10.25921/66nr-kv23_Not Applicable",
"title": "Adult Japanese eel, Anguilla japonica, by mid water trawl net, water temperature and salinity by CTD, and other parameters collected from the research vessel Kaiyo-maru, cruise KY1302, in the North Pacific from 2013-05-23 to 2013-07-16 (NCEI Accession 0224416)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-05-23",
"end_date": "2013-07-16",
"bbox": "140.35, 10.5, 143.55, 20",
@@ -691,7 +691,7 @@
{
"id": "10.25921/9hsn-xq82_Not Applicable",
"title": "A combined globally mapped carbon dioxide (CO2) flux estimate based on the Surface Ocean CO2 Atlas Database (SOCAT) and Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) biogeochemistry floats from 1982 to 2017 (NCEI Accession 0191304)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1982-01-01",
"end_date": "2017-12-31",
"bbox": "-180, -89.5, 180, 89.5",
@@ -704,7 +704,7 @@
{
"id": "10.25921/9hsn-xq82_Not Applicable",
"title": "A combined globally mapped carbon dioxide (CO2) flux estimate based on the Surface Ocean CO2 Atlas Database (SOCAT) and Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) biogeochemistry floats from 1982 to 2017 (NCEI Accession 0191304)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1982-01-01",
"end_date": "2017-12-31",
"bbox": "-180, -89.5, 180, 89.5",
@@ -743,7 +743,7 @@
{
"id": "10.25921/c1sn-9631_Not Applicable",
"title": "A comprehensive global oceanic dataset of discrete measurements of helium isotope and tritium during the hydrographic cruises on various ships from 1952-10-21 to 2016-01-22 (NCEI Accession 0176626)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1952-10-21",
"end_date": "2016-01-22",
"bbox": "-179.98, -82.38, 180, 90",
@@ -756,7 +756,7 @@
{
"id": "10.25921/c1sn-9631_Not Applicable",
"title": "A comprehensive global oceanic dataset of discrete measurements of helium isotope and tritium during the hydrographic cruises on various ships from 1952-10-21 to 2016-01-22 (NCEI Accession 0176626)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1952-10-21",
"end_date": "2016-01-22",
"bbox": "-179.98, -82.38, 180, 90",
@@ -1393,7 +1393,7 @@
{
"id": "10.3334/cdiac/otg.carina_omex2_Not Applicable",
"title": "Alkalinity, temperature, salinity and other variables collected from discrete sample and profile observations using CTD, bottle and other instruments from the BELGICA, CHARLES DARWIN and METEOR in the North Atlantic Ocean from 1997-06-01 to 1999-09-01 (NCEI Accession 0115763)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1997-06-01",
"end_date": "1999-09-01",
"bbox": "-10.6353, 36.5522, -7.0757, 47.7569",
@@ -1406,7 +1406,7 @@
{
"id": "10.3334/cdiac/otg.carina_omex2_Not Applicable",
"title": "Alkalinity, temperature, salinity and other variables collected from discrete sample and profile observations using CTD, bottle and other instruments from the BELGICA, CHARLES DARWIN and METEOR in the North Atlantic Ocean from 1997-06-01 to 1999-09-01 (NCEI Accession 0115763)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-06-01",
"end_date": "1999-09-01",
"bbox": "-10.6353, 36.5522, -7.0757, 47.7569",
@@ -1471,7 +1471,7 @@
{
"id": "10.3334/cdiac/otg.pacifica_49nz20040901_Not Applicable",
"title": "Alkalinity, temperature, salinity and other variables collected from discrete sample and profile observations using CTD, Coulometer for DIC measurement and other instruments from MIRAI in the Arctic Ocean and Beaufort Sea from 2004-09-01 to 2004-10-13 (NCEI Accession 0112357)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2004-09-01",
"end_date": "2004-10-13",
"bbox": "179.501, 67, -144.988, 76.581",
@@ -1484,7 +1484,7 @@
{
"id": "10.3334/cdiac/otg.pacifica_49nz20040901_Not Applicable",
"title": "Alkalinity, temperature, salinity and other variables collected from discrete sample and profile observations using CTD, Coulometer for DIC measurement and other instruments from MIRAI in the Arctic Ocean and Beaufort Sea from 2004-09-01 to 2004-10-13 (NCEI Accession 0112357)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-09-01",
"end_date": "2004-10-13",
"bbox": "179.501, 67, -144.988, 76.581",
@@ -1770,7 +1770,7 @@
{
"id": "10.7289/v51v5bzm_Not Applicable",
"title": "Aerial Surveys of Arctic Marine Mammals (ASAMM) collected by National Marine Mammal Laboratory, Bureau of Ocean Energy Management, and other agencies in the Arctic Ocean, Bering, Chukchi and Beaufort Seas from 1979-04-21 to 2019-10-29 (NCEI Accession 0039614)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1979-04-21",
"end_date": "2019-10-29",
"bbox": "-174.01, 57.73, -125.25, 76.15",
@@ -1783,7 +1783,7 @@
{
"id": "10.7289/v51v5bzm_Not Applicable",
"title": "Aerial Surveys of Arctic Marine Mammals (ASAMM) collected by National Marine Mammal Laboratory, Bureau of Ocean Energy Management, and other agencies in the Arctic Ocean, Bering, Chukchi and Beaufort Seas from 1979-04-21 to 2019-10-29 (NCEI Accession 0039614)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1979-04-21",
"end_date": "2019-10-29",
"bbox": "-174.01, 57.73, -125.25, 76.15",
@@ -2511,7 +2511,7 @@
{
"id": "12-hourly_interpolated_surface_velocity_from_buoys",
"title": "12-Hourly Interpolated Surface Velocity from Buoys",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1979-01-01",
"end_date": "2009-12-02",
"bbox": "-180, 74, 180, 90",
@@ -2524,7 +2524,7 @@
{
"id": "12-hourly_interpolated_surface_velocity_from_buoys",
"title": "12-Hourly Interpolated Surface Velocity from Buoys",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1979-01-01",
"end_date": "2009-12-02",
"bbox": "-180, 74, 180, 90",
@@ -2628,7 +2628,7 @@
{
"id": "16920eb2-2eaf-4629-8337-3626e70e4770",
"title": "Africa - Photovoltaic Solar Electricity Potential",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2008-12-31",
"bbox": "-24.960938, -35.859375, 61.523438, 46.40625",
@@ -2641,7 +2641,7 @@
{
"id": "16920eb2-2eaf-4629-8337-3626e70e4770",
"title": "Africa - Photovoltaic Solar Electricity Potential",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2008-12-31",
"bbox": "-24.960938, -35.859375, 61.523438, 46.40625",
@@ -2667,7 +2667,7 @@
{
"id": "1747-ESDD",
"title": "Alaskan Geologic Photography Collection from USGS",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1898-01-01",
"end_date": "",
"bbox": "-179, 50, -140, 72",
@@ -2680,7 +2680,7 @@
{
"id": "1747-ESDD",
"title": "Alaskan Geologic Photography Collection from USGS",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1898-01-01",
"end_date": "",
"bbox": "-179, 50, -140, 72",
@@ -2992,7 +2992,7 @@
{
"id": "1994-1997_S_GW_GG04_AN_ISOTOPE",
"title": "A Preliminary Study on Oxygen Isotopes of Ice Cores from Collins Ice Cap, King George Island, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1994-01-01",
"end_date": "1997-12-30",
"bbox": "-58.97, -62.17, -58.97, -62.17",
@@ -3005,7 +3005,7 @@
{
"id": "1994-1997_S_GW_GG04_AN_ISOTOPE",
"title": "A Preliminary Study on Oxygen Isotopes of Ice Cores from Collins Ice Cap, King George Island, Antarctica",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1994-01-01",
"end_date": "1997-12-30",
"bbox": "-58.97, -62.17, -58.97, -62.17",
@@ -3161,7 +3161,7 @@
{
"id": "1996-1997_13-13_S_OC_OC05_LO_O011301_000_R0_Y",
"title": "1996-1997 Raw data of CTD in Prydz Bay region of the southern Indian Ocean, CHINARE-13",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-01-01",
"end_date": "1997-01-01",
"bbox": "70, -70, 78, -64",
@@ -3174,7 +3174,7 @@
{
"id": "1996-1997_13-13_S_OC_OC05_LO_O011301_000_R0_Y",
"title": "1996-1997 Raw data of CTD in Prydz Bay region of the southern Indian Ocean, CHINARE-13",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1997-01-01",
"end_date": "1997-01-01",
"bbox": "70, -70, 78, -64",
@@ -4188,7 +4188,7 @@
{
"id": "200708_CEAMARC_CASO_TRACE_ELEMENT_SAMPLES_1",
"title": "2007-08 CEAMARC-CASO VOYAGE TRACE ELEMENT SAMPLING AROUND AN ICEBERG",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2008-01-01",
"end_date": "2008-03-20",
"bbox": "139.01488, -67.07104, 150.06479, -42.88246",
@@ -4201,7 +4201,7 @@
{
"id": "200708_CEAMARC_CASO_TRACE_ELEMENT_SAMPLES_1",
"title": "2007-08 CEAMARC-CASO VOYAGE TRACE ELEMENT SAMPLING AROUND AN ICEBERG",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2008-01-01",
"end_date": "2008-03-20",
"bbox": "139.01488, -67.07104, 150.06479, -42.88246",
@@ -4214,7 +4214,7 @@
{
"id": "200712_imnavait_field",
"title": "200712_Imnavait_field",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-06-22",
"end_date": "2012-06-22",
"bbox": "-180, -90, 180, 90",
@@ -4227,7 +4227,7 @@
{
"id": "200712_imnavait_field",
"title": "200712_Imnavait_field",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2012-06-22",
"end_date": "2012-06-22",
"bbox": "-180, -90, 180, 90",
@@ -4318,7 +4318,7 @@
{
"id": "200811_barrow_field_photos",
"title": "200811_Barrow_field_photos",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2008-11-01",
"end_date": "2008-12-01",
"bbox": "-156.7, 71, -156.4, 71.5",
@@ -4331,7 +4331,7 @@
{
"id": "200811_barrow_field_photos",
"title": "200811_Barrow_field_photos",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2008-11-01",
"end_date": "2008-12-01",
"bbox": "-156.7, 71, -156.4, 71.5",
@@ -4344,7 +4344,7 @@
{
"id": "2008_carbon_water_and_energy_balance_unburned_site",
"title": "2008 carbon, water, and Energy balance Unburned site",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2008-06-01",
"end_date": "2008-08-31",
"bbox": "-150.3, 68.9, -150.3, 68.9",
@@ -4357,7 +4357,7 @@
{
"id": "2008_carbon_water_and_energy_balance_unburned_site",
"title": "2008 carbon, water, and Energy balance Unburned site",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2008-06-01",
"end_date": "2008-08-31",
"bbox": "-150.3, 68.9, -150.3, 68.9",
@@ -4370,7 +4370,7 @@
{
"id": "2008_carbon_water_energy_balance_moderately_burned_site",
"title": "2008 carbon, water, energy balance moderately burned site",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2008-06-01",
"end_date": "2008-08-31",
"bbox": "-150.2, 69, -150.2, 69",
@@ -4383,7 +4383,7 @@
{
"id": "2008_carbon_water_energy_balance_moderately_burned_site",
"title": "2008 carbon, water, energy balance moderately burned site",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2008-06-01",
"end_date": "2008-08-31",
"bbox": "-150.2, 69, -150.2, 69",
@@ -4682,7 +4682,7 @@
{
"id": "2010_hydgrographic_chlorophyll_cdom_fluor_opt_backscatt_data_coll_acro_tow_prof",
"title": "2010 Hydgrographic, chlorophyll and CDOM fluorescence, and optical backscattering data collected using an Acrobat towed profiler",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-08-21",
"end_date": "2010-08-31",
"bbox": "-158, 71.3, -153.5, 72",
@@ -4695,7 +4695,7 @@
{
"id": "2010_hydgrographic_chlorophyll_cdom_fluor_opt_backscatt_data_coll_acro_tow_prof",
"title": "2010 Hydgrographic, chlorophyll and CDOM fluorescence, and optical backscattering data collected using an Acrobat towed profiler",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2010-08-21",
"end_date": "2010-08-31",
"bbox": "-158, 71.3, -153.5, 72",
@@ -4708,7 +4708,7 @@
{
"id": "2010_niskin_bottle_data_chlorophyll_nutrients_picoplankton",
"title": "2010 Niskin Bottle Data (chlorophyll, nutrients, picoplankton)",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-08-21",
"end_date": "2010-09-08",
"bbox": "-158, 71.3, -153.5, 72",
@@ -4721,7 +4721,7 @@
{
"id": "2010_niskin_bottle_data_chlorophyll_nutrients_picoplankton",
"title": "2010 Niskin Bottle Data (chlorophyll, nutrients, picoplankton)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2010-08-21",
"end_date": "2010-09-08",
"bbox": "-158, 71.3, -153.5, 72",
@@ -5488,7 +5488,7 @@
{
"id": "234Th_data_0",
"title": "234Th and POC data in the North Pacific",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1997-11-12",
"end_date": "2008-10-28",
"bbox": "142.5, 35, 145, 57",
@@ -5501,7 +5501,7 @@
{
"id": "234Th_data_0",
"title": "234Th and POC data in the North Pacific",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-11-12",
"end_date": "2008-10-28",
"bbox": "142.5, 35, 145, 57",
@@ -5566,7 +5566,7 @@
{
"id": "28458e44db959dd2b1e920457964665327a333f6",
"title": "3 year daily average solar exposure map Mali 3Km GRAS December 2008-2011",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-15, 8, 5, 28",
@@ -5579,7 +5579,7 @@
{
"id": "28458e44db959dd2b1e920457964665327a333f6",
"title": "3 year daily average solar exposure map Mali 3Km GRAS December 2008-2011",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-15, 8, 5, 28",
@@ -5709,7 +5709,7 @@
{
"id": "3-hourly_interpolated_buoy_data",
"title": "3-Hourly Interpolated Buoy Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2004-01-01",
"end_date": "2005-12-01",
"bbox": "-180, 45, 180, 90",
@@ -5722,7 +5722,7 @@
{
"id": "3-hourly_interpolated_buoy_data",
"title": "3-Hourly Interpolated Buoy Data",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-01-01",
"end_date": "2005-12-01",
"bbox": "-180, 45, 180, 90",
@@ -6021,7 +6021,7 @@
{
"id": "39234_Not Applicable",
"title": "Agrihan Island IKONOS Imagery - IKONOS Imagery for the Northern Mariana Islands, 2001-2003",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2003-01-01",
"bbox": "-180, -90, 180, 90",
@@ -6034,7 +6034,7 @@
{
"id": "39234_Not Applicable",
"title": "Agrihan Island IKONOS Imagery - IKONOS Imagery for the Northern Mariana Islands, 2001-2003",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2003-01-01",
"bbox": "-180, -90, 180, 90",
@@ -6047,7 +6047,7 @@
{
"id": "39235_Not Applicable",
"title": "Aguijan Island IKONOS Imagery - IKONOS Imagery for the Northern Mariana Islands, 2001-2003",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2003-01-01",
"bbox": "-180, -90, 180, 90",
@@ -6060,7 +6060,7 @@
{
"id": "39235_Not Applicable",
"title": "Aguijan Island IKONOS Imagery - IKONOS Imagery for the Northern Mariana Islands, 2001-2003",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2003-01-01",
"bbox": "-180, -90, 180, 90",
@@ -6528,7 +6528,7 @@
{
"id": "39332_Not Applicable",
"title": "2000 Photo Mosaics and Hyperspectral Imagery for the Main Eight Hawaiian Islands Utilized to Map Shallow Water Benthic Habitats",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2000-01-01",
"bbox": "-180, -90, 180, 90",
@@ -6541,7 +6541,7 @@
{
"id": "39332_Not Applicable",
"title": "2000 Photo Mosaics and Hyperspectral Imagery for the Main Eight Hawaiian Islands Utilized to Map Shallow Water Benthic Habitats",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2000-01-01",
"bbox": "-180, -90, 180, 90",
@@ -6684,7 +6684,7 @@
{
"id": "39383_Not Applicable",
"title": "Accuracy Assessment Field Data for the Main Eight Hawaiian Islands UTM Zone 4",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-01-01",
"end_date": "2006-01-01",
"bbox": "-180, -90, 180, 90",
@@ -6697,7 +6697,7 @@
{
"id": "39383_Not Applicable",
"title": "Accuracy Assessment Field Data for the Main Eight Hawaiian Islands UTM Zone 4",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2004-01-01",
"end_date": "2006-01-01",
"bbox": "-180, -90, 180, 90",
@@ -6840,7 +6840,7 @@
{
"id": "39423_Not Applicable",
"title": "Accuracy Assessment Field Data for Benthic Habitat Maps of Palau",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2006-01-01",
"end_date": "2007-01-01",
"bbox": "-180, -90, 180, 90",
@@ -6853,7 +6853,7 @@
{
"id": "39423_Not Applicable",
"title": "Accuracy Assessment Field Data for Benthic Habitat Maps of Palau",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2006-01-01",
"end_date": "2007-01-01",
"bbox": "-180, -90, 180, 90",
@@ -7139,7 +7139,7 @@
{
"id": "39462_Not Applicable",
"title": "1999 Photomosaics of Puerto Rico and U.S. Virgin Islands Utilized to Map Shallow Water Benthic Habitats of the Region",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1999-02-01",
"end_date": "1999-12-01",
"bbox": "-180, -90, 180, 90",
@@ -7152,7 +7152,7 @@
{
"id": "39462_Not Applicable",
"title": "1999 Photomosaics of Puerto Rico and U.S. Virgin Islands Utilized to Map Shallow Water Benthic Habitats of the Region",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1999-02-01",
"end_date": "1999-12-01",
"bbox": "-180, -90, 180, 90",
@@ -7243,7 +7243,7 @@
{
"id": "39483_Not Applicable",
"title": "1992 Seagrass and Mangrove Habitats of the Salt River Bay National Historical Park and Ecological Preserve",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1992-01-31",
"end_date": "1992-01-31",
"bbox": "-180, -90, 180, 90",
@@ -7256,7 +7256,7 @@
{
"id": "39483_Not Applicable",
"title": "1992 Seagrass and Mangrove Habitats of the Salt River Bay National Historical Park and Ecological Preserve",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1992-01-31",
"end_date": "1992-01-31",
"bbox": "-180, -90, 180, 90",
@@ -7373,7 +7373,7 @@
{
"id": "39556_Not Applicable",
"title": "1993 Average Monthly Sea Surface Temperature for California",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1993-01-01",
"end_date": "1993-12-31",
"bbox": "-180, -90, 180, 90",
@@ -7386,7 +7386,7 @@
{
"id": "39556_Not Applicable",
"title": "1993 Average Monthly Sea Surface Temperature for California",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1993-01-01",
"end_date": "1993-12-31",
"bbox": "-180, -90, 180, 90",
@@ -7399,7 +7399,7 @@
{
"id": "39557_Not Applicable",
"title": "1994 Average Monthly Sea Surface Temperature for California",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1994-01-01",
"end_date": "1994-12-31",
"bbox": "-180, -90, 180, 90",
@@ -7412,7 +7412,7 @@
{
"id": "39557_Not Applicable",
"title": "1994 Average Monthly Sea Surface Temperature for California",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1994-01-01",
"end_date": "1994-12-31",
"bbox": "-180, -90, 180, 90",
@@ -7451,7 +7451,7 @@
{
"id": "39559_Not Applicable",
"title": "1996 Average Monthly Sea Surface Temperature for California",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1996-01-01",
"end_date": "1996-12-31",
"bbox": "-180, -90, 180, 90",
@@ -7464,7 +7464,7 @@
{
"id": "39559_Not Applicable",
"title": "1996 Average Monthly Sea Surface Temperature for California",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-01-01",
"end_date": "1996-12-31",
"bbox": "-180, -90, 180, 90",
@@ -7477,7 +7477,7 @@
{
"id": "39560_Not Applicable",
"title": "1997 Average Monthly Sea Surface Temperature for California",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1997-01-01",
"end_date": "1997-12-31",
"bbox": "-180, -90, 180, 90",
@@ -7490,7 +7490,7 @@
{
"id": "39560_Not Applicable",
"title": "1997 Average Monthly Sea Surface Temperature for California",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-01-01",
"end_date": "1997-12-31",
"bbox": "-180, -90, 180, 90",
@@ -7555,7 +7555,7 @@
{
"id": "39563_Not Applicable",
"title": "2000 Average Monthly Sea Surface Temperature for California",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2000-12-31",
"bbox": "-180, -90, 180, 90",
@@ -7568,7 +7568,7 @@
{
"id": "39563_Not Applicable",
"title": "2000 Average Monthly Sea Surface Temperature for California",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2000-12-31",
"bbox": "-180, -90, 180, 90",
@@ -7581,7 +7581,7 @@
{
"id": "39564_Not Applicable",
"title": "2001 Average Monthly Sea Surface Temperature for California",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2001-12-31",
"bbox": "-180, -90, 180, 90",
@@ -7594,7 +7594,7 @@
{
"id": "39564_Not Applicable",
"title": "2001 Average Monthly Sea Surface Temperature for California",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2001-12-31",
"bbox": "-180, -90, 180, 90",
@@ -7607,7 +7607,7 @@
{
"id": "39565_Not Applicable",
"title": "2002 Average Monthly Sea Surface Temperature for California",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2002-01-01",
"end_date": "2002-12-31",
"bbox": "-180, -90, 180, 90",
@@ -7620,7 +7620,7 @@
{
"id": "39565_Not Applicable",
"title": "2002 Average Monthly Sea Surface Temperature for California",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-01-01",
"end_date": "2002-12-31",
"bbox": "-180, -90, 180, 90",
@@ -7763,7 +7763,7 @@
{
"id": "39589_Not Applicable",
"title": "A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Subsurface Current Model Outputs",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2006-09-01",
"end_date": "2006-09-01",
"bbox": "-180, -90, 180, 90",
@@ -7776,7 +7776,7 @@
{
"id": "39589_Not Applicable",
"title": "A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Subsurface Current Model Outputs",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2006-09-01",
"end_date": "2006-09-01",
"bbox": "-180, -90, 180, 90",
@@ -7789,7 +7789,7 @@
{
"id": "39590_Not Applicable",
"title": "A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Surface Current Model Outputs",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2006-01-01",
"end_date": "2006-01-01",
"bbox": "-180, -90, 180, 90",
@@ -7802,7 +7802,7 @@
{
"id": "39590_Not Applicable",
"title": "A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Surface Current Model Outputs",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2006-01-01",
"end_date": "2006-01-01",
"bbox": "-180, -90, 180, 90",
@@ -7867,7 +7867,7 @@
{
"id": "39623_Not Applicable",
"title": "A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Kriged Predictive Map of Zooplankton Samples",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2006-09-01",
"end_date": "2006-09-01",
"bbox": "-180, -90, 180, 90",
@@ -7880,7 +7880,7 @@
{
"id": "39623_Not Applicable",
"title": "A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Kriged Predictive Map of Zooplankton Samples",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2006-09-01",
"end_date": "2006-09-01",
"bbox": "-180, -90, 180, 90",
@@ -7893,7 +7893,7 @@
{
"id": "39624_Not Applicable",
"title": "A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Kriged Probability Map of Zooplankton Samples",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2006-09-01",
"end_date": "2006-09-01",
"bbox": "-180, -90, 180, 90",
@@ -7906,7 +7906,7 @@
{
"id": "39624_Not Applicable",
"title": "A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Kriged Probability Map of Zooplankton Samples",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2006-09-01",
"end_date": "2006-09-01",
"bbox": "-180, -90, 180, 90",
@@ -8205,7 +8205,7 @@
{
"id": "3d_snow_models_4.0",
"title": "3D_Snow_Models",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ENVIDAT STAC Catalog",
"state_date": "2022-01-01",
"end_date": "2022-01-01",
"bbox": "9.8471832, 46.8146287, 9.8471832, 46.8146287",
@@ -8218,7 +8218,7 @@
{
"id": "3d_snow_models_4.0",
"title": "3D_Snow_Models",
- "catalog": "ENVIDAT STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2022-01-01",
"end_date": "2022-01-01",
"bbox": "9.8471832, 46.8146287, 9.8471832, 46.8146287",
@@ -8296,7 +8296,7 @@
{
"id": "42ad984d-a92e-41c2-af23-f28ecd22018d_1",
"title": "AFRICA CITIES POPULATION DATABASE (ACPD)",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1990-10-26",
"end_date": "1990-10-26",
"bbox": "-20, -38, 54, 38",
@@ -8309,7 +8309,7 @@
{
"id": "42ad984d-a92e-41c2-af23-f28ecd22018d_1",
"title": "AFRICA CITIES POPULATION DATABASE (ACPD)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1990-10-26",
"end_date": "1990-10-26",
"bbox": "-20, -38, 54, 38",
@@ -9401,7 +9401,7 @@
{
"id": "96159374900008",
"title": "Alexander Island Microclimate Data",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1992-01-01",
"end_date": "1997-01-01",
"bbox": "-68, -72, -68, -72",
@@ -9414,7 +9414,7 @@
{
"id": "96159374900008",
"title": "Alexander Island Microclimate Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1992-01-01",
"end_date": "1997-01-01",
"bbox": "-68, -72, -68, -72",
@@ -9427,7 +9427,7 @@
{
"id": "96159393396972",
"title": "Adelaide Island Microclimate Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1995-01-01",
"end_date": "1997-01-01",
"bbox": "-68, -68, -68, -68",
@@ -9440,7 +9440,7 @@
{
"id": "96159393396972",
"title": "Adelaide Island Microclimate Data",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1995-01-01",
"end_date": "1997-01-01",
"bbox": "-68, -68, -68, -68",
@@ -9570,7 +9570,7 @@
{
"id": "A Fusion Dataset for Crop Type Classification in Germany_1",
"title": "A Fusion Dataset for Crop Type Classification in Germany",
- "catalog": "ALL STAC Catalog",
+ "catalog": "MLHUB STAC Catalog",
"state_date": "2020-01-01",
"end_date": "2023-01-01",
"bbox": "13.6339485, 52.4179888, 14.3529903, 52.8494418",
@@ -9583,7 +9583,7 @@
{
"id": "A Fusion Dataset for Crop Type Classification in Germany_1",
"title": "A Fusion Dataset for Crop Type Classification in Germany",
- "catalog": "MLHUB STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-01-01",
"end_date": "2023-01-01",
"bbox": "13.6339485, 52.4179888, 14.3529903, 52.8494418",
@@ -9622,7 +9622,7 @@
{
"id": "A crop type dataset for consistent land cover classification in Central Asia_1",
"title": "A crop type dataset for consistent land cover classification in Central Asia",
- "catalog": "ALL STAC Catalog",
+ "catalog": "MLHUB STAC Catalog",
"state_date": "2020-01-01",
"end_date": "2023-01-01",
"bbox": "60.2013297, 37.4241018, 72.3539419, 41.8252151",
@@ -9635,7 +9635,7 @@
{
"id": "A crop type dataset for consistent land cover classification in Central Asia_1",
"title": "A crop type dataset for consistent land cover classification in Central Asia",
- "catalog": "MLHUB STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-01-01",
"end_date": "2023-01-01",
"bbox": "60.2013297, 37.4241018, 72.3539419, 41.8252151",
@@ -10350,7 +10350,7 @@
{
"id": "AAD_voyage_soundings_HI513_1",
"title": "Acoustic depth soundings collected on Australian Antarctic Division voyages, 1997/98, 1998/99 and 2003/04 to 2011/12",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-09-23",
"end_date": "2012-02-11",
"bbox": "30, -70, 170, -42",
@@ -10363,7 +10363,7 @@
{
"id": "AAD_voyage_soundings_HI513_1",
"title": "Acoustic depth soundings collected on Australian Antarctic Division voyages, 1997/98, 1998/99 and 2003/04 to 2011/12",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1997-09-23",
"end_date": "2012-02-11",
"bbox": "30, -70, 170, -42",
@@ -10701,7 +10701,7 @@
{
"id": "AAS_3051_AbatusMicrosatellites_2",
"title": "Abatus Microsatellites data set",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2009-10-01",
"end_date": "2013-03-31",
"bbox": "77.987556, -68.584139, 77.94931, -68.565625",
@@ -10714,7 +10714,7 @@
{
"id": "AAS_3051_AbatusMicrosatellites_2",
"title": "Abatus Microsatellites data set",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2009-10-01",
"end_date": "2013-03-31",
"bbox": "77.987556, -68.584139, 77.94931, -68.565625",
@@ -10857,7 +10857,7 @@
{
"id": "AAS_3145_Advection_1",
"title": "Advection shapes Southern Ocean microbial assemblages independent of distance and environment effects",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2012-01-20",
"end_date": "2012-02-07",
"bbox": "113, -65, 115, -37",
@@ -10870,7 +10870,7 @@
{
"id": "AAS_3145_Advection_1",
"title": "Advection shapes Southern Ocean microbial assemblages independent of distance and environment effects",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-01-20",
"end_date": "2012-02-07",
"bbox": "113, -65, 115, -37",
@@ -10974,7 +10974,7 @@
{
"id": "AAS_3326_bathymetric_grid_casey_2013-2015_1",
"title": "A high resolution bathymetric grid of the nearshore area at Casey station, Antarctica",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-12-23",
"end_date": "2015-01-30",
"bbox": "110.3633, -66.3122, 110.5703, -66.2311",
@@ -10987,7 +10987,7 @@
{
"id": "AAS_3326_bathymetric_grid_casey_2013-2015_1",
"title": "A high resolution bathymetric grid of the nearshore area at Casey station, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2013-12-23",
"end_date": "2015-01-30",
"bbox": "110.3633, -66.3122, 110.5703, -66.2311",
@@ -11442,7 +11442,7 @@
{
"id": "AAS_4036_aerial_mosaic_macquarie_jan2015_1",
"title": "Aerial photograph mosaic of Macquarie Island isthmus, 31 January 2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2015-01-31",
"end_date": "2015-01-31",
"bbox": "158.9332, -54.5013, 158.9414, -54.4972",
@@ -11455,7 +11455,7 @@
{
"id": "AAS_4036_aerial_mosaic_macquarie_jan2015_1",
"title": "Aerial photograph mosaic of Macquarie Island isthmus, 31 January 2015",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-01-31",
"end_date": "2015-01-31",
"bbox": "158.9332, -54.5013, 158.9414, -54.4972",
@@ -11871,7 +11871,7 @@
{
"id": "AAS_4075_ABN_continuousGas-CFA_1",
"title": "ABN continuous gas CFA - methane (CH4) and carbon monoxide (CO)",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-12-01",
"end_date": "2014-01-31",
"bbox": "111.366531, -71.166889, 111.366531, -71.166889",
@@ -11884,7 +11884,7 @@
{
"id": "AAS_4075_ABN_continuousGas-CFA_1",
"title": "ABN continuous gas CFA - methane (CH4) and carbon monoxide (CO)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2013-12-01",
"end_date": "2014-01-31",
"bbox": "111.366531, -71.166889, 111.366531, -71.166889",
@@ -12014,7 +12014,7 @@
{
"id": "AAS_4087_Fulmarine_petrel_tracking_study_Hop_Island_2015_16_1",
"title": "AAS 4087 Fulmarine petrel tracking study, Hop Island, 2015/16",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2015-11-01",
"end_date": "2016-03-31",
"bbox": "68.55469, -69.225, 81.91406, -64.62388",
@@ -12027,7 +12027,7 @@
{
"id": "AAS_4087_Fulmarine_petrel_tracking_study_Hop_Island_2015_16_1",
"title": "AAS 4087 Fulmarine petrel tracking study, Hop Island, 2015/16",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-11-01",
"end_date": "2016-03-31",
"bbox": "68.55469, -69.225, 81.91406, -64.62388",
@@ -12131,7 +12131,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Bechervaise_2013_1",
"title": "Adelie penguin occupancy survey of Bechervaise Island, 2013",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-01-09",
"end_date": "2013-01-09",
"bbox": "62.806, -67.588, 62.808, -67.586",
@@ -12144,7 +12144,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Bechervaise_2013_1",
"title": "Adelie penguin occupancy survey of Bechervaise Island, 2013",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2013-01-09",
"end_date": "2013-01-09",
"bbox": "62.806, -67.588, 62.808, -67.586",
@@ -12157,7 +12157,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Bechervaise_2016_1",
"title": "Adelie penguin occupancy survey of Bechervaise Island, 2016",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-12-21",
"end_date": "2016-12-21",
"bbox": "62.806, -67.588, 62.808, -67.586",
@@ -12170,7 +12170,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Bechervaise_2016_1",
"title": "Adelie penguin occupancy survey of Bechervaise Island, 2016",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2016-12-21",
"end_date": "2016-12-21",
"bbox": "62.806, -67.588, 62.808, -67.586",
@@ -12261,7 +12261,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Chick_Henry_2012_1",
"title": "Adelie penguin occupancy survey of Chick and Henry Islands, 2012",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-01-26",
"end_date": "2012-01-26",
"bbox": "120.5, -66.876, 121.03, -66.789",
@@ -12274,7 +12274,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Chick_Henry_2012_1",
"title": "Adelie penguin occupancy survey of Chick and Henry Islands, 2012",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2012-01-26",
"end_date": "2012-01-26",
"bbox": "120.5, -66.876, 121.03, -66.789",
@@ -12339,7 +12339,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Knox_2011_1",
"title": "Adelie penguin occupancy survey of islands along the Knox Coast, 2011",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2011-01-01",
"end_date": "2011-01-31",
"bbox": "107.08, -66.55, 109.33, -66.45",
@@ -12352,7 +12352,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Knox_2011_1",
"title": "Adelie penguin occupancy survey of islands along the Knox Coast, 2011",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-01-01",
"end_date": "2011-01-31",
"bbox": "107.08, -66.55, 109.33, -66.45",
@@ -12443,7 +12443,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Murray_2010_1",
"title": "Adelie penguin occupancy survey of Murray Monolith, 2010",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2010-12-10",
"end_date": "2010-12-10",
"bbox": "66.8874, -67.7847, 66.8884, -67.7837",
@@ -12456,7 +12456,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Murray_2010_1",
"title": "Adelie penguin occupancy survey of Murray Monolith, 2010",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-12-10",
"end_date": "2010-12-10",
"bbox": "66.8874, -67.7847, 66.8884, -67.7837",
@@ -12521,7 +12521,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Robinson_2006_1",
"title": "Adelie penguin occupancy survey of the Robinson Group, 2006",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2006-11-01",
"end_date": "2006-11-30",
"bbox": "63.435, -67.445, 63.443, -67.435",
@@ -12534,7 +12534,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Robinson_2006_1",
"title": "Adelie penguin occupancy survey of the Robinson Group, 2006",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2006-11-01",
"end_date": "2006-11-30",
"bbox": "63.435, -67.445, 63.443, -67.435",
@@ -12547,7 +12547,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Robinson_2013_1",
"title": "Adelie penguin occupancy survey of the Robinson Group, 2013",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-11-29",
"end_date": "2013-11-29",
"bbox": "63.435, -67.445, 63.443, -67.435",
@@ -12560,7 +12560,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Robinson_2013_1",
"title": "Adelie penguin occupancy survey of the Robinson Group, 2013",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2013-11-29",
"end_date": "2013-11-29",
"bbox": "63.435, -67.445, 63.443, -67.435",
@@ -12599,7 +12599,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Rookery_2014_1",
"title": "Adelie penguin occupancy survey of the Rookery Island Group, 2014",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2014-12-04",
"end_date": "2014-12-04",
"bbox": "62.51, -67.61, 62.52, -67.59",
@@ -12612,7 +12612,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Rookery_2014_1",
"title": "Adelie penguin occupancy survey of the Rookery Island Group, 2014",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2014-12-04",
"end_date": "2014-12-04",
"bbox": "62.51, -67.61, 62.52, -67.59",
@@ -12677,7 +12677,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Stanton_2015_1",
"title": "Adelie penguin occupancy survey of the Stanton Group, 2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2015-02-15",
"end_date": "2015-02-15",
"bbox": "61.608, -67.527, 61.618, -67.517",
@@ -12690,7 +12690,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Stanton_2015_1",
"title": "Adelie penguin occupancy survey of the Stanton Group, 2015",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-02-15",
"end_date": "2015-02-15",
"bbox": "61.608, -67.527, 61.618, -67.517",
@@ -12807,7 +12807,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Vestfold_2012_1",
"title": "Adelie penguin occupancy survey of the Vestfold Hills, 2012",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2012-12-13",
"end_date": "2012-12-13",
"bbox": "78.15, -68.6, 78.35, -68.4",
@@ -12820,7 +12820,7 @@
{
"id": "AAS_4088_Adelie_occupancy_Vestfold_2012_1",
"title": "Adelie penguin occupancy survey of the Vestfold Hills, 2012",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-12-13",
"end_date": "2012-12-13",
"bbox": "78.15, -68.6, 78.35, -68.4",
@@ -13314,7 +13314,7 @@
{
"id": "AAS_4102_AcousticTrackingLog2013_1",
"title": "Acoustic whale tracking log of the 2013 Antarctic Blue Whale Voyage to the Southern Ocean",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-01-31",
"end_date": "2013-03-16",
"bbox": "140, -70, -170, -40",
@@ -13327,7 +13327,7 @@
{
"id": "AAS_4102_AcousticTrackingLog2013_1",
"title": "Acoustic whale tracking log of the 2013 Antarctic Blue Whale Voyage to the Southern Ocean",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2013-01-31",
"end_date": "2013-03-16",
"bbox": "140, -70, -170, -40",
@@ -13353,7 +13353,7 @@
{
"id": "AAS_4102_all_photo_ID_images_2012_1",
"title": "All identification photos taken of whales during the two blue whale voyages in the Bonney Upwelling, Januray and March 2012",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-01-12",
"end_date": "2012-03-30",
"bbox": "141, -39.5, 143, -38",
@@ -13366,7 +13366,7 @@
{
"id": "AAS_4102_all_photo_ID_images_2012_1",
"title": "All identification photos taken of whales during the two blue whale voyages in the Bonney Upwelling, Januray and March 2012",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2012-01-12",
"end_date": "2012-03-30",
"bbox": "141, -39.5, 143, -38",
@@ -13379,7 +13379,7 @@
{
"id": "AAS_4102_all_photo_ID_images_2013_1",
"title": "All identification photos taken of Antarctic blue whales during the Antarctic blue whale voyage 2013",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-01-31",
"end_date": "2013-03-16",
"bbox": "140, -70, -170, -40",
@@ -13392,7 +13392,7 @@
{
"id": "AAS_4102_all_photo_ID_images_2013_1",
"title": "All identification photos taken of Antarctic blue whales during the Antarctic blue whale voyage 2013",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2013-01-31",
"end_date": "2013-03-16",
"bbox": "140, -70, -170, -40",
@@ -13574,7 +13574,7 @@
{
"id": "AAS_4124_CEAMARC200708_BenthicStills_1",
"title": "Abundances of broad benthic functional groups in the CEAMARC region 2007/08",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-12-22",
"end_date": "2008-01-20",
"bbox": "138.86719, -67.23806, 146.20605, -64.94216",
@@ -13587,7 +13587,7 @@
{
"id": "AAS_4124_CEAMARC200708_BenthicStills_1",
"title": "Abundances of broad benthic functional groups in the CEAMARC region 2007/08",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2007-12-22",
"end_date": "2008-01-20",
"bbox": "138.86719, -67.23806, 146.20605, -64.94216",
@@ -13678,7 +13678,7 @@
{
"id": "AAS_4124_pelagic_regionalisation_1",
"title": "A circumpolar pelagic regionalisation of the Southern Ocean",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-10-01",
"end_date": "2016-03-31",
"bbox": "-180, -80, 180, -40",
@@ -13691,7 +13691,7 @@
{
"id": "AAS_4124_pelagic_regionalisation_1",
"title": "A circumpolar pelagic regionalisation of the Southern Ocean",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2012-10-01",
"end_date": "2016-03-31",
"bbox": "-180, -80, 180, -40",
@@ -14016,7 +14016,7 @@
{
"id": "AAS_4156_Macquarie_Island_unnamed_lake_1",
"title": "2000 year record of environmental change from an unnamed lake on Macquarie Island",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-07-01",
"end_date": "2019-06-30",
"bbox": "158.74969, -54.78485, 158.96118, -54.47004",
@@ -14029,7 +14029,7 @@
{
"id": "AAS_4156_Macquarie_Island_unnamed_lake_1",
"title": "2000 year record of environmental change from an unnamed lake on Macquarie Island",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2012-07-01",
"end_date": "2019-06-30",
"bbox": "158.74969, -54.78485, 158.96118, -54.47004",
@@ -15485,7 +15485,7 @@
{
"id": "AAS_4346_Airborne_Ocean_Sensors_2",
"title": "Airborne-deployed ocean sensors in the Southern Ocean, 2016-2018, Level 0 data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2016-11-01",
"end_date": "2020-01-31",
"bbox": "99, -66.8, 121, -65",
@@ -15498,7 +15498,7 @@
{
"id": "AAS_4346_Airborne_Ocean_Sensors_2",
"title": "Airborne-deployed ocean sensors in the Southern Ocean, 2016-2018, Level 0 data",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-11-01",
"end_date": "2020-01-31",
"bbox": "99, -66.8, 121, -65",
@@ -15979,7 +15979,7 @@
{
"id": "AAS_4446_Kerguelen_Geochronology_1",
"title": "40Ar/39Ar geochronology data of basalt samples from the Kerguelen Plateau and Broken Ridge",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-07-01",
"end_date": "2019-06-30",
"bbox": "59.76563, -64.32087, 103.71094, -23.88584",
@@ -15992,7 +15992,7 @@
{
"id": "AAS_4446_Kerguelen_Geochronology_1",
"title": "40Ar/39Ar geochronology data of basalt samples from the Kerguelen Plateau and Broken Ridge",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2017-07-01",
"end_date": "2019-06-30",
"bbox": "59.76563, -64.32087, 103.71094, -23.88584",
@@ -16239,7 +16239,7 @@
{
"id": "AAS_974_Concordia_2009to2011_1min_1",
"title": "Absolute vertical electric field data raw and selected data - Concordia from 2006-2011; processed 1-minute averages",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2009-01-01",
"end_date": "2011-12-31",
"bbox": "123.4, -71.15, 123.5, -71.05",
@@ -16252,7 +16252,7 @@
{
"id": "AAS_974_Concordia_2009to2011_1min_1",
"title": "Absolute vertical electric field data raw and selected data - Concordia from 2006-2011; processed 1-minute averages",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2009-01-01",
"end_date": "2011-12-31",
"bbox": "123.4, -71.15, 123.5, -71.05",
@@ -16447,7 +16447,7 @@
{
"id": "ABLVIS1B_1",
"title": "ABoVE LVIS L1B Geolocated Return Energy Waveforms V001",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-06-29",
"end_date": "2017-07-17",
"bbox": "-158, 48, -104, 72",
@@ -16460,7 +16460,7 @@
{
"id": "ABLVIS1B_1",
"title": "ABoVE LVIS L1B Geolocated Return Energy Waveforms V001",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2017-06-29",
"end_date": "2017-07-17",
"bbox": "-158, 48, -104, 72",
@@ -16525,7 +16525,7 @@
{
"id": "ABOA_bb",
"title": "ABOA seismic broad band station",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-13.41, -73.04, -13.41, -73.04",
@@ -16538,7 +16538,7 @@
{
"id": "ABOA_bb",
"title": "ABOA seismic broad band station",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-13.41, -73.04, -13.41, -73.04",
@@ -16551,7 +16551,7 @@
{
"id": "ABOLVIS1A_1",
"title": "ABoVE LVIS L1A Geotagged Images V001",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-06-29",
"end_date": "2017-07-17",
"bbox": "-158, 48, -104, 72",
@@ -16564,7 +16564,7 @@
{
"id": "ABOLVIS1A_1",
"title": "ABoVE LVIS L1A Geotagged Images V001",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2017-06-29",
"end_date": "2017-07-17",
"bbox": "-158, 48, -104, 72",
@@ -16577,7 +16577,7 @@
{
"id": "ABoVE_ASCENDS_Backscatter_2051_1",
"title": "ABoVE/ASCENDS: Atmospheric Backscattering Coefficient Profiles from CO2 Sounder, 2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2017-07-20",
"end_date": "2017-08-08",
"bbox": "-165.68, 34.59, -98.15, 71.27",
@@ -16590,7 +16590,7 @@
{
"id": "ABoVE_ASCENDS_Backscatter_2051_1",
"title": "ABoVE/ASCENDS: Atmospheric Backscattering Coefficient Profiles from CO2 Sounder, 2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-07-20",
"end_date": "2017-08-08",
"bbox": "-165.68, 34.59, -98.15, 71.27",
@@ -16629,7 +16629,7 @@
{
"id": "ABoVE_ASCENDS_XCO2_2050_1",
"title": "ABoVE/ASCENDS: Active Sensing of CO2, CH4, and Water Vapor, Alaska and Canada, 2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-07-20",
"end_date": "2017-08-08",
"bbox": "-165.68, 34.59, -98.1, 71.28",
@@ -16642,7 +16642,7 @@
{
"id": "ABoVE_ASCENDS_XCO2_2050_1",
"title": "ABoVE/ASCENDS: Active Sensing of CO2, CH4, and Water Vapor, Alaska and Canada, 2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2017-07-20",
"end_date": "2017-08-08",
"bbox": "-165.68, 34.59, -98.1, 71.28",
@@ -16681,7 +16681,7 @@
{
"id": "ABoVE_AirSWOT_Water_Mask_1707_1",
"title": "ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2017-07-09",
"end_date": "2017-08-17",
"bbox": "-152.18, 43.27, -98.64, 76.28",
@@ -16694,7 +16694,7 @@
{
"id": "ABoVE_AirSWOT_Water_Mask_1707_1",
"title": "ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-07-09",
"end_date": "2017-08-17",
"bbox": "-152.18, 43.27, -98.64, 76.28",
@@ -16707,7 +16707,7 @@
{
"id": "ABoVE_Airborne_AVIRIS_NG_V3_2362_3",
"title": "ABoVE: AVIRIS-NG Imaging Spectroscopy for Alaska, Canada, and Iceland, 2017-2022, V3",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2017-06-24",
"end_date": "2022-08-19",
"bbox": "-166.65, 52.16, 28.22, 71.38",
@@ -16720,7 +16720,7 @@
{
"id": "ABoVE_Airborne_AVIRIS_NG_V3_2362_3",
"title": "ABoVE: AVIRIS-NG Imaging Spectroscopy for Alaska, Canada, and Iceland, 2017-2022, V3",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-06-24",
"end_date": "2022-08-19",
"bbox": "-166.65, 52.16, 28.22, 71.38",
@@ -16772,7 +16772,7 @@
{
"id": "ABoVE_Atmospheric_Flask_Data_1717_1",
"title": "ABoVE: Atmospheric Gas Concentrations from Airborne Flasks, Arctic-CAP, 2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2017-04-27",
"end_date": "2017-11-04",
"bbox": "-165.48, 58.08, -111.57, 71.27",
@@ -16785,7 +16785,7 @@
{
"id": "ABoVE_Atmospheric_Flask_Data_1717_1",
"title": "ABoVE: Atmospheric Gas Concentrations from Airborne Flasks, Arctic-CAP, 2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-04-27",
"end_date": "2017-11-04",
"bbox": "-165.48, 58.08, -111.57, 71.27",
@@ -16811,7 +16811,7 @@
{
"id": "ABoVE_Concise_Experiment_Plan_1617_1.1",
"title": "A Concise Experiment Plan for the Arctic-Boreal Vulnerability Experiment",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2014-01-01",
"end_date": "2021-12-31",
"bbox": "-176.12, 39.42, -66.92, 81.61",
@@ -16824,7 +16824,7 @@
{
"id": "ABoVE_Concise_Experiment_Plan_1617_1.1",
"title": "A Concise Experiment Plan for the Arctic-Boreal Vulnerability Experiment",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2014-01-01",
"end_date": "2021-12-31",
"bbox": "-176.12, 39.42, -66.92, 81.61",
@@ -16850,7 +16850,7 @@
{
"id": "ABoVE_Fire_Severity_dNBR_1564_1",
"title": "ABoVE: Landsat-derived Burn Scar dNBR across Alaska and Canada, 1985-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1985-01-01",
"end_date": "2015-12-31",
"bbox": "-168.42, 50.25, -101.74, 71.36",
@@ -16863,7 +16863,7 @@
{
"id": "ABoVE_Fire_Severity_dNBR_1564_1",
"title": "ABoVE: Landsat-derived Burn Scar dNBR across Alaska and Canada, 1985-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1985-01-01",
"end_date": "2015-12-31",
"bbox": "-168.42, 50.25, -101.74, 71.36",
@@ -16902,7 +16902,7 @@
{
"id": "ABoVE_Forage_Lichen_Maps_1867_1",
"title": "ABoVE: Lichen Forage Cover over Fortymile Caribou Range, Alaska and Yukon, 2000-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2017-08-01",
"bbox": "-153.86, 58.61, -128.26, 70.09",
@@ -16915,7 +16915,7 @@
{
"id": "ABoVE_Forage_Lichen_Maps_1867_1",
"title": "ABoVE: Lichen Forage Cover over Fortymile Caribou Range, Alaska and Yukon, 2000-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2017-08-01",
"bbox": "-153.86, 58.61, -128.26, 70.09",
@@ -16954,7 +16954,7 @@
{
"id": "ABoVE_Frac_Open_Water_1362_1",
"title": "ABoVE: Fractional Open Water Cover for Pan-Arctic and ABoVE-Domain Regions, 2002-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2002-06-20",
"end_date": "2015-12-31",
"bbox": "-180, 39.38, 180, 90",
@@ -16967,7 +16967,7 @@
{
"id": "ABoVE_Frac_Open_Water_1362_1",
"title": "ABoVE: Fractional Open Water Cover for Pan-Arctic and ABoVE-Domain Regions, 2002-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-06-20",
"end_date": "2015-12-31",
"bbox": "-180, 39.38, 180, 90",
@@ -16980,7 +16980,7 @@
{
"id": "ABoVE_GrowingSeason_Lake_Color_1866_1",
"title": "ABoVE: Lake Growing Season Green Surface Reflectance Trends, AK and Canada, 1984-2019",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1984-07-01",
"end_date": "2019-09-01",
"bbox": "-168.1, 49.54, -81.23, 75",
@@ -16993,7 +16993,7 @@
{
"id": "ABoVE_GrowingSeason_Lake_Color_1866_1",
"title": "ABoVE: Lake Growing Season Green Surface Reflectance Trends, AK and Canada, 1984-2019",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1984-07-01",
"end_date": "2019-09-01",
"bbox": "-168.1, 49.54, -81.23, 75",
@@ -17136,7 +17136,7 @@
{
"id": "ABoVE_Open_Water_Map_1643_1",
"title": "ABoVE: AirSWOT Color-Infrared Imagery Over Alaska and Canada, 2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2017-07-09",
"end_date": "2017-08-17",
"bbox": "-149.26, 46.85, -98.64, 69.47",
@@ -17149,7 +17149,7 @@
{
"id": "ABoVE_Open_Water_Map_1643_1",
"title": "ABoVE: AirSWOT Color-Infrared Imagery Over Alaska and Canada, 2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-07-09",
"end_date": "2017-08-17",
"bbox": "-149.26, 46.85, -98.64, 69.47",
@@ -17188,7 +17188,7 @@
{
"id": "ABoVE_Particles_WRF_AK_NWCa_1895_1",
"title": "ABoVE: Level-4 WRF-STILT Particle Trajectories for Circumpolar Receptors, 2016-2019",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-07-24",
"end_date": "2019-12-31",
"bbox": "-180, 10, 180, 90",
@@ -17201,7 +17201,7 @@
{
"id": "ABoVE_Particles_WRF_AK_NWCa_1895_1",
"title": "ABoVE: Level-4 WRF-STILT Particle Trajectories for Circumpolar Receptors, 2016-2019",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-07-24",
"end_date": "2019-12-31",
"bbox": "-180, 10, 180, 90",
@@ -17214,7 +17214,7 @@
{
"id": "ABoVE_Planning_Field_Sites_1582_1",
"title": "ABoVE: Directory of Field Sites Associated with 2017 ABoVE Airborne Campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2017-04-01",
"end_date": "2017-04-01",
"bbox": "-166.01, 52.71, -103.6, 71.33",
@@ -17227,7 +17227,7 @@
{
"id": "ABoVE_Planning_Field_Sites_1582_1",
"title": "ABoVE: Directory of Field Sites Associated with 2017 ABoVE Airborne Campaign",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-04-01",
"end_date": "2017-04-01",
"bbox": "-166.01, 52.71, -103.6, 71.33",
@@ -17240,7 +17240,7 @@
{
"id": "ABoVE_Plot_Data_Burned_Sites_1744_1",
"title": "ABoVE: Synthesis of Burned and Unburned Forest Site Data, AK and Canada, 1983-2016",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1983-01-01",
"end_date": "2016-08-08",
"bbox": "-150.9, 53.19, -88.61, 67.23",
@@ -17253,7 +17253,7 @@
{
"id": "ABoVE_Plot_Data_Burned_Sites_1744_1",
"title": "ABoVE: Synthesis of Burned and Unburned Forest Site Data, AK and Canada, 1983-2016",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1983-01-01",
"end_date": "2016-08-08",
"bbox": "-150.9, 53.19, -88.61, 67.23",
@@ -17357,7 +17357,7 @@
{
"id": "ABoVE_Soil_ThawDepth_Moisture_1903_1",
"title": "ABoVE: Soil Moisture and Active Layer Thickness in Alaska and NWT, Canada, 2008-2020",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2008-06-22",
"end_date": "2020-08-15",
"bbox": "-165.97, 60.45, -111.37, 71.32",
@@ -17370,7 +17370,7 @@
{
"id": "ABoVE_Soil_ThawDepth_Moisture_1903_1",
"title": "ABoVE: Soil Moisture and Active Layer Thickness in Alaska and NWT, Canada, 2008-2020",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2008-06-22",
"end_date": "2020-08-15",
"bbox": "-165.97, 60.45, -111.37, 71.32",
@@ -17435,7 +17435,7 @@
{
"id": "ABoVE_reference_grid_v2_1527_2.1",
"title": "ABoVE: Study Domain and Standard Reference Grids, Version 2",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2014-01-01",
"end_date": "2023-04-20",
"bbox": "-180, -90, 180, 90",
@@ -17448,7 +17448,7 @@
{
"id": "ABoVE_reference_grid_v2_1527_2.1",
"title": "ABoVE: Study Domain and Standard Reference Grids, Version 2",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2014-01-01",
"end_date": "2023-04-20",
"bbox": "-180, -90, 180, 90",
@@ -17461,7 +17461,7 @@
{
"id": "ACCLIP_AerosolCloud_AircraftRemoteSensing_WB57_Data_1",
"title": "ACCLIP WB-57 Aerosol and Cloud Remotely Sensed Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2022-07-14",
"end_date": "2022-09-14",
"bbox": "180, 16.6, -180, 61.5",
@@ -17474,7 +17474,7 @@
{
"id": "ACCLIP_AerosolCloud_AircraftRemoteSensing_WB57_Data_1",
"title": "ACCLIP WB-57 Aerosol and Cloud Remotely Sensed Data",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2022-07-14",
"end_date": "2022-09-14",
"bbox": "180, 16.6, -180, 61.5",
@@ -17513,7 +17513,7 @@
{
"id": "ACCLIP_AircraftInSitu_WB57_Water_Data_1",
"title": "ACCLIP WB-57 Aircraft Water In-situ Data",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2022-07-14",
"end_date": "2022-09-14",
"bbox": "180, 16.6, -180, 61.5",
@@ -17526,7 +17526,7 @@
{
"id": "ACCLIP_AircraftInSitu_WB57_Water_Data_1",
"title": "ACCLIP WB-57 Aircraft Water In-situ Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2022-07-14",
"end_date": "2022-09-14",
"bbox": "180, 16.6, -180, 61.5",
@@ -17539,7 +17539,7 @@
{
"id": "ACCLIP_Cloud_AircraftInSitu_WB57_Data_1",
"title": "ACCLIP WB-57 Aircraft In-situ Cloud Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2022-07-14",
"end_date": "2022-09-15",
"bbox": "180, 16.6, -180, 61.5",
@@ -17552,7 +17552,7 @@
{
"id": "ACCLIP_Cloud_AircraftInSitu_WB57_Data_1",
"title": "ACCLIP WB-57 Aircraft In-situ Cloud Data",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2022-07-14",
"end_date": "2022-09-15",
"bbox": "180, 16.6, -180, 61.5",
@@ -17591,7 +17591,7 @@
{
"id": "ACCLIP_MetNav_AircraftInSitu_WB57_Data_1",
"title": "ACCLIP WB-57 Meteorological and Navigational Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2022-07-14",
"end_date": "2022-09-14",
"bbox": "180, 16.6, -180, 61.5",
@@ -17604,7 +17604,7 @@
{
"id": "ACCLIP_MetNav_AircraftInSitu_WB57_Data_1",
"title": "ACCLIP WB-57 Meteorological and Navigational Data",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2022-07-14",
"end_date": "2022-09-14",
"bbox": "180, 16.6, -180, 61.5",
@@ -17617,7 +17617,7 @@
{
"id": "ACCLIP_Model_WB57_Data_1",
"title": "ACCLIP WB-57 Aircraft Model Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2022-07-14",
"end_date": "2022-09-14",
"bbox": "180, 16.6, -180, 61.5",
@@ -17630,7 +17630,7 @@
{
"id": "ACCLIP_Model_WB57_Data_1",
"title": "ACCLIP WB-57 Aircraft Model Data",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2022-07-14",
"end_date": "2022-09-14",
"bbox": "180, 16.6, -180, 61.5",
@@ -17669,7 +17669,7 @@
{
"id": "ACE-ASIA_0",
"title": "Aerosol Characterization Experiment (ACE) - Asia",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "2001-03-15",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -17682,7 +17682,7 @@
{
"id": "ACE-ASIA_0",
"title": "Aerosol Characterization Experiment (ACE) - Asia",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-03-15",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -17877,7 +17877,7 @@
{
"id": "ACE_0",
"title": "Aerosol Characterization Experiment (ACE)",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-06-20",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -17890,7 +17890,7 @@
{
"id": "ACE_0",
"title": "Aerosol Characterization Experiment (ACE)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "1997-06-20",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -17916,7 +17916,7 @@
{
"id": "ACE_EPAM_LEVEL2",
"title": "Advanced Composition Explorer (ACE) Electron, Proton, and Alpha Monitor (EPAM) Level 2 Data",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-08-25",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -17929,7 +17929,7 @@
{
"id": "ACE_EPAM_LEVEL2",
"title": "Advanced Composition Explorer (ACE) Electron, Proton, and Alpha Monitor (EPAM) Level 2 Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1997-08-25",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -17942,7 +17942,7 @@
{
"id": "ACE_LEVEL2",
"title": "Advanced Composition Explorer (ACE) CRIS Level 2 Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1997-08-25",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -17955,7 +17955,7 @@
{
"id": "ACE_LEVEL2",
"title": "Advanced Composition Explorer (ACE) CRIS Level 2 Data",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-08-25",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -17968,7 +17968,7 @@
{
"id": "ACE_MAG_LEVEL2",
"title": "Advanced Composition Explorer (ACE) Magnetic Field Experiment (MAG) Level 2 Data",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-08-25",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -17981,7 +17981,7 @@
{
"id": "ACE_MAG_LEVEL2",
"title": "Advanced Composition Explorer (ACE) Magnetic Field Experiment (MAG) Level 2 Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1997-08-25",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -18020,7 +18020,7 @@
{
"id": "ACE_SEPICA_LEVEL2",
"title": "Advanced Composition Explorer (ACE) Solar Energetic Particle Charge Analyser (SEPICA) Level 2 Data",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-08-25",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -18033,7 +18033,7 @@
{
"id": "ACE_SEPICA_LEVEL2",
"title": "Advanced Composition Explorer (ACE) Solar Energetic Particle Charge Analyser (SEPICA) Level 2 Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1997-08-25",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -18072,7 +18072,7 @@
{
"id": "ACE_SWEPAM_LEVEL2",
"title": "Advanced Composition Explorer (ACE) Solar Wind Electron, Proton, and Alpha Monitor (SWEPAM) Level 2 Data",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-08-25",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -18085,7 +18085,7 @@
{
"id": "ACE_SWEPAM_LEVEL2",
"title": "Advanced Composition Explorer (ACE) Solar Wind Electron, Proton, and Alpha Monitor (SWEPAM) Level 2 Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1997-08-25",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -18098,7 +18098,7 @@
{
"id": "ACE_SWICS_SWIMS_LEVEL2",
"title": "Advanced Composition Explorer (ACE) Solar Wind Ion Composition Spectrometer (SWICS) and Solar Wind Ion Mass Spectrometer (SWIMS) Level 2 Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1997-08-25",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -18111,7 +18111,7 @@
{
"id": "ACE_SWICS_SWIMS_LEVEL2",
"title": "Advanced Composition Explorer (ACE) Solar Wind Ion Composition Spectrometer (SWICS) and Solar Wind Ion Mass Spectrometer (SWIMS) Level 2 Data",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-08-25",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -18124,7 +18124,7 @@
{
"id": "ACE_ULEIS_LEVEL2",
"title": "Advanced Composition Explorer (ACE) Ultra Low Energy Isotope Spectrometer (ULEIS) Level 2 Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1997-08-25",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -18137,7 +18137,7 @@
{
"id": "ACE_ULEIS_LEVEL2",
"title": "Advanced Composition Explorer (ACE) Ultra Low Energy Isotope Spectrometer (ULEIS) Level 2 Data",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-08-25",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -18176,7 +18176,7 @@
{
"id": "ACIDRAINSENDAI",
"title": "Acid Precipitation Survey",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1975-01-01",
"end_date": "",
"bbox": "140, 38, 140, 38",
@@ -18189,7 +18189,7 @@
{
"id": "ACIDRAINSENDAI",
"title": "Acid Precipitation Survey",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1975-01-01",
"end_date": "",
"bbox": "140, 38, 140, 38",
@@ -18241,7 +18241,7 @@
{
"id": "ACOS_L2S_9r",
"title": "ACOS GOSAT/TANSO-FTS Level 2 Full Physics Standard Product V9r (ACOS_L2S) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2009-04-20",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -18254,7 +18254,7 @@
{
"id": "ACOS_L2S_9r",
"title": "ACOS GOSAT/TANSO-FTS Level 2 Full Physics Standard Product V9r (ACOS_L2S) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2009-04-20",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -18293,7 +18293,7 @@
{
"id": "ACOS_L2_Lite_FP_9r",
"title": "ACOS GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files V9r (ACOS_L2_Lite_FP) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2009-04-20",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -18306,7 +18306,7 @@
{
"id": "ACOS_L2_Lite_FP_9r",
"title": "ACOS GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files V9r (ACOS_L2_Lite_FP) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2009-04-20",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -18319,7 +18319,7 @@
{
"id": "ACR3L2DM_1",
"title": "ACRIM III Level 2 Daily Mean Data V001",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC STAC Catalog",
"state_date": "2000-04-05",
"end_date": "2013-11-09",
"bbox": "-180, -90, 180, 90",
@@ -18332,7 +18332,7 @@
{
"id": "ACR3L2DM_1",
"title": "ACRIM III Level 2 Daily Mean Data V001",
- "catalog": "LARC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-04-05",
"end_date": "2013-11-09",
"bbox": "-180, -90, 180, 90",
@@ -18371,7 +18371,7 @@
{
"id": "ACRIMII_TSI_UARS_NAT_1",
"title": "Active Cavity Radiometer Irradiance Monitor (ACRIM) II Total Solar Irradiance (TSI) aboard UARS in Native format",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "1991-10-04",
"end_date": "2001-11-01",
"bbox": "180, -90, -180, 90",
@@ -18384,7 +18384,7 @@
{
"id": "ACRIMII_TSI_UARS_NAT_1",
"title": "Active Cavity Radiometer Irradiance Monitor (ACRIM) II Total Solar Irradiance (TSI) aboard UARS in Native format",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1991-10-04",
"end_date": "2001-11-01",
"bbox": "180, -90, -180, 90",
@@ -18423,7 +18423,7 @@
{
"id": "ACTAMERICA_Hskping_1574_1.1",
"title": "ACT-America: L1 Meteorological and Aircraft Navigational Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-05-27",
"end_date": "2019-07-27",
"bbox": "-106.49, 27.23, -71.91, 50.55",
@@ -18436,7 +18436,7 @@
{
"id": "ACTAMERICA_Hskping_1574_1.1",
"title": "ACT-America: L1 Meteorological and Aircraft Navigational Data",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-05-27",
"end_date": "2019-07-27",
"bbox": "-106.49, 27.23, -71.91, 50.55",
@@ -18501,7 +18501,7 @@
{
"id": "ACTAMERICA_Merge_1593_1.2",
"title": "ACT-America: L3 Merged In Situ Atmospheric Trace Gases and Flask Data, Eastern USA",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-07-11",
"end_date": "2019-07-27",
"bbox": "-106.49, 27.23, -72.66, 50.55",
@@ -18514,7 +18514,7 @@
{
"id": "ACTAMERICA_Merge_1593_1.2",
"title": "ACT-America: L3 Merged In Situ Atmospheric Trace Gases and Flask Data, Eastern USA",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-07-11",
"end_date": "2019-07-27",
"bbox": "-106.49, 27.23, -72.66, 50.55",
@@ -18527,7 +18527,7 @@
{
"id": "ACTAMERICA_PFP_1575_1.2",
"title": "ACT-America: L2 In Situ Atmospheric Gas Concentrations from Flasks, Eastern USA",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-05-27",
"end_date": "2019-07-27",
"bbox": "-105.89, 27.79, -72.94, 49.4",
@@ -18540,7 +18540,7 @@
{
"id": "ACTAMERICA_PFP_1575_1.2",
"title": "ACT-America: L2 In Situ Atmospheric Gas Concentrations from Flasks, Eastern USA",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-05-27",
"end_date": "2019-07-27",
"bbox": "-105.89, 27.79, -72.94, 49.4",
@@ -18553,7 +18553,7 @@
{
"id": "ACTAMERICA_PICARRO_1556_1.2",
"title": "ACT-America: L2 In Situ Atmospheric CO2, CO, CH4, and O3 Concentrations, Eastern USA",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-07-11",
"end_date": "2019-07-27",
"bbox": "-110, 25, -70, 50.55",
@@ -18566,7 +18566,7 @@
{
"id": "ACTAMERICA_PICARRO_1556_1.2",
"title": "ACT-America: L2 In Situ Atmospheric CO2, CO, CH4, and O3 Concentrations, Eastern USA",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-07-11",
"end_date": "2019-07-27",
"bbox": "-110, 25, -70, 50.55",
@@ -18579,7 +18579,7 @@
{
"id": "ACTAMERICA_WRF_Chem_Output_1884_1",
"title": "ACT-America: WRF-Chem Baseline Simulations for North America, 2016-2019",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-06-29",
"end_date": "2019-07-31",
"bbox": "-150.39, 12.99, -41.61, 62.84",
@@ -18592,7 +18592,7 @@
{
"id": "ACTAMERICA_WRF_Chem_Output_1884_1",
"title": "ACT-America: WRF-Chem Baseline Simulations for North America, 2016-2019",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-06-29",
"end_date": "2019-07-31",
"bbox": "-150.39, 12.99, -41.61, 62.84",
@@ -18605,7 +18605,7 @@
{
"id": "ACTIVATE-FLEXPART_1",
"title": "ACTIVATE FLEXible PARTicle (FLEXPART) Dispersion Model Back-trajectories",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-02-14",
"end_date": "2022-06-30",
"bbox": "180, 0, -180, 90",
@@ -18618,7 +18618,7 @@
{
"id": "ACTIVATE-FLEXPART_1",
"title": "ACTIVATE FLEXible PARTicle (FLEXPART) Dispersion Model Back-trajectories",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2020-02-14",
"end_date": "2022-06-30",
"bbox": "180, 0, -180, 90",
@@ -18709,7 +18709,7 @@
{
"id": "ACTIVATE_Aerosol_AircraftInSitu_Falcon_Data_1",
"title": "ACTIVATE Falcon In Situ Aerosol Data",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-02-14",
"end_date": "2022-06-30",
"bbox": "-85, 25, -58.5, 50",
@@ -18722,7 +18722,7 @@
{
"id": "ACTIVATE_Aerosol_AircraftInSitu_Falcon_Data_1",
"title": "ACTIVATE Falcon In Situ Aerosol Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2020-02-14",
"end_date": "2022-06-30",
"bbox": "-85, 25, -58.5, 50",
@@ -18761,7 +18761,7 @@
{
"id": "ACTIVATE_Merge_Data_1",
"title": "ACTIVATE Falcon Aircraft Merge Data Files",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2020-02-14",
"end_date": "2022-06-30",
"bbox": "-85, 25, -58.5, 50",
@@ -18774,7 +18774,7 @@
{
"id": "ACTIVATE_Merge_Data_1",
"title": "ACTIVATE Falcon Aircraft Merge Data Files",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-02-14",
"end_date": "2022-06-30",
"bbox": "-85, 25, -58.5, 50",
@@ -18787,7 +18787,7 @@
{
"id": "ACTIVATE_MetNav_AircraftInSitu_Falcon_Data_1",
"title": "ACTIVATE Falcon In-Situ Meteorological and Navigational Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2020-02-10",
"end_date": "2022-06-20",
"bbox": "-85, 25, -58.5, 50",
@@ -18800,7 +18800,7 @@
{
"id": "ACTIVATE_MetNav_AircraftInSitu_Falcon_Data_1",
"title": "ACTIVATE Falcon In-Situ Meteorological and Navigational Data",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-02-10",
"end_date": "2022-06-20",
"bbox": "-85, 25, -58.5, 50",
@@ -18813,7 +18813,7 @@
{
"id": "ACTIVATE_MetNav_AircraftInSitu_KingAir_Data_1",
"title": "ACTIVATE King Air Meteorological and Navigational Data",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-12-16",
"end_date": "2022-06-30",
"bbox": "-85, 25, -58.5, 50",
@@ -18826,7 +18826,7 @@
{
"id": "ACTIVATE_MetNav_AircraftInSitu_KingAir_Data_1",
"title": "ACTIVATE King Air Meteorological and Navigational Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2019-12-16",
"end_date": "2022-06-30",
"bbox": "-85, 25, -58.5, 50",
@@ -18839,7 +18839,7 @@
{
"id": "ACTIVATE_Miscellaneous_Data_1",
"title": "ACTIVATE Miscellaneous and Ancillary Data",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-02-10",
"end_date": "2022-06-30",
"bbox": "-85, 25, -58.5, 50",
@@ -18852,7 +18852,7 @@
{
"id": "ACTIVATE_Miscellaneous_Data_1",
"title": "ACTIVATE Miscellaneous and Ancillary Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2020-02-10",
"end_date": "2022-06-30",
"bbox": "-85, 25, -58.5, 50",
@@ -18891,7 +18891,7 @@
{
"id": "ACTIVATE_TraceGas_AircraftInSitu_Falcon_Data_1",
"title": "ACTIVATE Falcon In Situ Trace Gas Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2020-02-14",
"end_date": "2022-06-30",
"bbox": "-85, 25, -58.5, 50",
@@ -18904,7 +18904,7 @@
{
"id": "ACTIVATE_TraceGas_AircraftInSitu_Falcon_Data_1",
"title": "ACTIVATE Falcon In Situ Trace Gas Data",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-02-14",
"end_date": "2022-06-30",
"bbox": "-85, 25, -58.5, 50",
@@ -18917,7 +18917,7 @@
{
"id": "ACT_CASA_Ensemble_Prior_Fluxes_1675_1.1",
"title": "ACT-America: Gridded Ensembles of Surface Biogenic Carbon Fluxes, 2003-2019",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-01",
"end_date": "2019-12-31",
"bbox": "-176, 0.5, -24.5, 70.5",
@@ -18930,7 +18930,7 @@
{
"id": "ACT_CASA_Ensemble_Prior_Fluxes_1675_1.1",
"title": "ACT-America: Gridded Ensembles of Surface Biogenic Carbon Fluxes, 2003-2019",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2003-01-01",
"end_date": "2019-12-31",
"bbox": "-176, 0.5, -24.5, 70.5",
@@ -19099,7 +19099,7 @@
{
"id": "ADCP_5MINUTE_SO",
"title": "ACDP Data, 5min. ensemble avrgs. of ocean current velocities, Mar-Sept 2001-2002, Drake Passage and Continental Margin off Western Antarctic Peninsula, GLOBEC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2001-03-19",
"end_date": "2002-09-17",
"bbox": "-78, -71, -60, -52",
@@ -19112,7 +19112,7 @@
{
"id": "ADCP_5MINUTE_SO",
"title": "ACDP Data, 5min. ensemble avrgs. of ocean current velocities, Mar-Sept 2001-2002, Drake Passage and Continental Margin off Western Antarctic Peninsula, GLOBEC",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-03-19",
"end_date": "2002-09-17",
"bbox": "-78, -71, -60, -52",
@@ -19203,7 +19203,7 @@
{
"id": "ADEOS-II_AMSR_L2_AP_NA",
"title": "ADEOS-II/AMSR L2 Amount of Precipitation",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19216,7 +19216,7 @@
{
"id": "ADEOS-II_AMSR_L2_AP_NA",
"title": "ADEOS-II/AMSR L2 Amount of Precipitation",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19229,7 +19229,7 @@
{
"id": "ADEOS-II_AMSR_L2_CLW_NA",
"title": "ADEOS-II/AMSR L2 Cloud Liquid Water",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19242,7 +19242,7 @@
{
"id": "ADEOS-II_AMSR_L2_CLW_NA",
"title": "ADEOS-II/AMSR L2 Cloud Liquid Water",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19255,7 +19255,7 @@
{
"id": "ADEOS-II_AMSR_L2_IC_NA",
"title": "ADEOS-II/AMSR L2 Ice Concentration",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19268,7 +19268,7 @@
{
"id": "ADEOS-II_AMSR_L2_IC_NA",
"title": "ADEOS-II/AMSR L2 Ice Concentration",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19281,7 +19281,7 @@
{
"id": "ADEOS-II_AMSR_L2_SM_NA",
"title": "ADEOS-II/AMSR L2 Soil Moisture",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19294,7 +19294,7 @@
{
"id": "ADEOS-II_AMSR_L2_SM_NA",
"title": "ADEOS-II/AMSR L2 Soil Moisture",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19307,7 +19307,7 @@
{
"id": "ADEOS-II_AMSR_L2_SST_NA",
"title": "ADEOS-II/AMSR L2 Sea Surface Temperature",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19320,7 +19320,7 @@
{
"id": "ADEOS-II_AMSR_L2_SST_NA",
"title": "ADEOS-II/AMSR L2 Sea Surface Temperature",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19333,7 +19333,7 @@
{
"id": "ADEOS-II_AMSR_L2_SSW_NA",
"title": "ADEOS-II/AMSR L2 Sea Surface Wind",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19346,7 +19346,7 @@
{
"id": "ADEOS-II_AMSR_L2_SSW_NA",
"title": "ADEOS-II/AMSR L2 Sea Surface Wind",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19437,7 +19437,7 @@
{
"id": "ADEOS-II_AMSR_L3_AP_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Amount of Precipitation (1month,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19450,7 +19450,7 @@
{
"id": "ADEOS-II_AMSR_L3_AP_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Amount of Precipitation (1month,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19463,7 +19463,7 @@
{
"id": "ADEOS-II_AMSR_L3_CLW_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Cloud Liquid Water (1day,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19476,7 +19476,7 @@
{
"id": "ADEOS-II_AMSR_L3_CLW_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Cloud Liquid Water (1day,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19515,7 +19515,7 @@
{
"id": "ADEOS-II_AMSR_L3_IC_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Ice Concentration (1day,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19528,7 +19528,7 @@
{
"id": "ADEOS-II_AMSR_L3_IC_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Ice Concentration (1day,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19619,7 +19619,7 @@
{
"id": "ADEOS-II_AMSR_L3_SST_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Sea Surface Temperature (1day,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19632,7 +19632,7 @@
{
"id": "ADEOS-II_AMSR_L3_SST_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Sea Surface Temperature (1day,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19671,7 +19671,7 @@
{
"id": "ADEOS-II_AMSR_L3_SSW_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Sea Surface Wind (1day,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19684,7 +19684,7 @@
{
"id": "ADEOS-II_AMSR_L3_SSW_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Sea Surface Wind (1day,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19697,7 +19697,7 @@
{
"id": "ADEOS-II_AMSR_L3_SSW_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Sea Surface Wind (1month,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19710,7 +19710,7 @@
{
"id": "ADEOS-II_AMSR_L3_SSW_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Sea Surface Wind (1month,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19723,7 +19723,7 @@
{
"id": "ADEOS-II_AMSR_L3_SWE_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Snow Water Equivalent (1day,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19736,7 +19736,7 @@
{
"id": "ADEOS-II_AMSR_L3_SWE_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Snow Water Equivalent (1day,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19749,7 +19749,7 @@
{
"id": "ADEOS-II_AMSR_L3_SWE_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Snow Water Equivalent (1month,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19762,7 +19762,7 @@
{
"id": "ADEOS-II_AMSR_L3_SWE_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 Snow Water Equivalent (1month,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19801,7 +19801,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_10.65GHz-H_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 10.65GHz-V Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19814,7 +19814,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_10.65GHz-H_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 10.65GHz-V Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19827,7 +19827,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_10.65GHz-V_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19840,7 +19840,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_10.65GHz-V_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19853,7 +19853,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_10.65GHz-V_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19866,7 +19866,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_10.65GHz-V_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19905,7 +19905,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_18.7GHz-H_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 18.7GHz-H Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19918,7 +19918,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_18.7GHz-H_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 18.7GHz-H Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19931,7 +19931,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_18.7GHz-V_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 18.7GHz-V Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19944,7 +19944,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_18.7GHz-V_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 18.7GHz-V Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19983,7 +19983,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_23.8GHz-H_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 23.8GHz-H Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -19996,7 +19996,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_23.8GHz-H_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 23.8GHz-H Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20061,7 +20061,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_23.8GHz-V_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 23.8GHz-V Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20074,7 +20074,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_23.8GHz-V_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 23.8GHz-V Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20087,7 +20087,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_36.5GHz-H_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 36.5GHz-H Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20100,7 +20100,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_36.5GHz-H_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 36.5GHz-H Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20139,7 +20139,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_36.5GHz-V_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20152,7 +20152,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_36.5GHz-V_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20165,7 +20165,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_36.5GHz-V_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20178,7 +20178,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_36.5GHz-V_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20217,7 +20217,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_50.3GHz-H_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 50.3GHz-H Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20230,7 +20230,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_50.3GHz-H_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 50.3GHz-H Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20269,7 +20269,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_50.3GHz-V_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 50.3GHz-V Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20282,7 +20282,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_50.3GHz-V_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 50.3GHz-V Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20321,7 +20321,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_52.8GHz-H_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 52.8GHz-H Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20334,7 +20334,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_52.8GHz-H_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 52.8GHz-H Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20347,7 +20347,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_52.8GHz-V_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 52.8GHz-V Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20360,7 +20360,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_52.8GHz-V_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 52.8GHz-V Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20399,7 +20399,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_6GHz-H_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20412,7 +20412,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_6GHz-H_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20425,7 +20425,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_6GHz-H_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20438,7 +20438,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_6GHz-H_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20451,7 +20451,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_6GHz-V_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 6GHz-V Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20464,7 +20464,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_6GHz-V_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 6GHz-V Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20529,7 +20529,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_89.0GHz-H_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 89.0GHz-H Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20542,7 +20542,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_89.0GHz-H_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 89.0GHz-H Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20555,7 +20555,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_89.0GHz-V_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20568,7 +20568,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_89.0GHz-V_1day_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1day,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20581,7 +20581,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_89.0GHz-V_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20594,7 +20594,7 @@
{
"id": "ADEOS-II_AMSR_L3_TB_89.0GHz-V_1month_0.25deg_NA",
"title": "ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1month,0.25deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-04-02",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20659,7 +20659,7 @@
{
"id": "ADEOS-II_GLI_L1A_250m_NA",
"title": "ADEOS/2GLI L1A 250m",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20672,7 +20672,7 @@
{
"id": "ADEOS-II_GLI_L1A_250m_NA",
"title": "ADEOS/2GLI L1A 250m",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20711,7 +20711,7 @@
{
"id": "ADEOS-II_GLI_L1A_SWIR_1km_NA",
"title": "ADEOS-II/GLI L1A Short-wavelength infrared (1km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20724,7 +20724,7 @@
{
"id": "ADEOS-II_GLI_L1A_SWIR_1km_NA",
"title": "ADEOS-II/GLI L1A Short-wavelength infrared (1km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20737,7 +20737,7 @@
{
"id": "ADEOS-II_GLI_L1A_VNIR_1km_NA",
"title": "ADEOS-II/GLI L1A Visible and near infrared (1km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20750,7 +20750,7 @@
{
"id": "ADEOS-II_GLI_L1A_VNIR_1km_NA",
"title": "ADEOS-II/GLI L1A Visible and near infrared (1km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20815,7 +20815,7 @@
{
"id": "ADEOS-II_GLI_L1B_SLPT_1km_NA",
"title": "ADEOS-II/GLI L1B Satellite Position (1km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20828,7 +20828,7 @@
{
"id": "ADEOS-II_GLI_L1B_SLPT_1km_NA",
"title": "ADEOS-II/GLI L1B Satellite Position (1km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20841,7 +20841,7 @@
{
"id": "ADEOS-II_GLI_L1B_SWIR_1km_NA",
"title": "ADEOS-II/GLI L1B Short-wavelength infrared (1km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20854,7 +20854,7 @@
{
"id": "ADEOS-II_GLI_L1B_SWIR_1km_NA",
"title": "ADEOS-II/GLI L1B Short-wavelength infrared (1km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20971,7 +20971,7 @@
{
"id": "ADEOS-II_GLI_L2_ARAE_NA",
"title": "ADEOS-II/GLI L2 Aerosol Angstrom Exponent",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20984,7 +20984,7 @@
{
"id": "ADEOS-II_GLI_L2_ARAE_NA",
"title": "ADEOS-II/GLI L2 Aerosol Angstrom Exponent",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -20997,7 +20997,7 @@
{
"id": "ADEOS-II_GLI_L2_AROP_NA",
"title": "ADEOS-II/GLI L2 Aerosol Optical Thickness",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21010,7 +21010,7 @@
{
"id": "ADEOS-II_GLI_L2_AROP_NA",
"title": "ADEOS-II/GLI L2 Aerosol Optical Thickness",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21023,7 +21023,7 @@
{
"id": "ADEOS-II_GLI_L2_CLER_i_e_NA",
"title": "ADEOS-II/GLI L2 Cloud Effective Particle Radius of ice cloud by emission method",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21036,7 +21036,7 @@
{
"id": "ADEOS-II_GLI_L2_CLER_i_e_NA",
"title": "ADEOS-II/GLI L2 Cloud Effective Particle Radius of ice cloud by emission method",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21049,7 +21049,7 @@
{
"id": "ADEOS-II_GLI_L2_CLER_w_r_NA",
"title": "ADEOS-II/GLI L2 Cloud Effective Particle Radius of water cloud by reflection method",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21062,7 +21062,7 @@
{
"id": "ADEOS-II_GLI_L2_CLER_w_r_NA",
"title": "ADEOS-II/GLI L2 Cloud Effective Particle Radius of water cloud by reflection method",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21101,7 +21101,7 @@
{
"id": "ADEOS-II_GLI_L2_CLFR_NA",
"title": "ADEOS-II/GLI L2 Cloud fraction",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21114,7 +21114,7 @@
{
"id": "ADEOS-II_GLI_L2_CLFR_NA",
"title": "ADEOS-II/GLI L2 Cloud fraction",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21179,7 +21179,7 @@
{
"id": "ADEOS-II_GLI_L2_CLOP_i_r_NA",
"title": "ADEOS-II/GLI Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21192,7 +21192,7 @@
{
"id": "ADEOS-II_GLI_L2_CLOP_i_r_NA",
"title": "ADEOS-II/GLI Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21257,7 +21257,7 @@
{
"id": "ADEOS-II_GLI_L2_CLTT_w_r_NA",
"title": "ADEOS-II/GLI L2 Cloud Top Temperature of water cloud by reflection method",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21270,7 +21270,7 @@
{
"id": "ADEOS-II_GLI_L2_CLTT_w_r_NA",
"title": "ADEOS-II/GLI L2 Cloud Top Temperature of water cloud by reflection method",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21283,7 +21283,7 @@
{
"id": "ADEOS-II_GLI_L2_CLWP_w_r_NA",
"title": "ADEOS-II/GLI L2 Cloud Liquid Water Path of water cloud by reflection method",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21296,7 +21296,7 @@
{
"id": "ADEOS-II_GLI_L2_CLWP_w_r_NA",
"title": "ADEOS-II/GLI L2 Cloud Liquid Water Path of water cloud by reflection method",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21387,7 +21387,7 @@
{
"id": "ADEOS-II_GLI_L2_SNGI_NA",
"title": "ADEOS-II/GLI L2 Snow Grain and Impurities",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21400,7 +21400,7 @@
{
"id": "ADEOS-II_GLI_L2_SNGI_NA",
"title": "ADEOS-II/GLI L2 Snow Grain and Impurities",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21491,7 +21491,7 @@
{
"id": "ADEOS-II_GLI_L3B_ARAE_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Aerosol Angstrom Exponent (1month,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21504,7 +21504,7 @@
{
"id": "ADEOS-II_GLI_L3B_ARAE_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Aerosol Angstrom Exponent (1month,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21517,7 +21517,7 @@
{
"id": "ADEOS-II_GLI_L3B_AROP_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (16days,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21530,7 +21530,7 @@
{
"id": "ADEOS-II_GLI_L3B_AROP_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (16days,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21543,7 +21543,7 @@
{
"id": "ADEOS-II_GLI_L3B_AROP_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (1month,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21556,7 +21556,7 @@
{
"id": "ADEOS-II_GLI_L3B_AROP_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (1month,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21595,7 +21595,7 @@
{
"id": "ADEOS-II_GLI_L3B_CLER_i_e_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21608,7 +21608,7 @@
{
"id": "ADEOS-II_GLI_L3B_CLER_i_e_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21621,7 +21621,7 @@
{
"id": "ADEOS-II_GLI_L3B_CLER_w_r_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of water cloud by reflection method (16days,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21634,7 +21634,7 @@
{
"id": "ADEOS-II_GLI_L3B_CLER_w_r_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of water cloud by reflection method (16days,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21725,7 +21725,7 @@
{
"id": "ADEOS-II_GLI_L3B_CLHT_w_e_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Cloud Top Height of water cloud by emission method (16days,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21738,7 +21738,7 @@
{
"id": "ADEOS-II_GLI_L3B_CLHT_w_e_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Cloud Top Height of water cloud by emission method (16days,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21803,7 +21803,7 @@
{
"id": "ADEOS-II_GLI_L3B_CLOP_i_e_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21816,7 +21816,7 @@
{
"id": "ADEOS-II_GLI_L3B_CLOP_i_e_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21829,7 +21829,7 @@
{
"id": "ADEOS-II_GLI_L3B_CLOP_i_r_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (16days,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21842,7 +21842,7 @@
{
"id": "ADEOS-II_GLI_L3B_CLOP_i_r_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (16days,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21855,7 +21855,7 @@
{
"id": "ADEOS-II_GLI_L3B_CLOP_i_r_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (1month,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21868,7 +21868,7 @@
{
"id": "ADEOS-II_GLI_L3B_CLOP_i_r_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (1month,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21985,7 +21985,7 @@
{
"id": "ADEOS-II_GLI_L3B_CLTT_w_r_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Cloud Top Temperature of water cloud by reflection method (16days,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -21998,7 +21998,7 @@
{
"id": "ADEOS-II_GLI_L3B_CLTT_w_r_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 Binned Cloud Top Temperature of water cloud by reflection method (16days,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22089,7 +22089,7 @@
{
"id": "ADEOS-II_GLI_L3B_CS_1day_9km_NA",
"title": "ADEOS-II/GLI L3 Binned Ocean Color (1day,9 km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22102,7 +22102,7 @@
{
"id": "ADEOS-II_GLI_L3B_CS_1day_9km_NA",
"title": "ADEOS-II/GLI L3 Binned Ocean Color (1day,9 km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22167,7 +22167,7 @@
{
"id": "ADEOS-II_GLI_L3B_LA_1day_9km_NA",
"title": "ADEOS-II/GLI L3 Binned Aerosol radiance (1day,9 km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22180,7 +22180,7 @@
{
"id": "ADEOS-II_GLI_L3B_LA_1day_9km_NA",
"title": "ADEOS-II/GLI L3 Binned Aerosol radiance (1day,9 km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22271,7 +22271,7 @@
{
"id": "ADEOS-II_GLI_L3B_NW_1month_9km_NA",
"title": "ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (1month,9 km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22284,7 +22284,7 @@
{
"id": "ADEOS-II_GLI_L3B_NW_1month_9km_NA",
"title": "ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (1month,9 km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22323,7 +22323,7 @@
{
"id": "ADEOS-II_GLI_L3B_SNWGS_16days_1-12deg_NA",
"title": "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (16days,1/12deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22336,7 +22336,7 @@
{
"id": "ADEOS-II_GLI_L3B_SNWGS_16days_1-12deg_NA",
"title": "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (16days,1/12deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22349,7 +22349,7 @@
{
"id": "ADEOS-II_GLI_L3B_SNWGS_1month_1-12deg_NA",
"title": "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (1month,1/12deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22362,7 +22362,7 @@
{
"id": "ADEOS-II_GLI_L3B_SNWGS_1month_1-12deg_NA",
"title": "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (1month,1/12deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22427,7 +22427,7 @@
{
"id": "ADEOS-II_GLI_L3B_SNWI_16days_1-12deg_NA",
"title": "ADEOS-II/GLI L3 Binned Snow impurities (16days,1/12deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22440,7 +22440,7 @@
{
"id": "ADEOS-II_GLI_L3B_SNWI_16days_1-12deg_NA",
"title": "ADEOS-II/GLI L3 Binned Snow impurities (16days,1/12deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22453,7 +22453,7 @@
{
"id": "ADEOS-II_GLI_L3B_SNWI_1month_1-12deg_NA",
"title": "ADEOS-II/GLI L3 Binned Snow impurities (1month,1/12deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22466,7 +22466,7 @@
{
"id": "ADEOS-II_GLI_L3B_SNWI_1month_1-12deg_NA",
"title": "ADEOS-II/GLI L3 Binned Snow impurities (1month,1/12deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22531,7 +22531,7 @@
{
"id": "ADEOS-II_GLI_L3B_ST_1day_9km_NA",
"title": "ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1day,9 km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22544,7 +22544,7 @@
{
"id": "ADEOS-II_GLI_L3B_ST_1day_9km_NA",
"title": "ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1day,9 km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22557,7 +22557,7 @@
{
"id": "ADEOS-II_GLI_L3B_ST_1month_9km_NA",
"title": "ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1month,9 km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22570,7 +22570,7 @@
{
"id": "ADEOS-II_GLI_L3B_ST_1month_9km_NA",
"title": "ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1month,9 km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22635,7 +22635,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_ARAE_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Aerosol Angstrom Exponent (1month,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22648,7 +22648,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_ARAE_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Aerosol Angstrom Exponent (1month,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22739,7 +22739,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CDOM_1month_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (1month,9km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22752,7 +22752,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CDOM_1month_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (1month,9km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22791,7 +22791,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CHLA_1day_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Chlorophyll-a (1day,9km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22804,7 +22804,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CHLA_1day_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Chlorophyll-a (1day,9km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22895,7 +22895,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLER_i_e_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22908,7 +22908,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLER_i_e_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22973,7 +22973,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLFR_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud fraction (16days,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22986,7 +22986,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLFR_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud fraction (16days,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -22999,7 +22999,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLFR_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud fraction (1month,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23012,7 +23012,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLFR_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud fraction (1month,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23051,7 +23051,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLHT_w_e_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Top Height of water cloud by emission method (1month,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23064,7 +23064,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLHT_w_e_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Top Height of water cloud by emission method (1month,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23103,7 +23103,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLOP_i_e_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23116,7 +23116,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLOP_i_e_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23129,7 +23129,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLOP_i_r_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (16days,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23142,7 +23142,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLOP_i_r_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (16days,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23155,7 +23155,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLOP_i_r_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (1month,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23168,7 +23168,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLOP_i_r_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (1month,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23259,7 +23259,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLTT_i_e_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of ice cloud by emission method (1month,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23272,7 +23272,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLTT_i_e_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of ice cloud by emission method (1month,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23285,7 +23285,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLTT_w_r_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of water cloud by reflection method (16days,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23298,7 +23298,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLTT_w_r_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of water cloud by reflection method (16days,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23337,7 +23337,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLWP_w_r_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (16days,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23350,7 +23350,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLWP_w_r_16days_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (16days,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23363,7 +23363,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLWP_w_r_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (1month,1/4deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23376,7 +23376,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_CLWP_w_r_1month_1-4deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (1month,1/4deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23389,7 +23389,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_K490_1day_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (1day,9km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23402,7 +23402,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_K490_1day_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (1day,9km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23441,7 +23441,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_K490_8days_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (8days,9km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23454,7 +23454,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_K490_8days_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (8days,9km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23467,7 +23467,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_LA_1day_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Aerosol radiance (1day,9km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23480,7 +23480,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_LA_1day_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Aerosol radiance (1day,9km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23493,7 +23493,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_LA_1month_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Aerosol radiance (1month,9km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23506,7 +23506,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_LA_1month_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Aerosol radiance (1month,9km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23519,7 +23519,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_LA_8days_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Aerosol radiance (8days,9km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23532,7 +23532,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_LA_8days_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Aerosol radiance (8days,9km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23597,7 +23597,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_NW_8days_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (8days,9km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23610,7 +23610,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_NW_8days_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (8days,9km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23623,7 +23623,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SNWGS_16days_1-12deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (16days,1/12deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23636,7 +23636,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SNWGS_16days_1-12deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (16days,1/12deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23649,7 +23649,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SNWGS_1month_1-12deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (1month,1/12deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23662,7 +23662,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SNWGS_1month_1-12deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (1month,1/12deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23675,7 +23675,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SNWG_16days_1-12deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (16days,1/12deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23688,7 +23688,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SNWG_16days_1-12deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (16days,1/12deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23701,7 +23701,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SNWG_1month_1-12deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (1month,1/12deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23714,7 +23714,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SNWG_1month_1-12deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (1month,1/12deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23727,7 +23727,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SNWI_16days_1-12deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Snow impurities (16days,1/12deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23740,7 +23740,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SNWI_16days_1-12deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Snow impurities (16days,1/12deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23779,7 +23779,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SNWTS_16days_1-12deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Snow surface temperature (16days,1/12deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23792,7 +23792,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SNWTS_16days_1-12deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Snow surface temperature (16days,1/12deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23805,7 +23805,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SNWTS_1month_1-12deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Snow surface temperature (1month,1/12deg)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23818,7 +23818,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SNWTS_1month_1-12deg_NA",
"title": "ADEOS-II/GLI L3 STA Map Snow surface temperature (1month,1/12deg)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23857,7 +23857,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SS_1month_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Suspended solid weight (1month,9km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23870,7 +23870,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_SS_1month_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Suspended solid weight (1month,9km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23909,7 +23909,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_ST_ALL_1day_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (1day,9km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23922,7 +23922,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_ST_ALL_1day_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (1day,9km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23961,7 +23961,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_ST_ALL_8days_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (8days,9km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23974,7 +23974,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_ST_ALL_8days_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (8days,9km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -23987,7 +23987,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_ST_DayNight_1day_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1day,9km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -24000,7 +24000,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_ST_DayNight_1day_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1day,9km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -24013,7 +24013,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_ST_DayNight_1month_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1month,9km)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -24026,7 +24026,7 @@
{
"id": "ADEOS-II_GLI_L3STA_Map_ST_DayNight_1month_9km_NA",
"title": "ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1month,9km)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-24",
"end_date": "2003-10-25",
"bbox": "-180, -90, 180, 90",
@@ -24091,7 +24091,7 @@
{
"id": "ADEOS_AVNIR_L1A_MU_NA",
"title": "ADEOS/AVNIR L1A Multispectral band",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-10-30",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24104,7 +24104,7 @@
{
"id": "ADEOS_AVNIR_L1A_MU_NA",
"title": "ADEOS/AVNIR L1A Multispectral band",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-10-30",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24143,7 +24143,7 @@
{
"id": "ADEOS_AVNIR_L1B2_MU_NA",
"title": "ADEOS/AVNIR L1B2 Multispectral band",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-10-30",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24156,7 +24156,7 @@
{
"id": "ADEOS_AVNIR_L1B2_MU_NA",
"title": "ADEOS/AVNIR L1B2 Multispectral band",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-10-30",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24195,7 +24195,7 @@
{
"id": "ADEOS_OCTS_L1A_GAC_TI_NA",
"title": "ADEOS/OCTS L1A GAC Thermal infrared",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24208,7 +24208,7 @@
{
"id": "ADEOS_OCTS_L1A_GAC_TI_NA",
"title": "ADEOS/OCTS L1A GAC Thermal infrared",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24247,7 +24247,7 @@
{
"id": "ADEOS_OCTS_L1A_RTC_TI_NA",
"title": "ADEOS/OCTS L1A RTC Thermal infrared",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24260,7 +24260,7 @@
{
"id": "ADEOS_OCTS_L1A_RTC_TI_NA",
"title": "ADEOS/OCTS L1A RTC Thermal infrared",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24299,7 +24299,7 @@
{
"id": "ADEOS_OCTS_L2_GAC_OC1_NA",
"title": "ADEOS/OCTS L2 GAC Ocean Color (OC1)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24312,7 +24312,7 @@
{
"id": "ADEOS_OCTS_L2_GAC_OC1_NA",
"title": "ADEOS/OCTS L2 GAC Ocean Color (OC1)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24325,7 +24325,7 @@
{
"id": "ADEOS_OCTS_L2_GAC_OC2_NA",
"title": "ADEOS/OCTS L2 GAC Ocean Color (OC2)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24338,7 +24338,7 @@
{
"id": "ADEOS_OCTS_L2_GAC_OC2_NA",
"title": "ADEOS/OCTS L2 GAC Ocean Color (OC2)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24351,7 +24351,7 @@
{
"id": "ADEOS_OCTS_L2_GAC_SST_NA",
"title": "ADEOS/OCTS L2 GAC Sea Surface Temperature",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24364,7 +24364,7 @@
{
"id": "ADEOS_OCTS_L2_GAC_SST_NA",
"title": "ADEOS/OCTS L2 GAC Sea Surface Temperature",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24403,7 +24403,7 @@
{
"id": "ADEOS_OCTS_L2_RTC_OC1_NA",
"title": "ADEOS/OCTS L2 RTC Ocean Color (OC1)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24416,7 +24416,7 @@
{
"id": "ADEOS_OCTS_L2_RTC_OC1_NA",
"title": "ADEOS/OCTS L2 RTC Ocean Color (OC1)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24455,7 +24455,7 @@
{
"id": "ADEOS_OCTS_L2_RTC_SST_NA",
"title": "ADEOS/OCTS L2 RTC Sea Surface Temperature (SST)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24468,7 +24468,7 @@
{
"id": "ADEOS_OCTS_L2_RTC_SST_NA",
"title": "ADEOS/OCTS L2 RTC Sea Surface Temperature (SST)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -24559,7 +24559,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_OCC_1week_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Ocean Color (OCC) (1-Week)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -24572,7 +24572,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_OCC_1week_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Ocean Color (OCC) (1-Week)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -24663,7 +24663,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_OCK_1week_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Ocean Color (OCK) (1-Week)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -24676,7 +24676,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_OCK_1week_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Ocean Color (OCK) (1-Week)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -24689,7 +24689,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_OCK_1year_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Ocean Color (OCK) (1-Year)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -24702,7 +24702,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_OCK_1year_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Ocean Color (OCK) (1-Year)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -24793,7 +24793,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_OCL_1year_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Ocean Color (OCL) (1-Year)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -24806,7 +24806,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_OCL_1year_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Ocean Color (OCL) (1-Year)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -24871,7 +24871,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_OCP_1week_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Ocean Color (OCP) (1-Week)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -24884,7 +24884,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_OCP_1week_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Ocean Color (OCP) (1-Week)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -24897,7 +24897,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_OCP_1year_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Ocean Color (OCP) (1-Year)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -24910,7 +24910,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_OCP_1year_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Ocean Color (OCP) (1-Year)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -24949,7 +24949,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_SST_1month_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Sea Surface Temperature (1-Month)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -24962,7 +24962,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_SST_1month_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Sea Surface Temperature (1-Month)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -24975,7 +24975,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_SST_1week_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Sea Surface Temperature (1-Week)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -24988,7 +24988,7 @@
{
"id": "ADEOS_OCTS_L3BM_GAC_SST_1week_NA",
"title": "ADEOS OCTS L3 GAC Binned Map Sea Surface Temperature (1-Week)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -25131,7 +25131,7 @@
{
"id": "ADEOS_OCTS_L3B_GAC_OC_1day_NA",
"title": "ADEOS OCTS L3 GAC Binned Ocean Color (1-Day)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -25144,7 +25144,7 @@
{
"id": "ADEOS_OCTS_L3B_GAC_OC_1day_NA",
"title": "ADEOS OCTS L3 GAC Binned Ocean Color (1-Day)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -25157,7 +25157,7 @@
{
"id": "ADEOS_OCTS_L3B_GAC_OC_1month_NA",
"title": "ADEOS OCTS L3 GAC Binned Ocean Color (1-Month)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -25170,7 +25170,7 @@
{
"id": "ADEOS_OCTS_L3B_GAC_OC_1month_NA",
"title": "ADEOS OCTS L3 GAC Binned Ocean Color (1-Month)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -25183,7 +25183,7 @@
{
"id": "ADEOS_OCTS_L3B_GAC_OC_1week_NA",
"title": "ADEOS OCTS L3 GAC Binned Ocean Color (1-Week)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -25196,7 +25196,7 @@
{
"id": "ADEOS_OCTS_L3B_GAC_OC_1week_NA",
"title": "ADEOS OCTS L3 GAC Binned Ocean Color (1-Week)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -25287,7 +25287,7 @@
{
"id": "ADEOS_OCTS_L3B_GAC_SST_1week_NA",
"title": "ADEOS OCTS L3 GAC Binned Sea Surface Temperature (1-Week)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -25300,7 +25300,7 @@
{
"id": "ADEOS_OCTS_L3B_GAC_SST_1week_NA",
"title": "ADEOS OCTS L3 GAC Binned Sea Surface Temperature (1-Week)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -25313,7 +25313,7 @@
{
"id": "ADEOS_OCTS_L3B_GAC_SST_1year_NA",
"title": "ADEOS OCTS L3 GAC Binned Sea Surface Temperature (1-Year)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -25326,7 +25326,7 @@
{
"id": "ADEOS_OCTS_L3B_GAC_SST_1year_NA",
"title": "ADEOS OCTS L3 GAC Binned Sea Surface Temperature (1-Year)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -25365,7 +25365,7 @@
{
"id": "ADEOS_OCTS_L3B_GAC_VI_1month_NA",
"title": "ADEOS OCTS L3 GAC Binned Vegetation indices (1-Month)",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -25378,7 +25378,7 @@
{
"id": "ADEOS_OCTS_L3B_GAC_VI_1month_NA",
"title": "ADEOS OCTS L3 GAC Binned Vegetation indices (1-Month)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-07-06",
"bbox": "-180, -90, 180, 90",
@@ -25521,7 +25521,7 @@
{
"id": "ADEOS_OCTS_L3M_RTC_SST_NA",
"title": "ADEOS OCTS L3 RTC Map Sea Surface Temperature",
- "catalog": "JAXA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-22",
"bbox": "-180, -90, 180, 90",
@@ -25534,7 +25534,7 @@
{
"id": "ADEOS_OCTS_L3M_RTC_SST_NA",
"title": "ADEOS OCTS L3 RTC Map Sea Surface Temperature",
- "catalog": "ALL STAC Catalog",
+ "catalog": "JAXA STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-22",
"bbox": "-180, -90, 180, 90",
@@ -25547,7 +25547,7 @@
{
"id": "ADS_WRI",
"title": "Africa Data Sampler (ADS): Digital Data Sets for Africa Available from the World Resources Institute (WRI)",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-16, -35, 55, 40",
@@ -25560,7 +25560,7 @@
{
"id": "ADS_WRI",
"title": "Africa Data Sampler (ADS): Digital Data Sets for Africa Available from the World Resources Institute (WRI)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-16, -35, 55, 40",
@@ -25573,7 +25573,7 @@
{
"id": "AERDB_D3_ABI_G16_1",
"title": "ABI G16 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LAADS STAC Catalog",
"state_date": "2019-05-01",
"end_date": "2020-05-01",
"bbox": "-180, -90, 180, 90",
@@ -25586,7 +25586,7 @@
{
"id": "AERDB_D3_ABI_G16_1",
"title": "ABI G16 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid",
- "catalog": "LAADS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-05-01",
"end_date": "2020-05-01",
"bbox": "-180, -90, 180, 90",
@@ -25820,7 +25820,7 @@
{
"id": "AERDB_M3_ABI_G16_1",
"title": "ABI G16 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid",
- "catalog": "LAADS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-05-01",
"end_date": "2020-05-01",
"bbox": "-180, -90, 180, 90",
@@ -25833,7 +25833,7 @@
{
"id": "AERDB_M3_ABI_G16_1",
"title": "ABI G16 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LAADS STAC Catalog",
"state_date": "2019-05-01",
"end_date": "2020-05-01",
"bbox": "-180, -90, 180, 90",
@@ -26002,7 +26002,7 @@
{
"id": "AERIALDIGI",
"title": "Aircraft Scanners",
- "catalog": "ALL STAC Catalog",
+ "catalog": "USGS_LTA STAC Catalog",
"state_date": "1987-10-06",
"end_date": "",
"bbox": "-180, 24, -60, 72",
@@ -26014,34 +26014,34 @@
},
{
"id": "AERIALDIGI",
- "title": "Aircraft Scanners - AERIALDIGI",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "title": "Aircraft Scanners",
+ "catalog": "ALL STAC Catalog",
"state_date": "1987-10-06",
"end_date": "",
"bbox": "-180, 24, -60, 72",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/AERIALDIGI",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/USGS_LTA/collections/AERIALDIGI",
"description": "The National Aeronautics and Space Administration (NASA) Aircraft Scanners data set contains digital imagery acquired from several multispectral scanners, including Daedalus thematic mapper simulator scanners and the thermal infrared multispectral scanner. Data are collected from selected areas over the conterminous United States, Alaska, and Hawaii by NASA ER-2 and NASA C-130B aircraft, operating from the NASA Ames Research Center in Moffett Field, California, and by NASA Learjet aircraft, operating from Stennis Space Center in Bay St. Louis, Mississippi. Limited international acquisitions also are available. In cooperation with the Jet Propulsion Laboratory and Daedalus Enterprises,Inc., NASA developed several multispectral sensors. The data acquired from these sensors supports NASA's Airborne Science and Applications Program and have been identified as precursors to the instruments scheduled to fly on Earth Observing System platforms. THEMATIC MAPPER SIMULATOR The Thematic Mapper Simulator (TMS) sensor is a line scanning device designed for a variety of Earth science applications. Flown aboard NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field of View of 1.25 milliradians with a ground resolution of 81 feet (25 meters) at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per second with 716 pixels per scan line. Swath width is 8.3 nautical miles (15.4 kilometers) at 65,000 feet while the scanner's Field of View is 42.5 degrees. NS-001 MULTISPECTRAL SCANNER The NS-001multispectral scanner is a line scanning device designed to simulate Landsat thematic mapper (TM) sensor performance, including a near infrared/short-wave infrared band used in applications similar to those of the TM sensor (e.g., Earth resources mapping, vegetation/land cover mapping, geologic studies). Flown aboard NASA C-130B aircraft, the NS-001 sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a variable scan rate (10 to 100 scans per second) with 699 pixels per scan line, but the available motor drive supply restricts the maximum stable scan speed to approximately 85 revolutions per second. A scan rate of 100 revolutions per second is possible, but not probable, for short scan lines; therefore, a combination of factors, including aircraft flight requirements and maximum scan speed, prevent scanner operation below 1,500 feet. Swath width is 3.9 nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or field of regard for the sensor is 100 degrees, plus or minus 15 degrees for roll compensation. THERMAL INFRARED MULTISPECTRAL SCANNER The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line scanning device originally designed for geologic applications. Flown aboard NASA C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698 pixels per scan line. Swath width is 2.6 nautical miles (4.8 kilometers) at 10,000 feet while the scanner's Field of View is 76.56 degrees.",
"license": "proprietary"
},
{
"id": "AERIALDIGI",
- "title": "Aircraft Scanners",
- "catalog": "USGS_LTA STAC Catalog",
+ "title": "Aircraft Scanners - AERIALDIGI",
+ "catalog": "ALL STAC Catalog",
"state_date": "1987-10-06",
"end_date": "",
"bbox": "-180, 24, -60, 72",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/USGS_LTA/collections/AERIALDIGI",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/CEOS_EXTRA/collections/AERIALDIGI",
"description": "The National Aeronautics and Space Administration (NASA) Aircraft Scanners data set contains digital imagery acquired from several multispectral scanners, including Daedalus thematic mapper simulator scanners and the thermal infrared multispectral scanner. Data are collected from selected areas over the conterminous United States, Alaska, and Hawaii by NASA ER-2 and NASA C-130B aircraft, operating from the NASA Ames Research Center in Moffett Field, California, and by NASA Learjet aircraft, operating from Stennis Space Center in Bay St. Louis, Mississippi. Limited international acquisitions also are available. In cooperation with the Jet Propulsion Laboratory and Daedalus Enterprises,Inc., NASA developed several multispectral sensors. The data acquired from these sensors supports NASA's Airborne Science and Applications Program and have been identified as precursors to the instruments scheduled to fly on Earth Observing System platforms. THEMATIC MAPPER SIMULATOR The Thematic Mapper Simulator (TMS) sensor is a line scanning device designed for a variety of Earth science applications. Flown aboard NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field of View of 1.25 milliradians with a ground resolution of 81 feet (25 meters) at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per second with 716 pixels per scan line. Swath width is 8.3 nautical miles (15.4 kilometers) at 65,000 feet while the scanner's Field of View is 42.5 degrees. NS-001 MULTISPECTRAL SCANNER The NS-001multispectral scanner is a line scanning device designed to simulate Landsat thematic mapper (TM) sensor performance, including a near infrared/short-wave infrared band used in applications similar to those of the TM sensor (e.g., Earth resources mapping, vegetation/land cover mapping, geologic studies). Flown aboard NASA C-130B aircraft, the NS-001 sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a variable scan rate (10 to 100 scans per second) with 699 pixels per scan line, but the available motor drive supply restricts the maximum stable scan speed to approximately 85 revolutions per second. A scan rate of 100 revolutions per second is possible, but not probable, for short scan lines; therefore, a combination of factors, including aircraft flight requirements and maximum scan speed, prevent scanner operation below 1,500 feet. Swath width is 3.9 nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or field of regard for the sensor is 100 degrees, plus or minus 15 degrees for roll compensation. THERMAL INFRARED MULTISPECTRAL SCANNER The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line scanning device originally designed for geologic applications. Flown aboard NASA C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698 pixels per scan line. Swath width is 2.6 nautical miles (4.8 kilometers) at 10,000 feet while the scanner's Field of View is 76.56 degrees.",
"license": "proprietary"
},
{
"id": "AERIALDIGI",
"title": "Aircraft Scanners - AERIALDIGI",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1987-10-06",
"end_date": "",
"bbox": "-180, 24, -60, 72",
@@ -26366,7 +26366,7 @@
{
"id": "AFOLVIS1A_1",
"title": "AfriSAR LVIS L1A Geotagged Images V001",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2016-02-20",
"end_date": "2016-03-08",
"bbox": "8, -2, 12, 1",
@@ -26379,7 +26379,7 @@
{
"id": "AFOLVIS1A_1",
"title": "AfriSAR LVIS L1A Geotagged Images V001",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-02-20",
"end_date": "2016-03-08",
"bbox": "8, -2, 12, 1",
@@ -26717,7 +26717,7 @@
{
"id": "AIRG2SSD_IRonly_006",
"title": "AIRS/Aqua L2G Precipitation Estimate (AIRS-only) V006 (AIRG2SSD_IRonly) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-08-30",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -26730,7 +26730,7 @@
{
"id": "AIRG2SSD_IRonly_006",
"title": "AIRS/Aqua L2G Precipitation Estimate (AIRS-only) V006 (AIRG2SSD_IRonly) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-08-30",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -26769,7 +26769,7 @@
{
"id": "AIRH2CCF_006",
"title": "AIRS/Aqua L2 Cloud-Cleared Infrared Radiances (AIRS+AMSU+HSB) V006 (AIRH2CCF) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-08-30",
"end_date": "2003-02-05",
"bbox": "-180, -90, 180, 90",
@@ -26782,7 +26782,7 @@
{
"id": "AIRH2CCF_006",
"title": "AIRS/Aqua L2 Cloud-Cleared Infrared Radiances (AIRS+AMSU+HSB) V006 (AIRH2CCF) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-08-30",
"end_date": "2003-02-05",
"bbox": "-180, -90, 180, 90",
@@ -26964,7 +26964,7 @@
{
"id": "AIRH3SPD_006",
"title": "AIRS/Aqua L3 Daily Support Daily Product (AIRS+AMSU+HSB) 1 degree x 1 degree V006 (AIRH3SPD) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-08-31",
"end_date": "2003-02-06",
"bbox": "-180, -90, 180, 90",
@@ -26977,7 +26977,7 @@
{
"id": "AIRH3SPD_006",
"title": "AIRS/Aqua L3 Daily Support Daily Product (AIRS+AMSU+HSB) 1 degree x 1 degree V006 (AIRH3SPD) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-08-31",
"end_date": "2003-02-06",
"bbox": "-180, -90, 180, 90",
@@ -27146,7 +27146,7 @@
{
"id": "AIRHBRAD_005",
"title": "AIRS/Aqua L1B HSB geolocated and calibrated brightness temperatures V005 (AIRHBRAD) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-05-24",
"end_date": "2003-11-20",
"bbox": "-180, -90, 180, 90",
@@ -27159,7 +27159,7 @@
{
"id": "AIRHBRAD_005",
"title": "AIRS/Aqua L1B HSB geolocated and calibrated brightness temperatures V005 (AIRHBRAD) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-05-24",
"end_date": "2003-11-20",
"bbox": "-180, -90, 180, 90",
@@ -27172,7 +27172,7 @@
{
"id": "AIRI2CCF_006",
"title": "AIRS/Aqua L2 Cloud-Cleared Infrared Radiances (AIRS+AMSU) V006 (AIRI2CCF) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-08-30",
"end_date": "2016-09-24",
"bbox": "-180, -90, 180, 90",
@@ -27185,7 +27185,7 @@
{
"id": "AIRI2CCF_006",
"title": "AIRS/Aqua L2 Cloud-Cleared Infrared Radiances (AIRS+AMSU) V006 (AIRI2CCF) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-08-30",
"end_date": "2016-09-24",
"bbox": "-180, -90, 180, 90",
@@ -27315,7 +27315,7 @@
{
"id": "AIRIBRAD_NRT_BUFR_005",
"title": "AIRS/Aqua L1B Near Real Time (NRT) Infrared (IR) geolocated and calibrated radiances in BUFR format V005 (AIRIBRAD_NRT_BUFR) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2015-12-15",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -27328,7 +27328,7 @@
{
"id": "AIRIBRAD_NRT_BUFR_005",
"title": "AIRS/Aqua L1B Near Real Time (NRT) Infrared (IR) geolocated and calibrated radiances in BUFR format V005 (AIRIBRAD_NRT_BUFR) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-12-15",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -27341,7 +27341,7 @@
{
"id": "AIRICRAD_6.7",
"title": "AIRS/Aqua L1C Infrared (IR) resampled and corrected radiances V6.7 (AIRICRAD) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-08-30",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -27354,7 +27354,7 @@
{
"id": "AIRICRAD_6.7",
"title": "AIRS/Aqua L1C Infrared (IR) resampled and corrected radiances V6.7 (AIRICRAD) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-08-30",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -27445,7 +27445,7 @@
{
"id": "AIRMISR_CLAMS_2001_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the CLAMS 2001 Campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2001-07-12",
"end_date": "2001-08-02",
"bbox": "-78.82, 35.64, -74.01, 39.99",
@@ -27458,7 +27458,7 @@
{
"id": "AIRMISR_CLAMS_2001_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the CLAMS 2001 Campaign",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-07-12",
"end_date": "2001-08-02",
"bbox": "-78.82, 35.64, -74.01, 39.99",
@@ -27471,7 +27471,7 @@
{
"id": "AIRMISR_HARVARD_2003_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Harvard 2003 Campaign",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-08-24",
"end_date": "2003-08-24",
"bbox": "-72.45, 42.28, -71.81, 42.78",
@@ -27484,7 +27484,7 @@
{
"id": "AIRMISR_HARVARD_2003_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Harvard 2003 Campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2003-08-24",
"end_date": "2003-08-24",
"bbox": "-72.45, 42.28, -71.81, 42.78",
@@ -27549,7 +27549,7 @@
{
"id": "AIRMISR_LUNAR_LAKE_2000_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Lunar Lake 2000 Campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2000-06-11",
"end_date": "2000-06-11",
"bbox": "-117.5, 36.86, -114.6, 39",
@@ -27562,7 +27562,7 @@
{
"id": "AIRMISR_LUNAR_LAKE_2000_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Lunar Lake 2000 Campaign",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-06-11",
"end_date": "2000-06-11",
"bbox": "-117.5, 36.86, -114.6, 39",
@@ -27575,7 +27575,7 @@
{
"id": "AIRMISR_LUNAR_LAKE_2001_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Lunar Lake 2001 Campaign",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-06-30",
"end_date": "2001-06-30",
"bbox": "-116.32, 38.13, -115.36, 38.73",
@@ -27588,7 +27588,7 @@
{
"id": "AIRMISR_LUNAR_LAKE_2001_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Lunar Lake 2001 Campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2001-06-30",
"end_date": "2001-06-30",
"bbox": "-116.32, 38.13, -115.36, 38.73",
@@ -27627,7 +27627,7 @@
{
"id": "AIRMISR_MORGAN_MONROE_2003_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Morgan Monore 2003 Campaign",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-08-19",
"end_date": "2003-08-19",
"bbox": "-86.76, 39.05, -86.03, 39.6",
@@ -27640,7 +27640,7 @@
{
"id": "AIRMISR_MORGAN_MONROE_2003_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Morgan Monore 2003 Campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2003-08-19",
"end_date": "2003-08-19",
"bbox": "-86.76, 39.05, -86.03, 39.6",
@@ -27653,7 +27653,7 @@
{
"id": "AIRMISR_ROGERS_LAKE_2001_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Roger's Lake 2001 Campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2001-06-06",
"end_date": "2001-06-06",
"bbox": "-118.06, 34.75, -117.51, 35.33",
@@ -27666,7 +27666,7 @@
{
"id": "AIRMISR_ROGERS_LAKE_2001_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Roger's Lake 2001 Campaign",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-06-06",
"end_date": "2001-06-06",
"bbox": "-118.06, 34.75, -117.51, 35.33",
@@ -27679,7 +27679,7 @@
{
"id": "AIRMISR_SAFARI_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Southern African Fire Atmosphere Research Initiative 2000 Field Campaign",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-09-06",
"end_date": "2000-09-14",
"bbox": "9.08, -24.69, 31.49, -15.18",
@@ -27692,7 +27692,7 @@
{
"id": "AIRMISR_SAFARI_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Southern African Fire Atmosphere Research Initiative 2000 Field Campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2000-09-06",
"end_date": "2000-09-14",
"bbox": "9.08, -24.69, 31.49, -15.18",
@@ -27705,7 +27705,7 @@
{
"id": "AIRMISR_SERC_2003_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the SERC 2003 Campaign",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-08-20",
"end_date": "2003-08-20",
"bbox": "-76.85, 38.6, -76.28, 39.06",
@@ -27718,7 +27718,7 @@
{
"id": "AIRMISR_SERC_2003_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the SERC 2003 Campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2003-08-20",
"end_date": "2003-08-20",
"bbox": "-76.85, 38.6, -76.28, 39.06",
@@ -27757,7 +27757,7 @@
{
"id": "AIRMISR_WISCONSIN_2000_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Wisconsin 2000 Campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2000-03-03",
"end_date": "2000-03-03",
"bbox": "-98, 35.9, -90.2, 43.9",
@@ -27770,7 +27770,7 @@
{
"id": "AIRMISR_WISCONSIN_2000_1",
"title": "Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Wisconsin 2000 Campaign",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-03-03",
"end_date": "2000-03-03",
"bbox": "-98, 35.9, -90.2, 43.9",
@@ -27822,7 +27822,7 @@
{
"id": "AIRS2CCF_NRT_006",
"title": "AIRS/Aqua L2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) V006 (AIRS2CCF_NRT) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2016-10-15",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -27835,7 +27835,7 @@
{
"id": "AIRS2CCF_NRT_006",
"title": "AIRS/Aqua L2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) V006 (AIRS2CCF_NRT) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-10-15",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -27861,7 +27861,7 @@
{
"id": "AIRS2RET_006",
"title": "AIRS/Aqua L2 Standard Physical Retrieval (AIRS-only) V006 (AIRS2RET) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-08-30",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -27874,7 +27874,7 @@
{
"id": "AIRS2RET_006",
"title": "AIRS/Aqua L2 Standard Physical Retrieval (AIRS-only) V006 (AIRS2RET) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-08-30",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -27900,7 +27900,7 @@
{
"id": "AIRS2RET_NRT_006",
"title": "AIRS/Aqua L2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) V006 (AIRS2RET_NRT) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-10-15",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -27913,7 +27913,7 @@
{
"id": "AIRS2RET_NRT_006",
"title": "AIRS/Aqua L2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) V006 (AIRS2RET_NRT) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2016-10-15",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -27939,7 +27939,7 @@
{
"id": "AIRS2SPC_005",
"title": "AIRS/Aqua L2 CO2 support retrieval (AIRS-only) V005 (AIRS2SPC) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-01-01",
"end_date": "2017-03-01",
"bbox": "-180, -90, 180, 90",
@@ -27952,7 +27952,7 @@
{
"id": "AIRS2SPC_005",
"title": "AIRS/Aqua L2 CO2 support retrieval (AIRS-only) V005 (AIRS2SPC) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2010-01-01",
"end_date": "2017-03-01",
"bbox": "-180, -90, 180, 90",
@@ -27965,7 +27965,7 @@
{
"id": "AIRS2STC_005",
"title": "AIRS/Aqua L2 CO2 in the free troposphere (AIRS-only) V005 (AIRS2STC) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2010-01-01",
"end_date": "2017-03-01",
"bbox": "-180, -60, 180, 90",
@@ -27978,7 +27978,7 @@
{
"id": "AIRS2STC_005",
"title": "AIRS/Aqua L2 CO2 in the free troposphere (AIRS-only) V005 (AIRS2STC) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-01-01",
"end_date": "2017-03-01",
"bbox": "-180, -60, 180, 90",
@@ -27991,7 +27991,7 @@
{
"id": "AIRS2SUP_006",
"title": "AIRS/Aqua L2 Support Retrieval (AIRS-only) V006 (AIRS2SUP) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-08-30",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28004,7 +28004,7 @@
{
"id": "AIRS2SUP_006",
"title": "AIRS/Aqua L2 Support Retrieval (AIRS-only) V006 (AIRS2SUP) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-08-30",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28030,7 +28030,7 @@
{
"id": "AIRS2SUP_NRT_006",
"title": "AIRS/Aqua L2 Near Real Time (NRT) Support Retrieval (AIRS-only) V006 (AIRS2SUP_NRT) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-10-15",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28043,7 +28043,7 @@
{
"id": "AIRS2SUP_NRT_006",
"title": "AIRS/Aqua L2 Near Real Time (NRT) Support Retrieval (AIRS-only) V006 (AIRS2SUP_NRT) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2016-10-15",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28095,7 +28095,7 @@
{
"id": "AIRS3C2D_005",
"title": "AIRS/Aqua L3 daily CO2 in the free troposphere (AIRS-only) 2.5 degrees x 2 degrees V005 (AIRS3C2D) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2010-01-01",
"end_date": "2017-02-28",
"bbox": "-180, -60, 180, 90",
@@ -28108,7 +28108,7 @@
{
"id": "AIRS3C2D_005",
"title": "AIRS/Aqua L3 daily CO2 in the free troposphere (AIRS-only) 2.5 degrees x 2 degrees V005 (AIRS3C2D) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-01-01",
"end_date": "2017-02-28",
"bbox": "-180, -60, 180, 90",
@@ -28147,7 +28147,7 @@
{
"id": "AIRS3QP5_006",
"title": "AIRS/Aqua L3 5-day Quantization in Physical Units (AIRS-only) 5 degrees x 5 degrees V006 (AIRS3QP5) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-09-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28160,7 +28160,7 @@
{
"id": "AIRS3QP5_006",
"title": "AIRS/Aqua L3 5-day Quantization in Physical Units (AIRS-only) 5 degrees x 5 degrees V006 (AIRS3QP5) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-09-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28173,7 +28173,7 @@
{
"id": "AIRS3QPM_006",
"title": "AIRS/Aqua L3 Monthly Quantization in Physical Units (AIRS-only) 5 degrees x 5 degrees V006 (AIRS3QPM) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-09-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28186,7 +28186,7 @@
{
"id": "AIRS3QPM_006",
"title": "AIRS/Aqua L3 Monthly Quantization in Physical Units (AIRS-only) 5 degrees x 5 degrees V006 (AIRS3QPM) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-09-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28199,7 +28199,7 @@
{
"id": "AIRS3SP8_006",
"title": "AIRS/Aqua L3 8-day Support Product (AIRS-only) 1 degree X 1 degree V006 (AIRS3SP8) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-09-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28212,7 +28212,7 @@
{
"id": "AIRS3SP8_006",
"title": "AIRS/Aqua L3 8-day Support Product (AIRS-only) 1 degree X 1 degree V006 (AIRS3SP8) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-09-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28264,7 +28264,7 @@
{
"id": "AIRS3SPM_006",
"title": "AIRS/Aqua L3 Monthly Support Product (AIRS-only) 1 degree x 1 degree V006 (AIRS3SPM) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-09-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28277,7 +28277,7 @@
{
"id": "AIRS3SPM_006",
"title": "AIRS/Aqua L3 Monthly Support Product (AIRS-only) 1 degree x 1 degree V006 (AIRS3SPM) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-09-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28303,7 +28303,7 @@
{
"id": "AIRS3ST8_006",
"title": "AIRS/Aqua L3 8-day Standard Physical Retrieval (AIRS-only) 1 degree X 1 degree V006 (AIRS3ST8) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-09-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28316,7 +28316,7 @@
{
"id": "AIRS3ST8_006",
"title": "AIRS/Aqua L3 8-day Standard Physical Retrieval (AIRS-only) 1 degree X 1 degree V006 (AIRS3ST8) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-09-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28472,7 +28472,7 @@
{
"id": "AIRSAR_NASA_JPL",
"title": "AirSAR Data and Images Database at NASA/JPL",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1993-01-01",
"end_date": "",
"bbox": "-130, 20, -65, 50",
@@ -28485,7 +28485,7 @@
{
"id": "AIRSAR_NASA_JPL",
"title": "AirSAR Data and Images Database at NASA/JPL",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1993-01-01",
"end_date": "",
"bbox": "-130, 20, -65, 50",
@@ -28550,7 +28550,7 @@
{
"id": "AIRSAR_TOP_C-DEM_STOKES_1",
"title": "AIRSAR_TOPSAR_C-BAND_DEM_AND_STOKES",
- "catalog": "ASF STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1993-06-08",
"end_date": "2004-12-04",
"bbox": "-172.880269, -27.388834, -49.704356, 69.25925",
@@ -28563,7 +28563,7 @@
{
"id": "AIRSAR_TOP_C-DEM_STOKES_1",
"title": "AIRSAR_TOPSAR_C-BAND_DEM_AND_STOKES",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ASF STAC Catalog",
"state_date": "1993-06-08",
"end_date": "2004-12-04",
"bbox": "-172.880269, -27.388834, -49.704356, 69.25925",
@@ -28576,7 +28576,7 @@
{
"id": "AIRSAR_TOP_DEM_1",
"title": "AIRSAR_TOPSAR_DEM",
- "catalog": "ASF STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1993-06-08",
"end_date": "2004-12-04",
"bbox": "-172.880269, -27.388834, -49.704356, 69.25925",
@@ -28589,7 +28589,7 @@
{
"id": "AIRSAR_TOP_DEM_1",
"title": "AIRSAR_TOPSAR_DEM",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ASF STAC Catalog",
"state_date": "1993-06-08",
"end_date": "2004-12-04",
"bbox": "-172.880269, -27.388834, -49.704356, 69.25925",
@@ -28602,7 +28602,7 @@
{
"id": "AIRSAR_TOP_DEM_C_1",
"title": "AIRSAR_TOPSAR_DEM_C",
- "catalog": "ASF STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1993-06-08",
"end_date": "2004-12-04",
"bbox": "-172.880269, -27.388834, -49.704356, 69.25925",
@@ -28615,7 +28615,7 @@
{
"id": "AIRSAR_TOP_DEM_C_1",
"title": "AIRSAR_TOPSAR_DEM_C",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ASF STAC Catalog",
"state_date": "1993-06-08",
"end_date": "2004-12-04",
"bbox": "-172.880269, -27.388834, -49.704356, 69.25925",
@@ -28628,7 +28628,7 @@
{
"id": "AIRSAR_TOP_DEM_L_1",
"title": "AIRSAR_TOPSAR_DEM_L",
- "catalog": "ASF STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1993-06-08",
"end_date": "2004-12-04",
"bbox": "-172.880269, -27.388834, -49.704356, 69.25925",
@@ -28641,7 +28641,7 @@
{
"id": "AIRSAR_TOP_DEM_L_1",
"title": "AIRSAR_TOPSAR_DEM_L",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ASF STAC Catalog",
"state_date": "1993-06-08",
"end_date": "2004-12-04",
"bbox": "-172.880269, -27.388834, -49.704356, 69.25925",
@@ -28745,7 +28745,7 @@
{
"id": "AIRSM_CPR_MAT_3.2",
"title": "AIRS-AMSU variables-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRSM_CPR_MAT) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2006-06-15",
"end_date": "2012-12-14",
"bbox": "-180, -90, 180, 90",
@@ -28758,7 +28758,7 @@
{
"id": "AIRSM_CPR_MAT_3.2",
"title": "AIRS-AMSU variables-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRSM_CPR_MAT) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2006-06-15",
"end_date": "2012-12-14",
"bbox": "-180, -90, 180, 90",
@@ -28797,7 +28797,7 @@
{
"id": "AIRS_CPR_MAT_3.2",
"title": "AIRS-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRS_CPR_MAT) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2006-06-15",
"end_date": "2012-12-14",
"bbox": "-180, -90, 180, 90",
@@ -28810,7 +28810,7 @@
{
"id": "AIRS_CPR_MAT_3.2",
"title": "AIRS-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRS_CPR_MAT) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2006-06-15",
"end_date": "2012-12-14",
"bbox": "-180, -90, 180, 90",
@@ -28849,7 +28849,7 @@
{
"id": "AIRVBQAP_005",
"title": "AIRS/Aqua L1B Visible/Near Infrared (VIS/NIR) quality assurance subset V005 (AIRVBQAP) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-08-30",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28862,7 +28862,7 @@
{
"id": "AIRVBQAP_005",
"title": "AIRS/Aqua L1B Visible/Near Infrared (VIS/NIR) quality assurance subset V005 (AIRVBQAP) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-08-30",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28927,7 +28927,7 @@
{
"id": "AIRVBRAD_NRT_005",
"title": "AIRS/Aqua L1B Near Real Time (NRT) Visible/Near Infrared (VIS/NIR) geolocated and calibrated radiances V005 (AIRVBRAD_NRT) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2018-11-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28940,7 +28940,7 @@
{
"id": "AIRVBRAD_NRT_005",
"title": "AIRS/Aqua L1B Near Real Time (NRT) Visible/Near Infrared (VIS/NIR) geolocated and calibrated radiances V005 (AIRVBRAD_NRT) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2018-11-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -28992,7 +28992,7 @@
{
"id": "AIRX2SPC_005",
"title": "AIRS/Aqua L2 CO2 support retrieval (AIRS+AMSU) V005 (AIRX2SPC) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-09-01",
"end_date": "2012-03-01",
"bbox": "-180, -90, 180, 90",
@@ -29005,7 +29005,7 @@
{
"id": "AIRX2SPC_005",
"title": "AIRS/Aqua L2 CO2 support retrieval (AIRS+AMSU) V005 (AIRX2SPC) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-09-01",
"end_date": "2012-03-01",
"bbox": "-180, -90, 180, 90",
@@ -29018,7 +29018,7 @@
{
"id": "AIRX2STC_005",
"title": "AIRS/Aqua L2 CO2 in the free troposphere (AIRS+AMSU) V005 (AIRX2STC) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-09-01",
"end_date": "2012-03-01",
"bbox": "-180, -60, 180, 90",
@@ -29031,7 +29031,7 @@
{
"id": "AIRX2STC_005",
"title": "AIRS/Aqua L2 CO2 in the free troposphere (AIRS+AMSU) V005 (AIRX2STC) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-09-01",
"end_date": "2012-03-01",
"bbox": "-180, -60, 180, 90",
@@ -29044,7 +29044,7 @@
{
"id": "AIRX2SUP_006",
"title": "AIRS/Aqua L2 Support Retrieval (AIRS+AMSU) V006 (AIRX2SUP) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-08-30",
"end_date": "2016-09-25",
"bbox": "-180, -90, 180, 90",
@@ -29057,7 +29057,7 @@
{
"id": "AIRX2SUP_006",
"title": "AIRS/Aqua L2 Support Retrieval (AIRS+AMSU) V006 (AIRX2SUP) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-08-30",
"end_date": "2016-09-25",
"bbox": "-180, -90, 180, 90",
@@ -29135,7 +29135,7 @@
{
"id": "AIRX3C2M_005",
"title": "AIRS/Aqua L3 Monthly CO2 in the free troposphere (AIRS+AMSU) 2.5 degrees x 2 degrees V005 (AIRX3C2M) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-09-01",
"end_date": "2012-02-29",
"bbox": "-180, -60, 180, 90",
@@ -29148,7 +29148,7 @@
{
"id": "AIRX3C2M_005",
"title": "AIRS/Aqua L3 Monthly CO2 in the free troposphere (AIRS+AMSU) 2.5 degrees x 2 degrees V005 (AIRX3C2M) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-09-01",
"end_date": "2012-02-29",
"bbox": "-180, -60, 180, 90",
@@ -29161,7 +29161,7 @@
{
"id": "AIRX3QP5_006",
"title": "AIRS/Aqua L3 5-day Quantization in Physical Units (AIRS+AMSU) 5 degrees x 5 degrees V006 (AIRX3QP5) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-09-01",
"end_date": "2016-09-26",
"bbox": "-180, -90, 180, 90",
@@ -29174,7 +29174,7 @@
{
"id": "AIRX3QP5_006",
"title": "AIRS/Aqua L3 5-day Quantization in Physical Units (AIRS+AMSU) 5 degrees x 5 degrees V006 (AIRX3QP5) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-09-01",
"end_date": "2016-09-26",
"bbox": "-180, -90, 180, 90",
@@ -29187,7 +29187,7 @@
{
"id": "AIRX3QPM_006",
"title": "AIRS/Aqua L3 Monthly Quantization in Physical Units (AIRS+AMSU) 5 degrees x 5 degrees V006 (AIRX3QPM) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-09-01",
"end_date": "2016-10-01",
"bbox": "-180, -90, 180, 90",
@@ -29200,7 +29200,7 @@
{
"id": "AIRX3QPM_006",
"title": "AIRS/Aqua L3 Monthly Quantization in Physical Units (AIRS+AMSU) 5 degrees x 5 degrees V006 (AIRX3QPM) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-09-01",
"end_date": "2016-10-01",
"bbox": "-180, -90, 180, 90",
@@ -29213,7 +29213,7 @@
{
"id": "AIRX3SP8_006",
"title": "AIRS/Aqua L3 8-day Support Multiday Product (AIRS+AMSU) 1 degree x 1 degree V006 (AIRX3SP8) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-09-01",
"end_date": "2016-10-01",
"bbox": "-180, -90, 180, 90",
@@ -29226,7 +29226,7 @@
{
"id": "AIRX3SP8_006",
"title": "AIRS/Aqua L3 8-day Support Multiday Product (AIRS+AMSU) 1 degree x 1 degree V006 (AIRX3SP8) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-09-01",
"end_date": "2016-10-01",
"bbox": "-180, -90, 180, 90",
@@ -29278,7 +29278,7 @@
{
"id": "AIRX3SPM_006",
"title": "AIRS/Aqua L3 Monthly Support Product (AIRS+AMSU) 1 degree x 1 degree V006 (AIRX3SPM) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-09-01",
"end_date": "2016-10-01",
"bbox": "-180, -90, 180, 90",
@@ -29291,7 +29291,7 @@
{
"id": "AIRX3SPM_006",
"title": "AIRS/Aqua L3 Monthly Support Product (AIRS+AMSU) 1 degree x 1 degree V006 (AIRX3SPM) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-09-01",
"end_date": "2016-10-01",
"bbox": "-180, -90, 180, 90",
@@ -29447,7 +29447,7 @@
{
"id": "AIRXBCAL_005",
"title": "AIRS/Aqua L1B Calibration subset V005 (AIRXBCAL) at GES DISC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GES_DISC STAC Catalog",
"state_date": "2002-08-31",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -29460,7 +29460,7 @@
{
"id": "AIRXBCAL_005",
"title": "AIRS/Aqua L1B Calibration subset V005 (AIRXBCAL) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-08-31",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -29629,7 +29629,7 @@
{
"id": "AK_AVHRR",
"title": "Alaska AVHRR Twice-Monthly Composites",
- "catalog": "USGS_LTA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1990-06-16",
"end_date": "",
"bbox": "-179, 51, -116, 70",
@@ -29642,7 +29642,7 @@
{
"id": "AK_AVHRR",
"title": "Alaska AVHRR Twice-Monthly Composites",
- "catalog": "ALL STAC Catalog",
+ "catalog": "USGS_LTA STAC Catalog",
"state_date": "1990-06-16",
"end_date": "",
"bbox": "-179, 51, -116, 70",
@@ -29655,7 +29655,7 @@
{
"id": "AK_North_Slope_NEE_CH4_Flux_1562_1",
"title": "ABoVE: CO2 and CH4 Fluxes and Meteorology at Flux Tower Sites, Alaska, 2015-2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2015-01-01",
"end_date": "2017-03-09",
"bbox": "-157.41, 68.49, -155.75, 71.28",
@@ -29668,7 +29668,7 @@
{
"id": "AK_North_Slope_NEE_CH4_Flux_1562_1",
"title": "ABoVE: CO2 and CH4 Fluxes and Meteorology at Flux Tower Sites, Alaska, 2015-2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-01-01",
"end_date": "2017-03-09",
"bbox": "-157.41, 68.49, -155.75, 71.28",
@@ -29720,7 +29720,7 @@
{
"id": "AK_Yukon_PFT_TopCover_2032_1.1",
"title": "ABoVE: Modeled Top Cover by Plant Functional Type over Alaska and Yukon, 1985-2020",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1985-01-01",
"end_date": "2020-12-31",
"bbox": "-176.1, 51, -122.5, 75.91",
@@ -29733,7 +29733,7 @@
{
"id": "AK_Yukon_PFT_TopCover_2032_1.1",
"title": "ABoVE: Modeled Top Cover by Plant Functional Type over Alaska and Yukon, 1985-2020",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1985-01-01",
"end_date": "2020-12-31",
"bbox": "-176.1, 51, -122.5, 75.91",
@@ -29759,7 +29759,7 @@
{
"id": "ALERA",
"title": "ALERA AFES-LETKF experimental ensemble reanalysis",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2005-06-01",
"end_date": "2007-01-10",
"bbox": "-180, -90, 180, 90",
@@ -29772,7 +29772,7 @@
{
"id": "ALERA",
"title": "ALERA AFES-LETKF experimental ensemble reanalysis",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2005-06-01",
"end_date": "2007-01-10",
"bbox": "-180, -90, 180, 90",
@@ -30019,7 +30019,7 @@
{
"id": "ALT_Maps_AK_CA_2332_1",
"title": "ABoVE: Upscaled Active Layer Thickness in Northern Alaska, 2014-2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2014-01-01",
"end_date": "2017-12-31",
"bbox": "-171.8, 59.35, -133.05, 74.72",
@@ -30032,7 +30032,7 @@
{
"id": "ALT_Maps_AK_CA_2332_1",
"title": "ABoVE: Upscaled Active Layer Thickness in Northern Alaska, 2014-2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2014-01-01",
"end_date": "2017-12-31",
"bbox": "-171.8, 59.35, -133.05, 74.72",
@@ -30435,7 +30435,7 @@
{
"id": "ANARE-26_1",
"title": "A qualitative investigation into scavenging of airborne sea salt over Macquarie Island.",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1961-01-24",
"end_date": "1963-03-31",
"bbox": "158.8833, -54.6333, 158.8833, -54.6333",
@@ -30448,7 +30448,7 @@
{
"id": "ANARE-26_1",
"title": "A qualitative investigation into scavenging of airborne sea salt over Macquarie Island.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1961-01-24",
"end_date": "1963-03-31",
"bbox": "158.8833, -54.6333, 158.8833, -54.6333",
@@ -30643,7 +30643,7 @@
{
"id": "APG_ATLAS_1.0",
"title": "Alaska PaleoGlacier Atlas: A Geospatial Compilation of Pleistocene Glacier Extents",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "172, 51, -130, 72",
@@ -30656,7 +30656,7 @@
{
"id": "APG_ATLAS_1.0",
"title": "Alaska PaleoGlacier Atlas: A Geospatial Compilation of Pleistocene Glacier Extents",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "172, 51, -130, 72",
@@ -33321,7 +33321,7 @@
{
"id": "ARB_48_IN_LIDAR_1",
"title": "Aerosol Research Branch (ARB) 48 inch Lidar Data",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1982-06-14",
"end_date": "2001-12-04",
"bbox": "-76.378, 37.1, -76.3, 37.106",
@@ -33334,7 +33334,7 @@
{
"id": "ARB_48_IN_LIDAR_1",
"title": "Aerosol Research Branch (ARB) 48 inch Lidar Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "1982-06-14",
"end_date": "2001-12-04",
"bbox": "-76.378, 37.1, -76.3, 37.106",
@@ -34023,7 +34023,7 @@
{
"id": "ARNd0086_103",
"title": "Alaska basemap",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-170, 51, -130, 72",
@@ -34036,7 +34036,7 @@
{
"id": "ARNd0086_103",
"title": "Alaska basemap",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-170, 51, -130, 72",
@@ -34179,7 +34179,7 @@
{
"id": "ASAC_1004_1",
"title": "Air sampling and analysis from Antarctic firn and ice",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1976-06-30",
"end_date": "1998-12-31",
"bbox": "111, -66.8, 114, -65.8",
@@ -34192,7 +34192,7 @@
{
"id": "ASAC_1004_1",
"title": "Air sampling and analysis from Antarctic firn and ice",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1976-06-30",
"end_date": "1998-12-31",
"bbox": "111, -66.8, 114, -65.8",
@@ -35713,7 +35713,7 @@
{
"id": "ASAC_1219_AAT_APen_D_73_1",
"title": "Adelie Penguin Distributions in the Davis Area, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1973-11-08",
"end_date": "1973-11-14",
"bbox": "77, -69, 79, -68",
@@ -35726,7 +35726,7 @@
{
"id": "ASAC_1219_AAT_APen_D_73_1",
"title": "Adelie Penguin Distributions in the Davis Area, Antarctica",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1973-11-08",
"end_date": "1973-11-14",
"bbox": "77, -69, 79, -68",
@@ -36532,7 +36532,7 @@
{
"id": "ASAC_194_1",
"title": "A Study of the Nitrogen-fixing Microbiota of Macquarie Island Plant Communities",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1990-12-01",
"end_date": "1991-01-31",
"bbox": "158, -54.5, 159, -54",
@@ -36545,7 +36545,7 @@
{
"id": "ASAC_194_1",
"title": "A Study of the Nitrogen-fixing Microbiota of Macquarie Island Plant Communities",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1990-12-01",
"end_date": "1991-01-31",
"bbox": "158, -54.5, 159, -54",
@@ -36688,7 +36688,7 @@
{
"id": "ASAC_2201_Casey_SRE2_1",
"title": "A manipulative field experiment examining the effect of contaminated sediment on the recruitment of soft-sediment infauna (Mar 1998 - Feb 1999).",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1998-02-11",
"end_date": "1999-02-11",
"bbox": "110.52252, -66.2941, 110.54701, -66.27913",
@@ -36701,7 +36701,7 @@
{
"id": "ASAC_2201_Casey_SRE2_1",
"title": "A manipulative field experiment examining the effect of contaminated sediment on the recruitment of soft-sediment infauna (Mar 1998 - Feb 1999).",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1998-02-11",
"end_date": "1999-02-11",
"bbox": "110.52252, -66.2941, 110.54701, -66.27913",
@@ -36818,7 +36818,7 @@
{
"id": "ASAC_2201_HCL_0.5_1",
"title": "0.5 hour 1 M HCl extraction data for the Windmill Islands marine sediments",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1997-10-01",
"end_date": "1999-03-31",
"bbox": "110, -66, 110, -66",
@@ -36831,7 +36831,7 @@
{
"id": "ASAC_2201_HCL_0.5_1",
"title": "0.5 hour 1 M HCl extraction data for the Windmill Islands marine sediments",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-10-01",
"end_date": "1999-03-31",
"bbox": "110, -66, 110, -66",
@@ -36844,7 +36844,7 @@
{
"id": "ASAC_2201_HCL_4_1",
"title": "4 hour 1 M HCl extraction data for the Windmill Islands marine sediments",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1997-10-01",
"end_date": "1999-03-31",
"bbox": "110, -66, 110, -66",
@@ -36857,7 +36857,7 @@
{
"id": "ASAC_2201_HCL_4_1",
"title": "4 hour 1 M HCl extraction data for the Windmill Islands marine sediments",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-10-01",
"end_date": "1999-03-31",
"bbox": "110, -66, 110, -66",
@@ -37312,7 +37312,7 @@
{
"id": "ASAC_2357_2",
"title": "10 year trend of levels of organochlorine pollutants in Antarctic seabirds",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2003-12-16",
"end_date": "2004-01-18",
"bbox": "77.59, -68.93, 77.99, -68.755",
@@ -37325,7 +37325,7 @@
{
"id": "ASAC_2357_2",
"title": "10 year trend of levels of organochlorine pollutants in Antarctic seabirds",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-12-16",
"end_date": "2004-01-18",
"bbox": "77.59, -68.93, 77.99, -68.755",
@@ -37962,7 +37962,7 @@
{
"id": "ASAC_2720_ADCP_1",
"title": "ADCP data collected during the SAZ-SENSE voyage, January-February 2007",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2007-01-17",
"end_date": "2007-02-20",
"bbox": "140.3, -54.27, 153.81, -43.05",
@@ -37975,7 +37975,7 @@
{
"id": "ASAC_2720_ADCP_1",
"title": "ADCP data collected during the SAZ-SENSE voyage, January-February 2007",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-01-17",
"end_date": "2007-02-20",
"bbox": "140.3, -54.27, 153.81, -43.05",
@@ -38001,7 +38001,7 @@
{
"id": "ASAC_2722_Adelie_Rauer_Vestfold_Nov1993_1",
"title": "Adelie penguin colony boundaries at the Rauer Group and the Vestfold Hills, November 1993",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1993-11-24",
"end_date": "1993-11-24",
"bbox": "77.6292, -68.8433, 78.5775, -68.3486",
@@ -38014,7 +38014,7 @@
{
"id": "ASAC_2722_Adelie_Rauer_Vestfold_Nov1993_1",
"title": "Adelie penguin colony boundaries at the Rauer Group and the Vestfold Hills, November 1993",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1993-11-24",
"end_date": "1993-11-24",
"bbox": "77.6292, -68.8433, 78.5775, -68.3486",
@@ -38183,7 +38183,7 @@
{
"id": "ASAC_2904_1",
"title": "Aliens in Antarctica - project to study exotic species and visitors in the Antarctic",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-09-30",
"end_date": "2011-03-31",
"bbox": "-180, -90, 180, -53",
@@ -38196,7 +38196,7 @@
{
"id": "ASAC_2904_1",
"title": "Aliens in Antarctica - project to study exotic species and visitors in the Antarctic",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2007-09-30",
"end_date": "2011-03-31",
"bbox": "-180, -90, 180, -53",
@@ -38209,7 +38209,7 @@
{
"id": "ASAC_2904_Food_1",
"title": "Aliens in Antarctica Project - Inspection of fresh food for alien propagules",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2007-10-19",
"end_date": "2008-03-14",
"bbox": "60, -67, 160, -54",
@@ -38222,7 +38222,7 @@
{
"id": "ASAC_2904_Food_1",
"title": "Aliens in Antarctica Project - Inspection of fresh food for alien propagules",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-10-19",
"end_date": "2008-03-14",
"bbox": "60, -67, 160, -54",
@@ -38937,7 +38937,7 @@
{
"id": "ASAC_555_1",
"title": "A Survey of the Freshwater Macroinvertebrates in Streams and Lakes of Macquarie Island",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1992-11-13",
"end_date": "1992-12-03",
"bbox": "158.7925, -54.7651, 158.9351, -54.5143",
@@ -38950,7 +38950,7 @@
{
"id": "ASAC_555_1",
"title": "A Survey of the Freshwater Macroinvertebrates in Streams and Lakes of Macquarie Island",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1992-11-13",
"end_date": "1992-12-03",
"bbox": "158.7925, -54.7651, 158.9351, -54.5143",
@@ -40276,52 +40276,52 @@
{
"id": "ATL02_006",
"title": "ATLAS/ICESat-2 L1B Converted Telemetry Data V006",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2541211133-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2541211133-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL02_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2547589158-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2547589158-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL02_006",
"description": "This data set (ATL02) contains science-unit-converted time-ordered telemetry data, calibrated for instrument effects, downlinked from the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The data are used by the ATLAS/ICESat-2 Science Investigator-led Processing System (SIPS) for system-level, quality control analysis and as source data for ATLAS/ICESat-2 Level-2 products and Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations.",
"license": "proprietary"
},
{
"id": "ATL02_006",
"title": "ATLAS/ICESat-2 L1B Converted Telemetry Data V006",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2547589158-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2547589158-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL02_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2541211133-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2541211133-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL02_006",
"description": "This data set (ATL02) contains science-unit-converted time-ordered telemetry data, calibrated for instrument effects, downlinked from the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The data are used by the ATLAS/ICESat-2 Science Investigator-led Processing System (SIPS) for system-level, quality control analysis and as source data for ATLAS/ICESat-2 Level-2 products and Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations.",
"license": "proprietary"
},
{
"id": "ATL03_006",
"title": "ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2596864127-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2596864127-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL03_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2559919423-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2559919423-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL03_006",
"description": "This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03.",
"license": "proprietary"
},
{
"id": "ATL03_006",
"title": "ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2559919423-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2559919423-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL03_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2596864127-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2596864127-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL03_006",
"description": "This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03.",
"license": "proprietary"
},
@@ -40341,26 +40341,26 @@
{
"id": "ATL04_006",
"title": "ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL04_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553327-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553327-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL04_006",
"description": "ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.",
"license": "proprietary"
},
{
"id": "ATL04_006",
"title": "ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553327-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553327-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL04_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL04_006",
"description": "ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.",
"license": "proprietary"
},
@@ -40406,26 +40406,26 @@
{
"id": "ATL07_006",
"title": "ATLAS/ICESat-2 L3A Sea Ice Height V006",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2018-10-14",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2713030505-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2713030505-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL07_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2564625052-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2564625052-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL07_006",
"description": "The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.",
"license": "proprietary"
},
{
"id": "ATL07_006",
"title": "ATLAS/ICESat-2 L3A Sea Ice Height V006",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2018-10-14",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2564625052-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2564625052-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL07_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2713030505-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2713030505-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL07_006",
"description": "The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.",
"license": "proprietary"
},
@@ -40445,26 +40445,26 @@
{
"id": "ATL08_006",
"title": "ATLAS/ICESat-2 L3A Land and Vegetation Height V006",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2018-10-14",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553260-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553260-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL08_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2565090645-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565090645-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL08_006",
"description": "This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.",
"license": "proprietary"
},
{
"id": "ATL08_006",
"title": "ATLAS/ICESat-2 L3A Land and Vegetation Height V006",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2018-10-14",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2565090645-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2565090645-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL08_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553260-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553260-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL08_006",
"description": "This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.",
"license": "proprietary"
},
@@ -40484,26 +40484,26 @@
{
"id": "ATL09_006",
"title": "ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL09_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL09_006",
"description": "This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.",
"license": "proprietary"
},
{
"id": "ATL09_006",
"title": "ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL09_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL09_006",
"description": "This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.",
"license": "proprietary"
},
@@ -40549,52 +40549,52 @@
{
"id": "ATL11_006",
"title": "ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2019-03-29",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2750966856-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2750966856-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL11_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2752556504-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2752556504-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL11_006",
"description": "This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations.",
"license": "proprietary"
},
{
"id": "ATL11_006",
"title": "ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2019-03-29",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2752556504-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2752556504-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL11_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2750966856-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2750966856-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL11_006",
"description": "This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations.",
"license": "proprietary"
},
{
"id": "ATL12_006",
"title": "ATLAS/ICESat-2 L3A Ocean Surface Height V006",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL12_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL12_006",
"description": "This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.",
"license": "proprietary"
},
{
"id": "ATL12_006",
"title": "ATLAS/ICESat-2 L3A Ocean Surface Height V006",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL12_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL12_006",
"description": "This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory.",
"license": "proprietary"
},
@@ -40614,26 +40614,26 @@
{
"id": "ATL13_006",
"title": "ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL13_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2684928243-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2684928243-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL13_006",
"description": "This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit).",
"license": "proprietary"
},
{
"id": "ATL13_006",
"title": "ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2684928243-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2684928243-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL13_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL13_006",
"description": "This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit).",
"license": "proprietary"
},
@@ -40744,78 +40744,78 @@
{
"id": "ATL16_005",
"title": "ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2769337070-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2769337070-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL16_005",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997243-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997243-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL16_005",
"description": "This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.",
"license": "proprietary"
},
{
"id": "ATL16_005",
"title": "ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997243-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997243-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL16_005",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2769337070-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2769337070-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL16_005",
"description": "This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.",
"license": "proprietary"
},
{
"id": "ATL17_005",
"title": "ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL17_005",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL17_005",
"description": "This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.",
"license": "proprietary"
},
{
"id": "ATL17_005",
"title": "ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL17_005",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL17_005",
"description": "This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency.",
"license": "proprietary"
},
{
"id": "ATL19_003",
"title": "ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -88, 180, 88",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL19_003",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL19_003",
"description": "This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography.",
"license": "proprietary"
},
{
"id": "ATL19_003",
"title": "ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -88, 180, 88",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL19_003",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL19_003",
"description": "This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography.",
"license": "proprietary"
},
@@ -40874,52 +40874,52 @@
{
"id": "ATL22_003",
"title": "ATLAS/ICESat-2 L3B Mean Inland Surface Water Data V003",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2018-10-14",
"end_date": "",
"bbox": "-180, -88, 180, 88",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2738530540-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2738530540-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL22_003",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2761722214-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2761722214-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL22_003",
"description": "ATL22 is a derivative of the continuous Level 3A ATL13 Along Track Inland Surface Water Data product. ATL13 contains the high-resolution, along-track inland water surface profiles derived from analysis of the geolocated photon clouds from the ATL03 product. Starting from ATL13, ATL22 computes the mean surface water quantities with no additional photon analysis. The two data products, ATL22 and ATL13, can be used in conjunction as they include the same orbit and water body nomenclature independent from version numbers.",
"license": "proprietary"
},
{
"id": "ATL22_003",
"title": "ATLAS/ICESat-2 L3B Mean Inland Surface Water Data V003",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2018-10-14",
"end_date": "",
"bbox": "-180, -88, 180, 88",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2761722214-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2761722214-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL22_003",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2738530540-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2738530540-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL22_003",
"description": "ATL22 is a derivative of the continuous Level 3A ATL13 Along Track Inland Surface Water Data product. ATL13 contains the high-resolution, along-track inland water surface profiles derived from analysis of the geolocated photon clouds from the ATL03 product. Starting from ATL13, ATL22 computes the mean surface water quantities with no additional photon analysis. The two data products, ATL22 and ATL13, can be used in conjunction as they include the same orbit and water body nomenclature independent from version numbers.",
"license": "proprietary"
},
{
"id": "ATL23_001",
"title": "ATLAS/ICESat-2 L3B Monthly 3-Month Gridded Dynamic Ocean Topography V001",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -88, 180, 88",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2692731693-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2692731693-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL23_001",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2765424272-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2765424272-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL23_001",
"description": "This data set contains 3-month gridded averages of dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided. Both single beam and all-beam gridded averages are available. Simple averages, degree-of-freedom averages, and averages interpolated to the center of grid cells are included, as well as uncertainty estimates.",
"license": "proprietary"
},
{
"id": "ATL23_001",
"title": "ATLAS/ICESat-2 L3B Monthly 3-Month Gridded Dynamic Ocean Topography V001",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2018-10-13",
"end_date": "",
"bbox": "-180, -88, 180, 88",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2765424272-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2765424272-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/ATL23_001",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2692731693-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2692731693-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/ATL23_001",
"description": "This data set contains 3-month gridded averages of dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided. Both single beam and all-beam gridded averages are available. Simple averages, degree-of-freedom averages, and averages interpolated to the center of grid cells are included, as well as uncertainty estimates.",
"license": "proprietary"
},
@@ -42642,7 +42642,7 @@
{
"id": "AWI-EDMED_542_8",
"title": "Aeromagnetic surveys of the Southern Ross Sea and North Victoria Land (Antarctica), 1990/1991, (project GANOVEX VI)",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1990-12-01",
"end_date": "1991-03-30",
"bbox": "-180, -90, 180, -63",
@@ -42655,7 +42655,7 @@
{
"id": "AWI-EDMED_542_8",
"title": "Aeromagnetic surveys of the Southern Ross Sea and North Victoria Land (Antarctica), 1990/1991, (project GANOVEX VI)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1990-12-01",
"end_date": "1991-03-30",
"bbox": "-180, -90, 180, -63",
@@ -42668,7 +42668,7 @@
{
"id": "A_Biotic_Database_of_Indo-Pacific_Marine_Mollusks_1.0",
"title": "A Biotic Database of Indo-Pacific Marine Mollusks",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1824-01-01",
"end_date": "2002-12-31",
"bbox": "-179, -62.98, 180, 72",
@@ -42681,7 +42681,7 @@
{
"id": "A_Biotic_Database_of_Indo-Pacific_Marine_Mollusks_1.0",
"title": "A Biotic Database of Indo-Pacific Marine Mollusks",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1824-01-01",
"end_date": "2002-12-31",
"bbox": "-179, -62.98, 180, 72",
@@ -42730,6 +42730,19 @@
"description": "This annotated library contains both a data set and a data product. The data set contains a sub-sample of underwater recordings made around Antarctica from 2005-2017. These recordings were curated and sub-sampled from a variety of national and academic recording campaigns. Recordings were made using a variety of different instruments, and sub-samples span 11 different combinations of site and year. Spatial coverage of the recordings includes sites in the Western Antarctic Peninsula, Atlantic, Indian, and Pacific sectors. Temporal coverage of recordings covers a representative sample throughout each recording year for the years of 2005, 2013, 2014, 2015, and 2017. The focus is on low-frequency sounds of blue and fin whales, so curated recordings have been downsampled to sample rates of either 250, 500, 1000 or 2000 Hz. Recordings are all in 16-bit wav format. The file name of each wav file contains a timestamp with the date and time of the start of that file. Recordings are contained in the /wav/ subfolder for each site-year (e.g. Casey2014/wav). The data product is in the form of annotations that describe the times within each WAV file that contain detections of blue and fin whale sounds. Each annotations are stored as a row in a tab-separated text file (with descriptive column headers), and each text file describes a particular type of sound. These annotation text files are formatted as Selection Tables that can be directly imported into the software program Raven Pro 1.5 (Cornell Bioacoustics Laboratory). Full description of the details of the creation and use of this dataset are described in the draft manuscript contained in the documentation folder.",
"license": "proprietary"
},
+ {
+ "id": "Acoustic_Data_Cape_Floristic_2372_1",
+ "title": "BioSCape: BioSoundSCape Acoustic Recordings, South Africa, 2023",
+ "catalog": "ORNL_CLOUD STAC Catalog",
+ "state_date": "2023-06-05",
+ "end_date": "2023-12-16",
+ "bbox": "18.01, -34.82, 23.92, -31.37",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3366080352-ORNL_CLOUD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3366080352-ORNL_CLOUD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/ORNL_CLOUD/collections/Acoustic_Data_Cape_Floristic_2372_1",
+ "description": "This dataset holds in situ sound recordings from sites in Greater Cape Floristic Region (GCFR), South Africa from June to December 2023. The recordings were collected as part of the Biodiversity Survey of the Cape (BioSCape) project, a multi-agency, NASA-led research project that integrates airborne imaging spectroscopy and lidar with a suite of measurements of biodiversity. BioSoundSCape is a BioSCape subproject seeking to relate ground-based measures of bioacoustic diversity to remote imagery. AudioMoth recorders were deployed at sites for 4 to 10 days of data collection (median = 7), and programmed to record 1 min of every 10, thus providing temporal sampling through day and night. Each recording was saved in a waveform audio file format with 16-bit digitization depth and a 48 kHz sampling rate. The recordings contain a wide range of environmental sounds such as biophony (e.g., birds, frogs, insects), anthropophony (e.g,. automobiles, airplanes) and geophony (e.g,. wind, rain). Sampling locations were stratified with respect to elevation, broad land use/land cover types, and time since wildfire disturbance. Most sites were within protected fynbos and Afromontane forest ecosystems. There were 538 sites in the wet season and 543 sites in the dry season, with most sites co-located between seasons. All sites were located within AVIRIS-NG hyperspectral acquisitions and 61% of sites were in LVIS lidar acquisitions. The dataset includes site information in tabular form and photographs of field sites.",
+ "license": "proprietary"
+ },
{
"id": "Acoustic_Data_SonomaCounty_CA_2341_1",
"title": "Soundscapes to Landscapes Acoustic Recordings, Sonoma County, CA, 2017-2022",
@@ -42772,7 +42785,7 @@
{
"id": "Active_Fluorescence_2001_0",
"title": "Active fluorescence measurements in the Gulf Stream in 2001",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-06-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -42785,7 +42798,7 @@
{
"id": "Active_Fluorescence_2001_0",
"title": "Active fluorescence measurements in the Gulf Stream in 2001",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "2001-06-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -42798,7 +42811,7 @@
{
"id": "Active_Layer_Thaw_Depths_1701_1",
"title": "ABoVE: Soil Active Layer Thaw Depths at CRREL sites near Fairbanks, Alaska, 2014-2018",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2014-10-15",
"end_date": "2018-10-15",
"bbox": "-147.74, 64.87, -147.61, 64.95",
@@ -42811,7 +42824,7 @@
{
"id": "Active_Layer_Thaw_Depths_1701_1",
"title": "ABoVE: Soil Active Layer Thaw Depths at CRREL sites near Fairbanks, Alaska, 2014-2018",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2014-10-15",
"end_date": "2018-10-15",
"bbox": "-147.74, 64.87, -147.61, 64.95",
@@ -42824,7 +42837,7 @@
{
"id": "Adelie_Aerial_Photography_Casey20102011_1",
"title": "Aerial photography from the Casey region taken during January 2011 used for Adelie penguin analysis",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2011-01-02",
"end_date": "2011-01-23",
"bbox": "108, -67, 111, -66",
@@ -42837,7 +42850,7 @@
{
"id": "Adelie_Aerial_Photography_Casey20102011_1",
"title": "Aerial photography from the Casey region taken during January 2011 used for Adelie penguin analysis",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-01-02",
"end_date": "2011-01-23",
"bbox": "108, -67, 111, -66",
@@ -42941,7 +42954,7 @@
{
"id": "Aeolian_Processes_McMurdo",
"title": "Aeolian Processes in the Dry Valleys",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-01-01",
"end_date": "2003-02-28",
"bbox": "162.00787, -77.6042, 163.13045, -77.36601",
@@ -42954,7 +42967,7 @@
{
"id": "Aeolian_Processes_McMurdo",
"title": "Aeolian Processes in the Dry Valleys",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2002-01-01",
"end_date": "2003-02-28",
"bbox": "162.00787, -77.6042, 163.13045, -77.36601",
@@ -43019,7 +43032,7 @@
{
"id": "Aeolus-CalVal-HALO_DC8_1",
"title": "Aeolus CalVal HALO Aerosol and Water Vapor Profiles and Images",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-04-17",
"end_date": "2019-04-30",
"bbox": "-159, 5, -113, 52",
@@ -43032,7 +43045,7 @@
{
"id": "Aeolus-CalVal-HALO_DC8_1",
"title": "Aeolus CalVal HALO Aerosol and Water Vapor Profiles and Images",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2019-04-17",
"end_date": "2019-04-30",
"bbox": "-159, 5, -113, 52",
@@ -43136,7 +43149,7 @@
{
"id": "Aerosol_opt_char_at_BTN_station",
"title": "Aerosol optical characteristics at BTN station",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-12-01",
"end_date": "2002-02-28",
"bbox": "164.1, -74.7, 164.1, -74.7",
@@ -43149,7 +43162,7 @@
{
"id": "Aerosol_opt_char_at_BTN_station",
"title": "Aerosol optical characteristics at BTN station",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2001-12-01",
"end_date": "2002-02-28",
"bbox": "164.1, -74.7, 164.1, -74.7",
@@ -43188,7 +43201,7 @@
{
"id": "AfriSAR_AGB_Maps_1681_1",
"title": "AfriSAR: Aboveground Biomass for Lope, Mabounie, Mondah, and Rabi Sites, Gabon",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-02-01",
"end_date": "2016-03-31",
"bbox": "9.3, -1.95, 11.64, 0.61",
@@ -43201,7 +43214,7 @@
{
"id": "AfriSAR_AGB_Maps_1681_1",
"title": "AfriSAR: Aboveground Biomass for Lope, Mabounie, Mondah, and Rabi Sites, Gabon",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-02-01",
"end_date": "2016-03-31",
"bbox": "9.3, -1.95, 11.64, 0.61",
@@ -43266,7 +43279,7 @@
{
"id": "African_Marine_Atlas",
"title": "African Marine Atlas",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -43279,7 +43292,7 @@
{
"id": "African_Marine_Atlas",
"title": "African Marine Atlas",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -43357,7 +43370,7 @@
{
"id": "AirMOSS_Field_Data_Harvard_1677_1",
"title": "AirMOSS: In Situ Soil Moisture and Tree Measurements, Harvard Forest, 2012-2013",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-10-15",
"end_date": "2013-08-22",
"bbox": "-72.18, 42.54, -71.18, 42.55",
@@ -43370,7 +43383,7 @@
{
"id": "AirMOSS_Field_Data_Harvard_1677_1",
"title": "AirMOSS: In Situ Soil Moisture and Tree Measurements, Harvard Forest, 2012-2013",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2012-10-15",
"end_date": "2013-08-22",
"bbox": "-72.18, 42.54, -71.18, 42.55",
@@ -43383,7 +43396,7 @@
{
"id": "AirMOSS_L1_Sigma0_BERMS_1406_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, BERMS, Canada, 2012-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2012-10-04",
"end_date": "2015-09-28",
"bbox": "-106.68, 53.56, -104.14, 54.02",
@@ -43396,7 +43409,7 @@
{
"id": "AirMOSS_L1_Sigma0_BERMS_1406_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, BERMS, Canada, 2012-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-10-04",
"end_date": "2015-09-28",
"bbox": "-106.68, 53.56, -104.14, 54.02",
@@ -43409,7 +43422,7 @@
{
"id": "AirMOSS_L1_Sigma0_Chamel_1407_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Chamela, Mexico, 2012-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-06-15",
"end_date": "2015-04-21",
"bbox": "-105.25, 19.29, -104.16, 20.3",
@@ -43422,7 +43435,7 @@
{
"id": "AirMOSS_L1_Sigma0_Chamel_1407_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Chamela, Mexico, 2012-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2013-06-15",
"end_date": "2015-04-21",
"bbox": "-105.25, 19.29, -104.16, 20.3",
@@ -43435,7 +43448,7 @@
{
"id": "AirMOSS_L1_Sigma0_DukeFr_1408_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Duke Forest, 2012-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-10-13",
"end_date": "2015-09-10",
"bbox": "-80.04, 35.39, -78.5, 36.43",
@@ -43448,7 +43461,7 @@
{
"id": "AirMOSS_L1_Sigma0_DukeFr_1408_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Duke Forest, 2012-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2012-10-13",
"end_date": "2015-09-10",
"bbox": "-80.04, 35.39, -78.5, 36.43",
@@ -43461,7 +43474,7 @@
{
"id": "AirMOSS_L1_Sigma0_Harvrd_1409_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Harvard Forest, 2012-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2012-10-15",
"end_date": "2015-09-09",
"bbox": "-72.39, 42.18, -71.85, 43.56",
@@ -43474,7 +43487,7 @@
{
"id": "AirMOSS_L1_Sigma0_Harvrd_1409_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Harvard Forest, 2012-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-10-15",
"end_date": "2015-09-09",
"bbox": "-72.39, 42.18, -71.85, 43.56",
@@ -43513,7 +43526,7 @@
{
"id": "AirMOSS_L1_Sigma0_LaSelv_1411_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, La Selva, 2012-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2013-02-20",
"end_date": "2015-02-24",
"bbox": "-85.14, 9.74, -83.27, 11.05",
@@ -43526,7 +43539,7 @@
{
"id": "AirMOSS_L1_Sigma0_LaSelv_1411_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, La Selva, 2012-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-02-20",
"end_date": "2015-02-24",
"bbox": "-85.14, 9.74, -83.27, 11.05",
@@ -43565,7 +43578,7 @@
{
"id": "AirMOSS_L1_Sigma0_Moisst_1413_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, MOISST, 2012-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2012-10-24",
"end_date": "2015-08-14",
"bbox": "-99, 35.78, -96.82, 36.89",
@@ -43578,7 +43591,7 @@
{
"id": "AirMOSS_L1_Sigma0_Moisst_1413_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, MOISST, 2012-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-10-24",
"end_date": "2015-08-14",
"bbox": "-99, 35.78, -96.82, 36.89",
@@ -43591,7 +43604,7 @@
{
"id": "AirMOSS_L1_Sigma0_TonziR_1414_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Tonzi Ranch, 2012-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2013-02-05",
"end_date": "2015-05-31",
"bbox": "-121.2, 37.38, -119.93, 38.59",
@@ -43604,7 +43617,7 @@
{
"id": "AirMOSS_L1_Sigma0_TonziR_1414_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Tonzi Ranch, 2012-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-02-05",
"end_date": "2015-05-31",
"bbox": "-121.2, 37.38, -119.93, 38.59",
@@ -43617,7 +43630,7 @@
{
"id": "AirMOSS_L1_Sigma0_Walnut_1415_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Walnut Gulch, 2012-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-09-20",
"end_date": "2015-09-01",
"bbox": "-111.24, 31.58, -109.48, 32.08",
@@ -43630,7 +43643,7 @@
{
"id": "AirMOSS_L1_Sigma0_Walnut_1415_1",
"title": "AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Walnut Gulch, 2012-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2012-09-20",
"end_date": "2015-09-01",
"bbox": "-111.24, 31.58, -109.48, 32.08",
@@ -43643,7 +43656,7 @@
{
"id": "AirMOSS_L2_3_RZ_Soil_Moisture_1418_1.1",
"title": "AirMOSS: L2/3 Volumetric Soil Moisture Profiles Derived From Radar, 2012-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2012-09-18",
"end_date": "2015-09-29",
"bbox": "-123.28, 9.88, -68.32, 54.13",
@@ -43656,7 +43669,7 @@
{
"id": "AirMOSS_L2_3_RZ_Soil_Moisture_1418_1.1",
"title": "AirMOSS: L2/3 Volumetric Soil Moisture Profiles Derived From Radar, 2012-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-09-18",
"end_date": "2015-09-29",
"bbox": "-123.28, 9.88, -68.32, 54.13",
@@ -43695,7 +43708,7 @@
{
"id": "AirMOSS_L2_Inground_Soil_Moist_1416_1",
"title": "AirMOSS: L2 Hourly In-Ground Soil Moisture at AirMOSS Sites, 2011-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2011-09-01",
"end_date": "2015-12-31",
"bbox": "-121.56, 19.51, -72.17, 53.92",
@@ -43708,7 +43721,7 @@
{
"id": "AirMOSS_L2_Inground_Soil_Moist_1416_1",
"title": "AirMOSS: L2 Hourly In-Ground Soil Moisture at AirMOSS Sites, 2011-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-09-01",
"end_date": "2015-12-31",
"bbox": "-121.56, 19.51, -72.17, 53.92",
@@ -43721,7 +43734,7 @@
{
"id": "AirMOSS_L2_Precipitation_1417_1",
"title": "AirMOSS: L2 Hourly Precipitation at AirMOSS Sites, 2011-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2011-09-01",
"end_date": "2015-12-31",
"bbox": "-121.56, 19.51, -72.17, 53.92",
@@ -43734,7 +43747,7 @@
{
"id": "AirMOSS_L2_Precipitation_1417_1",
"title": "AirMOSS: L2 Hourly Precipitation at AirMOSS Sites, 2011-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-09-01",
"end_date": "2015-12-31",
"bbox": "-121.56, 19.51, -72.17, 53.92",
@@ -43773,7 +43786,7 @@
{
"id": "AirMOSS_L4_RZ_Soil_Moisture_1421_1",
"title": "AirMOSS: L4 Modeled Volumetric Root Zone Soil Moisture, 2012-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-09-21",
"end_date": "2015-09-28",
"bbox": "-123.28, 19.12, -68.12, 54.13",
@@ -43786,7 +43799,7 @@
{
"id": "AirMOSS_L4_RZ_Soil_Moisture_1421_1",
"title": "AirMOSS: L4 Modeled Volumetric Root Zone Soil Moisture, 2012-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2012-09-21",
"end_date": "2015-09-28",
"bbox": "-123.28, 19.12, -68.12, 54.13",
@@ -43851,7 +43864,7 @@
{
"id": "AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data_6",
"title": "AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the NASA ACEPOL flight campaign Oct-Nov 2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2017-10-19",
"end_date": "2017-11-09",
"bbox": "180, -90, -180, 90",
@@ -43864,7 +43877,7 @@
{
"id": "AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data_6",
"title": "AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the NASA ACEPOL flight campaign Oct-Nov 2017",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-10-19",
"end_date": "2017-11-09",
"bbox": "180, -90, -180, 90",
@@ -43877,7 +43890,7 @@
{
"id": "AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data_6",
"title": "AirMSPI version 6 ellipsoid-projected georegistered radiance product acquired during the CalWater-2 flight campaign Jan-Feb 2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2015-01-20",
"end_date": "2015-02-24",
"bbox": "180, -90, -180, 90",
@@ -43890,7 +43903,7 @@
{
"id": "AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data_6",
"title": "AirMSPI version 6 ellipsoid-projected georegistered radiance product acquired during the CalWater-2 flight campaign Jan-Feb 2015",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-01-20",
"end_date": "2015-02-24",
"bbox": "180, -90, -180, 90",
@@ -43929,7 +43942,7 @@
{
"id": "AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data_6",
"title": "AirMSPI version 6 terrain-projected georegistered radiance product acquired during the FIREX-AQ flight campaign",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-08-01",
"end_date": "2019-08-22",
"bbox": "180, -90, -180, 90",
@@ -43942,7 +43955,7 @@
{
"id": "AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data_6",
"title": "AirMSPI version 6 terrain-projected georegistered radiance product acquired during the FIREX-AQ flight campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2019-08-01",
"end_date": "2019-08-22",
"bbox": "180, -90, -180, 90",
@@ -43981,7 +43994,7 @@
{
"id": "AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data_6",
"title": "AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the ImPACT-PM flight campaign",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-07-05",
"end_date": "2016-07-08",
"bbox": "180, -90, -180, 90",
@@ -43994,7 +44007,7 @@
{
"id": "AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data_6",
"title": "AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the ImPACT-PM flight campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2016-07-05",
"end_date": "2016-07-08",
"bbox": "180, -90, -180, 90",
@@ -44059,7 +44072,7 @@
{
"id": "AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data_5",
"title": "AirMSPI version 5 ellipsoid-projected georegistered radiance product acquired during the NASA PODEX flight campaign January-February 2013",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2013-01-14",
"end_date": "2013-02-06",
"bbox": "-130, 28, -114, 42.5",
@@ -44072,7 +44085,7 @@
{
"id": "AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data_5",
"title": "AirMSPI version 5 ellipsoid-projected georegistered radiance product acquired during the NASA PODEX flight campaign January-February 2013",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-01-14",
"end_date": "2013-02-06",
"bbox": "-130, 28, -114, 42.5",
@@ -44085,7 +44098,7 @@
{
"id": "AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data_5",
"title": "AirMSPI version 5 terrain-projected georegistered radiance product acquired during the NASA PODEX flight campaign Jan-Feb 2013",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2013-01-14",
"end_date": "2013-02-06",
"bbox": "-130, 28, -114, 42.5",
@@ -44098,7 +44111,7 @@
{
"id": "AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data_5",
"title": "AirMSPI version 5 terrain-projected georegistered radiance product acquired during the NASA PODEX flight campaign Jan-Feb 2013",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-01-14",
"end_date": "2013-02-06",
"bbox": "-130, 28, -114, 42.5",
@@ -44111,7 +44124,7 @@
{
"id": "AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data_6",
"title": "AirMSPI verison 6 ellipsoid-projected georegistered radiance product acquired during the RADEX flight campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2015-11-10",
"end_date": "2015-12-13",
"bbox": "180, -90, -180, 90",
@@ -44124,7 +44137,7 @@
{
"id": "AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data_6",
"title": "AirMSPI verison 6 ellipsoid-projected georegistered radiance product acquired during the RADEX flight campaign",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-11-10",
"end_date": "2015-12-13",
"bbox": "180, -90, -180, 90",
@@ -44137,7 +44150,7 @@
{
"id": "AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data_6",
"title": "AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the RADEX flight campaign",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-11-10",
"end_date": "2015-12-13",
"bbox": "180, -90, -180, 90",
@@ -44150,7 +44163,7 @@
{
"id": "AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data_6",
"title": "AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the RADEX flight campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2015-11-10",
"end_date": "2015-12-13",
"bbox": "180, -90, -180, 90",
@@ -44163,7 +44176,7 @@
{
"id": "AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data_5",
"title": "AirMSPI ellipsoid-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign August-September 2013, V005",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2013-08-01",
"end_date": "2013-09-23",
"bbox": "-127, 14, -73, 53",
@@ -44176,7 +44189,7 @@
{
"id": "AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data_5",
"title": "AirMSPI ellipsoid-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign August-September 2013, V005",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-08-01",
"end_date": "2013-09-23",
"bbox": "-127, 14, -73, 53",
@@ -44189,7 +44202,7 @@
{
"id": "AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data_5",
"title": "AirMSPI terrain-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign August-September 2013, V005",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2013-08-01",
"end_date": "2013-09-23",
"bbox": "-126, 15, -74, 52",
@@ -44202,7 +44215,7 @@
{
"id": "AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data_5",
"title": "AirMSPI terrain-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign August-September 2013, V005",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-08-01",
"end_date": "2013-09-23",
"bbox": "-126, 15, -74, 52",
@@ -44241,7 +44254,7 @@
{
"id": "AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data_6",
"title": "AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the SPEX-PR flight campaign",
- "catalog": "LARC_ASDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-02-02",
"end_date": "2016-02-09",
"bbox": "180, -90, -180, 90",
@@ -44254,7 +44267,7 @@
{
"id": "AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data_6",
"title": "AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the SPEX-PR flight campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LARC_ASDC STAC Catalog",
"state_date": "2016-02-02",
"end_date": "2016-02-09",
"bbox": "180, -90, -180, 90",
@@ -44267,7 +44280,7 @@
{
"id": "AirSWOT_Orthomosaic_WaterMask_1655_1",
"title": "ABoVE: AirSWOT Radar, Orthomosaic, and Water Masks, Yukon Flats Basin, Alaska, 2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2015-06-15",
"end_date": "2015-06-15",
"bbox": "-148, 65.93, -145, 66.9",
@@ -44280,7 +44293,7 @@
{
"id": "AirSWOT_Orthomosaic_WaterMask_1655_1",
"title": "ABoVE: AirSWOT Radar, Orthomosaic, and Water Masks, Yukon Flats Basin, Alaska, 2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-06-15",
"end_date": "2015-06-15",
"bbox": "-148, 65.93, -145, 66.9",
@@ -44306,7 +44319,7 @@
{
"id": "Airborne_radiotracers",
"title": "Airborne radiotracers",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1995-12-01",
"end_date": "2004-02-28",
"bbox": "164.1, -74.72, 164.12, -74.65",
@@ -44319,7 +44332,7 @@
{
"id": "Airborne_radiotracers",
"title": "Airborne radiotracers",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1995-12-01",
"end_date": "2004-02-28",
"bbox": "164.1, -74.72, 164.12, -74.65",
@@ -44384,7 +44397,7 @@
{
"id": "Alaska_Lake_Pond_Maps_2134_1",
"title": "ABoVE: Lake and Pond Extents in Alaskan Boreal and Tundra Subregions, 2019-2021",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-05-19",
"end_date": "2021-09-28",
"bbox": "-164.4, 60.76, -143.84, 67.21",
@@ -44397,7 +44410,7 @@
{
"id": "Alaska_Lake_Pond_Maps_2134_1",
"title": "ABoVE: Lake and Pond Extents in Alaskan Boreal and Tundra Subregions, 2019-2021",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2019-05-19",
"end_date": "2021-09-28",
"bbox": "-164.4, 60.76, -143.84, 67.21",
@@ -44462,7 +44475,7 @@
{
"id": "Albedo_Boreal_North_America_1605_1.1",
"title": "ABoVE: MODIS-Derived Daily Mean Blue Sky Albedo for Northern North America, 2000-2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-02-24",
"end_date": "2017-04-22",
"bbox": "-173.09, 41.68, -52.62, 79.08",
@@ -44475,7 +44488,7 @@
{
"id": "Albedo_Boreal_North_America_1605_1.1",
"title": "ABoVE: MODIS-Derived Daily Mean Blue Sky Albedo for Northern North America, 2000-2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2000-02-24",
"end_date": "2017-04-22",
"bbox": "-173.09, 41.68, -52.62, 79.08",
@@ -44605,7 +44618,7 @@
{
"id": "Aliens_in_Antarctica_survey_data_1",
"title": "Aliens in Antarctica - General Visitor Survey and Visitor Clothing Survey data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2007-09-01",
"end_date": "2008-03-31",
"bbox": "62, -67, 160, -54",
@@ -44618,7 +44631,7 @@
{
"id": "Aliens_in_Antarctica_survey_data_1",
"title": "Aliens in Antarctica - General Visitor Survey and Visitor Clothing Survey data",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-09-01",
"end_date": "2008-03-31",
"bbox": "62, -67, 160, -54",
@@ -44683,7 +44696,7 @@
{
"id": "Annual_30m_AGB_1808_1",
"title": "ABoVE: Annual Aboveground Biomass for Boreal Forests of ABoVE Core Domain, 1984-2014",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1984-01-01",
"end_date": "2014-12-31",
"bbox": "-165.41, 51.78, -101.74, 69.73",
@@ -44696,7 +44709,7 @@
{
"id": "Annual_30m_AGB_1808_1",
"title": "ABoVE: Annual Aboveground Biomass for Boreal Forests of ABoVE Core Domain, 1984-2014",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1984-01-01",
"end_date": "2014-12-31",
"bbox": "-165.41, 51.78, -101.74, 69.73",
@@ -44722,7 +44735,7 @@
{
"id": "Annual_Landcover_ABoVE_1691_1",
"title": "ABoVE: Landsat-derived Annual Dominant Land Cover Across ABoVE Core Domain, 1984-2014",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1984-01-01",
"end_date": "2014-12-31",
"bbox": "-170.01, 50.26, -98.97, 76.23",
@@ -44735,7 +44748,7 @@
{
"id": "Annual_Landcover_ABoVE_1691_1",
"title": "ABoVE: Landsat-derived Annual Dominant Land Cover Across ABoVE Core Domain, 1984-2014",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1984-01-01",
"end_date": "2014-12-31",
"bbox": "-170.01, 50.26, -98.97, 76.23",
@@ -44748,7 +44761,7 @@
{
"id": "Annual_Seasonality_Greenness_1698_1",
"title": "ABoVE: Annual Phenology Derived from Landsat across the ABoVE Core Domain, 1984-2014",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1984-01-01",
"end_date": "2014-12-31",
"bbox": "-170.01, 50.26, -98.97, 75.01",
@@ -44761,7 +44774,7 @@
{
"id": "Annual_Seasonality_Greenness_1698_1",
"title": "ABoVE: Annual Phenology Derived from Landsat across the ABoVE Core Domain, 1984-2014",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1984-01-01",
"end_date": "2014-12-31",
"bbox": "-170.01, 50.26, -98.97, 75.01",
@@ -46373,7 +46386,7 @@
{
"id": "ArcOD_2006B1",
"title": "Abundance and diversity of the Amphipoda (Crustacea) from the Greenlandic shelf",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2001-10-23",
"end_date": "2004-10-29",
"bbox": "-52.58, 60, -37.2, 64.42",
@@ -46386,7 +46399,7 @@
{
"id": "ArcOD_2006B1",
"title": "Abundance and diversity of the Amphipoda (Crustacea) from the Greenlandic shelf",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-10-23",
"end_date": "2004-10-29",
"bbox": "-52.58, 60, -37.2, 64.42",
@@ -46659,7 +46672,7 @@
{
"id": "B031_ChickCon_1.0",
"title": "Adelie penguin chick measurements from the California Avian Data Center hosted by Point Reyes Blue Conservation Science",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-12-25",
"end_date": "2017-01-31",
"bbox": "165.9, -77.6, 169.4, -76.9",
@@ -46672,7 +46685,7 @@
{
"id": "B031_ChickCon_1.0",
"title": "Adelie penguin chick measurements from the California Avian Data Center hosted by Point Reyes Blue Conservation Science",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1996-12-25",
"end_date": "2017-01-31",
"bbox": "165.9, -77.6, 169.4, -76.9",
@@ -46685,7 +46698,7 @@
{
"id": "B031_chickcount_1.0",
"title": "Adelie penguin chick counts 1997-2014 from the California Avian Data Center hosted by Point Blue Conservation Science",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1997-01-15",
"end_date": "2017-01-31",
"bbox": "165.9, -77.6, 169.4, -76.9",
@@ -46698,7 +46711,7 @@
{
"id": "B031_chickcount_1.0",
"title": "Adelie penguin chick counts 1997-2014 from the California Avian Data Center hosted by Point Blue Conservation Science",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-01-15",
"end_date": "2017-01-31",
"bbox": "165.9, -77.6, 169.4, -76.9",
@@ -46763,7 +46776,7 @@
{
"id": "B031_resight_1.0",
"title": "Adelie penguin resighting data from the California Avian Data Center hosted by Point Blue Conservation Science",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-12-15",
"end_date": "2017-01-31",
"bbox": "165.9, -77.6, 169.4, -76.9",
@@ -46776,7 +46789,7 @@
{
"id": "B031_resight_1.0",
"title": "Adelie penguin resighting data from the California Avian Data Center hosted by Point Blue Conservation Science",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1997-12-15",
"end_date": "2017-01-31",
"bbox": "165.9, -77.6, 169.4, -76.9",
@@ -48674,7 +48687,7 @@
{
"id": "BANd0216_113",
"title": "Administrative map of Vietnam",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "101.43, 7.75, 110.25, 24.05",
@@ -48687,7 +48700,7 @@
{
"id": "BANd0216_113",
"title": "Administrative map of Vietnam",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "101.43, 7.75, 110.25, 24.05",
@@ -48895,7 +48908,7 @@
{
"id": "BESTsed25",
"title": "Accumulation of Dioxins and Furans in Sediment and Biota in the Lower Columbia Wauna River Area",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1991-09-01",
"end_date": "1991-09-01",
"bbox": "-123, 47, -122, 48",
@@ -48908,7 +48921,7 @@
{
"id": "BESTsed25",
"title": "Accumulation of Dioxins and Furans in Sediment and Biota in the Lower Columbia Wauna River Area",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1991-09-01",
"end_date": "1991-09-01",
"bbox": "-123, 47, -122, 48",
@@ -49649,7 +49662,7 @@
{
"id": "BROKE-West_ACS_1",
"title": "ACS data collected on the BROKE-West voyage of the Aurora Australis, 2006",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2006-01-17",
"end_date": "2006-02-28",
"bbox": "30, -69.1, 80, -59.8",
@@ -49662,7 +49675,7 @@
{
"id": "BROKE-West_ACS_1",
"title": "ACS data collected on the BROKE-West voyage of the Aurora Australis, 2006",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2006-01-17",
"end_date": "2006-02-28",
"bbox": "30, -69.1, 80, -59.8",
@@ -49909,7 +49922,7 @@
{
"id": "BROKE_Documentation_Logs_1",
"title": "A collection of logs and documentation associated with the BROKE voyage of the Aurora Australis in the 1995/1996 season",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1996-01-19",
"end_date": "1996-03-31",
"bbox": "70, -67, 165, -44",
@@ -49922,7 +49935,7 @@
{
"id": "BROKE_Documentation_Logs_1",
"title": "A collection of logs and documentation associated with the BROKE voyage of the Aurora Australis in the 1995/1996 season",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-01-19",
"end_date": "1996-03-31",
"bbox": "70, -67, 165, -44",
@@ -50299,7 +50312,7 @@
{
"id": "Biology_Log_Adelie_Rookery_1957_Gardner_1",
"title": "Adelie Penguin rookery observations made at Gardner Island in 1957",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1957-12-02",
"end_date": "1957-12-27",
"bbox": "77.867, -68.583, 77.867, -68.583",
@@ -50312,7 +50325,7 @@
{
"id": "Biology_Log_Adelie_Rookery_1957_Gardner_1",
"title": "Adelie Penguin rookery observations made at Gardner Island in 1957",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1957-12-02",
"end_date": "1957-12-27",
"bbox": "77.867, -68.583, 77.867, -68.583",
@@ -50585,7 +50598,7 @@
{
"id": "Biology_Log_Mawson_1958_1962_1",
"title": "A log of biological observations made at Mawson, Davis and Wilkes stations between 1958 and 1962",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1958-01-01",
"end_date": "1962-12-31",
"bbox": "62, -68, 110, -66",
@@ -50598,7 +50611,7 @@
{
"id": "Biology_Log_Mawson_1958_1962_1",
"title": "A log of biological observations made at Mawson, Davis and Wilkes stations between 1958 and 1962",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1958-01-01",
"end_date": "1962-12-31",
"bbox": "62, -68, 110, -66",
@@ -50611,7 +50624,7 @@
{
"id": "Biology_Log_Mawson_1971_1974_1",
"title": "A log of biological and sea ice observations made at Mawson station between 1971 and 1974",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1971-01-01",
"end_date": "1974-12-31",
"bbox": "62, -67, 63, -66",
@@ -50624,7 +50637,7 @@
{
"id": "Biology_Log_Mawson_1971_1974_1",
"title": "A log of biological and sea ice observations made at Mawson station between 1971 and 1974",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1971-01-01",
"end_date": "1974-12-31",
"bbox": "62, -67, 63, -66",
@@ -50637,7 +50650,7 @@
{
"id": "Biology_Log_Mawson_1977_1978_1",
"title": "A log of biological and sea ice observations made at Mawson station between 1977 and 1978",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1977-01-01",
"end_date": "1978-01-31",
"bbox": "62, -67, 63, -66",
@@ -50650,7 +50663,7 @@
{
"id": "Biology_Log_Mawson_1977_1978_1",
"title": "A log of biological and sea ice observations made at Mawson station between 1977 and 1978",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1977-01-01",
"end_date": "1978-01-31",
"bbox": "62, -67, 63, -66",
@@ -50663,7 +50676,7 @@
{
"id": "Biology_Log_Mawson_1980_1981_1",
"title": "A log of biological observations at Mawson station during 1980 and 1981",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1980-04-18",
"end_date": "1981-12-26",
"bbox": "62, -67, 63, -66",
@@ -50676,7 +50689,7 @@
{
"id": "Biology_Log_Mawson_1980_1981_1",
"title": "A log of biological observations at Mawson station during 1980 and 1981",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1980-04-18",
"end_date": "1981-12-26",
"bbox": "62, -67, 63, -66",
@@ -50689,7 +50702,7 @@
{
"id": "Biology_Log_Mawson_1982_1",
"title": "A log of biological observations at Mawson station in 1982",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1982-01-01",
"end_date": "1983-01-09",
"bbox": "62, -67, 63, -66",
@@ -50702,7 +50715,7 @@
{
"id": "Biology_Log_Mawson_1982_1",
"title": "A log of biological observations at Mawson station in 1982",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1982-01-01",
"end_date": "1983-01-09",
"bbox": "62, -67, 63, -66",
@@ -50780,7 +50793,7 @@
{
"id": "Biology_Log_Mawson_Pintardo_Petrels_1972_1988_1",
"title": "A log of biological observations of Pintardo Petrels made at Mawson station between 1972 and 1988",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1971-02-10",
"end_date": "1988-11-03",
"bbox": "62, -67, 63, -66",
@@ -50793,7 +50806,7 @@
{
"id": "Biology_Log_Mawson_Pintardo_Petrels_1972_1988_1",
"title": "A log of biological observations of Pintardo Petrels made at Mawson station between 1972 and 1988",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1971-02-10",
"end_date": "1988-11-03",
"bbox": "62, -67, 63, -66",
@@ -50832,7 +50845,7 @@
{
"id": "Biology_Log_Mawson_Skuas_1982_1990_1",
"title": "A log of biological observations at Mawson station of skuas from 1982 to 1990",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1982-03-10",
"end_date": "1990-10-22",
"bbox": "62, -67, 63, -66",
@@ -50845,7 +50858,7 @@
{
"id": "Biology_Log_Mawson_Skuas_1982_1990_1",
"title": "A log of biological observations at Mawson station of skuas from 1982 to 1990",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1982-03-10",
"end_date": "1990-10-22",
"bbox": "62, -67, 63, -66",
@@ -51014,7 +51027,7 @@
{
"id": "Biology_Log_Wilkes_Bird_Banding_1962_1963_1",
"title": "A log of bird banding and zoological observations made at Wilkes Station and the Windmill Islands, 1962-1963",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1962-01-01",
"end_date": "1963-12-31",
"bbox": "110, -67, 111, -66",
@@ -51027,7 +51040,7 @@
{
"id": "Biology_Log_Wilkes_Bird_Banding_1962_1963_1",
"title": "A log of bird banding and zoological observations made at Wilkes Station and the Windmill Islands, 1962-1963",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1962-01-01",
"end_date": "1963-12-31",
"bbox": "110, -67, 111, -66",
@@ -51079,7 +51092,7 @@
{
"id": "Biology_Log_Wilkes_Zoology_1959_1961_1",
"title": "A log of zoological observations made at Wilkes Station and the Windmill Islands, 1959-1961",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1959-01-01",
"end_date": "1961-12-31",
"bbox": "110, -67, 111, -66",
@@ -51092,7 +51105,7 @@
{
"id": "Biology_Log_Wilkes_Zoology_1959_1961_1",
"title": "A log of zoological observations made at Wilkes Station and the Windmill Islands, 1959-1961",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1959-01-01",
"end_date": "1961-12-31",
"bbox": "110, -67, 111, -66",
@@ -51339,7 +51352,7 @@
{
"id": "BurnedArea_Emissions_AK_YT_NWT_1812_2",
"title": "ABoVE: Ignitions, Burned Area, and Emissions of Fires in AK, YT, and NWT, 2001-2018",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2018-12-31",
"bbox": "-167, 51.63, -99.98, 79.26",
@@ -51352,7 +51365,7 @@
{
"id": "BurnedArea_Emissions_AK_YT_NWT_1812_2",
"title": "ABoVE: Ignitions, Burned Area, and Emissions of Fires in AK, YT, and NWT, 2001-2018",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2018-12-31",
"bbox": "-167, 51.63, -99.98, 79.26",
@@ -57657,7 +57670,7 @@
{
"id": "CDIAC_DB1004",
"title": "Alaskan Historical Climatology Network (HCN) Serial Temperature and Precipitation Data/CDIAC, DB1004",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1828-01-01",
"end_date": "1990-12-31",
"bbox": "-180, 50, -130, 75",
@@ -57670,7 +57683,7 @@
{
"id": "CDIAC_DB1004",
"title": "Alaskan Historical Climatology Network (HCN) Serial Temperature and Precipitation Data/CDIAC, DB1004",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1828-01-01",
"end_date": "1990-12-31",
"bbox": "-180, 50, -130, 75",
@@ -57735,7 +57748,7 @@
{
"id": "CDIAC_NDP072_ORNL/CDIAC-120",
"title": "A Database of Woody Vegetation Responses to Elevated Atmospheric CO2, CDIAC/NDP-072",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-177.1, 13.71, -61.48, 76.63",
@@ -57748,7 +57761,7 @@
{
"id": "CDIAC_NDP072_ORNL/CDIAC-120",
"title": "A Database of Woody Vegetation Responses to Elevated Atmospheric CO2, CDIAC/NDP-072",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-177.1, 13.71, -61.48, 76.63",
@@ -57761,7 +57774,7 @@
{
"id": "CDIAC_NDP073",
"title": "A Database of Herbaceous Vegetation Responses to Elevated Atmospheric CO2, CDAIC/NDP-073",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-177.1, 13.71, -61.48, 76.63",
@@ -57774,7 +57787,7 @@
{
"id": "CDIAC_NDP073",
"title": "A Database of Herbaceous Vegetation Responses to Elevated Atmospheric CO2, CDAIC/NDP-073",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-177.1, 13.71, -61.48, 76.63",
@@ -57800,7 +57813,7 @@
{
"id": "CDIAC_NDP43A",
"title": "A Coastal Hazards Data Base for the U.S. East Coast, CDIAC NDP-043A",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-80, 25, -65, 45",
@@ -57813,7 +57826,7 @@
{
"id": "CDIAC_NDP43A",
"title": "A Coastal Hazards Data Base for the U.S. East Coast, CDIAC NDP-043A",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-80, 25, -65, 45",
@@ -57852,7 +57865,7 @@
{
"id": "CDIAC_TR051",
"title": "A Comprehensive Precipitation Data Set for Global Land Areas, CDIAC/TR051",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1851-01-01",
"end_date": "1989-12-31",
"bbox": "-180, -60, 180, 80",
@@ -57865,7 +57878,7 @@
{
"id": "CDIAC_TR051",
"title": "A Comprehensive Precipitation Data Set for Global Land Areas, CDIAC/TR051",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1851-01-01",
"end_date": "1989-12-31",
"bbox": "-180, -60, 180, 80",
@@ -57878,7 +57891,7 @@
{
"id": "CDMO_acemet01-12.02m",
"title": "ACE Basin National Estuarine Research Reserve Meteorological Metadata January - December 2002 Latest Update: February 11, 2005",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2002-01-01",
"end_date": "2002-12-31",
"bbox": "-80.67007, 32.32975, -80.27775, 32.669712",
@@ -57891,7 +57904,7 @@
{
"id": "CDMO_acemet01-12.02m",
"title": "ACE Basin National Estuarine Research Reserve Meteorological Metadata January - December 2002 Latest Update: February 11, 2005",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-01-01",
"end_date": "2002-12-31",
"bbox": "-80.67007, 32.32975, -80.27775, 32.669712",
@@ -57982,7 +57995,7 @@
{
"id": "CDMO_acenut01-12.02m",
"title": "ACE Basin NERR Nutrient Metadata January-December 2002 Latest Update: December 15, 2004",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2002-01-01",
"end_date": "2002-12-31",
"bbox": "-80.67007, 32.32975, -80.27775, 32.669712",
@@ -57995,7 +58008,7 @@
{
"id": "CDMO_acenut01-12.02m",
"title": "ACE Basin NERR Nutrient Metadata January-December 2002 Latest Update: December 15, 2004",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-01-01",
"end_date": "2002-12-31",
"bbox": "-80.67007, 32.32975, -80.27775, 32.669712",
@@ -58086,7 +58099,7 @@
{
"id": "CDMO_acewq01-12.01m",
"title": "ACE Basin NERR Water Quality Metadata January-December 2001 Latest update: August 20, 2002",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2001-12-31",
"bbox": "-80.67007, 32.32975, -80.27775, 32.669712",
@@ -58099,7 +58112,7 @@
{
"id": "CDMO_acewq01-12.01m",
"title": "ACE Basin NERR Water Quality Metadata January-December 2001 Latest update: August 20, 2002",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2001-12-31",
"bbox": "-80.67007, 32.32975, -80.27775, 32.669712",
@@ -58138,7 +58151,7 @@
{
"id": "CDMO_acewq01-12.04m",
"title": "ACE Basin (ACE) National Estuarine Research Reserve Water Quality Metadata January-December 2004 Report Latest edit: May 6, 2005",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2004-12-31",
"bbox": "-80.67007, 32.32975, -80.27775, 32.669712",
@@ -58151,7 +58164,7 @@
{
"id": "CDMO_acewq01-12.04m",
"title": "ACE Basin (ACE) National Estuarine Research Reserve Water Quality Metadata January-December 2004 Report Latest edit: May 6, 2005",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2004-12-31",
"bbox": "-80.67007, 32.32975, -80.27775, 32.669712",
@@ -58190,7 +58203,7 @@
{
"id": "CDMO_acewq01-12.97m",
"title": "ACE Basin National Estuarine Research Reserve January-December 1997 Water Quality Metadata Report Latest Update: September 26, 2001",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1997-01-01",
"end_date": "1997-12-31",
"bbox": "-80.67007, 32.32975, -80.27775, 32.669712",
@@ -58203,7 +58216,7 @@
{
"id": "CDMO_acewq01-12.97m",
"title": "ACE Basin National Estuarine Research Reserve January-December 1997 Water Quality Metadata Report Latest Update: September 26, 2001",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-01-01",
"end_date": "1997-12-31",
"bbox": "-80.67007, 32.32975, -80.27775, 32.669712",
@@ -58216,7 +58229,7 @@
{
"id": "CDMO_acewq01-12.98m",
"title": "ACE Basin National Estuarine Research Reserve January-December 1998 Water Quality Metadata Report Latest Update: September 26, 2001",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1998-01-01",
"end_date": "1998-12-31",
"bbox": "-80.67007, 32.32975, -80.27775, 32.669712",
@@ -58229,7 +58242,7 @@
{
"id": "CDMO_acewq01-12.98m",
"title": "ACE Basin National Estuarine Research Reserve January-December 1998 Water Quality Metadata Report Latest Update: September 26, 2001",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1998-01-01",
"end_date": "1998-12-31",
"bbox": "-80.67007, 32.32975, -80.27775, 32.669712",
@@ -58268,7 +58281,7 @@
{
"id": "CDMO_acewq03-12.95m",
"title": "ACE Basin National Estuarine Research Reserve March - December 1995 Metadata Report edited: 9/19/97",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1995-03-01",
"end_date": "1995-12-31",
"bbox": "-80.67007, 32.32975, -80.27775, 32.669712",
@@ -58281,7 +58294,7 @@
{
"id": "CDMO_acewq03-12.95m",
"title": "ACE Basin National Estuarine Research Reserve March - December 1995 Metadata Report edited: 9/19/97",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1995-03-01",
"end_date": "1995-12-31",
"bbox": "-80.67007, 32.32975, -80.27775, 32.669712",
@@ -58294,7 +58307,7 @@
{
"id": "CE1d0023_173",
"title": "Administrative boundaries of Mohtamadeyas in Tunisia; 1989",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1974-01-01",
"end_date": "1989-01-01",
"bbox": "7, 30, 12, 35",
@@ -58307,7 +58320,7 @@
{
"id": "CE1d0023_173",
"title": "Administrative boundaries of Mohtamadeyas in Tunisia; 1989",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1974-01-01",
"end_date": "1989-01-01",
"bbox": "7, 30, 12, 35",
@@ -58320,7 +58333,7 @@
{
"id": "CE1d0029_173",
"title": "Agroclimatological Zones, Jordan; 1977",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1977-01-01",
"end_date": "1980-01-01",
"bbox": "34, 29, 39, 33",
@@ -58333,7 +58346,7 @@
{
"id": "CE1d0029_173",
"title": "Agroclimatological Zones, Jordan; 1977",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1977-01-01",
"end_date": "1980-01-01",
"bbox": "34, 29, 39, 33",
@@ -58346,7 +58359,7 @@
{
"id": "CE1d0038_173",
"title": "Administrative Units Boundaries of Jordan; 1977",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1974-01-01",
"end_date": "1977-01-01",
"bbox": "34, 29, 39, 33",
@@ -58359,7 +58372,7 @@
{
"id": "CE1d0038_173",
"title": "Administrative Units Boundaries of Jordan; 1977",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1974-01-01",
"end_date": "1977-01-01",
"bbox": "34, 29, 39, 33",
@@ -58515,7 +58528,7 @@
{
"id": "CEAMARC_CASO_200708_V3_Biogeochemistry_EIMS_1",
"title": "AAV30708 Biogeochemistry - EIMS Data Collected on the CEAMARC Cruise of the Aurora Australis 2007-2008",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2007-12-16",
"end_date": "2008-01-27",
"bbox": "141.76285, -67.04925, 147.85347, -43.12521",
@@ -58528,7 +58541,7 @@
{
"id": "CEAMARC_CASO_200708_V3_Biogeochemistry_EIMS_1",
"title": "AAV30708 Biogeochemistry - EIMS Data Collected on the CEAMARC Cruise of the Aurora Australis 2007-2008",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-12-16",
"end_date": "2008-01-27",
"bbox": "141.76285, -67.04925, 147.85347, -43.12521",
@@ -58541,7 +58554,7 @@
{
"id": "CEAMARC_CASO_200708_V3_Biogeochemistry_PCO2_1",
"title": "AAV30708 Biogeochemistry PCO2 Data Collected on the CEAMARC Cruise of the Aurora Australis 2007-2008",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2007-12-16",
"end_date": "2008-01-27",
"bbox": "141.76285, -67.04925, 147.85347, -43.12521",
@@ -58554,7 +58567,7 @@
{
"id": "CEAMARC_CASO_200708_V3_Biogeochemistry_PCO2_1",
"title": "AAV30708 Biogeochemistry PCO2 Data Collected on the CEAMARC Cruise of the Aurora Australis 2007-2008",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-12-16",
"end_date": "2008-01-27",
"bbox": "141.76285, -67.04925, 147.85347, -43.12521",
@@ -58580,7 +58593,7 @@
{
"id": "CEAMARC_CASO_200708_V3_IMAGES_1",
"title": "2007-08 Voyage 3 of the Aurora Australis, CEAMARC-CASO Image Data - Stills and Video",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2007-12-16",
"end_date": "2008-01-27",
"bbox": "139.01488, -67.07104, 150.06479, -42.88246",
@@ -58593,7 +58606,7 @@
{
"id": "CEAMARC_CASO_200708_V3_IMAGES_1",
"title": "2007-08 Voyage 3 of the Aurora Australis, CEAMARC-CASO Image Data - Stills and Video",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-12-16",
"end_date": "2008-01-27",
"bbox": "139.01488, -67.07104, 150.06479, -42.88246",
@@ -58632,7 +58645,7 @@
{
"id": "CEAMARC_CASO_200708_V3_MINERALOGY_1",
"title": "2007-08 Voyage 3 of the Aurora Australis, CEAMARC-CASO Mineralogy Biota Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2007-12-16",
"end_date": "2008-01-27",
"bbox": "139.01488, -67.07104, 150.06479, -42.88246",
@@ -58645,7 +58658,7 @@
{
"id": "CEAMARC_CASO_200708_V3_MINERALOGY_1",
"title": "2007-08 Voyage 3 of the Aurora Australis, CEAMARC-CASO Mineralogy Biota Data",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-12-16",
"end_date": "2008-01-27",
"bbox": "139.01488, -67.07104, 150.06479, -42.88246",
@@ -58710,7 +58723,7 @@
{
"id": "CEAMARC_Diatom_Absolute_Abundance_1",
"title": "Absolute abundance of diatoms from CEAMARC cores",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-06-01",
"end_date": "2012-07-31",
"bbox": "139, -67.5, 146, -65",
@@ -58723,7 +58736,7 @@
{
"id": "CEAMARC_Diatom_Absolute_Abundance_1",
"title": "Absolute abundance of diatoms from CEAMARC cores",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2012-06-01",
"end_date": "2012-07-31",
"bbox": "139, -67.5, 146, -65",
@@ -58762,7 +58775,7 @@
{
"id": "CEDAR_Imager",
"title": "Airglow/Aurora Video Imaging Data and All-Sky Camera Data from the CEDAR Data Base at NCAR/HAO",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1987-07-29",
"end_date": "1990-03-30",
"bbox": "-155, 20, 16, 79",
@@ -58775,7 +58788,7 @@
{
"id": "CEDAR_Imager",
"title": "Airglow/Aurora Video Imaging Data and All-Sky Camera Data from the CEDAR Data Base at NCAR/HAO",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1987-07-29",
"end_date": "1990-03-30",
"bbox": "-155, 20, 16, 79",
@@ -61141,7 +61154,7 @@
{
"id": "CH-OG-1-GPS-30S_0.0",
"title": "30 sec GPS ground tracking data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2001-05-28",
"end_date": "",
"bbox": "-63.51, -45.69, 170.42, 78.87",
@@ -61154,7 +61167,7 @@
{
"id": "CH-OG-1-GPS-30S_0.0",
"title": "30 sec GPS ground tracking data",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-05-28",
"end_date": "",
"bbox": "-63.51, -45.69, 170.42, 78.87",
@@ -61193,7 +61206,7 @@
{
"id": "CH4_Flux_BigTrail_Goldstream_1778_1",
"title": "ABoVE: Methane Flux across Two Thermokarst Lake Ecosystems, Interior Alaska, 2018",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2018-07-17",
"end_date": "2018-07-29",
"bbox": "-147.85, 64.92, -147.82, 64.92",
@@ -61206,7 +61219,7 @@
{
"id": "CH4_Flux_BigTrail_Goldstream_1778_1",
"title": "ABoVE: Methane Flux across Two Thermokarst Lake Ecosystems, Interior Alaska, 2018",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2018-07-17",
"end_date": "2018-07-29",
"bbox": "-147.85, 64.92, -147.82, 64.92",
@@ -61271,7 +61284,7 @@
{
"id": "CIESIN0122",
"title": "Africa Real Time Environmental Monitoring Information System (ARTEMIS)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1982-01-01",
"end_date": "",
"bbox": "-20, -35, 60, 40",
@@ -61284,7 +61297,7 @@
{
"id": "CIESIN0122",
"title": "Africa Real Time Environmental Monitoring Information System (ARTEMIS)",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1982-01-01",
"end_date": "",
"bbox": "-20, -35, 60, 40",
@@ -61375,7 +61388,7 @@
{
"id": "CIESIN_AfSIS_MODIS_ALB2012_2012.00",
"title": "AfSIS MODIS Collection: Albedo, 2012 Release",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-02-01",
"end_date": "2012-06-30",
"bbox": "-20, -40, 60, 40",
@@ -61388,7 +61401,7 @@
{
"id": "CIESIN_AfSIS_MODIS_ALB2012_2012.00",
"title": "AfSIS MODIS Collection: Albedo, 2012 Release",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2000-02-01",
"end_date": "2012-06-30",
"bbox": "-20, -40, 60, 40",
@@ -61401,7 +61414,7 @@
{
"id": "CIESIN_AfSIS_MODIS_LAIFPAR2012_2012.00",
"title": "AfSIS MODIS Collection: Leaf Area Index - FPAR, 2012 Release",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2000-02-01",
"end_date": "2012-06-30",
"bbox": "-20, -40, 60, 40",
@@ -61414,7 +61427,7 @@
{
"id": "CIESIN_AfSIS_MODIS_LAIFPAR2012_2012.00",
"title": "AfSIS MODIS Collection: Leaf Area Index - FPAR, 2012 Release",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-02-01",
"end_date": "2012-06-30",
"bbox": "-20, -40, 60, 40",
@@ -61427,7 +61440,7 @@
{
"id": "CIESIN_AfSIS_MODIS_LCT2012_2012.00",
"title": "AfSIS MODIS Collection: Land Cover Type, 2012 Release",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2009-12-31",
"bbox": "-20, -40, 60, 40",
@@ -61440,7 +61453,7 @@
{
"id": "CIESIN_AfSIS_MODIS_LCT2012_2012.00",
"title": "AfSIS MODIS Collection: Land Cover Type, 2012 Release",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2009-12-31",
"bbox": "-20, -40, 60, 40",
@@ -61479,7 +61492,7 @@
{
"id": "CIESIN_AfSIS_MODIS_PP2012_2014.00",
"title": "AfSIS MODIS Collection: Primary Productivity, 2012 Release",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2010-12-31",
"bbox": "-20, -40, 60, 40",
@@ -61492,7 +61505,7 @@
{
"id": "CIESIN_AfSIS_MODIS_PP2012_2014.00",
"title": "AfSIS MODIS Collection: Primary Productivity, 2012 Release",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2010-12-31",
"bbox": "-20, -40, 60, 40",
@@ -62636,7 +62649,7 @@
{
"id": "CIESIN_SEDAC_EPI_2008_2008.00",
"title": "2008 Environmental Performance Index (EPI)",
- "catalog": "SEDAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1994-01-01",
"end_date": "2007-12-31",
"bbox": "-180, -55, 180, 90",
@@ -62649,7 +62662,7 @@
{
"id": "CIESIN_SEDAC_EPI_2008_2008.00",
"title": "2008 Environmental Performance Index (EPI)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SEDAC STAC Catalog",
"state_date": "1994-01-01",
"end_date": "2007-12-31",
"bbox": "-180, -55, 180, 90",
@@ -62662,7 +62675,7 @@
{
"id": "CIESIN_SEDAC_EPI_2010_2010.00",
"title": "2010 Environmental Performance Index (EPI)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SEDAC STAC Catalog",
"state_date": "1994-01-01",
"end_date": "2009-12-31",
"bbox": "-180, -55, 180, 90",
@@ -62675,7 +62688,7 @@
{
"id": "CIESIN_SEDAC_EPI_2010_2010.00",
"title": "2010 Environmental Performance Index (EPI)",
- "catalog": "SEDAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1994-01-01",
"end_date": "2009-12-31",
"bbox": "-180, -55, 180, 90",
@@ -62688,7 +62701,7 @@
{
"id": "CIESIN_SEDAC_EPI_2012_2012.00",
"title": "2012 Environmental Performance Index and Pilot Trend Environmental Performance Index",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SEDAC STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2010-12-31",
"bbox": "-180, -55, 180, 90",
@@ -62701,7 +62714,7 @@
{
"id": "CIESIN_SEDAC_EPI_2012_2012.00",
"title": "2012 Environmental Performance Index and Pilot Trend Environmental Performance Index",
- "catalog": "SEDAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2010-12-31",
"bbox": "-180, -55, 180, 90",
@@ -62740,7 +62753,7 @@
{
"id": "CIESIN_SEDAC_EPI_2016_2016.00",
"title": "2016 Environmental Performance Index (EPI)",
- "catalog": "SEDAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1950-01-01",
"end_date": "2016-12-31",
"bbox": "-180, -55, 180, 90",
@@ -62753,7 +62766,7 @@
{
"id": "CIESIN_SEDAC_EPI_2016_2016.00",
"title": "2016 Environmental Performance Index (EPI)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SEDAC STAC Catalog",
"state_date": "1950-01-01",
"end_date": "2016-12-31",
"bbox": "-180, -55, 180, 90",
@@ -62766,7 +62779,7 @@
{
"id": "CIESIN_SEDAC_EPI_2018_2018.00",
"title": "2018 Environmental Performance Index (EPI)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SEDAC STAC Catalog",
"state_date": "1950-01-01",
"end_date": "2018-12-31",
"bbox": "-180, -55, 180, 90",
@@ -62779,7 +62792,7 @@
{
"id": "CIESIN_SEDAC_EPI_2018_2018.00",
"title": "2018 Environmental Performance Index (EPI)",
- "catalog": "SEDAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1950-01-01",
"end_date": "2018-12-31",
"bbox": "-180, -55, 180, 90",
@@ -62792,7 +62805,7 @@
{
"id": "CIESIN_SEDAC_EPI_2020_2020.00",
"title": "2020 Environmental Performance Index (EPI)",
- "catalog": "SEDAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1950-01-01",
"end_date": "2020-12-31",
"bbox": "-180, -55, 180, 90",
@@ -62805,7 +62818,7 @@
{
"id": "CIESIN_SEDAC_EPI_2020_2020.00",
"title": "2020 Environmental Performance Index (EPI)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SEDAC STAC Catalog",
"state_date": "1950-01-01",
"end_date": "2020-12-31",
"bbox": "-180, -55, 180, 90",
@@ -62896,7 +62909,7 @@
{
"id": "CIESIN_SEDAC_ESI_2002_2002.00",
"title": "2002 Environmental Sustainability Index (ESI)",
- "catalog": "SEDAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1980-01-01",
"end_date": "2000-12-31",
"bbox": "-180, -55, 180, 90",
@@ -62909,7 +62922,7 @@
{
"id": "CIESIN_SEDAC_ESI_2002_2002.00",
"title": "2002 Environmental Sustainability Index (ESI)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SEDAC STAC Catalog",
"state_date": "1980-01-01",
"end_date": "2000-12-31",
"bbox": "-180, -55, 180, 90",
@@ -67407,7 +67420,7 @@
{
"id": "CNDA-ESP_ANT94-0905_LIQ_05",
"title": "Adaptive strategies of lichen species to cold environments: Antarctica and the Mediterranean high mountains.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1995-01-19",
"end_date": "1995-02-09",
"bbox": "-60, -63, -60, -63",
@@ -67420,7 +67433,7 @@
{
"id": "CNDA-ESP_ANT94-0905_LIQ_05",
"title": "Adaptive strategies of lichen species to cold environments: Antarctica and the Mediterranean high mountains.",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1995-01-19",
"end_date": "1995-02-09",
"bbox": "-60, -63, -60, -63",
@@ -67433,7 +67446,7 @@
{
"id": "CNDP_HES_20230103_CHALLENGE_ALS_1.0",
"title": "Algae sampling of the project CHALLENGE-2",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2023-01-03",
"end_date": "2023-02-28",
"bbox": "-70.1938725, -68.1163134, -56.8344988, -61.085064",
@@ -67446,7 +67459,7 @@
{
"id": "CNDP_HES_20230103_CHALLENGE_ALS_1.0",
"title": "Algae sampling of the project CHALLENGE-2",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2023-01-03",
"end_date": "2023-02-28",
"bbox": "-70.1938725, -68.1163134, -56.8344988, -61.085064",
@@ -67576,7 +67589,7 @@
{
"id": "CNNADC_1999_ARCTIC_MAP",
"title": "1:5000000 map of Arctic Ocean area",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -67589,7 +67602,7 @@
{
"id": "CNNADC_1999_ARCTIC_MAP",
"title": "1:5000000 map of Arctic Ocean area",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -67628,7 +67641,7 @@
{
"id": "CNNADC_2006_ZhongshanStation_Antarctica_2006",
"title": "2006 Zhongshan station earth tide data - CNNADC_2006_ZhongshanStation_Antarctica_2006",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2006-04-01",
"end_date": "2006-11-30",
"bbox": "-180, -90, 180, 90",
@@ -67641,7 +67654,7 @@
{
"id": "CNNADC_2006_ZhongshanStation_Antarctica_2006",
"title": "2006 Zhongshan station earth tide data - CNNADC_2006_ZhongshanStation_Antarctica_2006",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2006-04-01",
"end_date": "2006-11-30",
"bbox": "-180, -90, 180, 90",
@@ -67953,7 +67966,7 @@
{
"id": "CPL_ABL_Top_Height_1825_1",
"title": "ACT-America: CPL-derived Atmospheric Boundary Layer Top Height, Eastern US, 2016-2018",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-07-18",
"end_date": "2018-05-20",
"bbox": "-106.49, 27.25, -64, 50",
@@ -67966,7 +67979,7 @@
{
"id": "CPL_ABL_Top_Height_1825_1",
"title": "ACT-America: CPL-derived Atmospheric Boundary Layer Top Height, Eastern US, 2016-2018",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-07-18",
"end_date": "2018-05-20",
"bbox": "-106.49, 27.25, -64, 50",
@@ -68044,7 +68057,7 @@
{
"id": "CSIRO_Albatross_primaryprod",
"title": "Albatross Bay Primary Productivity",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1986-01-01",
"end_date": "1992-12-31",
"bbox": "141.5, -13, 142, -12.5",
@@ -68057,7 +68070,7 @@
{
"id": "CSIRO_Albatross_primaryprod",
"title": "Albatross Bay Primary Productivity",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1986-01-01",
"end_date": "1992-12-31",
"bbox": "141.5, -13, 142, -12.5",
@@ -68161,7 +68174,7 @@
{
"id": "CSU_fueltreatment_Fontainebleauwildfirestudy",
"title": "1999 Fontainebleau Wildfire study",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-88.71972, 30.401943, -88.71972, 30.401943",
@@ -68174,7 +68187,7 @@
{
"id": "CSU_fueltreatment_Fontainebleauwildfirestudy",
"title": "1999 Fontainebleau Wildfire study",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-88.71972, 30.401943, -88.71972, 30.401943",
@@ -68187,7 +68200,7 @@
{
"id": "CSU_fueltreatment_HiMeadow",
"title": "2000 Hi Meadow Wildfire Study",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-105.372, 39.368, -105.337, 39.403",
@@ -68200,7 +68213,7 @@
{
"id": "CSU_fueltreatment_HiMeadow",
"title": "2000 Hi Meadow Wildfire Study",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-105.372, 39.368, -105.337, 39.403",
@@ -69227,7 +69240,7 @@
{
"id": "CZM_moris_algonquin_hubline_lng_arc",
"title": "Algonquin Hubline natural gas pipeline, Massachusetts Bay, Massachusetts",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2004-11-04",
"end_date": "",
"bbox": "-70.964935, 42.244022, -70.774414, 42.54302",
@@ -69240,7 +69253,7 @@
{
"id": "CZM_moris_algonquin_hubline_lng_arc",
"title": "Algonquin Hubline natural gas pipeline, Massachusetts Bay, Massachusetts",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-11-04",
"end_date": "",
"bbox": "-70.964935, 42.244022, -70.774414, 42.54302",
@@ -69357,7 +69370,7 @@
{
"id": "Canada_Boreal_Forest_Greenness_1587_1",
"title": "ABoVE: Peak Greenness for Canadian Boreal Forest from Landsat 5 TM Imagery, 1984-2011",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "2014-12-31",
"bbox": "-124.47, 45.32, -53.91, 63.44",
@@ -69370,7 +69383,7 @@
{
"id": "Canada_Boreal_Forest_Greenness_1587_1",
"title": "ABoVE: Peak Greenness for Canadian Boreal Forest from Landsat 5 TM Imagery, 1984-2011",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1970-01-01",
"end_date": "2014-12-31",
"bbox": "-124.47, 45.32, -53.91, 63.44",
@@ -69513,7 +69526,7 @@
{
"id": "Catlin_Arctic_Survey_0",
"title": "2011 R/V Catlin cruise in the Arctic Ocean",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "2011-03-17",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -69526,7 +69539,7 @@
{
"id": "Catlin_Arctic_Survey_0",
"title": "2011 R/V Catlin cruise in the Arctic Ocean",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-03-17",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -69825,7 +69838,7 @@
{
"id": "CosRay_Notes_Charts_1959-1986_1",
"title": "A collection of some comsic ray physics notes and charts from Antarctica in the period 1959-1986",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1959-01-01",
"end_date": "1986-12-31",
"bbox": "60, -69, 159, -54",
@@ -69838,7 +69851,7 @@
{
"id": "CosRay_Notes_Charts_1959-1986_1",
"title": "A collection of some comsic ray physics notes and charts from Antarctica in the period 1959-1986",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1959-01-01",
"end_date": "1986-12-31",
"bbox": "60, -69, 159, -54",
@@ -70150,7 +70163,7 @@
{
"id": "DB_Trophic_1",
"title": "A compilation of dietary and related data from the Southern Ocean",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1960-12-21",
"end_date": "2010-03-20",
"bbox": "-180, -80, 180, -40",
@@ -70163,7 +70176,7 @@
{
"id": "DB_Trophic_1",
"title": "A compilation of dietary and related data from the Southern Ocean",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1960-12-21",
"end_date": "2010-03-20",
"bbox": "-180, -80, 180, -40",
@@ -71658,7 +71671,7 @@
{
"id": "DLG100K",
"title": "1:100,000-scale Digital Line Graphs (DLG) from the U.S. Geological Survey",
- "catalog": "ALL STAC Catalog",
+ "catalog": "USGS_LTA STAC Catalog",
"state_date": "1987-06-19",
"end_date": "",
"bbox": "-126, 24, -66, 49",
@@ -71671,7 +71684,7 @@
{
"id": "DLG100K",
"title": "1:100,000-scale Digital Line Graphs (DLG) from the U.S. Geological Survey",
- "catalog": "USGS_LTA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1987-06-19",
"end_date": "",
"bbox": "-126, 24, -66, 49",
@@ -72152,7 +72165,7 @@
{
"id": "Dall_Sheep_Population_Dynamics_1640_1",
"title": "ABoVE: Dall Sheep Lamb Recruitment and Climate Data, Alaska and NW Canada, 2000-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2015-12-31",
"bbox": "-163.28, 59.6, -123.55, 69.71",
@@ -72165,7 +72178,7 @@
{
"id": "Dall_Sheep_Population_Dynamics_1640_1",
"title": "ABoVE: Dall Sheep Lamb Recruitment and Climate Data, Alaska and NW Canada, 2000-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2015-12-31",
"bbox": "-163.28, 59.6, -123.55, 69.71",
@@ -72542,7 +72555,7 @@
{
"id": "Decadal_Water_Maps_1324_1.1",
"title": "ABoVE: Surface Water Extent, Boreal and Tundra Regions, North America, 1991-2011",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1990-01-01",
"end_date": "2012-12-31",
"bbox": "-177.48, 41.7, -53.94, 82.37",
@@ -72555,7 +72568,7 @@
{
"id": "Decadal_Water_Maps_1324_1.1",
"title": "ABoVE: Surface Water Extent, Boreal and Tundra Regions, North America, 1991-2011",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1990-01-01",
"end_date": "2012-12-31",
"bbox": "-177.48, 41.7, -53.94, 82.37",
@@ -73478,7 +73491,7 @@
{
"id": "EANET",
"title": "Acid Deposition Monitoring Network in East Asia Data (EANET)",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -73491,7 +73504,7 @@
{
"id": "EANET",
"title": "Acid Deposition Monitoring Network in East Asia Data (EANET)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -73517,7 +73530,7 @@
{
"id": "EARTH_CRUST_AK_PETROGRAPH_THIN1",
"title": "Alaskan Rocks - Petrographic Thin Sections; USGS, Anchorage",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1891-01-01",
"end_date": "",
"bbox": "-179, 50, -140, 72",
@@ -73530,7 +73543,7 @@
{
"id": "EARTH_CRUST_AK_PETROGRAPH_THIN1",
"title": "Alaskan Rocks - Petrographic Thin Sections; USGS, Anchorage",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1891-01-01",
"end_date": "",
"bbox": "-179, 50, -140, 72",
@@ -73556,7 +73569,7 @@
{
"id": "EARTH_CRUST_USGS_AK_NOTEBOOKS1",
"title": "Alaskan Geologic Field Notebooks; USGS, Anchorage",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1891-01-01",
"end_date": "",
"bbox": "-179, 50, -140, 72",
@@ -73569,7 +73582,7 @@
{
"id": "EARTH_CRUST_USGS_AK_NOTEBOOKS1",
"title": "Alaskan Geologic Field Notebooks; USGS, Anchorage",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1891-01-01",
"end_date": "",
"bbox": "-179, 50, -140, 72",
@@ -73933,7 +73946,7 @@
{
"id": "EARTH_LAND_USGS_ALASKA_FOSSILS1",
"title": "Alaskan Fossil Identification File",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1898-01-01",
"end_date": "",
"bbox": "-180, 53, -130, 74",
@@ -73946,7 +73959,7 @@
{
"id": "EARTH_LAND_USGS_ALASKA_FOSSILS1",
"title": "Alaskan Fossil Identification File",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1898-01-01",
"end_date": "",
"bbox": "-180, 53, -130, 74",
@@ -73985,7 +73998,7 @@
{
"id": "EARTH_LAND_USGS_AMES_AIR_PHOTOS",
"title": "Aerial Photographs (from AMES Pilot Land Data System); USGS EDC, Sioux Falls",
- "catalog": "USGS_LTA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, 20, -60, 50",
@@ -73998,7 +74011,7 @@
{
"id": "EARTH_LAND_USGS_AMES_AIR_PHOTOS",
"title": "Aerial Photographs (from AMES Pilot Land Data System); USGS EDC, Sioux Falls",
- "catalog": "ALL STAC Catalog",
+ "catalog": "USGS_LTA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, 20, -60, 50",
@@ -74102,7 +74115,7 @@
{
"id": "ECA014",
"title": "Air-Water Distribution of POPs Along a North-South Atlantic Transect",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -74115,7 +74128,7 @@
{
"id": "ECA014",
"title": "Air-Water Distribution of POPs Along a North-South Atlantic Transect",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -74167,7 +74180,7 @@
{
"id": "ECA060",
"title": "A 2000-year record of mercury and ancient civilizations in seal hairs from King George Island, West Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1999-02-01",
"end_date": "2002-02-28",
"bbox": "-180, -90, 180, 90",
@@ -74180,7 +74193,7 @@
{
"id": "ECA060",
"title": "A 2000-year record of mercury and ancient civilizations in seal hairs from King George Island, West Antarctica",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1999-02-01",
"end_date": "2002-02-28",
"bbox": "-180, -90, 180, 90",
@@ -77144,7 +77157,7 @@
{
"id": "EOSWEBSTER_CLIMCALC_NE_US",
"title": "A Spatial Model of Atmospheric Deposition For the Northeastern U.S.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-77, 38, -66, 48",
@@ -77157,7 +77170,7 @@
{
"id": "EOSWEBSTER_CLIMCALC_NE_US",
"title": "A Spatial Model of Atmospheric Deposition For the Northeastern U.S.",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-77, 38, -66, 48",
@@ -77209,7 +77222,7 @@
{
"id": "EPA_AQA",
"title": "Air Quality Atlas",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-109.35, 25.19, -88.54, 37.43",
@@ -77222,7 +77235,7 @@
{
"id": "EPA_AQA",
"title": "Air Quality Atlas",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-109.35, 25.19, -88.54, 37.43",
@@ -77976,7 +77989,7 @@
{
"id": "ERS_CONT_500_ANT_1",
"title": "500 metre interval contours of Antarctica derived from ERS radar altimetry data.",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-01",
"end_date": "2003-01-31",
"bbox": "-180, -82, 180, -65",
@@ -77989,7 +78002,7 @@
{
"id": "ERS_CONT_500_ANT_1",
"title": "500 metre interval contours of Antarctica derived from ERS radar altimetry data.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2003-01-01",
"end_date": "2003-01-31",
"bbox": "-180, -82, 180, -65",
@@ -78054,7 +78067,7 @@
{
"id": "ERS_DTM_TIN_ANT_1",
"title": "A digital terrain model of Antarctica in Triangulated Irregular Network (TIN) format, derived from ERS Radar Altimetry.",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-01",
"end_date": "2003-01-31",
"bbox": "-180, -82, 180, -65",
@@ -78067,7 +78080,7 @@
{
"id": "ERS_DTM_TIN_ANT_1",
"title": "A digital terrain model of Antarctica in Triangulated Irregular Network (TIN) format, derived from ERS Radar Altimetry.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2003-01-01",
"end_date": "2003-01-31",
"bbox": "-180, -82, 180, -65",
@@ -78457,7 +78470,7 @@
{
"id": "Ecosystem_Map_SRD_PAD_1947_1",
"title": "ABoVE: Wetland Type, Slave River and Peace-Athabasca Deltas, Canada, 2007 and 2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2006-06-14",
"end_date": "2019-05-28",
"bbox": "-115.29, 57.77, -109.64, 61.79",
@@ -78470,7 +78483,7 @@
{
"id": "Ecosystem_Map_SRD_PAD_1947_1",
"title": "ABoVE: Wetland Type, Slave River and Peace-Athabasca Deltas, Canada, 2007 and 2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2006-06-14",
"end_date": "2019-05-28",
"bbox": "-115.29, 57.77, -109.64, 61.79",
@@ -78756,7 +78769,7 @@
{
"id": "Eurobis_618_1",
"title": "70 samples data of Kiel Bay (EUROBIS)",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1995-05-29",
"end_date": "",
"bbox": "10.3944, 54.3814, 10.3944, 54.3814",
@@ -78769,7 +78782,7 @@
{
"id": "Eurobis_618_1",
"title": "70 samples data of Kiel Bay (EUROBIS)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1995-05-29",
"end_date": "",
"bbox": "10.3944, 54.3814, 10.3944, 54.3814",
@@ -78873,7 +78886,7 @@
{
"id": "FAUNA_PENGUIN_COLONY_1",
"title": "A census of penguin colony counts (provided to OBIS) from the year 1900 to 1996 in the Antarctic Region",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1901-01-01",
"end_date": "1996-12-31",
"bbox": "-180, -80, 180, -45",
@@ -78886,7 +78899,7 @@
{
"id": "FAUNA_PENGUIN_COLONY_1",
"title": "A census of penguin colony counts (provided to OBIS) from the year 1900 to 1996 in the Antarctic Region",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1901-01-01",
"end_date": "1996-12-31",
"bbox": "-180, -80, 180, -45",
@@ -78964,7 +78977,7 @@
{
"id": "FEDMAC_ALPS",
"title": "Airborne Laser Polarization Sensor (ALPS) Experiment During the Forest Ecosystem Dynamics - Multisensor Airborne Campaign",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1990-09-09",
"end_date": "1990-09-11",
"bbox": "-68, 45, -68, 45",
@@ -78977,7 +78990,7 @@
{
"id": "FEDMAC_ALPS",
"title": "Airborne Laser Polarization Sensor (ALPS) Experiment During the Forest Ecosystem Dynamics - Multisensor Airborne Campaign",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1990-09-09",
"end_date": "1990-09-11",
"bbox": "-68, 45, -68, 45",
@@ -81434,7 +81447,7 @@
{
"id": "Fire_Emissions_NWT_1561_1",
"title": "ABoVE: Wildfire Carbon Emissions and Burned Plot Characteristics, NWT, CA, 2014-2016",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2014-07-02",
"end_date": "2016-08-01",
"bbox": "-136.13, 56.25, -102, 71.7",
@@ -81447,7 +81460,7 @@
{
"id": "Fire_Emissions_NWT_1561_1",
"title": "ABoVE: Wildfire Carbon Emissions and Burned Plot Characteristics, NWT, CA, 2014-2016",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2014-07-02",
"end_date": "2016-08-01",
"bbox": "-136.13, 56.25, -102, 71.7",
@@ -81694,7 +81707,7 @@
{
"id": "Frac_FuelComponent_Maps_Tundra_1761_1",
"title": "ABoVE: Distribution Maps of Wildland Fire Fuel Components across Alaskan Tundra, 2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2013-01-01",
"end_date": "2017-12-31",
"bbox": "-170.01, 57.39, -132.49, 72.52",
@@ -81707,7 +81720,7 @@
{
"id": "Frac_FuelComponent_Maps_Tundra_1761_1",
"title": "ABoVE: Distribution Maps of Wildland Fire Fuel Components across Alaskan Tundra, 2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-01-01",
"end_date": "2017-12-31",
"bbox": "-170.01, 57.39, -132.49, 72.52",
@@ -82409,7 +82422,7 @@
{
"id": "GB-NERC-BAS-AEDC-00250",
"title": "AFI 01/27_01 - Dyke intrusions as tracers of continental break-up processes - Rock samples collected in Dronning Maud Land in 2000/2001",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2000-09-01",
"end_date": "2006-12-01",
"bbox": "-5.5, -74, 1, -72",
@@ -82422,7 +82435,7 @@
{
"id": "GB-NERC-BAS-AEDC-00250",
"title": "AFI 01/27_01 - Dyke intrusions as tracers of continental break-up processes - Rock samples collected in Dronning Maud Land in 2000/2001",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-09-01",
"end_date": "2006-12-01",
"bbox": "-5.5, -74, 1, -72",
@@ -82487,7 +82500,7 @@
{
"id": "GB-NERC-BAS-AEDC-00262",
"title": "AFI 02/30_02 - The status of dark septate fungi in Antarctic plant and soil communities - Analysis of fungal cultures, plant and soil samples collected from the northern Antarctic Peninsula region in 2002/2003",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2002-07-01",
"end_date": "2005-06-30",
"bbox": "-68.35, -67.6, -36.48333, -54.28333",
@@ -82500,7 +82513,7 @@
{
"id": "GB-NERC-BAS-AEDC-00262",
"title": "AFI 02/30_02 - The status of dark septate fungi in Antarctic plant and soil communities - Analysis of fungal cultures, plant and soil samples collected from the northern Antarctic Peninsula region in 2002/2003",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-07-01",
"end_date": "2005-06-30",
"bbox": "-68.35, -67.6, -36.48333, -54.28333",
@@ -82513,7 +82526,7 @@
{
"id": "GB-NERC-BAS-AEDC-00272",
"title": "AFI 04/17_02 - Glacial-interglacial changes in the lost drainage basin on the West Antarctic Ice Sheet - Sediment cores collected in the Bellingshausen Sea, 2004",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2004-01-23",
"end_date": "2004-02-13",
"bbox": "-90, -73, -76, -69",
@@ -82526,7 +82539,7 @@
{
"id": "GB-NERC-BAS-AEDC-00272",
"title": "AFI 04/17_02 - Glacial-interglacial changes in the lost drainage basin on the West Antarctic Ice Sheet - Sediment cores collected in the Bellingshausen Sea, 2004",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-01-23",
"end_date": "2004-02-13",
"bbox": "-90, -73, -76, -69",
@@ -82565,7 +82578,7 @@
{
"id": "GB-NERC-BAS-AEDC-00276",
"title": "AFI 02/36_02 - Geochemical Tracing of Pacific-to-Atlantic Mantle Flow through the Drake Passage/Scotia Sea Gateway - Rock samples collected by dredging in the Scotia Sea, February and March 2004",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2004-02-19",
"end_date": "2004-03-03",
"bbox": "-55, -58, -40, -54",
@@ -82578,7 +82591,7 @@
{
"id": "GB-NERC-BAS-AEDC-00276",
"title": "AFI 02/36_02 - Geochemical Tracing of Pacific-to-Atlantic Mantle Flow through the Drake Passage/Scotia Sea Gateway - Rock samples collected by dredging in the Scotia Sea, February and March 2004",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-02-19",
"end_date": "2004-03-03",
"bbox": "-55, -58, -40, -54",
@@ -82643,7 +82656,7 @@
{
"id": "GB-NERC-BAS-AEDC-00279",
"title": "AFI 02/36_04 - Geochemical Tracing of Pacific-to-Atlantic Mantle Flow through the Drake Passage/Scotia Sea Gateway - Geochemical analysis of rock samples collected by dredging in the Scotia Sea, February and March 2004",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-10-01",
"end_date": "2005-09-30",
"bbox": "-55, -58, -40, -54",
@@ -82656,7 +82669,7 @@
{
"id": "GB-NERC-BAS-AEDC-00279",
"title": "AFI 02/36_04 - Geochemical Tracing of Pacific-to-Atlantic Mantle Flow through the Drake Passage/Scotia Sea Gateway - Geochemical analysis of rock samples collected by dredging in the Scotia Sea, February and March 2004",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2001-10-01",
"end_date": "2005-09-30",
"bbox": "-55, -58, -40, -54",
@@ -82695,7 +82708,7 @@
{
"id": "GB-NERC-BAS-AEDC-00289",
"title": "AFI 02/48_02 - Ice-rafted debris on the Antarctic continental margin and dynamics of the Antarctic Ice Sheet - Vibro gravity cores, and sediments data collected from the Weddell Sea, Marguerite Bay, Feb - March 2002",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2002-02-01",
"end_date": "2002-03-01",
"bbox": "-72, -68.5, -69, -66",
@@ -82708,7 +82721,7 @@
{
"id": "GB-NERC-BAS-AEDC-00289",
"title": "AFI 02/48_02 - Ice-rafted debris on the Antarctic continental margin and dynamics of the Antarctic Ice Sheet - Vibro gravity cores, and sediments data collected from the Weddell Sea, Marguerite Bay, Feb - March 2002",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-02-01",
"end_date": "2002-03-01",
"bbox": "-72, -68.5, -69, -66",
@@ -82747,7 +82760,7 @@
{
"id": "GB-NERC-BAS-AEDC-00293",
"title": "AFI 04/09_01 - Improving ice-core interpretation - AWS data, Rothschild, Latady and Smyley Islands, 2005",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2005-01-08",
"end_date": "2006-02-11",
"bbox": "-79, -73, -72.5, -69.5",
@@ -82760,7 +82773,7 @@
{
"id": "GB-NERC-BAS-AEDC-00293",
"title": "AFI 04/09_01 - Improving ice-core interpretation - AWS data, Rothschild, Latady and Smyley Islands, 2005",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2005-01-08",
"end_date": "2006-02-11",
"bbox": "-79, -73, -72.5, -69.5",
@@ -82877,7 +82890,7 @@
{
"id": "GB-NERC-BAS-AEDC-00342",
"title": "AFI 07/02_01 - Subglacial Lake Ellsworth - SEISMIC data, Antarctica, 2007/08",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2007-11-09",
"end_date": "2008-02-03",
"bbox": "-91.01667, -79.86667, -89.21667, -79.25",
@@ -82890,7 +82903,7 @@
{
"id": "GB-NERC-BAS-AEDC-00342",
"title": "AFI 07/02_01 - Subglacial Lake Ellsworth - SEISMIC data, Antarctica, 2007/08",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-11-09",
"end_date": "2008-02-03",
"bbox": "-91.01667, -79.86667, -89.21667, -79.25",
@@ -82903,7 +82916,7 @@
{
"id": "GB-NERC-BAS-AEDC-00343",
"title": "AFI 07/02_02 Subglacial Lake Ellsworth - GPS data, Antarctica, 2007/08",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2007-11-09",
"end_date": "",
"bbox": "-91.01667, -79.86667, -89.21667, -79.25",
@@ -82916,7 +82929,7 @@
{
"id": "GB-NERC-BAS-AEDC-00343",
"title": "AFI 07/02_02 Subglacial Lake Ellsworth - GPS data, Antarctica, 2007/08",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-11-09",
"end_date": "",
"bbox": "-91.01667, -79.86667, -89.21667, -79.25",
@@ -82981,7 +82994,7 @@
{
"id": "GB-NERC-BAS-AEDC-00348",
"title": "AFI 07/02_05 - Subglacial Lake Ellsworth - ICE CORE samples, Antarctica, 2007/08",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-11-09",
"end_date": "2008-02-03",
"bbox": "-91.01667, -79.86667, -89.21667, -79.25",
@@ -82994,7 +83007,7 @@
{
"id": "GB-NERC-BAS-AEDC-00348",
"title": "AFI 07/02_05 - Subglacial Lake Ellsworth - ICE CORE samples, Antarctica, 2007/08",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2007-11-09",
"end_date": "2008-02-03",
"bbox": "-91.01667, -79.86667, -89.21667, -79.25",
@@ -83137,7 +83150,7 @@
{
"id": "GB-NERC-BAS-AEDC-00368",
"title": "AFI 01/05_03 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - GPS data, 2004/06",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-11-18",
"end_date": "2006-02-28",
"bbox": "-85, -78.25, -82, -77.75",
@@ -83150,7 +83163,7 @@
{
"id": "GB-NERC-BAS-AEDC-00368",
"title": "AFI 01/05_03 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - GPS data, 2004/06",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2004-11-18",
"end_date": "2006-02-28",
"bbox": "-85, -78.25, -82, -77.75",
@@ -83189,7 +83202,7 @@
{
"id": "GB-NERC-BAS-AEDC-00371",
"title": "AFI 01/05_05 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Ice core samples, 2004/06",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2005-01-24",
"end_date": "2005-01-26",
"bbox": "-83.9, -78.14, -83.9, -78.14",
@@ -83202,7 +83215,7 @@
{
"id": "GB-NERC-BAS-AEDC-00371",
"title": "AFI 01/05_05 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Ice core samples, 2004/06",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2005-01-24",
"end_date": "2005-01-26",
"bbox": "-83.9, -78.14, -83.9, -78.14",
@@ -83215,7 +83228,7 @@
{
"id": "GB-NERC-BAS-AEDC-00373",
"title": "AFI 01/05_06 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Radar data, 2004/06",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2005-01-21",
"end_date": "2005-02-13",
"bbox": "-85, -78.25, -83, -78",
@@ -83228,7 +83241,7 @@
{
"id": "GB-NERC-BAS-AEDC-00373",
"title": "AFI 01/05_06 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Radar data, 2004/06",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2005-01-21",
"end_date": "2005-02-13",
"bbox": "-85, -78.25, -83, -78",
@@ -83241,7 +83254,7 @@
{
"id": "GB-NERC-BAS-AEDC-00374",
"title": "AFI 01/05_07 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Weather data, 2004/06",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2004-12-30",
"end_date": "2005-02-20",
"bbox": "-83.9, -78.14, -83.9, -78.14",
@@ -83254,7 +83267,7 @@
{
"id": "GB-NERC-BAS-AEDC-00374",
"title": "AFI 01/05_07 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Weather data, 2004/06",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-12-30",
"end_date": "2005-02-20",
"bbox": "-83.9, -78.14, -83.9, -78.14",
@@ -83293,7 +83306,7 @@
{
"id": "GB-NERC-BAS-AEDC-00400",
"title": "AFI 02/37_01 - Identifying terranes in the Antarctic Peninsula using primitive basalt dykes as lithospheric probes - Rock samples collected from Palmer Land and Graham Land in the 2001/2002 field season.",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-11-01",
"end_date": "2002-02-28",
"bbox": "-65, -73, -63, -65",
@@ -83306,7 +83319,7 @@
{
"id": "GB-NERC-BAS-AEDC-00400",
"title": "AFI 02/37_01 - Identifying terranes in the Antarctic Peninsula using primitive basalt dykes as lithospheric probes - Rock samples collected from Palmer Land and Graham Land in the 2001/2002 field season.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2001-11-01",
"end_date": "2002-02-28",
"bbox": "-65, -73, -63, -65",
@@ -83319,7 +83332,7 @@
{
"id": "GB-NERC-BAS-AEDC-00401",
"title": "AFI 02/37_02 - Identifying terranes in the Antarctic Peninsula using primitive basalt dykes as lithospheric probes - Geochemical and petrographic analysis of rock samples, 2001/02",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2001-11-01",
"end_date": "2002-02-28",
"bbox": "-65, -73, -63, -65",
@@ -83332,7 +83345,7 @@
{
"id": "GB-NERC-BAS-AEDC-00401",
"title": "AFI 02/37_02 - Identifying terranes in the Antarctic Peninsula using primitive basalt dykes as lithospheric probes - Geochemical and petrographic analysis of rock samples, 2001/02",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-11-01",
"end_date": "2002-02-28",
"bbox": "-65, -73, -63, -65",
@@ -83345,7 +83358,7 @@
{
"id": "GB-NERC-BAS-AEDC-00423",
"title": "AFI 01/07_02 - Observations of Antarctic Precipitation processes - Ice Nuclei & Meteorological Data, Mount Rex, Antarctica Jan-Feb 2002",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-01-17",
"end_date": "2002-02-17",
"bbox": "75, -74.63, 75, -74.63",
@@ -83358,7 +83371,7 @@
{
"id": "GB-NERC-BAS-AEDC-00423",
"title": "AFI 01/07_02 - Observations of Antarctic Precipitation processes - Ice Nuclei & Meteorological Data, Mount Rex, Antarctica Jan-Feb 2002",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2002-01-17",
"end_date": "2002-02-17",
"bbox": "75, -74.63, 75, -74.63",
@@ -83371,7 +83384,7 @@
{
"id": "GB-NERC-BAS-PDC-00499",
"title": "ACES-FOCAS: Forcings from the Ocean, Clouds, Atmosphere and Sea-ice",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2008-01-01",
"end_date": "2008-02-28",
"bbox": "-180, -90, 180, 90",
@@ -83384,7 +83397,7 @@
{
"id": "GB-NERC-BAS-PDC-00499",
"title": "ACES-FOCAS: Forcings from the Ocean, Clouds, Atmosphere and Sea-ice",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2008-01-01",
"end_date": "2008-02-28",
"bbox": "-180, -90, 180, 90",
@@ -83397,7 +83410,7 @@
{
"id": "GB-NERC-BAS-PDC-00500",
"title": "ACES-ACCENT: Antarctic Climate Change and Nonlinear Teleconnections",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -83410,7 +83423,7 @@
{
"id": "GB-NERC-BAS-PDC-00500",
"title": "ACES-ACCENT: Antarctic Climate Change and Nonlinear Teleconnections",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -92497,7 +92510,7 @@
{
"id": "GGD239_1",
"title": "Active layer physical processes at Broeggerhalvoya, western Spitsbergen, Version 1",
- "catalog": "NSIDCV0 STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1985-07-01",
"end_date": "1986-06-30",
"bbox": "12.462, 78.958, 12.462, 78.958",
@@ -92510,7 +92523,7 @@
{
"id": "GGD239_1",
"title": "Active layer physical processes at Broeggerhalvoya, western Spitsbergen, Version 1",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NSIDCV0 STAC Catalog",
"state_date": "1985-07-01",
"end_date": "1986-06-30",
"bbox": "12.462, 78.958, 12.462, 78.958",
@@ -92549,7 +92562,7 @@
{
"id": "GGD249_1",
"title": "Active layer thickness and ground temperatures, Svea, Svalbard, Version 1",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NSIDCV0 STAC Catalog",
"state_date": "1987-07-01",
"end_date": "1996-05-31",
"bbox": "16.683, 77.9, 16.683, 77.9",
@@ -92562,7 +92575,7 @@
{
"id": "GGD249_1",
"title": "Active layer thickness and ground temperatures, Svea, Svalbard, Version 1",
- "catalog": "NSIDCV0 STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1987-07-01",
"end_date": "1996-05-31",
"bbox": "16.683, 77.9, 16.683, 77.9",
@@ -92731,7 +92744,7 @@
{
"id": "GGD622_1",
"title": "Active-Layer Depth of a Finnish Palsa Bog, Version 1",
- "catalog": "NSIDCV0 STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1993-09-08",
"end_date": "2002-10-14",
"bbox": "27.17, 69.82, 27.17, 69.82",
@@ -92744,7 +92757,7 @@
{
"id": "GGD622_1",
"title": "Active-Layer Depth of a Finnish Palsa Bog, Version 1",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NSIDCV0 STAC Catalog",
"state_date": "1993-09-08",
"end_date": "2002-10-14",
"bbox": "27.17, 69.82, 27.17, 69.82",
@@ -93030,26 +93043,26 @@
{
"id": "GLAH04_033",
"title": "GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH04_033",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH04_033",
"description": "Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.",
"license": "proprietary"
},
{
"id": "GLAH04_033",
"title": "GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH04_033",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH04_033",
"description": "Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing.",
"license": "proprietary"
},
@@ -93108,26 +93121,26 @@
{
"id": "GLAH07_033",
"title": "GLAS/ICESat L1B Global Backscatter Data (HDF5) V033",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH07_033",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH07_033",
"description": "GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product.",
"license": "proprietary"
},
{
"id": "GLAH07_033",
"title": "GLAS/ICESat L1B Global Backscatter Data (HDF5) V033",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH07_033",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH07_033",
"description": "GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product.",
"license": "proprietary"
},
@@ -93160,26 +93173,26 @@
{
"id": "GLAH09_033",
"title": "GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH09_033",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH09_033",
"description": "GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product.",
"license": "proprietary"
},
{
"id": "GLAH09_033",
"title": "GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH09_033",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/GLAH09_033",
"description": "GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product.",
"license": "proprietary"
},
@@ -93235,19 +93248,6 @@
"description": "GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product.",
"license": "proprietary"
},
- {
- "id": "GLAH12_034",
- "title": "GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034",
- "catalog": "NSIDC_ECS STAC Catalog",
- "state_date": "2003-02-20",
- "end_date": "2009-10-11",
- "bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH12_034",
- "description": "GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (\u00b1 50\u00b0 latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.",
- "license": "proprietary"
- },
{
"id": "GLAH12_034",
"title": "GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034",
@@ -93262,15 +93262,15 @@
"license": "proprietary"
},
{
- "id": "GLAH13_034",
- "title": "GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034",
+ "id": "GLAH12_034",
+ "title": "GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034",
"catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000464-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000464-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH13_034",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH12_034",
"description": "GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (\u00b1 50\u00b0 latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.",
"license": "proprietary"
},
@@ -93288,15 +93288,15 @@
"license": "proprietary"
},
{
- "id": "GLAH14_034",
- "title": "GLAS/ICESat L2 Global Land Surface Altimetry Data (HDF5) V034",
+ "id": "GLAH13_034",
+ "title": "GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034",
"catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2003-02-20",
"end_date": "2009-10-11",
"bbox": "-180, -86, 180, 86",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000443-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000443-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH14_034",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000464-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000464-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH13_034",
"description": "GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (\u00b1 50\u00b0 latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.",
"license": "proprietary"
},
@@ -93313,6 +93313,19 @@
"description": "GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (\u00b1 50\u00b0 latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.",
"license": "proprietary"
},
+ {
+ "id": "GLAH14_034",
+ "title": "GLAS/ICESat L2 Global Land Surface Altimetry Data (HDF5) V034",
+ "catalog": "NSIDC_ECS STAC Catalog",
+ "state_date": "2003-02-20",
+ "end_date": "2009-10-11",
+ "bbox": "-180, -86, 180, 86",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000443-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000443-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/GLAH14_034",
+ "description": "GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (\u00b1 50\u00b0 latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product.",
+ "license": "proprietary"
+ },
{
"id": "GLAH15_034",
"title": "GLAS/ICESat L2 Ocean Altimetry Data (HDF5) V034",
@@ -94187,7 +94200,7 @@
{
"id": "GNVd0188_104",
"title": "30 arc-second DEM for Africa",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-07-23",
"end_date": "1996-07-23",
"bbox": "-20, -35, 60, 40",
@@ -94200,7 +94213,7 @@
{
"id": "GNVd0188_104",
"title": "30 arc-second DEM for Africa",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1996-07-23",
"end_date": "1996-07-23",
"bbox": "-20, -35, 60, 40",
@@ -94239,7 +94252,7 @@
{
"id": "GNVd0190_104",
"title": "30 arc-second DEM for Europe",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1995-09-22",
"end_date": "1995-09-22",
"bbox": "-25, 35, 22, 85",
@@ -94252,7 +94265,7 @@
{
"id": "GNVd0190_104",
"title": "30 arc-second DEM for Europe",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1995-09-22",
"end_date": "1995-09-22",
"bbox": "-25, 35, 22, 85",
@@ -97619,7 +97632,7 @@
{
"id": "GPP_MODIS_Alaska_Canada_2024_1",
"title": "ABoVE: Light-Curve Modelling of Gridded GPP Using MODIS MAIAC and Flux Tower Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2018-01-01",
"bbox": "-172.08, 50.06, -73.64, 79.75",
@@ -97632,7 +97645,7 @@
{
"id": "GPP_MODIS_Alaska_Canada_2024_1",
"title": "ABoVE: Light-Curve Modelling of Gridded GPP Using MODIS MAIAC and Flux Tower Data",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2018-01-01",
"bbox": "-172.08, 50.06, -73.64, 79.75",
@@ -98165,7 +98178,7 @@
{
"id": "GSI_ABSOLUT_GRAVITY_ANT",
"title": "Absolute gravity measurement",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1992-01-01",
"end_date": "",
"bbox": "39.5, -69, 39.5, -69",
@@ -98178,7 +98191,7 @@
{
"id": "GSI_ABSOLUT_GRAVITY_ANT",
"title": "Absolute gravity measurement",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1992-01-01",
"end_date": "",
"bbox": "39.5, -69, 39.5, -69",
@@ -99179,7 +99192,7 @@
{
"id": "Global_Microbial_Biomass_C_N_P_1264_1",
"title": "A Compilation of Global Soil Microbial Biomass Carbon, Nitrogen, and Phosphorus Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1977-11-16",
"end_date": "2012-06-01",
"bbox": "-180, -90, 177.9, 79",
@@ -99192,7 +99205,7 @@
{
"id": "Global_Microbial_Biomass_C_N_P_1264_1",
"title": "A Compilation of Global Soil Microbial Biomass Carbon, Nitrogen, and Phosphorus Data",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1977-11-16",
"end_date": "2012-06-01",
"bbox": "-180, -90, 177.9, 79",
@@ -99231,7 +99244,7 @@
{
"id": "Global_Phosphorus_Hedley_Fract_1230_1",
"title": "A Global Database of Soil Phosphorus Compiled from Studies Using Hedley Fractionation",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1985-01-01",
"end_date": "2010-12-31",
"bbox": "-117.86, -42.5, 117.6, 63.23",
@@ -99244,7 +99257,7 @@
{
"id": "Global_Phosphorus_Hedley_Fract_1230_1",
"title": "A Global Database of Soil Phosphorus Compiled from Studies Using Hedley Fractionation",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1985-01-01",
"end_date": "2010-12-31",
"bbox": "-117.86, -42.5, 117.6, 63.23",
@@ -99257,7 +99270,7 @@
{
"id": "Global_RTSG_Flux_1078_1",
"title": "A Global Database of Gas Fluxes from Soils after Rewetting or Thawing, Version 1.0",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1956-01-01",
"end_date": "2009-12-31",
"bbox": "-149.63, -36.45, 160.52, 74.5",
@@ -99270,7 +99283,7 @@
{
"id": "Global_RTSG_Flux_1078_1",
"title": "A Global Database of Gas Fluxes from Soils after Rewetting or Thawing, Version 1.0",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1956-01-01",
"end_date": "2009-12-31",
"bbox": "-149.63, -36.45, 160.52, 74.5",
@@ -99361,7 +99374,7 @@
{
"id": "Globalsoil_ESM",
"title": "A Global Soil Dataset for Earth System Modeling",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -99374,7 +99387,7 @@
{
"id": "Globalsoil_ESM",
"title": "A Global Soil Dataset for Earth System Modeling",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -99595,7 +99608,7 @@
{
"id": "Great_Slave_Lake_Ecosystem_Map_1695_1",
"title": "ABoVE: Ecosystem Map, Great Slave Lake Area, Northwest Territories, Canada, 1997-2011",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-09-25",
"end_date": "2011-09-14",
"bbox": "-123.04, 58.51, -109.46, 65.15",
@@ -99608,7 +99621,7 @@
{
"id": "Great_Slave_Lake_Ecosystem_Map_1695_1",
"title": "ABoVE: Ecosystem Map, Great Slave Lake Area, Northwest Territories, Canada, 1997-2011",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1997-09-25",
"end_date": "2011-09-14",
"bbox": "-123.04, 58.51, -109.46, 65.15",
@@ -99972,7 +99985,7 @@
{
"id": "HALO_LiDAR_AOP_ML_Heights_1833_1",
"title": "ACT-America: HALO Lidar Measurements of AOP and ML Heights, 2019",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2019-06-17",
"end_date": "2019-07-28",
"bbox": "-102, 28, -73, 50",
@@ -99985,7 +99998,7 @@
{
"id": "HALO_LiDAR_AOP_ML_Heights_1833_1",
"title": "ACT-America: HALO Lidar Measurements of AOP and ML Heights, 2019",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-06-17",
"end_date": "2019-07-28",
"bbox": "-102, 28, -73, 50",
@@ -102364,7 +102377,7 @@
{
"id": "ICRAF_AfSIS_AfrHySRTM",
"title": "Africa Soil Information Service (AfSIS): Hydrologically Corrected/Adjusted SRTM DEM (AfrHySRTM)",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-17.535833, -34.83917, 51.413334, 37.345833",
@@ -102377,7 +102390,7 @@
{
"id": "ICRAF_AfSIS_AfrHySRTM",
"title": "Africa Soil Information Service (AfSIS): Hydrologically Corrected/Adjusted SRTM DEM (AfrHySRTM)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-17.535833, -34.83917, 51.413334, 37.345833",
@@ -102416,7 +102429,7 @@
{
"id": "ICRAF_AfSIS_TWI",
"title": "Africa Soil Information Service (AfSIS): Topographic Wetness Index (TWI)",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-17.535833, -34.83917, 51.413334, 37.345833",
@@ -102429,7 +102442,7 @@
{
"id": "ICRAF_AfSIS_TWI",
"title": "Africa Soil Information Service (AfSIS): Topographic Wetness Index (TWI)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-17.535833, -34.83917, 51.413334, 37.345833",
@@ -103040,7 +103053,7 @@
{
"id": "IMERG_Precip_Canada_Alaska_2097_1",
"title": "ABoVE: Bias-Corrected IMERG Monthly Precipitation for Alaska and Canada, 2000-2020",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2000-06-01",
"end_date": "2020-12-31",
"bbox": "-179.3, 40.8, -48.5, 72",
@@ -103053,7 +103066,7 @@
{
"id": "IMERG_Precip_Canada_Alaska_2097_1",
"title": "ABoVE: Bias-Corrected IMERG Monthly Precipitation for Alaska and Canada, 2000-2020",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-06-01",
"end_date": "2020-12-31",
"bbox": "-179.3, 40.8, -48.5, 72",
@@ -103105,7 +103118,7 @@
{
"id": "INC_NCMF",
"title": "A Nature Characterization Map of Flanders",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-5.29, 40.65, 10.4, 51.82",
@@ -103118,7 +103131,7 @@
{
"id": "INC_NCMF",
"title": "A Nature Characterization Map of Flanders",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-5.29, 40.65, 10.4, 51.82",
@@ -104353,7 +104366,7 @@
{
"id": "ISPOL2004_AAD_BuoyData_1",
"title": "AAD buoy data collected during ISPOL 2004, Western Weddell Sea",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-11-28",
"end_date": "2005-01-01",
"bbox": "-54.7666, -68.1666, -54.7666, -68.1666",
@@ -104366,7 +104379,7 @@
{
"id": "ISPOL2004_AAD_BuoyData_1",
"title": "AAD buoy data collected during ISPOL 2004, Western Weddell Sea",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2004-11-28",
"end_date": "2005-01-01",
"bbox": "-54.7666, -68.1666, -54.7666, -68.1666",
@@ -104626,7 +104639,7 @@
{
"id": "Interior_Alaska_Subsistence_1725_1",
"title": "ABoVE: Subsistence Resource Use Areas of Interior Alaskan Communities, 2011-2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-01-01",
"end_date": "2017-12-31",
"bbox": "-176.65, 51.71, -131.52, 70.15",
@@ -104639,7 +104652,7 @@
{
"id": "Interior_Alaska_Subsistence_1725_1",
"title": "ABoVE: Subsistence Resource Use Areas of Interior Alaskan Communities, 2011-2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2011-01-01",
"end_date": "2017-12-31",
"bbox": "-176.65, 51.71, -131.52, 70.15",
@@ -104665,7 +104678,7 @@
{
"id": "InundationMap_YkFlats_PeaceAth_1901_1",
"title": "ABoVE: Wetland Inundation Coverage at Yukon Flats, AK and PA Delta, Canada, 2017-2019",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2017-05-21",
"end_date": "2019-10-26",
"bbox": "-146.43, 58.25, -110.92, 66.81",
@@ -104678,7 +104691,7 @@
{
"id": "InundationMap_YkFlats_PeaceAth_1901_1",
"title": "ABoVE: Wetland Inundation Coverage at Yukon Flats, AK and PA Delta, Canada, 2017-2019",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-05-21",
"end_date": "2019-10-26",
"bbox": "-146.43, 58.25, -110.92, 66.81",
@@ -105328,7 +105341,7 @@
{
"id": "JCADM_USA_PENGUINS",
"title": "Adelie Penguin ecology",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1995-12-25",
"end_date": "2001-01-20",
"bbox": "166.17, -77.58, 169.25, -76.92",
@@ -105341,7 +105354,7 @@
{
"id": "JCADM_USA_PENGUINS",
"title": "Adelie Penguin ecology",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1995-12-25",
"end_date": "2001-01-20",
"bbox": "166.17, -77.58, 169.25, -76.92",
@@ -105536,7 +105549,7 @@
{
"id": "JGOFS_EQPAC_CYANOBACT_NANOPLANK",
"title": "Abundance, Biovolume and Biomass of Cyanobacteria and Eukaryotic Pico- and Nanoplankton Measured during the JGOFS Equatorial Pacific Process Study",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1992-02-03",
"end_date": "1992-10-21",
"bbox": "-140, -17, -140, 12",
@@ -105549,7 +105562,7 @@
{
"id": "JGOFS_EQPAC_CYANOBACT_NANOPLANK",
"title": "Abundance, Biovolume and Biomass of Cyanobacteria and Eukaryotic Pico- and Nanoplankton Measured during the JGOFS Equatorial Pacific Process Study",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1992-02-03",
"end_date": "1992-10-21",
"bbox": "-140, -17, -140, 12",
@@ -105562,7 +105575,7 @@
{
"id": "JGOFS_EQPAC_DINOFLAG",
"title": "Abundance, Biovolume and Biomass of Heterotrophic Dinoflagellates Measured during the JGOFS Equatorial Pacific Process Study",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1992-02-03",
"end_date": "1992-10-21",
"bbox": "-140, -17, -140, 12",
@@ -105575,7 +105588,7 @@
{
"id": "JGOFS_EQPAC_DINOFLAG",
"title": "Abundance, Biovolume and Biomass of Heterotrophic Dinoflagellates Measured during the JGOFS Equatorial Pacific Process Study",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1992-02-03",
"end_date": "1992-10-21",
"bbox": "-140, -17, -140, 12",
@@ -105770,7 +105783,7 @@
{
"id": "K009_1975_1976_NZ_2",
"title": "A survey of the Miers and Marshall Valley and Walcott Bay area for dating the formation of the major landforms",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1975-12-09",
"end_date": "1977-01-06",
"bbox": "161.6666, -77.5166, 161.6666, -77.5166",
@@ -105783,7 +105796,7 @@
{
"id": "K009_1975_1976_NZ_2",
"title": "A survey of the Miers and Marshall Valley and Walcott Bay area for dating the formation of the major landforms",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1975-12-09",
"end_date": "1977-01-06",
"bbox": "161.6666, -77.5166, 161.6666, -77.5166",
@@ -105796,7 +105809,7 @@
{
"id": "K009_1979_1980_NZ_1",
"title": "A study of the glacial history of the McMurdo Oasis by the dating of lacustre carbonates",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1979-12-10",
"end_date": "1980-01-15",
"bbox": "163.1833, -77.6166, 163.1833, -77.6166",
@@ -105809,7 +105822,7 @@
{
"id": "K009_1979_1980_NZ_1",
"title": "A study of the glacial history of the McMurdo Oasis by the dating of lacustre carbonates",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1979-12-10",
"end_date": "1980-01-15",
"bbox": "163.1833, -77.6166, 163.1833, -77.6166",
@@ -105822,7 +105835,7 @@
{
"id": "K012_1978_1980_NZ_1",
"title": "A series of experiments to characterize the neuromuscular transmission in Antarctic fishes (Pagothenia borchgrevinki) and the effects of temperature on these reactions",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1978-11-08",
"end_date": "1979-12-06",
"bbox": "166.75, -77.85, 166.75, -77.85",
@@ -105835,7 +105848,7 @@
{
"id": "K012_1978_1980_NZ_1",
"title": "A series of experiments to characterize the neuromuscular transmission in Antarctic fishes (Pagothenia borchgrevinki) and the effects of temperature on these reactions",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1978-11-08",
"end_date": "1979-12-06",
"bbox": "166.75, -77.85, 166.75, -77.85",
@@ -105848,7 +105861,7 @@
{
"id": "K014_1969_1970_NZ_1",
"title": "A feasibility study of marine investigations at Cape Bird: Plankton sampling, water temperature, conductivity and chlorophyll content",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1969-10-01",
"end_date": "1970-02-15",
"bbox": "166.6833, -77.1667, 166.6833, -77.1667",
@@ -105861,7 +105874,7 @@
{
"id": "K014_1969_1970_NZ_1",
"title": "A feasibility study of marine investigations at Cape Bird: Plankton sampling, water temperature, conductivity and chlorophyll content",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1969-10-01",
"end_date": "1970-02-15",
"bbox": "166.6833, -77.1667, 166.6833, -77.1667",
@@ -105952,7 +105965,7 @@
{
"id": "K014_1982_1983_NZ_1",
"title": "Adelie penguin and skua census and analysis of stomach contents of adelie penguins from Cape Hallett",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1983-01-17",
"end_date": "1983-01-22",
"bbox": "170.2667, -72.3167, 170.2667, -72.3167",
@@ -105965,7 +105978,7 @@
{
"id": "K014_1982_1983_NZ_1",
"title": "Adelie penguin and skua census and analysis of stomach contents of adelie penguins from Cape Hallett",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1983-01-17",
"end_date": "1983-01-22",
"bbox": "170.2667, -72.3167, 170.2667, -72.3167",
@@ -105978,7 +105991,7 @@
{
"id": "K014_1982_1983_NZ_3",
"title": "A distribution of vegetation survey and an environmental assessment carried out to identify any damage caused by previous occupation of the area by man at Cape Hallett's Specially Protected Area No. 7",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1983-01-01",
"end_date": "1983-02-28",
"bbox": "170.2667, -72.3167, 170.2667, -72.3167",
@@ -105991,7 +106004,7 @@
{
"id": "K014_1982_1983_NZ_3",
"title": "A distribution of vegetation survey and an environmental assessment carried out to identify any damage caused by previous occupation of the area by man at Cape Hallett's Specially Protected Area No. 7",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1983-01-01",
"end_date": "1983-02-28",
"bbox": "170.2667, -72.3167, 170.2667, -72.3167",
@@ -106030,7 +106043,7 @@
{
"id": "K017_1967_1968_NZ_2",
"title": "A study on the siting, establishment and maintenance of territories in the South Polar Skua (Catharacta maccormicki)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1967-11-10",
"end_date": "1968-02-15",
"bbox": "166.6833, -77.1667, 166.6833, -77.1667",
@@ -106043,7 +106056,7 @@
{
"id": "K017_1967_1968_NZ_2",
"title": "A study on the siting, establishment and maintenance of territories in the South Polar Skua (Catharacta maccormicki)",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1967-11-10",
"end_date": "1968-02-15",
"bbox": "166.6833, -77.1667, 166.6833, -77.1667",
@@ -106056,7 +106069,7 @@
{
"id": "K022_1977_1978_NZ_1",
"title": "A biological reconnaissance of the photoreceptors of invertebrates and fish from the Ross Sea, identifying the micro fauna and flora of Dry Valley lakes and other organism from the Ross Sea region",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1977-11-22",
"end_date": "1978-01-13",
"bbox": "160, -78.75, 168, -77",
@@ -106069,7 +106082,7 @@
{
"id": "K022_1977_1978_NZ_1",
"title": "A biological reconnaissance of the photoreceptors of invertebrates and fish from the Ross Sea, identifying the micro fauna and flora of Dry Valley lakes and other organism from the Ross Sea region",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1977-11-22",
"end_date": "1978-01-13",
"bbox": "160, -78.75, 168, -77",
@@ -106108,7 +106121,7 @@
{
"id": "K029_1999_2000_NZ_1",
"title": "A molecular analysis of penguin and chewing lice coevolution from Adelie (Pygoscelis adeliae) and Emperor (Aptenodytes forsteri) penguins",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1999-11-08",
"end_date": "1999-11-18",
"bbox": "166.1655, -77.5555, 169.2705, -77.4541",
@@ -106121,7 +106134,7 @@
{
"id": "K029_1999_2000_NZ_1",
"title": "A molecular analysis of penguin and chewing lice coevolution from Adelie (Pygoscelis adeliae) and Emperor (Aptenodytes forsteri) penguins",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1999-11-08",
"end_date": "1999-11-18",
"bbox": "166.1655, -77.5555, 169.2705, -77.4541",
@@ -106212,7 +106225,7 @@
{
"id": "K042_1980_1981_NZ_1",
"title": "A seismic refraction survey on sea ice near Butter Point, New Harbour, McMurdo Sound",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1980-11-26",
"end_date": "1980-12-03",
"bbox": "164.12, -77.39, 164.12, -77.39",
@@ -106225,7 +106238,7 @@
{
"id": "K042_1980_1981_NZ_1",
"title": "A seismic refraction survey on sea ice near Butter Point, New Harbour, McMurdo Sound",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1980-11-26",
"end_date": "1980-12-03",
"bbox": "164.12, -77.39, 164.12, -77.39",
@@ -106342,7 +106355,7 @@
{
"id": "K043_2006_2008_NZ_2",
"title": "Algal response to transplantation with a ice core flipping experiment, Terra Nova Bay, Ross Sea",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2006-11-03",
"end_date": "2006-12-09",
"bbox": "164.5, -74.8333, 164.5, -74.8333",
@@ -106355,7 +106368,7 @@
{
"id": "K043_2006_2008_NZ_2",
"title": "Algal response to transplantation with a ice core flipping experiment, Terra Nova Bay, Ross Sea",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2006-11-03",
"end_date": "2006-12-09",
"bbox": "164.5, -74.8333, 164.5, -74.8333",
@@ -106368,7 +106381,7 @@
{
"id": "K048_1992_1993_NZ_1",
"title": "A collection of lithospheric xenoliths from the Executive Committee Range and Mt Murphy Volcanic Complex in West Antarctica and the McMurdo Volcanic Province in McMurdo Sound",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1992-11-14",
"end_date": "1992-12-01",
"bbox": "-166, -78.4, -166.41667, -75.3667",
@@ -106381,7 +106394,7 @@
{
"id": "K048_1992_1993_NZ_1",
"title": "A collection of lithospheric xenoliths from the Executive Committee Range and Mt Murphy Volcanic Complex in West Antarctica and the McMurdo Volcanic Province in McMurdo Sound",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1992-11-14",
"end_date": "1992-12-01",
"bbox": "-166, -78.4, -166.41667, -75.3667",
@@ -106446,7 +106459,7 @@
{
"id": "K053_1990_1991_NZ_2",
"title": "Algae cultures from air trap samples, snow samples and algal surveys from Scott Base, the Ross Ice Shelf and Victoria Valley to determine the dispersal of algae by wind within Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1990-12-19",
"end_date": "1991-01-28",
"bbox": "161.5, -77.85, 166.75, -77.25",
@@ -106459,7 +106472,7 @@
{
"id": "K053_1990_1991_NZ_2",
"title": "Algae cultures from air trap samples, snow samples and algal surveys from Scott Base, the Ross Ice Shelf and Victoria Valley to determine the dispersal of algae by wind within Antarctica",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1990-12-19",
"end_date": "1991-01-28",
"bbox": "161.5, -77.85, 166.75, -77.25",
@@ -106498,7 +106511,7 @@
{
"id": "K054_1988_1989_NZ_3",
"title": "A survey of the density of starfish and sea urchins to determine the grazing pressure of these species on a sponge dominated reef, Cape Armitage",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1988-10-14",
"end_date": "1988-11-24",
"bbox": "166.6667, -77.85, 166.6667, -77.85",
@@ -106511,7 +106524,7 @@
{
"id": "K054_1988_1989_NZ_3",
"title": "A survey of the density of starfish and sea urchins to determine the grazing pressure of these species on a sponge dominated reef, Cape Armitage",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1988-10-14",
"end_date": "1988-11-24",
"bbox": "166.6667, -77.85, 166.6667, -77.85",
@@ -106524,7 +106537,7 @@
{
"id": "K057_1999_2000_NZ_2",
"title": "A partitioning experiments to determine the aetiology of x-cell disease",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1999-11-01",
"end_date": "1999-12-30",
"bbox": "166.75, -77.85, 166.75, -77.85",
@@ -106537,7 +106550,7 @@
{
"id": "K057_1999_2000_NZ_2",
"title": "A partitioning experiments to determine the aetiology of x-cell disease",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1999-11-01",
"end_date": "1999-12-30",
"bbox": "166.75, -77.85, 166.75, -77.85",
@@ -106576,7 +106589,7 @@
{
"id": "K061_1992_1995_NZ_1",
"title": "A comparative examination of the origin, structure and metamorphism of the Skelton and Koettlitz Group (basement lithologies) in South Victoria Land, Antarctica.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1992-11-19",
"end_date": "1994-12-20",
"bbox": "160, -79, 165, -74",
@@ -106589,7 +106602,7 @@
{
"id": "K061_1992_1995_NZ_1",
"title": "A comparative examination of the origin, structure and metamorphism of the Skelton and Koettlitz Group (basement lithologies) in South Victoria Land, Antarctica.",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1992-11-19",
"end_date": "1994-12-20",
"bbox": "160, -79, 165, -74",
@@ -106602,7 +106615,7 @@
{
"id": "K061_2001_2002_NZ_2",
"title": "A reconstruction of the record of volcanic processes within the vent of a large and explosive basaltic eruption in the Mawson Formation in the Allan Hills",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-11-28",
"end_date": "2001-12-22",
"bbox": "159.65, -78.7333, 159.65, -78.7333",
@@ -106615,7 +106628,7 @@
{
"id": "K061_2001_2002_NZ_2",
"title": "A reconstruction of the record of volcanic processes within the vent of a large and explosive basaltic eruption in the Mawson Formation in the Allan Hills",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2001-11-28",
"end_date": "2001-12-22",
"bbox": "159.65, -78.7333, 159.65, -78.7333",
@@ -106628,7 +106641,7 @@
{
"id": "K062_2003_2004_NZ_1",
"title": "Age determination of the detrital zircon component of crustal slices of Ross Orogen from the Skelton Glacier and Royal Society Ranges areas",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-12-04",
"end_date": "2004-11-22",
"bbox": "161, -79, 163, -78.25",
@@ -106641,7 +106654,7 @@
{
"id": "K062_2003_2004_NZ_1",
"title": "Age determination of the detrital zircon component of crustal slices of Ross Orogen from the Skelton Glacier and Royal Society Ranges areas",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2003-12-04",
"end_date": "2004-11-22",
"bbox": "161, -79, 163, -78.25",
@@ -106654,7 +106667,7 @@
{
"id": "K063_1987_1988_NZ_2",
"title": "Adelie penguin weights before and after foraging trips from three groups of penguins: control, single egg removed and penned females",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1987-11-01",
"end_date": "1989-02-06",
"bbox": "166.68, -77.17, 166.68, -77.17",
@@ -106667,7 +106680,7 @@
{
"id": "K063_1987_1988_NZ_2",
"title": "Adelie penguin weights before and after foraging trips from three groups of penguins: control, single egg removed and penned females",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1987-11-01",
"end_date": "1989-02-06",
"bbox": "166.68, -77.17, 166.68, -77.17",
@@ -106706,7 +106719,7 @@
{
"id": "K081_1983_1986_NZ_1",
"title": "Algal composition, physico-chemical features, photosynthetic carbon metabolism, nitrogen cycling and the structure and metabolic properties of algal mats in lakes and streams of southern Victoria Land",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1983-11-05",
"end_date": "1986-02-03",
"bbox": "161, -78.25, 167, -77.25",
@@ -106719,7 +106732,7 @@
{
"id": "K081_1983_1986_NZ_1",
"title": "Algal composition, physico-chemical features, photosynthetic carbon metabolism, nitrogen cycling and the structure and metabolic properties of algal mats in lakes and streams of southern Victoria Land",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1983-11-05",
"end_date": "1986-02-03",
"bbox": "161, -78.25, 167, -77.25",
@@ -106875,7 +106888,7 @@
{
"id": "K138_1992_1993_NZ_1",
"title": "A study of global (Very Low Frequency) VLF propagation with emphasis on the effects of stratospheric ionisation and glacial ice in Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1992-11-10",
"end_date": "1992-12-05",
"bbox": "165, -78, 175, -43",
@@ -106888,7 +106901,7 @@
{
"id": "K138_1992_1993_NZ_1",
"title": "A study of global (Very Low Frequency) VLF propagation with emphasis on the effects of stratospheric ionisation and glacial ice in Antarctica",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1992-11-10",
"end_date": "1992-12-05",
"bbox": "165, -78, 175, -43",
@@ -107070,7 +107083,7 @@
{
"id": "KOPRI-KPDC-00000001_1",
"title": "2007 Seismic Data, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2007-12-08",
"end_date": "2007-12-11",
"bbox": "-63.593556, -62.777306, -61.092444, -61.466739",
@@ -107083,7 +107096,7 @@
{
"id": "KOPRI-KPDC-00000001_1",
"title": "2007 Seismic Data, Antarctica",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-12-08",
"end_date": "2007-12-11",
"bbox": "-63.593556, -62.777306, -61.092444, -61.466739",
@@ -107148,7 +107161,7 @@
{
"id": "KOPRI-KPDC-00000004_1",
"title": "2002 Seismic Data, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2002-12-18",
"end_date": "2002-12-21",
"bbox": "-50.500417, -60.016, -47.001556, -59.247",
@@ -107161,7 +107174,7 @@
{
"id": "KOPRI-KPDC-00000004_1",
"title": "2002 Seismic Data, Antarctica",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-12-18",
"end_date": "2002-12-21",
"bbox": "-50.500417, -60.016, -47.001556, -59.247",
@@ -107174,7 +107187,7 @@
{
"id": "KOPRI-KPDC-00000005_1",
"title": "2001 Seismic Data, Antarctica",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-12-15",
"end_date": "2001-12-19",
"bbox": "-52.37845, -62.5604, -49.249567, -59.814483",
@@ -107187,7 +107200,7 @@
{
"id": "KOPRI-KPDC-00000005_1",
"title": "2001 Seismic Data, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2001-12-15",
"end_date": "2001-12-19",
"bbox": "-52.37845, -62.5604, -49.249567, -59.814483",
@@ -107213,7 +107226,7 @@
{
"id": "KOPRI-KPDC-00000007_1",
"title": "2000 Seismic Data, Antarctica",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-12-04",
"end_date": "2000-12-08",
"bbox": "-52.378444, -62.5604, -49.249567, -59.814639",
@@ -107226,7 +107239,7 @@
{
"id": "KOPRI-KPDC-00000007_1",
"title": "2000 Seismic Data, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2000-12-04",
"end_date": "2000-12-08",
"bbox": "-52.378444, -62.5604, -49.249567, -59.814639",
@@ -107304,7 +107317,7 @@
{
"id": "KOPRI-KPDC-00000011_1",
"title": "1996 Seismic Data, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "1996-12-17",
"end_date": "1996-12-26",
"bbox": "-62.766667, -63.583333, -60.233333, -62.733333",
@@ -107317,7 +107330,7 @@
{
"id": "KOPRI-KPDC-00000011_1",
"title": "1996 Seismic Data, Antarctica",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-12-17",
"end_date": "1996-12-26",
"bbox": "-62.766667, -63.583333, -60.233333, -62.733333",
@@ -107369,7 +107382,7 @@
{
"id": "KOPRI-KPDC-00000014_1",
"title": "1994 Seismic Data, Antarctica",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1994-12-19",
"end_date": "1994-12-27",
"bbox": "-59.352778, -63.060278, -56.167778, -62.030833",
@@ -107382,7 +107395,7 @@
{
"id": "KOPRI-KPDC-00000014_1",
"title": "1994 Seismic Data, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "1994-12-19",
"end_date": "1994-12-27",
"bbox": "-59.352778, -63.060278, -56.167778, -62.030833",
@@ -107395,7 +107408,7 @@
{
"id": "KOPRI-KPDC-00000015_1",
"title": "1999 Seismic Data, Antarctica",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1999-12-29",
"end_date": "2000-01-01",
"bbox": "-69.238889, -65.787222, -66.314722, -63.994444",
@@ -107408,7 +107421,7 @@
{
"id": "KOPRI-KPDC-00000015_1",
"title": "1999 Seismic Data, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "1999-12-29",
"end_date": "2000-01-01",
"bbox": "-69.238889, -65.787222, -66.314722, -63.994444",
@@ -107772,7 +107785,7 @@
{
"id": "KOPRI-KPDC-00000043_1",
"title": "2000 Sediment Core, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2000-12-08",
"end_date": "2000-12-10",
"bbox": "-68.527222, -65.264722, -66.956111, -64.021389",
@@ -107785,7 +107798,7 @@
{
"id": "KOPRI-KPDC-00000043_1",
"title": "2000 Sediment Core, Antarctica",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-12-08",
"end_date": "2000-12-10",
"bbox": "-68.527222, -65.264722, -66.956111, -64.021389",
@@ -107798,7 +107811,7 @@
{
"id": "KOPRI-KPDC-00000044_1",
"title": "2001 Sediment Core, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2001-12-19",
"end_date": "2001-12-21",
"bbox": "-58.026667, -61.925556, -52.468056, -60.802778",
@@ -107811,7 +107824,7 @@
{
"id": "KOPRI-KPDC-00000044_1",
"title": "2001 Sediment Core, Antarctica",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-12-19",
"end_date": "2001-12-21",
"bbox": "-58.026667, -61.925556, -52.468056, -60.802778",
@@ -107980,7 +107993,7 @@
{
"id": "KOPRI-KPDC-00000051_1",
"title": "1994 Sediment Core, Antarctica",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1994-12-31",
"end_date": "1995-01-02",
"bbox": "-58.026667, -62.42, -57.739722, -62.32",
@@ -107993,7 +108006,7 @@
{
"id": "KOPRI-KPDC-00000051_1",
"title": "1994 Sediment Core, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "1994-12-31",
"end_date": "1995-01-02",
"bbox": "-58.026667, -62.42, -57.739722, -62.32",
@@ -108032,7 +108045,7 @@
{
"id": "KOPRI-KPDC-00000053_1",
"title": "1996 Sediment Core, Antarctica",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-12-16",
"end_date": "1996-12-16",
"bbox": "-60.151944, -62.100278, -59.717778, -62.051389",
@@ -108045,7 +108058,7 @@
{
"id": "KOPRI-KPDC-00000053_1",
"title": "1996 Sediment Core, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "1996-12-16",
"end_date": "1996-12-16",
"bbox": "-60.151944, -62.100278, -59.717778, -62.051389",
@@ -108084,7 +108097,7 @@
{
"id": "KOPRI-KPDC-00000055_1",
"title": "1998 Sediment Core, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "1998-12-11",
"end_date": "1998-12-12",
"bbox": "-66.32, -63.95, -63.47, -62.943333",
@@ -108097,7 +108110,7 @@
{
"id": "KOPRI-KPDC-00000055_1",
"title": "1998 Sediment Core, Antarctica",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1998-12-11",
"end_date": "1998-12-12",
"bbox": "-66.32, -63.95, -63.47, -62.943333",
@@ -108136,7 +108149,7 @@
{
"id": "KOPRI-KPDC-00000057_1",
"title": "2005 Sediment Core, Antarctica",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2005-12-26",
"end_date": "2005-12-28",
"bbox": "-57.808611, -61.3075, -56.389722, -60.925833",
@@ -108149,7 +108162,7 @@
{
"id": "KOPRI-KPDC-00000057_1",
"title": "2005 Sediment Core, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2005-12-26",
"end_date": "2005-12-28",
"bbox": "-57.808611, -61.3075, -56.389722, -60.925833",
@@ -108162,7 +108175,7 @@
{
"id": "KOPRI-KPDC-00000058_1",
"title": "2006 Sediment Core, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2006-12-10",
"end_date": "2006-12-11",
"bbox": "-61.138333, -61.503333, -58.722222, -61.284444",
@@ -108175,7 +108188,7 @@
{
"id": "KOPRI-KPDC-00000058_1",
"title": "2006 Sediment Core, Antarctica",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2006-12-10",
"end_date": "2006-12-11",
"bbox": "-61.138333, -61.503333, -58.722222, -61.284444",
@@ -108240,7 +108253,7 @@
{
"id": "KOPRI-KPDC-00000061_1",
"title": "2012 Sediment Core, Antarctica (Amundsen Sea Project)",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-12-07",
"end_date": "2011-12-07",
"bbox": "-180, -90, 180, 90",
@@ -108253,7 +108266,7 @@
{
"id": "KOPRI-KPDC-00000061_1",
"title": "2012 Sediment Core, Antarctica (Amundsen Sea Project)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2011-12-07",
"end_date": "2011-12-07",
"bbox": "-180, -90, 180, 90",
@@ -109124,7 +109137,7 @@
{
"id": "KOPRI-KPDC-00000125_1",
"title": "Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2004",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-01-01",
"end_date": "2004-12-31",
"bbox": "180, -84.959305, 0.5, 84.574702",
@@ -109137,7 +109150,7 @@
{
"id": "KOPRI-KPDC-00000125_1",
"title": "Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2004",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2004-01-01",
"end_date": "2004-12-31",
"bbox": "180, -84.959305, 0.5, 84.574702",
@@ -109228,7 +109241,7 @@
{
"id": "KOPRI-KPDC-00000129_1",
"title": "Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2008",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2008-01-01",
"end_date": "2008-12-31",
"bbox": "180, -84.959305, 0.5, 84.574702",
@@ -109241,7 +109254,7 @@
{
"id": "KOPRI-KPDC-00000129_1",
"title": "Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2008",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2008-01-01",
"end_date": "2008-12-31",
"bbox": "180, -84.959305, 0.5, 84.574702",
@@ -109254,7 +109267,7 @@
{
"id": "KOPRI-KPDC-00000130_1",
"title": "Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2009",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2009-01-01",
"end_date": "2009-12-31",
"bbox": "180, -84.959305, 0.5, 84.574702",
@@ -109267,7 +109280,7 @@
{
"id": "KOPRI-KPDC-00000130_1",
"title": "Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2009",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2009-01-01",
"end_date": "2009-12-31",
"bbox": "180, -84.959305, 0.5, 84.574702",
@@ -109280,7 +109293,7 @@
{
"id": "KOPRI-KPDC-00000131_1",
"title": "Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2010",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2010-01-01",
"end_date": "2010-12-31",
"bbox": "180, -84.959305, 0.5, 84.574702",
@@ -109293,7 +109306,7 @@
{
"id": "KOPRI-KPDC-00000131_1",
"title": "Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2010",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-01-01",
"end_date": "2010-12-31",
"bbox": "180, -84.959305, 0.5, 84.574702",
@@ -109306,7 +109319,7 @@
{
"id": "KOPRI-KPDC-00000132_1",
"title": "Advanced Microwave Scanning Radiometer from EOS (AMSR-E), 2011",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2011-01-01",
"end_date": "2011-12-31",
"bbox": "180, -84.959305, 0.5, 84.574702",
@@ -109319,7 +109332,7 @@
{
"id": "KOPRI-KPDC-00000132_1",
"title": "Advanced Microwave Scanning Radiometer from EOS (AMSR-E), 2011",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-01-01",
"end_date": "2011-12-31",
"bbox": "180, -84.959305, 0.5, 84.574702",
@@ -110515,7 +110528,7 @@
{
"id": "KOPRI-KPDC-00000223_1",
"title": "All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2009",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2009-02-21",
"end_date": "2009-04-18",
"bbox": "-58.783333, -62.216667, -58.783333, -62.216667",
@@ -110528,7 +110541,7 @@
{
"id": "KOPRI-KPDC-00000223_1",
"title": "All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2009",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2009-02-21",
"end_date": "2009-04-18",
"bbox": "-58.783333, -62.216667, -58.783333, -62.216667",
@@ -110541,7 +110554,7 @@
{
"id": "KOPRI-KPDC-00000224_1",
"title": "All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2010",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2010-02-15",
"end_date": "2010-10-31",
"bbox": "-58.783333, -62.216667, -58.783333, -62.216667",
@@ -110554,7 +110567,7 @@
{
"id": "KOPRI-KPDC-00000224_1",
"title": "All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2010",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-02-15",
"end_date": "2010-10-31",
"bbox": "-58.783333, -62.216667, -58.783333, -62.216667",
@@ -110567,7 +110580,7 @@
{
"id": "KOPRI-KPDC-00000225_1",
"title": "All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2011",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-03-08",
"end_date": "2011-10-28",
"bbox": "-58.783333, -62.216667, -58.783333, -62.216667",
@@ -110580,7 +110593,7 @@
{
"id": "KOPRI-KPDC-00000225_1",
"title": "All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2011",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2011-03-08",
"end_date": "2011-10-28",
"bbox": "-58.783333, -62.216667, -58.783333, -62.216667",
@@ -111321,7 +111334,7 @@
{
"id": "KOPRI-KPDC-00000278_1",
"title": "Aerosol Scattering Coefficients in the Antarctic ocean, 2011-2012",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2011-11-15",
"end_date": "2012-02-01",
"bbox": "-180, -90, 180, 90",
@@ -111334,7 +111347,7 @@
{
"id": "KOPRI-KPDC-00000278_1",
"title": "Aerosol Scattering Coefficients in the Antarctic ocean, 2011-2012",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-11-15",
"end_date": "2012-02-01",
"bbox": "-180, -90, 180, 90",
@@ -111698,7 +111711,7 @@
{
"id": "KOPRI-KPDC-00000304_1",
"title": "All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2012",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2012-02-14",
"end_date": "2012-11-04",
"bbox": "-58.783333, -62.216667, -58.783333, -62.216667",
@@ -111711,7 +111724,7 @@
{
"id": "KOPRI-KPDC-00000304_1",
"title": "All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2012",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-02-14",
"end_date": "2012-11-04",
"bbox": "-58.783333, -62.216667, -58.783333, -62.216667",
@@ -111789,7 +111802,7 @@
{
"id": "KOPRI-KPDC-00000310_1",
"title": "Air-sea turbulent fluxes on the Arctic in the summer of 2004",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-01-01",
"end_date": "2004-12-31",
"bbox": "-58.783333, -62.216667, -58.783333, -62.216667",
@@ -111802,7 +111815,7 @@
{
"id": "KOPRI-KPDC-00000310_1",
"title": "Air-sea turbulent fluxes on the Arctic in the summer of 2004",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2004-01-01",
"end_date": "2004-12-31",
"bbox": "-58.783333, -62.216667, -58.783333, -62.216667",
@@ -111945,7 +111958,7 @@
{
"id": "KOPRI-KPDC-00000321_2",
"title": "2013 CTD Data, Ross Sea of Antarctic",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-01-27",
"end_date": "2013-02-19",
"bbox": "163.0785, -76.478667, 179.505833, -71.866667",
@@ -111958,7 +111971,7 @@
{
"id": "KOPRI-KPDC-00000321_2",
"title": "2013 CTD Data, Ross Sea of Antarctic",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2013-01-27",
"end_date": "2013-02-19",
"bbox": "163.0785, -76.478667, 179.505833, -71.866667",
@@ -112647,7 +112660,7 @@
{
"id": "KOPRI-KPDC-00000370_1",
"title": "A study on the distribution characteristics of dissolved inorganic carbon (DIC) in the Amundsen Sea in 2011.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2010-12-20",
"end_date": "2011-01-22",
"bbox": "-180, -90, 180, 90",
@@ -112660,7 +112673,7 @@
{
"id": "KOPRI-KPDC-00000370_1",
"title": "A study on the distribution characteristics of dissolved inorganic carbon (DIC) in the Amundsen Sea in 2011.",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-12-20",
"end_date": "2011-01-22",
"bbox": "-180, -90, 180, 90",
@@ -113466,7 +113479,7 @@
{
"id": "KOPRI-KPDC-00000432_1",
"title": "A study on the distribution characteristics of pH in the Amundsen Sea in 2012.",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-01-22",
"end_date": "2012-03-11",
"bbox": "-180, -90, 180, 90",
@@ -113479,7 +113492,7 @@
{
"id": "KOPRI-KPDC-00000432_1",
"title": "A study on the distribution characteristics of pH in the Amundsen Sea in 2012.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2012-01-22",
"end_date": "2012-03-11",
"bbox": "-180, -90, 180, 90",
@@ -113908,7 +113921,7 @@
{
"id": "KOPRI-KPDC-00000464_1",
"title": "2013 CTD Data, in Chukchi Borderland/Mendeleev Ridge of Arctic",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2013-08-21",
"end_date": "2013-09-25",
"bbox": "-179.715167, 69.988167, -134.155167, 77.500667",
@@ -113921,7 +113934,7 @@
{
"id": "KOPRI-KPDC-00000464_1",
"title": "2013 CTD Data, in Chukchi Borderland/Mendeleev Ridge of Arctic",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-08-21",
"end_date": "2013-09-25",
"bbox": "-179.715167, 69.988167, -134.155167, 77.500667",
@@ -113934,7 +113947,7 @@
{
"id": "KOPRI-KPDC-00000465_1",
"title": "2013 LADCP Data, in Chukchi Borderland, Arctic",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2013-08-21",
"end_date": "2013-09-25",
"bbox": "179.715167, 69.988167, 178.9955, 77.5",
@@ -113947,7 +113960,7 @@
{
"id": "KOPRI-KPDC-00000465_1",
"title": "2013 LADCP Data, in Chukchi Borderland, Arctic",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-08-21",
"end_date": "2013-09-25",
"bbox": "179.715167, 69.988167, 178.9955, 77.5",
@@ -114012,7 +114025,7 @@
{
"id": "KOPRI-KPDC-00000470_1",
"title": "2011 Mooring Data, Antarctic",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2010-12-31",
"end_date": "2012-03-02",
"bbox": "-117.72235, -73.271333, -114.9705, -72.402717",
@@ -114025,7 +114038,7 @@
{
"id": "KOPRI-KPDC-00000470_1",
"title": "2011 Mooring Data, Antarctic",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-12-31",
"end_date": "2012-03-02",
"bbox": "-117.72235, -73.271333, -114.9705, -72.402717",
@@ -114116,7 +114129,7 @@
{
"id": "KOPRI-KPDC-00000476_1",
"title": "Air-sea turbulent fluxes on the Amundsen Sea of 2011 (ANA01C)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2010-12-21",
"end_date": "2011-01-22",
"bbox": "-180, -90, 180, 90",
@@ -114129,7 +114142,7 @@
{
"id": "KOPRI-KPDC-00000476_1",
"title": "Air-sea turbulent fluxes on the Amundsen Sea of 2011 (ANA01C)",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-12-21",
"end_date": "2011-01-22",
"bbox": "-180, -90, 180, 90",
@@ -114766,7 +114779,7 @@
{
"id": "KOPRI-KPDC-00000523_2",
"title": "2015 ARAON Arctic geological expedition: Box Core(BC) sediment data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2015-08-27",
"end_date": "2015-09-06",
"bbox": "178.870742, 73.620362, 176.540425, 76.602687",
@@ -114779,7 +114792,7 @@
{
"id": "KOPRI-KPDC-00000523_2",
"title": "2015 ARAON Arctic geological expedition: Box Core(BC) sediment data",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-08-27",
"end_date": "2015-09-06",
"bbox": "178.870742, 73.620362, 176.540425, 76.602687",
@@ -114818,7 +114831,7 @@
{
"id": "KOPRI-KPDC-00000525_2",
"title": "2015 ARAON Arctic geological expedition: Gravity Core(GC) sediment data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2015-08-27",
"end_date": "2015-09-06",
"bbox": "-166.51978, 73.620935, -166.432032, 73.634698",
@@ -114831,7 +114844,7 @@
{
"id": "KOPRI-KPDC-00000525_2",
"title": "2015 ARAON Arctic geological expedition: Gravity Core(GC) sediment data",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-08-27",
"end_date": "2015-09-06",
"bbox": "-166.51978, 73.620935, -166.432032, 73.634698",
@@ -114844,7 +114857,7 @@
{
"id": "KOPRI-KPDC-00000526_2",
"title": "2015 ARAON Arctic geological expedition: Jumbo piston core (JPC) sediment data",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-08-27",
"end_date": "2015-09-06",
"bbox": "178.734385, 73.620362, -161.168018, 76.602687",
@@ -114857,7 +114870,7 @@
{
"id": "KOPRI-KPDC-00000526_2",
"title": "2015 ARAON Arctic geological expedition: Jumbo piston core (JPC) sediment data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2015-08-27",
"end_date": "2015-09-06",
"bbox": "178.734385, 73.620362, -161.168018, 76.602687",
@@ -115403,7 +115416,7 @@
{
"id": "KOPRI-KPDC-00000568_1",
"title": "All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2013",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2013-03-01",
"end_date": "2013-10-31",
"bbox": "-58.47, -62.13, -58.47, -62.13",
@@ -115416,7 +115429,7 @@
{
"id": "KOPRI-KPDC-00000568_1",
"title": "All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2013",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-03-01",
"end_date": "2013-10-31",
"bbox": "-58.47, -62.13, -58.47, -62.13",
@@ -115702,7 +115715,7 @@
{
"id": "KOPRI-KPDC-00000589_1",
"title": "Air temperature and humidity in Cambridge Bay, Canada in 2012",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2012-07-11",
"end_date": "2013-08-04",
"bbox": "-180, -90, 180, 90",
@@ -115715,7 +115728,7 @@
{
"id": "KOPRI-KPDC-00000589_1",
"title": "Air temperature and humidity in Cambridge Bay, Canada in 2012",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-07-11",
"end_date": "2013-08-04",
"bbox": "-180, -90, 180, 90",
@@ -115780,7 +115793,7 @@
{
"id": "KOPRI-KPDC-00000593_1",
"title": "Air temperature and humidity in Cambridge Bay, Canada in 2014",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2014-06-01",
"end_date": "2015-08-31",
"bbox": "-180, -90, 180, 90",
@@ -115793,7 +115806,7 @@
{
"id": "KOPRI-KPDC-00000593_1",
"title": "Air temperature and humidity in Cambridge Bay, Canada in 2014",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2014-06-01",
"end_date": "2015-08-31",
"bbox": "-180, -90, 180, 90",
@@ -116144,7 +116157,7 @@
{
"id": "KOPRI-KPDC-00000620_1",
"title": "2015-2016 JBS_micro-climate data_HOBO_soil temp.,PAR,air temp.,relative humidity",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-02-09",
"end_date": "2015-02-13",
"bbox": "164.191389, -74.632806, 164.229972, -74.613",
@@ -116157,7 +116170,7 @@
{
"id": "KOPRI-KPDC-00000620_1",
"title": "2015-2016 JBS_micro-climate data_HOBO_soil temp.,PAR,air temp.,relative humidity",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2015-02-09",
"end_date": "2015-02-13",
"bbox": "164.191389, -74.632806, 164.229972, -74.613",
@@ -117535,7 +117548,7 @@
{
"id": "KOPRI-KPDC-00000724_1",
"title": "Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2013",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2014-10-08",
"end_date": "2014-10-08",
"bbox": "-58.766667, -62.216667, -58.766667, -62.216667",
@@ -117548,7 +117561,7 @@
{
"id": "KOPRI-KPDC-00000724_1",
"title": "Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2013",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2014-10-08",
"end_date": "2014-10-08",
"bbox": "-58.766667, -62.216667, -58.766667, -62.216667",
@@ -117717,7 +117730,7 @@
{
"id": "KOPRI-KPDC-00000737_1",
"title": "2014-15 Jang Bogo Station micro climate data_HOBO_soil temp.,PAR,air temp.,relative humidity",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2015-10-06",
"end_date": "2015-10-06",
"bbox": "-180, -90, 180, 90",
@@ -117730,7 +117743,7 @@
{
"id": "KOPRI-KPDC-00000737_1",
"title": "2014-15 Jang Bogo Station micro climate data_HOBO_soil temp.,PAR,air temp.,relative humidity",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-10-06",
"end_date": "2015-10-06",
"bbox": "-180, -90, 180, 90",
@@ -118029,7 +118042,7 @@
{
"id": "KOPRI-KPDC-00000760_1",
"title": "Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2016-2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2016-12-28",
"end_date": "2017-02-15",
"bbox": "153.936483, -75.389942, 159.216086, -75.059956",
@@ -118042,7 +118055,7 @@
{
"id": "KOPRI-KPDC-00000760_1",
"title": "Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2016-2017",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-12-28",
"end_date": "2017-02-15",
"bbox": "153.936483, -75.389942, 159.216086, -75.059956",
@@ -118133,7 +118146,7 @@
{
"id": "KOPRI-KPDC-00000767_1",
"title": "2016-2017 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-01-14",
"end_date": "2017-01-27",
"bbox": "-58.788436, -62.240056, -58.719694, -62.218583",
@@ -118146,7 +118159,7 @@
{
"id": "KOPRI-KPDC-00000767_1",
"title": "2016-2017 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2016-01-14",
"end_date": "2017-01-27",
"bbox": "-58.788436, -62.240056, -58.719694, -62.218583",
@@ -118185,7 +118198,7 @@
{
"id": "KOPRI-KPDC-00000770_1",
"title": "Aerosol Number Concentration (>10nm) from King Sejong Station collected in 2010-2016.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2010-01-01",
"end_date": "2016-12-31",
"bbox": "-58.78, -62.22, -58.78, -62.22",
@@ -118198,7 +118211,7 @@
{
"id": "KOPRI-KPDC-00000770_1",
"title": "Aerosol Number Concentration (>10nm) from King Sejong Station collected in 2010-2016.",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-01-01",
"end_date": "2016-12-31",
"bbox": "-58.78, -62.22, -58.78, -62.22",
@@ -118263,7 +118276,7 @@
{
"id": "KOPRI-KPDC-00000775_1",
"title": "Aerosol Size Distribution from King Sejong Station collected in 2010-2016.",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-01-01",
"end_date": "2016-12-31",
"bbox": "-58.78, -62.22, -58.78, -62.22",
@@ -118276,7 +118289,7 @@
{
"id": "KOPRI-KPDC-00000775_1",
"title": "Aerosol Size Distribution from King Sejong Station collected in 2010-2016.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2010-01-01",
"end_date": "2016-12-31",
"bbox": "-58.78, -62.22, -58.78, -62.22",
@@ -118822,7 +118835,7 @@
{
"id": "KOPRI-KPDC-00000816_2",
"title": "All-sky aurora (proton) Image, Longyearbyen, Norway, 2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2017-01-01",
"end_date": "2017-02-28",
"bbox": "16.12, 78.48, 16.12, 78.48",
@@ -118835,7 +118848,7 @@
{
"id": "KOPRI-KPDC-00000816_2",
"title": "All-sky aurora (proton) Image, Longyearbyen, Norway, 2017",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-01-01",
"end_date": "2017-02-28",
"bbox": "16.12, 78.48, 16.12, 78.48",
@@ -120564,7 +120577,7 @@
{
"id": "KOPRI-KPDC-00000947_1",
"title": "Advanced Very High Resolution Radiometer (AVHRR) around the Jang Bogo Station, 2015-2016",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-03-03",
"end_date": "2016-02-15",
"bbox": "164.233333, -74.616667, 164.233333, -74.616667",
@@ -120577,7 +120590,7 @@
{
"id": "KOPRI-KPDC-00000947_1",
"title": "Advanced Very High Resolution Radiometer (AVHRR) around the Jang Bogo Station, 2015-2016",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2015-03-03",
"end_date": "2016-02-15",
"bbox": "164.233333, -74.616667, 164.233333, -74.616667",
@@ -121357,7 +121370,7 @@
{
"id": "KOPRI-KPDC-00001008_2",
"title": "2018 KOPRI North Greenland Sirius Passet collection 1",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2021-08-02",
"end_date": "2021-08-02",
"bbox": "-42.228333, 82.793333, -42.228333, 82.793333",
@@ -121370,7 +121383,7 @@
{
"id": "KOPRI-KPDC-00001008_2",
"title": "2018 KOPRI North Greenland Sirius Passet collection 1",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2021-08-02",
"end_date": "2021-08-02",
"bbox": "-42.228333, 82.793333, -42.228333, 82.793333",
@@ -122761,7 +122774,7 @@
{
"id": "KOPRI-KPDC-00001112_4",
"title": "All-sky aurora (proton) image, Longyearbyen, Norway, 2018",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2018-01-01",
"end_date": "2018-02-28",
"bbox": "16.040746, 78.147909, 16.040746, 78.147909",
@@ -122774,7 +122787,7 @@
{
"id": "KOPRI-KPDC-00001112_4",
"title": "All-sky aurora (proton) image, Longyearbyen, Norway, 2018",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2018-01-01",
"end_date": "2018-02-28",
"bbox": "16.040746, 78.147909, 16.040746, 78.147909",
@@ -122930,7 +122943,7 @@
{
"id": "KOPRI-KPDC-00001124_4",
"title": "All-sky aurora (electron) image, Jang Bogo Station, Antarctica, 2018",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2018-03-01",
"end_date": "2018-10-31",
"bbox": "164.2273, -74.6202, 164.2273, -74.6202",
@@ -122943,7 +122956,7 @@
{
"id": "KOPRI-KPDC-00001124_4",
"title": "All-sky aurora (electron) image, Jang Bogo Station, Antarctica, 2018",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2018-03-01",
"end_date": "2018-10-31",
"bbox": "164.2273, -74.6202, 164.2273, -74.6202",
@@ -123008,7 +123021,7 @@
{
"id": "KOPRI-KPDC-00001129_1",
"title": "Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2018",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-06-19",
"end_date": "2018-06-18",
"bbox": "-105.133333, 69.1, -105.133333, 69.1",
@@ -123021,7 +123034,7 @@
{
"id": "KOPRI-KPDC-00001129_1",
"title": "Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2018",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2017-06-19",
"end_date": "2018-06-18",
"bbox": "-105.133333, 69.1, -105.133333, 69.1",
@@ -123632,7 +123645,7 @@
{
"id": "KOPRI-KPDC-00001177_3",
"title": "Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2018",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2018-11-18",
"end_date": "2019-01-14",
"bbox": "154.838627, -75.536572, 155.93514, -75.246428",
@@ -123645,7 +123658,7 @@
{
"id": "KOPRI-KPDC-00001177_3",
"title": "Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2018",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2018-11-18",
"end_date": "2019-01-14",
"bbox": "154.838627, -75.536572, 155.93514, -75.246428",
@@ -124711,7 +124724,7 @@
{
"id": "KOPRI-KPDC-00001265_3",
"title": "All-sky aurora (proton) image, KHO Longyearbyen, 2019",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2019-01-01",
"end_date": "2019-04-15",
"bbox": "16.03412, 78.15174, 16.03412, 78.15174",
@@ -124724,7 +124737,7 @@
{
"id": "KOPRI-KPDC-00001265_3",
"title": "All-sky aurora (proton) image, KHO Longyearbyen, 2019",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-01-01",
"end_date": "2019-04-15",
"bbox": "16.03412, 78.15174, 16.03412, 78.15174",
@@ -124854,7 +124867,7 @@
{
"id": "KOPRI-KPDC-00001275_3",
"title": "All-sky airglow image, King Sejong Station, 2019",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2019-03-11",
"end_date": "2019-09-30",
"bbox": "-58.78804, -62.22268, -58.78804, -62.22268",
@@ -124867,7 +124880,7 @@
{
"id": "KOPRI-KPDC-00001275_3",
"title": "All-sky airglow image, King Sejong Station, 2019",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-03-11",
"end_date": "2019-09-30",
"bbox": "-58.78804, -62.22268, -58.78804, -62.22268",
@@ -126804,7 +126817,7 @@
{
"id": "KOPRI-KPDC-00001423_2",
"title": "2019 Arctic Araon Cruise (ARA10C) sediment cores (multiple, gravity, and box cores)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2019-08-29",
"end_date": "2019-09-20",
"bbox": "167.676767, 73.69587, 179.98125, 77.132017",
@@ -126817,7 +126830,7 @@
{
"id": "KOPRI-KPDC-00001423_2",
"title": "2019 Arctic Araon Cruise (ARA10C) sediment cores (multiple, gravity, and box cores)",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-08-29",
"end_date": "2019-09-20",
"bbox": "167.676767, 73.69587, 179.98125, 77.132017",
@@ -127766,7 +127779,7 @@
{
"id": "KOPRI-KPDC-00001498_2",
"title": "Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2019",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-01-19",
"end_date": "2020-01-26",
"bbox": "-58.789338, -62.240538, -58.721474, -62.220364",
@@ -127779,7 +127792,7 @@
{
"id": "KOPRI-KPDC-00001498_2",
"title": "Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2019",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2019-01-19",
"end_date": "2020-01-26",
"bbox": "-58.789338, -62.240538, -58.721474, -62.220364",
@@ -127844,7 +127857,7 @@
{
"id": "KOPRI-KPDC-00001505_5",
"title": "All-sky airglow image, King Sejong Station, 2020",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-02-18",
"end_date": "2020-09-23",
"bbox": "-58.47, -62.13, -58.47, -62.13",
@@ -127857,7 +127870,7 @@
{
"id": "KOPRI-KPDC-00001505_5",
"title": "All-sky airglow image, King Sejong Station, 2020",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2020-02-18",
"end_date": "2020-09-23",
"bbox": "-58.47, -62.13, -58.47, -62.13",
@@ -127922,7 +127935,7 @@
{
"id": "KOPRI-KPDC-00001509_1",
"title": "2019-2020 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-01-19",
"end_date": "2020-01-26",
"bbox": "-58.788436, -62.240056, -58.719694, -62.218583",
@@ -127935,7 +127948,7 @@
{
"id": "KOPRI-KPDC-00001509_1",
"title": "2019-2020 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2019-01-19",
"end_date": "2020-01-26",
"bbox": "-58.788436, -62.240056, -58.719694, -62.218583",
@@ -129378,7 +129391,7 @@
{
"id": "KOPRI-KPDC-00001632_1",
"title": "A study on the distribution characteristics of stable oxygen isotope in the Amundsen Sea in 2011",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-12-20",
"end_date": "2011-01-20",
"bbox": "-145, -74.6, -112, -72.5",
@@ -129391,7 +129404,7 @@
{
"id": "KOPRI-KPDC-00001632_1",
"title": "A study on the distribution characteristics of stable oxygen isotope in the Amundsen Sea in 2011",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2010-12-20",
"end_date": "2011-01-20",
"bbox": "-145, -74.6, -112, -72.5",
@@ -129885,7 +129898,7 @@
{
"id": "KOPRI-KPDC-00001671_3",
"title": "2018&19 Multibeam data of Terra Nova Bay (around Jang Bogo station)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2019-02-14",
"end_date": "2019-02-15",
"bbox": "163.984928, -74.73604, 164.57053, -74.610485",
@@ -129898,7 +129911,7 @@
{
"id": "KOPRI-KPDC-00001671_3",
"title": "2018&19 Multibeam data of Terra Nova Bay (around Jang Bogo station)",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-02-14",
"end_date": "2019-02-15",
"bbox": "163.984928, -74.73604, 164.57053, -74.610485",
@@ -129911,7 +129924,7 @@
{
"id": "KOPRI-KPDC-00001672_3",
"title": "2016&17 Multibeam data of Terra Nova Bay (around Jang Bogo station)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2017-01-29",
"end_date": "2017-02-06",
"bbox": "163.984928, -74.73604, 164.57053, -74.610485",
@@ -129924,7 +129937,7 @@
{
"id": "KOPRI-KPDC-00001672_3",
"title": "2016&17 Multibeam data of Terra Nova Bay (around Jang Bogo station)",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-01-29",
"end_date": "2017-02-06",
"bbox": "163.984928, -74.73604, 164.57053, -74.610485",
@@ -131237,7 +131250,7 @@
{
"id": "KOPRI-KPDC-00001778_2",
"title": "2020/21 season Korean Route Traverse based GPS GIS data",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-12-01",
"end_date": "2020-12-31",
"bbox": "164.2362, -74.6281, 164.2362, -74.6281",
@@ -131250,7 +131263,7 @@
{
"id": "KOPRI-KPDC-00001778_2",
"title": "2020/21 season Korean Route Traverse based GPS GIS data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2020-12-01",
"end_date": "2020-12-31",
"bbox": "164.2362, -74.6281, 164.2362, -74.6281",
@@ -131497,7 +131510,7 @@
{
"id": "KOPRI-KPDC-00001797_2",
"title": "Age characteristics of Antarctic scallops (Adamussium colbecki)",
- "catalog": "AMD_KOPRI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-02-21",
"end_date": "2019-03-01",
"bbox": "164.243867, -74.627661, 164.243867, -74.627661",
@@ -131510,7 +131523,7 @@
{
"id": "KOPRI-KPDC-00001797_2",
"title": "Age characteristics of Antarctic scallops (Adamussium colbecki)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_KOPRI STAC Catalog",
"state_date": "2019-02-21",
"end_date": "2019-03-01",
"bbox": "164.243867, -74.627661, 164.243867, -74.627661",
@@ -133421,7 +133434,7 @@
{
"id": "Kyle-Ferrar_Igneous_Province",
"title": "40Ar/39Ar dates of Jurassic igneous rocks from Antarctica",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, -62.83",
@@ -133434,7 +133447,7 @@
{
"id": "Kyle-Ferrar_Igneous_Province",
"title": "40Ar/39Ar dates of Jurassic igneous rocks from Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, -62.83",
@@ -133473,7 +133486,7 @@
{
"id": "L2B_Wind_Products_3.0",
"title": "Aeolus Scientific L2B Rayleigh/Mie wind product",
- "catalog": "ESA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-04-20",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -133486,7 +133499,7 @@
{
"id": "L2B_Wind_Products_3.0",
"title": "Aeolus Scientific L2B Rayleigh/Mie wind product",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ESA STAC Catalog",
"state_date": "2020-04-20",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -133746,7 +133759,7 @@
{
"id": "LAI_Woody_Plants_1231_1",
"title": "A Global Database of Field-observed Leaf Area Index in Woody Plant Species, 1932-2011",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1932-01-01",
"end_date": "2011-12-31",
"bbox": "-164.78, -54.2, 175.62, 78.42",
@@ -133759,7 +133772,7 @@
{
"id": "LAI_Woody_Plants_1231_1",
"title": "A Global Database of Field-observed Leaf Area Index in Woody Plant Species, 1932-2011",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1932-01-01",
"end_date": "2011-12-31",
"bbox": "-164.78, -54.2, 175.62, 78.42",
@@ -135371,7 +135384,7 @@
{
"id": "LGB_10m_traverse_1",
"title": "10 m firn temperature data: LGB traverses 1990-95",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1989-11-01",
"end_date": "1995-02-28",
"bbox": "54, -77, 78, -69",
@@ -135384,7 +135397,7 @@
{
"id": "LGB_10m_traverse_1",
"title": "10 m firn temperature data: LGB traverses 1990-95",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1989-11-01",
"end_date": "1995-02-28",
"bbox": "54, -77, 78, -69",
@@ -136788,7 +136801,7 @@
{
"id": "Lake_Wetland_Classes_UAVSAR_1883_1",
"title": "ABoVE: Lake and Wetland Classification from L-band SAR, Alaska and Canada, 2017-2019",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-01-01",
"end_date": "2019-09-19",
"bbox": "-149.16, 53.71, -107.86, 67.91",
@@ -136801,7 +136814,7 @@
{
"id": "Lake_Wetland_Classes_UAVSAR_1883_1",
"title": "ABoVE: Lake and Wetland Classification from L-band SAR, Alaska and Canada, 2017-2019",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2017-01-01",
"end_date": "2019-09-19",
"bbox": "-149.16, 53.71, -107.86, 67.91",
@@ -137113,7 +137126,7 @@
{
"id": "Leaf_Carbon_Nutrients_1106_1",
"title": "A Global Database of Carbon and Nutrient Concentrations of Green and Senesced Leaves",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1970-01-01",
"end_date": "2009-12-31",
"bbox": "-159.7, -50, 176.9, 68.5",
@@ -137126,7 +137139,7 @@
{
"id": "Leaf_Carbon_Nutrients_1106_1",
"title": "A Global Database of Carbon and Nutrient Concentrations of Green and Senesced Leaves",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "2009-12-31",
"bbox": "-159.7, -50, 176.9, 68.5",
@@ -137165,7 +137178,7 @@
{
"id": "Level_2A_aerosol_cloud_optical_products_3.0",
"title": "Aeolus L2A Aerosol/Cloud optical product",
- "catalog": "ESA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2021-05-26",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -137178,7 +137191,7 @@
{
"id": "Level_2A_aerosol_cloud_optical_products_3.0",
"title": "Aeolus L2A Aerosol/Cloud optical product",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ESA STAC Catalog",
"state_date": "2021-05-26",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -137204,7 +137217,7 @@
{
"id": "LiDAR_Tundra_Forest_AK_1782_1",
"title": "ABoVE: Terrestrial Lidar Scanning Forest-Tundra Ecotone, Brooks Range, Alaska, 2016",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-06-14",
"end_date": "2016-06-25",
"bbox": "-149.76, 67.97, -149.71, 68.02",
@@ -137217,7 +137230,7 @@
{
"id": "LiDAR_Tundra_Forest_AK_1782_1",
"title": "ABoVE: Terrestrial Lidar Scanning Forest-Tundra Ecotone, Brooks Range, Alaska, 2016",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-06-14",
"end_date": "2016-06-25",
"bbox": "-149.76, 67.97, -149.71, 68.02",
@@ -140851,7 +140864,7 @@
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C2763289461-LPCLOUD.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2763289461-LPCLOUD.html",
"href": "https://cmr.earthdata.nasa.gov/stac/LPCLOUD/collections/MCD19A2_006",
- "description": "The MCD19A2 Version 6 data product was decommissioned on July 31, 2023. The MCD19A2 Version 6 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 1 kilometer (km) pixel resolution. The MCD19A2 product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1 product. The MCD19A2 AOD data product contains the following Science Dataset (SDS) layers: blue band AOD at 0.47 \u00b5m, green band AOD at 0.55 \u00b5m, AOD uncertainty, fine mode fraction over water, column water vapor over land and clouds (in cm), smoke injection height (m above ground), AOD QA, AOD model at 1km, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, and glint angle at 5km. A low-resolution browse image is also included showing AOD of the blue band at 0.47 \u00b5m created using a composite of all available orbits. Each SDS layer within each MCD19A2 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer. ",
+ "description": "The MCD19A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MCD19A2 Version 6.1 data product (https://doi.org/10.5067/MODIS/MCD19A2.061). The MCD19A2 Version 6 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 1 kilometer (km) pixel resolution. The MCD19A2 product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1 product. The MCD19A2 AOD data product contains the following Science Dataset (SDS) variables: blue band AOD at 0.47 micron, green band AOD at 0.55 micron, AOD uncertainty, fine mode fraction over water, column water vapor over land and clouds (in cm), smoke injection height (m above ground), AOD QA, AOD model at 1 km, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, and glint angle at 5 km. A low-resolution browse image is also included showing AOD of the blue band at 0.47 micron created using a composite of all available orbits. Each SDS variable within each MCD19A2 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS variable. Improvements/Changes from Previous Versions *New product for MODIS Version 6. ",
"license": "proprietary"
},
{
@@ -141956,7 +141969,7 @@
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C1623882456-LPDAAC_ECS.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1623882456-LPDAAC_ECS.html",
"href": "https://cmr.earthdata.nasa.gov/stac/LPDAAC_ECS/collections/MCD43GF_006",
- "description": "The Daily Moderate Resolution Imaging Spectroradiometer (MODIS) (Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6 product suite, with additional processing to provide a gap-filled, snow-free product. The highest quality full inversion values were used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the quality layer for each band. To generate a snow-free product, pixels with ephemeral snow were removed using the MCD43D41 (https://doi.org/10.5067/MODIS/MCD43D41.006) snow flags. The underlying MCD43D utilizes a BRDF model derived from all available high quality cloud clear reflectance data over a 16 day moving window centered on and emphasizing the daily day of interest (the ninth day of each retrieval period as reflected in the Julian date in the filename). This 30arc second BRDF model is then used to produce the Albedo and NBAR products (MCD43D). These BRDF model parameters are computed for MODIS spectral bands 1 through 7 (0.47 um, 0.55 um, 0.67 um, 0.86 um, 1.24 um, 1.64 um, 2.1 um), as well as the shortwave infrared band (0.3-5.0um), visible band (0.3-0.7 um), and near-infrared (0.7-5.0 um) broad bands. The MCD43GF product includes 67 layers containing black-sky albedo (BSA) at local solar noon, isotropic (ISO), volumetric (VOL), geometric (GEO), quality (QA), Nadir BRDF-Adjusted Reflectance (NBAR) at local solar noon, and white-sky albedo (WSA). Due to the large file size, each data layer is distributed as a separate HDF file. Users are encouraged to download the quality layers for each of the 10 bands to check quality assessment information before using the BRDF/Albedo data. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications (https://www.umb.edu/spectralmass/terra_aqua_modis/v006). The MCD43 product is not recommended for solar zenith angles beyond 70 degrees. The MODIS BRDF/Albedo products have achieved stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation. Improvements/Changes from Previous Versions Observations are weighted to estimate the BRDF/Albedo on the ninth day of the 16-day period. *\tMCD43 products use the snow status weighted to the ninth day instead of the majority snow/no-snow observations from the 16-day period. *\tBetter quality at high latitudes from use of all available observations for the acquisition period. Collection 5 used only four observations per day. *\tThe MCD43 products use L2G-lite surface reflectance as input. *\tIn cases where insufficient high-quality reflectances are obtained, a database with archetypal BRDF parameters is used to supplement the observational data and perform a lower quality magnitude inversion. This database is continually updated with the latest full inversion retrieval for each pixel. *\tCMG Albedo is estimated using all the clear-sky observations within the 1,000 m grid as opposed to aggregating from the 500 m albedo. Important Quality Information The incorrect representation of the aerosol quantities (low average high) in the C6 MYD09 and MOD09 surface reflectance products may have impacted down stream products particularly over arid bright surfaces (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/displayCase.cgi?esdt=MOD09&caseNum=PM_MOD09_20010&caseLocation=cases_data&type=C6). This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. ",
+ "description": "The Daily Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6 product suite, with additional processing to provide a gap-filled, snow-free product. The highest quality full inversion values were used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the quality layer for each band. To generate a snow-free product, pixels with ephemeral snow were removed using the MCD43D41 (https://doi.org/10.5067/MODIS/MCD43D41.006) snow flags. The underlying MCD43D utilizes a BRDF model derived from all available high quality cloud clear reflectance data over a 16 day moving window centered on and emphasizing the daily day of interest (the ninth day of each retrieval period as reflected in the Julian date in the filename). This 30 arc second BRDF model is then used to produce the Albedo and NBAR products (MCD43D). These BRDF model parameters are computed for MODIS spectral bands 1 through 7 (0.47 um, 0.55 um, 0.67 um, 0.86 um, 1.24 um, 1.64 um, 2.1 um), as well as the shortwave infrared band (0.3-5.0 um), visible band (0.3-0.7 um), and near-infrared (0.7-5.0 um) broad bands. The MCD43GF product includes 67 variables containing black-sky albedo (BSA) at local solar noon, isotropic model parameter (ISO), volumetric model parameter (VOL), geometric model parameter (GEO), quality (QA), Nadir BRDF-Adjusted Reflectance (NBAR) at local solar noon, and white-sky albedo (WSA). Due to the large file size, each data variable is distributed as a separate HDF file. Users are encouraged to download the quality variable for each of the 10 bands to check quality assessment information before using the BRDF/Albedo data. The MCD43 product is not recommended for solar zenith angles beyond 70 degrees. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. Improvements/Changes from Previous Versions *\tObservations are weighted to estimate the BRDF/Albedo on the ninth day of the 16-day period. *\tMCD43 products use the snow status weighted to the ninth day instead of the majority snow/no-snow observations from the 16-day period. *\tBetter quality at high latitudes from use of all available observations for the acquisition period. Collection 5 used only four observations per day. *\tThe MCD43 products use L2G-lite surface reflectance as input. *\tIn cases where insufficient high-quality reflectances are obtained, a database with archetypal BRDF parameters is used to supplement the observational data and perform a lower quality magnitude inversion. This database is continually updated with the latest full inversion retrieval for each pixel. *\tCMG Albedo is estimated using all the clear-sky observations within the 1,000 m grid as opposed to aggregating from the 500 m albedo. Important Quality Information The incorrect representation of the aerosol quantities (low average high) in the C6 MYD09 and MOD09 surface reflectance products may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. ",
"license": "proprietary"
},
{
@@ -142755,7 +142768,7 @@
{
"id": "MFLL_CO2_Weighting_Functions_1891_1",
"title": "ACT-America: L2 Weighting Functions for Airborne Lidar Column-avg CO2, Eastern USA",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-05-27",
"end_date": "2018-05-20",
"bbox": "-106.05, 27.23, -71.91, 49.11",
@@ -142768,7 +142781,7 @@
{
"id": "MFLL_CO2_Weighting_Functions_1891_1",
"title": "ACT-America: L2 Weighting Functions for Airborne Lidar Column-avg CO2, Eastern USA",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-05-27",
"end_date": "2018-05-20",
"bbox": "-106.05, 27.23, -71.91, 49.11",
@@ -143425,7 +143438,7 @@
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C185127378-LARC.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C185127378-LARC.html",
"href": "https://cmr.earthdata.nasa.gov/stac/LARC/collections/MIANACP_1",
- "description": "MIANACP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Aerosol Climatology Product version 1. It is 1) the microphysical and scattering characteristics of pure aerosol upon which routine retrievals are based; 2) mixtures of pure aerosol to be compared with MISR observations; and 3) likelihood value assigned to each mode geographically. The ACP describes mixtures of up to three component aerosol types from a list of eight components, in varying proportions. ACP component aerosol particle data quality depends on the ACP input data, which are based on aerosol particles described in the literature, and consider MISR-specific sensitivity to particle size, single-scattering albedo, and shape, and shape - roughly: small, medium and large; dirty and clean; spherical and nonspherical [Kahn et al. , 1998; 2001]. Also reported in the ACP are the mixtures of these components used by the retrieval algorithm. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.",
+ "description": "MIANACP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Aerosol Climatology Product version 1. It is 1) the microphysical and scattering characteristics of pure aerosol upon which routine retrievals are based, 2) mixtures of pure aerosol to be compared with MISR observations, and 3) the likelihood value assigned to each mode geographically. The ACP describes mixtures of up to three component aerosol types from a list of eight components in varying proportions. ACP component aerosol particle data quality depends on the ACP input data, which are based on aerosol particles described in the literature and consider MISR-specific sensitivity to particle size, single-scattering albedo, and shape, and shape - roughly: small, medium, and large; dirty and clean; spherical and nonspherical [Kahn et al., 1998; 2001]. Also reported in the ACP are the mixtures of these components used by the retrieval algorithm. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.",
"license": "proprietary"
},
{
@@ -143438,7 +143451,7 @@
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C183897339-LARC.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C183897339-LARC.html",
"href": "https://cmr.earthdata.nasa.gov/stac/LARC/collections/MIANCAGP_1",
- "description": "MIANCAGP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Geographic Product version 1. It is a set of 233 pre-computed files. Each AGP file pertains to a single Terra orbital path. MISR production software relies on information in the AGP, such as digital terrain elevation, as input to the algorithms which generate MISR products. The AGP contains eleven fields of geographical data. This product consists primarily of geolocation data on a Space Oblique Mercator (SOM) Grid. It has 233 parts, corresponding to the 233 repeat orbits of the EOS-AM1 Spacecraft. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.",
+ "description": "MIANCAGP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Geographic Product version 1. It is a set of 233 pre-computed files. Each AGP file pertains to a single Terra orbital path. MISR production software relies on information in the AGP, such as digital terrain elevation, as input to the algorithms that generate MISR products. The AGP contains eleven fields of geographical data. This product consists primarily of geolocation data on a Space Oblique Mercator (SOM) Grid. It has 233 parts, corresponding to the 233 repeat orbits of the EOS-AM1 Spacecraft. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.",
"license": "proprietary"
},
{
@@ -144679,7 +144692,7 @@
{
"id": "MI_alk_clones_1",
"title": "Alkane mono-oxygenase clone library from Macquarie Island soil",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2008-01-01",
"end_date": "2008-03-30",
"bbox": "158.93, -54.491, 158.931, -54.49",
@@ -144692,7 +144705,7 @@
{
"id": "MI_alk_clones_1",
"title": "Alkane mono-oxygenase clone library from Macquarie Island soil",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2008-01-01",
"end_date": "2008-03-30",
"bbox": "158.93, -54.491, 158.931, -54.49",
@@ -150204,7 +150217,7 @@
{
"id": "MODIS_MAIAC_Reflectance_1700_1",
"title": "ABoVE: Corrected MODIS MAIAC Reflectance at Tower Sites, Alaska and Canada, 2000-2016",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2000-02-24",
"end_date": "2016-07-31",
"bbox": "-157.41, 42.64, -74.04, 71.32",
@@ -150217,7 +150230,7 @@
{
"id": "MODIS_MAIAC_Reflectance_1700_1",
"title": "ABoVE: Corrected MODIS MAIAC Reflectance at Tower Sites, Alaska and Canada, 2000-2016",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-02-24",
"end_date": "2016-07-31",
"bbox": "-157.41, 42.64, -74.04, 71.32",
@@ -151413,7 +151426,7 @@
{
"id": "MURI_Camouflage_0",
"title": "A Multi University Research Initiative (MURI) Camouflage Project",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "2010-06-14",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -151426,7 +151439,7 @@
{
"id": "MURI_Camouflage_0",
"title": "A Multi University Research Initiative (MURI) Camouflage Project",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-06-14",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -153103,7 +153116,7 @@
{
"id": "Main_Melt_Onset_Dates_1841_1.1",
"title": "ABoVE: Passive Microwave-derived Annual Snowpack Main Melt Onset Date Maps, 1988-2018",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1988-02-09",
"end_date": "2018-02-10",
"bbox": "-180, 51.61, -107.83, 72.41",
@@ -153116,7 +153129,7 @@
{
"id": "Main_Melt_Onset_Dates_1841_1.1",
"title": "ABoVE: Passive Microwave-derived Annual Snowpack Main Melt Onset Date Maps, 1988-2018",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1988-02-09",
"end_date": "2018-02-10",
"bbox": "-180, 51.61, -107.83, 72.41",
@@ -153129,7 +153142,7 @@
{
"id": "MaineInvasives",
"title": "A Historical Record of Sponges, Bryozoa and Ascidians on the Coast of Maine: 1843-1980 (Bigelow Laboratory for Ocean Sciences)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1843-01-01",
"end_date": "1980-12-31",
"bbox": "-70.7, 42.6, -66.9, 45.2",
@@ -153142,7 +153155,7 @@
{
"id": "MaineInvasives",
"title": "A Historical Record of Sponges, Bryozoa and Ascidians on the Coast of Maine: 1843-1980 (Bigelow Laboratory for Ocean Sciences)",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1843-01-01",
"end_date": "1980-12-31",
"bbox": "-70.7, 42.6, -66.9, 45.2",
@@ -153233,7 +153246,7 @@
{
"id": "Marine_Virus_Southern_Ocean_Evans_IPY71_NL_1",
"title": "Abundances of algae, bacteria, viruses, and heterotrophic nanoflagellates in the Southern Ocean and determination of grazing and viral lysis of the algae",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-01-16",
"end_date": "2007-02-18",
"bbox": "140, -54, 155, -43",
@@ -153246,7 +153259,7 @@
{
"id": "Marine_Virus_Southern_Ocean_Evans_IPY71_NL_1",
"title": "Abundances of algae, bacteria, viruses, and heterotrophic nanoflagellates in the Southern Ocean and determination of grazing and viral lysis of the algae",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2007-01-16",
"end_date": "2007-02-18",
"bbox": "140, -54, 155, -43",
@@ -153337,7 +153350,7 @@
{
"id": "MassGIS_GISDATA.COQHMOSAICSDVDS_POLY.xm",
"title": "2001 MrSID Mosaics DVD Index",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-02-01",
"end_date": "",
"bbox": "-73.54455, 41.19853, -69.8716, 42.908627",
@@ -153350,7 +153363,7 @@
{
"id": "MassGIS_GISDATA.COQHMOSAICSDVDS_POLY.xm",
"title": "2001 MrSID Mosaics DVD Index",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2007-02-01",
"end_date": "",
"bbox": "-73.54455, 41.19853, -69.8716, 42.908627",
@@ -153363,7 +153376,7 @@
{
"id": "MassGIS_GISDATA.COQHMOSAICS_POLY",
"title": "2001 MrSID Mosaics Index",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2002-08-01",
"end_date": "",
"bbox": "-73.54455, 41.19853, -69.8716, 42.908627",
@@ -153376,7 +153389,7 @@
{
"id": "MassGIS_GISDATA.COQHMOSAICS_POLY",
"title": "2001 MrSID Mosaics Index",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-08-01",
"end_date": "",
"bbox": "-73.54455, 41.19853, -69.8716, 42.908627",
@@ -153441,7 +153454,7 @@
{
"id": "MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY",
"title": "2005 MrSID Mosaics DVD Index",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-02-01",
"end_date": "",
"bbox": "-73.54455, 41.19853, -69.8716, 42.908627",
@@ -153454,7 +153467,7 @@
{
"id": "MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY",
"title": "2005 MrSID Mosaics DVD Index",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2007-02-01",
"end_date": "",
"bbox": "-73.54455, 41.19853, -69.8716, 42.908627",
@@ -153467,7 +153480,7 @@
{
"id": "MassGIS_GISDATA.IMG_BWORTHOS",
"title": "1:5,000 Black and White Digital Orthophoto Images",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1992-01-01",
"end_date": "1999-12-31",
"bbox": "-73.54455, 41.198524, -69.87159, 42.908627",
@@ -153480,7 +153493,7 @@
{
"id": "MassGIS_GISDATA.IMG_BWORTHOS",
"title": "1:5,000 Black and White Digital Orthophoto Images",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1992-01-01",
"end_date": "1999-12-31",
"bbox": "-73.54455, 41.198524, -69.87159, 42.908627",
@@ -153493,7 +153506,7 @@
{
"id": "MassGIS_GISDATA.IMG_COQ2001",
"title": "1:5,000 Color Ortho Imagery",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2001-04-01",
"end_date": "",
"bbox": "-73.54455, 41.19853, -69.8716, 42.908627",
@@ -153506,7 +153519,7 @@
{
"id": "MassGIS_GISDATA.IMG_COQ2001",
"title": "1:5,000 Color Ortho Imagery",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-04-01",
"end_date": "",
"bbox": "-73.54455, 41.19853, -69.8716, 42.908627",
@@ -153649,7 +153662,7 @@
{
"id": "Maxwell_Bay_Beaches_data",
"title": "Ages and Elevations of Raised Beaches around Maxwell Bay, South Shetland Islands",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "0500-01-01",
"end_date": "2007-04-30",
"bbox": "-59, -62.3, -58.833, -62.1",
@@ -153662,7 +153675,7 @@
{
"id": "Maxwell_Bay_Beaches_data",
"title": "Ages and Elevations of Raised Beaches around Maxwell Bay, South Shetland Islands",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "0500-01-01",
"end_date": "2007-04-30",
"bbox": "-59, -62.3, -58.833, -62.1",
@@ -153675,7 +153688,7 @@
{
"id": "McMurdo_Predator_Prey_Acoustics",
"title": "Acoustic records near McMurdo Station, Antarctica, 2012 - 2015.",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -153688,7 +153701,7 @@
{
"id": "McMurdo_Predator_Prey_Acoustics",
"title": "Acoustic records near McMurdo Station, Antarctica, 2012 - 2015.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -153701,7 +153714,7 @@
{
"id": "McMurdo_Predator_Prey_Adelie_Penguins",
"title": "Adelie Penguins at Cape Royds, Antarctica, 2012 - 2015.",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -153714,7 +153727,7 @@
{
"id": "McMurdo_Predator_Prey_Adelie_Penguins",
"title": "Adelie Penguins at Cape Royds, Antarctica, 2012 - 2015.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -153805,7 +153818,7 @@
{
"id": "Meteorology_Log_Commonwealth_Bay_1977_1978_1",
"title": "A log of meteorological observations made at Commonwealth Bay between 1977 and 1978",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1977-01-01",
"end_date": "1978-12-31",
"bbox": "142.5, -67, 142.5, -67",
@@ -153818,7 +153831,7 @@
{
"id": "Meteorology_Log_Commonwealth_Bay_1977_1978_1",
"title": "A log of meteorological observations made at Commonwealth Bay between 1977 and 1978",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1977-01-01",
"end_date": "1978-12-31",
"bbox": "142.5, -67, 142.5, -67",
@@ -153935,7 +153948,7 @@
{
"id": "Monthly_Hydrological_Fluxes_1647_1",
"title": "ABoVE: Monthly Hydrological Fluxes for Canada and Alaska, 1979-2018",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1979-01-01",
"end_date": "2018-04-01",
"bbox": "-172.25, 41.75, -53.43, 83.12",
@@ -153948,7 +153961,7 @@
{
"id": "Monthly_Hydrological_Fluxes_1647_1",
"title": "ABoVE: Monthly Hydrological Fluxes for Canada and Alaska, 1979-2018",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1979-01-01",
"end_date": "2018-04-01",
"bbox": "-172.25, 41.75, -53.43, 83.12",
@@ -156015,7 +156028,7 @@
{
"id": "NBId0001_101",
"title": "Africa Outline, Integrated Terrain Units, Agric. Landuse, Soils, Vegetation",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-20, -35, 55, 40",
@@ -156028,7 +156041,7 @@
{
"id": "NBId0001_101",
"title": "Africa Outline, Integrated Terrain Units, Agric. Landuse, Soils, Vegetation",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-20, -35, 55, 40",
@@ -156184,7 +156197,7 @@
{
"id": "NBId0022_101",
"title": "Africa Olson World Ecosystems",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "16, -35, 55, 40",
@@ -156197,7 +156210,7 @@
{
"id": "NBId0022_101",
"title": "Africa Olson World Ecosystems",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "16, -35, 55, 40",
@@ -156288,7 +156301,7 @@
{
"id": "NBId0036_101",
"title": "Africa Lakes and Rivers (World Data Bank II)",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-30, -45, 60, 40",
@@ -156301,7 +156314,7 @@
{
"id": "NBId0036_101",
"title": "Africa Lakes and Rivers (World Data Bank II)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-30, -45, 60, 40",
@@ -156340,7 +156353,7 @@
{
"id": "NBId0043_101",
"title": "Africa Integrated Elevation and Bathymetry",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-30, -45, 60, 40",
@@ -156353,7 +156366,7 @@
{
"id": "NBId0043_101",
"title": "Africa Integrated Elevation and Bathymetry",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-30, -45, 60, 40",
@@ -156639,7 +156652,7 @@
{
"id": "NBId0203_101",
"title": "Africa Water Balance high/lowland crops, 1987",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-20, -35, 55, 40",
@@ -156652,7 +156665,7 @@
{
"id": "NBId0203_101",
"title": "Africa Water Balance high/lowland crops, 1987",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-20, -35, 55, 40",
@@ -156704,7 +156717,7 @@
{
"id": "NBId0211_101",
"title": "Africa Irrigation Potential, Best soils, 1987",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-20, -35, 55, 40",
@@ -156717,7 +156730,7 @@
{
"id": "NBId0211_101",
"title": "Africa Irrigation Potential, Best soils, 1987",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-20, -35, 55, 40",
@@ -156730,7 +156743,7 @@
{
"id": "NBId0216_101",
"title": "Africa Number of Wet Days per Year and Wind Velocity, 1984",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-20, -35, 55, 40",
@@ -156743,7 +156756,7 @@
{
"id": "NBId0216_101",
"title": "Africa Number of Wet Days per Year and Wind Velocity, 1984",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-20, -35, 55, 40",
@@ -156808,7 +156821,7 @@
{
"id": "NBId0223_101",
"title": "Africa Zobler Soils (Texture Classes, Slope, Phases), 1987",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-20, -35, 55, 40",
@@ -156821,7 +156834,7 @@
{
"id": "NBId0223_101",
"title": "Africa Zobler Soils (Texture Classes, Slope, Phases), 1987",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-20, -35, 55, 40",
@@ -156860,7 +156873,7 @@
{
"id": "NBId0236_101",
"title": "Africa Cattle Type (East Coast Fever Project), 1989",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-20, -35, 55, 40",
@@ -156873,7 +156886,7 @@
{
"id": "NBId0236_101",
"title": "Africa Cattle Type (East Coast Fever Project), 1989",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-20, -35, 55, 40",
@@ -156886,7 +156899,7 @@
{
"id": "NBId0248_101",
"title": "Africa Wilson & Henderson-Sellers Secondary Vegetation Classes and Class Reliability, 1985",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-20, -35, 55, 40",
@@ -156899,7 +156912,7 @@
{
"id": "NBId0248_101",
"title": "Africa Wilson & Henderson-Sellers Secondary Vegetation Classes and Class Reliability, 1985",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-20, -35, 55, 40",
@@ -156990,7 +157003,7 @@
{
"id": "NCAR_DS474.0",
"title": "AARI Russian North Polar Drifting Station Data, from NSIDC",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1937-05-01",
"end_date": "1991-03-31",
"bbox": "-180, -90, 180, 90",
@@ -157003,7 +157016,7 @@
{
"id": "NCAR_DS474.0",
"title": "AARI Russian North Polar Drifting Station Data, from NSIDC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1937-05-01",
"end_date": "1991-03-31",
"bbox": "-180, -90, 180, 90",
@@ -157068,7 +157081,7 @@
{
"id": "NCAR_DS871.0",
"title": "ADAPTE: Minimum and Maximum Temperature and Relative Humidity for Latin American Cities Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2006-12-31",
"bbox": "-180, -90, 180, 90",
@@ -157081,7 +157094,7 @@
{
"id": "NCAR_DS871.0",
"title": "ADAPTE: Minimum and Maximum Temperature and Relative Humidity for Latin American Cities Data",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2006-12-31",
"bbox": "-180, -90, 180, 90",
@@ -157094,7 +157107,7 @@
{
"id": "NCEI DSI 1167_01_Not Applicable",
"title": "Active Marine Station Metadata",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-05-18",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -157107,7 +157120,7 @@
{
"id": "NCEI DSI 1167_01_Not Applicable",
"title": "Active Marine Station Metadata",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2012-05-18",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -157432,7 +157445,7 @@
{
"id": "NCEI DSI 9799_Not Applicable",
"title": "African Historical Precipitation Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1850-01-01",
"end_date": "1984-12-31",
"bbox": "-25, -31, 52, 28",
@@ -157445,7 +157458,7 @@
{
"id": "NCEI DSI 9799_Not Applicable",
"title": "African Historical Precipitation Data",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1850-01-01",
"end_date": "1984-12-31",
"bbox": "-25, -31, 52, 28",
@@ -158251,7 +158264,7 @@
{
"id": "NESP_2015_SRW",
"title": "2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2015-02-09",
"end_date": "2015-07-09",
"bbox": "113.02734, -36.59789, 138.69141, -29.993",
@@ -158264,7 +158277,7 @@
{
"id": "NESP_2015_SRW",
"title": "2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-02-09",
"end_date": "2015-07-09",
"bbox": "113.02734, -36.59789, 138.69141, -29.993",
@@ -158303,7 +158316,7 @@
{
"id": "NESP_2016_SRW_3",
"title": "2016 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-08-24",
"end_date": "2016-08-29",
"bbox": "113.02734, -36.59789, 138.69141, -29.993",
@@ -158316,7 +158329,7 @@
{
"id": "NESP_2016_SRW_3",
"title": "2016 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2016-08-24",
"end_date": "2016-08-29",
"bbox": "113.02734, -36.59789, 138.69141, -29.993",
@@ -158329,7 +158342,7 @@
{
"id": "NESP_2017_SRW_1",
"title": "2017 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-08-23",
"end_date": "2017-08-27",
"bbox": "113.02734, -36.59789, 138.69141, -29.993",
@@ -158342,7 +158355,7 @@
{
"id": "NESP_2017_SRW_1",
"title": "2017 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2017-08-23",
"end_date": "2017-08-27",
"bbox": "113.02734, -36.59789, 138.69141, -29.993",
@@ -158381,7 +158394,7 @@
{
"id": "NESP_2019_SRW_1",
"title": "2019 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-08-18",
"end_date": "2019-08-24",
"bbox": "113.02734, -36.59789, 138.69141, -29.993",
@@ -158394,7 +158407,7 @@
{
"id": "NESP_2019_SRW_1",
"title": "2019 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2019-08-18",
"end_date": "2019-08-24",
"bbox": "113.02734, -36.59789, 138.69141, -29.993",
@@ -158485,7 +158498,7 @@
{
"id": "NGA178\n _1.0",
"title": "Advanced Terrestrial Simulator",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -158498,7 +158511,7 @@
{
"id": "NGA178\n _1.0",
"title": "Advanced Terrestrial Simulator",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -158537,7 +158550,7 @@
{
"id": "NGA232\n _1.0",
"title": "A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra: Supporting Data",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -158550,7 +158563,7 @@
{
"id": "NGA232\n _1.0",
"title": "A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra: Supporting Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -161007,7 +161020,7 @@
{
"id": "NSF-ANT-1142074-penguins_1.0",
"title": "Adelie penguin satellite position and dive data for NSF-ANT-1142074 from the California Avian Data Center hosted by Point Blue Conservation Science",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2012-12-15",
"end_date": "2013-01-31",
"bbox": "165.9, -77.6, 169.4, -76.9",
@@ -161020,7 +161033,7 @@
{
"id": "NSF-ANT-1142074-penguins_1.0",
"title": "Adelie penguin satellite position and dive data for NSF-ANT-1142074 from the California Avian Data Center hosted by Point Blue Conservation Science",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-12-15",
"end_date": "2013-01-31",
"bbox": "165.9, -77.6, 169.4, -76.9",
@@ -161319,7 +161332,7 @@
{
"id": "NSF-ANT09-44042",
"title": "Acoustic Assessment of Southern Ocean Salps and Their Ecosystem Impact",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_USAPDC STAC Catalog",
"state_date": "2010-09-01",
"end_date": "2013-08-31",
"bbox": "-70, -66, -50, -59",
@@ -161332,7 +161345,7 @@
{
"id": "NSF-ANT09-44042",
"title": "Acoustic Assessment of Southern Ocean Salps and Their Ecosystem Impact",
- "catalog": "AMD_USAPDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-09-01",
"end_date": "2013-08-31",
"bbox": "-70, -66, -50, -59",
@@ -161345,7 +161358,7 @@
{
"id": "NSF-ANT09-44358",
"title": "Adelie Penguin Response to Climate Change at the Individual, Colony and Metapopulation Levels - NSF-ANT09-44358",
- "catalog": "AMD_USAPDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2010-09-15",
"end_date": "2015-08-31",
"bbox": "165.9, -77.6, 169.4, -76.9",
@@ -161358,7 +161371,7 @@
{
"id": "NSF-ANT09-44358",
"title": "Adelie Penguin Response to Climate Change at the Individual, Colony and Metapopulation Levels - NSF-ANT09-44358",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_USAPDC STAC Catalog",
"state_date": "2010-09-15",
"end_date": "2015-08-31",
"bbox": "165.9, -77.6, 169.4, -76.9",
@@ -161449,7 +161462,7 @@
{
"id": "NSF-ANT10-43485_1",
"title": "A New Reconstruction of the Last West Antarctic Ice Sheet Deglaciation in the Ross Sea",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_USAPDC STAC Catalog",
"state_date": "2011-07-01",
"end_date": "2015-06-30",
"bbox": "-160, -78, -150, -68",
@@ -161462,7 +161475,7 @@
{
"id": "NSF-ANT10-43485_1",
"title": "A New Reconstruction of the Last West Antarctic Ice Sheet Deglaciation in the Ross Sea",
- "catalog": "AMD_USAPDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-07-01",
"end_date": "2015-06-30",
"bbox": "-160, -78, -150, -68",
@@ -161475,7 +161488,7 @@
{
"id": "NSF-ANT10-43517",
"title": "A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea",
- "catalog": "AMD_USAPDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-07-01",
"end_date": "2015-06-30",
"bbox": "163.5, -78.32, 165.35, -77.57",
@@ -161488,7 +161501,7 @@
{
"id": "NSF-ANT10-43517",
"title": "A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_USAPDC STAC Catalog",
"state_date": "2011-07-01",
"end_date": "2015-06-30",
"bbox": "163.5, -78.32, 165.35, -77.57",
@@ -161501,7 +161514,7 @@
{
"id": "NSF-ANT10-43554_1",
"title": "Activation of high-elevation alluvial fans in the Transantarctic Mountains - a proxy for Plio-Pleistocene warmth along East Antarctic ice margins",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_USAPDC STAC Catalog",
"state_date": "2011-07-01",
"end_date": "2015-06-30",
"bbox": "161.5, -77.5, 161.5, -77.5",
@@ -161514,7 +161527,7 @@
{
"id": "NSF-ANT10-43554_1",
"title": "Activation of high-elevation alluvial fans in the Transantarctic Mountains - a proxy for Plio-Pleistocene warmth along East Antarctic ice margins",
- "catalog": "AMD_USAPDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-07-01",
"end_date": "2015-06-30",
"bbox": "161.5, -77.5, 161.5, -77.5",
@@ -161527,7 +161540,7 @@
{
"id": "NSF-ANT10-43621",
"title": "A Comparison of Conjugate Auroral Electojet Indices",
- "catalog": "AMD_USAPDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-06-01",
"end_date": "2013-05-31",
"bbox": "-180, -79.5, 180, -54.5",
@@ -161540,7 +161553,7 @@
{
"id": "NSF-ANT10-43621",
"title": "A Comparison of Conjugate Auroral Electojet Indices",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_USAPDC STAC Catalog",
"state_date": "2011-06-01",
"end_date": "2013-05-31",
"bbox": "-180, -79.5, 180, -54.5",
@@ -161631,7 +161644,7 @@
{
"id": "NSF-ANT12-41487",
"title": "A Planning Workshop for a McMurdo Dry Valleys Terrestrial Observation Network",
- "catalog": "AMD_USAPDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-06-01",
"end_date": "2013-05-31",
"bbox": "-180, -90, 180, 90",
@@ -161644,7 +161657,7 @@
{
"id": "NSF-ANT12-41487",
"title": "A Planning Workshop for a McMurdo Dry Valleys Terrestrial Observation Network",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_USAPDC STAC Catalog",
"state_date": "2012-06-01",
"end_date": "2013-05-31",
"bbox": "-180, -90, 180, 90",
@@ -162801,7 +162814,7 @@
{
"id": "NSIDC-0326_1",
"title": "Ablation Rates of Taylor Glacier, Antarctica",
- "catalog": "AMD_USAPDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-11-19",
"end_date": "2011-01-12",
"bbox": "160.1, -77.9, 162.2, -77.6",
@@ -162814,7 +162827,7 @@
{
"id": "NSIDC-0326_1",
"title": "Ablation Rates of Taylor Glacier, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_USAPDC STAC Catalog",
"state_date": "2002-11-19",
"end_date": "2011-01-12",
"bbox": "160.1, -77.9, 162.2, -77.6",
@@ -163191,7 +163204,7 @@
{
"id": "NSIDC-0504_1",
"title": "Alkanes in Firn Air Samples, Antarctica and Greenland",
- "catalog": "AMD_USAPDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2005-12-01",
"end_date": "2009-01-31",
"bbox": "-38.3833, -79.47, 112.09, 72.5833",
@@ -163204,7 +163217,7 @@
{
"id": "NSIDC-0504_1",
"title": "Alkanes in Firn Air Samples, Antarctica and Greenland",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_USAPDC STAC Catalog",
"state_date": "2005-12-01",
"end_date": "2009-01-31",
"bbox": "-38.3833, -79.47, 112.09, 72.5833",
@@ -164881,7 +164894,7 @@
{
"id": "NorthSlope_NEE_TVPRM_1920_1",
"title": "ABoVE: TVPRM Simulated Net Ecosystem Exchange, Alaskan North Slope, 2008-2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2008-01-01",
"end_date": "2017-12-31",
"bbox": "-177.47, 56.09, -128.59, 77.26",
@@ -164894,7 +164907,7 @@
{
"id": "NorthSlope_NEE_TVPRM_1920_1",
"title": "ABoVE: TVPRM Simulated Net Ecosystem Exchange, Alaskan North Slope, 2008-2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2008-01-01",
"end_date": "2017-12-31",
"bbox": "-177.47, 56.09, -128.59, 77.26",
@@ -166454,7 +166467,7 @@
{
"id": "OCTS_L1_1",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Data Regional Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -166467,7 +166480,7 @@
{
"id": "OCTS_L1_1",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Data Regional Data",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -166506,7 +166519,7 @@
{
"id": "OCTS_L2_IOP_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Regional Data",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -166519,7 +166532,7 @@
{
"id": "OCTS_L2_IOP_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Regional Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -166584,7 +166597,7 @@
{
"id": "OCTS_L2_OC_2022.0",
"title": "ADEOS-I OCTS Level-2 Regional Ocean Color (OC) Data, version 2022.0",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_CLOUD STAC Catalog",
"state_date": "1996-10-31",
"end_date": "1997-06-29",
"bbox": "-180, -90, 180, 90",
@@ -166597,7 +166610,7 @@
{
"id": "OCTS_L2_OC_2022.0",
"title": "ADEOS-I OCTS Level-2 Regional Ocean Color (OC) Data, version 2022.0",
- "catalog": "OB_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-10-31",
"end_date": "1997-06-29",
"bbox": "-180, -90, 180, 90",
@@ -166610,7 +166623,7 @@
{
"id": "OCTS_L3b_CHL_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll (CHL) Global Binned Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -166623,7 +166636,7 @@
{
"id": "OCTS_L3b_CHL_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll (CHL) Global Binned Data",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -166662,7 +166675,7 @@
{
"id": "OCTS_L3b_IOP_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Binned Data",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -166675,7 +166688,7 @@
{
"id": "OCTS_L3b_IOP_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Binned Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -166688,7 +166701,7 @@
{
"id": "OCTS_L3b_IOP_2022.0",
"title": "ADEOS-I OCTS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_CLOUD STAC Catalog",
"state_date": "1996-10-31",
"end_date": "1997-06-29",
"bbox": "-180, -90, 180, 90",
@@ -166701,7 +166714,7 @@
{
"id": "OCTS_L3b_IOP_2022.0",
"title": "ADEOS-I OCTS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0",
- "catalog": "OB_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-10-31",
"end_date": "1997-06-29",
"bbox": "-180, -90, 180, 90",
@@ -166740,7 +166753,7 @@
{
"id": "OCTS_L3b_KD_2022.0",
"title": "ADEOS-I OCTS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0",
- "catalog": "OB_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-10-31",
"end_date": "1997-06-29",
"bbox": "-180, -90, 180, 90",
@@ -166753,7 +166766,7 @@
{
"id": "OCTS_L3b_KD_2022.0",
"title": "ADEOS-I OCTS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_CLOUD STAC Catalog",
"state_date": "1996-10-31",
"end_date": "1997-06-29",
"bbox": "-180, -90, 180, 90",
@@ -167052,7 +167065,7 @@
{
"id": "OCTS_L3m_IOP_2022.0",
"title": "ADEOS-I OCTS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_CLOUD STAC Catalog",
"state_date": "1996-10-31",
"end_date": "1997-06-29",
"bbox": "-180, -90, 180, 90",
@@ -167065,7 +167078,7 @@
{
"id": "OCTS_L3m_IOP_2022.0",
"title": "ADEOS-I OCTS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0",
- "catalog": "OB_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-10-31",
"end_date": "1997-06-29",
"bbox": "-180, -90, 180, 90",
@@ -167078,7 +167091,7 @@
{
"id": "OCTS_L3m_KD_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Mapped Data",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -167091,7 +167104,7 @@
{
"id": "OCTS_L3m_KD_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Mapped Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -167104,7 +167117,7 @@
{
"id": "OCTS_L3m_KD_2022.0",
"title": "ADEOS-I OCTS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_CLOUD STAC Catalog",
"state_date": "1996-10-31",
"end_date": "1997-06-29",
"bbox": "-180, -90, 180, 90",
@@ -167117,7 +167130,7 @@
{
"id": "OCTS_L3m_KD_2022.0",
"title": "ADEOS-I OCTS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0",
- "catalog": "OB_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-10-31",
"end_date": "1997-06-29",
"bbox": "-180, -90, 180, 90",
@@ -167130,7 +167143,7 @@
{
"id": "OCTS_L3m_PAR_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Mapped Data",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -167143,7 +167156,7 @@
{
"id": "OCTS_L3m_PAR_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Mapped Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -167156,7 +167169,7 @@
{
"id": "OCTS_L3m_PAR_2022.0",
"title": "ADEOS-I OCTS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_CLOUD STAC Catalog",
"state_date": "1996-10-31",
"end_date": "1997-06-29",
"bbox": "-180, -90, 180, 90",
@@ -167169,7 +167182,7 @@
{
"id": "OCTS_L3m_PAR_2022.0",
"title": "ADEOS-I OCTS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0",
- "catalog": "OB_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-10-31",
"end_date": "1997-06-29",
"bbox": "-180, -90, 180, 90",
@@ -167182,7 +167195,7 @@
{
"id": "OCTS_L3m_PIC_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Mapped Data",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -167195,7 +167208,7 @@
{
"id": "OCTS_L3m_PIC_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Mapped Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -167234,7 +167247,7 @@
{
"id": "OCTS_L3m_POC_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Mapped Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -167247,7 +167260,7 @@
{
"id": "OCTS_L3m_POC_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Mapped Data",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -167286,7 +167299,7 @@
{
"id": "OCTS_L3m_RRS_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Mapped Data",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -167299,7 +167312,7 @@
{
"id": "OCTS_L3m_RRS_2014",
"title": "ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Mapped Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "1996-11-01",
"end_date": "1997-06-30",
"bbox": "-180, -90, 180, 90",
@@ -167312,7 +167325,7 @@
{
"id": "OCTS_L3m_RRS_2022.0",
"title": "ADEOS-I OCTS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0",
- "catalog": "OB_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-10-31",
"end_date": "1997-06-29",
"bbox": "-180, -90, 180, 90",
@@ -167325,7 +167338,7 @@
{
"id": "OCTS_L3m_RRS_2022.0",
"title": "ADEOS-I OCTS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_CLOUD STAC Catalog",
"state_date": "1996-10-31",
"end_date": "1997-06-29",
"bbox": "-180, -90, 180, 90",
@@ -167364,7 +167377,7 @@
{
"id": "OFR_94-212",
"title": "A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1980-05-01",
"end_date": "1988-09-06",
"bbox": "-122, 46, -122, 46",
@@ -167377,7 +167390,7 @@
{
"id": "OFR_94-212",
"title": "A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1980-05-01",
"end_date": "1988-09-06",
"bbox": "-122, 46, -122, 46",
@@ -167390,7 +167403,7 @@
{
"id": "OFR_95-55",
"title": "A Compilation of Sulphur Dioxide and Carbon Dioxide Emission-Rate Data from Cook Inlet Volcanoes, Alaska During the Period from 1990 to 1994",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1990-03-20",
"end_date": "1994-07-07",
"bbox": "-154, 56, -152, 62",
@@ -167403,7 +167416,7 @@
{
"id": "OFR_95-55",
"title": "A Compilation of Sulphur Dioxide and Carbon Dioxide Emission-Rate Data from Cook Inlet Volcanoes, Alaska During the Period from 1990 to 1994",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1990-03-20",
"end_date": "1994-07-07",
"bbox": "-154, 56, -152, 62",
@@ -168726,19 +168739,6 @@
"description": "This Level-2G daily global gridded product OMCLDRRG is based on the pixel level OMI Level-2 CLDRR product OMCLDRR. This level-2G global cloud product (OMCLDRRG) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The algorithm lead for the products OMCLDRR and OMCLDRRG is NASA OMI scientist Dr. Joanna Joinner. OMCLDRRG data product is a special Level-2G Gridded Global Product where pixel level data (OMCLDRR)are binned into 0.25x0.25 degree global grids. It contains the OMCLDRR data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMCLDRRG data products are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (~14 orbits). The average file size for the OMCLDRRG data product is about 75 Mbytes.",
"license": "proprietary"
},
- {
- "id": "OMCLDRR_003",
- "title": "OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 (OMCLDRR) at GES DISC",
- "catalog": "GES_DISC STAC Catalog",
- "state_date": "2004-10-01",
- "end_date": "",
- "bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/GES_DISC/collections/OMCLDRR_003",
- "description": "The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.",
- "license": "proprietary"
- },
{
"id": "OMCLDRR_003",
"title": "OMI/Aura Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 NRT",
@@ -168752,6 +168752,19 @@
"description": "The reprocessed Aura OMI Version 003 Level 2 Cloud Data Product OMCLDRR is made available (in April 2012) to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). http://disc.gsfc.nasa.gov/Aura/OMI/omcldrr_v003.shtml ) Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product (OMCLDRR) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. OMCLDRR files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml . A short OMCLDRR Readme Document that includes brief algorithm description and data quality is also provided by the OMCLDRR Algorithm lead. The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products are archived at the NASA GES DISC. For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/instruments/omi/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMCLDRR data file contains one swath which consists of two groups: Data fields: Two Effective Cloud Fraction and two Cloud Top Pressures that are based on two different clear and cloudy scene reflectivity criteria, Chlorophyll Amount, Effective Reflectivity (394.1 micron), UV Aerosol Index (based on 360 and 388 nm), and many Auxiliary Algorithm Parameter and Quality Flags. Geolocation Fields: Latitude, Longitude, Time, Solar Zenith Angle, Viewing Zenith Angle, Relative Azimuth Angle, Terrain Height, and Ground Pixel Quality Flags. OMI Atmospheric data and documents are available from the following sites: http://disc.gsfc.nasa.gov/Aura/OMI/ http://mirador.gsfc.nasa.gov/",
"license": "proprietary"
},
+ {
+ "id": "OMCLDRR_003",
+ "title": "OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 (OMCLDRR) at GES DISC",
+ "catalog": "GES_DISC STAC Catalog",
+ "state_date": "2004-10-01",
+ "end_date": "",
+ "bbox": "-180, -90, 180, 90",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/GES_DISC/collections/OMCLDRR_003",
+ "description": "The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.",
+ "license": "proprietary"
+ },
{
"id": "OMCLDRR_004",
"title": "OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V004 (OMCLDRR) at GES DISC",
@@ -169883,19 +169896,6 @@
"description": "This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved Without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains UV aerosol index, cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes.",
"license": "proprietary"
},
- {
- "id": "OMTO3_003",
- "title": "OMI/Aura Ozone (O3) Total Column 1-Orbit L2 Swath 13x24 km V003 NRT",
- "catalog": "OMINRT STAC Catalog",
- "state_date": "2004-07-15",
- "end_date": "",
- "bbox": "-180, -90, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000140-OMINRT.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000140-OMINRT.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMTO3_003",
- "description": "The OMI/Aura Level-2 Total Column Ozone Data Product OMTO3 Near Real Time data is made available from the OMI SIPS NASA for the public access. The Ozone Monitoring Instrument (OMI)was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator's (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. This level-2 global total column ozone product (OMTO3)is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI additional hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrieval (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and extensive quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3 and the algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia ( Pawan.K.Bhartia@nasa.gov). OMTO3 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is about 35 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMTO3 data file contains one swath which consists of two groups: Data fields: OMI Total Ozone,Effective Reflectivity (331 - 360 nm), N-value, Cloud Fraction, Cloud Top Pressure, O3 below Cloud, UV Aerosol Index, SO2 index, Wavelength used in the algorithm, many Auxiliary Algorithm Parameter and Quality Flags Geolocation Fields: Latitude, Longitude, Time, Relative Azimuth, Solar Zenith and Azimuth, Viewing Zenith and Azimuth angles, Spacecraft Altitude, Latitude, Longitude, Terrain Height, Ground Pixel Quality Flags.For the full set of Aura data products available from the GES DISC, please see the link http://disc.sci.gsfc.nasa.gov/Aura/ .",
- "license": "proprietary"
- },
{
"id": "OMTO3_003",
"title": "OMI/Aura Ozone(O3) Total Column 1-Orbit L2 Swath 13x24 km V003 (OMTO3) at GES DISC",
@@ -169909,6 +169909,19 @@
"description": "The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Version 003) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI provides two Level-2 (OMTO3 and OMDOAO3) total column ozone products at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB.",
"license": "proprietary"
},
+ {
+ "id": "OMTO3_003",
+ "title": "OMI/Aura Ozone (O3) Total Column 1-Orbit L2 Swath 13x24 km V003 NRT",
+ "catalog": "OMINRT STAC Catalog",
+ "state_date": "2004-07-15",
+ "end_date": "",
+ "bbox": "-180, -90, 180, 90",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000140-OMINRT.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000000140-OMINRT.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/OMINRT/collections/OMTO3_003",
+ "description": "The OMI/Aura Level-2 Total Column Ozone Data Product OMTO3 Near Real Time data is made available from the OMI SIPS NASA for the public access. The Ozone Monitoring Instrument (OMI)was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator's (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. This level-2 global total column ozone product (OMTO3)is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI additional hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrieval (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and extensive quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3 and the algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia ( Pawan.K.Bhartia@nasa.gov). OMTO3 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is about 35 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMTO3 data file contains one swath which consists of two groups: Data fields: OMI Total Ozone,Effective Reflectivity (331 - 360 nm), N-value, Cloud Fraction, Cloud Top Pressure, O3 below Cloud, UV Aerosol Index, SO2 index, Wavelength used in the algorithm, many Auxiliary Algorithm Parameter and Quality Flags Geolocation Fields: Latitude, Longitude, Time, Relative Azimuth, Solar Zenith and Azimuth, Viewing Zenith and Azimuth angles, Spacecraft Altitude, Latitude, Longitude, Terrain Height, Ground Pixel Quality Flags.For the full set of Aura data products available from the GES DISC, please see the link http://disc.sci.gsfc.nasa.gov/Aura/ .",
+ "license": "proprietary"
+ },
{
"id": "OMTO3_CPR_003",
"title": "OMI/Aura Level 2 Ozone (O3) Total Column 1-Orbit Subset and Collocated Swath along CloudSat track 200-km wide at 13x24 km2 resolution",
@@ -171485,7 +171498,7 @@
{
"id": "PASSCAL_ABBA",
"title": "Adirondack Broad Band Array (ABBA)",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1995-01-01",
"end_date": "1996-12-31",
"bbox": "-74.5, 43.5, -73.8, 44.4",
@@ -171498,7 +171511,7 @@
{
"id": "PASSCAL_ABBA",
"title": "Adirondack Broad Band Array (ABBA)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1995-01-01",
"end_date": "1996-12-31",
"bbox": "-74.5, 43.5, -73.8, 44.4",
@@ -171511,7 +171524,7 @@
{
"id": "PASSCAL_ALAR",
"title": "Aleutian Arc Seismic Experiment",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -171524,7 +171537,7 @@
{
"id": "PASSCAL_ALAR",
"title": "Aleutian Arc Seismic Experiment",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -171563,7 +171576,7 @@
{
"id": "PASSCAL_WABASH",
"title": "A comprehensive geophysical investigation to assess seismic hazards in the coassesment of seismicity in the Wabash Valley",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1995-11-01",
"end_date": "1996-06-30",
"bbox": "-88.1706, 38.2057, -88.1706, 38.2057",
@@ -171576,7 +171589,7 @@
{
"id": "PASSCAL_WABASH",
"title": "A comprehensive geophysical investigation to assess seismic hazards in the coassesment of seismicity in the Wabash Valley",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1995-11-01",
"end_date": "1996-06-30",
"bbox": "-88.1706, 38.2057, -88.1706, 38.2057",
@@ -172018,7 +172031,7 @@
{
"id": "POSTER-03CYCLONE_Not Applicable",
"title": "2003 Tropical Cyclones of the World",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2003-01-08",
"end_date": "2003-12-21",
"bbox": "-180, -65, 180, 65",
@@ -172031,7 +172044,7 @@
{
"id": "POSTER-03CYCLONE_Not Applicable",
"title": "2003 Tropical Cyclones of the World",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-08",
"end_date": "2003-12-21",
"bbox": "-180, -65, 180, 65",
@@ -172044,7 +172057,7 @@
{
"id": "POSTER-2004 Hurricanes_Not Applicable",
"title": "2004 Landfalling Hurricanes Poster",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2004-08-13",
"end_date": "2004-09-25",
"bbox": "-91, 8, -33, 46.5",
@@ -172057,7 +172070,7 @@
{
"id": "POSTER-2004 Hurricanes_Not Applicable",
"title": "2004 Landfalling Hurricanes Poster",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-08-13",
"end_date": "2004-09-25",
"bbox": "-91, 8, -33, 46.5",
@@ -172070,7 +172083,7 @@
{
"id": "POSTER-2005 Atl Hurricanes_Not Applicable",
"title": "2005 Atlantic Hurricanes Poster",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2005-07-03",
"end_date": "2005-12-08",
"bbox": "-97, 20, -65, 40.5",
@@ -172083,7 +172096,7 @@
{
"id": "POSTER-2005 Atl Hurricanes_Not Applicable",
"title": "2005 Atlantic Hurricanes Poster",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2005-07-03",
"end_date": "2005-12-08",
"bbox": "-97, 20, -65, 40.5",
@@ -172720,7 +172733,7 @@
{
"id": "Passive_Microwave_Snowoff_Data_1711_1.1",
"title": "ABoVE: Passive Microwave-derived Annual Snowoff Date Maps, 1988-2018",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1988-01-01",
"end_date": "2018-12-31",
"bbox": "-180, 37.98, 180, 90",
@@ -172733,7 +172746,7 @@
{
"id": "Passive_Microwave_Snowoff_Data_1711_1.1",
"title": "ABoVE: Passive Microwave-derived Annual Snowoff Date Maps, 1988-2018",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1988-01-01",
"end_date": "2018-12-31",
"bbox": "-180, 37.98, 180, 90",
@@ -172837,7 +172850,7 @@
{
"id": "Permafrost_Thaw_Depth_YK_1598_1",
"title": "ABoVE: Permafrost Measurements and Distribution Across the Y-K Delta, Alaska, 2016",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2009-06-27",
"end_date": "2016-07-17",
"bbox": "-165.69, 61.17, -165.03, 61.29",
@@ -172850,7 +172863,7 @@
{
"id": "Permafrost_Thaw_Depth_YK_1598_1",
"title": "ABoVE: Permafrost Measurements and Distribution Across the Y-K Delta, Alaska, 2016",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2009-06-27",
"end_date": "2016-07-17",
"bbox": "-165.69, 61.17, -165.03, 61.29",
@@ -173058,7 +173071,7 @@
{
"id": "PolInSAR_Canopy_Height_1589_1",
"title": "AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-02-27",
"end_date": "2016-03-08",
"bbox": "9.29, -0.35, 11.83, 0.24",
@@ -173071,7 +173084,7 @@
{
"id": "PolInSAR_Canopy_Height_1589_1",
"title": "AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-02-27",
"end_date": "2016-03-08",
"bbox": "9.29, -0.35, 11.83, 0.24",
@@ -173123,7 +173136,7 @@
{
"id": "Polarimetric_CT_1601_1",
"title": "AfriSAR: Canopy Structure Derived from PolInSAR and Coherence TomoSAR NISAR tools",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-02-25",
"end_date": "2016-03-08",
"bbox": "9.17, -2.08, 11.86, 0.61",
@@ -173136,7 +173149,7 @@
{
"id": "Polarimetric_CT_1601_1",
"title": "AfriSAR: Canopy Structure Derived from PolInSAR and Coherence TomoSAR NISAR tools",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-02-25",
"end_date": "2016-03-08",
"bbox": "9.17, -2.08, 11.86, 0.61",
@@ -173188,7 +173201,7 @@
{
"id": "PostFire_Tree_Regeneration_1955_1.1",
"title": "ABoVE: Synthesis of Post-Fire Regeneration Across Boreal North America",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1989-01-01",
"end_date": "2018-12-31",
"bbox": "-152.2, 49.12, -71.01, 66.96",
@@ -173201,7 +173214,7 @@
{
"id": "PostFire_Tree_Regeneration_1955_1.1",
"title": "ABoVE: Synthesis of Post-Fire Regeneration Across Boreal North America",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1989-01-01",
"end_date": "2018-12-31",
"bbox": "-152.2, 49.12, -71.01, 66.96",
@@ -173214,7 +173227,7 @@
{
"id": "Post_Fire_C_Emissions_1787_1",
"title": "ABoVE: Spatial Estimates of Carbon Combustion from Wildfires across SK, Canada, 2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-04-06",
"end_date": "2015-08-11",
"bbox": "-116.06, 51.19, -100.17, 61.24",
@@ -173227,7 +173240,7 @@
{
"id": "Post_Fire_C_Emissions_1787_1",
"title": "ABoVE: Spatial Estimates of Carbon Combustion from Wildfires across SK, Canada, 2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2015-04-06",
"end_date": "2015-08-11",
"bbox": "-116.06, 51.19, -100.17, 61.24",
@@ -173487,7 +173500,7 @@
{
"id": "Profile_based_PBL_heights_1706_1.1",
"title": "ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern USA",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-07-18",
"end_date": "2019-07-26",
"bbox": "-106.36, 28.65, -73.13, 49.49",
@@ -173500,7 +173513,7 @@
{
"id": "Profile_based_PBL_heights_1706_1.1",
"title": "ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern USA",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-07-18",
"end_date": "2019-07-26",
"bbox": "-106.36, 28.65, -73.13, 49.49",
@@ -174527,7 +174540,7 @@
{
"id": "Radial_Growth_PRI_1781_1",
"title": "ABoVE: Photochemical Reflectance and Tree Growth, Brooks Range, Alaska, 2018-2019",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2018-05-01",
"end_date": "2019-09-13",
"bbox": "-149.76, 67.97, -149.72, 68.02",
@@ -174540,7 +174553,7 @@
{
"id": "Radial_Growth_PRI_1781_1",
"title": "ABoVE: Photochemical Reflectance and Tree Growth, Brooks Range, Alaska, 2018-2019",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2018-05-01",
"end_date": "2019-09-13",
"bbox": "-149.76, 67.97, -149.72, 68.02",
@@ -174553,7 +174566,7 @@
{
"id": "Rain-on-Snow_Data_1611_1",
"title": "ABoVE: Rain-on-Snow Frequency and Distribution during Cold Seasons, Alaska, 2003-2016",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-11-01",
"end_date": "2016-12-31",
"bbox": "-175.4, 48.62, -111.54, 73.85",
@@ -174566,7 +174579,7 @@
{
"id": "Rain-on-Snow_Data_1611_1",
"title": "ABoVE: Rain-on-Snow Frequency and Distribution during Cold Seasons, Alaska, 2003-2016",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2002-11-01",
"end_date": "2016-12-31",
"bbox": "-175.4, 48.62, -111.54, 73.85",
@@ -174761,7 +174774,7 @@
{
"id": "RiSCC_Outcomes_Bibliography_1",
"title": "A bibliography containing references to the outcomes of the RiSCC project from the Antarctic and subantarctic regions",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1994-01-01",
"end_date": "2006-12-31",
"bbox": "-180, -70, 180, -50",
@@ -174774,7 +174787,7 @@
{
"id": "RiSCC_Outcomes_Bibliography_1",
"title": "A bibliography containing references to the outcomes of the RiSCC project from the Antarctic and subantarctic regions",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1994-01-01",
"end_date": "2006-12-31",
"bbox": "-180, -70, 180, -50",
@@ -174787,7 +174800,7 @@
{
"id": "RiSCC_Research_Support_Bibliography_1",
"title": "A bibliography containing references to the research support of the RiSCC project from the Antarctic and subantarctic regions",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1875-01-01",
"end_date": "2004-12-31",
"bbox": "-180, -70, 180, -50",
@@ -174800,7 +174813,7 @@
{
"id": "RiSCC_Research_Support_Bibliography_1",
"title": "A bibliography containing references to the research support of the RiSCC project from the Antarctic and subantarctic regions",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1875-01-01",
"end_date": "2004-12-31",
"bbox": "-180, -70, 180, -50",
@@ -174813,7 +174826,7 @@
{
"id": "River_Ice_Breakup_Freezeup_1697_1",
"title": "ABoVE: River Ice Breakup and Freeze-up Stages, Yukon River Basin, Alaska, 1972-2016",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1972-11-04",
"end_date": "2016-11-30",
"bbox": "-160.07, 62.9, -142.99, 66.36",
@@ -174826,7 +174839,7 @@
{
"id": "River_Ice_Breakup_Freezeup_1697_1",
"title": "ABoVE: River Ice Breakup and Freeze-up Stages, Yukon River Basin, Alaska, 1972-2016",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1972-11-04",
"end_date": "2016-11-30",
"bbox": "-160.07, 62.9, -142.99, 66.36",
@@ -178492,7 +178505,7 @@
{
"id": "SIPEX_II_AUV_1",
"title": "3-D mapping of sea ice draft with an autonomous underwater vehicle",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-09-28",
"end_date": "2012-10-13",
"bbox": "115, -65, 125, -60",
@@ -178505,7 +178518,7 @@
{
"id": "SIPEX_II_AUV_1",
"title": "3-D mapping of sea ice draft with an autonomous underwater vehicle",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2012-09-28",
"end_date": "2012-10-13",
"bbox": "115, -65, 125, -60",
@@ -178531,7 +178544,7 @@
{
"id": "SIPEX_II_Albedo_1",
"title": "Albedos for 300-2500nm for thin sea ice covered with frost flowers, nilas, snow, and slush collected during SIPEX II",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2012-09-14",
"end_date": "2012-11-04",
"bbox": "113, -66, 147, -42",
@@ -178544,7 +178557,7 @@
{
"id": "SIPEX_II_Albedo_1",
"title": "Albedos for 300-2500nm for thin sea ice covered with frost flowers, nilas, snow, and slush collected during SIPEX II",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-09-14",
"end_date": "2012-11-04",
"bbox": "113, -66, 147, -42",
@@ -179077,7 +179090,7 @@
{
"id": "SIZEX-89-SAR",
"title": "Airborne X- and C-band SAR Images of Sea Ice in the Barents Sea",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1989-02-15",
"end_date": "1989-02-27",
"bbox": "15, 74, 25, 77",
@@ -179090,7 +179103,7 @@
{
"id": "SIZEX-89-SAR",
"title": "Airborne X- and C-band SAR Images of Sea Ice in the Barents Sea",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1989-02-15",
"end_date": "1989-02-27",
"bbox": "15, 74, 25, 77",
@@ -179662,7 +179675,7 @@
{
"id": "SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0",
"title": "ACEX 2004 ODEN TRACK",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-08-08",
"end_date": "2004-09-13",
"bbox": "19.045, 69.727, 175.94, 89.999",
@@ -179675,7 +179688,7 @@
{
"id": "SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0",
"title": "ACEX 2004 ODEN TRACK",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2004-08-08",
"end_date": "2004-09-13",
"bbox": "19.045, 69.727, 175.94, 89.999",
@@ -179688,7 +179701,7 @@
{
"id": "SMHI_IPY_ACEX-2004-Seismic",
"title": "ACEX 2004 Seismic",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-08-08",
"end_date": "2004-09-13",
"bbox": "139.0632, 87.917, 140.31, 87.977",
@@ -179701,7 +179714,7 @@
{
"id": "SMHI_IPY_ACEX-2004-Seismic",
"title": "ACEX 2004 Seismic",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2004-08-08",
"end_date": "2004-09-13",
"bbox": "139.0632, 87.917, 140.31, 87.977",
@@ -179714,7 +179727,7 @@
{
"id": "SMHI_IPY_ACEX-2004-Sites_1.0",
"title": "ACEX 2004 Sites",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-08-08",
"end_date": "2004-09-13",
"bbox": "-4.05029, 69.727, 19.045, 89.999",
@@ -179727,7 +179740,7 @@
{
"id": "SMHI_IPY_ACEX-2004-Sites_1.0",
"title": "ACEX 2004 Sites",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2004-08-08",
"end_date": "2004-09-13",
"bbox": "-4.05029, 69.727, 19.045, 89.999",
@@ -179740,7 +179753,7 @@
{
"id": "SMHI_IPY_AGAVE2007-track_1.0",
"title": "AGAVE2007 track",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2007-07-01",
"end_date": "2007-08-09",
"bbox": "-180, -90, 180, 90",
@@ -179753,7 +179766,7 @@
{
"id": "SMHI_IPY_AGAVE2007-track_1.0",
"title": "AGAVE2007 track",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2007-07-01",
"end_date": "2007-08-09",
"bbox": "-180, -90, 180, 90",
@@ -182493,6 +182506,32 @@
"description": "The data set presents snow pit measurements collected during the NASA SnowEx March 2023 Intensive Observation Period (IOP) in Alaska, USA to use for calibration and validation with coincident airborne SWESARR and lidar measurements as part of the strategy focused on snow water equivalence (SWE) and snow depth (HS). In total, 170 snow pits were excavated between the five sites at locations representing a range of snow depth, vegetation, and topographic conditions. Three study areas represented boreal forest snow near Fairbanks, AK: Farmers Loop Creamers Field (FLCF), Caribou Poker Creek Research Watershed (CPCRW), and Bonanza Creek Experimental Forest (BCEF). Two study areas represented Arctic tundra snow: Arctic Coastal Plain (ACP) and Upper Kuparuk Toolik (UKT).",
"license": "proprietary"
},
+ {
+ "id": "SNEX23_OCT23_GSR_1",
+ "title": "SnowEx23 October 23 Ground Surface Roughness Reconstruction V001",
+ "catalog": "NSIDC_ECS STAC Catalog",
+ "state_date": "2023-10-17",
+ "end_date": "2023-10-28",
+ "bbox": "-149.6, 64.8, -147.5, 68.7",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3333536572-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3333536572-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SNEX23_OCT23_GSR_1",
+ "description": "This data set presents ground surface roughness data collected during the NASA SnowEx 2023 field campaign between 17 and 28 October 2023. The data are formatted as point clouds, compiled from images acquired using a digital camera. Images were collected from 22 snow pits located across three study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Caribou Poker Creek watershed (CPCW) and Farmers Loop Creamers Field (FLCF), two boreal forest sites near Fairbanks, Alaska. The raw imagery from which these data are derived are available as SnowEx23 Oct23 Ground Surface Roughness Imagery, Version 1.",
+ "license": "proprietary"
+ },
+ {
+ "id": "SNEX23_OCT23_GSR_Raw_1",
+ "title": "SnowEx23 Oct23 Ground Surface Roughness Imagery V001",
+ "catalog": "NSIDC_ECS STAC Catalog",
+ "state_date": "2023-10-17",
+ "end_date": "2023-10-28",
+ "bbox": "-149.6, 64.8, -147.5, 68.7",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3333536645-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3333536645-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SNEX23_OCT23_GSR_Raw_1",
+ "description": "This data set presents photographs of snow pit ground surface collected using a digital camera during the NASA SnowEx 2023 field campaign between 17 and 28 October 2023. The images were collected from 22 snow pits located across three study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Caribou Poker Creek watershed (CPCW) and Farmers Loop Creamers Field (FLCF), two boreal forest sites near Fairbanks, Alaska. These photographs were used to derive point cloud data representative of ground surface roughness, which are available as SnowEx23 Oct23 Ground Surface Roughness Reconstruction, Version 1.",
+ "license": "proprietary"
+ },
{
"id": "SNEX23_SSA_1",
"title": "SnowEx23 Laser Snow Microstructure Specific Surface Area Data V001",
@@ -183107,7 +183146,7 @@
{
"id": "SNPEMAWSON04-05_1",
"title": "A GIS dataset of Snow Petrel nests mapped in the Mawson region during the 2004-2005 season",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-12-10",
"end_date": "2005-04-25",
"bbox": "62.25, -67.6, 63.5, -67.3",
@@ -183120,7 +183159,7 @@
{
"id": "SNPEMAWSON04-05_1",
"title": "A GIS dataset of Snow Petrel nests mapped in the Mawson region during the 2004-2005 season",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2004-12-10",
"end_date": "2005-04-25",
"bbox": "62.25, -67.6, 63.5, -67.3",
@@ -183497,7 +183536,7 @@
{
"id": "SOE_greenhouse_gas_1",
"title": "Air sampling for greenhouse gas concentrations and associated species",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1984-11-01",
"end_date": "",
"bbox": "62, -90, 159, -41",
@@ -183510,7 +183549,7 @@
{
"id": "SOE_greenhouse_gas_1",
"title": "Air sampling for greenhouse gas concentrations and associated species",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1984-11-01",
"end_date": "",
"bbox": "62, -90, 159, -41",
@@ -184550,26 +184589,26 @@
{
"id": "SPL1AP_002",
"title": "SMAP L1A Radiometer Time-Ordered Parsed Telemetry V002",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -86.4, 180, 86.4",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000001801-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000001801-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1AP_002",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661641-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661641-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1AP_002",
"description": "Each Level-1A (L1A) granule incorporates all radiometer data downlinked from the Soil Moisture Active Passive (SMAP) spacecraft for one specific half orbit. The data are scaled instrument counts of the following:
- The first four raw moments of the fullband channel for both vertical and horizontal polarizations
- The complex cross-correlations of the fullband channel
- The 16 subband channels for both vertical and horizontal polarizations
",
"license": "proprietary"
},
{
"id": "SPL1AP_002",
"title": "SMAP L1A Radiometer Time-Ordered Parsed Telemetry V002",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -86.4, 180, 86.4",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661641-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661641-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1AP_002",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1000001801-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1000001801-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1AP_002",
"description": "Each Level-1A (L1A) granule incorporates all radiometer data downlinked from the Soil Moisture Active Passive (SMAP) spacecraft for one specific half orbit. The data are scaled instrument counts of the following:
- The first four raw moments of the fullband channel for both vertical and horizontal polarizations
- The complex cross-correlations of the fullband channel
- The 16 subband channels for both vertical and horizontal polarizations
",
"license": "proprietary"
},
@@ -184771,26 +184810,26 @@
{
"id": "SPL1BTB_006",
"title": "SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V006",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -86.4, 180, 86.4",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463679-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463679-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1BTB_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661904-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661904-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1BTB_006",
"description": "This Level-1B (L1B) product provides calibrated estimates of time-ordered geolocated brightness temperatures measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band brightness temperatures are referenced to the Earth's surface with undesired and erroneous radiometric sources removed.",
"license": "proprietary"
},
{
"id": "SPL1BTB_006",
"title": "SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V006",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -86.4, 180, 86.4",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661904-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938661904-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1BTB_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463679-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463679-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1BTB_006",
"description": "This Level-1B (L1B) product provides calibrated estimates of time-ordered geolocated brightness temperatures measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band brightness temperatures are referenced to the Earth's surface with undesired and erroneous radiometric sources removed.",
"license": "proprietary"
},
@@ -184798,7 +184837,7 @@
"id": "SPL1BTB_NRT_105",
"title": "Near Real-time SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V105",
"catalog": "NSIDC_ECS STAC Catalog",
- "state_date": "2024-12-05",
+ "state_date": "2024-12-23",
"end_date": "",
"bbox": "-180, -86.4, 180, 86.4",
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C2257958430-NSIDC_ECS.umm_json",
@@ -184953,26 +184992,26 @@
{
"id": "SPL1CTB_E_004",
"title": "SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures V004",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -85.044, 180, 85.044",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463717-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463717-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1CTB_E_004",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663435-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663435-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1CTB_E_004",
"description": "This enhanced Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract enhanced information from SMAP antenna temperatures before they are converted to brightness temperatures. The resulting brightness temperatures are posted to an Earth-fixed, 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal.",
"license": "proprietary"
},
{
"id": "SPL1CTB_E_004",
"title": "SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures V004",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -85.044, 180, 85.044",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663435-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663435-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL1CTB_E_004",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463717-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463717-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL1CTB_E_004",
"description": "This enhanced Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract enhanced information from SMAP antenna temperatures before they are converted to brightness temperatures. The resulting brightness temperatures are posted to an Earth-fixed, 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal.",
"license": "proprietary"
},
@@ -185122,26 +185161,26 @@
{
"id": "SPL2SMAP_S_003",
"title": "SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -60, 180, 60",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMAP_S_003",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMAP_S_003",
"description": "This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution.",
"license": "proprietary"
},
{
"id": "SPL2SMAP_S_003",
"title": "SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -60, 180, 60",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMAP_S_003",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMAP_S_003",
"description": "This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution.",
"license": "proprietary"
},
@@ -185200,26 +185239,26 @@
{
"id": "SPL2SMP_E_006",
"title": "SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -85.044, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMP_E_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMP_E_006",
"description": "This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product].",
"license": "proprietary"
},
{
"id": "SPL2SMP_E_006",
"title": "SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -85.044, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL2SMP_E_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL2SMP_E_006",
"description": "This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product].",
"license": "proprietary"
},
@@ -185227,7 +185266,7 @@
"id": "SPL2SMP_NRT_107",
"title": "Near Real-time SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V107",
"catalog": "NSIDC_ECS STAC Catalog",
- "state_date": "2024-12-05",
+ "state_date": "2024-12-23",
"end_date": "",
"bbox": "-180, -85.044, 180, 85.044",
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C2312096175-NSIDC_ECS.umm_json",
@@ -185265,52 +185304,52 @@
{
"id": "SPL3FTP_004",
"title": "SMAP L3 Radiometer Global and Northern Hemisphere Daily 36 km EASE-Grid Freeze/Thaw State V004",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -85.044, 180, 85.044",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664170-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664170-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3FTP_004",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463838-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463838-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3FTP_004",
"description": "This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0.",
"license": "proprietary"
},
{
"id": "SPL3FTP_004",
"title": "SMAP L3 Radiometer Global and Northern Hemisphere Daily 36 km EASE-Grid Freeze/Thaw State V004",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -85.044, 180, 85.044",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463838-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463838-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3FTP_004",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664170-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664170-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3FTP_004",
"description": "This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0.",
"license": "proprietary"
},
{
"id": "SPL3FTP_E_004",
"title": "SMAP Enhanced L3 Radiometer Global and Northern Hemisphere Daily 9 km EASE-Grid Freeze/Thaw State V004",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -85.044, 180, 85.044",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463920-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463920-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3FTP_E_004",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664439-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664439-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3FTP_E_004",
"description": "This enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP enhanced Level-1C brightness temperatures (SPL1CTB_E). Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures. The data are then posted to two 9 km Earth-fixed, Equal-Area Scalable Earth Grids, Version 2.0 (EASE-Grid 2.0): a global cylindrical and a Northern Hemisphere azimuthal.",
"license": "proprietary"
},
{
"id": "SPL3FTP_E_004",
"title": "SMAP Enhanced L3 Radiometer Global and Northern Hemisphere Daily 9 km EASE-Grid Freeze/Thaw State V004",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -85.044, 180, 85.044",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664439-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664439-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3FTP_E_004",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463920-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463920-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3FTP_E_004",
"description": "This enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP enhanced Level-1C brightness temperatures (SPL1CTB_E). Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures. The data are then posted to two 9 km Earth-fixed, Equal-Area Scalable Earth Grids, Version 2.0 (EASE-Grid 2.0): a global cylindrical and a Northern Hemisphere azimuthal.",
"license": "proprietary"
},
@@ -185343,26 +185382,26 @@
{
"id": "SPL3SMA_003",
"title": "SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture V003",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2015-04-13",
"end_date": "2015-07-07",
"bbox": "-180, -85.044, 180, 85.044",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303828-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303828-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3SMA_003",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2872766452-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2872766452-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3SMA_003",
"description": "This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).",
"license": "proprietary"
},
{
"id": "SPL3SMA_003",
"title": "SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture V003",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2015-04-13",
"end_date": "2015-07-07",
"bbox": "-180, -85.044, 180, 85.044",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2872766452-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2872766452-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3SMA_003",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303828-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1236303828-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3SMA_003",
"description": "This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).",
"license": "proprietary"
},
@@ -185395,26 +185434,26 @@
{
"id": "SPL3SMP_E_006",
"title": "SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture V006",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -85.044, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463943-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463943-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3SMP_E_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664763-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664763-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3SMP_E_006",
"description": "This enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures. Backus-Gilbert optimal interpolation techniques are used to extract information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection.",
"license": "proprietary"
},
{
"id": "SPL3SMP_E_006",
"title": "SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture V006",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -85.044, 180, 90",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664763-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938664763-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL3SMP_E_006",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463943-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2776463943-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL3SMP_E_006",
"description": "This enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures. Backus-Gilbert optimal interpolation techniques are used to extract information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection.",
"license": "proprietary"
},
@@ -185447,52 +185486,52 @@
{
"id": "SPL4SMAU_007",
"title": "SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update V007",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -85.044, 180, 85.044",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2537927247-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2537927247-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4SMAU_007",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665508-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665508-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4SMAU_007",
"description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: - SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D)
- SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3)
- SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG).
For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.",
"license": "proprietary"
},
{
"id": "SPL4SMAU_007",
"title": "SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update V007",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -85.044, 180, 85.044",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665508-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665508-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4SMAU_007",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2537927247-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2537927247-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4SMAU_007",
"description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: - SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D)
- SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3)
- SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG).
For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.",
"license": "proprietary"
},
{
"id": "SPL4SMGP_007",
"title": "SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007",
- "catalog": "NSIDC_ECS STAC Catalog",
+ "catalog": "NSIDC_CPRD STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -85.044, 180, 85.044",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4SMGP_007",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4SMGP_007",
"description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.",
"license": "proprietary"
},
{
"id": "SPL4SMGP_007",
"title": "SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007",
- "catalog": "NSIDC_CPRD STAC Catalog",
+ "catalog": "NSIDC_ECS STAC Catalog",
"state_date": "2015-03-31",
"end_date": "",
"bbox": "-180, -85.044, 180, 85.044",
- "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.umm_json",
- "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.html",
- "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_CPRD/collections/SPL4SMGP_007",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SPL4SMGP_007",
"description": "SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection.",
"license": "proprietary"
},
@@ -186123,7 +186162,7 @@
{
"id": "SRDB_V5_1827_5",
"title": "A Global Database of Soil Respiration Data, Version 5.0",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1961-01-01",
"end_date": "2017-12-31",
"bbox": "-163.71, -78.02, 175.9, 81.8",
@@ -186136,7 +186175,7 @@
{
"id": "SRDB_V5_1827_5",
"title": "A Global Database of Soil Respiration Data, Version 5.0",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1961-01-01",
"end_date": "2017-12-31",
"bbox": "-163.71, -78.02, 175.9, 81.8",
@@ -187511,6 +187550,19 @@
"description": "These lidar measurements were collected in April and August 2022 in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods were selected to characterize differences during \"leaf-off\u201d and \"leaf-on\" conditions.",
"license": "proprietary"
},
+ {
+ "id": "SV19MA_SAR_1",
+ "title": "SMAPVEX19-22 Massachusetts UAVSAR Mosaics V001",
+ "catalog": "NSIDC_ECS STAC Catalog",
+ "state_date": "2022-04-25",
+ "end_date": "2022-07-25",
+ "bbox": "-72.33, 42.32, -71.91, 42.72",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3306692478-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3306692478-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SV19MA_SAR_1",
+ "description": "This data set consists of mosaicked Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images corrected for terrain-flattened gamma. Data image files at three different polarization configurations were composited daily between April to July 2022 in the vicinity of Petersham, Massachusetts during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign. The location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.",
+ "license": "proprietary"
+ },
{
"id": "SV19MA_TNET_1",
"title": "SMAPVEX19-22 Massachusetts Temporary Soil Moisture Network V001",
@@ -187576,6 +187628,19 @@
"description": "These lidar measurements were collected in April and August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods were selected to characterize differences during \"leaf-off\" and \"leaf-on\" conditions.",
"license": "proprietary"
},
+ {
+ "id": "SV19MB_SAR_1",
+ "title": "SMAPVEX19-22 Millbrook UAVSAR Mosaics V001",
+ "catalog": "NSIDC_ECS STAC Catalog",
+ "state_date": "2022-04-28",
+ "end_date": "2022-07-25",
+ "bbox": "-73.81, 41.66, -73.42, 42.05",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3306692820-NSIDC_ECS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3306692820-NSIDC_ECS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/NSIDC_ECS/collections/SV19MB_SAR_1",
+ "description": "This data set consists of mosaicked Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images corrected for terrain-flattened gamma. Data image files at three different polarization configurations were composited daily between April to July 2022 in the vicinity of Millbrook, New York during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign. The location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas.",
+ "license": "proprietary"
+ },
{
"id": "SV19MB_TNET_1",
"title": "SMAPVEX19-22 Millbrook Temporary Soil Moisture Network V001",
@@ -188788,7 +188853,7 @@
{
"id": "Salt_Marsh_Biomass_CONUS_2348_1",
"title": "Aboveground Biomass Estimates for Salt Marsh for the Contiguous United States, 2020",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-01-01",
"end_date": "2020-12-31",
"bbox": "-124.74, 24.52, -66.93, 49",
@@ -188801,7 +188866,7 @@
{
"id": "Salt_Marsh_Biomass_CONUS_2348_1",
"title": "Aboveground Biomass Estimates for Salt Marsh for the Contiguous United States, 2020",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2020-01-01",
"end_date": "2020-12-31",
"bbox": "-124.74, 24.52, -66.93, 49",
@@ -188853,7 +188918,7 @@
{
"id": "Sat_ActiveLayer_Thickness_Maps_1760_1",
"title": "ABoVE: Active Layer Thickness from Remote Sensing Permafrost Model, Alaska, 2001-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2015-12-31",
"bbox": "-179.18, 55.57, -132.58, 70.21",
@@ -188866,7 +188931,7 @@
{
"id": "Sat_ActiveLayer_Thickness_Maps_1760_1",
"title": "ABoVE: Active Layer Thickness from Remote Sensing Permafrost Model, Alaska, 2001-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2015-12-31",
"bbox": "-179.18, 55.57, -132.58, 70.21",
@@ -188892,7 +188957,7 @@
{
"id": "Scambos_PLR1441432",
"title": "A Low-power, Quick-install Polar Observation System ('AMIGOS-II') for Monitoring Climate-ice-ocean Interactions",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2014-06-01",
"end_date": "2015-05-31",
"bbox": "-180, -90, 180, 90",
@@ -188905,7 +188970,7 @@
{
"id": "Scambos_PLR1441432",
"title": "A Low-power, Quick-install Polar Observation System ('AMIGOS-II') for Monitoring Climate-ice-ocean Interactions",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2014-06-01",
"end_date": "2015-05-31",
"bbox": "-180, -90, 180, 90",
@@ -189685,7 +189750,7 @@
{
"id": "Seasonality_Tundra_Vegetation_1606_1",
"title": "ABoVE: Climate Drivers of Pan-Arctic Tundra Vegetation Productivity, 1982-2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1982-01-01",
"end_date": "2015-12-31",
"bbox": "-180, 70, 180, 90",
@@ -189698,7 +189763,7 @@
{
"id": "Seasonality_Tundra_Vegetation_1606_1",
"title": "ABoVE: Climate Drivers of Pan-Arctic Tundra Vegetation Productivity, 1982-2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1982-01-01",
"end_date": "2015-12-31",
"bbox": "-180, 70, 180, 90",
@@ -189893,7 +189958,7 @@
{
"id": "Skelton_Aeromag_Data",
"title": "Aeromagnetic data centered over Skelton Neve, Antarctica: A Web Site for Distribution of Data and Maps (on-line edition)",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-01-01",
"end_date": "1998-12-31",
"bbox": "153.5, -79.7, 166.7, -77.5",
@@ -189906,7 +189971,7 @@
{
"id": "Skelton_Aeromag_Data",
"title": "Aeromagnetic data centered over Skelton Neve, Antarctica: A Web Site for Distribution of Data and Maps (on-line edition)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1997-01-01",
"end_date": "1998-12-31",
"bbox": "153.5, -79.7, 166.7, -77.5",
@@ -189958,7 +190023,7 @@
{
"id": "SnowMeltDuration_PMicrowave_1843_1.1",
"title": "ABoVE: Passive Microwave-derived Annual Snow Melt Duration Date Maps, 1988-2018",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1988-02-09",
"end_date": "2018-07-20",
"bbox": "-180, 51.6, -107.83, 72.41",
@@ -189971,7 +190036,7 @@
{
"id": "SnowMeltDuration_PMicrowave_1843_1.1",
"title": "ABoVE: Passive Microwave-derived Annual Snow Melt Duration Date Maps, 1988-2018",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1988-02-09",
"end_date": "2018-07-20",
"bbox": "-180, 51.6, -107.83, 72.41",
@@ -189984,7 +190049,7 @@
{
"id": "Snow_Cover_Extent_and_Depth_1757_1",
"title": "ABoVE: High Resolution Cloud-Free Snow Cover Extent and Snow Depth, Alaska, 2001-2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2017-12-30",
"bbox": "-179.18, 55.57, -132.58, 71.42",
@@ -189997,7 +190062,7 @@
{
"id": "Snow_Cover_Extent_and_Depth_1757_1",
"title": "ABoVE: High Resolution Cloud-Free Snow Cover Extent and Snow Depth, Alaska, 2001-2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2017-12-30",
"bbox": "-179.18, 55.57, -132.58, 71.42",
@@ -190049,7 +190114,7 @@
{
"id": "Snowpack_Dall_Sheep_Track_1583_1",
"title": "ABoVE: Dall Sheep Track Sinking Depths, Snow Depth, Hardness, and Density, 2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-03-19",
"end_date": "2017-03-22",
"bbox": "-143.06, 62.26, -143.01, 62.28",
@@ -190062,7 +190127,7 @@
{
"id": "Snowpack_Dall_Sheep_Track_1583_1",
"title": "ABoVE: Dall Sheep Track Sinking Depths, Snow Depth, Hardness, and Density, 2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2017-03-19",
"end_date": "2017-03-22",
"bbox": "-143.06, 62.26, -143.01, 62.28",
@@ -190114,7 +190179,7 @@
{
"id": "Soil_ActiveLayer_Properties_AK_2315_1",
"title": "ABoVE: Active Layer Soil Characteristics at Selected Sites Across Alaska",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-08-09",
"end_date": "2018-07-07",
"bbox": "-149.53, 63.88, -146.56, 68.56",
@@ -190127,7 +190192,7 @@
{
"id": "Soil_ActiveLayer_Properties_AK_2315_1",
"title": "ABoVE: Active Layer Soil Characteristics at Selected Sites Across Alaska",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-08-09",
"end_date": "2018-07-07",
"bbox": "-149.53, 63.88, -146.56, 68.56",
@@ -190179,7 +190244,7 @@
{
"id": "Soil_Temp_Moisture_Alaska_1869_1",
"title": "ABoVE: Soil Temperature and VWC at Unburned and Burned Sites Across Alaska, 2016-2023",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-08-11",
"end_date": "2023-09-02",
"bbox": "-163.24, 61.27, -146.56, 68.99",
@@ -190192,7 +190257,7 @@
{
"id": "Soil_Temp_Moisture_Alaska_1869_1",
"title": "ABoVE: Soil Temperature and VWC at Unburned and Burned Sites Across Alaska, 2016-2023",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-08-11",
"end_date": "2023-09-02",
"bbox": "-163.24, 61.27, -146.56, 68.99",
@@ -190205,7 +190270,7 @@
{
"id": "Soil_Temperature_Profiles_AK_1767_1",
"title": "ABoVE: Soil Temperature Profiles, USArray Seismic Stations, AK and Canada, 2016-2019",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-06-25",
"end_date": "2019-08-22",
"bbox": "-163.18, 63.89, -134.34, 69.92",
@@ -190218,7 +190283,7 @@
{
"id": "Soil_Temperature_Profiles_AK_1767_1",
"title": "ABoVE: Soil Temperature Profiles, USArray Seismic Stations, AK and Canada, 2016-2019",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-06-25",
"end_date": "2019-08-22",
"bbox": "-163.18, 63.89, -134.34, 69.92",
@@ -190257,7 +190322,7 @@
{
"id": "Southern_Boreal_Plot_Attribute_1740_1",
"title": "ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2016-05-30",
"end_date": "2016-06-16",
"bbox": "-109.17, 54.09, -104.69, 57.36",
@@ -190270,7 +190335,7 @@
{
"id": "Southern_Boreal_Plot_Attribute_1740_1",
"title": "ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-05-30",
"end_date": "2016-06-16",
"bbox": "-109.17, 54.09, -104.69, 57.36",
@@ -190348,7 +190413,7 @@
{
"id": "Survey_1988_89_Mawson_npcms_1",
"title": "1988/89 Summer season, surveying and mapping program, Mawson - North Prince Charles Mountains - Davis",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1988-10-01",
"end_date": "1989-02-28",
"bbox": "62, -70, 79, -66",
@@ -190361,7 +190426,7 @@
{
"id": "Survey_1988_89_Mawson_npcms_1",
"title": "1988/89 Summer season, surveying and mapping program, Mawson - North Prince Charles Mountains - Davis",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1988-10-01",
"end_date": "1989-02-28",
"bbox": "62, -70, 79, -66",
@@ -198889,7 +198954,7 @@
{
"id": "Tidal_Marsh_Biomass_US_V1-1_1879_1.1",
"title": "Aboveground Biomass High-Resolution Maps for Selected US Tidal Marshes, 2015",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-08-01",
"end_date": "2015-09-01",
"bbox": "-122.73, 25.09, -69.93, 47.12",
@@ -198902,7 +198967,7 @@
{
"id": "Tidal_Marsh_Biomass_US_V1-1_1879_1.1",
"title": "Aboveground Biomass High-Resolution Maps for Selected US Tidal Marshes, 2015",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2015-08-01",
"end_date": "2015-09-01",
"bbox": "-122.73, 25.09, -69.93, 47.12",
@@ -199136,7 +199201,7 @@
{
"id": "Tundra_Greeness_Temp_Trends_1893_1",
"title": "ABoVE: Landsat Tundra Greenness and Summer Air Temperatures, Arctic Tundra, 1985-2016",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1985-07-01",
"end_date": "2016-08-31",
"bbox": "-180, 31.49, 180, 90",
@@ -199149,7 +199214,7 @@
{
"id": "Tundra_Greeness_Temp_Trends_1893_1",
"title": "ABoVE: Landsat Tundra Greenness and Summer Air Temperatures, Arctic Tundra, 1985-2016",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1985-07-01",
"end_date": "2016-08-31",
"bbox": "-180, 31.49, 180, 90",
@@ -200020,7 +200085,7 @@
{
"id": "UKASSEL_GLOBAL_IRRIGATED_AREA",
"title": "A Digital Global Map of Irrigated Areas",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1995-01-01",
"end_date": "1995-12-31",
"bbox": "-180, -90, 180, 90",
@@ -200033,7 +200098,7 @@
{
"id": "UKASSEL_GLOBAL_IRRIGATED_AREA",
"title": "A Digital Global Map of Irrigated Areas",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1995-01-01",
"end_date": "1995-12-31",
"bbox": "-180, -90, 180, 90",
@@ -200046,7 +200111,7 @@
{
"id": "UM0405_26_aerosol_optical",
"title": "Aerosol optical thickness - UM0405_26_aerosol_optical",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-12-31",
"end_date": "2005-01-25",
"bbox": "18, -68, 115, -32",
@@ -200059,7 +200124,7 @@
{
"id": "UM0405_26_aerosol_optical",
"title": "Aerosol optical thickness - UM0405_26_aerosol_optical",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2004-12-31",
"end_date": "2005-01-25",
"bbox": "18, -68, 115, -32",
@@ -200163,7 +200228,7 @@
{
"id": "UNEP_GRID_SF_AFRICA_third version",
"title": "Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1960-01-01",
"end_date": "1990-12-31",
"bbox": "-18, -35, 52, 35",
@@ -200176,7 +200241,7 @@
{
"id": "UNEP_GRID_SF_AFRICA_third version",
"title": "Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1960-01-01",
"end_date": "1990-12-31",
"bbox": "-18, -35, 52, 35",
@@ -200319,7 +200384,7 @@
{
"id": "USAP-1043471",
"title": "A Study of Atmospheric Dust in the WAIS Divide Ice Core Based on Sr-Nd-Pb-He Isotopes",
- "catalog": "AMD_USAPDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-08-01",
"end_date": "2015-07-31",
"bbox": "-112.5, -79.5, -112.086, -79.468",
@@ -200332,7 +200397,7 @@
{
"id": "USAP-1043471",
"title": "A Study of Atmospheric Dust in the WAIS Divide Ice Core Based on Sr-Nd-Pb-He Isotopes",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_USAPDC STAC Catalog",
"state_date": "2011-08-01",
"end_date": "2015-07-31",
"bbox": "-112.5, -79.5, -112.086, -79.468",
@@ -200670,7 +200735,7 @@
{
"id": "USAP-1643722_1",
"title": "A High Resolution Atmospheric Methane Record from the South Pole Ice Core",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_USAPDC STAC Catalog",
"state_date": "2017-02-01",
"end_date": "2019-01-31",
"bbox": "180, -90, 180, -90",
@@ -200683,7 +200748,7 @@
{
"id": "USAP-1643722_1",
"title": "A High Resolution Atmospheric Methane Record from the South Pole Ice Core",
- "catalog": "AMD_USAPDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-02-01",
"end_date": "2019-01-31",
"bbox": "180, -90, 180, -90",
@@ -200748,7 +200813,7 @@
{
"id": "USAP-1644234_1",
"title": "A Test of Global and Antarctic Models for Cosmogenic-nuclide Production Rates using High-precision Dating of 40Ar/39Ar Lava Flows from Mount Erebus",
- "catalog": "AMD_USAPDC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-07-15",
"end_date": "2022-06-30",
"bbox": "166.17, -77.7, 167.75, -77.3",
@@ -200761,7 +200826,7 @@
{
"id": "USAP-1644234_1",
"title": "A Test of Global and Antarctic Models for Cosmogenic-nuclide Production Rates using High-precision Dating of 40Ar/39Ar Lava Flows from Mount Erebus",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AMD_USAPDC STAC Catalog",
"state_date": "2017-07-15",
"end_date": "2022-06-30",
"bbox": "166.17, -77.7, 167.75, -77.3",
@@ -201359,7 +201424,7 @@
{
"id": "USARC_AERIAL_PHOTOS",
"title": "Aerial Photography of Antarctica",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, -62.83",
@@ -201372,7 +201437,7 @@
{
"id": "USARC_AERIAL_PHOTOS",
"title": "Aerial Photography of Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, -62.83",
@@ -201541,7 +201606,7 @@
{
"id": "USGS-DDS-3",
"title": "A Geologic Map of the Sea Floor in Western Massachusetts Bay, Constructed from Digital Sidescan-Sonar Images, Photography, and Sediment Samples",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-71.5, 42, -70, 43",
@@ -201554,7 +201619,7 @@
{
"id": "USGS-DDS-3",
"title": "A Geologic Map of the Sea Floor in Western Massachusetts Bay, Constructed from Digital Sidescan-Sonar Images, Photography, and Sediment Samples",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-71.5, 42, -70, 43",
@@ -201567,7 +201632,7 @@
{
"id": "USGS-DDS-33_1.0",
"title": "3-D Reservoir Characterization of the House Creek Oil Field, Powder River Basin, Wyoming, V1.00",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-111.4, 40.65, -103.7, 45.35",
@@ -201580,7 +201645,7 @@
{
"id": "USGS-DDS-33_1.0",
"title": "3-D Reservoir Characterization of the House Creek Oil Field, Powder River Basin, Wyoming, V1.00",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-111.4, 40.65, -103.7, 45.35",
@@ -201619,7 +201684,7 @@
{
"id": "USGS-DDS_30_P-10_cells",
"title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Joaquin Basin Province",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1990-12-01",
"end_date": "1990-12-01",
"bbox": "-121.388916, 34.890034, -118.58517, 37.83907",
@@ -201632,7 +201697,7 @@
{
"id": "USGS-DDS_30_P-10_cells",
"title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Joaquin Basin Province",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1990-12-01",
"end_date": "1990-12-01",
"bbox": "-121.388916, 34.890034, -118.58517, 37.83907",
@@ -202100,7 +202165,7 @@
{
"id": "USGS_DDS_P13_conventional",
"title": "1995 National Oil and Gas Assessment Conventional Plays within the Ventura Basin Province",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1996-01-01",
"end_date": "1996-12-31",
"bbox": "-120.58227, 33.84158, -117.37425, 34.824276",
@@ -202113,7 +202178,7 @@
{
"id": "USGS_DDS_P13_conventional",
"title": "1995 National Oil and Gas Assessment Conventional Plays within the Ventura Basin Province",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-01-01",
"end_date": "1996-12-31",
"bbox": "-120.58227, 33.84158, -117.37425, 34.824276",
@@ -202152,7 +202217,7 @@
{
"id": "USGS_DDS_P14_conventional",
"title": "1995 National Oil and Gas Assessment Conventional Plays within the Los Angeles Basin Province",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-01-01",
"end_date": "1996-12-31",
"bbox": "-119.63631, 32.7535, -117.52315, 34.17464",
@@ -202165,7 +202230,7 @@
{
"id": "USGS_DDS_P14_conventional",
"title": "1995 National Oil and Gas Assessment Conventional Plays within the Los Angeles Basin Province",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1996-01-01",
"end_date": "1996-12-31",
"bbox": "-119.63631, 32.7535, -117.52315, 34.17464",
@@ -202178,7 +202243,7 @@
{
"id": "USGS_DDS_P15_cells",
"title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Diego - Oceanside Province",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1990-12-01",
"end_date": "1990-12-01",
"bbox": "-117.75433, 32.527184, -115.904816, 34.236046",
@@ -202191,7 +202256,7 @@
{
"id": "USGS_DDS_P15_cells",
"title": "1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Diego - Oceanside Province",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1990-12-01",
"end_date": "1990-12-01",
"bbox": "-117.75433, 32.527184, -115.904816, 34.236046",
@@ -202360,7 +202425,7 @@
{
"id": "USGS_DDS_P19_conventional",
"title": "1995 National Oil and Gas Assessment Conventional Plays within the Eastern Great Basin Province",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1996-01-01",
"end_date": "1996-12-31",
"bbox": "-117.02622, 35.002083, -111.170425, 43.022377",
@@ -202373,7 +202438,7 @@
{
"id": "USGS_DDS_P19_conventional",
"title": "1995 National Oil and Gas Assessment Conventional Plays within the Eastern Great Basin Province",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-01-01",
"end_date": "1996-12-31",
"bbox": "-117.02622, 35.002083, -111.170425, 43.022377",
@@ -202438,7 +202503,7 @@
{
"id": "USGS_DDS_P20_conventional",
"title": "1995 National Oil and Gas Assessment Conventional Plays within the Uinta - Piceance Basin Province",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-01-01",
"end_date": "1996-12-31",
"bbox": "-111.486916, 38.14689, -105.87804, 40.85869",
@@ -202451,7 +202516,7 @@
{
"id": "USGS_DDS_P20_conventional",
"title": "1995 National Oil and Gas Assessment Conventional Plays within the Uinta - Piceance Basin Province",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1996-01-01",
"end_date": "1996-12-31",
"bbox": "-111.486916, 38.14689, -105.87804, 40.85869",
@@ -202529,7 +202594,7 @@
{
"id": "USGS_DS-845_PierScoutDatabase_1.0",
"title": "A pier-scour database: 2,427 field and laboratory measurements of pier scour",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "19.6, 16.916668, -52.62, 83.1",
@@ -202542,7 +202607,7 @@
{
"id": "USGS_DS-845_PierScoutDatabase_1.0",
"title": "A pier-scour database: 2,427 field and laboratory measurements of pier scour",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "19.6, 16.916668, -52.62, 83.1",
@@ -204037,7 +204102,7 @@
{
"id": "USGS_NPS_AcadiaAccuracy_Final",
"title": "Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-10-01",
"end_date": "2003-10-01",
"bbox": "-75.262726, 43.99941, -68.044304, 44.48051",
@@ -204050,7 +204115,7 @@
{
"id": "USGS_NPS_AcadiaAccuracy_Final",
"title": "Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "2003-10-01",
"end_date": "2003-10-01",
"bbox": "-75.262726, 43.99941, -68.044304, 44.48051",
@@ -204193,7 +204258,7 @@
{
"id": "USGS_OFR-97-792",
"title": "500,000 Year-old Stable Isotopic Record from Devils Hole, USGS OFR-97-792",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-116.3, 36.42, -116.3, 36.42",
@@ -204206,7 +204271,7 @@
{
"id": "USGS_OFR-97-792",
"title": "500,000 Year-old Stable Isotopic Record from Devils Hole, USGS OFR-97-792",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-116.3, 36.42, -116.3, 36.42",
@@ -205623,7 +205688,7 @@
{
"id": "USGS_OFR_2003_247_1.0",
"title": "A Digital Geological Map Database For the State of Oklahoma",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-103, 33, -94, 37",
@@ -205636,7 +205701,7 @@
{
"id": "USGS_OFR_2003_247_1.0",
"title": "A Digital Geological Map Database For the State of Oklahoma",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-103, 33, -94, 37",
@@ -205870,7 +205935,7 @@
{
"id": "USGS_OFR_2004_1058",
"title": "2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-01-01",
"end_date": "",
"bbox": "-168, 46, -126, 76",
@@ -205883,7 +205948,7 @@
{
"id": "USGS_OFR_2004_1058",
"title": "2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "2002-01-01",
"end_date": "",
"bbox": "-168, 46, -126, 76",
@@ -206169,7 +206234,7 @@
{
"id": "USGS_OFR_2004_1249",
"title": "A Forest Vegetation Database for Western Oregon",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-124.96, 41.58, -116.06, 46.68",
@@ -206182,7 +206247,7 @@
{
"id": "USGS_OFR_2004_1249",
"title": "A Forest Vegetation Database for Western Oregon",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-124.96, 41.58, -116.06, 46.68",
@@ -206442,7 +206507,7 @@
{
"id": "USGS_OFR_2005_1148_1.0",
"title": "Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-80.82, 39.43, -74.41, 42.56",
@@ -206455,7 +206520,7 @@
{
"id": "USGS_OFR_2005_1148_1.0",
"title": "Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-80.82, 39.43, -74.41, 42.56",
@@ -207131,7 +207196,7 @@
{
"id": "USGS_OFR_2007_1169",
"title": "2005 Hydrographic Survey of South San Francisco Bay, California",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-126, 37, -122, 42",
@@ -207144,7 +207209,7 @@
{
"id": "USGS_OFR_2007_1169",
"title": "2005 Hydrographic Survey of South San Francisco Bay, California",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-126, 37, -122, 42",
@@ -207911,7 +207976,7 @@
{
"id": "USGS_P-11_conventional",
"title": "1995 National Oil and Gas Assessment Conventional Plays within the Central Coastal Province",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-01-01",
"end_date": "1996-12-31",
"bbox": "-123.80987, 34.66294, -118.997696, 39.082233",
@@ -207924,7 +207989,7 @@
{
"id": "USGS_P-11_conventional",
"title": "1995 National Oil and Gas Assessment Conventional Plays within the Central Coastal Province",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1996-01-01",
"end_date": "1996-12-31",
"bbox": "-123.80987, 34.66294, -118.997696, 39.082233",
@@ -208093,7 +208158,7 @@
{
"id": "USGS_SESC_SturgeonBiblio_3",
"title": "A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi.",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -208106,7 +208171,7 @@
{
"id": "USGS_SESC_SturgeonBiblio_3",
"title": "A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -208171,7 +208236,7 @@
{
"id": "USGS_SOFIA_ASR_04",
"title": "A retrospective and critical review of aquifer and storage (ASR) sites and conceptual framework of the Upper Floridian aquifer in south Florida",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1999-10-01",
"end_date": "2004-09-30",
"bbox": "-82.55795, 24.441917, -79.84407, 27.586416",
@@ -208184,7 +208249,7 @@
{
"id": "USGS_SOFIA_ASR_04",
"title": "A retrospective and critical review of aquifer and storage (ASR) sites and conceptual framework of the Upper Floridian aquifer in south Florida",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1999-10-01",
"end_date": "2004-09-30",
"bbox": "-82.55795, 24.441917, -79.84407, 27.586416",
@@ -210056,7 +210121,7 @@
{
"id": "USGS_SOFIA_la_florida",
"title": "A Land of Flowers on a Latitude of Deserts: Aiding Conservation and Management of Florida's Biodiversity by Using Predictions from \"Down-Scaled\" AOGCM Climate Scenarios in Combination with Ecological Modeling",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "2000-12-31",
"bbox": "-92, 23, -75, 38.24",
@@ -210069,7 +210134,7 @@
{
"id": "USGS_SOFIA_la_florida",
"title": "A Land of Flowers on a Latitude of Deserts: Aiding Conservation and Management of Florida's Biodiversity by Using Predictions from \"Down-Scaled\" AOGCM Climate Scenarios in Combination with Ecological Modeling",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "2000-12-31",
"bbox": "-92, 23, -75, 38.24",
@@ -210862,7 +210927,7 @@
{
"id": "USGS_cont1994",
"title": "1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-117.07194, 34.095333, -115.98976, 34.64026",
@@ -210875,7 +210940,7 @@
{
"id": "USGS_cont1994",
"title": "1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-117.07194, 34.095333, -115.98976, 34.64026",
@@ -210888,7 +210953,7 @@
{
"id": "USGS_cont1996",
"title": "1996 Water-Table Contours of the Mojave River, the Morongo, and the Fort Irwin Ground-Water Basins, San Bernardino County, California",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-117.63461, 34.109745, -115.98707, 35.31552",
@@ -210901,7 +210966,7 @@
{
"id": "USGS_cont1996",
"title": "1996 Water-Table Contours of the Mojave River, the Morongo, and the Fort Irwin Ground-Water Basins, San Bernardino County, California",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-117.63461, 34.109745, -115.98707, 35.31552",
@@ -211746,7 +211811,7 @@
{
"id": "UTC_1990countyboundaries",
"title": "1990 County Boundaries of the United States",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1972-01-01",
"end_date": "1990-12-31",
"bbox": "-177.1, 13.71, -61.48, 76.63",
@@ -211759,7 +211824,7 @@
{
"id": "UTC_1990countyboundaries",
"title": "1990 County Boundaries of the United States",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1972-01-01",
"end_date": "1990-12-31",
"bbox": "-177.1, 13.71, -61.48, 76.63",
@@ -215776,7 +215841,7 @@
{
"id": "VMS_Genomics_1",
"title": "2010/11 VMS Geonomics sampling - data collected from the VMS (Voyage Marine Science) voyage of the Aurora Australis",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2011-01-04",
"end_date": "2011-02-06",
"bbox": "140, -67, 150, -42",
@@ -215789,7 +215854,7 @@
{
"id": "VMS_Genomics_1",
"title": "2010/11 VMS Geonomics sampling - data collected from the VMS (Voyage Marine Science) voyage of the Aurora Australis",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-01-04",
"end_date": "2011-02-06",
"bbox": "140, -67, 150, -42",
@@ -216345,6 +216410,19 @@
"description": "The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VNP13A3) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; pixel reliability; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VNP13A3 product: EVI and NDVI.",
"license": "proprietary"
},
+ {
+ "id": "VNP13A4N_2",
+ "title": "VIIRS/NPP Vegetation Indices 8-Day L3 Global 500m SIN Grid",
+ "catalog": "LANCEMODIS STAC Catalog",
+ "state_date": "2025-01-01",
+ "end_date": "",
+ "bbox": "-180, -90, 180, 90",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3363997464-LANCEMODIS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3363997464-LANCEMODIS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/LANCEMODIS/collections/VNP13A4N_2",
+ "description": "The VIIRS Near Real Time (NRT) Vegetation Indices 8-Day L3 Global 500m SIN Grid data, short-name VNP13A4N are provided everyday at 500-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes and global and regional climate. These data also may be used for characterizing land surface biophysical properties and processes including primary production and land cover conversion. Note: This is a near real-time product only. Standard historical data and imagery for VNP13A4N (8-Day 500m) are not available. The only 500m standard Vegetation Indices product available is a 16-Day composite (VNP13A1). So, users can either use VNP13A1, use the NDVI standard products from LAADS web (https://ladsweb.modaps.eosdis.nasa.gov/search/), or access the science quality VNP09A1 data and create the VI product of their own.",
+ "license": "proprietary"
+ },
{
"id": "VNP13C1_001",
"title": "VIIRS/NPP Vegetation Indices 16-Day L3 Global 0.05Deg CMG V001",
@@ -218500,7 +218578,7 @@
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C2980666614-LAADS.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C2980666614-LAADS.html",
"href": "https://cmr.earthdata.nasa.gov/stac/LAADS/collections/VNP46A1_2",
- "description": "The VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night product, short-name VNP46A1 is a daily, top-of-atmosphere, at-sensor nighttime radiance product. This product is available at 15 arc-second spatial resolution from January 2012 onward. The VNP46A1/VJ146A1 product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. ",
+ "description": "The VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night product, short-name VNP46A1 is a daily, top-of-atmosphere, at-sensor nighttime radiance product. This product is available at 15 arc-second spatial resolution from January 2012 onward. The VNP46A1/VJ146A1 product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. The current v2.0 collection contains several changes and differences relative to the previous v1.0 collection. These include radiance data format change from unsigned integer to floating-point, from exclusively for land surfaces coverage to both land and water surfaces, updated Mandatory_Quality_Flag layer, and others. Consult the v2.0-specific Black Marble User Guide for additional details at: https://landweb.modaps.eosdis.nasa.gov/data/userguide/BlackMarbleUserGuide_Collection2.0_20241203.pdf ",
"license": "proprietary"
},
{
@@ -218529,6 +218607,19 @@
"description": "The second of the two VIIRS DNB-based datasets is a daily moonlight- and atmosphere-corrected Nighttime Lights (NTL) product called VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 500m Linear Lat Lon Grid. Known by its short-name, VNP46A2, this product contains seven Science Data Sets (SDS) that include DNB BRDF-Corrected NTL, Gap-Filled DNB BRDF-Corrected NTL, DNB Lunar Irradiance, Latest High-Quality Retrieval, Mandatory Quality Flag, Cloud Mask Quality Flag, and Snow Flag. VNP46A2 products are provided in standard Hierarchical Data Format\u2013Earth Observing System (HDF-EOS5) format. ",
"license": "proprietary"
},
+ {
+ "id": "VNP46A2_NRT_2",
+ "title": "VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 15 arc-second Linear Lat Lon Grid NRT",
+ "catalog": "LANCEMODIS STAC Catalog",
+ "state_date": "2025-01-01",
+ "end_date": "",
+ "bbox": "-180, -90, 180, 90",
+ "url": "https://cmr.earthdata.nasa.gov/search/concepts/C3363944097-LANCEMODIS.umm_json",
+ "metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3363944097-LANCEMODIS.html",
+ "href": "https://cmr.earthdata.nasa.gov/stac/LANCEMODIS/collections/VNP46A2_NRT_2",
+ "description": "The second of the two VIIRS DNB-based datasets is a daily moonlight- and atmosphere-corrected Nighttime Lights (NTL) product called VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 500m Linear Lat Lon Grid. Known by its short-name, VNP46A2, this product contains seven Science Data Sets (SDS) that include DNB BRDF-Corrected NTL, Gap-Filled DNB BRDF-Corrected NTL, DNB Lunar Irradiance, Latest High-Quality Retrieval, Mandatory Quality Flag, Cloud Mask Quality Flag, and Snow Flag. VNP46A2 products are provided in standard Hierarchical Data Format\u2013Earth Observing System (HDF-EOS5) format. ",
+ "license": "proprietary"
+ },
{
"id": "VNP46A3_1",
"title": "VIIRS/NPP Lunar BRDF-Adjusted Nighttime Lights Monthly L3 Global 15 arc-second Linear Lat Lon Grid",
@@ -218623,7 +218714,7 @@
{
"id": "Veg_Soil_Tundra_Burned_Area_2119_1",
"title": "ABoVE: Post-Fire and Unburned Field Site Data, Anaktuvuk River Fire Area, 2008-2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2008-07-03",
"end_date": "2017-07-23",
"bbox": "-151.18, 69.02, -150.03, 69.36",
@@ -218636,7 +218727,7 @@
{
"id": "Veg_Soil_Tundra_Burned_Area_2119_1",
"title": "ABoVE: Post-Fire and Unburned Field Site Data, Anaktuvuk River Fire Area, 2008-2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2008-07-03",
"end_date": "2017-07-23",
"bbox": "-151.18, 69.02, -150.03, 69.36",
@@ -218818,7 +218909,7 @@
{
"id": "WARd0002_108",
"title": "Administration Division Maps Of Poland",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "24, 14, 49, 54",
@@ -218831,7 +218922,7 @@
{
"id": "WARd0002_108",
"title": "Administration Division Maps Of Poland",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "24, 14, 49, 54",
@@ -219182,7 +219273,7 @@
{
"id": "WIND_3DP",
"title": "3-D Plasma and Energetic Particle Investigation on WIND",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1994-11-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -219195,7 +219286,7 @@
{
"id": "WIND_3DP",
"title": "3-D Plasma and Energetic Particle Investigation on WIND",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1994-11-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -219221,7 +219312,7 @@
{
"id": "WISPMAWSON04-05_1",
"title": "A GIS dataset of Wilson's storm petrel nests mapped in the Mawson region during the 2004-2005 season",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-12-10",
"end_date": "2005-04-25",
"bbox": "62.18384, -67.68587, 63.40759, -67.47282",
@@ -219234,7 +219325,7 @@
{
"id": "WISPMAWSON04-05_1",
"title": "A GIS dataset of Wilson's storm petrel nests mapped in the Mawson region during the 2004-2005 season",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2004-12-10",
"end_date": "2005-04-25",
"bbox": "62.18384, -67.68587, 63.40759, -67.47282",
@@ -219520,7 +219611,7 @@
{
"id": "WYGISC_HYDRO100K",
"title": "1:100,000-scale Hydrography for Wyoming (enhanced DLGs)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-111.36555, 40.944794, -103.783806, 44.99391",
@@ -219533,7 +219624,7 @@
{
"id": "WYGISC_HYDRO100K",
"title": "1:100,000-scale Hydrography for Wyoming (enhanced DLGs)",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-111.36555, 40.944794, -103.783806, 44.99391",
@@ -219780,7 +219871,7 @@
{
"id": "Wildfire_Effects_Spruce_Field_1595_1",
"title": "ABoVE: Characterization of Burned and Unburned Spruce Forest Sites, Tanana, AK, 2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-07-26",
"end_date": "2017-07-28",
"bbox": "-152.42, 65.1, -151.95, 65.23",
@@ -219793,7 +219884,7 @@
{
"id": "Wildfire_Effects_Spruce_Field_1595_1",
"title": "ABoVE: Characterization of Burned and Unburned Spruce Forest Sites, Tanana, AK, 2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2017-07-26",
"end_date": "2017-07-28",
"bbox": "-152.42, 65.1, -151.95, 65.23",
@@ -219845,7 +219936,7 @@
{
"id": "Wildfires_Date_of_Burning_1559_1.1",
"title": "ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2019-12-31",
"bbox": "-178.84, 41.75, -53.83, 70.16",
@@ -219858,7 +219949,7 @@
{
"id": "Wildfires_Date_of_Burning_1559_1.1",
"title": "ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2019-12-31",
"bbox": "-178.84, 41.75, -53.83, 70.16",
@@ -219871,7 +219962,7 @@
{
"id": "Wildfires_NWT_Canada_1548_1",
"title": "ABoVE: Burn Severity, Fire Progression, and Field Data, NWT, Canada, 2015-2016",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-05-20",
"end_date": "2016-08-08",
"bbox": "-135.54, 59.93, -106.76, 68.33",
@@ -219884,7 +219975,7 @@
{
"id": "Wildfires_NWT_Canada_1548_1",
"title": "ABoVE: Burn Severity, Fire Progression, and Field Data, NWT, Canada, 2015-2016",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2015-05-20",
"end_date": "2016-08-08",
"bbox": "-135.54, 59.93, -106.76, 68.33",
@@ -219923,7 +220014,7 @@
{
"id": "Wildfires_NWT_Canada_2019_1900_1",
"title": "ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2019",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2018-08-16",
"end_date": "2019-09-05",
"bbox": "-117.43, 60.92, -113.02, 62.57",
@@ -219936,7 +220027,7 @@
{
"id": "Wildfires_NWT_Canada_2019_1900_1",
"title": "ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2019",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2018-08-16",
"end_date": "2019-09-05",
"bbox": "-117.43, 60.92, -113.02, 62.57",
@@ -219975,7 +220066,7 @@
{
"id": "Wolves_Denning_Pups_Climate_1846_1",
"title": "ABoVE: Wolf Denning Phenology and Reproductive Success, Alaska and Canada, 2000-2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "2000-03-29",
"end_date": "2017-08-31",
"bbox": "-154.58, 52.97, -112.97, 67.84",
@@ -219988,7 +220079,7 @@
{
"id": "Wolves_Denning_Pups_Climate_1846_1",
"title": "ABoVE: Wolf Denning Phenology and Reproductive Success, Alaska and Canada, 2000-2017",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-03-29",
"end_date": "2017-08-31",
"bbox": "-154.58, 52.97, -112.97, 67.84",
@@ -220079,7 +220170,7 @@
{
"id": "XAERDT_L2_ABI_G16_1",
"title": "ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km",
- "catalog": "LAADS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-01-01",
"end_date": "2023-01-02",
"bbox": "-180, -90, 180, 90",
@@ -220092,7 +220183,7 @@
{
"id": "XAERDT_L2_ABI_G16_1",
"title": "ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LAADS STAC Catalog",
"state_date": "2019-01-01",
"end_date": "2023-01-02",
"bbox": "-180, -90, 180, 90",
@@ -220131,7 +220222,7 @@
{
"id": "XAERDT_L2_AHI_H08_1",
"title": "AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km",
- "catalog": "ALL STAC Catalog",
+ "catalog": "LAADS STAC Catalog",
"state_date": "2019-01-01",
"end_date": "2022-12-31",
"bbox": "-180, -90, 180, 90",
@@ -220144,7 +220235,7 @@
{
"id": "XAERDT_L2_AHI_H08_1",
"title": "AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km",
- "catalog": "LAADS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-01-01",
"end_date": "2022-12-31",
"bbox": "-180, -90, 180, 90",
@@ -220638,7 +220729,7 @@
{
"id": "aamhcpex_1",
"title": "AAMH CPEX",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GHRC_DAAC STAC Catalog",
"state_date": "2017-05-26",
"end_date": "2017-07-16",
"bbox": "154.716, 0.6408, -19.5629, 44.9689",
@@ -220651,7 +220742,7 @@
{
"id": "aamhcpex_1",
"title": "AAMH CPEX",
- "catalog": "GHRC_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-05-26",
"end_date": "2017-07-16",
"bbox": "154.716, 0.6408, -19.5629, 44.9689",
@@ -220677,7 +220768,7 @@
{
"id": "above-and-below-ground-herbivore-communities-along-elevation_1.0",
"title": "Above- and below-ground herbivore communities along elevation",
- "catalog": "ENVIDAT STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-01-01",
"end_date": "2020-01-01",
"bbox": "5.95587, 45.81802, 10.49203, 47.80838",
@@ -220690,7 +220781,7 @@
{
"id": "above-and-below-ground-herbivore-communities-along-elevation_1.0",
"title": "Above- and below-ground herbivore communities along elevation",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ENVIDAT STAC Catalog",
"state_date": "2020-01-01",
"end_date": "2020-01-01",
"bbox": "5.95587, 45.81802, 10.49203, 47.80838",
@@ -220729,7 +220820,7 @@
{
"id": "accum-measurements-domec-traverse-1982_1",
"title": "Accumulation Measurements from Pioneerskaya to Dome C, 1982-84",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1982-01-01",
"end_date": "1984-12-31",
"bbox": "124.5, -78.5, 93, -67",
@@ -220742,7 +220833,7 @@
{
"id": "accum-measurements-domec-traverse-1982_1",
"title": "Accumulation Measurements from Pioneerskaya to Dome C, 1982-84",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1982-01-01",
"end_date": "1984-12-31",
"bbox": "124.5, -78.5, 93, -67",
@@ -220768,7 +220859,7 @@
{
"id": "accumulation_lawdome_1960_1",
"title": "Accumulation Measurements, Law Dome 1959-1960",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1959-01-01",
"end_date": "1960-12-31",
"bbox": "110, -67, 115, -65",
@@ -220781,7 +220872,7 @@
{
"id": "accumulation_lawdome_1960_1",
"title": "Accumulation Measurements, Law Dome 1959-1960",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1959-01-01",
"end_date": "1960-12-31",
"bbox": "110, -67, 115, -65",
@@ -220794,7 +220885,7 @@
{
"id": "aces1am_1",
"title": "ACES Aircraft and Mechanical Data",
- "catalog": "GHRC_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-07-10",
"end_date": "2002-08-30",
"bbox": "-85, 23, -81, 26",
@@ -220807,7 +220898,7 @@
{
"id": "aces1am_1",
"title": "ACES Aircraft and Mechanical Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GHRC_DAAC STAC Catalog",
"state_date": "2002-07-10",
"end_date": "2002-08-30",
"bbox": "-85, 23, -81, 26",
@@ -220820,7 +220911,7 @@
{
"id": "aces1cont_1",
"title": "ACES CONTINUOUS DATA V1",
- "catalog": "GHRC_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-07-10",
"end_date": "2002-08-30",
"bbox": "-85, 23, -81, 26",
@@ -220833,7 +220924,7 @@
{
"id": "aces1cont_1",
"title": "ACES CONTINUOUS DATA V1",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GHRC_DAAC STAC Catalog",
"state_date": "2002-07-10",
"end_date": "2002-08-30",
"bbox": "-85, 23, -81, 26",
@@ -220872,7 +220963,7 @@
{
"id": "aces1log_1",
"title": "ACES LOG DATA",
- "catalog": "GHRC_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-07-10",
"end_date": "2002-08-30",
"bbox": "-85, 23, -81, 26",
@@ -220885,7 +220976,7 @@
{
"id": "aces1log_1",
"title": "ACES LOG DATA",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GHRC_DAAC STAC Catalog",
"state_date": "2002-07-10",
"end_date": "2002-08-30",
"bbox": "-85, 23, -81, 26",
@@ -220924,7 +221015,7 @@
{
"id": "aces1trig_1",
"title": "ACES TRIGGERED DATA",
- "catalog": "GHRC_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-07-10",
"end_date": "2002-08-30",
"bbox": "-85, 23, -81, 26",
@@ -220937,7 +221028,7 @@
{
"id": "aces1trig_1",
"title": "ACES TRIGGERED DATA",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GHRC_DAAC STAC Catalog",
"state_date": "2002-07-10",
"end_date": "2002-08-30",
"bbox": "-85, 23, -81, 26",
@@ -220950,7 +221041,7 @@
{
"id": "acoustic_charts_v6_1994_95_1",
"title": "Acoustic Sounder Charts from Australian Antarctic Division Voyage 6 1994/95 (BANGSS)",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1995-02-06",
"end_date": "1995-04-12",
"bbox": "60, -69.393, 147.473, -42.882",
@@ -220963,7 +221054,7 @@
{
"id": "acoustic_charts_v6_1994_95_1",
"title": "Acoustic Sounder Charts from Australian Antarctic Division Voyage 6 1994/95 (BANGSS)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1995-02-06",
"end_date": "1995-04-12",
"bbox": "60, -69.393, 147.473, -42.882",
@@ -221054,7 +221145,7 @@
{
"id": "active_layer_arcss_grid_atqasuk_alaska_2011",
"title": "Active Layer ARCSS grid Atqasuk, Alaska 2011",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2011-06-17",
"end_date": "2011-08-12",
"bbox": "-157, 70, -156, 71",
@@ -221067,7 +221158,7 @@
{
"id": "active_layer_arcss_grid_atqasuk_alaska_2011",
"title": "Active Layer ARCSS grid Atqasuk, Alaska 2011",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-06-17",
"end_date": "2011-08-12",
"bbox": "-157, 70, -156, 71",
@@ -221080,7 +221171,7 @@
{
"id": "active_layer_arcss_grid_atqasuk_alaska_2012",
"title": "Active Layer ARCSS grid Atqasuk, Alaska 2012",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-06-09",
"end_date": "2012-08-18",
"bbox": "-156, 70, -157, 71",
@@ -221093,7 +221184,7 @@
{
"id": "active_layer_arcss_grid_atqasuk_alaska_2012",
"title": "Active Layer ARCSS grid Atqasuk, Alaska 2012",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2012-06-09",
"end_date": "2012-08-18",
"bbox": "-156, 70, -157, 71",
@@ -221158,7 +221249,7 @@
{
"id": "active_layer_arcss_grid_barrow_alaska_2012",
"title": "Active Layer ARCSS grid Barrow, Alaska 2012",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2012-06-09",
"end_date": "2012-08-18",
"bbox": "-156.6, 71, -156.5, 71.5",
@@ -221171,7 +221262,7 @@
{
"id": "active_layer_arcss_grid_barrow_alaska_2012",
"title": "Active Layer ARCSS grid Barrow, Alaska 2012",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-06-09",
"end_date": "2012-08-18",
"bbox": "-156.6, 71, -156.5, 71.5",
@@ -221236,7 +221327,7 @@
{
"id": "active_layer_nims_grid_barrow_alaska_2011",
"title": "Active Layer NIMS grid Barrow, Alaska 2011",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-06-14",
"end_date": "2011-08-09",
"bbox": "-156.6, 71, -156.5, 71.5",
@@ -221249,7 +221340,7 @@
{
"id": "active_layer_nims_grid_barrow_alaska_2011",
"title": "Active Layer NIMS grid Barrow, Alaska 2011",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2011-06-14",
"end_date": "2011-08-09",
"bbox": "-156.6, 71, -156.5, 71.5",
@@ -221262,7 +221353,7 @@
{
"id": "active_layer_nims_grid_barrow_alaska_2012",
"title": "Active Layer NIMS grid Barrow, Alaska 2012",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2012-06-09",
"end_date": "2012-08-18",
"bbox": "-156.6, 71, -156.5, 71.5",
@@ -221275,7 +221366,7 @@
{
"id": "active_layer_nims_grid_barrow_alaska_2012",
"title": "Active Layer NIMS grid Barrow, Alaska 2012",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-06-09",
"end_date": "2012-08-18",
"bbox": "-156.6, 71, -156.5, 71.5",
@@ -221353,7 +221444,7 @@
{
"id": "adpe-aat-census_1",
"title": "Adelie penguin census from records from 1931 to 2007 AAT region",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1931-02-13",
"end_date": "2006-12-08",
"bbox": "38.2, -69.6, 89.5, -65.8",
@@ -221366,7 +221457,7 @@
{
"id": "adpe-aat-census_1",
"title": "Adelie penguin census from records from 1931 to 2007 AAT region",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1931-02-13",
"end_date": "2006-12-08",
"bbox": "38.2, -69.6, 89.5, -65.8",
@@ -221444,7 +221535,7 @@
{
"id": "aerial_mosaics_macquarie_2017_2",
"title": "Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2017-01-15",
"end_date": "2017-02-15",
"bbox": "158.874, -54.506, 158.954, -54.483",
@@ -221457,7 +221548,7 @@
{
"id": "aerial_mosaics_macquarie_2017_2",
"title": "Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-01-15",
"end_date": "2017-02-15",
"bbox": "158.874, -54.506, 158.954, -54.483",
@@ -221587,7 +221678,7 @@
{
"id": "aerial_photographs_from_columbia_glacier_1976-2010",
"title": "Aerial Photographs from Columbia Glacier, 1976-2010",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1976-07-24",
"end_date": "2011-06-15",
"bbox": "-146.895, 61.22, -146.895, 61.22",
@@ -221600,7 +221691,7 @@
{
"id": "aerial_photographs_from_columbia_glacier_1976-2010",
"title": "Aerial Photographs from Columbia Glacier, 1976-2010",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1976-07-24",
"end_date": "2011-06-15",
"bbox": "-146.895, 61.22, -146.895, 61.22",
@@ -221613,7 +221704,7 @@
{
"id": "aerial_rpa_nov2016_1",
"title": "Aerial photographs of Davis and Heidemann Valley taken with Remotely Piloted Aircraft, November 2016",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2016-11-07",
"end_date": "2016-11-20",
"bbox": "77.9619, -68.5811, 78.0131, -68.5731",
@@ -221626,7 +221717,7 @@
{
"id": "aerial_rpa_nov2016_1",
"title": "Aerial photographs of Davis and Heidemann Valley taken with Remotely Piloted Aircraft, November 2016",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2016-11-07",
"end_date": "2016-11-20",
"bbox": "77.9619, -68.5811, 78.0131, -68.5731",
@@ -221639,7 +221730,7 @@
{
"id": "aerial_surveys_vestfold_2017-18_1",
"title": "Aerial surveys of Davis station and an area of the Vestfold Hills to the north-east of the station 2017/18",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-11-19",
"end_date": "2018-01-31",
"bbox": "77.8923, -68.6067, 78.2235, -68.4809",
@@ -221652,7 +221743,7 @@
{
"id": "aerial_surveys_vestfold_2017-18_1",
"title": "Aerial surveys of Davis station and an area of the Vestfold Hills to the north-east of the station 2017/18",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2017-11-19",
"end_date": "2018-01-31",
"bbox": "77.8923, -68.6067, 78.2235, -68.4809",
@@ -221665,7 +221756,7 @@
{
"id": "aerosol-data-davos-wolfgang_1.0",
"title": "Aerosol Data Davos Wolfgang",
- "catalog": "ENVIDAT STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-01-01",
"end_date": "2020-01-01",
"bbox": "9.853594, 46.835577, 9.853594, 46.835577",
@@ -221678,7 +221769,7 @@
{
"id": "aerosol-data-davos-wolfgang_1.0",
"title": "Aerosol Data Davos Wolfgang",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ENVIDAT STAC Catalog",
"state_date": "2020-01-01",
"end_date": "2020-01-01",
"bbox": "9.853594, 46.835577, 9.853594, 46.835577",
@@ -221691,7 +221782,7 @@
{
"id": "aerosol-data-weissfluhjoch_1.0",
"title": "Aerosol Data Weissfluhjoch",
- "catalog": "ENVIDAT STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-01-01",
"end_date": "2020-01-01",
"bbox": "9.806475, 46.832964, 9.806475, 46.832964",
@@ -221704,7 +221795,7 @@
{
"id": "aerosol-data-weissfluhjoch_1.0",
"title": "Aerosol Data Weissfluhjoch",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ENVIDAT STAC Catalog",
"state_date": "2020-01-01",
"end_date": "2020-01-01",
"bbox": "9.806475, 46.832964, 9.806475, 46.832964",
@@ -221964,7 +222055,7 @@
{
"id": "agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0",
"title": "Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using substance and energy flow analysis",
- "catalog": "ENVIDAT STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2022-01-01",
"end_date": "2022-01-01",
"bbox": "5.95587, 45.81802, 10.49203, 47.80838",
@@ -221977,7 +222068,7 @@
{
"id": "agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0",
"title": "Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using substance and energy flow analysis",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ENVIDAT STAC Catalog",
"state_date": "2022-01-01",
"end_date": "2022-01-01",
"bbox": "5.95587, 45.81802, 10.49203, 47.80838",
@@ -222016,7 +222107,7 @@
{
"id": "air_temperature_observations_in_the_arctic_1979-2004",
"title": "Air Temperature Observations in the Arctic 1979-2004",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1979-01-01",
"end_date": "2005-12-01",
"bbox": "-180, 14.5, 180, 90",
@@ -222029,7 +222120,7 @@
{
"id": "air_temperature_observations_in_the_arctic_1979-2004",
"title": "Air Temperature Observations in the Arctic 1979-2004",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1979-01-01",
"end_date": "2005-12-01",
"bbox": "-180, 14.5, 180, 90",
@@ -222380,7 +222471,7 @@
{
"id": "amprimpacts_1",
"title": "Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GHRC_DAAC STAC Catalog",
"state_date": "2020-01-18",
"end_date": "2023-03-02",
"bbox": "-124.153, 26.507, -64.366, 49.31",
@@ -222393,7 +222484,7 @@
{
"id": "amprimpacts_1",
"title": "Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS",
- "catalog": "GHRC_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-01-18",
"end_date": "2023-03-02",
"bbox": "-124.153, 26.507, -64.366, 49.31",
@@ -222588,10 +222679,10 @@
{
"id": "amsua15sp_1",
"title": "ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15",
- "catalog": "GHRC_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1998-08-03",
"end_date": "",
- "bbox": "-180, -90, 180, 89.756",
+ "bbox": "-180, -90, 180, 90",
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.html",
"href": "https://cmr.earthdata.nasa.gov/stac/GHRC_DAAC/collections/amsua15sp_1",
@@ -222601,10 +222692,10 @@
{
"id": "amsua15sp_1",
"title": "ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GHRC_DAAC STAC Catalog",
"state_date": "1998-08-03",
"end_date": "",
- "bbox": "-180, -90, 180, 89.756",
+ "bbox": "-180, -90, 180, 90",
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.html",
"href": "https://cmr.earthdata.nasa.gov/stac/GHRC_DAAC/collections/amsua15sp_1",
@@ -222614,7 +222705,7 @@
{
"id": "amsua16sp_1",
"title": "ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-16",
- "catalog": "GHRC_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-05-27",
"end_date": "2009-07-30",
"bbox": "-180, -89.91, 180, 89.73",
@@ -222627,7 +222718,7 @@
{
"id": "amsua16sp_1",
"title": "ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-16",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GHRC_DAAC STAC Catalog",
"state_date": "2001-05-27",
"end_date": "2009-07-30",
"bbox": "-180, -89.91, 180, 89.73",
@@ -222809,7 +222900,7 @@
{
"id": "apr3cpexaw_1",
"title": "Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-AW",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GHRC_DAAC STAC Catalog",
"state_date": "2021-08-20",
"end_date": "2021-09-04",
"bbox": "-80.7804, 11.8615, -45.6417, 34.046",
@@ -222822,7 +222913,7 @@
{
"id": "apr3cpexaw_1",
"title": "Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-AW",
- "catalog": "GHRC_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2021-08-20",
"end_date": "2021-09-04",
"bbox": "-80.7804, 11.8615, -45.6417, 34.046",
@@ -222900,7 +222991,7 @@
{
"id": "asas",
"title": "Advanced Solid-state Array Spectroradiometer (ASAS)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "USGS_LTA STAC Catalog",
"state_date": "1988-06-26",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -222913,7 +223004,7 @@
{
"id": "asas",
"title": "Advanced Solid-state Array Spectroradiometer (ASAS)",
- "catalog": "USGS_LTA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1988-06-26",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -223199,7 +223290,7 @@
{
"id": "atrs",
"title": "Airborne Coherant Radar Sounding Data",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, -70",
@@ -223212,7 +223303,7 @@
{
"id": "atrs",
"title": "Airborne Coherant Radar Sounding Data",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, -70",
@@ -223667,7 +223758,7 @@
{
"id": "avapsimpacts_1",
"title": "Advanced Vertical Atmospheric Profiling System Dropsondes (AVAPS) IMPACTS",
- "catalog": "ALL STAC Catalog",
+ "catalog": "GHRC_DAAC STAC Catalog",
"state_date": "2020-01-12",
"end_date": "2023-02-28",
"bbox": "-77.815, 33.54, -65.44, 44.17",
@@ -223680,7 +223771,7 @@
{
"id": "avapsimpacts_1",
"title": "Advanced Vertical Atmospheric Profiling System Dropsondes (AVAPS) IMPACTS",
- "catalog": "GHRC_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-01-12",
"end_date": "2023-02-28",
"bbox": "-77.815, 33.54, -65.44, 44.17",
@@ -224070,7 +224161,7 @@
{
"id": "bds_dragonfly",
"title": "A Checklist of British and Irish Dragonfly Species",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1998-01-01",
"end_date": "",
"bbox": "-8.41, 49.49, 2.39, 59.07",
@@ -224083,7 +224174,7 @@
{
"id": "bds_dragonfly",
"title": "A Checklist of British and Irish Dragonfly Species",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1998-01-01",
"end_date": "",
"bbox": "-8.41, 49.49, 2.39, 59.07",
@@ -224109,7 +224200,7 @@
{
"id": "bech_nest_locations_1",
"title": "Adelie Penguin nest locations on Bechervaise Island",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-02-01",
"end_date": "2000-02-22",
"bbox": "62.8084, -67.5879, 62.8152, -67.5863",
@@ -224122,7 +224213,7 @@
{
"id": "bech_nest_locations_1",
"title": "Adelie Penguin nest locations on Bechervaise Island",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2000-02-01",
"end_date": "2000-02-22",
"bbox": "62.8084, -67.5879, 62.8152, -67.5863",
@@ -224733,7 +224824,7 @@
{
"id": "breeding_success_BI_1",
"title": "Adelie penguin breeding success for Bechervaise Island, Mawson",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1990-10-01",
"end_date": "2005-02-01",
"bbox": "62.8055, -67.5916, 62.825, -67.5861",
@@ -224746,7 +224837,7 @@
{
"id": "breeding_success_BI_1",
"title": "Adelie penguin breeding success for Bechervaise Island, Mawson",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1990-10-01",
"end_date": "2005-02-01",
"bbox": "62.8055, -67.5916, 62.825, -67.5861",
@@ -224785,7 +224876,7 @@
{
"id": "bromwich_0337948_1",
"title": "A 45-Y Hindcast of Antarctic Surface Mass Balance Using Polar MM5",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1979-01-01",
"end_date": "2002-08-31",
"bbox": "-180, -90, 180, -60",
@@ -224798,7 +224889,7 @@
{
"id": "bromwich_0337948_1",
"title": "A 45-Y Hindcast of Antarctic Surface Mass Balance Using Polar MM5",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1979-01-01",
"end_date": "2002-08-31",
"bbox": "-180, -90, 180, -60",
@@ -226241,7 +226332,7 @@
{
"id": "chesapeake_val_2013_0",
"title": "2013 Chesapeake Bay measurements",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-04-11",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -226254,7 +226345,7 @@
{
"id": "chesapeake_val_2013_0",
"title": "2013 Chesapeake Bay measurements",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "2013-04-11",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -226358,7 +226449,7 @@
{
"id": "climate_temps_1",
"title": "ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1901-01-01",
"end_date": "2002-12-31",
"bbox": "-180, -80, 180, -17",
@@ -226371,7 +226462,7 @@
{
"id": "climate_temps_1",
"title": "ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1901-01-01",
"end_date": "2002-12-31",
"bbox": "-180, -80, 180, -17",
@@ -226969,7 +227060,7 @@
{
"id": "darling_sst_00",
"title": "2000 Seawater Temperatures at the Darling Marine Center",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2000-12-31",
"bbox": "-71.31, 42.85, -66.74, 47.67",
@@ -226982,7 +227073,7 @@
{
"id": "darling_sst_00",
"title": "2000 Seawater Temperatures at the Darling Marine Center",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-01-01",
"end_date": "2000-12-31",
"bbox": "-71.31, 42.85, -66.74, 47.67",
@@ -227021,7 +227112,7 @@
{
"id": "darling_sst_82-93",
"title": "1982-1989 and 1993 Seawater Temperatures at the Darling Marine Center",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1982-03-01",
"end_date": "1993-12-31",
"bbox": "-71.31, 42.85, -66.74, 47.67",
@@ -227034,7 +227125,7 @@
{
"id": "darling_sst_82-93",
"title": "1982-1989 and 1993 Seawater Temperatures at the Darling Marine Center",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1982-03-01",
"end_date": "1993-12-31",
"bbox": "-71.31, 42.85, -66.74, 47.67",
@@ -227827,7 +227918,7 @@
{
"id": "doi:10.25921/sta3-3b95_Not Applicable",
"title": "2014-2015 Untrawlable Habitat Strategic Initiative (UHSI) Video and Still Imagery Data Collection",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2014-09-08",
"end_date": "2015-05-08",
"bbox": "-84.4, 27.7, -83.4, 29.7",
@@ -227840,7 +227931,7 @@
{
"id": "doi:10.25921/sta3-3b95_Not Applicable",
"title": "2014-2015 Untrawlable Habitat Strategic Initiative (UHSI) Video and Still Imagery Data Collection",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2014-09-08",
"end_date": "2015-05-08",
"bbox": "-84.4, 27.7, -83.4, 29.7",
@@ -227918,7 +228009,7 @@
{
"id": "doi:10.7289/V5862DPB_Not Applicable",
"title": "Airborne Magnetic Trackline Database",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1958-12-06",
"end_date": "2011-02-26",
"bbox": "-180, -90, 180, 90",
@@ -227931,7 +228022,7 @@
{
"id": "doi:10.7289/V5862DPB_Not Applicable",
"title": "Airborne Magnetic Trackline Database",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1958-12-06",
"end_date": "2011-02-26",
"bbox": "-180, -90, 180, 90",
@@ -228984,7 +229075,7 @@
{
"id": "envidat-lwf-34_2019-03-06",
"title": "10-HS Pfynwald",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ENVIDAT STAC Catalog",
"state_date": "2019-01-01",
"end_date": "2019-01-01",
"bbox": "7.61211, 46.30279, 7.61211, 46.30279",
@@ -228997,7 +229088,7 @@
{
"id": "envidat-lwf-34_2019-03-06",
"title": "10-HS Pfynwald",
- "catalog": "ENVIDAT STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2019-01-01",
"end_date": "2019-01-01",
"bbox": "7.61211, 46.30279, 7.61211, 46.30279",
@@ -230921,7 +231012,7 @@
{
"id": "fife_hydrology_strm_15m_1_1",
"title": "15 Minute Stream Flow Data: USGS (FIFE)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ORNL_CLOUD STAC Catalog",
"state_date": "1984-12-25",
"end_date": "1988-03-04",
"bbox": "-96.6, 39.1, -96.6, 39.1",
@@ -230934,7 +231025,7 @@
{
"id": "fife_hydrology_strm_15m_1_1",
"title": "15 Minute Stream Flow Data: USGS (FIFE)",
- "catalog": "ORNL_CLOUD STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1984-12-25",
"end_date": "1988-03-04",
"bbox": "-96.6, 39.1, -96.6, 39.1",
@@ -231779,7 +231870,7 @@
{
"id": "finnarp_aerosols",
"title": "Aerosol measurements at ABOA / FINNARP 2009",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -231792,7 +231883,7 @@
{
"id": "finnarp_aerosols",
"title": "Aerosol measurements at ABOA / FINNARP 2009",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -231922,7 +232013,7 @@
{
"id": "foraging_trip_duration_BI_1",
"title": "Adelie penguin foraging trip duration, Bechervaise Island, Mawson",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1991-10-01",
"end_date": "2005-02-01",
"bbox": "62.8055, -67.5916, 62.825, -67.5861",
@@ -231935,7 +232026,7 @@
{
"id": "foraging_trip_duration_BI_1",
"title": "Adelie penguin foraging trip duration, Bechervaise Island, Mawson",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1991-10-01",
"end_date": "2005-02-01",
"bbox": "62.8055, -67.5916, 62.825, -67.5861",
@@ -233157,7 +233248,7 @@
{
"id": "geodata_0290",
"title": "Administrative Boundaries - First Level (ESRI)",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1998-01-01",
"end_date": "1998-12-31",
"bbox": "-180, -90, 180, -60.5033",
@@ -233170,7 +233261,7 @@
{
"id": "geodata_0290",
"title": "Administrative Boundaries - First Level (ESRI)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1998-01-01",
"end_date": "1998-12-31",
"bbox": "-180, -90, 180, -60.5033",
@@ -234951,7 +235042,7 @@
{
"id": "geodata_1672",
"title": "Agricultural Area",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1961-01-01",
"end_date": "2008-12-31",
"bbox": "-180, -90, 180, -60.5033",
@@ -234964,7 +235055,7 @@
{
"id": "geodata_1672",
"title": "Agricultural Area",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1961-01-01",
"end_date": "2008-12-31",
"bbox": "-180, -90, 180, -60.5033",
@@ -236069,7 +236160,7 @@
{
"id": "geodata_2134",
"title": "Agricultural Area Irrigated",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2008-12-31",
"bbox": "-180, -90, 180, -60.5033",
@@ -236082,7 +236173,7 @@
{
"id": "geodata_2134",
"title": "Agricultural Area Irrigated",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-01-01",
"end_date": "2008-12-31",
"bbox": "-180, -90, 180, -60.5033",
@@ -236342,7 +236433,7 @@
{
"id": "geodata_2217",
"title": "Agricultural Area Certified Organic",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "2003-01-01",
"end_date": "2008-12-31",
"bbox": "-180, -90, 180, -60.5033",
@@ -236355,7 +236446,7 @@
{
"id": "geodata_2217",
"title": "Agricultural Area Certified Organic",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-01",
"end_date": "2008-12-31",
"bbox": "-180, -90, 180, -60.5033",
@@ -237889,7 +237980,7 @@
{
"id": "gomc_156",
"title": "Adopt-a-Tide Pool",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1990-01-01",
"end_date": "",
"bbox": "-70.923, 42.489, -70.763, 42.577",
@@ -237902,7 +237993,7 @@
{
"id": "gomc_156",
"title": "Adopt-a-Tide Pool",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1990-01-01",
"end_date": "",
"bbox": "-70.923, 42.489, -70.763, 42.577",
@@ -237928,7 +238019,7 @@
{
"id": "gomc_219",
"title": "2001 Long Island Sound Study Ambient Water Quality and Monitoring Program",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-74.3, 40.5, -71.75, 41.5",
@@ -237941,7 +238032,7 @@
{
"id": "gomc_219",
"title": "2001 Long Island Sound Study Ambient Water Quality and Monitoring Program",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-74.3, 40.5, -71.75, 41.5",
@@ -237980,7 +238071,7 @@
{
"id": "gomc_40",
"title": "Air Quality Monitoring In New Brunswick",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-145.27, 37.3, -48.11, 87.61",
@@ -237993,7 +238084,7 @@
{
"id": "gomc_40",
"title": "Air Quality Monitoring In New Brunswick",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-145.27, 37.3, -48.11, 87.61",
@@ -238084,7 +238175,7 @@
{
"id": "gov.noaa.ncdc:C01599_beta6",
"title": "Adaptive Ecosystem Climatology Beta 6 Satellite Climatology",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1980-01-01",
"end_date": "2012-12-31",
"bbox": "-135, 22.9276, -62.987, 53",
@@ -238097,7 +238188,7 @@
{
"id": "gov.noaa.ncdc:C01599_beta6",
"title": "Adaptive Ecosystem Climatology Beta 6 Satellite Climatology",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1980-01-01",
"end_date": "2012-12-31",
"bbox": "-135, 22.9276, -62.987, 53",
@@ -238357,7 +238448,7 @@
{
"id": "gov.noaa.nodc:0000035_Not Applicable",
"title": "1996 - Early 1998 CRETM/LMER Phytoplankton Data (NCEI Accession 0000035)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1996-07-09",
"end_date": "1998-03-06",
"bbox": "-124.003, 46.179833, -123.183167, 46.261667",
@@ -238370,7 +238461,7 @@
{
"id": "gov.noaa.nodc:0000035_Not Applicable",
"title": "1996 - Early 1998 CRETM/LMER Phytoplankton Data (NCEI Accession 0000035)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1996-07-09",
"end_date": "1998-03-06",
"bbox": "-124.003, 46.179833, -123.183167, 46.261667",
@@ -238383,7 +238474,7 @@
{
"id": "gov.noaa.nodc:0000052_Not Applicable",
"title": "1988 Resurrection Bay Zooplankton Data Set from 01 March 1988 to 28 June 1988 (NCEI Accession 0000052)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1988-03-01",
"end_date": "1988-06-28",
"bbox": "-149.4083, 59.9117, -149.3583, 60.02",
@@ -238396,7 +238487,7 @@
{
"id": "gov.noaa.nodc:0000052_Not Applicable",
"title": "1988 Resurrection Bay Zooplankton Data Set from 01 March 1988 to 28 June 1988 (NCEI Accession 0000052)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1988-03-01",
"end_date": "1988-06-28",
"bbox": "-149.4083, 59.9117, -149.3583, 60.02",
@@ -238630,7 +238721,7 @@
{
"id": "gov.noaa.nodc:0000366_Not Applicable",
"title": "Air/delta/sea surface temperature, pressure, and other data from MISS GAIL in a world-wide distribution from 21 October 1957 to 18 April 1961 (NCEI Accession 0000366)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1957-10-21",
"end_date": "1961-04-18",
"bbox": "18.7, -43.033333, 16.3, 64.033333",
@@ -238643,7 +238734,7 @@
{
"id": "gov.noaa.nodc:0000366_Not Applicable",
"title": "Air/delta/sea surface temperature, pressure, and other data from MISS GAIL in a world-wide distribution from 21 October 1957 to 18 April 1961 (NCEI Accession 0000366)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1957-10-21",
"end_date": "1961-04-18",
"bbox": "18.7, -43.033333, 16.3, 64.033333",
@@ -238721,7 +238812,7 @@
{
"id": "gov.noaa.nodc:0000501_Not Applicable",
"title": "A unified, long-term, Caribbean-wide initiative to identity the factors responsible for sustaining mangrove wetland, seagrass meadow, and coral reef productivity, February 1993 - October 1998 (NCEI Accession 0000501)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1993-02-12",
"end_date": "1998-10-15",
"bbox": "-90.583333, 9.583333, -59.633333, 24.05",
@@ -238734,7 +238825,7 @@
{
"id": "gov.noaa.nodc:0000501_Not Applicable",
"title": "A unified, long-term, Caribbean-wide initiative to identity the factors responsible for sustaining mangrove wetland, seagrass meadow, and coral reef productivity, February 1993 - October 1998 (NCEI Accession 0000501)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1993-02-12",
"end_date": "1998-10-15",
"bbox": "-90.583333, 9.583333, -59.633333, 24.05",
@@ -238942,7 +239033,7 @@
{
"id": "gov.noaa.nodc:0000861_Not Applicable",
"title": "A Hydrographic Survey of the Scotia Sea, 15 March 1999 to 22 April 1999 (NCEI Accession 0000861)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1999-03-15",
"end_date": "1999-04-22",
"bbox": "-68.260333, -67.576667, -2.296667, 10",
@@ -238955,7 +239046,7 @@
{
"id": "gov.noaa.nodc:0000861_Not Applicable",
"title": "A Hydrographic Survey of the Scotia Sea, 15 March 1999 to 22 April 1999 (NCEI Accession 0000861)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1999-03-15",
"end_date": "1999-04-22",
"bbox": "-68.260333, -67.576667, -2.296667, 10",
@@ -239228,7 +239319,7 @@
{
"id": "gov.noaa.nodc:0001746_Not Applicable",
"title": "ALINE time series (NCEI Accession 0001746)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1989-01-01",
"end_date": "2001-01-01",
"bbox": "141, 37, 150, 44",
@@ -239241,7 +239332,7 @@
{
"id": "gov.noaa.nodc:0001746_Not Applicable",
"title": "ALINE time series (NCEI Accession 0001746)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1989-01-01",
"end_date": "2001-01-01",
"bbox": "141, 37, 150, 44",
@@ -239267,7 +239358,7 @@
{
"id": "gov.noaa.nodc:0001941_Not Applicable",
"title": "Aerial surveys of bowhead and beluga whales along with incidental sighting of other marine mammals in the Bering, Beaufort and Chukchi Seas for the Bowhead Whale Aerial Survey Project (BWASP), 1979 - 2004 (NCEI Accession 0001941)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1979-04-01",
"end_date": "2004-10-18",
"bbox": "-174.01, 57.72, -125.25, 76.14",
@@ -239280,7 +239371,7 @@
{
"id": "gov.noaa.nodc:0001941_Not Applicable",
"title": "Aerial surveys of bowhead and beluga whales along with incidental sighting of other marine mammals in the Bering, Beaufort and Chukchi Seas for the Bowhead Whale Aerial Survey Project (BWASP), 1979 - 2004 (NCEI Accession 0001941)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1979-04-01",
"end_date": "2004-10-18",
"bbox": "-174.01, 57.72, -125.25, 76.14",
@@ -239423,7 +239514,7 @@
{
"id": "gov.noaa.nodc:0002198_Not Applicable",
"title": "A survey to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002198)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1999-09-01",
"end_date": "2002-08-01",
"bbox": "-96, 23.49, -85.47, 29.33",
@@ -239436,7 +239527,7 @@
{
"id": "gov.noaa.nodc:0002198_Not Applicable",
"title": "A survey to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002198)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1999-09-01",
"end_date": "2002-08-01",
"bbox": "-96, 23.49, -85.47, 29.33",
@@ -239475,7 +239566,7 @@
{
"id": "gov.noaa.nodc:0002295_Not Applicable",
"title": "A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002295)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1999-09-01",
"end_date": "2002-08-20",
"bbox": "-92.01, 23.79, -85.49, 25.49",
@@ -239488,7 +239579,7 @@
{
"id": "gov.noaa.nodc:0002295_Not Applicable",
"title": "A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002295)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1999-09-01",
"end_date": "2002-08-20",
"bbox": "-92.01, 23.79, -85.49, 25.49",
@@ -239553,7 +239644,7 @@
{
"id": "gov.noaa.nodc:0002650_Not Applicable",
"title": "A survey of the marine biota of the island of Lanai, Hawaii, to determine the presence and impact of marine non-indigenous and cryptogenic species, February - March 2005 (NCEI Accession 0002650)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2005-02-28",
"end_date": "2005-03-04",
"bbox": "-157.05, 20.73, -156.88, 20.92",
@@ -239566,7 +239657,7 @@
{
"id": "gov.noaa.nodc:0002650_Not Applicable",
"title": "A survey of the marine biota of the island of Lanai, Hawaii, to determine the presence and impact of marine non-indigenous and cryptogenic species, February - March 2005 (NCEI Accession 0002650)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2005-02-28",
"end_date": "2005-03-04",
"bbox": "-157.05, 20.73, -156.88, 20.92",
@@ -239618,7 +239709,7 @@
{
"id": "gov.noaa.nodc:0014906_Not Applicable",
"title": "Aerial sightings of bowhead whales and other marine mammals by the US Department of the Interior's Minerals Management Service, 1979 - 2006, in the Bering, Chukchi and Beaufort Seas (NCEI Accession 0014906)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1979-04-01",
"end_date": "2006-10-31",
"bbox": "-174.01, 57.72, -125.25, 76.14",
@@ -239631,7 +239722,7 @@
{
"id": "gov.noaa.nodc:0014906_Not Applicable",
"title": "Aerial sightings of bowhead whales and other marine mammals by the US Department of the Interior's Minerals Management Service, 1979 - 2006, in the Bering, Chukchi and Beaufort Seas (NCEI Accession 0014906)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1979-04-01",
"end_date": "2006-10-31",
"bbox": "-174.01, 57.72, -125.25, 76.14",
@@ -239722,7 +239813,7 @@
{
"id": "gov.noaa.nodc:0046934_Not Applicable",
"title": "Acropora Spatial Survey Data of the Upper Florida Keys National Marine Sanctuary, 2005 - 2007 (NCEI Accession 0046934)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2005-01-01",
"end_date": "2007-12-31",
"bbox": "-81.41079, 24.54466, -80.19632, 25.29129",
@@ -239735,7 +239826,7 @@
{
"id": "gov.noaa.nodc:0046934_Not Applicable",
"title": "Acropora Spatial Survey Data of the Upper Florida Keys National Marine Sanctuary, 2005 - 2007 (NCEI Accession 0046934)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2005-01-01",
"end_date": "2007-12-31",
"bbox": "-81.41079, 24.54466, -80.19632, 25.29129",
@@ -239839,7 +239930,7 @@
{
"id": "gov.noaa.nodc:0061208_Not Applicable",
"title": "Algal, coral, and other data collected by ROV and scuba diver videography from M.V. FLING and M.V. SPREE for Post-Hurricane Assessment of Sensitive Habitats of the Flower Garden Banks Vicinity project from November 13, 2005 to June 23, 2007 (NCEI Accession 0061208)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2005-11-13",
"end_date": "2007-05-23",
"bbox": "-93.58, 27.85, -92.45, 28.3",
@@ -239852,7 +239943,7 @@
{
"id": "gov.noaa.nodc:0061208_Not Applicable",
"title": "Algal, coral, and other data collected by ROV and scuba diver videography from M.V. FLING and M.V. SPREE for Post-Hurricane Assessment of Sensitive Habitats of the Flower Garden Banks Vicinity project from November 13, 2005 to June 23, 2007 (NCEI Accession 0061208)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2005-11-13",
"end_date": "2007-05-23",
"bbox": "-93.58, 27.85, -92.45, 28.3",
@@ -241776,7 +241867,7 @@
{
"id": "gov.noaa.nodc:0125596_Not Applicable",
"title": "Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2009-03-18 to 2012-12-10 (NCEI Accession 0125596)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2009-03-18",
"end_date": "2012-12-10",
"bbox": "-51.493, -34.504, -44.498, -34.499",
@@ -241789,7 +241880,7 @@
{
"id": "gov.noaa.nodc:0125596_Not Applicable",
"title": "Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2009-03-18 to 2012-12-10 (NCEI Accession 0125596)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2009-03-18",
"end_date": "2012-12-10",
"bbox": "-51.493, -34.504, -44.498, -34.499",
@@ -241802,7 +241893,7 @@
{
"id": "gov.noaa.nodc:0125597_Not Applicable",
"title": "Acoustic travel time, bottom pressure, and near bottom current velocities from inverted echo sounders in the Atlantic Ocean from 2004-09-27 to 2016-02-25 (NCEI Accession 0125597)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2004-09-27",
"end_date": "2016-02-25",
"bbox": "-76.84, 26.491, -72.004, 26.516",
@@ -241815,7 +241906,7 @@
{
"id": "gov.noaa.nodc:0125597_Not Applicable",
"title": "Acoustic travel time, bottom pressure, and near bottom current velocities from inverted echo sounders in the Atlantic Ocean from 2004-09-27 to 2016-02-25 (NCEI Accession 0125597)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2004-09-27",
"end_date": "2016-02-25",
"bbox": "-76.84, 26.491, -72.004, 26.516",
@@ -241893,7 +241984,7 @@
{
"id": "gov.noaa.nodc:0130929_Not Applicable",
"title": "AFSC/REFM: Isolation by distance (IBD) Alaskan fish stock structure modeling (NCEI Accession 0130929)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1980-01-01",
"end_date": "2012-01-01",
"bbox": "170, 50, -160, 62",
@@ -241906,7 +241997,7 @@
{
"id": "gov.noaa.nodc:0130929_Not Applicable",
"title": "AFSC/REFM: Isolation by distance (IBD) Alaskan fish stock structure modeling (NCEI Accession 0130929)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1980-01-01",
"end_date": "2012-01-01",
"bbox": "170, 50, -160, 62",
@@ -242153,7 +242244,7 @@
{
"id": "gov.noaa.nodc:0148759_Not Applicable",
"title": "AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Helheim Glacier Ice Front from 2009-08-11 to 2016-02-20 (NCEI Accession 0148759)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2009-08-11",
"end_date": "2016-02-20",
"bbox": "-38.146, 66.329, -38.146, 66.329",
@@ -242166,7 +242257,7 @@
{
"id": "gov.noaa.nodc:0148759_Not Applicable",
"title": "AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Helheim Glacier Ice Front from 2009-08-11 to 2016-02-20 (NCEI Accession 0148759)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2009-08-11",
"end_date": "2016-02-20",
"bbox": "-38.146, 66.329, -38.146, 66.329",
@@ -242283,7 +242374,7 @@
{
"id": "gov.noaa.nodc:0156425_Not Applicable",
"title": "Absolute Geostrophic Velocity Inverted from the Polar Science Center Hydrographic Climatology (PHC3.0) of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156425)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1900-01-01",
"end_date": "1998-12-31",
"bbox": "-180, 45, 180, 90",
@@ -242296,7 +242387,7 @@
{
"id": "gov.noaa.nodc:0156425_Not Applicable",
"title": "Absolute Geostrophic Velocity Inverted from the Polar Science Center Hydrographic Climatology (PHC3.0) of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156425)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1900-01-01",
"end_date": "1998-12-31",
"bbox": "-180, 45, 180, 90",
@@ -242322,7 +242413,7 @@
{
"id": "gov.noaa.nodc:0156765_Not Applicable",
"title": "Age and Growth of Spotted Sea Trout in the Gulf of Mexico from 1994 to 1996 (NCEI Accession 0156765)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1994-05-06",
"end_date": "1996-08-30",
"bbox": "-87.6, 29.6, -84.7, 30.6",
@@ -242335,7 +242426,7 @@
{
"id": "gov.noaa.nodc:0156765_Not Applicable",
"title": "Age and Growth of Spotted Sea Trout in the Gulf of Mexico from 1994 to 1996 (NCEI Accession 0156765)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1994-05-06",
"end_date": "1996-08-30",
"bbox": "-87.6, 29.6, -84.7, 30.6",
@@ -242452,7 +242543,7 @@
{
"id": "gov.noaa.nodc:0159386_Not Applicable",
"title": "Airborne eXpendable BathyThermographs (AXBT) data from Ocean Surveys in the Gulf of Mexico during Hurricane Lili 2002-10-02 to 2002-10-04 (NCEI Accession 0159386)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-10-02",
"end_date": "2002-10-04",
"bbox": "-88.672, 22.203, -84.062, 26.433",
@@ -242465,7 +242556,7 @@
{
"id": "gov.noaa.nodc:0159386_Not Applicable",
"title": "Airborne eXpendable BathyThermographs (AXBT) data from Ocean Surveys in the Gulf of Mexico during Hurricane Lili 2002-10-02 to 2002-10-04 (NCEI Accession 0159386)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2002-10-02",
"end_date": "2002-10-04",
"bbox": "-88.672, 22.203, -84.062, 26.433",
@@ -242478,7 +242569,7 @@
{
"id": "gov.noaa.nodc:0159419_Not Applicable",
"title": "ADCP, CTD, MIDAS, and cruise track data collected from R/V Pelican in Galveston and Trinity Bay, Texas and the Gulf of Mexico from 2013-10-17 to 2013-10-20 (NCEI Accession 0159419)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2013-10-17",
"end_date": "2013-10-20",
"bbox": "-94.9828, 26.16133, -88, 29.69641",
@@ -242491,7 +242582,7 @@
{
"id": "gov.noaa.nodc:0159419_Not Applicable",
"title": "ADCP, CTD, MIDAS, and cruise track data collected from R/V Pelican in Galveston and Trinity Bay, Texas and the Gulf of Mexico from 2013-10-17 to 2013-10-20 (NCEI Accession 0159419)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2013-10-17",
"end_date": "2013-10-20",
"bbox": "-94.9828, 26.16133, -88, 29.69641",
@@ -242517,7 +242608,7 @@
{
"id": "gov.noaa.nodc:0161311_Not Applicable",
"title": "A Comprehensive Inventory of Alabama Coastal Zone Wetland Habitats (Swamps, Marshes, Submersed Grassbeds) from 1980 to 1982 (NCEI Accession 0161311)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1979-01-01",
"end_date": "1982-12-31",
"bbox": "-88.431, 30.2129, -87.328, 31.0701",
@@ -242530,7 +242621,7 @@
{
"id": "gov.noaa.nodc:0161311_Not Applicable",
"title": "A Comprehensive Inventory of Alabama Coastal Zone Wetland Habitats (Swamps, Marshes, Submersed Grassbeds) from 1980 to 1982 (NCEI Accession 0161311)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1979-01-01",
"end_date": "1982-12-31",
"bbox": "-88.431, 30.2129, -87.328, 31.0701",
@@ -242660,7 +242751,7 @@
{
"id": "gov.noaa.nodc:0163212_Not Applicable",
"title": "Acoustic Travel Time and Hydrostatic Pressure in Sermilik Fjord in Southeastern Greenland from 2011-08-23 to 2016-08-11 (NCEI Accession 0163212)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2011-08-23",
"end_date": "2016-08-11",
"bbox": "-37.8998, 65.5268, -37.6336, 66.2449",
@@ -242673,7 +242764,7 @@
{
"id": "gov.noaa.nodc:0163212_Not Applicable",
"title": "Acoustic Travel Time and Hydrostatic Pressure in Sermilik Fjord in Southeastern Greenland from 2011-08-23 to 2016-08-11 (NCEI Accession 0163212)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-08-23",
"end_date": "2016-08-11",
"bbox": "-37.8998, 65.5268, -37.6336, 66.2449",
@@ -243193,7 +243284,7 @@
{
"id": "gov.noaa.nodc:0172043_Not Applicable",
"title": "ADCP, CTD, and continuous data from the Multiple Instrument Data Acquisition System (MIDAS) collected in the Southeast of the Mississippi River Delta aboard the R/V Pelican from 2012-11-28 to 2012-12-19 (NCEI Accession 0172043)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-11-28",
"end_date": "2012-12-19",
"bbox": "-94.0863, 25.7961, -87.2228, 28.9733",
@@ -243206,7 +243297,7 @@
{
"id": "gov.noaa.nodc:0172043_Not Applicable",
"title": "ADCP, CTD, and continuous data from the Multiple Instrument Data Acquisition System (MIDAS) collected in the Southeast of the Mississippi River Delta aboard the R/V Pelican from 2012-11-28 to 2012-12-19 (NCEI Accession 0172043)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2012-11-28",
"end_date": "2012-12-19",
"bbox": "-94.0863, 25.7961, -87.2228, 28.9733",
@@ -243219,7 +243310,7 @@
{
"id": "gov.noaa.nodc:0172377_Not Applicable",
"title": "Abundance and biomass of parrotfishes (Labridae, Scarinae) in St.Croix, U.S. Virgin Islands 2015 to 2016 (NCEI Accession 0172377)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2015-07-21",
"end_date": "2016-08-05",
"bbox": "-64.9199, 17.63764, -64.47889, 17.82709",
@@ -243232,7 +243323,7 @@
{
"id": "gov.noaa.nodc:0172377_Not Applicable",
"title": "Abundance and biomass of parrotfishes (Labridae, Scarinae) in St.Croix, U.S. Virgin Islands 2015 to 2016 (NCEI Accession 0172377)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2015-07-21",
"end_date": "2016-08-05",
"bbox": "-64.9199, 17.63764, -64.47889, 17.82709",
@@ -243310,7 +243401,7 @@
{
"id": "gov.noaa.nodc:0175745_Not Applicable",
"title": "Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2011-07-07 to 2016-10-29 (NCEI Accession 0175745)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2011-07-07",
"end_date": "2016-10-29",
"bbox": "-51.5, -34.503, -44.5, -34.5",
@@ -243323,7 +243414,7 @@
{
"id": "gov.noaa.nodc:0175745_Not Applicable",
"title": "Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2011-07-07 to 2016-10-29 (NCEI Accession 0175745)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2011-07-07",
"end_date": "2016-10-29",
"bbox": "-51.5, -34.503, -44.5, -34.5",
@@ -243336,7 +243427,7 @@
{
"id": "gov.noaa.nodc:0175783_Not Applicable",
"title": "Agulhas Current transport derived from satellite altimetry observations in Indian Ocean from 1992-10-14 to 2016-12-28 (NCEI Accession 0175783)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1992-10-14",
"end_date": "2016-12-28",
"bbox": "27, -40, 30, -34",
@@ -243349,7 +243440,7 @@
{
"id": "gov.noaa.nodc:0175783_Not Applicable",
"title": "Agulhas Current transport derived from satellite altimetry observations in Indian Ocean from 1992-10-14 to 2016-12-28 (NCEI Accession 0175783)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1992-10-14",
"end_date": "2016-12-28",
"bbox": "27, -40, 30, -34",
@@ -243427,7 +243518,7 @@
{
"id": "gov.noaa.nodc:0185753_Not Applicable",
"title": "Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Laurentian in Lake Huron, Great Lakes from 2006-09-01 to 2012-12-31 (NCEI Accession 0185753)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2006-09-01",
"end_date": "2012-12-31",
"bbox": "-84.5, 43.2, -79.8, 46.3",
@@ -243440,7 +243531,7 @@
{
"id": "gov.noaa.nodc:0185753_Not Applicable",
"title": "Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Laurentian in Lake Huron, Great Lakes from 2006-09-01 to 2012-12-31 (NCEI Accession 0185753)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2006-09-01",
"end_date": "2012-12-31",
"bbox": "-84.5, 43.2, -79.8, 46.3",
@@ -243453,7 +243544,7 @@
{
"id": "gov.noaa.nodc:0186561_Not Applicable",
"title": "2003 Marine Fisheries Initiative (MARFIN) Gulf of Mexico and South Atlantic angler survey (NCEI Accession 0186561)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2003-01-01",
"end_date": "2003-12-31",
"bbox": "-98, 25, -80, 31",
@@ -243466,7 +243557,7 @@
{
"id": "gov.noaa.nodc:0186561_Not Applicable",
"title": "2003 Marine Fisheries Initiative (MARFIN) Gulf of Mexico and South Atlantic angler survey (NCEI Accession 0186561)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2003-01-01",
"end_date": "2003-12-31",
"bbox": "-98, 25, -80, 31",
@@ -243492,7 +243583,7 @@
{
"id": "gov.noaa.nodc:0194300_Not Applicable",
"title": "ADCP, CTD, water and sediment chemistry, and underway sensor data collected aboard R/V Endeavor cruise EN505 in the Gulf of Mexico from 2012-04-11 to 2012-04-24 (NCEI Accession 0194300)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2012-04-11",
"end_date": "2012-04-24",
"bbox": "-90.5895, 27.2111, -87.42629, 30.35717",
@@ -243505,7 +243596,7 @@
{
"id": "gov.noaa.nodc:0194300_Not Applicable",
"title": "ADCP, CTD, water and sediment chemistry, and underway sensor data collected aboard R/V Endeavor cruise EN505 in the Gulf of Mexico from 2012-04-11 to 2012-04-24 (NCEI Accession 0194300)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2012-04-11",
"end_date": "2012-04-24",
"bbox": "-90.5895, 27.2111, -87.42629, 30.35717",
@@ -243635,7 +243726,7 @@
{
"id": "gov.noaa.nodc:0209071_Not Applicable",
"title": "ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean from 2009-12-01 to 2010-03-23 (NCEI Accession 0209071)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2009-12-01",
"end_date": "2010-03-23",
"bbox": "11.2067, -5.8778, 11.2067, -5.8778",
@@ -243648,7 +243739,7 @@
{
"id": "gov.noaa.nodc:0209071_Not Applicable",
"title": "ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean from 2009-12-01 to 2010-03-23 (NCEI Accession 0209071)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2009-12-01",
"end_date": "2010-03-23",
"bbox": "11.2067, -5.8778, 11.2067, -5.8778",
@@ -243713,7 +243804,7 @@
{
"id": "gov.noaa.nodc:0209226_Not Applicable",
"title": "Acropora cervicornis outplanting scores in the Florida Reef Tract from 2006-01-01 to 2099-12-31 (NCEI Accession 0209226)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2006-01-01",
"end_date": "2099-12-31",
"bbox": "-82.9771, 24.4437, -80.0646, 26.3438",
@@ -243726,7 +243817,7 @@
{
"id": "gov.noaa.nodc:0209226_Not Applicable",
"title": "Acropora cervicornis outplanting scores in the Florida Reef Tract from 2006-01-01 to 2099-12-31 (NCEI Accession 0209226)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2006-01-01",
"end_date": "2099-12-31",
"bbox": "-82.9771, 24.4437, -80.0646, 26.3438",
@@ -243856,7 +243947,7 @@
{
"id": "gov.noaa.nodc:0221188_Not Applicable",
"title": "3-dimensional current velocity and other parameters taken by ADCP from the offshore supply ship Gerry Bordelon in Gulf of Mexico on 2017-09-24 (NCEI Accession 0221188)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2017-09-24",
"end_date": "2017-09-24",
"bbox": "-88.974, 28.932, -88.965, 28.944",
@@ -243869,7 +243960,7 @@
{
"id": "gov.noaa.nodc:0221188_Not Applicable",
"title": "3-dimensional current velocity and other parameters taken by ADCP from the offshore supply ship Gerry Bordelon in Gulf of Mexico on 2017-09-24 (NCEI Accession 0221188)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2017-09-24",
"end_date": "2017-09-24",
"bbox": "-88.974, 28.932, -88.965, 28.944",
@@ -243934,7 +244025,7 @@
{
"id": "gov.noaa.nodc:0226205_Not Applicable",
"title": "ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30 (NCEI Accession 0226205)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "2020-03-28",
"end_date": "2020-03-30",
"bbox": "-88.576242, 27.591893, -82.438911, 30.342877",
@@ -243947,7 +244038,7 @@
{
"id": "gov.noaa.nodc:0226205_Not Applicable",
"title": "ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30 (NCEI Accession 0226205)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-03-28",
"end_date": "2020-03-30",
"bbox": "-88.576242, 27.591893, -82.438911, 30.342877",
@@ -244103,7 +244194,7 @@
{
"id": "gov.noaa.nodc:7000422_Not Applicable",
"title": "AIR PRESSURE and Other Data from GOSNOLD From NW Atlantic (limit-40 W) from 1969-10-28 to 1969-10-29 (NCEI Accession 7000422)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1969-10-28",
"end_date": "1969-10-29",
"bbox": "-72, 39, -71, 40",
@@ -244116,7 +244207,7 @@
{
"id": "gov.noaa.nodc:7000422_Not Applicable",
"title": "AIR PRESSURE and Other Data from GOSNOLD From NW Atlantic (limit-40 W) from 1969-10-28 to 1969-10-29 (NCEI Accession 7000422)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1969-10-28",
"end_date": "1969-10-29",
"bbox": "-72, 39, -71, 40",
@@ -244129,7 +244220,7 @@
{
"id": "gov.noaa.nodc:7000981_Not Applicable",
"title": "A summary of seawater chemistry analysis of stations in North Atlantic Ocean from 1970-06-20 to 1970-07-03 (NCEI Accession 7000981)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1970-06-01",
"end_date": "1970-07-01",
"bbox": "-29.33, 50.01, -14.2, 55.56",
@@ -244142,7 +244233,7 @@
{
"id": "gov.noaa.nodc:7000981_Not Applicable",
"title": "A summary of seawater chemistry analysis of stations in North Atlantic Ocean from 1970-06-20 to 1970-07-03 (NCEI Accession 7000981)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-06-01",
"end_date": "1970-07-01",
"bbox": "-29.33, 50.01, -14.2, 55.56",
@@ -244350,7 +244441,7 @@
{
"id": "gov.noaa.nodc:7300282_Not Applicable",
"title": "AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms from 1968-07-01 to 1970-12-31 (NCEI Accession 7300282)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1968-07-01",
"end_date": "1970-12-31",
"bbox": "113.9, -46.6, 179.8, -0.2",
@@ -244363,7 +244454,7 @@
{
"id": "gov.noaa.nodc:7300282_Not Applicable",
"title": "AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms from 1968-07-01 to 1970-12-31 (NCEI Accession 7300282)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1968-07-01",
"end_date": "1970-12-31",
"bbox": "113.9, -46.6, 179.8, -0.2",
@@ -244597,7 +244688,7 @@
{
"id": "gov.noaa.nodc:7601613_Not Applicable",
"title": "AIR PRESSURE and Other Data from TIDE STATIONS From North American Coastline-North and Others from 1972-01-01 to 1974-06-30 (NCEI Accession 7601613)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1972-01-01",
"end_date": "1974-06-30",
"bbox": "-77, 37, -76, 39",
@@ -244610,7 +244701,7 @@
{
"id": "gov.noaa.nodc:7601613_Not Applicable",
"title": "AIR PRESSURE and Other Data from TIDE STATIONS From North American Coastline-North and Others from 1972-01-01 to 1974-06-30 (NCEI Accession 7601613)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1972-01-01",
"end_date": "1974-06-30",
"bbox": "-77, 37, -76, 39",
@@ -244714,7 +244805,7 @@
{
"id": "gov.noaa.nodc:7700179_Not Applicable",
"title": "AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms From Labrador Sea from 1919-09-29 to 1976-04-26 (NCEI Accession 7700179)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1919-09-29",
"end_date": "1976-04-26",
"bbox": "-60, 44, 48, 80.5",
@@ -244727,7 +244818,7 @@
{
"id": "gov.noaa.nodc:7700179_Not Applicable",
"title": "AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms From Labrador Sea from 1919-09-29 to 1976-04-26 (NCEI Accession 7700179)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1919-09-29",
"end_date": "1976-04-26",
"bbox": "-60, 44, 48, 80.5",
@@ -248094,7 +248185,7 @@
{
"id": "gov.noaa.nodc:9400225_Not Applicable",
"title": "ABSORPTION, SCATTERING, ATTENUATION COEFFICIENTS and Other Data from SATELLITE From Gulf of Maine from 1985-01-01 to 1992-12-31 (NCEI Accession 9400225)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1985-01-01",
"end_date": "1992-12-31",
"bbox": "-70.9, 42, -65.7, 45",
@@ -248107,7 +248198,7 @@
{
"id": "gov.noaa.nodc:9400225_Not Applicable",
"title": "ABSORPTION, SCATTERING, ATTENUATION COEFFICIENTS and Other Data from SATELLITE From Gulf of Maine from 1985-01-01 to 1992-12-31 (NCEI Accession 9400225)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1985-01-01",
"end_date": "1992-12-31",
"bbox": "-70.9, 42, -65.7, 45",
@@ -248302,7 +248393,7 @@
{
"id": "gov.noaa.nodc:9600025_Not Applicable",
"title": "AIR PRESSURE and Other Data from SHI YAN 3 From Antarctic and Others from 1992-11-09 to 1993-02-24 (NCEI Accession 9600025)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1992-11-09",
"end_date": "1993-02-24",
"bbox": "158, -2, 158, -2",
@@ -248315,7 +248406,7 @@
{
"id": "gov.noaa.nodc:9600025_Not Applicable",
"title": "AIR PRESSURE and Other Data from SHI YAN 3 From Antarctic and Others from 1992-11-09 to 1993-02-24 (NCEI Accession 9600025)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1992-11-09",
"end_date": "1993-02-24",
"bbox": "158, -2, 158, -2",
@@ -248432,7 +248523,7 @@
{
"id": "gov.noaa.nodc:9700063_Not Applicable",
"title": "AIR PRESSURE and Other Data from NOODIN From Great Lakes from 1995-06-20 to 1996-11-14 (NCEI Accession 9700063)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1995-06-20",
"end_date": "1996-11-14",
"bbox": "-91.7, 47, -91.7, 47",
@@ -248445,7 +248536,7 @@
{
"id": "gov.noaa.nodc:9700063_Not Applicable",
"title": "AIR PRESSURE and Other Data from NOODIN From Great Lakes from 1995-06-20 to 1996-11-14 (NCEI Accession 9700063)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1995-06-20",
"end_date": "1996-11-14",
"bbox": "-91.7, 47, -91.7, 47",
@@ -248484,7 +248575,7 @@
{
"id": "gov.noaa.nodc:9700205_Not Applicable",
"title": "AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1992-02-02 to 1992-10-21 (NCEI Accession 9700205)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1992-02-02",
"end_date": "1992-10-21",
"bbox": "-146.293, -12.864, -104.392, 2.999",
@@ -248497,7 +248588,7 @@
{
"id": "gov.noaa.nodc:9700205_Not Applicable",
"title": "AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1992-02-02 to 1992-10-21 (NCEI Accession 9700205)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1992-02-02",
"end_date": "1992-10-21",
"bbox": "-146.293, -12.864, -104.392, 2.999",
@@ -248601,7 +248692,7 @@
{
"id": "gov.noaa.nodc:9800085_Not Applicable",
"title": "AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1995-01-09 to 1995-12-28 (NCEI Accession 9800085)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1995-01-09",
"end_date": "1995-12-28",
"bbox": "56.5, 9.9, 68.8, 24.1",
@@ -248614,7 +248705,7 @@
{
"id": "gov.noaa.nodc:9800085_Not Applicable",
"title": "AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1995-01-09 to 1995-12-28 (NCEI Accession 9800085)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1995-01-09",
"end_date": "1995-12-28",
"bbox": "56.5, 9.9, 68.8, 24.1",
@@ -248744,7 +248835,7 @@
{
"id": "gov.noaa.nodc:9800197_Not Applicable",
"title": "Algal species and other data collected using photographs in the southern coast of the island of Ofu from 1992-09-08 to 1992-09-11 (NCEI Accession 9800197)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1992-09-08",
"end_date": "1992-09-11",
"bbox": "-169.7, -14.2, -169.7, -14.2",
@@ -248757,7 +248848,7 @@
{
"id": "gov.noaa.nodc:9800197_Not Applicable",
"title": "Algal species and other data collected using photographs in the southern coast of the island of Ofu from 1992-09-08 to 1992-09-11 (NCEI Accession 9800197)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1992-09-08",
"end_date": "1992-09-11",
"bbox": "-169.7, -14.2, -169.7, -14.2",
@@ -248939,7 +249030,7 @@
{
"id": "gov.noaa.nodc:9900159_Not Applicable",
"title": "1999 Field Season CTD, chlorophyll A and transmissivity data from the CRETM and LMER Projects in the Columbia River and Frasier River estuaries, 19990616 to 19990718 (NCEI Accession 9900159)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "NOAA_NCEI STAC Catalog",
"state_date": "1999-06-16",
"end_date": "1999-07-18",
"bbox": "-124, 45, -122, 49.5",
@@ -248952,7 +249043,7 @@
{
"id": "gov.noaa.nodc:9900159_Not Applicable",
"title": "1999 Field Season CTD, chlorophyll A and transmissivity data from the CRETM and LMER Projects in the Columbia River and Frasier River estuaries, 19990616 to 19990718 (NCEI Accession 9900159)",
- "catalog": "NOAA_NCEI STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1999-06-16",
"end_date": "1999-07-18",
"bbox": "-124, 45, -122, 49.5",
@@ -255725,7 +255816,7 @@
{
"id": "insects_subsaharanAfrica",
"title": "A Checklist of the Insects of Subsaharan Africa",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2000-01-01",
"end_date": "",
"bbox": "13.68, -35.9, 33.98, -21.27",
@@ -255738,7 +255829,7 @@
{
"id": "insects_subsaharanAfrica",
"title": "A Checklist of the Insects of Subsaharan Africa",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2000-01-01",
"end_date": "",
"bbox": "13.68, -35.9, 33.98, -21.27",
@@ -255985,7 +256076,7 @@
{
"id": "joughin_0631973",
"title": "Airborne Radar-Derived Accumulation Rates over Pine Island and Thwaites Glaciers",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1980-01-01",
"end_date": "2009-12-31",
"bbox": "-124.8, -80.8, -86.7, -73.9",
@@ -255998,7 +256089,7 @@
{
"id": "joughin_0631973",
"title": "Airborne Radar-Derived Accumulation Rates over Pine Island and Thwaites Glaciers",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1980-01-01",
"end_date": "2009-12-31",
"bbox": "-124.8, -80.8, -86.7, -73.9",
@@ -256765,7 +256856,7 @@
{
"id": "lake_erie_aug_2014_0",
"title": "2014 Lake Erie measurements",
- "catalog": "OB_DAAC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2014-08-18",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -256778,7 +256869,7 @@
{
"id": "lake_erie_aug_2014_0",
"title": "2014 Lake Erie measurements",
- "catalog": "ALL STAC Catalog",
+ "catalog": "OB_DAAC STAC Catalog",
"state_date": "2014-08-18",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -257142,7 +257233,7 @@
{
"id": "latent-reserves-in-the-swiss-nfi_1.0",
"title": "'Latent reserves' within the Swiss NFI",
- "catalog": "ENVIDAT STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2020-01-01",
"end_date": "2020-01-01",
"bbox": "5.95587, 45.81802, 10.49203, 47.80838",
@@ -257155,7 +257246,7 @@
{
"id": "latent-reserves-in-the-swiss-nfi_1.0",
"title": "'Latent reserves' within the Swiss NFI",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ENVIDAT STAC Catalog",
"state_date": "2020-01-01",
"end_date": "2020-01-01",
"bbox": "5.95587, 45.81802, 10.49203, 47.80838",
@@ -258949,7 +259040,7 @@
{
"id": "macquarie_taspaws_grid_1",
"title": "A grid system used by the Parks and Wildlife Service, Tasmania, for Macquarie Island, 1974 to June 2001",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1974-01-01",
"end_date": "2001-06-02",
"bbox": "158.7322, -54.8011, 158.9781, -54.4714",
@@ -258962,7 +259053,7 @@
{
"id": "macquarie_taspaws_grid_1",
"title": "A grid system used by the Parks and Wildlife Service, Tasmania, for Macquarie Island, 1974 to June 2001",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1974-01-01",
"end_date": "2001-06-02",
"bbox": "158.7322, -54.8011, 158.9781, -54.4714",
@@ -259365,7 +259456,7 @@
{
"id": "mbs_wilhelm_msa_hooh_1",
"title": "15 year Wilhelm II Land MSA and HOOH shallow ice core record from Mount Brown South (MBS)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1984-01-01",
"end_date": "1998-12-31",
"bbox": "86.082, -69.13, 86.084, -69.12",
@@ -259378,7 +259469,7 @@
{
"id": "mbs_wilhelm_msa_hooh_1",
"title": "15 year Wilhelm II Land MSA and HOOH shallow ice core record from Mount Brown South (MBS)",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1984-01-01",
"end_date": "1998-12-31",
"bbox": "86.082, -69.13, 86.084, -69.12",
@@ -260977,7 +261068,7 @@
{
"id": "newcomb_bay_bathy_dem_1",
"title": "A bathymetric Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-02-01",
"end_date": "2000-02-05",
"bbox": "110.512, -66.282, 110.566, -66.256",
@@ -260990,7 +261081,7 @@
{
"id": "newcomb_bay_bathy_dem_1",
"title": "A bathymetric Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1997-02-01",
"end_date": "2000-02-05",
"bbox": "110.512, -66.282, 110.566, -66.256",
@@ -261367,7 +261458,7 @@
{
"id": "obrienbay_bathy_dem_1",
"title": "A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1997-03-31",
"end_date": "1997-03-31",
"bbox": "110.516, -66.297, 110.54, -66.293",
@@ -261380,7 +261471,7 @@
{
"id": "obrienbay_bathy_dem_1",
"title": "A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1997-03-31",
"end_date": "1997-03-31",
"bbox": "110.516, -66.297, 110.54, -66.293",
@@ -261718,7 +261809,7 @@
{
"id": "pfynwaldgasexchange_1.0",
"title": "2013-2020 gas exchange at Pfynwald",
- "catalog": "ALL STAC Catalog",
+ "catalog": "ENVIDAT STAC Catalog",
"state_date": "2021-01-01",
"end_date": "2021-01-01",
"bbox": "7.6105556, 46.3001905, 7.6163921, 46.3047564",
@@ -261731,7 +261822,7 @@
{
"id": "pfynwaldgasexchange_1.0",
"title": "2013-2020 gas exchange at Pfynwald",
- "catalog": "ENVIDAT STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2021-01-01",
"end_date": "2021-01-01",
"bbox": "7.6105556, 46.3001905, 7.6163921, 46.3047564",
@@ -264578,7 +264669,7 @@
{
"id": "scarmarbin_1647",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -264591,7 +264682,7 @@
{
"id": "scarmarbin_1647",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea.",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -264604,7 +264695,7 @@
{
"id": "scarmarbin_1648",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Cumacea.",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -264617,7 +264708,7 @@
{
"id": "scarmarbin_1648",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Cumacea.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -264630,7 +264721,7 @@
{
"id": "scarmarbin_1649",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Pycnogonida.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -264643,7 +264734,7 @@
{
"id": "scarmarbin_1649",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Pycnogonida.",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -264682,7 +264773,7 @@
{
"id": "scarmarbin_1716",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80 - scarmarbin_1716",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1979-12-27",
"end_date": "1980-02-07",
"bbox": "-180, -90, 180, 90",
@@ -264695,7 +264786,7 @@
{
"id": "scarmarbin_1716",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80 - scarmarbin_1716",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1979-12-27",
"end_date": "1980-02-07",
"bbox": "-180, -90, 180, 90",
@@ -264708,7 +264799,7 @@
{
"id": "scarmarbin_1772",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Ophiuroidea.",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -264721,7 +264812,7 @@
{
"id": "scarmarbin_1772",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Ophiuroidea.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -264734,7 +264825,7 @@
{
"id": "scarmarbin_1806",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Amphipoda (1997).",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -264747,7 +264838,7 @@
{
"id": "scarmarbin_1806",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Amphipoda (1997).",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -264760,7 +264851,7 @@
{
"id": "scarmarbin_1807",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1994).",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -264773,7 +264864,7 @@
{
"id": "scarmarbin_1807",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1994).",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -264786,7 +264877,7 @@
{
"id": "scarmarbin_1808",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1997).",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -264799,7 +264890,7 @@
{
"id": "scarmarbin_1808",
"title": "Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1997).",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -264812,7 +264903,7 @@
{
"id": "scarmarbin_987",
"title": "A Biotic Database of Indo-Pacific Marine Mollusks (Southern Ocean Collection)",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -264825,7 +264916,7 @@
{
"id": "scarmarbin_987",
"title": "A Biotic Database of Indo-Pacific Marine Mollusks (Southern Ocean Collection)",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -265020,7 +265111,7 @@
{
"id": "seamap47",
"title": "Aerial Surveys of Marine Birds and Mammals in Support of Oil Spill Response and Injury Assessment",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1994-06-13",
"end_date": "1997-11-22",
"bbox": "-124.81862, 33.78087, -118.39433, 41.182",
@@ -265033,7 +265124,7 @@
{
"id": "seamap47",
"title": "Aerial Surveys of Marine Birds and Mammals in Support of Oil Spill Response and Injury Assessment",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1994-06-13",
"end_date": "1997-11-22",
"bbox": "-124.81862, 33.78087, -118.39433, 41.182",
@@ -265280,7 +265371,7 @@
{
"id": "shirley_dem_1",
"title": "A digital elevation model (DEM) and orthophoto of Shirley Island, Windmill Islands, Antarctica",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2005-01-01",
"end_date": "2007-05-01",
"bbox": "110.473, -66.287, 110.509, -66.277",
@@ -265293,7 +265384,7 @@
{
"id": "shirley_dem_1",
"title": "A digital elevation model (DEM) and orthophoto of Shirley Island, Windmill Islands, Antarctica",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "2005-01-01",
"end_date": "2007-05-01",
"bbox": "110.473, -66.287, 110.509, -66.277",
@@ -265306,7 +265397,7 @@
{
"id": "simrad_SO",
"title": "Acoustic responses to water column features, Antarctic, Aug-Sept 2002, GLOBEC.",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2002-08-03",
"end_date": "2002-09-15",
"bbox": "-75.5, -68.75, -69.5, -65.75",
@@ -265319,7 +265410,7 @@
{
"id": "simrad_SO",
"title": "Acoustic responses to water column features, Antarctic, Aug-Sept 2002, GLOBEC.",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2002-08-03",
"end_date": "2002-09-15",
"bbox": "-75.5, -68.75, -69.5, -65.75",
@@ -265878,7 +265969,7 @@
{
"id": "sonobuoy_whale_SO",
"title": "Acoustic census of mysticete whales, Antarctic, Mar-Aug 2001, GLOBEC",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2001-03-21",
"end_date": "2001-08-28",
"bbox": "-77.2, -70.3, -61.5, -59",
@@ -265891,7 +265982,7 @@
{
"id": "sonobuoy_whale_SO",
"title": "Acoustic census of mysticete whales, Antarctic, Mar-Aug 2001, GLOBEC",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2001-03-21",
"end_date": "2001-08-28",
"bbox": "-77.2, -70.3, -61.5, -59",
@@ -265930,7 +266021,7 @@
{
"id": "sowers_0739491",
"title": "2008 South Pole Firn Air Methane Isotopes",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "2008-12-01",
"end_date": "2009-01-31",
"bbox": "-180, -90, 180, 90",
@@ -265943,7 +266034,7 @@
{
"id": "sowers_0739491",
"title": "2008 South Pole Firn Air Methane Isotopes",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "2008-12-01",
"end_date": "2009-01-31",
"bbox": "-180, -90, 180, 90",
@@ -270857,7 +270948,7 @@
{
"id": "usgs_nps_agatefossilbeds",
"title": "Agate Fossil Beds National Monument, Field Plots Data Base for Vegetation Mapping",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1995-07-10",
"end_date": "1995-08-15",
"bbox": "-103.8, 42.40833, -103.7, 42.44167",
@@ -270870,7 +270961,7 @@
{
"id": "usgs_nps_agatefossilbeds",
"title": "Agate Fossil Beds National Monument, Field Plots Data Base for Vegetation Mapping",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1995-07-10",
"end_date": "1995-08-15",
"bbox": "-103.8, 42.40833, -103.7, 42.44167",
@@ -270883,7 +270974,7 @@
{
"id": "usgs_nps_agatefossilbedsspatial",
"title": "Agate Fossil Beds National Monument Spatial Vegetation Data: Cover Type/Association Level of the National Vegetation Classification System",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1995-07-29",
"end_date": "1995-07-29",
"bbox": "-103.8, 42.40833, -103.7, 42.44167",
@@ -270896,7 +270987,7 @@
{
"id": "usgs_nps_agatefossilbedsspatial",
"title": "Agate Fossil Beds National Monument Spatial Vegetation Data: Cover Type/Association Level of the National Vegetation Classification System",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1995-07-29",
"end_date": "1995-07-29",
"bbox": "-103.8, 42.40833, -103.7, 42.44167",
@@ -271065,7 +271156,7 @@
{
"id": "usgs_npwrc_acutetoxicity_Version 06JUL2000",
"title": "Acute Toxicity of Fire-Control Chemicals, Nitrogenous Chemicals, and Surfactants to Rainbow Trout",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -271078,7 +271169,7 @@
{
"id": "usgs_npwrc_acutetoxicity_Version 06JUL2000",
"title": "Acute Toxicity of Fire-Control Chemicals, Nitrogenous Chemicals, and Surfactants to Rainbow Trout",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-180, -90, 180, 90",
@@ -271130,7 +271221,7 @@
{
"id": "usgs_npwrc_graywolves_Version 30APR2001",
"title": "Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-168, 43.5, -75, 55",
@@ -271143,7 +271234,7 @@
{
"id": "usgs_npwrc_graywolves_Version 30APR2001",
"title": "Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-168, 43.5, -75, 55",
@@ -271247,7 +271338,7 @@
{
"id": "usgsbrdasc00000004",
"title": "Air quality monitoring protocol - Denali National Park and Preserve",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1992-01-01",
"end_date": "1998-01-01",
"bbox": "-149, 63, -148, 64",
@@ -271260,7 +271351,7 @@
{
"id": "usgsbrdasc00000004",
"title": "Air quality monitoring protocol - Denali National Park and Preserve",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1992-01-01",
"end_date": "1998-01-01",
"bbox": "-149, 63, -148, 64",
@@ -271325,7 +271416,7 @@
{
"id": "usgsbrdnpwrcb00000013_Version 30SEP2002",
"title": "A Bibliography of Fisheries Biology in North and South Dakota",
- "catalog": "CEOS_EXTRA STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-104, 43, -96, 49",
@@ -271338,7 +271429,7 @@
{
"id": "usgsbrdnpwrcb00000013_Version 30SEP2002",
"title": "A Bibliography of Fisheries Biology in North and South Dakota",
- "catalog": "ALL STAC Catalog",
+ "catalog": "CEOS_EXTRA STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-104, 43, -96, 49",
@@ -272222,7 +272313,7 @@
{
"id": "winston_bathy_1",
"title": "A bathymetric survey of Winston Lagoon",
- "catalog": "ALL STAC Catalog",
+ "catalog": "AU_AADC STAC Catalog",
"state_date": "1987-01-09",
"end_date": "1987-01-14",
"bbox": "73.23557, -53.20274, 73.83911, -52.95006",
@@ -272235,7 +272326,7 @@
{
"id": "winston_bathy_1",
"title": "A bathymetric survey of Winston Lagoon",
- "catalog": "AU_AADC STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1987-01-09",
"end_date": "1987-01-14",
"bbox": "73.23557, -53.20274, 73.83911, -52.95006",
@@ -272355,7 +272446,7 @@
"catalog": "GHRC_DAAC STAC Catalog",
"state_date": "2013-01-01",
"end_date": "2023-12-31",
- "bbox": "-179.975, -89.975, 179.975, 89.975",
+ "bbox": "-180, -90, 180, 90",
"url": "https://cmr.earthdata.nasa.gov/search/concepts/C3301410475-GHRC_DAAC.umm_json",
"metadata": "https://cmr.earthdata.nasa.gov/search/concepts/C3301410475-GHRC_DAAC.html",
"href": "https://cmr.earthdata.nasa.gov/stac/GHRC_DAAC/collections/wwllnmth_1",
@@ -272365,7 +272456,7 @@
{
"id": "wygisc_wolphoyo",
"title": "Aerial Photos for Crazy Woman and Clear Creek Watersheds",
- "catalog": "ALL STAC Catalog",
+ "catalog": "SCIOPS STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-107, 44, -106.36, 44.75",
@@ -272378,7 +272469,7 @@
{
"id": "wygisc_wolphoyo",
"title": "Aerial Photos for Crazy Woman and Clear Creek Watersheds",
- "catalog": "SCIOPS STAC Catalog",
+ "catalog": "ALL STAC Catalog",
"state_date": "1970-01-01",
"end_date": "",
"bbox": "-107, 44, -106.36, 44.75",
diff --git a/nasa_cmr_catalog.tsv b/nasa_cmr_catalog.tsv
index 57f47e2..2f39dba 100644
--- a/nasa_cmr_catalog.tsv
+++ b/nasa_cmr_catalog.tsv
@@ -7,8 +7,8 @@ id title catalog state_date end_date bbox url description license
057dd6c36f0741d3b97f9eee688b7835_NA ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 05.2 FEDEO STAC Catalog 1978-11-01 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143472-FEDEO.umm_json The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.The v05.2 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all three of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717â739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070 proprietary
065f6040ef08485db989cbd89d536167_NA ESA Fire Climate Change Initiative (Fire_cci): Small Fire Dataset (SFD) Burned Area pixel product for Sub-Saharan Africa, version 1.1 FEDEO STAC Catalog 2016-01-01 2016-12-31 -20, -35, 55, 25 https://cmr.earthdata.nasa.gov/search/concepts/C2548142692-FEDEO.umm_json The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project has produced maps of global burned area developed from satellite observations. The Small Fire Dataset (SFD) pixel products have been obtained by combining spectral information from Sentinel-2 MSI data and thermal information from MODIS MOD14MD Collection 6 active fire products.This dataset is part of v1.1 of the Small Fire Dataset (also known as FireCCISFD11), which covers Sub-Saharan Africa for the year 2016. Data is available here at pixel resolution (0.00017966259 degrees, corresponding to approximately 20m at the Equator). Gridded data products are also available in a separate dataset. proprietary
0875b4675f1e46ebadb526e0b95505c5_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products) at 4km resolution, Version 6.0 FEDEO STAC Catalog 1997-09-04 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359430-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains all their Version 6.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. Data are also available as monthly climatologies.Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.This data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.) proprietary
-0944645 Age and Composition of the East Antarctic Shield ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214605206-SCIOPS.umm_json We completed a field season in Antarctica in 2010-11 with a 5-person field party. Ten sampling sites along the Transantarctic Mountains from the Convoy Range to Hatcher Bluffs were visited by helicopter or fixed-wing aircraft, where rock samples were collected. All samples were returned to the University of Minnesota-Duluth, where they were prepared for laboratory study. Laboratory work includes examination of polished thin sections by optical microscope and scanning electron microscope to determine textures, mineral assemblages, and mineral compositions. Samples of igneous and metamorphic rock clasts were crushed in order to isolate the mineral zircon; zircon from these samples was analyzed by U-Pb, O and Hf isotopic analysis in order to determine their ages and isotopic character. Monazite was identified in selected samples for U-Pb age dating in polished thin section. A suite of Ross Orogen granitoids was also prepared for zircon separation and for whole-rock geochemical analysis. Petrographic study is complete for over 300 samples of igneous and metamorphic rock clasts collected from glacial moraines on the ‘backside’ of the Transantarctic Mountains, mainly between the inlets to the Byrd through Shackleton Glaciers. We U-Pb, O and Hf analyses of zircon and monazite in igneous and metamorphic clasts, and in samples of TAM granitoids. proprietary
0944645 Age and Composition of the East Antarctic Shield SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214605206-SCIOPS.umm_json We completed a field season in Antarctica in 2010-11 with a 5-person field party. Ten sampling sites along the Transantarctic Mountains from the Convoy Range to Hatcher Bluffs were visited by helicopter or fixed-wing aircraft, where rock samples were collected. All samples were returned to the University of Minnesota-Duluth, where they were prepared for laboratory study. Laboratory work includes examination of polished thin sections by optical microscope and scanning electron microscope to determine textures, mineral assemblages, and mineral compositions. Samples of igneous and metamorphic rock clasts were crushed in order to isolate the mineral zircon; zircon from these samples was analyzed by U-Pb, O and Hf isotopic analysis in order to determine their ages and isotopic character. Monazite was identified in selected samples for U-Pb age dating in polished thin section. A suite of Ross Orogen granitoids was also prepared for zircon separation and for whole-rock geochemical analysis. Petrographic study is complete for over 300 samples of igneous and metamorphic rock clasts collected from glacial moraines on the ‘backside’ of the Transantarctic Mountains, mainly between the inlets to the Byrd through Shackleton Glaciers. We U-Pb, O and Hf analyses of zircon and monazite in igneous and metamorphic clasts, and in samples of TAM granitoids. proprietary
+0944645 Age and Composition of the East Antarctic Shield ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214605206-SCIOPS.umm_json We completed a field season in Antarctica in 2010-11 with a 5-person field party. Ten sampling sites along the Transantarctic Mountains from the Convoy Range to Hatcher Bluffs were visited by helicopter or fixed-wing aircraft, where rock samples were collected. All samples were returned to the University of Minnesota-Duluth, where they were prepared for laboratory study. Laboratory work includes examination of polished thin sections by optical microscope and scanning electron microscope to determine textures, mineral assemblages, and mineral compositions. Samples of igneous and metamorphic rock clasts were crushed in order to isolate the mineral zircon; zircon from these samples was analyzed by U-Pb, O and Hf isotopic analysis in order to determine their ages and isotopic character. Monazite was identified in selected samples for U-Pb age dating in polished thin section. A suite of Ross Orogen granitoids was also prepared for zircon separation and for whole-rock geochemical analysis. Petrographic study is complete for over 300 samples of igneous and metamorphic rock clasts collected from glacial moraines on the ‘backside’ of the Transantarctic Mountains, mainly between the inlets to the Byrd through Shackleton Glaciers. We U-Pb, O and Hf analyses of zircon and monazite in igneous and metamorphic clasts, and in samples of TAM granitoids. proprietary
0ac98747-eb94-4c9f-aef8-56f9d3a04740 Earthquake Risk-Annual Average Losses CEOS_EXTRA STAC Catalog 2012-01-01 2013-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848506-CEOS_EXTRA.umm_json The map (risk map) presents the results of earthquake annual average losses (AAL) per country at global level. The probabilistic risk assessment results were obtained from analitical formulation on CAPRA platform. Values for this map are expresed on UDS millions (AAL-absolute value) and millar (AAL/VALFIS-Exposed physical value), also include population count per country (VALHUM), VALFIS and VALHUM values are derived from Global Exposure Database 2013 (GED) implemented by UNIGE with support of ERN-AL. proprietary
0b23b3c771db4fff8958196432d978cb_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity data for the Greenland Margin from ERS-2 for winter 1995-1996, v1.1 (June 2016 release) FEDEO STAC Catalog 1995-09-02 1996-03-29 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143114-FEDEO.umm_json This dataset contains ice velocities for the Greenland margin for winter 1995-1996, which have been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. The data were derived from intensity-tracking of ERS-2 data acquired between 03-09-1995 and 29-03-1996. It provides components of the ice velocity and the magnitude of the velocity.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E). The horizontal velocity is provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid; the vertical displacement (z), derived from a digital elevation model, is also provided. Please note that previous versions of this product provided the horizontal velocities as true East and North velocities.Both a single NetCDF file (including all measurements and annotation), and separate geotiff files with the velocity components are provided. The product was generated by DTU Space - Microwaves and Remote Sensing. For further information please see the product user guide.Please note - this product was released on the Greenland Ice Sheets download page in June 2016, but an earlier product (also accidentally labelled v1.1) was available through the CCI Open Data Portal and the CEDA archive until 29th November 2016. Please now use the later v1.1 product. proprietary
0d2260ad4e2c42b6b14fe5b3308f5eaa_NA ESA Ozone Climate Change Initiative (Ozone CCI): Level 3 Total Ozone Merged Data Product, version 01 FEDEO STAC Catalog 1996-03-31 2011-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143081-FEDEO.umm_json This dataset is a monthly mean gridded total ozone data record (level 3) produced by the ESA Ozone Climate Change Initiative project (Ozone CCI). The dataset is a prototype of a merged harmonised ozone data record combining ozone data from the GOME instrument on ERS-2, the SCIAMACHY instrument on ENVISAT and the GOME-2 instrument on METOP-A, and covers the period between April 1996 to June 2011. proprietary
@@ -37,27 +37,27 @@ id title catalog state_date end_date bbox url description license
10.25921/0haq-t221_Not Applicable Chlorofluorocarbons, nutrients, dissolved oxygen, temperature, salinity and other measurements collected from discrete samples and profile observations during the R/V Meteor MT80/2 cruise (EXPOCODE 06MT20091126) in the Tropical Atlantic Ocean from 2009-11-26 to 2009-12-22 (NCEI Accession 0186104) NOAA_NCEI STAC Catalog 2009-11-26 2009-12-22 -31.03, 3.76, -15, 17.44 https://cmr.earthdata.nasa.gov/search/concepts/C2089379193-NOAA_NCEI.umm_json This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor MT80/2 cruise (EXPOCODE 06MT20091126) in the Tropical Atlantic Ocean from 2009-11-26 to 2009-12-22. These data include temperature, salinity, dissolved oxygen, nutrients, chlorofluorocarbons, nutrients and other measurements. R/V Meteor Cruise No. 80/2 was aimed at studying biogeochemical and physical processes in the tropical/subtropical Atlantic Ocean. Observations were carried out in the entire water column, from the sea floor to the sea surface. proprietary
10.25921/16y6-9e29_Not Applicable Chlorofluorocarbon (CFC-12), sulfur hexafluoride (SF6), water temperature, salinity, nutrients, dissolved oxygen and other measurements collected from discrete samples and profile observations during the R/V Meteor cruise M135 (EXPOCODE 06MT20170302) in the South Pacific Ocean from 2017-03-02 to 2017-04-07 (NCEI Accession 0232257) NOAA_NCEI STAC Catalog 2017-03-02 2017-04-07 -86, -31.03, -70, -10.67 https://cmr.earthdata.nasa.gov/search/concepts/C2089380481-NOAA_NCEI.umm_json This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor cruise M135 (EXPOCODE 06MT20170302) in the South Pacific Ocean from 2017-03-02 to 2017-04-07. These data include water temperature, salinity, dissolved oxygen, nitrate, nitrite, phosphate, silicate, chlorofluorocarbon-12 (CFC-12), sulfur hexafluoride (SF6) and other measurements. R/V Meteor Cruise was aimed at studying biogeochemical and physical processes in the tropical/subtropical Pacific Ocean. Observations were carried out in the entire water column, from the sea floor to the sea surface. proprietary
10.25921/3bmf-xc16_Not Applicable Carbon dioxide, hydrographic, and chemical discrete profile data obtained during the R/V N.B. Palmer cruise in the South Pacific Ocean on GO-SHIP/CLIVAR/SOCCOM Repeat Hydrography Sections P06W (EXPOCODE 320620170703) and P06E (EXPOCODE 320620170820) from 2017-07-03 to 2017-09-30 (NCEI Accession 0175744) NOAA_NCEI STAC Catalog 2017-07-03 2017-09-30 152.9, -32.5051, -71.585, -28.9597 https://cmr.earthdata.nasa.gov/search/concepts/C2089380674-NOAA_NCEI.umm_json This NCEI Accession includes discrete bottle measurements of dissolved inorganic carbon (DIC), total alkalinity, pH on total scale, partial pressure of CO2, dissolved organic carbon (DOC), CFCs, temperature, salinity, oxygen, nutrients, and other variables measured during R/V N.B. Palmer cruise in the South Pacific Ocean on GO-SHIP/CLIVAR/SOCCOM Repeat Hydrography Sections P06W (EXPOCODE 320620170703) and P06E (EXPOCODE 320620170820) from 2017-07-03 to 2017-09-30. The Pacific Ocean P06 repeat hydrographic line was reoccupied for the US Global Ocean Carbon and Repeat Hydrography Program. Reoccupation of the P06E transect occurred on the RVIB Nathaniel B Palmer from August 20, 2017 to September 30, 2017. The survey of P06 2017 consisted of CTDO, rosette, LADCP, chipod, water samples and underway measurements. The ship departed from the port of Papeete on the island of Tahiti, French Polynesia and completed the cruise in the port of Valparaiso, Chile. proprietary
-10.25921/3edp-9d76_Not Applicable Alabama Near Coastal Meteorological & Hydrographic Continuous Data Sampling from 2003 to present NOAA_NCEI STAC Catalog 2003-02-24 -88.213, 30.09, -87.56, 30.66713 https://cmr.earthdata.nasa.gov/search/concepts/C2089386300-NOAA_NCEI.umm_json The Alabama Real-time Coastal Observing System (ARCOS) with support of the Dauphin Island Sea Lab is a network of continuously sampling observing stations that collect observations of meteorological and hydrographic data from fixed stations operating across coastal Alabama. Data were collected from 2003 through the present and include parameters such as air temperature, relative humidity, solar and quantum radiation, barometric pressure, wind speed, wind direction, precipitation amounts, water temperature, salinity, dissolved oxygen, water height, and other water quality data. Stations, when possible, are designed to collect the same data in the same way, though there are exceptions given unique location needs (see individual accession abstracts for details). Stations are strategically placed to sample across salinity gradients, from delta to offshore, and the width of the coast. proprietary
10.25921/3edp-9d76_Not Applicable Alabama Near Coastal Meteorological & Hydrographic Continuous Data Sampling from 2003 to present ALL STAC Catalog 2003-02-24 -88.213, 30.09, -87.56, 30.66713 https://cmr.earthdata.nasa.gov/search/concepts/C2089386300-NOAA_NCEI.umm_json The Alabama Real-time Coastal Observing System (ARCOS) with support of the Dauphin Island Sea Lab is a network of continuously sampling observing stations that collect observations of meteorological and hydrographic data from fixed stations operating across coastal Alabama. Data were collected from 2003 through the present and include parameters such as air temperature, relative humidity, solar and quantum radiation, barometric pressure, wind speed, wind direction, precipitation amounts, water temperature, salinity, dissolved oxygen, water height, and other water quality data. Stations, when possible, are designed to collect the same data in the same way, though there are exceptions given unique location needs (see individual accession abstracts for details). Stations are strategically placed to sample across salinity gradients, from delta to offshore, and the width of the coast. proprietary
+10.25921/3edp-9d76_Not Applicable Alabama Near Coastal Meteorological & Hydrographic Continuous Data Sampling from 2003 to present NOAA_NCEI STAC Catalog 2003-02-24 -88.213, 30.09, -87.56, 30.66713 https://cmr.earthdata.nasa.gov/search/concepts/C2089386300-NOAA_NCEI.umm_json The Alabama Real-time Coastal Observing System (ARCOS) with support of the Dauphin Island Sea Lab is a network of continuously sampling observing stations that collect observations of meteorological and hydrographic data from fixed stations operating across coastal Alabama. Data were collected from 2003 through the present and include parameters such as air temperature, relative humidity, solar and quantum radiation, barometric pressure, wind speed, wind direction, precipitation amounts, water temperature, salinity, dissolved oxygen, water height, and other water quality data. Stations, when possible, are designed to collect the same data in the same way, though there are exceptions given unique location needs (see individual accession abstracts for details). Stations are strategically placed to sample across salinity gradients, from delta to offshore, and the width of the coast. proprietary
10.25921/43nw-j564_Not Applicable Chlorofluorocarbons (CFC-11, CFC-12), temperature, salinity, dissolved oxygen, and sulfur hexafluoride (SF6) collected from profile and discrete sample observations during the R/V Maria S. Merian cruise MSM28 (EXPOCODE 06M220130509) in the North Atlantic Ocean from 2013-05-09 to 2013-06-20 (NCEI Accession 0209339) NOAA_NCEI STAC Catalog 2013-05-09 2013-06-20 -53.91, 46.8, -11.45, 60.28 https://cmr.earthdata.nasa.gov/search/concepts/C2089379203-NOAA_NCEI.umm_json This NCEI Accession includes discrete profile measurements of chlorofluorocarbons (CFC-11, CFC-12), temperature, salinity, dissolved oxygen, and sulfur hexafluoride (SF6) collected during the R/V Maria S. Merian cruise MSM28 (EXPOCODE 06M220130509) in the North Atlantic Ocean from 2013-05-09 to 2013-06-20. proprietary
10.25921/50xm-z231_Not Applicable Chlorofluorocarbons, nutrients, dissolved oxygen, temperature, salinity and other measurements collected from discrete samples and profile observations during the R/V Aurora Australis cruise along the Repeat Hydrography Section S04I (EXPOCODE 09AR19960119) in the Southern Ocean from 1996-01-19 to 1996-03-23 (NCEI Accession 0186170) NOAA_NCEI STAC Catalog 1996-01-19 1996-03-23 75.9, -68.3, 150.5, -62.4 https://cmr.earthdata.nasa.gov/search/concepts/C2089379224-NOAA_NCEI.umm_json This NCEI Accession includes discrete sample and profile data collected during the R/V Aurora Australis cruise along the Repeat Hydrography Section S04I (EXPOCODE 09AR19960119) in the Southern Ocean from 1996-01-19 to 1996-03-23. These data include temperature, salinity, dissolved oxygen, nutrients, chlorofluorocarbons and other measurements. proprietary
10.25921/579p-6p65_Not Applicable Chlorofluorocarbon (CFC-12), sulfur hexafluoride (SF6), water temperature, salinity, nutrients, dissolved oxygen and other measurements collected from discrete samples and profile observations during the R/V Meteor cruise M130 (EXPOCODE 06MT20160828) in the Tropical Atlantic Ocean from 2016-08-28 to 2016-10-03 (NCEI Accession 0232190) NOAA_NCEI STAC Catalog 2016-08-28 2016-10-03 -35.88, -11.9, -18.7, 17.7 https://cmr.earthdata.nasa.gov/search/concepts/C2089380462-NOAA_NCEI.umm_json This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor cruise M130 (EXPOCODE 06MT20160828) in the Tropical Atlantic Ocean from 2016-08-28 to 2016-10-03. These data include water temperature, salinity, dissolved oxygen, nitrate, nitrite, phosphate, silicate, chlorofluorocarbon-12 (CFC-12), sulfur hexafluoride (SF6) and other measurements. R/V Meteor Cruise was aimed at studying biogeochemical and physical processes in the tropical/subtropical Atlantic Ocean. Observations were carried out in the entire water column, from the sea floor to the sea surface. proprietary
10.25921/58yq-7g68_Not Applicable Census Data of Colonial Penguins in Antarctica from 1977 to 2015 (NCEI Accession 0185113) NOAA_NCEI STAC Catalog 1977-10-01 2015-03-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2089379060-NOAA_NCEI.umm_json Census data were collected from two penguin monitoring sites in the Antarctic peninsula region between 1977 and 2015 using traditional census methods. Seabirds observed in this study are Adélie (Pygoscelis adeliae), chinstrap (P. antarctica), and gentoo (P. papua) penguins. The two study sites are the US AMLR Program sites at Cape Shirreff (Livingston Island) and Copacabana (King George Island) Antarctica. proprietary
-10.25921/5p69-y471_Not Applicable A global monthly climatology of total alkalinity (AT): a neural network approach (NCEI Accession 0222470) ALL STAC Catalog 1957-01-01 2018-12-31 -179.5, -77.5, 179.5, 89.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089378396-NOAA_NCEI.umm_json This NCEI accession contains global monthly climatology of oceanic total alkalinity (AT). Total alkalinity (AT) monthly climatology was created from a neural network approach (Broullón et al., 2019). The neural network was trained with GLODAPv2.2019 data (Olsen et al., 2019) using as predictor variables position (latitude, longitude and depth), temperature, salinity, phosphate, nitrate, silicate and dissolved oxygen. The relations extracted between these predictor variables and AT were used to obtain the climatology passing through the network global monthly climatologies of the predictor variables: temperature and salinity fields of the World Ocean Atlas version 2013 (WOA13), filtered WOA13 oxygen (fifth-order one-dimensional median filter through the depth dimension; see Broullón et al., 2019 for details) and nutrients computed using CANYON-B (Bittig et al., 2018) over the three previous fields. The obtained climatology has a 1ºx1º spatial resolution and 102 depth levels between 0 and 5500 m, with a monthly resolution from 0 to 1500 m and an annual resolution from 1550 to 5500m. proprietary
10.25921/5p69-y471_Not Applicable A global monthly climatology of total alkalinity (AT): a neural network approach (NCEI Accession 0222470) NOAA_NCEI STAC Catalog 1957-01-01 2018-12-31 -179.5, -77.5, 179.5, 89.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089378396-NOAA_NCEI.umm_json This NCEI accession contains global monthly climatology of oceanic total alkalinity (AT). Total alkalinity (AT) monthly climatology was created from a neural network approach (Broullón et al., 2019). The neural network was trained with GLODAPv2.2019 data (Olsen et al., 2019) using as predictor variables position (latitude, longitude and depth), temperature, salinity, phosphate, nitrate, silicate and dissolved oxygen. The relations extracted between these predictor variables and AT were used to obtain the climatology passing through the network global monthly climatologies of the predictor variables: temperature and salinity fields of the World Ocean Atlas version 2013 (WOA13), filtered WOA13 oxygen (fifth-order one-dimensional median filter through the depth dimension; see Broullón et al., 2019 for details) and nutrients computed using CANYON-B (Bittig et al., 2018) over the three previous fields. The obtained climatology has a 1ºx1º spatial resolution and 102 depth levels between 0 and 5500 m, with a monthly resolution from 0 to 1500 m and an annual resolution from 1550 to 5500m. proprietary
-10.25921/66nr-kv23_Not Applicable Adult Japanese eel, Anguilla japonica, by mid water trawl net, water temperature and salinity by CTD, and other parameters collected from the research vessel Kaiyo-maru, cruise KY1302, in the North Pacific from 2013-05-23 to 2013-07-16 (NCEI Accession 0224416) ALL STAC Catalog 2013-05-23 2013-07-16 140.35, 10.5, 143.55, 20 https://cmr.earthdata.nasa.gov/search/concepts/C2089378826-NOAA_NCEI.umm_json This dataset contains cruise report including data on adult Japanese eel, Anguilla japonica, by mid water trawl net, water temperature and salinity by CTD, and other parameters collected from the research vessel Kaiyo-maru in the North Pacific. The research report focuses on the reproductive biology of the Japanese eel (Anguilla japonica) and the larval feeding ecology. This is MSR RATS cruise U2013-005. These data are part of the World Data Services for Oceanography. Cruise report is in PDF. proprietary
+10.25921/5p69-y471_Not Applicable A global monthly climatology of total alkalinity (AT): a neural network approach (NCEI Accession 0222470) ALL STAC Catalog 1957-01-01 2018-12-31 -179.5, -77.5, 179.5, 89.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089378396-NOAA_NCEI.umm_json This NCEI accession contains global monthly climatology of oceanic total alkalinity (AT). Total alkalinity (AT) monthly climatology was created from a neural network approach (Broullón et al., 2019). The neural network was trained with GLODAPv2.2019 data (Olsen et al., 2019) using as predictor variables position (latitude, longitude and depth), temperature, salinity, phosphate, nitrate, silicate and dissolved oxygen. The relations extracted between these predictor variables and AT were used to obtain the climatology passing through the network global monthly climatologies of the predictor variables: temperature and salinity fields of the World Ocean Atlas version 2013 (WOA13), filtered WOA13 oxygen (fifth-order one-dimensional median filter through the depth dimension; see Broullón et al., 2019 for details) and nutrients computed using CANYON-B (Bittig et al., 2018) over the three previous fields. The obtained climatology has a 1ºx1º spatial resolution and 102 depth levels between 0 and 5500 m, with a monthly resolution from 0 to 1500 m and an annual resolution from 1550 to 5500m. proprietary
10.25921/66nr-kv23_Not Applicable Adult Japanese eel, Anguilla japonica, by mid water trawl net, water temperature and salinity by CTD, and other parameters collected from the research vessel Kaiyo-maru, cruise KY1302, in the North Pacific from 2013-05-23 to 2013-07-16 (NCEI Accession 0224416) NOAA_NCEI STAC Catalog 2013-05-23 2013-07-16 140.35, 10.5, 143.55, 20 https://cmr.earthdata.nasa.gov/search/concepts/C2089378826-NOAA_NCEI.umm_json This dataset contains cruise report including data on adult Japanese eel, Anguilla japonica, by mid water trawl net, water temperature and salinity by CTD, and other parameters collected from the research vessel Kaiyo-maru in the North Pacific. The research report focuses on the reproductive biology of the Japanese eel (Anguilla japonica) and the larval feeding ecology. This is MSR RATS cruise U2013-005. These data are part of the World Data Services for Oceanography. Cruise report is in PDF. proprietary
+10.25921/66nr-kv23_Not Applicable Adult Japanese eel, Anguilla japonica, by mid water trawl net, water temperature and salinity by CTD, and other parameters collected from the research vessel Kaiyo-maru, cruise KY1302, in the North Pacific from 2013-05-23 to 2013-07-16 (NCEI Accession 0224416) ALL STAC Catalog 2013-05-23 2013-07-16 140.35, 10.5, 143.55, 20 https://cmr.earthdata.nasa.gov/search/concepts/C2089378826-NOAA_NCEI.umm_json This dataset contains cruise report including data on adult Japanese eel, Anguilla japonica, by mid water trawl net, water temperature and salinity by CTD, and other parameters collected from the research vessel Kaiyo-maru in the North Pacific. The research report focuses on the reproductive biology of the Japanese eel (Anguilla japonica) and the larval feeding ecology. This is MSR RATS cruise U2013-005. These data are part of the World Data Services for Oceanography. Cruise report is in PDF. proprietary
10.25921/6k3e-3x27_Not Applicable Chlorofluorocarbons, nutrients, dissolved oxygen, temperature, salinity and other measurements collected from discrete samples and profile observations during the R/V Meteor MT31/1 cruise (EXPOCODE 06MT19941230) in the Mediterranean Sea from 1994-12-30 to 1995-03-22 (NCEI Accession 0174793) NOAA_NCEI STAC Catalog 1994-12-30 1995-03-22 -1, 32.1, 33.7, 41.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089380270-NOAA_NCEI.umm_json This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor MT31/1 cruise (EXPOCODE 06MT19941230) in the Mediterranean Sea from 1994-12-30 to 1995-03-22. These data include temperature, salinity, dissolved oxygen, nutrients, chlorofluorocarbons, helium, tritium and neon measurements. proprietary
10.25921/7c1m-rw73_2.61 GHRSST Level 3U OSPO dataset v2.61 from VIIRS on NOAA-20 Satellite (GDS version 2) GHRSSTCWIC STAC Catalog 2018-11-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2213644528-GHRSSTCWIC.umm_json NOAA-20 (hereafter, N20; also known as JPSS-1 or J1 prior to launch) is the second satellite in the US National Oceanic and Atmospheric Administration (NOAA) latest generation Joint Polar Satellite System (JPSS). N20 was launched on November 18, 2017. In conjunction with the first US satellite in JPSS series, Suomi National Polar-orbiting Partnership (S-NPP) satellite launched on October 28, 2011, N20 form the new NOAA polar constellation. The ACSPO N20/VIIRS L3U (Level 3 Uncollated) product is a gridded version of the ACSPO N20/VIIRS L2P product. The L3U output files are 10-minute granules in netCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). There are 144 granules per 24hr interval, with a total data volume of 500MB/day. Fill values are reported at all invalid pixels, including pixels with >5 km inland. For each valid water pixel (defined as ocean, sea, lake or river, and up to 5 km inland), the following layers are reported: SSTs, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed, and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST). Only L2P SSTs with QL=5 were gridded, so all valid SSTs are recommended for the users. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010). proprietary
10.25921/7swn-9p71_Not Applicable Chlorofluorocarbons (CFC-11, CFC-12), temperature, salinity and dissolved oxygen collected from profile and discrete sample observations during the R/V Maria S. Merian cruise MSM38 (EXPOCODE 06M220140507) in the North Atlantic Ocean from 2014-05-07 to 2014-06-05 (NCEI Accession 0209341) NOAA_NCEI STAC Catalog 2014-05-07 2014-06-05 -47.26, 38.59, -12.38, 52.58 https://cmr.earthdata.nasa.gov/search/concepts/C2089379221-NOAA_NCEI.umm_json This NCEI Accession includes discrete profile measurements of chlorofluorocarbons (CFC-11, CFC-12), temperature, salinity and dissolved oxygen collected during the R/V Maria S. Merian cruise MSM38 (EXPOCODE 06M220140507) in the North Atlantic Ocean from 2014-05-07 to 2014-06-05. proprietary
10.25921/8vaj-bk51_Not Applicable Atmospheric measurements of carbon dioxide (CO2) and methane (CH4) from the state of Utah from 2014-09-10 to 2018-04-01 (NCEI Accession 0183632) NOAA_NCEI STAC Catalog 2014-09-10 2018-04-01 -112.0697, 40.1434, -109.468, 41.7616 https://cmr.earthdata.nasa.gov/search/concepts/C2089378712-NOAA_NCEI.umm_json This data set contains atmospheric measurements of carbon dioxide (CO2) and methane (CH4) from 12 sites sites located across the state of Utah. Data are in Comma Separated Value (CSV) ASCII text with one file for each station. QA/QC flags, measurements precision and accuracy statistics and calibrated observations are also provided. proprietary
10.25921/91sj-y926_Not Applicable Chlorofluorocarbon (CFC-12), sulfur hexafluoride (SF6), water temperature, salinity, nutrients, dissolved oxygen and other measurements collected from discrete samples and profile observations during the R/V Meteor cruise M145 (EXPOCODE 06MT20180213) in the Tropical Atlantic Ocean from 2018-02-13 to 2018-03-14 (NCEI Accession 0232258) NOAA_NCEI STAC Catalog 2018-02-13 2018-03-14 -35.88, -11.5, -21.23, 17.61 https://cmr.earthdata.nasa.gov/search/concepts/C2089380490-NOAA_NCEI.umm_json This NCEI Accession includes discrete sample and profile data collected during the R/V Meteor cruise M145 (EXPOCODE 06MT20180213) in the Tropical Atlantic Ocean from 2018-02-13 to 2018-03-14. These data include water temperature, salinity, dissolved oxygen, nitrate, nitrite, phosphate, silicate, chlorofluorocarbon-12 (CFC-12), sulfur hexafluoride (SF6) and other measurements. R/V Meteor Cruise was aimed at studying biogeochemical and physical processes in the tropical/subtropical Atlantic Ocean. Observations were carried out in the entire water column, from the sea floor to the sea surface. proprietary
-10.25921/9hsn-xq82_Not Applicable A combined globally mapped carbon dioxide (CO2) flux estimate based on the Surface Ocean CO2 Atlas Database (SOCAT) and Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) biogeochemistry floats from 1982 to 2017 (NCEI Accession 0191304) NOAA_NCEI STAC Catalog 1982-01-01 2017-12-31 -180, -89.5, 180, 89.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089377555-NOAA_NCEI.umm_json This NCEI accession contains a combined globally mapped estimate of the air-sea exchange of carbon dioxide (CO2) based on Surface Ocean CO2 Atlas Database (SOCAT) partial pressure of CO2 (pCO2) and calculated pCO2 from Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) biogeochemistry floats from 1982 to 2017. The pCO2 fields were created using a 2-step neural network technique. In a first step, the global ocean is divided into 16 biogeochemical provinces using a self-organizing map. In a second step, the non-linear relationship between variables known to drive the surface ocean carbon system and gridded observations from the SOCAT dataset (Bakker et al., 2016) starting in 1982 in various combinations with calculated pCO2 from biogeochemical ARGO floats starting in 2014 from the SOCCOM project (Johnson et al., 2017) is reconstructed using a feed-forward neural network within each province separately. The final product is then produced by projecting these driving variables, i.e., surface temperature, chlorophyll, mixed layer depth, and atmospheric CO2 onto oceanic pCO2 using these non-linear relationships. This results in monthly pCO2 fields at 1°x1° resolution covering the entire globe with the exception of the Arctic Ocean and few marginal seas. The air-sea CO2 flux is then computed using a standard bulk formula. proprietary
10.25921/9hsn-xq82_Not Applicable A combined globally mapped carbon dioxide (CO2) flux estimate based on the Surface Ocean CO2 Atlas Database (SOCAT) and Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) biogeochemistry floats from 1982 to 2017 (NCEI Accession 0191304) ALL STAC Catalog 1982-01-01 2017-12-31 -180, -89.5, 180, 89.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089377555-NOAA_NCEI.umm_json This NCEI accession contains a combined globally mapped estimate of the air-sea exchange of carbon dioxide (CO2) based on Surface Ocean CO2 Atlas Database (SOCAT) partial pressure of CO2 (pCO2) and calculated pCO2 from Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) biogeochemistry floats from 1982 to 2017. The pCO2 fields were created using a 2-step neural network technique. In a first step, the global ocean is divided into 16 biogeochemical provinces using a self-organizing map. In a second step, the non-linear relationship between variables known to drive the surface ocean carbon system and gridded observations from the SOCAT dataset (Bakker et al., 2016) starting in 1982 in various combinations with calculated pCO2 from biogeochemical ARGO floats starting in 2014 from the SOCCOM project (Johnson et al., 2017) is reconstructed using a feed-forward neural network within each province separately. The final product is then produced by projecting these driving variables, i.e., surface temperature, chlorophyll, mixed layer depth, and atmospheric CO2 onto oceanic pCO2 using these non-linear relationships. This results in monthly pCO2 fields at 1°x1° resolution covering the entire globe with the exception of the Arctic Ocean and few marginal seas. The air-sea CO2 flux is then computed using a standard bulk formula. proprietary
+10.25921/9hsn-xq82_Not Applicable A combined globally mapped carbon dioxide (CO2) flux estimate based on the Surface Ocean CO2 Atlas Database (SOCAT) and Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) biogeochemistry floats from 1982 to 2017 (NCEI Accession 0191304) NOAA_NCEI STAC Catalog 1982-01-01 2017-12-31 -180, -89.5, 180, 89.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089377555-NOAA_NCEI.umm_json This NCEI accession contains a combined globally mapped estimate of the air-sea exchange of carbon dioxide (CO2) based on Surface Ocean CO2 Atlas Database (SOCAT) partial pressure of CO2 (pCO2) and calculated pCO2 from Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) biogeochemistry floats from 1982 to 2017. The pCO2 fields were created using a 2-step neural network technique. In a first step, the global ocean is divided into 16 biogeochemical provinces using a self-organizing map. In a second step, the non-linear relationship between variables known to drive the surface ocean carbon system and gridded observations from the SOCAT dataset (Bakker et al., 2016) starting in 1982 in various combinations with calculated pCO2 from biogeochemical ARGO floats starting in 2014 from the SOCCOM project (Johnson et al., 2017) is reconstructed using a feed-forward neural network within each province separately. The final product is then produced by projecting these driving variables, i.e., surface temperature, chlorophyll, mixed layer depth, and atmospheric CO2 onto oceanic pCO2 using these non-linear relationships. This results in monthly pCO2 fields at 1°x1° resolution covering the entire globe with the exception of the Arctic Ocean and few marginal seas. The air-sea CO2 flux is then computed using a standard bulk formula. proprietary
10.25921/ayf6-c438_2.70 GHRSST NOAA/STAR GOES-16 ABI L2P America Region SST v2.70 dataset (GDS version 2) GHRSSTCWIC STAC Catalog 2019-05-17 -135, -59, -15, 59 https://cmr.earthdata.nasa.gov/search/concepts/C2213636951-GHRSSTCWIC.umm_json GOES-16 (G16) is the first satellite in the US NOAA third generation of Geostationary Operational Environmental Satellites (GOES), a.k.a. GOES-R series (which will also include -S, -T, and -U). G16 was launched on 19 Nov 2016 and initially placed in an interim position at 89.5-deg W, between GOES-East and -West. Upon completion of Cal/Val in Dec 2018, it was moved to its permanent position at 75.2-deg W, and declared NOAA operational GOES-East on 18 Dec 2018. NOAA is responsible for all GOES-R products, including Sea Surface Temperature (SST) from the Advanced Baseline Imager (ABI). The ABI offers vastly enhanced capabilities for SST retrievals, over the heritage GOES-I/P Imager, including five narrow bands (centered at 3.9, 8.4, 10.3, 11.2, and 12.3 um) out of 16 that can be used for SST, as well as accurate sensor calibration, image navigation and co-registration, spectral fidelity, and sophisticated pre-processing (geo-rectification, radiance equalization, and mapping). From altitude 35,800 km, G16/ABI can accurately map SST in a Full Disk (FD) area from 15-135-deg W and 60S-60N, with spatial resolution 2km at nadir (degrading to 15km at view zenith angle, 67-deg) and temporal sampling of 10min (15min prior to 2 Apr 2019). The Level 2 Preprocessed (L2P) SST product is derived at the native sensor resolution using NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) system. ACSPO first processes every 10min FD data SSTs are derived from BTs using the ACSPO clear-sky mask (ACSM; Petrenko et al., 2010) and Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014). Currently, only 4 longwave bands centered at 8.4, 10.3, 11.2, and 12.3 um are used (the 3.9 microns was initially excluded, to minimize possible discontinuities in the diurnal cycle). The regression is tuned against quality controlled in situ SSTs from drifting and tropical mooring buoys in the NOAA iQuam system (Xu and Ignatov, 2014). The 10-min FD data are subsequently collated in time, to produce 1-hr L2P product, with improved coverage, and reduced cloud leakages and image noise, compared to each individual 10min image. In the collated L2P, SSTs and BTs are only reported in clear-sky water pixels (defined as ocean, sea, lake or river, and up to 5 km inland) and fill values elsewhere. The L2P is reported in netCDF4 GHRSST Data Specification version 2 (GDS2) format, 24 granules per day, with a total data volume of 0.6GB/day. In addition to SST, ACSPO files also include sun-sensor geometry, four BTs in ABI bands 11 (8.4um), 13 (10.3um), 14 (11.2um), and 15 (12.3um) and two reflectances in bands 2 and 3 (0.64um and 0.86um; used for cloud identification). The l2p_flags layer includes day/night, land, ice, twilight, and glint flags. Other variables include NCEP wind speed and ACSPO SST minus reference SST (Canadian Met Centre 0.1deg L4 SST). Pixel-level earth locations are not reported in the granules, as they remain unchanged from granule to granule. To obtain those, user has a choice of using a flat lat-lon file, or a Python script, both available at ftp://ftp.star.nesdis.noaa.gov/pub/socd4/coastwatch/sst/nrt/abi/nav/. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (SSES bias and standard deviation) are reported in each pixel. The ACSPO VIIRS L2P product is monitored and validated against in situ data (Xu and Ignatov, 2014) using the Satellite Quality Monitor SQUAM (Dash et al, 2010), and BTs are validated against RTM simulation in MICROS (Liang and Ignatov, 2011). A reduced size (0.2GB/day), equal-angle gridded (0.02-deg resolution), ACSPO L3C product is also available, where gridded L2P SSTs are reported, and BT layers omitted. proprietary
10.25921/b2g4-bs86_Not Applicable Benthic Epifauna Biomass and Abundance Data in the Chuckchi Sea, Arctic Marine Biodiversity Observing Network (AMBON) research cruise on the Norseman II from 2015-08-09 to 2015-09-03 (NCEI Accession 0177837) NOAA_NCEI STAC Catalog 2015-08-12 2015-09-03 -168.96, 67.67, -159.393, 72.496 https://cmr.earthdata.nasa.gov/search/concepts/C2089377383-NOAA_NCEI.umm_json This dataset contains benthic epifauna biomass and abundance data collected in the Chukchi Sea, U.S. Arctic during the 9 August - 3 September 2015 Arctic Marine Biodiversity Observing Network (AMBON) research cruise aboard the vessel Norseman II. The dataset contains two comma separated values (csv) files exported from Microsoft Excel. These data were generated from epifauna samples conducted using beam trawls during the research cruise. The data in the file named AMBON2015_epifauna_abundance_DWC.csv describes abundance per taxon of epibenthic invertebrates. The data in the file named AMBON2015_epifauna_biomass_DWC.csv describes biomass per taxon of epibenthic invertebrates. This dataset was transformed into a table structure using Darwin Core term names as column names. proprietary
-10.25921/c1sn-9631_Not Applicable A comprehensive global oceanic dataset of discrete measurements of helium isotope and tritium during the hydrographic cruises on various ships from 1952-10-21 to 2016-01-22 (NCEI Accession 0176626) ALL STAC Catalog 1952-10-21 2016-01-22 -179.98, -82.38, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376880-NOAA_NCEI.umm_json This NCEI accession consists of global oceanic database of tritium and helium isotope measurements made by numerous researchers and laboratories over a period exceeding 60 years: from 1952-10-21 to 2016-01-22 in the Pacific Ocean, Atlantic Ocean, Indian Ocean, Southern Ocean, Arctic Ocean, Mediterranean Sea, Baltic Sea, Black Sea. Tritium and helium isotope data provide key information on ocean circulation, ventilation, and mixing, as well as the rates of biogeochemical processes, and deep-ocean hydrothermal processes. The dataset includes approximately 60,000 valid tritium measurements, 63,000 valid helium isotope determinations, 57,000 dissolved helium concentrations, and 34,000 dissolved neon concentrations. Some quality control has been applied in that questionable data have been flagged and clearly compromised data excluded entirely. Appropriate metadata has been included: geographic location, date, and sample depth. When available, water temperature, salinity, and dissolved oxygen were included. Data quality flags and data originator information (including methodology) are also included. proprietary
10.25921/c1sn-9631_Not Applicable A comprehensive global oceanic dataset of discrete measurements of helium isotope and tritium during the hydrographic cruises on various ships from 1952-10-21 to 2016-01-22 (NCEI Accession 0176626) NOAA_NCEI STAC Catalog 1952-10-21 2016-01-22 -179.98, -82.38, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376880-NOAA_NCEI.umm_json This NCEI accession consists of global oceanic database of tritium and helium isotope measurements made by numerous researchers and laboratories over a period exceeding 60 years: from 1952-10-21 to 2016-01-22 in the Pacific Ocean, Atlantic Ocean, Indian Ocean, Southern Ocean, Arctic Ocean, Mediterranean Sea, Baltic Sea, Black Sea. Tritium and helium isotope data provide key information on ocean circulation, ventilation, and mixing, as well as the rates of biogeochemical processes, and deep-ocean hydrothermal processes. The dataset includes approximately 60,000 valid tritium measurements, 63,000 valid helium isotope determinations, 57,000 dissolved helium concentrations, and 34,000 dissolved neon concentrations. Some quality control has been applied in that questionable data have been flagged and clearly compromised data excluded entirely. Appropriate metadata has been included: geographic location, date, and sample depth. When available, water temperature, salinity, and dissolved oxygen were included. Data quality flags and data originator information (including methodology) are also included. proprietary
+10.25921/c1sn-9631_Not Applicable A comprehensive global oceanic dataset of discrete measurements of helium isotope and tritium during the hydrographic cruises on various ships from 1952-10-21 to 2016-01-22 (NCEI Accession 0176626) ALL STAC Catalog 1952-10-21 2016-01-22 -179.98, -82.38, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376880-NOAA_NCEI.umm_json This NCEI accession consists of global oceanic database of tritium and helium isotope measurements made by numerous researchers and laboratories over a period exceeding 60 years: from 1952-10-21 to 2016-01-22 in the Pacific Ocean, Atlantic Ocean, Indian Ocean, Southern Ocean, Arctic Ocean, Mediterranean Sea, Baltic Sea, Black Sea. Tritium and helium isotope data provide key information on ocean circulation, ventilation, and mixing, as well as the rates of biogeochemical processes, and deep-ocean hydrothermal processes. The dataset includes approximately 60,000 valid tritium measurements, 63,000 valid helium isotope determinations, 57,000 dissolved helium concentrations, and 34,000 dissolved neon concentrations. Some quality control has been applied in that questionable data have been flagged and clearly compromised data excluded entirely. Appropriate metadata has been included: geographic location, date, and sample depth. When available, water temperature, salinity, and dissolved oxygen were included. Data quality flags and data originator information (including methodology) are also included. proprietary
10.25921/c9h2-z342_Not Applicable Chlorofluorocarbons (CFC-11, CFC-12, CFC113), nutrients, dissolved oxygen, temperature, salinity and other measurements collected from discrete samples and profile observations during the R/V Knorr GEOTRACES 2011 cruise KN204A/B (EXPOCODE 316N20111106) in the North Atlantic Ocean from 2011-11-06 to 2011-12-11 (NCEI Accession 0186205) NOAA_NCEI STAC Catalog 2011-11-06 2011-12-11 -69.9, 17.1, -24.2, 39.9 https://cmr.earthdata.nasa.gov/search/concepts/C2089379253-NOAA_NCEI.umm_json This NCEI Accession includes discrete sample and profile data collected during the R/V Knorr GEOTRACES 2011 cruise KN204A/B (EXPOCODE 316N20111106) in the North Atlantic Ocean from 2011-11-06 to 2011-12-11. These data include temperature, salinity, dissolved oxygen, nutrients, and chlorofluorocarbons (CFC-11, CFC-12, CFC113). A hydrographic survey consisting of rosette/CTD sections and Bio-Optical casts in the mid-latitude eastern Atlantic Ocean was carried out during November-December 2011. The R/V Knorr departed Woods Hole, MA on 6 November 2011. The cruise ended in Praia, Cabo Verde on 11 December 2011. proprietary
10.25921/cnwq-y130_Not Applicable Chlorofluorocarbons (CFC-11, CFC-12), dissolved oxygen, temperature and salinity collected from profile and discrete sample observations during R/V Meteor cruise MT82.2 (EXPOCODE 06M320100804) in the North Atlantic Ocean from 2010-08-04 to 2010-09-01 (NCEI Accession 0209328) NOAA_NCEI STAC Catalog 2010-08-04 2010-09-01 -47.27, 46.9, -14.8, 52.93 https://cmr.earthdata.nasa.gov/search/concepts/C2089379120-NOAA_NCEI.umm_json This NCEI Accession includes discrete profile measurements of chlorofluorocarbons (CFC-11, CFC-12), dissolved oxygen, temperature and salinity collected during R/V Meteor cruise MT82.2 (EXPOCODE 06M320100804) in the North Atlantic Ocean from 2010-08-04 to 2010-09-01. proprietary
10.25921/cp7t-7118_Not Applicable Arctic Sea Ice Summer Melt Feature Classification from Operation IceBridge High-Resolution Optical Imagery, July 2016 and July 2017 (NCEI Accession 0209246) NOAA_NCEI STAC Catalog 2016-07-13 2017-07-25 -176.8, 72.8, -43.15, 84.56 https://cmr.earthdata.nasa.gov/search/concepts/C2089378857-NOAA_NCEI.umm_json The Arctic Sea Ice Summer Melt Feature Classification product is derived from high-resolution Digital Mapping System (DMS) imagery acquired during low-altitude NASA Operation IceBridge airborne surveys over Arctic sea ice. DMS images were acquired in July, 2016 and 2017. For each image, meaningful geophysical parameters have been derived: melt pond fraction, sea ice concentration, and pond color fraction. Melt pond fraction is the percentage of the sea ice surface that is ponded. Sea ice concentration is the percentage of ocean covered by sea ice. Pond color fraction is the partitioning of dark, medium, and light color ponds as a percentage of total ponded area. proprietary
@@ -106,14 +106,14 @@ id title catalog state_date end_date bbox url description license
10.25921/zrw8-kn24_Not Applicable A compilation of inorganic carbon system and other hydrographic and chemical discrete profile measurements obtained during the fifty five Line P cruises in the Northeast Pacific Ocean over the period from 1990 to 2019 (NCEI Accession 0234342) NOAA_NCEI STAC Catalog 1990-05-10 2019-06-19 -145, 48.65, -126.65, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2089380864-NOAA_NCEI.umm_json This NCEI Accession contains a compilation of inorganic carbon system and other hydrographic and chemical discrete profile measurements obtained during the fifty five Line P cruises in the Northeast Pacific Ocean over the period from 1990-05-10 to 2019-06-19. The data in the data set include dissolved inorganic carbon (DIC), total alkalinity (TA), water temperature, salinity, dissolved oxygen concentration and nutrients. The majority of the cruises from 1990 to 2015 have been reported elsewhere as individual files (e.g., GLODAP and PACIFICA databases). This data set is a combination of the available cruises into a single database, and extended the time series to June 2019. A secondary quality control was performed and the quality flags revised. Additionally, the suggested PACIFICA corrections for salinity, oxygen, dissolved inorganic carbon and nutrients were applied. Oxygen units were converted to µmol/kg when reported in ml/L. Nutrient concentrations were converted to µmol/kg from µmol/L. proprietary
10.3334/cdiac/otg.carina_77dn20010717_Not Applicable Alkalinity, temperature, salinity and other variables collected from discrete sample and profile observations using CTD, bottle and other instruments from the ODEN in the Arctic Ocean from 2001-07-17 to 2001-07-26 (NCEI Accession 0113589) NOAA_NCEI STAC Catalog 2001-07-17 2001-07-26 26.3936, 81.2861, 154.2917, 88.465 https://cmr.earthdata.nasa.gov/search/concepts/C2089372369-NOAA_NCEI.umm_json NODC Accession 0113589 includes chemical, discrete sample, physical and profile data collected from ODEN in the Arctic Ocean from 2001-07-17 to 2001-07-26 and retrieved during cruise CARINA/77DN20010717. These data include ALKALINITY, HYDROSTATIC PRESSURE, Potential temperature (theta), SALINITY and WATER TEMPERATURE. The instruments used to collect these data include CTD and bottle. These data were collected by Leif Anderson of Gothenburg University; Department of Analytical and Marine Chemistry as part of the CARINA/77DN20010717 data set. The CARINA (CARbon dioxide IN the Atlantic Ocean) data synthesis project is an international collaborative effort of the EU IP CARBOOCEAN, and U.S. partners. It has produced a merged internally consistent data set of open ocean subsurface measurements for biogeochemical investigations, in particular, studies involving the carbon system. The original focus area was the North Atlantic Ocean, but over time the geographic extent expanded and CARINA now includes data from the entire Atlantic, the Arctic Ocean, and the Southern Ocean. proprietary
10.3334/cdiac/otg.carina_77dn20010717_Not Applicable Alkalinity, temperature, salinity and other variables collected from discrete sample and profile observations using CTD, bottle and other instruments from the ODEN in the Arctic Ocean from 2001-07-17 to 2001-07-26 (NCEI Accession 0113589) ALL STAC Catalog 2001-07-17 2001-07-26 26.3936, 81.2861, 154.2917, 88.465 https://cmr.earthdata.nasa.gov/search/concepts/C2089372369-NOAA_NCEI.umm_json NODC Accession 0113589 includes chemical, discrete sample, physical and profile data collected from ODEN in the Arctic Ocean from 2001-07-17 to 2001-07-26 and retrieved during cruise CARINA/77DN20010717. These data include ALKALINITY, HYDROSTATIC PRESSURE, Potential temperature (theta), SALINITY and WATER TEMPERATURE. The instruments used to collect these data include CTD and bottle. These data were collected by Leif Anderson of Gothenburg University; Department of Analytical and Marine Chemistry as part of the CARINA/77DN20010717 data set. The CARINA (CARbon dioxide IN the Atlantic Ocean) data synthesis project is an international collaborative effort of the EU IP CARBOOCEAN, and U.S. partners. It has produced a merged internally consistent data set of open ocean subsurface measurements for biogeochemical investigations, in particular, studies involving the carbon system. The original focus area was the North Atlantic Ocean, but over time the geographic extent expanded and CARINA now includes data from the entire Atlantic, the Arctic Ocean, and the Southern Ocean. proprietary
-10.3334/cdiac/otg.carina_omex2_Not Applicable Alkalinity, temperature, salinity and other variables collected from discrete sample and profile observations using CTD, bottle and other instruments from the BELGICA, CHARLES DARWIN and METEOR in the North Atlantic Ocean from 1997-06-01 to 1999-09-01 (NCEI Accession 0115763) ALL STAC Catalog 1997-06-01 1999-09-01 -10.6353, 36.5522, -7.0757, 47.7569 https://cmr.earthdata.nasa.gov/search/concepts/C2089375405-NOAA_NCEI.umm_json NODC Accession 0115763 includes chemical, discrete sample, physical and profile data collected from BELGICA, CHARLES DARWIN and METEOR in the North Atlantic Ocean from 1997-06-01 to 1999-09-01 and retrieved during cruise OMEX2. These data include ALKALINITY, AMMONIUM, DISSOLVED OXYGEN, HYDROSTATIC PRESSURE, NITRATE, NITRITE, PHOSPHATE, Potential temperature (theta), SALINITY, SILICATE, UREA and WATER TEMPERATURE. The instruments used to collect these data include CTD and bottle. These data were collected by A. et al. Borges of University of Liege as part of the CARINA/OMEX2 data set. The CARINA (CARbon dioxide IN the Atlantic Ocean) data synthesis project is an international collaborative effort of the EU IP CARBOOCEAN, and U.S. partners. It has produced a merged internally consistent data set of open ocean subsurface measurements for biogeochemical investigations, in particular, studies involving the carbon system. The original focus area was the North Atlantic Ocean, but over time the geographic extent expanded and CARINA now includes data from the entire Atlantic, the Arctic Ocean, and the Southern Ocean. proprietary
10.3334/cdiac/otg.carina_omex2_Not Applicable Alkalinity, temperature, salinity and other variables collected from discrete sample and profile observations using CTD, bottle and other instruments from the BELGICA, CHARLES DARWIN and METEOR in the North Atlantic Ocean from 1997-06-01 to 1999-09-01 (NCEI Accession 0115763) NOAA_NCEI STAC Catalog 1997-06-01 1999-09-01 -10.6353, 36.5522, -7.0757, 47.7569 https://cmr.earthdata.nasa.gov/search/concepts/C2089375405-NOAA_NCEI.umm_json NODC Accession 0115763 includes chemical, discrete sample, physical and profile data collected from BELGICA, CHARLES DARWIN and METEOR in the North Atlantic Ocean from 1997-06-01 to 1999-09-01 and retrieved during cruise OMEX2. These data include ALKALINITY, AMMONIUM, DISSOLVED OXYGEN, HYDROSTATIC PRESSURE, NITRATE, NITRITE, PHOSPHATE, Potential temperature (theta), SALINITY, SILICATE, UREA and WATER TEMPERATURE. The instruments used to collect these data include CTD and bottle. These data were collected by A. et al. Borges of University of Liege as part of the CARINA/OMEX2 data set. The CARINA (CARbon dioxide IN the Atlantic Ocean) data synthesis project is an international collaborative effort of the EU IP CARBOOCEAN, and U.S. partners. It has produced a merged internally consistent data set of open ocean subsurface measurements for biogeochemical investigations, in particular, studies involving the carbon system. The original focus area was the North Atlantic Ocean, but over time the geographic extent expanded and CARINA now includes data from the entire Atlantic, the Arctic Ocean, and the Southern Ocean. proprietary
+10.3334/cdiac/otg.carina_omex2_Not Applicable Alkalinity, temperature, salinity and other variables collected from discrete sample and profile observations using CTD, bottle and other instruments from the BELGICA, CHARLES DARWIN and METEOR in the North Atlantic Ocean from 1997-06-01 to 1999-09-01 (NCEI Accession 0115763) ALL STAC Catalog 1997-06-01 1999-09-01 -10.6353, 36.5522, -7.0757, 47.7569 https://cmr.earthdata.nasa.gov/search/concepts/C2089375405-NOAA_NCEI.umm_json NODC Accession 0115763 includes chemical, discrete sample, physical and profile data collected from BELGICA, CHARLES DARWIN and METEOR in the North Atlantic Ocean from 1997-06-01 to 1999-09-01 and retrieved during cruise OMEX2. These data include ALKALINITY, AMMONIUM, DISSOLVED OXYGEN, HYDROSTATIC PRESSURE, NITRATE, NITRITE, PHOSPHATE, Potential temperature (theta), SALINITY, SILICATE, UREA and WATER TEMPERATURE. The instruments used to collect these data include CTD and bottle. These data were collected by A. et al. Borges of University of Liege as part of the CARINA/OMEX2 data set. The CARINA (CARbon dioxide IN the Atlantic Ocean) data synthesis project is an international collaborative effort of the EU IP CARBOOCEAN, and U.S. partners. It has produced a merged internally consistent data set of open ocean subsurface measurements for biogeochemical investigations, in particular, studies involving the carbon system. The original focus area was the North Atlantic Ocean, but over time the geographic extent expanded and CARINA now includes data from the entire Atlantic, the Arctic Ocean, and the Southern Ocean. proprietary
10.3334/cdiac/otg.clivar_mp_2003_Not Applicable Carbon Dioxide and Hydrographic Data Obtained During the MP (MANTRA/PIRANA) Cruises in the Pacific Ocean in 2002-2003 (NCEI Accession 0108077) NOAA_NCEI STAC Catalog 2002-07-01 2003-08-21 170, 18.5, -154.3, 28 https://cmr.earthdata.nasa.gov/search/concepts/C2089375872-NOAA_NCEI.umm_json NODC Accession 0108077 discrete profile chemical and physical data collected from R/V Ka'imikai-O-Kanaloa, R/V Kilo Moana and R/V Roger Revelle in the North Pacific Ocean from 2002-07-01 to 2003-08-21 during the MP-5, MP-6 and MP-9 cruises. These data include total alkalinity, dissolved inorganic carbon, salinity and temperature. The instruments used to collect these data include Alkalinity titrator, CTD, Coulometer for DIC measurement. These data were collected by Patricia L. Yager of University of Georgia; School of Marine Programs as part of the MP-5 cruise, MP-6 cruise and MP-9 cruise data set. proprietary
10.3334/cdiac/otg.clivar_s04p_2011_Not Applicable Carbon Dioxide, Hydrographic, and Chemical Data Obtained During the R/V Nathaniel B. Palmer Cruise in the Southern Ocean on CLIVAR Repeat Hydrography Section S04P (Feb. 19 - Apr. 23, 2011) (NCEI Accession 0109933) NOAA_NCEI STAC Catalog 2011-02-19 2011-04-23 165.692, -77.692, -66.582, -58.803 https://cmr.earthdata.nasa.gov/search/concepts/C2089372729-NOAA_NCEI.umm_json NCEI Accession 0109933 includes discrete sample data collected from NATHANIEL B. PALMER in the Southern Oceans from 2011-02-19 to 2011-04-23. These data include CHLOROFLUOROCARBON-11 (CFC-11), CHLOROFLUOROCARBON-113 (CFC-113), CHLOROFLUOROCARBON-12 (CFC-12), DELTA CARBON-13, DELTA CARBON-14, DELTA HELIUM-3, DISSOLVED INORGANIC CARBON (DIC), DISSOLVED ORGANIC CARBON, DISSOLVED OXYGEN, HELIUM, HYDROSTATIC PRESSURE, NEON, NITRATE, NITRITE, Partial pressure (or fugacity) of carbon dioxide - water, Potential temperature (theta), SALINITY, SEA SURFACE TEMPERATURE, SULFUR HEXAFLUORIDE (SF6), TOTAL ALKALINITY (TA), Total Dissolved Nitrogen (TDN), Tritium (Hydrogen isotope), WATER TEMPERATURE, pH, phosphate and silicate. The instruments used to collect these data include Alkalinity titrator, CTD, Coulometer for DIC measurement, bottle and spectrophotometer. These data were collected by Frank J. Millero and Dennis Hansell of Rosenstiel School of Marine and Atmospheric Science, Richard A. Feely and Christopher Sabine of US DOC; NOAA; OAR; Pacific Marine Environmental Laboratory and Andrew Dickson of University of California - San Diego; Scripps Institution of Oceanography as part of the CLIVAR_S04P_2011 data set. The International CLIVAR Global Ocean Carbon and Repeat Hydrography Program carries out a systematic and global re-occupation of select WOCE/JGOFS hydrographic sections to quantify changes in storage and transport of heat, fresh water, carbon dioxide (CO2), and related parameters. proprietary
10.3334/cdiac/otg.nac13v1_Not Applicable An Internally Consistent Dataset of Del13C-DIC Data in the North Atlantic Ocean (NCEI Accession 0164569) NOAA_NCEI STAC Catalog 1981-01-01 2014-12-12 -80, -47, 11, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2089377573-NOAA_NCEI.umm_json NCEI accession 0164569 presents a Del13C-DIC data set for the North Atlantic, which has undergone strict quality control. The data, all in all 6569 samples, originate from oceanographic research cruises that took place between 1981 and 2014. During a primary quality control step based on simple range tests obviously bad data has been flagged. In a second quality control step systematic biases between of all cruises were quantified through a crossover analysis. The data set consists of 32 cruises of which 24 could be compared quantitatively for systematic biases through an adequate crossover study. Additive adjustments were applied to 11 of the 24 cruises. Based on this analysis the internal consistency of this data set is estimated to be 0.017 o/oo. The NAC13v1.csv file contains the 13C data, a simple quality flag ('Del13Cf', 2: good, 9: bad/not measured) and a 2nd QC-flag ('Del13Cqc', 1: quality controlled, 0: not quality controlled). The NAC13v1_expocode.csv-File contains the allocation of the cruise numbers used in NAC13v1 and their EXPOCODEs as well as the respective cruise numbers in GLODAPv2 and CARINA. For this analysis some cruises that belong together were condensed to one, e.g. the TTO-NA cruises. proprietary
10.3334/cdiac/otg.ndp094_Not Applicable Climatological Distributions of pH, pCO2, Total CO2, Alkalinity, and CaCO3 Saturation in the Global Surface Ocean (NCEI Accession 0164568) NOAA_NCEI STAC Catalog 1957-01-01 2013-12-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089377567-NOAA_NCEI.umm_json Climatological mean monthly distributions of pH in the total H+ scale, total CO2 concentration (TCO2), and the degree of CaCO3 saturation for the global surface ocean waters (excluding coastal areas) are calculated using a data set for pCO2, alkalinity and nutrient concentrations in surface waters (depths less than 50 m), which is built upon the GLODAP, CARINA and LDEO database. The mutual consistency among these measured parameters is demonstrated using the inorganic carbon chemistry model with the dissociation constants for carbonic acid by Lueker et al. (2000) and for boric acid by Dickson (1990). The global ocean is divided into 24 regions, and the linear potential alkalinity (total alkalinity + nitrate) versus salinity relationships are established for each region. The mean monthly distributions of pH and carbon chemistry parameters for the reference year 2005 are computed using the climatological mean monthly pCO2 data adjusted to a reference year 2005 and the alkalinity estimated from the potential alkalinity versus salinity relationships. The climatological monthly mean values of pCO2 over the global ocean are compiled for a 4° x 5° grid for the reference year 2005, and the gridded data for each of 12 months are included in this database. This is updated version of Takahashi et al. (2009) for the reference year 2000 representing non-El Niño years using a database of about 6.5 million pCO2 data (less coastal areas of North and South America) observed in 1957-2012 (Takahashi et al., 2013). The equatorial zone (4°N-4°S) of the Pacific is excluded from the analysis because of the large interannual changes associated with the El Niño-Southern Oscillation events. The pH thus calculated ranges from 7.9 to 8.2. Lower values are located in the upwelling regions in the tropical Pacific and in the Arabian and Bering Seas; and higher values are found in the subpolar and polar waters during the spring-summer months of intense photosynthetic production. The vast areas of subtropical oceans have seasonally varying pH values ranging from 8.05 during warmer months to 8.15 during colder months. The warm tropical and subtropical waters are supersaturated by a factor of as much as 4.2 with respect to aragonite and 6.3 for calcite, whereas the cold subpolar and polar waters are less supersaturated only by 1.2 for aragonite and 2 for calcite because of the lower pH values resulting from greater TCO2 concentrations. In the western Arctic Ocean, aragonite undersaturation is observed. proprietary
-10.3334/cdiac/otg.pacifica_49nz20040901_Not Applicable Alkalinity, temperature, salinity and other variables collected from discrete sample and profile observations using CTD, Coulometer for DIC measurement and other instruments from MIRAI in the Arctic Ocean and Beaufort Sea from 2004-09-01 to 2004-10-13 (NCEI Accession 0112357) ALL STAC Catalog 2004-09-01 2004-10-13 179.501, 67, -144.988, 76.581 https://cmr.earthdata.nasa.gov/search/concepts/C2089375276-NOAA_NCEI.umm_json NCEI Accession 0112357 includes biological, chemical, discrete sample, physical and profile data collected from MIRAI in the Arctic Ocean and Beaufort Sea from 2004-09-01 to 2004-10-13. These data include AMMONIUM (NH4), CHLOROFLUOROCARBON-11 (CFC-11), CHLOROFLUOROCARBON-113 (CFC-113), CHLOROFLUOROCARBON-12 (CFC-12), CHLOROPHYLL A, DISSOLVED OXYGEN, Delta Oxygen-18, HYDROSTATIC PRESSURE, Methane (CH4), NITRATE, NITRITE, SALINITY, TOTAL ALKALINITY (TA), WATER TEMPERATURE, phosphate and silicate. The instruments used to collect these data include CTD, Coulometer for DIC measurement and bottle. These data were collected by Shigeto Nishino and Koji Shimada of Japan Agency for Marine-Earth Science and Technology (JAMSTEC) as part of the PACIFICA_49NZ20040901 data set. CDIAC associated the following cruise ID(s) with this data set: MR04-05 and PACIFICA_49NZ20040901 PACIFICA (PACIFic ocean Interior CArbon) was an international collaborative project for the data synthesis of ocean interior carbon and its related parameters in the Pacific Ocean. The North Pacific Marine Science Organization (PICES), Section of Carbon and Climate (S-CC) supported the project. proprietary
10.3334/cdiac/otg.pacifica_49nz20040901_Not Applicable Alkalinity, temperature, salinity and other variables collected from discrete sample and profile observations using CTD, Coulometer for DIC measurement and other instruments from MIRAI in the Arctic Ocean and Beaufort Sea from 2004-09-01 to 2004-10-13 (NCEI Accession 0112357) NOAA_NCEI STAC Catalog 2004-09-01 2004-10-13 179.501, 67, -144.988, 76.581 https://cmr.earthdata.nasa.gov/search/concepts/C2089375276-NOAA_NCEI.umm_json NCEI Accession 0112357 includes biological, chemical, discrete sample, physical and profile data collected from MIRAI in the Arctic Ocean and Beaufort Sea from 2004-09-01 to 2004-10-13. These data include AMMONIUM (NH4), CHLOROFLUOROCARBON-11 (CFC-11), CHLOROFLUOROCARBON-113 (CFC-113), CHLOROFLUOROCARBON-12 (CFC-12), CHLOROPHYLL A, DISSOLVED OXYGEN, Delta Oxygen-18, HYDROSTATIC PRESSURE, Methane (CH4), NITRATE, NITRITE, SALINITY, TOTAL ALKALINITY (TA), WATER TEMPERATURE, phosphate and silicate. The instruments used to collect these data include CTD, Coulometer for DIC measurement and bottle. These data were collected by Shigeto Nishino and Koji Shimada of Japan Agency for Marine-Earth Science and Technology (JAMSTEC) as part of the PACIFICA_49NZ20040901 data set. CDIAC associated the following cruise ID(s) with this data set: MR04-05 and PACIFICA_49NZ20040901 PACIFICA (PACIFic ocean Interior CArbon) was an international collaborative project for the data synthesis of ocean interior carbon and its related parameters in the Pacific Ocean. The North Pacific Marine Science Organization (PICES), Section of Carbon and Climate (S-CC) supported the project. proprietary
+10.3334/cdiac/otg.pacifica_49nz20040901_Not Applicable Alkalinity, temperature, salinity and other variables collected from discrete sample and profile observations using CTD, Coulometer for DIC measurement and other instruments from MIRAI in the Arctic Ocean and Beaufort Sea from 2004-09-01 to 2004-10-13 (NCEI Accession 0112357) ALL STAC Catalog 2004-09-01 2004-10-13 179.501, 67, -144.988, 76.581 https://cmr.earthdata.nasa.gov/search/concepts/C2089375276-NOAA_NCEI.umm_json NCEI Accession 0112357 includes biological, chemical, discrete sample, physical and profile data collected from MIRAI in the Arctic Ocean and Beaufort Sea from 2004-09-01 to 2004-10-13. These data include AMMONIUM (NH4), CHLOROFLUOROCARBON-11 (CFC-11), CHLOROFLUOROCARBON-113 (CFC-113), CHLOROFLUOROCARBON-12 (CFC-12), CHLOROPHYLL A, DISSOLVED OXYGEN, Delta Oxygen-18, HYDROSTATIC PRESSURE, Methane (CH4), NITRATE, NITRITE, SALINITY, TOTAL ALKALINITY (TA), WATER TEMPERATURE, phosphate and silicate. The instruments used to collect these data include CTD, Coulometer for DIC measurement and bottle. These data were collected by Shigeto Nishino and Koji Shimada of Japan Agency for Marine-Earth Science and Technology (JAMSTEC) as part of the PACIFICA_49NZ20040901 data set. CDIAC associated the following cruise ID(s) with this data set: MR04-05 and PACIFICA_49NZ20040901 PACIFICA (PACIFic ocean Interior CArbon) was an international collaborative project for the data synthesis of ocean interior carbon and its related parameters in the Pacific Ocean. The North Pacific Marine Science Organization (PICES), Section of Carbon and Climate (S-CC) supported the project. proprietary
10.3334/cdiac/otg.tsm_estoc_Not Applicable Carbon dioxide, temperature, salinity and other variables collected via time series monitoring from METEOR, POSEIDON and others in the North Atlantic Ocean from 1995-10-02 to 2009-11-25 (NCEI Accession 0100064) NOAA_NCEI STAC Catalog 1995-10-02 2009-11-25 -15.833, 29.066, -15.833, 29.066 https://cmr.earthdata.nasa.gov/search/concepts/C2089374894-NOAA_NCEI.umm_json NODC Accession 0100064 includes chemical, physical, time series and underway - surface data collected from METEOR, POSEIDON, TALIARTE and VICTOR HENSEN in the North Atlantic Ocean and South Atlantic Ocean from 1995-10-02 to 2009-11-25 and retrieved during cruise ESTOC cruises. These data include ALKALINITY - TOTAL, CARBON DIOXIDE - PARTIAL PRESSURE (pCO2), DISSOLVED INORGANIC CARBON, SALINITY, SEA SURFACE TEMPERATURE and pH. The instruments used to collect these data include Carbon dioxide (CO2) gas analyzer and Carbon dioxide (CO2) shower head chamber equilibrator. These data were collected by Melchor González Dávila of Universidad de Las Palmas de Gran Canaria as part of the ESTOC_Time_Series data set. proprietary
10.3334/cdiac/otg.tsm_tao170w_2s_Not Applicable Carbon dioxide, temperature, salinity and other variables collected via time series monitoring from MOORINGS in the North Pacific Ocean from 1998-06-22 to 2004-11-23 (NCEI Accession 0100079) NOAA_NCEI STAC Catalog 1998-06-22 2004-11-23 -170, 2, -170, 2 https://cmr.earthdata.nasa.gov/search/concepts/C2089375060-NOAA_NCEI.umm_json NODC Accession 0100079 includes chemical, time series and underway - surface data collected from MOORINGS in the North Pacific Ocean and South Pacific Ocean from 1998-06-22 to 2004-11-23. These data include CARBON DIOXIDE - PARTIAL PRESSURE - DIFFERENCE. The instruments used to collect these data include Carbon dioxide (CO2) gas analyzer and Carbon dioxide (CO2) laminar flow bubble equilibrator (for buoy measurement). These data were collected by Francisco Chavez of MONTEREY BAY AQUARIUM RESEARCH INSTITUTE as part of the Mooring TAO170W2S data set. CDIAC assigned the following cruise ID(s) to this data set: TAO170W2S_1998_2004, TAO170W2S_2007_2008. proprietary
10.3334/cdiac/otg.vos_alligatorhope_1999-2001_Not Applicable Carbon dioxide, temperature, salinity, and other variables collected via surface underway survey from Volunteer Observing Ship Alligator Hope in the North Pacific Ocean and South Pacific Ocean from 1999-11-12 to 2001-05-11 (NCEI Accession 0081049) NOAA_NCEI STAC Catalog 1999-11-12 2001-05-11 140, 34.46, -124, 56.99 https://cmr.earthdata.nasa.gov/search/concepts/C2089376304-NOAA_NCEI.umm_json VOS Alligator Hope Line proprietary
@@ -135,8 +135,8 @@ id title catalog state_date end_date bbox url description license
10.3334/cdiac/otg.vos_skaugran_1995-1999_Not Applicable Carbon dioxide, temperature, salinity, and other variables collected via surface underway survey from Volunteer Observing Ship SKAUGRAN in the North Pacific Ocean and South Pacific Ocean from 1995-03-29 to 1999-09-25 (NCEI Accession 0081047) NOAA_NCEI STAC Catalog 1995-03-29 1999-09-25 125.6, -27.44, -117.1, 55.99 https://cmr.earthdata.nasa.gov/search/concepts/C2089376285-NOAA_NCEI.umm_json VOS Skaugran Line proprietary
10.3334/cdiac/otg.vos_tully_1989_Not Applicable Carbon dioxide, temperature, salinity, and other variables collected via surface underway survey from Volunteer Observing Ship JOHN P. TULLY in the Arctic Ocean, Beaufort Sea and others from 1989-07-13 to 1989-09-27 (NCEI Accession 0081010) NOAA_NCEI STAC Catalog 1989-07-13 1989-09-27 -173.31, 48.1, -123.57, 71.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089375960-NOAA_NCEI.umm_json Underway measurements from R/V John P. Tully 1989 cruise proprietary
10.7289/v50r9mn2_Not Applicable Carbon Dioxide (CO2) mole fraction, CO2 flux, and others collected from Salt Lake City CO2 measurement network in Western U.S. from 2001-02-07 to 2015-10-23 (NCEI Accession 0170450) NOAA_NCEI STAC Catalog 2001-02-07 2015-10-23 -115, 37, -109, 42.99 https://cmr.earthdata.nasa.gov/search/concepts/C2089380359-NOAA_NCEI.umm_json This data set contains atmospheric measurements of carbon dioxide (CO2) from the Salt Lake City CO2 measurement network from 2001-2015 as well as several supporting data sets used to interpret the mixing ratio data. The additional data sets include atmospheric footprints (i.e. the upstream influence region on the atmospheric measurement site), fluxes of CO2 from anthropogenic and biological sources, and gridded population in the state of Utah. proprietary
-10.7289/v51v5bzm_Not Applicable Aerial Surveys of Arctic Marine Mammals (ASAMM) collected by National Marine Mammal Laboratory, Bureau of Ocean Energy Management, and other agencies in the Arctic Ocean, Bering, Chukchi and Beaufort Seas from 1979-04-21 to 2019-10-29 (NCEI Accession 0039614) NOAA_NCEI STAC Catalog 1979-04-21 2019-10-29 -174.01, 57.73, -125.25, 76.15 https://cmr.earthdata.nasa.gov/search/concepts/C2089375773-NOAA_NCEI.umm_json This archival package contains aerial survey data from the surveys described below. The Bureau of Ocean Energy Management (BOEM), formerly the Minerals Management Service (MMS), and its precursor, the Bureau of Land Management, have funded aerial surveys in the Beaufort, Chukchi, and Bering seas since 1979. In 2008, through an Interagency Agreement between MMS and the Alaska Fisheries Science Center (AFSC, National Marine Fisheries Service, National Oceanic and Atmospheric Administration), the Marine Mammal Laboratory (MML, a division of AFSC), formerly the National Marine Mammal Laboratory assumed co-management responsibilities for these surveys. Throughout the history of the surveys, they have been referred to as the Bowhead Whale Aerial Survey Project (BWASP) and the Chukchi Offshore Monitoring in Drilling Area (COMIDA) marine mammal aerial surveys, both of which are described in more detail below. The surveys are currently conducted under the auspices of a single study, Aerial Surveys of Arctic Marine Mammals (ASAMM). Consistent survey protocol has been in effect on surveys conducted since 1982. WESTERN BEAUFORT SEA Aerial surveys in the western Beaufort Sea (south of 72 degrees N, 140-157 degrees W) have been conducted each year since 1979. MMS personnel and contractors conducted the surveys from 1979-2007. From 2008-2019, the surveys were conducted by MML. The primary goal of the project, also known as BWASP, was to document bowhead whales (Balaena mysticetus) during their fall migration through the western Beaufort Sea, although data were also collected for all other marine mammals that were sighted during the surveys. The surveys were typically conducted during the open water (i.e., ice-free) months of September and October, when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Cross Island (village of Nuiqsut), and Utqiaġvik (formerly Barrow), Alaska. Additional surveys were conducted in the Beaufort Sea during spring and summer 1979-1986, and during summer 2011-2019. EASTERN CHUKCHI SEA Aerial surveys in the eastern Chukchi Sea (68-73 degrees N, 157-169 degrees W) were conducted by MMS (now BOEM) contractors from 1982-1991. From 2008-2019, the surveys were conducted by MML using similar methodology to the surveys conducted in previous years. Beginning in 2014, surveys were expanded south to 67 degrees N. The goal of the surveys, also known as the Chukchi Offshore Monitoring in Drilling Area (COMIDA) marine mammal aerial survey project, was to investigate the distribution and relative abundance of marine mammals in the Chukchi Sea Planning Area (CSPA) during the open water (i.e., ice-free) months of June to October, when various species are undertaking seasonal migrations through the area. However, from 1979-1984, surveys were also conducted during spring. NORTHERN BERING AND SOUTHERN CHUKCHI SEAS Aerial surveys in the northern Bering and southern Chukchi seas (63-68 degrees N, east of the International Date Line) were conducted by MMS (now BOEM) contractors from 1979-1985. The goal of these surveys was to investigate the distribution, abundance, migration timing, habitat relationships and behavior of endangered whales during the spring migration. Surveys were conducted from April-July. EASTERN BEAUFORT SEA AND AMUNDSEN GULF Aerial surveys in the eastern Beaufort Sea and Amundsen Gulf (67-73 degrees N, 118-140 degrees W), were conducted by MML from 5 to 27 August 2019, in collaboration with BOEM, North Slope Borough, Department of Fisheries and Oceans Canada, Inuvialuit Game Council, and Fisheries Joint Management Committee. The goal of these surveys, known as the ASAMM Bowhead Abundance (ABA) project, was to collect aerial survey data specific to estimating the abundance of the Bering-Chukchi-Beaufort Seas bowhead whale population. The primary ABA study area in its entirety includes the Beaufort Sea shelf and Amundsen Gulf (118-158 degrees W). proprietary
10.7289/v51v5bzm_Not Applicable Aerial Surveys of Arctic Marine Mammals (ASAMM) collected by National Marine Mammal Laboratory, Bureau of Ocean Energy Management, and other agencies in the Arctic Ocean, Bering, Chukchi and Beaufort Seas from 1979-04-21 to 2019-10-29 (NCEI Accession 0039614) ALL STAC Catalog 1979-04-21 2019-10-29 -174.01, 57.73, -125.25, 76.15 https://cmr.earthdata.nasa.gov/search/concepts/C2089375773-NOAA_NCEI.umm_json This archival package contains aerial survey data from the surveys described below. The Bureau of Ocean Energy Management (BOEM), formerly the Minerals Management Service (MMS), and its precursor, the Bureau of Land Management, have funded aerial surveys in the Beaufort, Chukchi, and Bering seas since 1979. In 2008, through an Interagency Agreement between MMS and the Alaska Fisheries Science Center (AFSC, National Marine Fisheries Service, National Oceanic and Atmospheric Administration), the Marine Mammal Laboratory (MML, a division of AFSC), formerly the National Marine Mammal Laboratory assumed co-management responsibilities for these surveys. Throughout the history of the surveys, they have been referred to as the Bowhead Whale Aerial Survey Project (BWASP) and the Chukchi Offshore Monitoring in Drilling Area (COMIDA) marine mammal aerial surveys, both of which are described in more detail below. The surveys are currently conducted under the auspices of a single study, Aerial Surveys of Arctic Marine Mammals (ASAMM). Consistent survey protocol has been in effect on surveys conducted since 1982. WESTERN BEAUFORT SEA Aerial surveys in the western Beaufort Sea (south of 72 degrees N, 140-157 degrees W) have been conducted each year since 1979. MMS personnel and contractors conducted the surveys from 1979-2007. From 2008-2019, the surveys were conducted by MML. The primary goal of the project, also known as BWASP, was to document bowhead whales (Balaena mysticetus) during their fall migration through the western Beaufort Sea, although data were also collected for all other marine mammals that were sighted during the surveys. The surveys were typically conducted during the open water (i.e., ice-free) months of September and October, when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Cross Island (village of Nuiqsut), and Utqiaġvik (formerly Barrow), Alaska. Additional surveys were conducted in the Beaufort Sea during spring and summer 1979-1986, and during summer 2011-2019. EASTERN CHUKCHI SEA Aerial surveys in the eastern Chukchi Sea (68-73 degrees N, 157-169 degrees W) were conducted by MMS (now BOEM) contractors from 1982-1991. From 2008-2019, the surveys were conducted by MML using similar methodology to the surveys conducted in previous years. Beginning in 2014, surveys were expanded south to 67 degrees N. The goal of the surveys, also known as the Chukchi Offshore Monitoring in Drilling Area (COMIDA) marine mammal aerial survey project, was to investigate the distribution and relative abundance of marine mammals in the Chukchi Sea Planning Area (CSPA) during the open water (i.e., ice-free) months of June to October, when various species are undertaking seasonal migrations through the area. However, from 1979-1984, surveys were also conducted during spring. NORTHERN BERING AND SOUTHERN CHUKCHI SEAS Aerial surveys in the northern Bering and southern Chukchi seas (63-68 degrees N, east of the International Date Line) were conducted by MMS (now BOEM) contractors from 1979-1985. The goal of these surveys was to investigate the distribution, abundance, migration timing, habitat relationships and behavior of endangered whales during the spring migration. Surveys were conducted from April-July. EASTERN BEAUFORT SEA AND AMUNDSEN GULF Aerial surveys in the eastern Beaufort Sea and Amundsen Gulf (67-73 degrees N, 118-140 degrees W), were conducted by MML from 5 to 27 August 2019, in collaboration with BOEM, North Slope Borough, Department of Fisheries and Oceans Canada, Inuvialuit Game Council, and Fisheries Joint Management Committee. The goal of these surveys, known as the ASAMM Bowhead Abundance (ABA) project, was to collect aerial survey data specific to estimating the abundance of the Bering-Chukchi-Beaufort Seas bowhead whale population. The primary ABA study area in its entirety includes the Beaufort Sea shelf and Amundsen Gulf (118-158 degrees W). proprietary
+10.7289/v51v5bzm_Not Applicable Aerial Surveys of Arctic Marine Mammals (ASAMM) collected by National Marine Mammal Laboratory, Bureau of Ocean Energy Management, and other agencies in the Arctic Ocean, Bering, Chukchi and Beaufort Seas from 1979-04-21 to 2019-10-29 (NCEI Accession 0039614) NOAA_NCEI STAC Catalog 1979-04-21 2019-10-29 -174.01, 57.73, -125.25, 76.15 https://cmr.earthdata.nasa.gov/search/concepts/C2089375773-NOAA_NCEI.umm_json This archival package contains aerial survey data from the surveys described below. The Bureau of Ocean Energy Management (BOEM), formerly the Minerals Management Service (MMS), and its precursor, the Bureau of Land Management, have funded aerial surveys in the Beaufort, Chukchi, and Bering seas since 1979. In 2008, through an Interagency Agreement between MMS and the Alaska Fisheries Science Center (AFSC, National Marine Fisheries Service, National Oceanic and Atmospheric Administration), the Marine Mammal Laboratory (MML, a division of AFSC), formerly the National Marine Mammal Laboratory assumed co-management responsibilities for these surveys. Throughout the history of the surveys, they have been referred to as the Bowhead Whale Aerial Survey Project (BWASP) and the Chukchi Offshore Monitoring in Drilling Area (COMIDA) marine mammal aerial surveys, both of which are described in more detail below. The surveys are currently conducted under the auspices of a single study, Aerial Surveys of Arctic Marine Mammals (ASAMM). Consistent survey protocol has been in effect on surveys conducted since 1982. WESTERN BEAUFORT SEA Aerial surveys in the western Beaufort Sea (south of 72 degrees N, 140-157 degrees W) have been conducted each year since 1979. MMS personnel and contractors conducted the surveys from 1979-2007. From 2008-2019, the surveys were conducted by MML. The primary goal of the project, also known as BWASP, was to document bowhead whales (Balaena mysticetus) during their fall migration through the western Beaufort Sea, although data were also collected for all other marine mammals that were sighted during the surveys. The surveys were typically conducted during the open water (i.e., ice-free) months of September and October, when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Cross Island (village of Nuiqsut), and Utqiaġvik (formerly Barrow), Alaska. Additional surveys were conducted in the Beaufort Sea during spring and summer 1979-1986, and during summer 2011-2019. EASTERN CHUKCHI SEA Aerial surveys in the eastern Chukchi Sea (68-73 degrees N, 157-169 degrees W) were conducted by MMS (now BOEM) contractors from 1982-1991. From 2008-2019, the surveys were conducted by MML using similar methodology to the surveys conducted in previous years. Beginning in 2014, surveys were expanded south to 67 degrees N. The goal of the surveys, also known as the Chukchi Offshore Monitoring in Drilling Area (COMIDA) marine mammal aerial survey project, was to investigate the distribution and relative abundance of marine mammals in the Chukchi Sea Planning Area (CSPA) during the open water (i.e., ice-free) months of June to October, when various species are undertaking seasonal migrations through the area. However, from 1979-1984, surveys were also conducted during spring. NORTHERN BERING AND SOUTHERN CHUKCHI SEAS Aerial surveys in the northern Bering and southern Chukchi seas (63-68 degrees N, east of the International Date Line) were conducted by MMS (now BOEM) contractors from 1979-1985. The goal of these surveys was to investigate the distribution, abundance, migration timing, habitat relationships and behavior of endangered whales during the spring migration. Surveys were conducted from April-July. EASTERN BEAUFORT SEA AND AMUNDSEN GULF Aerial surveys in the eastern Beaufort Sea and Amundsen Gulf (67-73 degrees N, 118-140 degrees W), were conducted by MML from 5 to 27 August 2019, in collaboration with BOEM, North Slope Borough, Department of Fisheries and Oceans Canada, Inuvialuit Game Council, and Fisheries Joint Management Committee. The goal of these surveys, known as the ASAMM Bowhead Abundance (ABA) project, was to collect aerial survey data specific to estimating the abundance of the Bering-Chukchi-Beaufort Seas bowhead whale population. The primary ABA study area in its entirety includes the Beaufort Sea shelf and Amundsen Gulf (118-158 degrees W). proprietary
10.7289/v52805n2_1.0 GHRSST Level 2P Western Atlantic Regional Skin Sea Surface Temperature from the Geostationary Operational Environmental Satellites (GOES) Imager on the GOES-13 satellite (GDS versions 1 and 2) GHRSSTCWIC STAC Catalog 2010-06-21 2018-01-08 -135, 50, -30, 65 https://cmr.earthdata.nasa.gov/search/concepts/C2213639676-GHRSSTCWIC.umm_json The Geostationary Operational Environmental Satellites (GOES) operated by the United States National Oceanic and Atmospheric Administration (NOAA) support weather forecasting, severe storm tracking, meteorology and oceanography research. Generally there are several GOES satellites in geosynchronous orbit at any one time viewing different earth locations including the GOES-13 launched 24 May 2006. The radiometer aboard the satellite, The GOES N-P Imager, is a five channel (one visible, four infrared) imaging radiometer designed to sense radiant and solar reflected energy from sampled areas of the earth. The multi-element spectral channels simultaneously sweep east-west and west-east along a north-to-south path by means of a two-axis mirror scan system retuning telemetry in 10-bit precision. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the far IR channels of GOES-13 at full resolution on a half hourly basis. In native satellite projection, vertically adjacent pixels are averaged and read out at every pixel. L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0. The full disk image is subsetted into granules representing distinct northern and southern regions. proprietary
10.7289/v52j68xx_Not Applicable AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present NOAA_NCEI STAC Catalog 1981-08-25 2021-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089385687-NOAA_NCEI.umm_json "The AVHRR Pathfinder Version 5.3 (PFV53) L3C Sea Surface Temperature data set is a collection of global, twice-daily (Day and Night) 4km sea surface temperature (SST) data produced by the NOAA National Centers for Environmental Information (NCEI). L3C is generated with measurements combined from a single instrument into a space-time grid. In this process multiple passes/scenes of data are combined. PFV53 was computed with data from the AVHRR instruments on board NOAA's polar orbiting satellite series using an entirely modernized system based on SeaDAS (version 6.4). This system incorporates several key changes from its predecessors (mainly version 5.2: PFV52). The SSTs in PFV53 are now available for all quality levels, including quality '0' which was left out of PFV52 due to a memory issue in the version 5.2 code. The Sun glint regions are better represented in the data. Cloud tree tests for NOAA-7 and NOAA-19 are now consistent with the rest of the sensors in contrast to PFV52 where they were inconsistent. Similar to all previous versions of Pathfinder this version also includes L3C products. The sst_dtime variable is still not included in L3C (it was not included in PFV52 either). The global and variables attributes in netCDF files are revised, have better CF and ACDD compliance, and are consistent with the NCEI netCDF templates. Anomalous hot-spots at land-water boundaries are better identified and flagged in PFV53. The PFV53 land mask has been updated (based on Global Lakes and Wetlands Database: Lakes and Wetlands Grid Level 3, 2015). Sea ice data over the Antarctic ice shelves are marked as ice and flagged as 100% ice cover. The PFV53 output are netCDF version 4 in ""classic"" mode, whereas in PFV52 the netCDF-4 files were not explicitly identified as ""classic"". An extra bit (bit 6) is used under l2p_flags variable to flag out the daytime unrealistic SST values (>39.8°C) that remain in pf_quality_level 4 to 7. Users are recommended to avoid these values. Importantly, PFV53 data provided in netCDF-4 (classic model, with internal compression and chunking) are nearly 100% compliant with the GHRSST Data Specification Version 2.0 (GDS2.0 revision 5) requirements. However, it must be noted that in L3C data the variables sses_bias, sses_standard_deviation, and sst_dtime are still empty. PFV53 data were collected through the operational periods of the NOAA-7 through NOAA-19 Polar Operational Environmental Satellites (POES), and are available from 1981 through Present. Data for all these years are available as multiple NCEI accessions. PFV5.3 production is running on operational mode and will be updated on quarterly basis." proprietary
10.7289/v53b5xcg_Not Applicable Bottle, discrete measurements of alkalinity, pH (on total scale), temperature, salinity, nutrients and other physical and chemical parameters from R/V Investigator SOCCOM cruise IN2016_v01 (EXPOCODE 096U20160108) in the Southern Ocean from 2016-01-08 to 2016-02-27 (NCEI Accession 0162618) NOAA_NCEI STAC Catalog 2016-01-08 2016-02-02 71.3, -54.6, 104.1, -35.8 https://cmr.earthdata.nasa.gov/search/concepts/C2089380389-NOAA_NCEI.umm_json NCEI Accession 0162618 includes discrete bottle measurements of Total Alkalinity, pH (on total scale), Oxygen, Nutrients, Temperature and Salinity from R/V Investigator SOCCOM cruise IN2016_v01 (EXPOCODE 096U20160108) in the Southern Ocean from 2016-01-08 to 2016-02-27. The R/V Investigator cruise IN2016_v01 is the part of the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project funded by National Science Foundation and the Heard Earth-Ocean-Biosphere Interactions (HEOBI) funded by multiple Australian agencies. proprietary
@@ -192,8 +192,8 @@ id title catalog state_date end_date bbox url description license
11c5f6df1abc41968d0b28fe36393c9d_NA ESA Aerosol Climate Change Initiative (Aerosol CCI): Level 3 aerosol products from MERIS (ALAMO algorithm), Version 2.2 FEDEO STAC Catalog 2008-01-01 2008-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143004-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 3 aerosol daily and monthly gridded products from MERIS for 2008, using the ALAMO algorithm, version 2.2. The data have been provided by Hygeos.For further details about these data products please see the linked documentation. proprietary
12-hourly_interpolated_surface_position_from_buoys 12-Hourly Interpolated Surface Position from Buoys SCIOPS STAC Catalog 1979-01-01 2009-12-01 -180, 60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600619-SCIOPS.umm_json This data set contains Arctic Ocean daily buoy positions interpolated to hours 0Z and 12Z. proprietary
12-hourly_interpolated_surface_position_from_buoys 12-Hourly Interpolated Surface Position from Buoys ALL STAC Catalog 1979-01-01 2009-12-01 -180, 60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600619-SCIOPS.umm_json This data set contains Arctic Ocean daily buoy positions interpolated to hours 0Z and 12Z. proprietary
-12-hourly_interpolated_surface_velocity_from_buoys 12-Hourly Interpolated Surface Velocity from Buoys SCIOPS STAC Catalog 1979-01-01 2009-12-02 -180, 74, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600621-SCIOPS.umm_json This data set contains 12-hourly interpolated surface velocity data from buoys. Point grid: Latitude 74N to 90N - 4 degree increment Longitude 0E to 320E - 20 and 40 degree increment. proprietary
12-hourly_interpolated_surface_velocity_from_buoys 12-Hourly Interpolated Surface Velocity from Buoys ALL STAC Catalog 1979-01-01 2009-12-02 -180, 74, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600621-SCIOPS.umm_json This data set contains 12-hourly interpolated surface velocity data from buoys. Point grid: Latitude 74N to 90N - 4 degree increment Longitude 0E to 320E - 20 and 40 degree increment. proprietary
+12-hourly_interpolated_surface_velocity_from_buoys 12-Hourly Interpolated Surface Velocity from Buoys SCIOPS STAC Catalog 1979-01-01 2009-12-02 -180, 74, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600621-SCIOPS.umm_json This data set contains 12-hourly interpolated surface velocity data from buoys. Point grid: Latitude 74N to 90N - 4 degree increment Longitude 0E to 320E - 20 and 40 degree increment. proprietary
12_hourly_interpolated_surface_air_pressure_from_buoys 12 Hourly Interpolated Surface Air Pressure from Buoys SCIOPS STAC Catalog 1979-01-01 2007-11-30 -180, 70, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600618-SCIOPS.umm_json Optimally interpolated atmospheric surface pressure over the Arctic Ocean Basin. Temporal format - twice daily (0Z and 12Z) Spatial format - 2 degree latitude x 10 degree longitude - latitude: 70 N - 90 N - longitude: 0 E - 350 E proprietary
12_hourly_interpolated_surface_air_pressure_from_buoys 12 Hourly Interpolated Surface Air Pressure from Buoys ALL STAC Catalog 1979-01-01 2007-11-30 -180, 70, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600618-SCIOPS.umm_json Optimally interpolated atmospheric surface pressure over the Arctic Ocean Basin. Temporal format - twice daily (0Z and 12Z) Spatial format - 2 degree latitude x 10 degree longitude - latitude: 70 N - 90 N - longitude: 0 E - 350 E proprietary
142052b9dc754f6da47a631e35ec4609_NA ESA Sea Level Climate Change Initiative (Sea_Level_cci): Time series of gridded Sea Level Anomalies (SLA), Version 2.0 FEDEO STAC Catalog 1993-01-01 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142503-FEDEO.umm_json As part of the European Space Agency's (ESA) Sea Level Climate Change Initiative (CCI) project, a multi-satellite merged time series of monthly gridded Sea Level Anomalies (SLA) has been produced from satellite altimeter measurements. The Sea Level Anomaly grids have been calculated after merging the altimetry mission measurements together into monthly grids, with a spatial resolution of 0.25 degrees. This version of the product is Version 2.0. The following DOI can be used to reference the monthly Sea Level Anomaly product: DOI: 10.5270/esa-sea_level_cci-MSLA-1993_2015-v_2.0-201612The complete collection of v2.0 products from the Sea Level CCI project can be referenced using the following DOI: 10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612When using or referring to the Sea Level cci products, please mention the associated DOIs and also use the following citation where a detailed description of the Sea Level_cci project and products can be found:Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993â2010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.For further information on the Sea Level CCI products, and to register for these projects please email: info-sealevel@esa-sealevel-cci.org proprietary
@@ -201,11 +201,11 @@ id title catalog state_date end_date bbox url description license
14c_of_soil_co2_from_ipy_itex_cross_site_comparison 14C of soil CO2 from IPY ITEX Cross Site Comparison ALL STAC Catalog 2008-01-16 2008-01-21 -157.4, -36.9, 147.29, 71.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214602443-SCIOPS.umm_json Study sites: Toolik Lake Field Station Alaska, USA 68.63 N, 149.57 W; Atqasuk, Alaska USA 70.45 N, 157.40 W; Barrow, Alaska, USA 71.30 N, 156.67 W; Latnjajaure, Sweden 68.35 N, 18.50 E; Falls Creek, Australia: Site 2-unburned 36.90 S 147.29 E; Site 3-burned 36.89 S 147.28 E. Additional sites will be added summer 2008, but the exact sites are not finalized. Purpose: Collect soil CO2 for analysis of radiocarbon to evaluate the age of the carbon respired in controls and warmed plots from across the ITEX network. Treatments: control and ITEX OTC warming experiment (1994-2007). Design: 5 replicates of each treatment at dry site and moist site. Sampling frequency: Once per peak season. proprietary
159-96_03 Alkali basalt volcanism along a subduction-related magmatic arc: The case of Puyuhuapi Quaternary volcanic line, Southern Andes (44deg20minS) SCIOPS STAC Catalog 1998-02-01 1998-05-02 -72.36, -42.22, -72.31, -42.14 https://cmr.earthdata.nasa.gov/search/concepts/C1214615254-SCIOPS.umm_json In Southern Chile, plate configuration is characterized by ridge-ridge-trench collision in correspondence of Taitao peninsula (Chile triple junction). The different converging rates of Nazca and Antarctic plates favored the formation of a forearc sliver (Chiloe block) limited to west by a dextral transcurrent fault system, Known as Liquine-Ofqui fault system (LOFS). During the Quatenary time, a series of monogenetic volcanic centers, as Puyuhuapi volcanic centers (PVC), formed along the LOFS. The PVC lavas have a primitive character; two groups and can be distinguihed. Group-1 rocks show a K-AlKaline affinity and are nepheline normative with olivine and plagioclase as dominant phases. Group-2 lavas have Na-affinity with olivine and hyperstene in the norm; olivine is the most abundant mineral phase. In contrast with overall alkaline affinity of PVC, the products from the neighboring central composite volcanoes are generally calcalkaline with the exception of the lavas from Maca Volcano, which show tholeiitic affinity. proprietary
159-96_03 Alkali basalt volcanism along a subduction-related magmatic arc: The case of Puyuhuapi Quaternary volcanic line, Southern Andes (44deg20minS) ALL STAC Catalog 1998-02-01 1998-05-02 -72.36, -42.22, -72.31, -42.14 https://cmr.earthdata.nasa.gov/search/concepts/C1214615254-SCIOPS.umm_json In Southern Chile, plate configuration is characterized by ridge-ridge-trench collision in correspondence of Taitao peninsula (Chile triple junction). The different converging rates of Nazca and Antarctic plates favored the formation of a forearc sliver (Chiloe block) limited to west by a dextral transcurrent fault system, Known as Liquine-Ofqui fault system (LOFS). During the Quatenary time, a series of monogenetic volcanic centers, as Puyuhuapi volcanic centers (PVC), formed along the LOFS. The PVC lavas have a primitive character; two groups and can be distinguihed. Group-1 rocks show a K-AlKaline affinity and are nepheline normative with olivine and plagioclase as dominant phases. Group-2 lavas have Na-affinity with olivine and hyperstene in the norm; olivine is the most abundant mineral phase. In contrast with overall alkaline affinity of PVC, the products from the neighboring central composite volcanoes are generally calcalkaline with the exception of the lavas from Maca Volcano, which show tholeiitic affinity. proprietary
-16920eb2-2eaf-4629-8337-3626e70e4770 Africa - Photovoltaic Solar Electricity Potential ALL STAC Catalog 2001-01-01 2008-12-31 -24.960938, -35.859375, 61.523438, 46.40625 https://cmr.earthdata.nasa.gov/search/concepts/C1214604070-SCIOPS.umm_json The map displays the quantity of energy that reached equator-oriented photovoltaic modules that are optimally-inclined to maximise yearly electricity yields. This map is computed from observations made by meteorological satellites. Click on map to enlarge. If you use this map, mention this copyright please: PVGIS copyright European Commission 2001-2008 and HelioClim-1 copyright Mines ParisTech / Armines 2001-2008. proprietary
16920eb2-2eaf-4629-8337-3626e70e4770 Africa - Photovoltaic Solar Electricity Potential SCIOPS STAC Catalog 2001-01-01 2008-12-31 -24.960938, -35.859375, 61.523438, 46.40625 https://cmr.earthdata.nasa.gov/search/concepts/C1214604070-SCIOPS.umm_json The map displays the quantity of energy that reached equator-oriented photovoltaic modules that are optimally-inclined to maximise yearly electricity yields. This map is computed from observations made by meteorological satellites. Click on map to enlarge. If you use this map, mention this copyright please: PVGIS copyright European Commission 2001-2008 and HelioClim-1 copyright Mines ParisTech / Armines 2001-2008. proprietary
+16920eb2-2eaf-4629-8337-3626e70e4770 Africa - Photovoltaic Solar Electricity Potential ALL STAC Catalog 2001-01-01 2008-12-31 -24.960938, -35.859375, 61.523438, 46.40625 https://cmr.earthdata.nasa.gov/search/concepts/C1214604070-SCIOPS.umm_json The map displays the quantity of energy that reached equator-oriented photovoltaic modules that are optimally-inclined to maximise yearly electricity yields. This map is computed from observations made by meteorological satellites. Click on map to enlarge. If you use this map, mention this copyright please: PVGIS copyright European Commission 2001-2008 and HelioClim-1 copyright Mines ParisTech / Armines 2001-2008. proprietary
16c633f003ef4d8481420f052356c11c_NA ESA Land Surface Temperature Climate Change Initiative (LST_cci): Monthly land surface temperature from ATSR-2 (Along-Track Scanning Radiometer 2), level 3 collated (L3C) global product (1995-2013), version 3.00 FEDEO STAC Catalog 1995-08-01 2003-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359482-FEDEO.umm_json This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.Daytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length.Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.The dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India â further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml.LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.Dataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods.The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards. proprietary
-1747-ESDD Alaskan Geologic Photography Collection from USGS ALL STAC Catalog 1898-01-01 -179, 50, -140, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231549077-CEOS_EXTRA.umm_json Collection of Alaskan photography taken throughout the state by explorers and field geologists. Subjects include geology and geologic phenomenon, earthquake damage, landscapes and people. Collection contains both photographs and slides. Library indexed by subject, locality, and year. Written requests accepted. Give as much information as possible to ensure successful search. Lists of many of the photographs submitted by Alaskan geologists are held in the technical data unit of the USGS branch of Alaskan geology in Anchorage, Alaska. proprietary
1747-ESDD Alaskan Geologic Photography Collection from USGS CEOS_EXTRA STAC Catalog 1898-01-01 -179, 50, -140, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231549077-CEOS_EXTRA.umm_json Collection of Alaskan photography taken throughout the state by explorers and field geologists. Subjects include geology and geologic phenomenon, earthquake damage, landscapes and people. Collection contains both photographs and slides. Library indexed by subject, locality, and year. Written requests accepted. Give as much information as possible to ensure successful search. Lists of many of the photographs submitted by Alaskan geologists are held in the technical data unit of the USGS branch of Alaskan geology in Anchorage, Alaska. proprietary
+1747-ESDD Alaskan Geologic Photography Collection from USGS ALL STAC Catalog 1898-01-01 -179, 50, -140, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231549077-CEOS_EXTRA.umm_json Collection of Alaskan photography taken throughout the state by explorers and field geologists. Subjects include geology and geologic phenomenon, earthquake damage, landscapes and people. Collection contains both photographs and slides. Library indexed by subject, locality, and year. Written requests accepted. Give as much information as possible to ensure successful search. Lists of many of the photographs submitted by Alaskan geologists are held in the technical data unit of the USGS branch of Alaskan geology in Anchorage, Alaska. proprietary
1751a072-d00b-42e8-8c7d-dc078f2ee40a Cyclones Winds - Hazard, Wind Speed 250RP CEOS_EXTRA STAC Catalog 1970-01-01 -180, -55, 179.7721, 59.768925 https://cmr.earthdata.nasa.gov/search/concepts/C2232848078-CEOS_EXTRA.umm_json "This file contains the geographical distribution of wind field intensities (peak velocity of 5 seconds gusts) for the entire globe, for 250 years return period. It was generated by integration of the intensity values contained in the files ""Wind_Atlantic.AME"", ""Wind_EastPacific.AME"", ""Wind_NorthIndian.AME"", ""Wind_SudIndian.AME"", ""Wind_SudPacific.AME"" and ""Wind_WestPacific.AME""." proprietary
17767027aa484505b7b732aee6619c74_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Helheim glacier from ERS-1, ERS-2 and Envisat data for 1996-2010, v1.1 FEDEO STAC Catalog 1996-05-28 2010-02-26 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143018-FEDEO.umm_json This dataset contains a time series of ice velocities for the Helheim glacier in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 29/05/1996 and 26/2/2010. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle of 35 days. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland). proprietary
198081050_1 MS Nella Dan Voyage V5 1980/81 (FIBEX) Track and Underway Data AU_AADC STAC Catalog 1981-01-19 1981-03-25 61.9, -69, 147.3, -43.2 https://cmr.earthdata.nasa.gov/search/concepts/C1214305277-AU_AADC.umm_json This dataset contains the underway data collected during the MS Nella Dan Voyage V5 1980/81 (FIBEX). Voyage name : First International BIOMASS Experiment Voyage leader: Knowles Ronald Kerry Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). proprietary
@@ -229,8 +229,8 @@ id title catalog state_date end_date bbox url description license
199394020_1 Aurora Australis Voyage 2 1993-94 Underway Data AU_AADC STAC Catalog 1993-10-12 1993-11-17 62, -69, 148, -44 https://cmr.earthdata.nasa.gov/search/concepts/C1214305522-AU_AADC.umm_json This dataset contains the underway data from Voyage 2 1993-94 of the Aurora Australis. This was a non-marine science voyage, but NoQalms data types were logged at 60-second intervals. The observations were taken between October and November 1993 en route from Hobart to Mawson to Davis and back to Hobart. See the Marine Science Support Report at the Related URL section. proprietary
199394040_1 Aurora Australis Voyage 4 1993-94 Underway Data AU_AADC STAC Catalog 1993-11-19 1993-12-17 62, -69, 148, -44 https://cmr.earthdata.nasa.gov/search/concepts/C1214305493-AU_AADC.umm_json This dataset contains the underway data from Voyage 4 1993-94 of the Aurora Australis. This was a non-marine science voyage, but NoQalms data types were logged at 20-second intervals. The observations were taken between November and December 1993 en route from Hobart to Davis to Mawson and back to Hobart. See the Marine Science Support Data Quality Report at the Related URL section. proprietary
199394070_1 Aurora Australis Voyage 7 (SHAM) 1993-94 Underway Data AU_AADC STAC Catalog 1994-01-01 1994-03-01 60, -69, 160, -45 https://cmr.earthdata.nasa.gov/search/concepts/C1214305494-AU_AADC.umm_json This dataset contains the underway data from Voyage 7 1993-94 (SHAM) of the Aurora Australis. This was a manned marine science voyage. DLS and NoQalms data types were logged. The observations were taken between January and February 1994. The Programmer's Report is available via the Related URL section (includes a section on Data Quality). XBT and CTD data were also obtained. proprietary
-1994-1997_S_GW_GG04_AN_ISOTOPE A Preliminary Study on Oxygen Isotopes of Ice Cores from Collins Ice Cap, King George Island, Antarctica ALL STAC Catalog 1994-01-01 1997-12-30 -58.97, -62.17, -58.97, -62.17 https://cmr.earthdata.nasa.gov/search/concepts/C1214608733-SCIOPS.umm_json Ice-cores of the Collins Ice Cap were all gained through the BZXJ-model ice-core drilling machine newly made by Lanzhou Institute of Glaciology and Geocryology, Chinese Academy of Sciences. During drilling and collecting ice-cores, strict protection measures against the pollution and melt were taken so that the sample as good as possible to satisfy the demands of physical and chemical analyses of ice-cores. Collected ice-cores were transported under frozen conditions from Antarctica to the low temperature laboratory of Polar Research Institute of China, partly to University of New Hampshire, USA, and were preserved under -25 degrees centigrade. Ice-cores were taken out before analyses, cut apart with a band saw on clean low-temperature working table. We scraped a few millimetres of surface ice to melt under normal air temperature. Oxygen isotope analyses of 0-13.96m depth ice-cores from Big Dome Summit of Collins Ice Cap were completed by the Glacier Research Group, Institute for the Study of Earth, Ocean and Space, University of New Hampshire, USA. Their sampling interval is 15-20cm, total is 87 samples. Oxygen isotope analyses of 13.96-20.02m depth and 27.78-30.52m depth ice-cores from Big Dome Summit of Collins Ice Cap and firn samples drawn from BDA, BDB, BDC and Small Dome Top (SDT) were completed in state key laboratory of mineralization in Nanjing University. Sampling interval (total of 10 samples) is between 30cm and 130cm, and the sampling interval of SDT (total of 20 samples) is 10-20cm. proprietary
1994-1997_S_GW_GG04_AN_ISOTOPE A Preliminary Study on Oxygen Isotopes of Ice Cores from Collins Ice Cap, King George Island, Antarctica SCIOPS STAC Catalog 1994-01-01 1997-12-30 -58.97, -62.17, -58.97, -62.17 https://cmr.earthdata.nasa.gov/search/concepts/C1214608733-SCIOPS.umm_json Ice-cores of the Collins Ice Cap were all gained through the BZXJ-model ice-core drilling machine newly made by Lanzhou Institute of Glaciology and Geocryology, Chinese Academy of Sciences. During drilling and collecting ice-cores, strict protection measures against the pollution and melt were taken so that the sample as good as possible to satisfy the demands of physical and chemical analyses of ice-cores. Collected ice-cores were transported under frozen conditions from Antarctica to the low temperature laboratory of Polar Research Institute of China, partly to University of New Hampshire, USA, and were preserved under -25 degrees centigrade. Ice-cores were taken out before analyses, cut apart with a band saw on clean low-temperature working table. We scraped a few millimetres of surface ice to melt under normal air temperature. Oxygen isotope analyses of 0-13.96m depth ice-cores from Big Dome Summit of Collins Ice Cap were completed by the Glacier Research Group, Institute for the Study of Earth, Ocean and Space, University of New Hampshire, USA. Their sampling interval is 15-20cm, total is 87 samples. Oxygen isotope analyses of 13.96-20.02m depth and 27.78-30.52m depth ice-cores from Big Dome Summit of Collins Ice Cap and firn samples drawn from BDA, BDB, BDC and Small Dome Top (SDT) were completed in state key laboratory of mineralization in Nanjing University. Sampling interval (total of 10 samples) is between 30cm and 130cm, and the sampling interval of SDT (total of 20 samples) is 10-20cm. proprietary
+1994-1997_S_GW_GG04_AN_ISOTOPE A Preliminary Study on Oxygen Isotopes of Ice Cores from Collins Ice Cap, King George Island, Antarctica ALL STAC Catalog 1994-01-01 1997-12-30 -58.97, -62.17, -58.97, -62.17 https://cmr.earthdata.nasa.gov/search/concepts/C1214608733-SCIOPS.umm_json Ice-cores of the Collins Ice Cap were all gained through the BZXJ-model ice-core drilling machine newly made by Lanzhou Institute of Glaciology and Geocryology, Chinese Academy of Sciences. During drilling and collecting ice-cores, strict protection measures against the pollution and melt were taken so that the sample as good as possible to satisfy the demands of physical and chemical analyses of ice-cores. Collected ice-cores were transported under frozen conditions from Antarctica to the low temperature laboratory of Polar Research Institute of China, partly to University of New Hampshire, USA, and were preserved under -25 degrees centigrade. Ice-cores were taken out before analyses, cut apart with a band saw on clean low-temperature working table. We scraped a few millimetres of surface ice to melt under normal air temperature. Oxygen isotope analyses of 0-13.96m depth ice-cores from Big Dome Summit of Collins Ice Cap were completed by the Glacier Research Group, Institute for the Study of Earth, Ocean and Space, University of New Hampshire, USA. Their sampling interval is 15-20cm, total is 87 samples. Oxygen isotope analyses of 13.96-20.02m depth and 27.78-30.52m depth ice-cores from Big Dome Summit of Collins Ice Cap and firn samples drawn from BDA, BDB, BDC and Small Dome Top (SDT) were completed in state key laboratory of mineralization in Nanjing University. Sampling interval (total of 10 samples) is between 30cm and 130cm, and the sampling interval of SDT (total of 20 samples) is 10-20cm. proprietary
199495010_1 Aurora Australis Voyage 1 1994-95 Underway Data AU_AADC STAC Catalog 1994-08-31 1994-10-19 79, -69, 159, -44 https://cmr.earthdata.nasa.gov/search/concepts/C1214305495-AU_AADC.umm_json This dataset contains the underway data from Voyage 1 1994-95 of the Aurora Australis. This was a resupply cruise, with limited marine science being carried out. NoQalms data types were logged at 20-second intervals. The observations were taken between August and October 1995 en route from Hobart to Macquarie Island to Davis and back to Hobart. See the Marine Science Support Data Quality Report via the Related URL section. proprietary
199495020_1 Aurora Australis Voyage 2 1994-95 Underway Data AU_AADC STAC Catalog 1994-10-22 1994-12-01 79, -69, 148, -44 https://cmr.earthdata.nasa.gov/search/concepts/C1214305523-AU_AADC.umm_json This dataset contains the underway data from Voyage 2 1994-95 of the Aurora Australis. This was an resupply cruise, but NoQalms data types were logged at 20-second intervals. The observations were taken between October and December 1994 en route from Hobart to Casey to Davis and back to Hobart. See the Marine Science Support Data Quality Report via the Related URL section. proprietary
199495030_1 Aurora Australis Voyage 3 (MIRTH) 1994-95 Underway Data AU_AADC STAC Catalog 1994-12-01 1994-12-10 148, -55, 159, -44 https://cmr.earthdata.nasa.gov/search/concepts/C1214305524-AU_AADC.umm_json This dataset contains the underway data from Voyage 3 1994-95 (MIRTH) of the Aurora Australis. This was a resupply voyage, but was also used as a marine science training cruise. NoQalms data types were logged at 20-second intervals. The observations were taken in December 1994 en route from Hobart to Macquarie Island and back to Hobart. The Programmer's and Data Quality Reports are available via the Related URL section. proprietary
@@ -242,8 +242,8 @@ id title catalog state_date end_date bbox url description license
199596030_1 Aurora Australis Voyage 3 1995-96 Underway Data AU_AADC STAC Catalog 1995-11-25 1996-01-01 60, -70, 150, -30 https://cmr.earthdata.nasa.gov/search/concepts/C1214305526-AU_AADC.umm_json This dataset contains the underway data from Voyage 3 1995-96 of the Aurora Australis. This was a non-marine science voyage that departed Fremantle for Casey, Bunger Hills, Mawson, Davis and Law Base, and returned to Hobart. The Marine Science Support Data Quality Report is available via the Related URL section. proprietary
199596040_1 Aurora Australis Voyage 4 (BROKE) 1995-96 Underway Data AU_AADC STAC Catalog 1996-01-19 1996-03-31 70, -67, 165, -44 https://cmr.earthdata.nasa.gov/search/concepts/C1214305527-AU_AADC.umm_json This dataset contains the underway data from Voyage 4 1995-96 (BROKE) of the Aurora Australis. This was a manned marine science cruise. The major projects were a hydro-acoustic/trawl krill population survey, and the MARGINEX oceanographic survey on bottom water formation. CTD data were also obtained. Marine Science Support Data Quality and Programmer's Reports are available via the Related URL section. proprietary
199596060_1 Aurora Australis Voyage 6 1995-96 Underway Data AU_AADC STAC Catalog 1996-04-02 1996-05-01 60, -70, 150, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1214305499-AU_AADC.umm_json This dataset contains the underway data from Voyage 6 1995-96 of the Aurora Australis. This voyage visited Davis and Casey from Hobart and included a small marine science component. The Marine Science Support Data Quality Report is available via the Related URL section. proprietary
-1996-1997_13-13_S_OC_OC05_LO_O011301_000_R0_Y 1996-1997 Raw data of CTD in Prydz Bay region of the southern Indian Ocean, CHINARE-13 SCIOPS STAC Catalog 1997-01-01 1997-01-01 70, -70, 78, -64 https://cmr.earthdata.nasa.gov/search/concepts/C1214587181-SCIOPS.umm_json A series of measurements in water temperature, conductivity and depth was carried out during the austral summer of 1996/97 within and the north of Prydz Bay, the southern Indian Ocean.25 oceanographic stations were successfully completed and 3.77MB CTD data were obtained. proprietary
1996-1997_13-13_S_OC_OC05_LO_O011301_000_R0_Y 1996-1997 Raw data of CTD in Prydz Bay region of the southern Indian Ocean, CHINARE-13 ALL STAC Catalog 1997-01-01 1997-01-01 70, -70, 78, -64 https://cmr.earthdata.nasa.gov/search/concepts/C1214587181-SCIOPS.umm_json A series of measurements in water temperature, conductivity and depth was carried out during the austral summer of 1996/97 within and the north of Prydz Bay, the southern Indian Ocean.25 oceanographic stations were successfully completed and 3.77MB CTD data were obtained. proprietary
+1996-1997_13-13_S_OC_OC05_LO_O011301_000_R0_Y 1996-1997 Raw data of CTD in Prydz Bay region of the southern Indian Ocean, CHINARE-13 SCIOPS STAC Catalog 1997-01-01 1997-01-01 70, -70, 78, -64 https://cmr.earthdata.nasa.gov/search/concepts/C1214587181-SCIOPS.umm_json A series of measurements in water temperature, conductivity and depth was carried out during the austral summer of 1996/97 within and the north of Prydz Bay, the southern Indian Ocean.25 oceanographic stations were successfully completed and 3.77MB CTD data were obtained. proprietary
199697010_1 Aurora Australis Voyage 1 (WASTE) 1996-97 Underway Data AU_AADC STAC Catalog 1996-08-22 1996-09-22 130, -67, 150, -44 https://cmr.earthdata.nasa.gov/search/concepts/C1214305528-AU_AADC.umm_json This dataset contains the underway data from Voyage 1 1996-97 (WASTE) of the Aurora Australis. This was a manned marine science cruise. CTD data were also obtained from Hobart to the ice edge along the WOCE SR3 line. Oceanographic data from this voyage are held by the Principal Investigator Dr. Steve Rintoul at CSIRO. Marine Science Support Data Quality and Programmer's Reports are available via the Related URL section. proprietary
199697020_1 Aurora Australis Voyage 2 1996-97 Underway Data AU_AADC STAC Catalog 1996-09-26 1996-11-24 70, -70, 150, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1214305529-AU_AADC.umm_json This dataset contains the underway data collected during the Aurora Australis Voyage 2 1996-97. This voyage departed Hobart for Casey and then travelled to Davis after completing some marine science research. Underway (meteorological, thermosalinograph and bathymetric) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report via the Related URL section. proprietary
199697030_1 Aurora Australis Voyage 3 1996/97 Underway Data AU_AADC STAC Catalog 1996-11-25 1996-12-05 140, -55, 160, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1214305540-AU_AADC.umm_json This dataset contains the underway data collected during the Aurora Australis Voyage 3 1996-97. This voyage visited Macquarie Island, leaving from and returning to Hobart. Underway (meteorological and water temperature) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). For further information, see the Marine Science Support Data Quality Report via the Related URL section. proprietary
@@ -321,22 +321,22 @@ id title catalog state_date end_date bbox url description license
200708030_1 Aurora Australis Voyage V3 2007/08 Track and Underway Data - CEAMARC-CASO Voyage AU_AADC STAC Catalog 2007-12-02 2008-01-27 139, -68.6, 150, -42.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214305587-AU_AADC.umm_json This dataset contains the underway data collected during the Aurora Australis Voyage V3 2007/08. Voyage Objectives : CEAMARC/CASO Marine Science Leader: Dr. Martin Riddle Deputy Leader: Miss. Sarah Robinson Undertake marine science as part of the CEAMARC-CASO program. CASO work was primarily undertaken on a later voyage, and this voyage mainly focussed on CEAMARC work. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). proprietary
200708040_1 Aurora Australis Voyage V4 2007/08 Track and Underway Data AU_AADC STAC Catalog 2008-01-26 2008-03-20 62, -69.1, 147.5, -42.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214305588-AU_AADC.umm_json This dataset contains the underway data collected during the Aurora Australis Voyage V4 2007/08. Voyage Objectives : MAWSON and CASEY RESUPPLY Personnel retrieval Voyage leader: Ms. Nicki Chilcott Deploy and retrieve personnel - Casey, Mawson, Davis. The need to retrieve personnel by ship is subject to review on implementation of intercontinental air transport. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). proprietary
200708060_1 Aurora Australis Voyage V6 2007/08 Track and Underway Data - CASO Voyage AU_AADC STAC Catalog 2008-03-22 2008-04-19 139, -66.6, 147, -42.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214305589-AU_AADC.umm_json This dataset contains the underway data collected during the Aurora Australis Voyage V6 2007/08. Voyage Objectives : CASO marine science Leader: Dr. Steve Rintoul Deputy Leader: Mr. Andrew Deep Undertake marine science as part of the CASO program. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). proprietary
-200708_CEAMARC_CASO_TRACE_ELEMENT_SAMPLES_1 2007-08 CEAMARC-CASO VOYAGE TRACE ELEMENT SAMPLING AROUND AN ICEBERG AU_AADC STAC Catalog 2008-01-01 2008-03-20 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214305618-AU_AADC.umm_json We collected surface seawater samples using trace clean 1L Nalgene bottles on the end of a long bamboo pole. We will analyse these samples for trace elements. Iron is the element of highest interest to our group. We will determine dissolved iron and total dissolvable iron concentrations. Samples collected from 7 sites: Sites 1, 2, 3, 4 were a transect perpendicular to the edge of the iceberg to try and determine if there is a iron concentration gradient relative to the iceberg. Sites 4, 5, 6 were along the edge of the iceberg to determine if there is any spatial variability along the iceberg edge. Site 7 was away from the iceberg to determine what the iron concentration is in the surrounding waters not influenced by the iceberg. proprietary
200708_CEAMARC_CASO_TRACE_ELEMENT_SAMPLES_1 2007-08 CEAMARC-CASO VOYAGE TRACE ELEMENT SAMPLING AROUND AN ICEBERG ALL STAC Catalog 2008-01-01 2008-03-20 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214305618-AU_AADC.umm_json We collected surface seawater samples using trace clean 1L Nalgene bottles on the end of a long bamboo pole. We will analyse these samples for trace elements. Iron is the element of highest interest to our group. We will determine dissolved iron and total dissolvable iron concentrations. Samples collected from 7 sites: Sites 1, 2, 3, 4 were a transect perpendicular to the edge of the iceberg to try and determine if there is a iron concentration gradient relative to the iceberg. Sites 4, 5, 6 were along the edge of the iceberg to determine if there is any spatial variability along the iceberg edge. Site 7 was away from the iceberg to determine what the iron concentration is in the surrounding waters not influenced by the iceberg. proprietary
-200712_imnavait_field 200712_Imnavait_field SCIOPS STAC Catalog 2012-06-22 2012-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214602312-SCIOPS.umm_json Imnavait field campaign data from December 2007. proprietary
+200708_CEAMARC_CASO_TRACE_ELEMENT_SAMPLES_1 2007-08 CEAMARC-CASO VOYAGE TRACE ELEMENT SAMPLING AROUND AN ICEBERG AU_AADC STAC Catalog 2008-01-01 2008-03-20 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214305618-AU_AADC.umm_json We collected surface seawater samples using trace clean 1L Nalgene bottles on the end of a long bamboo pole. We will analyse these samples for trace elements. Iron is the element of highest interest to our group. We will determine dissolved iron and total dissolvable iron concentrations. Samples collected from 7 sites: Sites 1, 2, 3, 4 were a transect perpendicular to the edge of the iceberg to try and determine if there is a iron concentration gradient relative to the iceberg. Sites 4, 5, 6 were along the edge of the iceberg to determine if there is any spatial variability along the iceberg edge. Site 7 was away from the iceberg to determine what the iron concentration is in the surrounding waters not influenced by the iceberg. proprietary
200712_imnavait_field 200712_Imnavait_field ALL STAC Catalog 2012-06-22 2012-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214602312-SCIOPS.umm_json Imnavait field campaign data from December 2007. proprietary
+200712_imnavait_field 200712_Imnavait_field SCIOPS STAC Catalog 2012-06-22 2012-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214602312-SCIOPS.umm_json Imnavait field campaign data from December 2007. proprietary
200802_imnavait_field 200802_Imnavait_field ALL STAC Catalog 2012-06-22 2012-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600384-SCIOPS.umm_json Imnavait field campaign data from February 2008. proprietary
200802_imnavait_field 200802_Imnavait_field SCIOPS STAC Catalog 2012-06-22 2012-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600384-SCIOPS.umm_json Imnavait field campaign data from February 2008. proprietary
200809010_1 Aurora Australis Voyage V1 2008/09 Track and Underway Data AU_AADC STAC Catalog 2008-10-12 2008-11-21 76.2, -68.6, 147.7, -42.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214305590-AU_AADC.umm_json This dataset contains the underway data collected during the Aurora Australis Voyage V1 2008/09. Voyage Objectives : Deploy and retrieve personnel - Davis Changeover and Resupply Ice radar project Voyage leader: Tony Worby Deploy and retrieve personnel - subject to availability of intercontinental air transport capability. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). proprietary
200809020_1 Aurora Australis Voyage V2 2008/09 Track and Underway Data AU_AADC STAC Catalog 2008-11-23 2008-12-26 72.4, -68.6, 147.5, -31.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214305591-AU_AADC.umm_json This dataset contains the underway data collected during the Aurora Australis Voyage V2 2008/09. Voyage Objectives : Casey Changeover and Davis Summer Personnel changeover Voyage leader: Robb Clifton Deploy and retrieve personnel from Casey and Davis. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). proprietary
200809030_1 Aurora Australis Voyage V3 2008/09 Track and Underway Data AU_AADC STAC Catalog 2008-12-30 2009-02-20 37.7, -68.4, 150.2, -31.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214305512-AU_AADC.umm_json This dataset contains the underway data collected during the Aurora Australis Voyage V3 2008/09. Voyage Objectives : Deploy and Retrieve Personnel - JARE. Conduct marine science en-route along 110E. Deploy and retrieve personnel and fully resupply Syowa Station via helicopter over 40 miles of fast ice. Load RTA cargo. Conduct marine science en-route along 150E. Voyage leader: Rob Bryson Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). proprietary
200809050_1 Aurora Australis Voyage V5 2008/09 Track and Underway Data AU_AADC STAC Catalog 2009-02-24 2009-03-26 76.1, -68.6, 159, -42.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214305592-AU_AADC.umm_json This dataset contains the underway data collected during the Aurora Australis Voyage V5 2008/09 (). Voyage Objectives : Davis Personnel Retrieval and Macquarie Island Resupply Voyage leader: Pete Perderson Personnel retrieval from Davis. Full resupply of Macquarie Island. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section). proprietary
-200811_barrow_field_photos 200811_Barrow_field_photos ALL STAC Catalog 2008-11-01 2008-12-01 -156.7, 71, -156.4, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600315-SCIOPS.umm_json Barrow field campaign photos from November 2008. proprietary
200811_barrow_field_photos 200811_Barrow_field_photos SCIOPS STAC Catalog 2008-11-01 2008-12-01 -156.7, 71, -156.4, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600315-SCIOPS.umm_json Barrow field campaign photos from November 2008. proprietary
-2008_carbon_water_and_energy_balance_unburned_site 2008 carbon, water, and Energy balance Unburned site SCIOPS STAC Catalog 2008-06-01 2008-08-31 -150.3, 68.9, -150.3, 68.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214600632-SCIOPS.umm_json Fluxes of C, water, and energy as measured at an eddy covariance met tower. Data are half-hourly averages collected June-August 2008 proprietary
+200811_barrow_field_photos 200811_Barrow_field_photos ALL STAC Catalog 2008-11-01 2008-12-01 -156.7, 71, -156.4, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600315-SCIOPS.umm_json Barrow field campaign photos from November 2008. proprietary
2008_carbon_water_and_energy_balance_unburned_site 2008 carbon, water, and Energy balance Unburned site ALL STAC Catalog 2008-06-01 2008-08-31 -150.3, 68.9, -150.3, 68.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214600632-SCIOPS.umm_json Fluxes of C, water, and energy as measured at an eddy covariance met tower. Data are half-hourly averages collected June-August 2008 proprietary
-2008_carbon_water_energy_balance_moderately_burned_site 2008 carbon, water, energy balance moderately burned site SCIOPS STAC Catalog 2008-06-01 2008-08-31 -150.2, 69, -150.2, 69 https://cmr.earthdata.nasa.gov/search/concepts/C1214600665-SCIOPS.umm_json This data set contains eddy covariance met tower data from 2008 at moderately-burned site in the Anaktuvuk River Burn. proprietary
+2008_carbon_water_and_energy_balance_unburned_site 2008 carbon, water, and Energy balance Unburned site SCIOPS STAC Catalog 2008-06-01 2008-08-31 -150.3, 68.9, -150.3, 68.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214600632-SCIOPS.umm_json Fluxes of C, water, and energy as measured at an eddy covariance met tower. Data are half-hourly averages collected June-August 2008 proprietary
2008_carbon_water_energy_balance_moderately_burned_site 2008 carbon, water, energy balance moderately burned site ALL STAC Catalog 2008-06-01 2008-08-31 -150.2, 69, -150.2, 69 https://cmr.earthdata.nasa.gov/search/concepts/C1214600665-SCIOPS.umm_json This data set contains eddy covariance met tower data from 2008 at moderately-burned site in the Anaktuvuk River Burn. proprietary
+2008_carbon_water_energy_balance_moderately_burned_site 2008 carbon, water, energy balance moderately burned site SCIOPS STAC Catalog 2008-06-01 2008-08-31 -150.2, 69, -150.2, 69 https://cmr.earthdata.nasa.gov/search/concepts/C1214600665-SCIOPS.umm_json This data set contains eddy covariance met tower data from 2008 at moderately-burned site in the Anaktuvuk River Burn. proprietary
2008_carbon_water_energy_balance_severely_burned_site 2008 carbon, water, energy balance severely burned site SCIOPS STAC Catalog 2008-06-01 2008-08-31 -150.3, 69, -150.3, 69 https://cmr.earthdata.nasa.gov/search/concepts/C1214601124-SCIOPS.umm_json This data set contains eddy covariance met tower data from severely burned site in the Anaktuvuk River burn. proprietary
2008_carbon_water_energy_balance_severely_burned_site 2008 carbon, water, energy balance severely burned site ALL STAC Catalog 2008-06-01 2008-08-31 -150.3, 69, -150.3, 69 https://cmr.earthdata.nasa.gov/search/concepts/C1214601124-SCIOPS.umm_json This data set contains eddy covariance met tower data from severely burned site in the Anaktuvuk River burn. proprietary
200904_imnavait_field 200904_Imnavait_field ALL STAC Catalog 2012-06-22 2012-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214602078-SCIOPS.umm_json Imnavait field campaign data from April 2009. proprietary
@@ -359,10 +359,10 @@ id title catalog state_date end_date bbox url description license
201011030_1 Aurora Australis Voyage 3 2010/11 Track and Underway Data AU_AADC STAC Catalog 2011-02-08 2011-03-16 60, -66, 147, -41.4 https://cmr.earthdata.nasa.gov/search/concepts/C1214305603-AU_AADC.umm_json "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website. These data are broadcast ""live"" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL). This dataset contains the underway data collected during Voyage 3 of the Aurora Australis Voyage in the 2010/11 season. Voyage Objectives: Mawson Resupply, Davis light essential Cargo deployment. Leader: Mr. Andy Cianchi Deputy Leader: Ms. Margaret Lindsay VM Trainee: Ms. Kate O'Malley Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section)." proprietary
201011040_1 Aurora Australis Voyage 4 2010/11 Track and Underway Data AU_AADC STAC Catalog 2011-03-18 2011-04-15 78, -67, 147, -41.4 https://cmr.earthdata.nasa.gov/search/concepts/C1214305604-AU_AADC.umm_json "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website. These data are broadcast ""live"" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL). This dataset contains the underway data collected during Voyage 4 of the Aurora Australis Voyage in the 2010/11 season. Voyage Objectives: Davis and Casey Summer Personnel Retrieval. Leader: Dr. Doug Thost Deputy Leader: Mr. George Osborne VM Trainee: Dr. Barbara Frankel Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section)." proprietary
201011050_1 Aurora Australis Voyage 5 2010/11 Track and Underway Data AU_AADC STAC Catalog 2011-04-17 2011-05-01 147, -54, 160, -41.4 https://cmr.earthdata.nasa.gov/search/concepts/C1214305605-AU_AADC.umm_json "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website. These data are broadcast ""live"" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL). This dataset contains the underway data collected during Voyage 5 of the Aurora Australis Voyage in the 2010/11 season. Voyage Objectives: Macquarie Island Resupply. Leader: Mr. Robb Clifton Deputy Leader: Ms. Leanne Millhouse VM Trainee: Mr. Martin Boyle Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section)." proprietary
-2010_hydgrographic_chlorophyll_cdom_fluor_opt_backscatt_data_coll_acro_tow_prof 2010 Hydgrographic, chlorophyll and CDOM fluorescence, and optical backscattering data collected using an Acrobat towed profiler SCIOPS STAC Catalog 2010-08-21 2010-08-31 -158, 71.3, -153.5, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214602406-SCIOPS.umm_json This data set contains the Acrobat files from data underway along transects conducted near Barrow, AK from August 21 - September 8, 2010. Details of the latitude, longitude, date, and time are listed in the event log that is archived at this site. Date /time (UTC day, decimal time), time, position, bottom depth, and measured variables are listed as separate columns in each file. Each Acrobat file is named according to the transect line sampled, the year, and the year day of data collection (e.g., line_2_2010_233.dat). Because of a leaky motor can, data could not be collected from all transects sampled during the AON work using the Acrobat. Data were collected from the R/V Annika Marie using an Acrobat (Sea Sciences Inc.) towed undulating vehicle equipped with a SeaBird SBE49 conductivity-temperature-depth (CTD) sensor, a Wetlabs EcoTriplet with chlorophyll and CDOM fluorescence and optical backscatter sensors, and a Wetlabs data logger system. Data were acquired in real time from near-surface (1-m) to a few meters off of the bottom or to a maximum depth of 60 m. The inter-profile distance was usually ~150 m over the shelf and ~1 km seaward of the shelf break. The CTD was calibrated pre-cruise. No correction of chlorophyll fluorescence was done as comparison with the extracted chlorophyll from accompanying Niskin bottle samples indicated that the factory calibration was very good. The WetLabs software that calculates density from the observed temperature and conductivity cannot do so at temperatures below 0°C and a value of -999.999 is returned. Therefore users of these data should re-calculate density. Units of chlorophyll and CDOM are µg/L. For optical backscattering, the particulate volume scattering coefficient at 117 degrees and 660 nm with the scattering of water at 117 degrees subtracted out is shown. proprietary
2010_hydgrographic_chlorophyll_cdom_fluor_opt_backscatt_data_coll_acro_tow_prof 2010 Hydgrographic, chlorophyll and CDOM fluorescence, and optical backscattering data collected using an Acrobat towed profiler ALL STAC Catalog 2010-08-21 2010-08-31 -158, 71.3, -153.5, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214602406-SCIOPS.umm_json This data set contains the Acrobat files from data underway along transects conducted near Barrow, AK from August 21 - September 8, 2010. Details of the latitude, longitude, date, and time are listed in the event log that is archived at this site. Date /time (UTC day, decimal time), time, position, bottom depth, and measured variables are listed as separate columns in each file. Each Acrobat file is named according to the transect line sampled, the year, and the year day of data collection (e.g., line_2_2010_233.dat). Because of a leaky motor can, data could not be collected from all transects sampled during the AON work using the Acrobat. Data were collected from the R/V Annika Marie using an Acrobat (Sea Sciences Inc.) towed undulating vehicle equipped with a SeaBird SBE49 conductivity-temperature-depth (CTD) sensor, a Wetlabs EcoTriplet with chlorophyll and CDOM fluorescence and optical backscatter sensors, and a Wetlabs data logger system. Data were acquired in real time from near-surface (1-m) to a few meters off of the bottom or to a maximum depth of 60 m. The inter-profile distance was usually ~150 m over the shelf and ~1 km seaward of the shelf break. The CTD was calibrated pre-cruise. No correction of chlorophyll fluorescence was done as comparison with the extracted chlorophyll from accompanying Niskin bottle samples indicated that the factory calibration was very good. The WetLabs software that calculates density from the observed temperature and conductivity cannot do so at temperatures below 0°C and a value of -999.999 is returned. Therefore users of these data should re-calculate density. Units of chlorophyll and CDOM are µg/L. For optical backscattering, the particulate volume scattering coefficient at 117 degrees and 660 nm with the scattering of water at 117 degrees subtracted out is shown. proprietary
-2010_niskin_bottle_data_chlorophyll_nutrients_picoplankton 2010 Niskin Bottle Data (chlorophyll, nutrients, picoplankton) SCIOPS STAC Catalog 2010-08-21 2010-09-08 -158, 71.3, -153.5, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214602157-SCIOPS.umm_json Arctic Observing Network (AON) Annual Observations of the Biological and Physical Marine Environment in the Chukchi and near-shore Beaufort Seas near Barrow, AK. Carin Ashjian, Woods Hole Oceanographic Institution Robert Campbell, University of Rhode Island Stephen Okkonen, University of Alaska Fairbanks NISKIN BOTTLE DATA This data set contains the nutrient concentrations (PO4, NO2+NO3, SiO4, NO2, and NH4), total chlorophyll a concentration, the concentration of coccoid cyanobacteria, photosynthetic eukaryotes, and diatoms, and the abundances of protists (dinoflagellates and ciliates) as both cells/ml and as �g C/L as well as sample depth, position (latitude and longitude, date, station number, and temperature, salinity, and fluorescence for water samples collected using Niskin bottles during August and September 2010. More information regarding sample collection and the associated CTD casts numbers can be found in the event log for this cruise. Niskin bottles were deployed either just above the CTD (40 m) or by hand on a line over the side (0 m and 10 m samples) and tripped by messenger. Water was sampled immediately upon recovery of the Niskins. For chlorophyll a analysis, 100 ml of seawater was filtered onto GF-F glass fiber filters in triplicate for each bottle. Two hundred ml subsamples for determination of microzooplankton biomass and abundance were preserved with 5% final concentration acid Lugol solution for inverted microscopy. For flow cytometry samples, 3 ml aliquots were pipetted into 4 ml cryovials and preserved with 0.2% final concentration of freshly made paraformaldehyde. The samples were gently mixed and let sit in the dark at room temperature for 10 minutes before quick-freezing and storage -80 oC until flow cytometric analysis was performed. Analyses of nutrient, chlorophyll a, and flow cytometry samples followed methods described in Ashjian et al. (2010) that are reproduced below. Analysis of microzooplankton abundance followed methods described in Sherr et al. (in review) that are reproduced below. Nutrient and chlorophyll a samples were frozen in a -20�C freezer immediately after collection and transferred to a -80�C freezer within 6-8 hours. Water for the abundance of < 5 �m photosynthetic picoplankton by flow cytometry was drawn into 60 ml, brown bottles and kept cold for ~6-8 hours before being subsampled and frozen at -80�C. Chlorophyll a concentrations were analyzed within 2 months. “The filters were extracted in 6 ml of 90% acetone in 13 x 100 mm glass culture tubes at -20 oC for 18 to 24 hours. At the end of the extraction period, the filter was carefully removed from each tube, and the chlorophyll a concentration determined using a calibrated Turner Designs fluorometer. A solid chlorophyll a standard was used to check for fluorometer drift at the beginning of each reading of chlorophyll a samples. Extracted chlorophyll values were used to ground-truth the chlorophyll fluorescence sensors on the Acrobat and the CTD.” (Ashjian et al., 2010) “Nutrient analyses were performed using a hybrid Technicon AutoAnalyzer IITM and Alpkem RFA300TM system following protocols modified from Gordon et al. (1995). Standard curves with four different concentrations were run daily at the beginning and end of each run. Fresh standards were made prior to each run by diluting a primary standard with low-nutrient surface seawater. Triplicate deionized water blanks were analyzed at the beginning and end of each run to correct for any baseline shifts. In this protocol, the coefficients of variation for duplicates at low nutrient concentrations are typically < 1% (Fleischbein et al., 1999) while at high nutrient concentrations coefficients of variation are 2–3 % for nitrate and silicate (Corwith andWheeler, 2002). “ (Ashjian et al., 2010). Nutrient analyses were conducted by Joe Jennings at Oregon State University. “In the laboratory, samples for the abundance of < 5 �m photosynthetic microbes were thawed and kept on ice in a dark container until subsamples of 500 �l were enumerated on a Becton–Dickinson FACSCaliber flow cytometer with a 488-nm laser (Sherr et al. 2005). Populations of coccoid cyanobacteria and of photosynthetic eukaryotes were distinguished by differences in side light scatter (SSC) and by fluorescence in orange (cyanobacteria) and in red (eukaryotic phytoplankton) wavelengths. “ (Ashjian et al., 2010). Microzooplankton were enumerated from the Lugol-preserved samples. “From 15 to 50 ml were settled for a minimum of 24 hours and then the whole slide inspected by inverted light microscopy. A Nikon inverted microscope mated to a computer digitizing system via a drawing tube was used to identify and measure microzooplankton cells and to convert linear dimensions to cell volumes using equations appropriate for individual cell shapes (Roff and Hopcroft, 1986). All ciliate and dinoflagellate cells in each sample were counted and sized. From 60 to 400 protist cells were counted and sized in each sample inspected. Cell biomass for dinoflagellates was estimated using an algorithm of Menden-Deuer and Lessard (2000) and for ciliates was estimated using the 0.19 pgC μm-3 value of Putt and Stoecker (1989). Ratios of heterotrophic dinoflagellate biomass, and of > 40 μm sized microzooplankton biomass, as a fraction of total microzooplankton biomass were also calculated.” Microzooplankton were enumerated by Celia Ross, under the direction of Evelyn and Barry Sherr, at Oregon State University. Fluorescence values from the fluorometer on the CTD were ground-truthed using the extracted chlorophyll a data; the chlorophyll fluorescence values reported here for each bottle are derived from those corrected values from the CTD fluorometer. Ashjian, C.J., Braund, S.R., Campbell, R.G., George, J.C., Kruse, J. Maslowski, W., Moore, S.E., Nicolson, C.R., Okkonen, S.R., Sherr, B.F., Sherr, E.B., Spitz, Y. 2010. Climate variability, oceanography, bowhead whale distribution, and I�upiat subsistence whaling near Barrow, AK. Arctic 63: 179-194. Menden-Deuer, S., Lessard, E., 2000. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnology and Oceanography 45, 569–579 Putt M., Stoecker D.K. 1989. An experimentally determined carbon: volume ratio for marine ‘‘oligotrichous’’ ciliates from estuarine and coastal waters. Limnology and Oceanography 34: 1097–1103. Roff J.C., Hopcroft R.R. 1986. High precision microcomputer based measuring system for ecological research. Canadian Journal of Fisheries and Aquatic Sciences 43: 2044–2048. Sherr, EB, Sherr, BF, Ross, C. Microzooplankton grazing impact in the Bering Sea during spring sea ice conditions. In review, Deep-Sea Research II. proprietary
+2010_hydgrographic_chlorophyll_cdom_fluor_opt_backscatt_data_coll_acro_tow_prof 2010 Hydgrographic, chlorophyll and CDOM fluorescence, and optical backscattering data collected using an Acrobat towed profiler SCIOPS STAC Catalog 2010-08-21 2010-08-31 -158, 71.3, -153.5, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214602406-SCIOPS.umm_json This data set contains the Acrobat files from data underway along transects conducted near Barrow, AK from August 21 - September 8, 2010. Details of the latitude, longitude, date, and time are listed in the event log that is archived at this site. Date /time (UTC day, decimal time), time, position, bottom depth, and measured variables are listed as separate columns in each file. Each Acrobat file is named according to the transect line sampled, the year, and the year day of data collection (e.g., line_2_2010_233.dat). Because of a leaky motor can, data could not be collected from all transects sampled during the AON work using the Acrobat. Data were collected from the R/V Annika Marie using an Acrobat (Sea Sciences Inc.) towed undulating vehicle equipped with a SeaBird SBE49 conductivity-temperature-depth (CTD) sensor, a Wetlabs EcoTriplet with chlorophyll and CDOM fluorescence and optical backscatter sensors, and a Wetlabs data logger system. Data were acquired in real time from near-surface (1-m) to a few meters off of the bottom or to a maximum depth of 60 m. The inter-profile distance was usually ~150 m over the shelf and ~1 km seaward of the shelf break. The CTD was calibrated pre-cruise. No correction of chlorophyll fluorescence was done as comparison with the extracted chlorophyll from accompanying Niskin bottle samples indicated that the factory calibration was very good. The WetLabs software that calculates density from the observed temperature and conductivity cannot do so at temperatures below 0°C and a value of -999.999 is returned. Therefore users of these data should re-calculate density. Units of chlorophyll and CDOM are µg/L. For optical backscattering, the particulate volume scattering coefficient at 117 degrees and 660 nm with the scattering of water at 117 degrees subtracted out is shown. proprietary
2010_niskin_bottle_data_chlorophyll_nutrients_picoplankton 2010 Niskin Bottle Data (chlorophyll, nutrients, picoplankton) ALL STAC Catalog 2010-08-21 2010-09-08 -158, 71.3, -153.5, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214602157-SCIOPS.umm_json Arctic Observing Network (AON) Annual Observations of the Biological and Physical Marine Environment in the Chukchi and near-shore Beaufort Seas near Barrow, AK. Carin Ashjian, Woods Hole Oceanographic Institution Robert Campbell, University of Rhode Island Stephen Okkonen, University of Alaska Fairbanks NISKIN BOTTLE DATA This data set contains the nutrient concentrations (PO4, NO2+NO3, SiO4, NO2, and NH4), total chlorophyll a concentration, the concentration of coccoid cyanobacteria, photosynthetic eukaryotes, and diatoms, and the abundances of protists (dinoflagellates and ciliates) as both cells/ml and as �g C/L as well as sample depth, position (latitude and longitude, date, station number, and temperature, salinity, and fluorescence for water samples collected using Niskin bottles during August and September 2010. More information regarding sample collection and the associated CTD casts numbers can be found in the event log for this cruise. Niskin bottles were deployed either just above the CTD (40 m) or by hand on a line over the side (0 m and 10 m samples) and tripped by messenger. Water was sampled immediately upon recovery of the Niskins. For chlorophyll a analysis, 100 ml of seawater was filtered onto GF-F glass fiber filters in triplicate for each bottle. Two hundred ml subsamples for determination of microzooplankton biomass and abundance were preserved with 5% final concentration acid Lugol solution for inverted microscopy. For flow cytometry samples, 3 ml aliquots were pipetted into 4 ml cryovials and preserved with 0.2% final concentration of freshly made paraformaldehyde. The samples were gently mixed and let sit in the dark at room temperature for 10 minutes before quick-freezing and storage -80 oC until flow cytometric analysis was performed. Analyses of nutrient, chlorophyll a, and flow cytometry samples followed methods described in Ashjian et al. (2010) that are reproduced below. Analysis of microzooplankton abundance followed methods described in Sherr et al. (in review) that are reproduced below. Nutrient and chlorophyll a samples were frozen in a -20�C freezer immediately after collection and transferred to a -80�C freezer within 6-8 hours. Water for the abundance of < 5 �m photosynthetic picoplankton by flow cytometry was drawn into 60 ml, brown bottles and kept cold for ~6-8 hours before being subsampled and frozen at -80�C. Chlorophyll a concentrations were analyzed within 2 months. “The filters were extracted in 6 ml of 90% acetone in 13 x 100 mm glass culture tubes at -20 oC for 18 to 24 hours. At the end of the extraction period, the filter was carefully removed from each tube, and the chlorophyll a concentration determined using a calibrated Turner Designs fluorometer. A solid chlorophyll a standard was used to check for fluorometer drift at the beginning of each reading of chlorophyll a samples. Extracted chlorophyll values were used to ground-truth the chlorophyll fluorescence sensors on the Acrobat and the CTD.” (Ashjian et al., 2010) “Nutrient analyses were performed using a hybrid Technicon AutoAnalyzer IITM and Alpkem RFA300TM system following protocols modified from Gordon et al. (1995). Standard curves with four different concentrations were run daily at the beginning and end of each run. Fresh standards were made prior to each run by diluting a primary standard with low-nutrient surface seawater. Triplicate deionized water blanks were analyzed at the beginning and end of each run to correct for any baseline shifts. In this protocol, the coefficients of variation for duplicates at low nutrient concentrations are typically < 1% (Fleischbein et al., 1999) while at high nutrient concentrations coefficients of variation are 2–3 % for nitrate and silicate (Corwith andWheeler, 2002). “ (Ashjian et al., 2010). Nutrient analyses were conducted by Joe Jennings at Oregon State University. “In the laboratory, samples for the abundance of < 5 �m photosynthetic microbes were thawed and kept on ice in a dark container until subsamples of 500 �l were enumerated on a Becton–Dickinson FACSCaliber flow cytometer with a 488-nm laser (Sherr et al. 2005). Populations of coccoid cyanobacteria and of photosynthetic eukaryotes were distinguished by differences in side light scatter (SSC) and by fluorescence in orange (cyanobacteria) and in red (eukaryotic phytoplankton) wavelengths. “ (Ashjian et al., 2010). Microzooplankton were enumerated from the Lugol-preserved samples. “From 15 to 50 ml were settled for a minimum of 24 hours and then the whole slide inspected by inverted light microscopy. A Nikon inverted microscope mated to a computer digitizing system via a drawing tube was used to identify and measure microzooplankton cells and to convert linear dimensions to cell volumes using equations appropriate for individual cell shapes (Roff and Hopcroft, 1986). All ciliate and dinoflagellate cells in each sample were counted and sized. From 60 to 400 protist cells were counted and sized in each sample inspected. Cell biomass for dinoflagellates was estimated using an algorithm of Menden-Deuer and Lessard (2000) and for ciliates was estimated using the 0.19 pgC μm-3 value of Putt and Stoecker (1989). Ratios of heterotrophic dinoflagellate biomass, and of > 40 μm sized microzooplankton biomass, as a fraction of total microzooplankton biomass were also calculated.” Microzooplankton were enumerated by Celia Ross, under the direction of Evelyn and Barry Sherr, at Oregon State University. Fluorescence values from the fluorometer on the CTD were ground-truthed using the extracted chlorophyll a data; the chlorophyll fluorescence values reported here for each bottle are derived from those corrected values from the CTD fluorometer. Ashjian, C.J., Braund, S.R., Campbell, R.G., George, J.C., Kruse, J. Maslowski, W., Moore, S.E., Nicolson, C.R., Okkonen, S.R., Sherr, B.F., Sherr, E.B., Spitz, Y. 2010. Climate variability, oceanography, bowhead whale distribution, and I�upiat subsistence whaling near Barrow, AK. Arctic 63: 179-194. Menden-Deuer, S., Lessard, E., 2000. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnology and Oceanography 45, 569–579 Putt M., Stoecker D.K. 1989. An experimentally determined carbon: volume ratio for marine ‘‘oligotrichous’’ ciliates from estuarine and coastal waters. Limnology and Oceanography 34: 1097–1103. Roff J.C., Hopcroft R.R. 1986. High precision microcomputer based measuring system for ecological research. Canadian Journal of Fisheries and Aquatic Sciences 43: 2044–2048. Sherr, EB, Sherr, BF, Ross, C. Microzooplankton grazing impact in the Bering Sea during spring sea ice conditions. In review, Deep-Sea Research II. proprietary
+2010_niskin_bottle_data_chlorophyll_nutrients_picoplankton 2010 Niskin Bottle Data (chlorophyll, nutrients, picoplankton) SCIOPS STAC Catalog 2010-08-21 2010-09-08 -158, 71.3, -153.5, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214602157-SCIOPS.umm_json Arctic Observing Network (AON) Annual Observations of the Biological and Physical Marine Environment in the Chukchi and near-shore Beaufort Seas near Barrow, AK. Carin Ashjian, Woods Hole Oceanographic Institution Robert Campbell, University of Rhode Island Stephen Okkonen, University of Alaska Fairbanks NISKIN BOTTLE DATA This data set contains the nutrient concentrations (PO4, NO2+NO3, SiO4, NO2, and NH4), total chlorophyll a concentration, the concentration of coccoid cyanobacteria, photosynthetic eukaryotes, and diatoms, and the abundances of protists (dinoflagellates and ciliates) as both cells/ml and as �g C/L as well as sample depth, position (latitude and longitude, date, station number, and temperature, salinity, and fluorescence for water samples collected using Niskin bottles during August and September 2010. More information regarding sample collection and the associated CTD casts numbers can be found in the event log for this cruise. Niskin bottles were deployed either just above the CTD (40 m) or by hand on a line over the side (0 m and 10 m samples) and tripped by messenger. Water was sampled immediately upon recovery of the Niskins. For chlorophyll a analysis, 100 ml of seawater was filtered onto GF-F glass fiber filters in triplicate for each bottle. Two hundred ml subsamples for determination of microzooplankton biomass and abundance were preserved with 5% final concentration acid Lugol solution for inverted microscopy. For flow cytometry samples, 3 ml aliquots were pipetted into 4 ml cryovials and preserved with 0.2% final concentration of freshly made paraformaldehyde. The samples were gently mixed and let sit in the dark at room temperature for 10 minutes before quick-freezing and storage -80 oC until flow cytometric analysis was performed. Analyses of nutrient, chlorophyll a, and flow cytometry samples followed methods described in Ashjian et al. (2010) that are reproduced below. Analysis of microzooplankton abundance followed methods described in Sherr et al. (in review) that are reproduced below. Nutrient and chlorophyll a samples were frozen in a -20�C freezer immediately after collection and transferred to a -80�C freezer within 6-8 hours. Water for the abundance of < 5 �m photosynthetic picoplankton by flow cytometry was drawn into 60 ml, brown bottles and kept cold for ~6-8 hours before being subsampled and frozen at -80�C. Chlorophyll a concentrations were analyzed within 2 months. “The filters were extracted in 6 ml of 90% acetone in 13 x 100 mm glass culture tubes at -20 oC for 18 to 24 hours. At the end of the extraction period, the filter was carefully removed from each tube, and the chlorophyll a concentration determined using a calibrated Turner Designs fluorometer. A solid chlorophyll a standard was used to check for fluorometer drift at the beginning of each reading of chlorophyll a samples. Extracted chlorophyll values were used to ground-truth the chlorophyll fluorescence sensors on the Acrobat and the CTD.” (Ashjian et al., 2010) “Nutrient analyses were performed using a hybrid Technicon AutoAnalyzer IITM and Alpkem RFA300TM system following protocols modified from Gordon et al. (1995). Standard curves with four different concentrations were run daily at the beginning and end of each run. Fresh standards were made prior to each run by diluting a primary standard with low-nutrient surface seawater. Triplicate deionized water blanks were analyzed at the beginning and end of each run to correct for any baseline shifts. In this protocol, the coefficients of variation for duplicates at low nutrient concentrations are typically < 1% (Fleischbein et al., 1999) while at high nutrient concentrations coefficients of variation are 2–3 % for nitrate and silicate (Corwith andWheeler, 2002). “ (Ashjian et al., 2010). Nutrient analyses were conducted by Joe Jennings at Oregon State University. “In the laboratory, samples for the abundance of < 5 �m photosynthetic microbes were thawed and kept on ice in a dark container until subsamples of 500 �l were enumerated on a Becton–Dickinson FACSCaliber flow cytometer with a 488-nm laser (Sherr et al. 2005). Populations of coccoid cyanobacteria and of photosynthetic eukaryotes were distinguished by differences in side light scatter (SSC) and by fluorescence in orange (cyanobacteria) and in red (eukaryotic phytoplankton) wavelengths. “ (Ashjian et al., 2010). Microzooplankton were enumerated from the Lugol-preserved samples. “From 15 to 50 ml were settled for a minimum of 24 hours and then the whole slide inspected by inverted light microscopy. A Nikon inverted microscope mated to a computer digitizing system via a drawing tube was used to identify and measure microzooplankton cells and to convert linear dimensions to cell volumes using equations appropriate for individual cell shapes (Roff and Hopcroft, 1986). All ciliate and dinoflagellate cells in each sample were counted and sized. From 60 to 400 protist cells were counted and sized in each sample inspected. Cell biomass for dinoflagellates was estimated using an algorithm of Menden-Deuer and Lessard (2000) and for ciliates was estimated using the 0.19 pgC μm-3 value of Putt and Stoecker (1989). Ratios of heterotrophic dinoflagellate biomass, and of > 40 μm sized microzooplankton biomass, as a fraction of total microzooplankton biomass were also calculated.” Microzooplankton were enumerated by Celia Ross, under the direction of Evelyn and Barry Sherr, at Oregon State University. Fluorescence values from the fluorometer on the CTD were ground-truthed using the extracted chlorophyll a data; the chlorophyll fluorescence values reported here for each bottle are derived from those corrected values from the CTD fluorometer. Ashjian, C.J., Braund, S.R., Campbell, R.G., George, J.C., Kruse, J. Maslowski, W., Moore, S.E., Nicolson, C.R., Okkonen, S.R., Sherr, B.F., Sherr, E.B., Spitz, Y. 2010. Climate variability, oceanography, bowhead whale distribution, and I�upiat subsistence whaling near Barrow, AK. Arctic 63: 179-194. Menden-Deuer, S., Lessard, E., 2000. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnology and Oceanography 45, 569–579 Putt M., Stoecker D.K. 1989. An experimentally determined carbon: volume ratio for marine ‘‘oligotrichous’’ ciliates from estuarine and coastal waters. Limnology and Oceanography 34: 1097–1103. Roff J.C., Hopcroft R.R. 1986. High precision microcomputer based measuring system for ecological research. Canadian Journal of Fisheries and Aquatic Sciences 43: 2044–2048. Sherr, EB, Sherr, BF, Ross, C. Microzooplankton grazing impact in the Bering Sea during spring sea ice conditions. In review, Deep-Sea Research II. proprietary
201104_imnavait_field 201104_Imnavait_field ALL STAC Catalog 2012-06-22 2012-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214601998-SCIOPS.umm_json Imnavait field campaign data from April 2011 proprietary
201104_imnavait_field 201104_Imnavait_field SCIOPS STAC Catalog 2012-06-22 2012-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214601998-SCIOPS.umm_json Imnavait field campaign data from April 2011 proprietary
201112000_1 Aurora Australis Trials Voyage 2011/12 Track and Underway Data AU_AADC STAC Catalog 2011-10-16 2011-10-21 147, -43.5, 147.5, -42.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214305620-AU_AADC.umm_json "On every voyage of the Aurora Australis, approximately 50 onboard sensors collect data on average every 10 seconds. These data are known as the underway datasets. The type of data collected include water and air temperature, wind speeds, ship speed and location, humidity, fluorescence, salinity and so on. For the full list of available data types, see the website. These data are broadcast ""live"" (every 30 minutes) back to Australia and are available via the Australian Oceanographic Data Centre's portal (see the provided link). Once the ship returns to port, the data are then transferred to Australian Antarctic Division servers where they are then made available via the Marine Science Data Search system (see the provided URL). This dataset contains the underway data collected during the Trials Voyage of the Aurora Australis Voyage in the 2011/12 season. Underway (meteorological) data are available online via the Australian Antarctic Division Data Centre web page (or via the Related URL section)." proprietary
@@ -421,14 +421,14 @@ id title catalog state_date end_date bbox url description license
20ec12f5d1f94e99aff2ed796264ee65_NA ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v4.0 FEDEO STAC Catalog 1997-01-01 2021-12-31 -180, 25, 180, 85 https://cmr.earthdata.nasa.gov/search/concepts/C3327359729-FEDEO.umm_json This dataset contains v4.0 permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m). Case A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year. proprietary
22254b5608ab430fa360d0ff7e71c34e_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Petermann glacier from ERS-1, ERS-2 and Envisat data for 1991-2010, v1.1 FEDEO STAC Catalog 1991-08-15 2010-06-01 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142729-FEDEO.umm_json This dataset contains a time series of ice velocities for the Petermann glacier in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 16/08/1991 and 01/06/2010. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 1 to 35 days are used. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland). proprietary
2282b4aeb9f24bc3a1e0961e4d545427_NA ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Along-Track Scanning Radiometer (ATSR) Level 3 Uncollated (L3U) Climate Data Record, version 2.1 FEDEO STAC Catalog 1991-11-01 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143215-FEDEO.umm_json This v2.1 SST_cci Along-Track Scanning Radiometer (ATSR) Level 3 Uncollated (L3U) Climate Data Record consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments. It covers the period between 11/1991 and 04/2012. The L3U products provide these SST data on a 0.05 regular latitude-longitude grid with with a single orbit per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR v2.0 and the Long Term product v1.1. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x proprietary
-234Th_data_0 234Th and POC data in the North Pacific ALL STAC Catalog 1997-11-12 2008-10-28 142.5, 35, 145, 57 https://cmr.earthdata.nasa.gov/search/concepts/C1667896877-SCIOPS.umm_json We had made time-series observations of 234Th and POC in the North Pacific. In this dataset, we present vertical profiles of 234Th, POC, PON, and Chlorophyll a in the North Pacific. These data will help further understanding of particle dynamics at the euphotic layer. proprietary
234Th_data_0 234Th and POC data in the North Pacific SCIOPS STAC Catalog 1997-11-12 2008-10-28 142.5, 35, 145, 57 https://cmr.earthdata.nasa.gov/search/concepts/C1667896877-SCIOPS.umm_json We had made time-series observations of 234Th and POC in the North Pacific. In this dataset, we present vertical profiles of 234Th, POC, PON, and Chlorophyll a in the North Pacific. These data will help further understanding of particle dynamics at the euphotic layer. proprietary
+234Th_data_0 234Th and POC data in the North Pacific ALL STAC Catalog 1997-11-12 2008-10-28 142.5, 35, 145, 57 https://cmr.earthdata.nasa.gov/search/concepts/C1667896877-SCIOPS.umm_json We had made time-series observations of 234Th and POC in the North Pacific. In this dataset, we present vertical profiles of 234Th, POC, PON, and Chlorophyll a in the North Pacific. These data will help further understanding of particle dynamics at the euphotic layer. proprietary
2457272c747f4d6ca33cb40833bd9cc2_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Zachariae and 79Fjord area from ERS-1, ERS-2 and Envisat data for 1991-2011, v1.1 FEDEO STAC Catalog 1991-07-31 2011-02-07 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142956-FEDEO.umm_json This dataset contains a time series of ice velocities for the Zachariae and 79Fjord area in Greenland derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 01/08/1991 and 07/02/2011. It provides components of the ice velocity and the magnitude of the velocity and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. The image pairs have a repeat cycle between 1 and 35 days. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland). proprietary
24dc5d5429434ccdb349db04a1a3233d_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Ice Velocity Map, Winter 2016-2017, v1.0 FEDEO STAC Catalog 2016-12-23 2017-02-27 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142785-FEDEO.umm_json This dataset provides an ice velocity map for the whole Greenland ice-sheet for the winter of 2016-2017, derived from Sentinel-1 SAR data acquired from 23/12/2016 to 27/02/2017, as part of the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project. In total approximately 1800 S-1A & S-1B scenes are used to derive the surface velocity applying feature tracking techniques. The ice velocity map is provided at 500m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity is provided in true meters per day, towards EASTING(vx) and NORTHING(vy) direction of the grid, and the vertical displacement (vz), derived from a digital elevation model is also provided. The product was generated by ENVEO (Earth Observation Information Technology GmbH). proprietary
2785ee1ec6274be39d11e7e7ce51b381_NA ESA Sea Level Climate Change Initiative (Sea_Level_cci): Fundamental Climate Data Records of sea level anomalies and altimeter standards, Version 2.0 FEDEO STAC Catalog 1993-01-01 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142554-FEDEO.umm_json As part of the European Space Agency's (ESA) Sea Level Climate Change Initiative (CCI) Project, Fundamental Climate Data Records (FCDRs) have been computed for all the altimeter missions used within the project. These FCDR's consist of along track values of sea level anomalies and altimeter standards for the period between 1993 and 2015. This version of the product is v2.0.The FCDR's are mono-mission products, derived from the respective altimeter level-2 products. They have been produced along the tracks of the different altimeters, with a resolution of 1Hz, corresponding to a ground distance close to 6km. The dataset is separated by altimeter mission, and divided into files by altimetric cycle corresponding to the repetivity of the mission. When using or referring to the Sea Level cci products, please mention the associated DOIs and also use the following citation where a detailed description of the Sea Level_cci project and products can be found:Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.: Improved sea level record over the satellite altimetry era (1993â2010) from the Climate Change Initiative project, Ocean Sci., 11, 67-82, doi:10.5194/os-11-67-2015, 2015.For further information on the Sea Level CCI products, and to register for these projects please email: info-sealevel@esa-sealevel-cci.org proprietary
27fc79c6e65f4302a18ec9788605c246_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Hagen glacier from ERS-1, ERS-2 and Envisat data for 1991-2010, v1.1 FEDEO STAC Catalog 1991-08-25 2010-05-07 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142954-FEDEO.umm_json This dataset contains a time series of ice velocities for the Hagen glacier in Greenland, derived from intensity-tracking of ERS-1, ERS-2 and Envisat data acquired between 26/08/1991 and 7/5/2010. It provides components of the ice velocity and the magnitude of the velocity, and has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 500m grid spacing. Image pairs with a repeat cycle of 6 to 35 days are used. The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by GEUS (Geological Survey of Denmark and Greenland). proprietary
-28458e44db959dd2b1e920457964665327a333f6 3 year daily average solar exposure map Mali 3Km GRAS December 2008-2011 ALL STAC Catalog 1970-01-01 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214603938-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for December. proprietary
28458e44db959dd2b1e920457964665327a333f6 3 year daily average solar exposure map Mali 3Km GRAS December 2008-2011 SCIOPS STAC Catalog 1970-01-01 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214603938-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for December. proprietary
+28458e44db959dd2b1e920457964665327a333f6 3 year daily average solar exposure map Mali 3Km GRAS December 2008-2011 ALL STAC Catalog 1970-01-01 -15, 8, 5, 28 https://cmr.earthdata.nasa.gov/search/concepts/C1214603938-SCIOPS.umm_json This map contains the 3 year (2008-2011) daily average solar exposure (in Kmh/m2/day) with a resolution of 3Km for Mali for December. proprietary
2940cda8-cf01-490a-a7ab-688bd54fb56a Earthquake Risk-Probable Maximum Losses CEOS_EXTRA STAC Catalog 2012-01-01 2013-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848573-CEOS_EXTRA.umm_json The map (risk map) presents the results of earthquake probable maximum loss (PML) per country at global level. The probabilistic risk assessment results were obtained from analitical formulation on CAPRA platform. Values for this map are expresed on UDS millions (PML-absolute value) and percentage (PML/VALFIS-Exposed physical value), also include population count per country (VALHUM), VALFIS and VALHUM values are derived from Global Exposure Database 2013 (GED) implemented by UNIGE with support of ERN-AL. proprietary
294b4075ddbc4464bb06742816813bdc_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CO2 from SCIAMACHY generated with the BESD algorithm (CO2_SCI_BESD), v02.01.02 FEDEO STAC Catalog 2003-01-08 2012-03-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142508-FEDEO.umm_json The CO2_SCI_BESD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (CO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) instrument on board the European Space Agency's (ESA's) environmental research satellite ENVISAT. It has been produced using the Bremen Optimal Estimation DOAS (BESD) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project.The Bremen Optimal Estimation DOAS (BESD) algorithm is a full physics algorithm which uses measurements in the O2-A absorption band to retrieve scattering information about clouds and aerosols. This is the Greenhouse Gases CCI baseline algorithm for deriving SCIAMACHY XCO2 data. A product has also been generated from the SCIAMACHY data using an alternative algorithm: the WFMD algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this BESD product. For more information regarding the differences between baseline and alternative algorithms please see the Greenhouse Gases CCI data products webpage.For further information on the product, including details of the BESD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents. proprietary
296f4386-4af1-4a73-866c-d9192ec18685_NA MERIS - Water Parameters - North Sea, 10-Day FEDEO STAC Catalog 2006-01-01 2010-03-10 -6.10393, 49.9616, 11.4301, 61.9523 https://cmr.earthdata.nasa.gov/search/concepts/C2207458047-FEDEO.umm_json The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of the North Sea derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides 10-day maps. proprietary
@@ -438,8 +438,8 @@ id title catalog state_date end_date bbox url description license
2e54b40f184b44c797db36e192d2b679_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the Jakobshavn Glacier from COSMO-SkyMed for 2012-2014, v1.0 FEDEO STAC Catalog 2012-06-01 2014-12-25 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142500-FEDEO.umm_json This dataset contains ice velocity time series of then Jakobshavn glacier in Greenland, derived from intensity-tracking of COSMO-SkyMed data acquired between 2/6/2012 and 25/12/2014. The ice velocity data is derived using 4-day COSMO-SkyMed offset-tracking pairs. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG3413: Latitude of true scale 70N, Reference Longitude 45E) with 250m grid spacing. Image pairs with a repeat cycle of 4 days have been used.The horizontal velocity is provided in true meters per day, towards EASTING(x) and NORTHING(y) direction of the grid, and the vertical displacement (z), derived from a digital elevation model, is also provided.The product was generated by DTU Space. For further details, please consult the document:T. Nagler, et al., Product User Guide (PUG) for the Greenland_Ice_Sheet_cci project of ESA's Climate Change Initiative, version 2.0. proprietary
2e656d34d016414c8d6bced18634772c_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from the Multi-Sensor UV Absorbing Aerosol Index (MS UVAI) algorithm, Version 1.7 FEDEO STAC Catalog 1978-11-01 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142580-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 Absorbing Aerosol Index (AAI) products, using the Multi-Sensor UVAI algorithm, Version 1.7. L3 products are provided as daily and monthly gridded products as well as a monthly climatology. For further details about these data products please see the linked documentation. proprietary
2f423ac3eb244567a12b283894b869de_NA ESA Cloud Climate Change Initiative (Cloud_cci): MERIS+AATSR monthly gridded cloud properties, Version 2.0 FEDEO STAC Catalog 2003-01-01 2011-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143246-FEDEO.umm_json The Cloud_cci MERIS+AATSR dataset was generated within the Cloud_cci project (http://www.esa-cloud-cci.org) which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. This dataset is based on MERIS and AATSR (onboard ENVISAT) measurements and contains a variety of cloud properties which were derived employing the Freie Universität Berlin AATSR MERIS Cloud (FAME-C) retrieval system. The core cloud properties contained in the Cloud_cci MERIS+AATSR dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. Level-3C product files contain monthly averages and histograms of the mentioned cloud properties together with propagated uncertainty measures. proprietary
-3-hourly_interpolated_buoy_data 3-Hourly Interpolated Buoy Data ALL STAC Catalog 2004-01-01 2005-12-01 -180, 45, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600640-SCIOPS.umm_json Raw observations position, sea level pressure and air temperature are interpolated to 3-hourly intervals. proprietary
3-hourly_interpolated_buoy_data 3-Hourly Interpolated Buoy Data SCIOPS STAC Catalog 2004-01-01 2005-12-01 -180, 45, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600640-SCIOPS.umm_json Raw observations position, sea level pressure and air temperature are interpolated to 3-hourly intervals. proprietary
+3-hourly_interpolated_buoy_data 3-Hourly Interpolated Buoy Data ALL STAC Catalog 2004-01-01 2005-12-01 -180, 45, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600640-SCIOPS.umm_json Raw observations position, sea level pressure and air temperature are interpolated to 3-hourly intervals. proprietary
3-hourly_interpolated_buoy_data_2004 3-Hourly Interpolated Buoy Data: 2004 SCIOPS STAC Catalog 2008-09-13 2009-03-31 -87.445, 85.214, -87.445, 85.214 https://cmr.earthdata.nasa.gov/search/concepts/C1214600589-SCIOPS.umm_json This data set contains raw observations position, sea level pressure and air temperature data interpolated to 3-hourly intervals for 2004. proprietary
3-hourly_interpolated_buoy_data_2004 3-Hourly Interpolated Buoy Data: 2004 ALL STAC Catalog 2008-09-13 2009-03-31 -87.445, 85.214, -87.445, 85.214 https://cmr.earthdata.nasa.gov/search/concepts/C1214600589-SCIOPS.umm_json This data set contains raw observations position, sea level pressure and air temperature data interpolated to 3-hourly intervals for 2004. proprietary
302939d341fa4013b6d96d231d6d4f40_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (ADV algorithm), Version 2.31 FEDEO STAC Catalog 1995-06-01 2003-04-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142616-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the ATSR-2 instrument on the ERS-2 satellite, derived using the ADV algorithm, version 2.31. It covers the period from 1995-2003.For further details about these data products please see the linked documentation. proprietary
@@ -462,10 +462,10 @@ id title catalog state_date end_date bbox url description license
39094_Not Applicable Average Seasonal Chlorophyll Geotifs of Stellwagen Bank National Marine Sanctuary NOAA_NCEI STAC Catalog 1998-01-01 2005-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656220-NOAA_NCEI.umm_json Average seasonal Chlorophyll imagery - Each image represents one three month season proprietary
39206_Not Applicable Benthic Habitat Maps of the U.S. Virgin Islands-St. Croix Prepared by Visual Interpretation from Remote Sensing Imagery Collected by NOAA, 1999 NOAA_NCEI STAC Catalog 1999-01-01 2001-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656111-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment;the United States Geological Survey; the National Park Service; and the National Geophysical Data Center to produce benthic habitat maps and georeferenced imagery for Puerto Rico and the U.S. Virgin Islands. This project was conducted in support of the U.S. Coral Reef Task Force.Twenty-one distinct benthic habitat types within eight zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs. Benthic features were mapped that covered an area of 1600 km^2. In all, 49 km^2 of unconsolidated sediment, 721 km^2 of submerged vegetation, 73 km^2 of mangroves, and 756 km^2 of coral reef and colonized hardbottom were mapped. proprietary
39207_Not Applicable Benthic Habitat Maps of the U.S. Virgin Islands-St. Thomas and St. John Prepared by Visual Interpretation from Remote Sensing Imagery Collected by NOAA, 1999 NOAA_NCEI STAC Catalog 1999-01-01 2001-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656120-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the United States Geological Survey; the National Park Service; and the National Geophysical Data Center, to produce benthic habitat maps and georeferenced imagery for Puerto Rico and the U.S. Virgin Islands. This project was conducted in support of the U.S. Coral Reef Task Force.Twenty-one distinct benthic habitat types within eight zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs. Benthic features were mapped that covered an area of 1600 km^2. In all, 49 km^2 of unconsolidated sediment, 721 km^2 of submerged vegetation, 73 km^2 of mangroves, and 756 km^2 of coral reef and colonized hardbottom were mapped. proprietary
-39234_Not Applicable Agrihan Island IKONOS Imagery - IKONOS Imagery for the Northern Mariana Islands, 2001-2003 NOAA_NCEI STAC Catalog 2000-01-01 2003-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656342-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer. proprietary
39234_Not Applicable Agrihan Island IKONOS Imagery - IKONOS Imagery for the Northern Mariana Islands, 2001-2003 ALL STAC Catalog 2000-01-01 2003-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656342-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer. proprietary
-39235_Not Applicable Aguijan Island IKONOS Imagery - IKONOS Imagery for the Northern Mariana Islands, 2001-2003 ALL STAC Catalog 2000-01-01 2003-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656351-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer. proprietary
+39234_Not Applicable Agrihan Island IKONOS Imagery - IKONOS Imagery for the Northern Mariana Islands, 2001-2003 NOAA_NCEI STAC Catalog 2000-01-01 2003-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656342-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer. proprietary
39235_Not Applicable Aguijan Island IKONOS Imagery - IKONOS Imagery for the Northern Mariana Islands, 2001-2003 NOAA_NCEI STAC Catalog 2000-01-01 2003-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656351-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer. proprietary
+39235_Not Applicable Aguijan Island IKONOS Imagery - IKONOS Imagery for the Northern Mariana Islands, 2001-2003 ALL STAC Catalog 2000-01-01 2003-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656351-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer. proprietary
39236_Not Applicable Alamagan Island IKONOS Imagery - IKONOS Imagery for the Northern Mariana Islands, 2001-2003 NOAA_NCEI STAC Catalog 2000-01-01 2003-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656363-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer. proprietary
39236_Not Applicable Alamagan Island IKONOS Imagery - IKONOS Imagery for the Northern Mariana Islands, 2001-2003 ALL STAC Catalog 2000-01-01 2003-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656363-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer. proprietary
39238_Not Applicable Anatahan Island IKONOS Imagery - IKONOS Imagery for the Northern Mariana Islands, 2001-2003 NOAA_NCEI STAC Catalog 2000-01-01 2003-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656385-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. IKONOS imagery was purchased to support the Pacific Islands Geographic Information System (GIS) project and the National Ocean Service's (NOS) coral mapping activities. One-meter panchromatic and four-meter multi-spectral data were purchased for each study area. The enhanced spectral resolution of multispectral imagery and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The IKONOS imagery was processed to minimize atmospheric and water column effects. Photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer. proprietary
@@ -501,8 +501,8 @@ id title catalog state_date end_date bbox url description license
39324_Not Applicable California halibut habitat suitability model for Channel Islands National Marine Sanctuary Biogeographic Assessment NOAA_NCEI STAC Catalog 2006-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656444-NOAA_NCEI.umm_json The Office of National Marine Sanctuaries (ONMS) is updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, ONMS and NOAA's National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS). proprietary
39326_Not Applicable Benthic Habitats of the Main Hawaiian Islands Prepared by Visual Interpretation from Remote Sensing Imagery Collected by NOAA Year 2000: Hawaii NOAA_NCEI STAC Catalog 2001-01-01 2002-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656248-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography, hyperspectral and IKONOS satellite imagery. Twenty-seven distinct benthic habitat types within eleven zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs and hyperspectral imagery. Benthic features were mapped that covered an area of 790 km^2. In all, 204 km^2 of unconsolidated sediment, 171 km^2 of submerged vegetation, and 415 km^2 of coral reef and colonized hardbottom were mapped. proprietary
39330_Not Applicable Benthic Habitats of Hawaii Derived From IKONOS and Quick Bird Satellite Imagery, 2004-2007 NOAA_NCEI STAC Catalog 2004-01-01 2007-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656291-NOAA_NCEI.umm_json This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of the Main Eight Hawaiian Islands by visual interpretation and manual delineation of IKONOS and Quick Bird satellite imagery.A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes fourteen zones. proprietary
-39332_Not Applicable 2000 Photo Mosaics and Hyperspectral Imagery for the Main Eight Hawaiian Islands Utilized to Map Shallow Water Benthic Habitats NOAA_NCEI STAC Catalog 2000-01-01 2000-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656309-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography and hyperspectral imagery. Aerial photographs were acquired for the Main Eight Hawaiian Islands Benthic Mapping Project in 2000 by NOAA Aircraft Operation Centers aircraft and National Geodetic Survey cameras and personnel. Approximately 1,500, color, 9 by 9 inch photos were taken of the coastal waters of the Main Eight Hawaiian Islands at 1:24,000 scale. Specific sun angle and maximum percent cloud cover restrictions were adhered to when possible during photography missions to ensure collection of high quality imagery for the purpose of benthic mapping. In addition, consecutive photos were taken at 60 percent overlap on individual flight lines and 30 percent overlap on adjacent flight lines to allow for orthorectification and elimination of sun glint. The enhanced spectral resolution of hyperspectral and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The AURORA hyperspectral imaging system collected 72 ten nm bands in visible and near infrared spectral range with a 3 meter pixel resolution. The data was processed to select band widths, which optimized feature detection in shallow and deep water. The digital scans of aerial photos and hyperspectral imagery were orthorectified to eliminate sources of spatial distortion. With these orthorectified images photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer. proprietary
39332_Not Applicable 2000 Photo Mosaics and Hyperspectral Imagery for the Main Eight Hawaiian Islands Utilized to Map Shallow Water Benthic Habitats ALL STAC Catalog 2000-01-01 2000-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656309-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography and hyperspectral imagery. Aerial photographs were acquired for the Main Eight Hawaiian Islands Benthic Mapping Project in 2000 by NOAA Aircraft Operation Centers aircraft and National Geodetic Survey cameras and personnel. Approximately 1,500, color, 9 by 9 inch photos were taken of the coastal waters of the Main Eight Hawaiian Islands at 1:24,000 scale. Specific sun angle and maximum percent cloud cover restrictions were adhered to when possible during photography missions to ensure collection of high quality imagery for the purpose of benthic mapping. In addition, consecutive photos were taken at 60 percent overlap on individual flight lines and 30 percent overlap on adjacent flight lines to allow for orthorectification and elimination of sun glint. The enhanced spectral resolution of hyperspectral and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The AURORA hyperspectral imaging system collected 72 ten nm bands in visible and near infrared spectral range with a 3 meter pixel resolution. The data was processed to select band widths, which optimized feature detection in shallow and deep water. The digital scans of aerial photos and hyperspectral imagery were orthorectified to eliminate sources of spatial distortion. With these orthorectified images photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer. proprietary
+39332_Not Applicable 2000 Photo Mosaics and Hyperspectral Imagery for the Main Eight Hawaiian Islands Utilized to Map Shallow Water Benthic Habitats NOAA_NCEI STAC Catalog 2000-01-01 2000-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656309-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography and hyperspectral imagery. Aerial photographs were acquired for the Main Eight Hawaiian Islands Benthic Mapping Project in 2000 by NOAA Aircraft Operation Centers aircraft and National Geodetic Survey cameras and personnel. Approximately 1,500, color, 9 by 9 inch photos were taken of the coastal waters of the Main Eight Hawaiian Islands at 1:24,000 scale. Specific sun angle and maximum percent cloud cover restrictions were adhered to when possible during photography missions to ensure collection of high quality imagery for the purpose of benthic mapping. In addition, consecutive photos were taken at 60 percent overlap on individual flight lines and 30 percent overlap on adjacent flight lines to allow for orthorectification and elimination of sun glint. The enhanced spectral resolution of hyperspectral and control of bandwidths of multispectral data yield an advantage over color aerial photography particularly when coral health and time series analysis of coral reef community structure are of interest. The AURORA hyperspectral imaging system collected 72 ten nm bands in visible and near infrared spectral range with a 3 meter pixel resolution. The data was processed to select band widths, which optimized feature detection in shallow and deep water. The digital scans of aerial photos and hyperspectral imagery were orthorectified to eliminate sources of spatial distortion. With these orthorectified images photointerpreters can accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor using a software interface such as the Habitat Digitizer. proprietary
39348_Not Applicable Benthic Habitats of Kahoolawe Derived From IKONOS and Quick Bird Satellite Imagery, 2004-2007 NOAA_NCEI STAC Catalog 2004-01-01 2007-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656288-NOAA_NCEI.umm_json This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of the Main Eight Hawaiian Islands by visual interpretation and manual delineation of IKONOS and Quick Bird satellite imagery.A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes fourteen zones. proprietary
39351_Not Applicable Benthic Habitats of the Main Hawaiian Islands Prepared by Visual Interpretation from Remote Sensing Imagery Collected by NOAA Year 2000: Kauai NOAA_NCEI STAC Catalog 2001-01-01 2002-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656325-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography, hyperspectral and IKONOS satellite imagery. Twenty-seven distinct benthic habitat types within eleven zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs and hyperspectral imagery. Benthic features were mapped that covered an area of 790 km^2. In all, 204 km^2 of unconsolidated sediment, 171 km^2 of submerged vegetation, and 415 km^2 of coral reef and colonized hardbottom were mapped. proprietary
39354_Not Applicable Benthic Habitats of Kauai Derived From IKONOS and Quick Bird Satellite Imagery, 2004-2006 NOAA_NCEI STAC Catalog 2004-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656358-NOAA_NCEI.umm_json This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of the Main Eight Hawaiian Islands by visual interpretation and manual delineation of IKONOS and Quick Bird satellite imagery.A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes fourteen zones. proprietary
@@ -513,8 +513,8 @@ id title catalog state_date end_date bbox url description license
39368_Not Applicable Accuracy Assessment Field Data for the Mariana Archipelago NOAA_NCEI STAC Catalog 2003-01-01 2004-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656313-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; BAE Systems Spectral Solutions; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to incorporate previously developed mapping methods to produce coral reef habitat maps for American Samoa, Guam and the Commonwealth of the Northern Mariana Islands. GPS field observations were used to establish the thematic accuracy of this thematic product. 1113 benthic habitat characterizations were completed for this work. proprietary
39375_Not Applicable Benthic Habitats of the Main Hawaiian Islands Prepared by Visual Interpretation from Remote Sensing Imagery Collected by NOAA Year 2000: Maui NOAA_NCEI STAC Catalog 2001-01-01 2002-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656394-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography, hyperspectral and IKONOS satellite imagery. Twenty-seven distinct benthic habitat types within eleven zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs and hyperspectral imagery. Benthic features were mapped that covered an area of 790 km^2. In all, 204 km^2 of unconsolidated sediment, 171 km^2 of submerged vegetation, and 415 km^2 of coral reef and colonized hardbottom were mapped. proprietary
39379_Not Applicable Benthic Habitat of Maui Derived From IKONOS and Quick Bird Satellite Imagery, 2004-2006 NOAA_NCEI STAC Catalog 2004-01-01 2007-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656453-NOAA_NCEI.umm_json This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of the Main Eight Hawaiian Islands by visual interpretation and manual delineation of IKONOS and Quick Bird satellite imagery.A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes fourteen zones. proprietary
-39383_Not Applicable Accuracy Assessment Field Data for the Main Eight Hawaiian Islands UTM Zone 4 NOAA_NCEI STAC Catalog 2004-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656290-NOAA_NCEI.umm_json This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to incorporate previously developed mapping methods to produce coral reef habitat maps for the Main Eight Hawaiian Islands. GPS field observations were used to establish the thematic accuracy of this thematic product. 638 benthic habitat characterizations were completed in UTM Zone 4 for this work. proprietary
39383_Not Applicable Accuracy Assessment Field Data for the Main Eight Hawaiian Islands UTM Zone 4 ALL STAC Catalog 2004-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656290-NOAA_NCEI.umm_json This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to incorporate previously developed mapping methods to produce coral reef habitat maps for the Main Eight Hawaiian Islands. GPS field observations were used to establish the thematic accuracy of this thematic product. 638 benthic habitat characterizations were completed in UTM Zone 4 for this work. proprietary
+39383_Not Applicable Accuracy Assessment Field Data for the Main Eight Hawaiian Islands UTM Zone 4 NOAA_NCEI STAC Catalog 2004-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656290-NOAA_NCEI.umm_json This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to incorporate previously developed mapping methods to produce coral reef habitat maps for the Main Eight Hawaiian Islands. GPS field observations were used to establish the thematic accuracy of this thematic product. 638 benthic habitat characterizations were completed in UTM Zone 4 for this work. proprietary
39384_Not Applicable Accuracy Assessment Field Data for the Main Eight Hawaiian Islands UTM Zone 5 NOAA_NCEI STAC Catalog 2004-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656301-NOAA_NCEI.umm_json This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to incorporate previously developed mapping methods to produce coral reef habitat maps for the Main Eight Hawaiian Islands. GPS field observations were used to establish the thematic accuracy of this thematic product. 39 benthic habitat characterizations were completed in UTM Zone 5 for this work. proprietary
39384_Not Applicable Accuracy Assessment Field Data for the Main Eight Hawaiian Islands UTM Zone 5 ALL STAC Catalog 2004-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656301-NOAA_NCEI.umm_json This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to incorporate previously developed mapping methods to produce coral reef habitat maps for the Main Eight Hawaiian Islands. GPS field observations were used to establish the thematic accuracy of this thematic product. 39 benthic habitat characterizations were completed in UTM Zone 5 for this work. proprietary
39392_Not Applicable Benthic Habitats of the Main Hawaiian Islands Prepared by Visual Interpretation from Remote Sensing Imagery Collected by NOAA Year 2000: Molokai NOAA_NCEI STAC Catalog 2001-01-01 2002-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656347-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography, hyperspectral and IKONOS satellite imagery. Twenty-seven distinct benthic habitat types within eleven zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs and hyperspectral imagery. Benthic features were mapped that covered an area of 790 km^2. In all, 204 km^2 of unconsolidated sediment, 171 km^2 of submerged vegetation, and 415 km^2 of coral reef and colonized hardbottom were mapped. proprietary
@@ -525,8 +525,8 @@ id title catalog state_date end_date bbox url description license
39411_Not Applicable Benthic Habitat of Oahu Derived From IKONOS and Quick Bird Satellite Imagery, 2004-2006 NOAA_NCEI STAC Catalog 2003-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656359-NOAA_NCEI.umm_json This project is a cooperative effort between the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, BAE Systems Spectral Solutions and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of the Main Eight Hawaiian Islands by visual interpretation and manual delineation of IKONOS and Quick Bird satellite imagery.A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes fourteen zones. proprietary
39413_Not Applicable Benthic Habitats of the Main Hawaiian Islands Prepared by Visual Interpretation from Remote Sensing Imagery Collected by NOAA Year 2000: Oahu (Section 1) NOAA_NCEI STAC Catalog 2001-01-01 2002-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656383-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment, the University of Hawaii, and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography, hyperspectral and IKONOS satellite imagery. Twenty-seven distinct benthic habitat types within eleven zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs and hyperspectral imagery. Benthic features were mapped that covered an area of 790 km^2. In all, 204 km^2 of unconsolidated sediment, 171 km^2 of submerged vegetation, and 415 km^2 of coral reef and colonized hardbottom were mapped. proprietary
39414_Not Applicable Benthic Habitats of the Main Hawaiian Islands Prepared by Visual Interpretation from Remote Sensing Imagery Collected by NOAA Year 2000: Oahu (Section 2) NOAA_NCEI STAC Catalog 2001-01-01 2002-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656395-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to develop coral reef mapping methods and compare benthic habitat maps generated by photointerpreting georeferenced color aerial photography, hyperspectral and IKONOS satellite imagery. Twenty-seven distinct benthic habitat types within eleven zones were mapped directly into a GIS system using visual interpretation of orthorectified aerial photographs and hyperspectral imagery. Benthic features were mapped that covered an area of 790 km^2. In all, 204 km^2 of unconsolidated sediment, 171 km^2 of submerged vegetation, and 415 km^2 of coral reef and colonized hardbottom were mapped. proprietary
-39423_Not Applicable Accuracy Assessment Field Data for Benthic Habitat Maps of Palau ALL STAC Catalog 2006-01-01 2007-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656456-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; IMSG; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to incorporate previously developed mapping methods to produce coral reef habitat maps for The Republic of Palau. GPS field observations were used to establish the thematic accuracy of this thematic product. 623 benthic habitat characterizations were completed in UTM Zone 53N for this work. proprietary
39423_Not Applicable Accuracy Assessment Field Data for Benthic Habitat Maps of Palau NOAA_NCEI STAC Catalog 2006-01-01 2007-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656456-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; IMSG; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to incorporate previously developed mapping methods to produce coral reef habitat maps for The Republic of Palau. GPS field observations were used to establish the thematic accuracy of this thematic product. 623 benthic habitat characterizations were completed in UTM Zone 53N for this work. proprietary
+39423_Not Applicable Accuracy Assessment Field Data for Benthic Habitat Maps of Palau ALL STAC Catalog 2006-01-01 2007-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656456-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; IMSG; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to incorporate previously developed mapping methods to produce coral reef habitat maps for The Republic of Palau. GPS field observations were used to establish the thematic accuracy of this thematic product. 623 benthic habitat characterizations were completed in UTM Zone 53N for this work. proprietary
39425_Not Applicable Benthic Habitats of Palau Derived From IKONOS Imagery, 2003-2006 NOAA_NCEI STAC Catalog 2003-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656477-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of Palau by visual interpretation and manual delineation of IKONOS satellite imagery. A two tiered habitat classification system was used in this work. The scheme integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes thirteen zones. proprietary
39426_Not Applicable Benthic Habitats of Puerto Rico and the U.S. Virgin Islands;Photomosaic of Puerto Rico (Arroyo), 1999 NOAA_NCEI STAC Catalog 1999-02-01 1999-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656497-NOAA_NCEI.umm_json Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor. proprietary
39427_Not Applicable Benthic Habitats of Puerto Rico and the U.S. Virgin Islands;Photomosaic of Puerto Rico (Barcelon), 1999 NOAA_NCEI STAC Catalog 1999-02-01 1999-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656459-NOAA_NCEI.umm_json Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor. proprietary
@@ -548,16 +548,16 @@ id title catalog state_date end_date bbox url description license
39459_Not Applicable Benthic Habitats of Puerto Rico and the U.S. Virgin Islands;Photomosaic of Puerto Rico (Rincon), 1999 NOAA_NCEI STAC Catalog 1999-02-01 1999-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656675-NOAA_NCEI.umm_json Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor. proprietary
39460_Not Applicable Benthic Habitats of Puerto Rico and the U.S. Virgin Islands;Photomosaic of Puerto Rico (Salinas), 1999 NOAA_NCEI STAC Catalog 1999-02-01 1999-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656706-NOAA_NCEI.umm_json Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor. proprietary
39461_Not Applicable Benthic Habitats of Puerto Rico and the U.S. Virgin Islands;Photomosaic of Puerto Rico (San Juan), 1999 NOAA_NCEI STAC Catalog 1999-02-01 1999-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656736-NOAA_NCEI.umm_json Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor. proprietary
-39462_Not Applicable 1999 Photomosaics of Puerto Rico and U.S. Virgin Islands Utilized to Map Shallow Water Benthic Habitats of the Region NOAA_NCEI STAC Catalog 1999-02-01 1999-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656765-NOAA_NCEI.umm_json Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However, spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor. proprietary
39462_Not Applicable 1999 Photomosaics of Puerto Rico and U.S. Virgin Islands Utilized to Map Shallow Water Benthic Habitats of the Region ALL STAC Catalog 1999-02-01 1999-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656765-NOAA_NCEI.umm_json Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However, spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor. proprietary
+39462_Not Applicable 1999 Photomosaics of Puerto Rico and U.S. Virgin Islands Utilized to Map Shallow Water Benthic Habitats of the Region NOAA_NCEI STAC Catalog 1999-02-01 1999-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656765-NOAA_NCEI.umm_json Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However, spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor. proprietary
39480_Not Applicable 1988 Mosaic of Aerial Photography of the Salt River Bay National Historical Park and Ecological Preserve NOAA_NCEI STAC Catalog 1988-11-24 1988-11-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656753-NOAA_NCEI.umm_json Aerial photographs taken by NOAA's National Geodetic Survey during 1988 were mosaicked and orthorectified by the Biogeography Branch. The resulting image was used to digitize benthic, land cover and mangrove habitat maps of the Salt River Bay National Historic Park and Ecological Preserve (National Park Service), on St. Croix, in the U.S. Virgin Islands.The mosaic is centered on the National Park Service Site, located on the north central coast of St. Croix, and extends beyond the park boundaries approximately 0.5 - 4.0 km. proprietary
39480_Not Applicable 1988 Mosaic of Aerial Photography of the Salt River Bay National Historical Park and Ecological Preserve ALL STAC Catalog 1988-11-24 1988-11-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656753-NOAA_NCEI.umm_json Aerial photographs taken by NOAA's National Geodetic Survey during 1988 were mosaicked and orthorectified by the Biogeography Branch. The resulting image was used to digitize benthic, land cover and mangrove habitat maps of the Salt River Bay National Historic Park and Ecological Preserve (National Park Service), on St. Croix, in the U.S. Virgin Islands.The mosaic is centered on the National Park Service Site, located on the north central coast of St. Croix, and extends beyond the park boundaries approximately 0.5 - 4.0 km. proprietary
39481_Not Applicable 1988 Seagrass and Mangrove Habitats of the Salt River Bay National Historical Park and Ecological Preserve NOAA_NCEI STAC Catalog 1988-11-24 1988-11-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656462-NOAA_NCEI.umm_json Habitat maps were created as part of a larger ecological assessment conducted by NOAA's National Ocean Service (NOS), Biogeography Branch, for Salt River Bay National Historic Park and Ecological Preserve (National Park Service).Aerial photographs were obtained for 1988 from the National Geodetic Survey, and were orthorectified by the Biogeography Branch. A classification scheme was set up with 20 benthic habitat types, 19 land cover types, and 13 mangrove habitat types. For this map of seagrass and mangrove habitats during 1988 only the 3 seagrass, and 14 mangrove classification categories were used. These were mapped directly into a GIS system through visual interpretation of orthorectified aerial photographs. proprietary
39481_Not Applicable 1988 Seagrass and Mangrove Habitats of the Salt River Bay National Historical Park and Ecological Preserve ALL STAC Catalog 1988-11-24 1988-11-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656462-NOAA_NCEI.umm_json Habitat maps were created as part of a larger ecological assessment conducted by NOAA's National Ocean Service (NOS), Biogeography Branch, for Salt River Bay National Historic Park and Ecological Preserve (National Park Service).Aerial photographs were obtained for 1988 from the National Geodetic Survey, and were orthorectified by the Biogeography Branch. A classification scheme was set up with 20 benthic habitat types, 19 land cover types, and 13 mangrove habitat types. For this map of seagrass and mangrove habitats during 1988 only the 3 seagrass, and 14 mangrove classification categories were used. These were mapped directly into a GIS system through visual interpretation of orthorectified aerial photographs. proprietary
39482_Not Applicable 1992 Mosaic of Aerial Photography of the Salt River Bay National Historical Park and Ecological Preserve NOAA_NCEI STAC Catalog 1992-01-31 1992-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656472-NOAA_NCEI.umm_json Aerial photographs taken by NOAA's National Geodetic Survey during 1992 were mosaicked and orthorectified by the Biogeography Branch. The resulting image was used to digitize benthic, land cover and mangrove habitat maps of the Salt River Bay National Historic Park and Ecological Preserve (National Park Service), on St. Croix, in the U.S. Virgin Islands.The mosaic is centered on the National Park Service Site, located on the north central coast of St. Croix, and in some areas extends beyond the park boundaries up to 2 km. proprietary
39482_Not Applicable 1992 Mosaic of Aerial Photography of the Salt River Bay National Historical Park and Ecological Preserve ALL STAC Catalog 1992-01-31 1992-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656472-NOAA_NCEI.umm_json Aerial photographs taken by NOAA's National Geodetic Survey during 1992 were mosaicked and orthorectified by the Biogeography Branch. The resulting image was used to digitize benthic, land cover and mangrove habitat maps of the Salt River Bay National Historic Park and Ecological Preserve (National Park Service), on St. Croix, in the U.S. Virgin Islands.The mosaic is centered on the National Park Service Site, located on the north central coast of St. Croix, and in some areas extends beyond the park boundaries up to 2 km. proprietary
-39483_Not Applicable 1992 Seagrass and Mangrove Habitats of the Salt River Bay National Historical Park and Ecological Preserve ALL STAC Catalog 1992-01-31 1992-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656483-NOAA_NCEI.umm_json Habitat maps were created as part of a larger ecological assessment conducted by NOAA's National Ocean Service (NOS), Biogeography Branch, for Salt River Bay National Historic Park and Ecological Preserve (National Park Service).Aerial photographs were obtained for 1992 from the National Geodetic Survey, and were orthorectified by the Biogeography Branch. A classification scheme was set up with 20 benthic habitat types, 19 land cover types, and 13 mangrove habitat types. For this map of seagrass and mangrove habitats during 1992 only the 3 seagrass, and 14 mangrove classification categories were used. These were mapped directly into a GIS system through visual interpretation of orthorectified aerial photographs. proprietary
39483_Not Applicable 1992 Seagrass and Mangrove Habitats of the Salt River Bay National Historical Park and Ecological Preserve NOAA_NCEI STAC Catalog 1992-01-31 1992-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656483-NOAA_NCEI.umm_json Habitat maps were created as part of a larger ecological assessment conducted by NOAA's National Ocean Service (NOS), Biogeography Branch, for Salt River Bay National Historic Park and Ecological Preserve (National Park Service).Aerial photographs were obtained for 1992 from the National Geodetic Survey, and were orthorectified by the Biogeography Branch. A classification scheme was set up with 20 benthic habitat types, 19 land cover types, and 13 mangrove habitat types. For this map of seagrass and mangrove habitats during 1992 only the 3 seagrass, and 14 mangrove classification categories were used. These were mapped directly into a GIS system through visual interpretation of orthorectified aerial photographs. proprietary
+39483_Not Applicable 1992 Seagrass and Mangrove Habitats of the Salt River Bay National Historical Park and Ecological Preserve ALL STAC Catalog 1992-01-31 1992-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656483-NOAA_NCEI.umm_json Habitat maps were created as part of a larger ecological assessment conducted by NOAA's National Ocean Service (NOS), Biogeography Branch, for Salt River Bay National Historic Park and Ecological Preserve (National Park Service).Aerial photographs were obtained for 1992 from the National Geodetic Survey, and were orthorectified by the Biogeography Branch. A classification scheme was set up with 20 benthic habitat types, 19 land cover types, and 13 mangrove habitat types. For this map of seagrass and mangrove habitats during 1992 only the 3 seagrass, and 14 mangrove classification categories were used. These were mapped directly into a GIS system through visual interpretation of orthorectified aerial photographs. proprietary
39484_Not Applicable Benthic and Landcover Characterization of Salt River Bay National Historical Park and Ecological Preserve NOAA_NCEI STAC Catalog 2000-01-20 2000-01-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656503-NOAA_NCEI.umm_json Habitat maps were created as part of a larger ecological assessment conducted by NOAA's National Ocean Service (NOS), Biogeography Branch, for Salt River Bay National Historic Park and Ecological Preserve (National Park Service). Aerial photographs were obtained for 2000 from the National Geodetic Survey, and were orthorectified by the Biogeography Branch. A classification scheme was set up with 20 benthic habitat types, 19 land cover types, and 13 mangrove habitat types. These habitats were mapped directly into a GIS system through visual interpretation of orthorectified aerial photographs. proprietary
39485_Not Applicable 2000 Mosaic of Aerial Photography of the Salt River Bay National Historical Park and Ecological Preserve ALL STAC Catalog 2000-01-20 2000-01-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656521-NOAA_NCEI.umm_json Aerial photographs taken by NOAA's National Geodetic Survey during 2000 were mosaicked and orthorectified by the Biogeography Branch. The resulting image was used to digitize benthic, land cover and mangrove habitat maps of the Salt River Bay National Historic Park and Ecological Preserve (National Park Service), on St. Croix, in the U.S. Virgin Islands.The mosaic is centered on the National Park Service Site, located on the north central coast of St. Croix, and extends beyond the park boundaries approximately 3.3 km to the east and west, and between 0.5 - 1.2 km to the north and south. proprietary
39485_Not Applicable 2000 Mosaic of Aerial Photography of the Salt River Bay National Historical Park and Ecological Preserve NOAA_NCEI STAC Catalog 2000-01-20 2000-01-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656521-NOAA_NCEI.umm_json Aerial photographs taken by NOAA's National Geodetic Survey during 2000 were mosaicked and orthorectified by the Biogeography Branch. The resulting image was used to digitize benthic, land cover and mangrove habitat maps of the Salt River Bay National Historic Park and Ecological Preserve (National Park Service), on St. Croix, in the U.S. Virgin Islands.The mosaic is centered on the National Park Service Site, located on the north central coast of St. Croix, and extends beyond the park boundaries approximately 3.3 km to the east and west, and between 0.5 - 1.2 km to the north and south. proprietary
@@ -566,26 +566,26 @@ id title catalog state_date end_date bbox url description license
39492_Not Applicable Benthic Habitats of the Southern Mariana Archipelago Derived from IKONOS Imagery, 2001-2003 NOAA_NCEI STAC Catalog 2002-01-01 2004-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656617-NOAA_NCEI.umm_json This project is a cooperative effort among the National Ocean Service, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment; the University of Hawaii; BAE Systems Spectral Solutions; and Analytical Laboratories of Hawaii, LLC. The goal of the work was to map the coral reef habitats of American Samoa, Guam and the Commonwealth of the Northern Mariana Islands by visual interpretation and manual delineation of IKONOS satellite imagery. A two tiered habitat classification system was tested and implemented in this work. It integrates geomorphologic reef structure and biological cover into a single scheme and subsets each into detail. It also includes thirteen zones. Benthic features were mapped that covered an area of 45.2 square kilometers of which 4.4 were unconsolidated sediment and 40.9 were coral reef and hard bottom. Of the coral reef and hard bottom class, 59.9% is colonized by greater than 10% coral cover. proprietary
39552_Not Applicable California sheephead habitat suitability model for Channel Islands National Marine Sanctuary Biogeographic Assessment NOAA_NCEI STAC Catalog 2006-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656589-NOAA_NCEI.umm_json The National Marine Sanctuary Program (NMSP) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, NMSP and NOAA?s National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS). proprietary
39555_Not Applicable California market squid habitat suitability model for Channel Islands National Marine Sanctuary Biogeographic Assessment NOAA_NCEI STAC Catalog 2006-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656625-NOAA_NCEI.umm_json The National Marine Sanctuary Program (NMSP) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, NMSP and NOAA?s National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS). proprietary
-39556_Not Applicable 1993 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 1993-01-01 1993-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656641-NOAA_NCEI.umm_json The NOAA/NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39556_Not Applicable 1993 Average Monthly Sea Surface Temperature for California NOAA_NCEI STAC Catalog 1993-01-01 1993-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656641-NOAA_NCEI.umm_json The NOAA/NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
-39557_Not Applicable 1994 Average Monthly Sea Surface Temperature for California NOAA_NCEI STAC Catalog 1994-01-01 1994-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656671-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
+39556_Not Applicable 1993 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 1993-01-01 1993-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656641-NOAA_NCEI.umm_json The NOAA/NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39557_Not Applicable 1994 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 1994-01-01 1994-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656671-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
+39557_Not Applicable 1994 Average Monthly Sea Surface Temperature for California NOAA_NCEI STAC Catalog 1994-01-01 1994-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656671-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39558_Not Applicable 1995 Average Monthly Sea Surface Temperature for California NOAA_NCEI STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656698-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39558_Not Applicable 1995 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656698-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
-39559_Not Applicable 1996 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 1996-01-01 1996-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656727-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39559_Not Applicable 1996 Average Monthly Sea Surface Temperature for California NOAA_NCEI STAC Catalog 1996-01-01 1996-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656727-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
-39560_Not Applicable 1997 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 1997-01-01 1997-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656756-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
+39559_Not Applicable 1996 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 1996-01-01 1996-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656727-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39560_Not Applicable 1997 Average Monthly Sea Surface Temperature for California NOAA_NCEI STAC Catalog 1997-01-01 1997-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656756-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
+39560_Not Applicable 1997 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 1997-01-01 1997-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656756-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39561_Not Applicable 1998 Average Monthly Sea Surface Temperature for California NOAA_NCEI STAC Catalog 1998-01-01 1998-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656465-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39561_Not Applicable 1998 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 1998-01-01 1998-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656465-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39562_Not Applicable 1999 Average Monthly Sea Surface Temperature for California NOAA_NCEI STAC Catalog 1999-01-01 1999-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656475-NOAA_NCEI.umm_json The NOAA/NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39562_Not Applicable 1999 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 1999-01-01 1999-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656475-NOAA_NCEI.umm_json The NOAA/NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
-39563_Not Applicable 2000 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656498-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39563_Not Applicable 2000 Average Monthly Sea Surface Temperature for California NOAA_NCEI STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656498-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
-39564_Not Applicable 2001 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 2001-01-01 2001-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656514-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
+39563_Not Applicable 2000 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656498-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39564_Not Applicable 2001 Average Monthly Sea Surface Temperature for California NOAA_NCEI STAC Catalog 2001-01-01 2001-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656514-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
-39565_Not Applicable 2002 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 2002-01-01 2002-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656528-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
+39564_Not Applicable 2001 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 2001-01-01 2001-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656514-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39565_Not Applicable 2002 Average Monthly Sea Surface Temperature for California NOAA_NCEI STAC Catalog 2002-01-01 2002-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656528-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
+39565_Not Applicable 2002 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 2002-01-01 2002-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656528-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39566_Not Applicable 2003 Average Monthly Sea Surface Temperature for California NOAA_NCEI STAC Catalog 2003-01-01 2003-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656549-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39566_Not Applicable 2003 Average Monthly Sea Surface Temperature for California ALL STAC Catalog 2003-01-01 2003-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656549-NOAA_NCEI.umm_json The NOAA/ NASA AVHRR Oceans Pathfinder sea surface temperature data are derived from the 5-channel Advanced Very High Resolution Radiometers (AVHRR) on board the NOAA -7, -9, -11, -14, -16 and -17 polar orbiting satellites. Daily, 8-day and monthly averaged data for both the ascending pass (daytime) and descending pass (nighttime) are available on equal-angle grids of 8192 pixels/360 degrees (nominally referred to as the 4km resolution, 4096 pixels/360 degrees (nominally referred to as the 9km resolution), 2048 pixels/360 degrees (nominally referred to as the 18km resolution), and 720 pixels/360 degrees (nominally referred to as the 54km resolution or 0.5 degree resolution).The monthly averaged daytime data was converted to an ESRI GRID format and the 12 monthly grid files were combined into one annual grid with a attribute field for each month. proprietary
39570_Not Applicable Benthic Community Characterization on Shallow (less than 30m) Hardbottom Shelf Habitats in St. Croix, USVI. A preliminary field survey to assess operational and logistical approaches to implement the National Coral Reef Monitoring Program (NCRMP) in the USVI. NOAA_NCEI STAC Catalog 2012-05-07 2012-05-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656587-NOAA_NCEI.umm_json Reef fish populations are a conspicuous and essential component of USVI coral reef ecosystems. Yet despite their importance, striking population and community level changes have occurred in the recent past due to fishing pressure and habitat degradation. The monitoring methodologies described in this document are necessary for understanding how natural and anthropogenic stressors are changing reef fish populations and communities and will be critical for their sustainable management. A collaborative research effort between the NOAA's National Centers for Coastal Ocean Science, Center for Coastal Monitoring and Assessment's Biogeography Branch (BB) and the National Park Service (NPS) has been used to inventory and assess reef fish populations in reef and reef-associated habitats in the northeast region of St. Croix from 2001-2011. The survey method previously used has been refined to enable broader region-wide coverage at the scale of the USVI yet maintains high precision at the Marine Protected Area (MPA) spatial level. Region-wide population metric estimates are required to effectively manage reef fisheries but are also imperative for spatial management and understanding ecosystem-level processes. For example, the ability to place protected fish resources in the context of the greater region not only allows for the evaluation of management actions but it also provides the ability to determine the ecological role of an MPA in the greater ecosystem. The monitoring method previously used by the Biogeography Branch and other partners in St. Croix and other regions within the USVI and Puerto Rico will be used to characterize and establish baseline data for future monitoring. St. Croix was chosen to serve as the first area to implement the protocol and to evaluate the logistics necessary to implement a long term monitoring program in the USVI as part of the National Coral Reef Monitoring Program (NCRMP). Characterization and monitoring of fish communities requires a quantitative measure of the spatial distribution and variation of those communities. These measures will enable managers to make targeted management decisions (e.g. where to allow mooring or where to allow recreational activities such as snorkeling and SCUBA diving). Additionally, the spatial setting, both within and outside protected regions allows managers to assess the impact, if any, of a change in regulation such as the prohibition of fishing. It also enables analysis of any differential effect (i.e. the effect may be the same throughout the region or it may be more effective toward an edge or center of a management area). To quantify patterns of spatial distribution and make meaningful interpretations, we must first have knowledge of the underlying variables determining species distribution. The basis for this work therefore, is the nearshore benthic habitats maps (less than 100 ft depth) created by NOAA's Biogeography Program in 2001 and NOS' bathymetry models. The sampling domain includes all hardbottom habitats around St. Croix at depths less than 30m. The benthic habitat map and a habitat classification scheme were used to create a sample frame constructed with 50 x 50 m grids. Grids were stratified based on three variables: Hardbottom habitat type, depth zone, and region/management area. Habitat within these grids was stratified into 5 habitat categories (scattered coral/ rock, pavement, bedrock, patch reef and linear reef) each with two depth classifications (shallow (0-11.9 m) and deep (12- 30m)). Further stratification was assigned based on management zones and region of the island. There are three managed areas in St. Croix. Two federal marine protected areas are managed by the Department of Interior's National Park Service: Buck Island Reef National Monument and Salt River Bay National Historical Park and Ecological Reserve. The St. Croix East End Marine Park is a territorial marine protected area managed by the USVI Department of Planning and Natural Resources. Other strata include specific regions of St. Croix: North, East, West, and South shores. Overall there were 70 possible strata: 5 habitat types, 2 depth zones and 8 management areas/regions. The monitoring objectives of this protocol are to determine status, trends, and variability in exploited reef fish species and communities within the USVI region and inside vs. outside different management zones, using measures such as relative abundance (density), spatial distribution, size structure and diversity. The survey design is optimized for nine economically and ecologically important species in the USVI: blue tang (Acanthurus coeruleus). queen triggerfish (Balistes vetula), coney (Cephalopholis fulva), red hind (Epinephelus guttatus), foureye butterflyfish (Chaetodon capistratus), French grunt (Haemulon flavolineatum), yellowtail snapper (Ocyurus chrysurus), stoplight parrotfish (Sparisoma viride) and threespot damselfish (Stegastes planifrons). These species were chosen to include a broad range of life history traits as well as a variety of habitat utilization patterns. The sample design is optimized with the respect to these species, but because all fish species are recorded, monitoring efforts also obtain important information about many non-targeted species, the overall trophic structure, and form the scientific basis for effective management actions. As such, the sample allocation for this mission is based upon the existing community metrics and the above species specific distribution from the northeast region of St. Croix. It was determined that 250 samples among the various strata would be sufficient to characterize hard bottom habitats around the island and have comparable coefficient of variation (CV) to values observed in the northeast region of St. Croix. The goal was to survey as many of the 250 sites as possible in a two week time period. We organized a strong science field team and completed 286 fish and benthic surveys around the island. proprietary
@@ -596,18 +596,18 @@ id title catalog state_date end_date bbox url description license
39578_Not Applicable Benthic substrate type off California NOAA_NCEI STAC Catalog 2006-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656759-NOAA_NCEI.umm_json The National Marine Sanctuary Program (NMSP) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, NMSP and NOAA's National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS). proprietary
39584_Not Applicable Adult thresher shark habitat suitability model for Channel Islands National Marine Sanctuary Biogeographic Assessment ALL STAC Catalog 2006-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656476-NOAA_NCEI.umm_json The National Marine Sanctuary Program (NMSP) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, NMSP and NOAAs National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS). proprietary
39584_Not Applicable Adult thresher shark habitat suitability model for Channel Islands National Marine Sanctuary Biogeographic Assessment NOAA_NCEI STAC Catalog 2006-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656476-NOAA_NCEI.umm_json The National Marine Sanctuary Program (NMSP) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, NMSP and NOAAs National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS). proprietary
-39589_Not Applicable A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Subsurface Current Model Outputs NOAA_NCEI STAC Catalog 2006-09-01 2006-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656564-NOAA_NCEI.umm_json Surface and sub-surface current model outputs were obtained from researchers at the University of Massachusetts-Boston to examine spatial and temporal current variability within the region around Stellwagen Bank National Marine Sancutary. proprietary
39589_Not Applicable A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Subsurface Current Model Outputs ALL STAC Catalog 2006-09-01 2006-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656564-NOAA_NCEI.umm_json Surface and sub-surface current model outputs were obtained from researchers at the University of Massachusetts-Boston to examine spatial and temporal current variability within the region around Stellwagen Bank National Marine Sancutary. proprietary
-39590_Not Applicable A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Surface Current Model Outputs ALL STAC Catalog 2006-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656573-NOAA_NCEI.umm_json Surface and sub-surface current model outputs were obtained from researchers at the University of Massachusetts-Boston to examine spatial and temporal current variability within the region around Stellwagen Bank National Marine Sancutary. proprietary
+39589_Not Applicable A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Subsurface Current Model Outputs NOAA_NCEI STAC Catalog 2006-09-01 2006-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656564-NOAA_NCEI.umm_json Surface and sub-surface current model outputs were obtained from researchers at the University of Massachusetts-Boston to examine spatial and temporal current variability within the region around Stellwagen Bank National Marine Sancutary. proprietary
39590_Not Applicable A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Surface Current Model Outputs NOAA_NCEI STAC Catalog 2006-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656573-NOAA_NCEI.umm_json Surface and sub-surface current model outputs were obtained from researchers at the University of Massachusetts-Boston to examine spatial and temporal current variability within the region around Stellwagen Bank National Marine Sancutary. proprietary
+39590_Not Applicable A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Surface Current Model Outputs ALL STAC Catalog 2006-01-01 2006-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656573-NOAA_NCEI.umm_json Surface and sub-surface current model outputs were obtained from researchers at the University of Massachusetts-Boston to examine spatial and temporal current variability within the region around Stellwagen Bank National Marine Sancutary. proprietary
39604_Not Applicable Benthic Habitats of Puerto Rico and the U.S. Virgin Islands;Photomosaic of U.S. Virgin Islands (St. John), 1999 NOAA_NCEI STAC Catalog 1999-02-01 1999-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656500-NOAA_NCEI.umm_json Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor. proprietary
39605_Not Applicable Benthic Habitats of Puerto Rico and the U.S. Virgin Islands;Photomosaic of U.S. Virgin Islands (St. Thomas), 1999 NOAA_NCEI STAC Catalog 1999-02-01 1999-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656515-NOAA_NCEI.umm_json Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor. proprietary
39606_Not Applicable Benthic Habitats of Puerto Rico and the U.S. Virgin Islands;Photomosaic of U.S. Virgin Islands (St. Croix-East), 1999 NOAA_NCEI STAC Catalog 1999-02-01 1999-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656530-NOAA_NCEI.umm_json Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor. proprietary
39607_Not Applicable Benthic Habitats of Puerto Rico and the U.S. Virgin Islands;Photomosaic of U.S. Virgin Islands (St. Croix-West), 1999 NOAA_NCEI STAC Catalog 1999-02-01 1999-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656551-NOAA_NCEI.umm_json Habitat maps of Puerto Rico and the U.S. Virgin Islands were created by visual interpretation of aerial photographs using the Habitat Digitizer Extension. Aerial photographs are valuable tools for natural resource managers and researchers since they provide an excellent record of the location and extent of habitats. However,spatial distortions in aerial photographs due to such factors as camera angle, lens characteristics, and relief displacement must be accounted for during analysis to prevent incorrect measurements of area, distance, and other spatial parameters. These distortions of scale within an image can be removed through orthorectification. During orthorectification, digital scans of aerial photos are subjected to algorithms that eliminate each source of spatial distortion. The result is a georeferenced digital mosaic of several photographs with uniform scale throughout the mosaic. Features near land are generally georeferenced with greater accuracy while the accuracy of features away from land is generally not as good. Where no land is in the original photographic frame only kinematic GPS locations and image tie points were used to georeference the images. After the orthorectified mosaics were created, photointerpreters were able to accurately and reliably delineate boundaries of features in the imagery as they appear on the computer monitor. proprietary
-39623_Not Applicable A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Kriged Predictive Map of Zooplankton Samples ALL STAC Catalog 2006-09-01 2006-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656499-NOAA_NCEI.umm_json Zooplankton communities have been well studied in the northeast Atlantic (Sherman et al., 1983) and on Georges Bank within the Gulf of Maine (Bigelow, 1927; Davis, 1984; Backus, 1987; Kane, 1993; Pershing et al., 2004). Few studies have examined zooplankton spatial patterns within the Gulf of Maine. Twelve years (1977-1988) of zooplankton data from the National Marine Fisheries Service Northeast Fisheries Science Center (NEFSC) Marine Resources Monitoring Assessment and Prediction Program (MARMAP) were obtained to examine spatial and temporal patterns. A subset of the entire database was selected to include all zooplankton surveys in the Gulf of Maine during this time period (Figure 1.7.4). Overall, 6,864 samples were collected within this area; sampling methodology is described in Sibunka and Silverman (1989). proprietary
39623_Not Applicable A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Kriged Predictive Map of Zooplankton Samples NOAA_NCEI STAC Catalog 2006-09-01 2006-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656499-NOAA_NCEI.umm_json Zooplankton communities have been well studied in the northeast Atlantic (Sherman et al., 1983) and on Georges Bank within the Gulf of Maine (Bigelow, 1927; Davis, 1984; Backus, 1987; Kane, 1993; Pershing et al., 2004). Few studies have examined zooplankton spatial patterns within the Gulf of Maine. Twelve years (1977-1988) of zooplankton data from the National Marine Fisheries Service Northeast Fisheries Science Center (NEFSC) Marine Resources Monitoring Assessment and Prediction Program (MARMAP) were obtained to examine spatial and temporal patterns. A subset of the entire database was selected to include all zooplankton surveys in the Gulf of Maine during this time period (Figure 1.7.4). Overall, 6,864 samples were collected within this area; sampling methodology is described in Sibunka and Silverman (1989). proprietary
-39624_Not Applicable A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Kriged Probability Map of Zooplankton Samples NOAA_NCEI STAC Catalog 2006-09-01 2006-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656512-NOAA_NCEI.umm_json Zooplankton communities have been well studied in the northeast Atlantic (Sherman et al., 1983) and on Georges Bank within the Gulf of Maine (Bigelow, 1927; Davis, 1984; Backus, 1987; Kane, 1993; Pershing et al., 2004). Few studies have examined zooplankton spatial patterns within the Gulf of Maine. Twelve years (1977-1988) of zooplankton data from the National Marine Fisheries Service Northeast Fisheries Science Center (NEFSC) Marine Resources Monitoring Assessment and Prediction Program (MARMAP) were obtained to examine spatial and temporal patterns. A subset of the entire database was selected to include all zooplankton surveys in the Gulf of Maine during this time period (Figure 1.7.4). Overall, 6,864 samples were collected within this area; sampling methodology is described in Sibunka and Silverman (1989). proprietary
+39623_Not Applicable A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Kriged Predictive Map of Zooplankton Samples ALL STAC Catalog 2006-09-01 2006-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656499-NOAA_NCEI.umm_json Zooplankton communities have been well studied in the northeast Atlantic (Sherman et al., 1983) and on Georges Bank within the Gulf of Maine (Bigelow, 1927; Davis, 1984; Backus, 1987; Kane, 1993; Pershing et al., 2004). Few studies have examined zooplankton spatial patterns within the Gulf of Maine. Twelve years (1977-1988) of zooplankton data from the National Marine Fisheries Service Northeast Fisheries Science Center (NEFSC) Marine Resources Monitoring Assessment and Prediction Program (MARMAP) were obtained to examine spatial and temporal patterns. A subset of the entire database was selected to include all zooplankton surveys in the Gulf of Maine during this time period (Figure 1.7.4). Overall, 6,864 samples were collected within this area; sampling methodology is described in Sibunka and Silverman (1989). proprietary
39624_Not Applicable A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Kriged Probability Map of Zooplankton Samples ALL STAC Catalog 2006-09-01 2006-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656512-NOAA_NCEI.umm_json Zooplankton communities have been well studied in the northeast Atlantic (Sherman et al., 1983) and on Georges Bank within the Gulf of Maine (Bigelow, 1927; Davis, 1984; Backus, 1987; Kane, 1993; Pershing et al., 2004). Few studies have examined zooplankton spatial patterns within the Gulf of Maine. Twelve years (1977-1988) of zooplankton data from the National Marine Fisheries Service Northeast Fisheries Science Center (NEFSC) Marine Resources Monitoring Assessment and Prediction Program (MARMAP) were obtained to examine spatial and temporal patterns. A subset of the entire database was selected to include all zooplankton surveys in the Gulf of Maine during this time period (Figure 1.7.4). Overall, 6,864 samples were collected within this area; sampling methodology is described in Sibunka and Silverman (1989). proprietary
+39624_Not Applicable A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Kriged Probability Map of Zooplankton Samples NOAA_NCEI STAC Catalog 2006-09-01 2006-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102656512-NOAA_NCEI.umm_json Zooplankton communities have been well studied in the northeast Atlantic (Sherman et al., 1983) and on Georges Bank within the Gulf of Maine (Bigelow, 1927; Davis, 1984; Backus, 1987; Kane, 1993; Pershing et al., 2004). Few studies have examined zooplankton spatial patterns within the Gulf of Maine. Twelve years (1977-1988) of zooplankton data from the National Marine Fisheries Service Northeast Fisheries Science Center (NEFSC) Marine Resources Monitoring Assessment and Prediction Program (MARMAP) were obtained to examine spatial and temporal patterns. A subset of the entire database was selected to include all zooplankton surveys in the Gulf of Maine during this time period (Figure 1.7.4). Overall, 6,864 samples were collected within this area; sampling methodology is described in Sibunka and Silverman (1989). proprietary
39909dc233b34118a80dd6fa8a7af553_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (SU algorithm), Version 4.3 FEDEO STAC Catalog 1995-06-01 2003-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143072-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises the Level 3 daily and monthly aerosol products from the ATSR-2 instrument on the ERS-2 satellite, using the Swansea University (SU) algorithm, version 4.3. Data cover the period 1995 - 2003.For further details about these data products please see the documentation. proprietary
39aba1ff-1a11-4e07-9efc-d49dd0b80a96_NA MERIS - Water Parameters - Lake Constance, Monthly FEDEO STAC Catalog 2006-01-01 2010-02-28 8.76585, 47.4928, 9.81505, 47.874 https://cmr.earthdata.nasa.gov/search/concepts/C2207457983-FEDEO.umm_json The Medium Resolution Imaging Spectrometer (MERIS) on Board ESA’s ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int/ This product developed in the frame of the MAPP project (MERIS Application and Regional Products Projects) represents the chlorophyll concentration of Lake Constance derived from MERIS data. The product is a cooperative effort of DLR-DFD and the Institute for Coastal Research at the GKSS Research Centre Geesthacht. DFD pre-processed up to the value added level whenever MERIS data for the North Sea region was received and positively checked for a water area large enough for a suitable interpretation. For more details the reader is referred tohttp://wdc.dlr.de/sensors/meris/ and http://wdc.dlr.de/sensors/meris/documents/Mapp_ATBD_final_i3r0dez2001.pdfThis product provides monthly maps. proprietary
3DIMG_L1B_STD INSAT-3D Imager Level-1B Full Acquisition Standard Product ISRO STAC Catalog 2013-10-01 0.843296, -81.04153, 163.15671, 81.04153 https://cmr.earthdata.nasa.gov/search/concepts/C1231649308-ISRO.umm_json INSAT-3D Imager Level-1B Standard Product containing 6 channels data in HDF-5 Format proprietary
@@ -630,15 +630,15 @@ id title catalog state_date end_date bbox url description license
3bdb21a4cd004e5f8cc148fea5f1d4e3_NA ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection at 4km resolution, Version 6.0 FEDEO STAC Catalog 1997-09-04 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3327359686-FEDEO.umm_json The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.This dataset contains the Version 6.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day, monthly and yearly composites) covering the period 1997 - 2022. It is computed from the Ocean Colour CCI Version 6.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. This data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection). proprietary
3bfe0c2d51544f72837a99306a74e359_NA ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Experimental Break-Adjusted COMBINED Product, Version 06.1 FEDEO STAC Catalog 1978-11-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143225-FEDEO.umm_json "An experimental break-adjusted soil-moisture product has been generated by the ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project for the first time with their v06.1 data release. The product attempts to reduce breaks in the final CCI product by matching the statistics of the datasets between merging periods. At v06.1, the break-adjustment process (explained in Preimesberger et al. 2020) is applied only to the COMBINED product, using ERA5 soil moisture as a reference. The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.1 COMBINED break-adjusted product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document and Preimesberger et al. 2020. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using all of the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717â739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.0013. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., ""Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,"" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." proprietary
3c324bb4ee394d0d876fe2e1db217378_NA ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 1.0 FEDEO STAC Catalog 1992-09-26 2019-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142668-FEDEO.umm_json "This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project.Lakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. The five thematic climate variables included in this dataset are:⢠Lake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.⢠Lake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.⢠Lake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. ⢠Lake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. ⢠Lake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).Data generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents." proprietary
-3d_snow_models_4.0 3D_Snow_Models ALL STAC Catalog 2022-01-01 2022-01-01 9.8471832, 46.8146287, 9.8471832, 46.8146287 https://cmr.earthdata.nasa.gov/search/concepts/C3226081402-ENVIDAT.umm_json The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel). proprietary
3d_snow_models_4.0 3D_Snow_Models ENVIDAT STAC Catalog 2022-01-01 2022-01-01 9.8471832, 46.8146287, 9.8471832, 46.8146287 https://cmr.earthdata.nasa.gov/search/concepts/C3226081402-ENVIDAT.umm_json The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel). proprietary
+3d_snow_models_4.0 3D_Snow_Models ALL STAC Catalog 2022-01-01 2022-01-01 9.8471832, 46.8146287, 9.8471832, 46.8146287 https://cmr.earthdata.nasa.gov/search/concepts/C3226081402-ENVIDAT.umm_json The dataset contains several snow models in the Standard Tesselated Geometry File Format (stl) for 3D visualization, printing and additive manufacturing. Different snow types are available (new snow, rounded snow, depth hoar, buried surface hoar, graupel). proprietary
3dd6bbdd-5dca-411e-b251-cdc325d703c4_NA METOP GOME-2 - Formaldehyde (HCHO) - Global FEDEO STAC Catalog 2007-01-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207457991-FEDEO.umm_json "The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational HCHO total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/" proprietary
3fe263d2-99ed-4751-b937-d26a31ab0606_NA AVHRR - Vegetation Index (NDVI) - Europe FEDEO STAC Catalog 1994-07-01 -24, 28, 57, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2207458021-FEDEO.umm_json "Every day, three successive NOAA-AVHRR scenes are used to derive a synthesis product in stereographic projection known as the ""Normalized Difference Vegetation Index"" for Europe and North Africa. It is calculated by dividing the difference in technical albedos between measurements in the near infrared and visible red part of the spectrum by the sum of both measurements. This value provides important information about the ""greenness"" and density of vegetation. Weekly and monthly thematic synthesis products are also derived from this daily operational product, at each step becoming successively free of clouds. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/" proprietary
4003949a-cb4b-41b7-9710-915269990bcd_NA IRS-1D - Panchromatic Images (PAN) - Europe FEDEO STAC Catalog 1999-01-01 2005-01-27 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458009-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 70 km x 70 km IRS PAN data provide a cost effective solution for mapping tasks up to 1:25'000 scale. proprietary
41e2300068b44fa190f24272dc08dcd0_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Ice Velocity time series for the 79-Fjord Glacier for 2015-2017 from Sentinel-1 data, v1.1 FEDEO STAC Catalog 2015-01-22 2017-03-22 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142592-FEDEO.umm_json This dataset contains a time series of ice velocities for the 79-Fjord Glacier in Greenland, derived from Sentinel-1 SAR (Synthetic Aperture Radar) data acquired between January 2015 and March 2017. It has been produced by the ESA Greenland Ice Sheet Climate Change Initiative (CCI) project.Data files are delivered in NetCDF format at 250m grid spacing in North Polar Stereographic projection (EPSG: 3413). The horizontal velocity components are provided in true meters per day, towards the EASTING(x) and NORTHING(y) directions of the grid. proprietary
41e9783d4caa447b99f653c065805579_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Surface Elevation Change from Cryosat-2, v2.2 FEDEO STAC Catalog 2011-01-01 2017-12-31 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142558-FEDEO.umm_json This data set is part of the ESA Greenland Ice sheet CCI project. The data set provides surface elevation changes (SEC) for the Greenland Ice sheet derived from Cryosat 2 satellite radar altimetry, for the time period between 2010 and 2017. The surface elevation change data are provided as 2-year means (2011-2012, 2012-2013, 2013-2014, 2014-2015, 2015-2016, and 2016-2017), and five-year means are also provided (2011-2015, 2012-2016, 2013-2017), along with their associated errors. Data are provided in both NetCDF and gridded ASCII format, as well as png plots.The algorithm used to devive the product is described in the paper âImplications of changing scattering properties on the Greenland ice sheet volume change from Cryosat-2 altimetryâ by S.B. Simonsen and L.S. Sørensen, Remote Sensing of the Environment, 190,pp.207-216, doi:10.1016/j.rse.2016.12.012 proprietary
-42ad984d-a92e-41c2-af23-f28ecd22018d_1 AFRICA CITIES POPULATION DATABASE (ACPD) CEOS_EXTRA STAC Catalog 1990-10-26 1990-10-26 -20, -38, 54, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2232847815-CEOS_EXTRA.umm_json "The African Cities Population Database (ACPD) has been produced by the Birkbeck College of the University of London in 1990 at the request of the United Nations Environment Programme (UNEP) in Nairobi, Kenya. The database contains head counts for 479 cities in Africa which either have a population of over 20,000 or are capitals of their nation state. Listed are the geographical location of the cities and their population sizes. The material is primarily derived from a 1988 report of the Economic Commission for Africa (ECA) and several issues of the United Nations Demographic Yearbook (1973-81). Severe problems were found with several countries such as Togo, Ghana and South Africa. For South Africa, the data were derived from the United Nations Demographic Yearbook 1987. WCPD is an Arc/Info point coverage. It has no projection, as the cities are located on the basis of their latitude and longitude. Coordinates were assigned on the basis of gazetteers or African maps. Each record in the data base contains details of the city name, country name, latitude and longitude of the city, and its population at a defined time. The Arc/Info attribute table contains the following fields: AREA Arc/Info item PERIMETER Arc/Info item ACPD# Arc/Info item ACPD-ID Arc/Info item ID-NUM Unique number for each city CITY City name COUNTRY Country name CITY-POP Population of city proper YEAR Latest available year of collection ACPD comes as an Arc/Info EXPORT file originally called ""ACPD.E00"" and contains 67 Kb of data. The file has a record length of 80 and a block size of 8000 (blocking factor = 100). The file can be read from tape using Arc/Info's TAPEREAD command or any other generic copy utility. If distributed on a diskette it can be read using the ordinary DOS 'COPY' command. The file has to be converted to Arc/Info internal format using its IMPORT command. References to the WCPD data set can be found in: - SERLL News, Issue No. 1, January 1991, Birkbeck College, London, UK. - D. Rhind. ""Cartographically-related research at Birkbeck College 1987-91"" in: The Cartographic Journal, Vol. 28, June 1991, pp. 63-66. The source of the WCPD data set as held by GRID is Birkbeck College, University of London, Department of Geography, London, UK." proprietary
42ad984d-a92e-41c2-af23-f28ecd22018d_1 AFRICA CITIES POPULATION DATABASE (ACPD) ALL STAC Catalog 1990-10-26 1990-10-26 -20, -38, 54, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2232847815-CEOS_EXTRA.umm_json "The African Cities Population Database (ACPD) has been produced by the Birkbeck College of the University of London in 1990 at the request of the United Nations Environment Programme (UNEP) in Nairobi, Kenya. The database contains head counts for 479 cities in Africa which either have a population of over 20,000 or are capitals of their nation state. Listed are the geographical location of the cities and their population sizes. The material is primarily derived from a 1988 report of the Economic Commission for Africa (ECA) and several issues of the United Nations Demographic Yearbook (1973-81). Severe problems were found with several countries such as Togo, Ghana and South Africa. For South Africa, the data were derived from the United Nations Demographic Yearbook 1987. WCPD is an Arc/Info point coverage. It has no projection, as the cities are located on the basis of their latitude and longitude. Coordinates were assigned on the basis of gazetteers or African maps. Each record in the data base contains details of the city name, country name, latitude and longitude of the city, and its population at a defined time. The Arc/Info attribute table contains the following fields: AREA Arc/Info item PERIMETER Arc/Info item ACPD# Arc/Info item ACPD-ID Arc/Info item ID-NUM Unique number for each city CITY City name COUNTRY Country name CITY-POP Population of city proper YEAR Latest available year of collection ACPD comes as an Arc/Info EXPORT file originally called ""ACPD.E00"" and contains 67 Kb of data. The file has a record length of 80 and a block size of 8000 (blocking factor = 100). The file can be read from tape using Arc/Info's TAPEREAD command or any other generic copy utility. If distributed on a diskette it can be read using the ordinary DOS 'COPY' command. The file has to be converted to Arc/Info internal format using its IMPORT command. References to the WCPD data set can be found in: - SERLL News, Issue No. 1, January 1991, Birkbeck College, London, UK. - D. Rhind. ""Cartographically-related research at Birkbeck College 1987-91"" in: The Cartographic Journal, Vol. 28, June 1991, pp. 63-66. The source of the WCPD data set as held by GRID is Birkbeck College, University of London, Department of Geography, London, UK." proprietary
+42ad984d-a92e-41c2-af23-f28ecd22018d_1 AFRICA CITIES POPULATION DATABASE (ACPD) CEOS_EXTRA STAC Catalog 1990-10-26 1990-10-26 -20, -38, 54, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2232847815-CEOS_EXTRA.umm_json "The African Cities Population Database (ACPD) has been produced by the Birkbeck College of the University of London in 1990 at the request of the United Nations Environment Programme (UNEP) in Nairobi, Kenya. The database contains head counts for 479 cities in Africa which either have a population of over 20,000 or are capitals of their nation state. Listed are the geographical location of the cities and their population sizes. The material is primarily derived from a 1988 report of the Economic Commission for Africa (ECA) and several issues of the United Nations Demographic Yearbook (1973-81). Severe problems were found with several countries such as Togo, Ghana and South Africa. For South Africa, the data were derived from the United Nations Demographic Yearbook 1987. WCPD is an Arc/Info point coverage. It has no projection, as the cities are located on the basis of their latitude and longitude. Coordinates were assigned on the basis of gazetteers or African maps. Each record in the data base contains details of the city name, country name, latitude and longitude of the city, and its population at a defined time. The Arc/Info attribute table contains the following fields: AREA Arc/Info item PERIMETER Arc/Info item ACPD# Arc/Info item ACPD-ID Arc/Info item ID-NUM Unique number for each city CITY City name COUNTRY Country name CITY-POP Population of city proper YEAR Latest available year of collection ACPD comes as an Arc/Info EXPORT file originally called ""ACPD.E00"" and contains 67 Kb of data. The file has a record length of 80 and a block size of 8000 (blocking factor = 100). The file can be read from tape using Arc/Info's TAPEREAD command or any other generic copy utility. If distributed on a diskette it can be read using the ordinary DOS 'COPY' command. The file has to be converted to Arc/Info internal format using its IMPORT command. References to the WCPD data set can be found in: - SERLL News, Issue No. 1, January 1991, Birkbeck College, London, UK. - D. Rhind. ""Cartographically-related research at Birkbeck College 1987-91"" in: The Cartographic Journal, Vol. 28, June 1991, pp. 63-66. The source of the WCPD data set as held by GRID is Birkbeck College, University of London, Department of Geography, London, UK." proprietary
42f7230ab55641cdac1bba84eabd446a_NA ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Uncollated (L3U) Climate Data Record, version 2.1 FEDEO STAC Catalog 1981-08-23 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142559-FEDEO.umm_json This v2.1 SST_cci Advanced Very High Resolution Radiometer (AVHRR) level 3 uncollated data (L3U) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L3U product provides these SST data on a 0.05 regular latitude-longitude grid with with a single orbit per file.The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST_cci accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.This CDR Version 2.1 product supercedes the CDR Version 2.0 product. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .When citing this dataset please also cite the associated data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x proprietary
43d73291472444e6b9c2d2420dbad7d6_NA ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 06.1 FEDEO STAC Catalog 1978-11-01 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143086-FEDEO.umm_json The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.The v06.1 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.The data set should be cited using the following references:1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717â739, https://doi.org/10.5194/essd-11-717-20192. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001 proprietary
43f81a9f-f903-43d4-8333-dcda52b2bc63 Global estimated risk index for flood hazard CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C2232847571-CEOS_EXTRA.umm_json This dataset includes an estimate of the global risk induced by flood hazard. Unit is estimated risk index from 1 (low) to 5 (extreme). This product was designed by UNEP/GRID-Europe for the Global Assessment Report on Risk Reduction (GAR). It was modeled using global data. Credit: UNEP/GRID-Europe. proprietary
@@ -722,10 +722,10 @@ id title catalog state_date end_date bbox url description license
94447955166780 Aeromagnetic Survey - Local Data ALL STAC Catalog 1973-01-01 -150, -90, -30, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214608586-SCIOPS.umm_json The acquistion in 1973 of an aeromagnetic system enabled the British Antarctic Survey (BAS) to initiate a systematic geophysical survey. In addition to a regional survey, areas of specific local geological interest were surveyed in greater detail. The first local datasets were collected during the 1970s and 1980s from four locations: Horseshoe Island, Graham Land; Neny Fjord, Graham Land; Staccato Peaks, Alexander Island; Beethoven Peninsula, Alexander Island. Subsequent surveys have expanded on this and metadata records for each survey are available by following the Related_URL link to the BAS data catalogue. These data have all been incorporated into the Antarctic Digital Magnetic Anomaly Project (ADMAP). proprietary
94447955166780 Aeromagnetic Survey - Local Data SCIOPS STAC Catalog 1973-01-01 -150, -90, -30, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214608586-SCIOPS.umm_json The acquistion in 1973 of an aeromagnetic system enabled the British Antarctic Survey (BAS) to initiate a systematic geophysical survey. In addition to a regional survey, areas of specific local geological interest were surveyed in greater detail. The first local datasets were collected during the 1970s and 1980s from four locations: Horseshoe Island, Graham Land; Neny Fjord, Graham Land; Staccato Peaks, Alexander Island; Beethoven Peninsula, Alexander Island. Subsequent surveys have expanded on this and metadata records for each survey are available by following the Related_URL link to the BAS data catalogue. These data have all been incorporated into the Antarctic Digital Magnetic Anomaly Project (ADMAP). proprietary
94f3670150de4bac90773806e26646f2_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Petermann Glacier between 2017-05-01 and 2017-09-14, generated using Sentinel-2 data, v1.1 FEDEO STAC Catalog 2017-04-30 2017-09-14 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143107-FEDEO.umm_json This dataset contains optical ice velocity time series and seasonal product of the Petermann Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-05-01 and 2017-09-14. It has been produced as part of the ESA Greenland Ice sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway. proprietary
-96159374900008 Alexander Island Microclimate Data SCIOPS STAC Catalog 1992-01-01 1997-01-01 -68, -72, -68, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1214608608-SCIOPS.umm_json The British Antarctic Survey has deployed data loggers at a number of locations on Alexander Island, to collect microclimate (micrometerological) data. Various types of logger are used, recording a number of parameters, including, temperature, relative humidity and wind speed. Sensors tend to be deployed at or near ground level and in and around particular types of vegetation, or other experimental sites, such a cloches. proprietary
96159374900008 Alexander Island Microclimate Data ALL STAC Catalog 1992-01-01 1997-01-01 -68, -72, -68, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1214608608-SCIOPS.umm_json The British Antarctic Survey has deployed data loggers at a number of locations on Alexander Island, to collect microclimate (micrometerological) data. Various types of logger are used, recording a number of parameters, including, temperature, relative humidity and wind speed. Sensors tend to be deployed at or near ground level and in and around particular types of vegetation, or other experimental sites, such a cloches. proprietary
-96159393396972 Adelaide Island Microclimate Data ALL STAC Catalog 1995-01-01 1997-01-01 -68, -68, -68, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214608609-SCIOPS.umm_json The British Antarctic Survey has deployed data loggers at a number of locations on Adelaide Island, to collect microclimate (micrometerological) data. Various types of logger are used, recording a number of parameters, including, temperature, relative humidity and wind speed. Sensors tend to be deployed at or near ground level and in and around particular types of vegetation, or other experimental sites, such a cloches. proprietary
+96159374900008 Alexander Island Microclimate Data SCIOPS STAC Catalog 1992-01-01 1997-01-01 -68, -72, -68, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1214608608-SCIOPS.umm_json The British Antarctic Survey has deployed data loggers at a number of locations on Alexander Island, to collect microclimate (micrometerological) data. Various types of logger are used, recording a number of parameters, including, temperature, relative humidity and wind speed. Sensors tend to be deployed at or near ground level and in and around particular types of vegetation, or other experimental sites, such a cloches. proprietary
96159393396972 Adelaide Island Microclimate Data SCIOPS STAC Catalog 1995-01-01 1997-01-01 -68, -68, -68, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214608609-SCIOPS.umm_json The British Antarctic Survey has deployed data loggers at a number of locations on Adelaide Island, to collect microclimate (micrometerological) data. Various types of logger are used, recording a number of parameters, including, temperature, relative humidity and wind speed. Sensors tend to be deployed at or near ground level and in and around particular types of vegetation, or other experimental sites, such a cloches. proprietary
+96159393396972 Adelaide Island Microclimate Data ALL STAC Catalog 1995-01-01 1997-01-01 -68, -68, -68, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214608609-SCIOPS.umm_json The British Antarctic Survey has deployed data loggers at a number of locations on Adelaide Island, to collect microclimate (micrometerological) data. Various types of logger are used, recording a number of parameters, including, temperature, relative humidity and wind speed. Sensors tend to be deployed at or near ground level and in and around particular types of vegetation, or other experimental sites, such a cloches. proprietary
96d5b75ea29946c5aab8214ddbab252b_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the SRPR (RemoTeC) Proxy Retrieval algorithm (CH4_GOS_SRPR), version 2.3.8 FEDEO STAC Catalog 2009-04-01 2015-12-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142712-FEDEO.umm_json The CH4_GOS_SRPR dataset is comprised of Level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the RemoTeC SRPR Proxy Retrieval algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the data is version 2.3.8, and forms part of the Climate Research Data Package 4. This Proxy Retrieval product has been generated using the RemoTeC SRPR algorithm, which is being jointly developed at SRON and KIT. This has been designated as an 'alternative' GHG CCI algorithm, and a separate product has also been generated by applying the baseline GHG CCI proxy algorithm (the University of Leicester OCPR algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the OCPR product generated with the baseline algorithm. For more information regarding the differences between the baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. As well as containing the key product, the product file contains information relevant for the use of the data, such as the vertical layering and averaging kernels. The parameters which are retrieved simultaneously with XCH4 are also included (e.g. surface albedo), in addition to retrieval diagnostics like quality of the fit and retrieval errors. For further details on the product, including the RemoTeC algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents. proprietary
971dc69b-a7a8-406e-a5bc-fad76b51156f Cyclones Winds - Hazard, Wind Speed 500RP CEOS_EXTRA STAC Catalog 1970-01-01 -180, -55, 179.7721, 59.768925 https://cmr.earthdata.nasa.gov/search/concepts/C2232849324-CEOS_EXTRA.umm_json "This file contains the geographical distribution of wind field intensities (peak velocity of 5 seconds gusts) for the entire globe, for 500 years return period. It was generated by integration of the intensity values contained in the files ""Wind_Atlantic.AME"", ""Wind_EastPacific.AME"", ""Wind_NorthIndian.AME"", ""Wind_SudIndian.AME"", ""Wind_SudPacific.AME"" and ""Wind_WestPacific.AME"". " proprietary
9740edfd-57ff-43f9-b4dc-1ecdd7012656_NA IRS-P6 Resourcesat-1 - Panchromatic Images (LISS-IV) - Europe, Mono Mode FEDEO STAC Catalog 2004-01-27 2010-01-01 -25, 30, 45, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2207458052-FEDEO.umm_json Indian Remote Sensing satellites (IRS) are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation. The IRS series provides many remote sensing services to India and international ground stations. With 5 m resolution and products covering areas up to 70 km x 70 km IRS LISS-IV mono data provide a cost effective solution for mapping tasks up to 1:25'000 scale. proprietary
@@ -735,12 +735,12 @@ id title catalog state_date end_date bbox url description license
9ed2813d2eda4d958e92ab3ce1ab1fe6_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 Merged Product generated with the EMMA algorithm (CH4_EMMA), version 1.2 FEDEO STAC Catalog 2009-06-01 2014-05-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143059-FEDEO.umm_json The CH4_EMMA dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) for methane (XCH4). It has been produced using the ensemble median algorithm EMMA to several different versions of the Japanes Greenhouse gases Observing Satellite (GOSAT) XCH4 data, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the product is v1.2, and forms part of the Climate Research Data Package 4.The ensemble median algorithm EMMA has been applied to level 2 data of several different retrieval products from the Japanese Greenhouse gases Observing Satellite (GOSAT) This is therefore a merged GOSAT XCH4 Level 2 product, which is primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). For further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR). proprietary
9f002827ba7d48f59019fcfd3577a57e_NA ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged CO2 Merged Product generated with the EMMA algorithm (CO2_EMMA), v2.2 FEDEO STAC Catalog 2009-05-31 2014-05-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143245-FEDEO.umm_json The CO2_EMMA dataset comprises of level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using the ensample median algorithm EMMA to produce a merged SCIAMACHY and GOSAT XCO2 Level 2 product, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the product is v2.2, and forms part of the Climate Research Data Package 4.The EMMA algorithm has been applied to level 2 data from multiple XCO2 retrievals from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. This merged SCIAMACHY and GOSAT XCO2 Level 2 product is primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). For further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR). proprietary
9f6324ebe92940b989ebf273d5f8bf33_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 2 aerosol products from AATSR (ADV Algorithm), Version 2.31 FEDEO STAC Catalog 2002-07-24 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142624-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 2 aerosol products from the AATSR instrument on ENVISAT, derived using the ADV algorithm, version 2.31. Data is available for the period 2002-2012.For further details about these data products please see the linked documentation. proprietary
-A Fusion Dataset for Crop Type Classification in Germany_1 A Fusion Dataset for Crop Type Classification in Germany ALL STAC Catalog 2020-01-01 2023-01-01 13.6339485, 52.4179888, 14.3529903, 52.8494418 https://cmr.earthdata.nasa.gov/search/concepts/C2781412484-MLHUB.umm_json This dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Brandenburg, Germany. There are nine crop types in this dataset from years 2018 and 2019: Wheat, Rye, Barley, Oats, Corn, Oil Seeds, Root Crops, Meadows, Forage Crops. The 2018 labels from one of the tiles are provided for training, and the 2019 labels from a neighboring tile will be used for scoring in the competition. Input imagery consist of time series of Sentinel-2, Sentinel-1 and Planet Fusion (daily and 5-day composite) data. You can access each source from a different collection. The Planet fusion data are made available under a CC-BY-SA license. As an exception to the AI4EO Terms and Conditions published on the competition website, you confirm, by participating in it, that you agree that your results will be made public under the same, open-source license. proprietary
A Fusion Dataset for Crop Type Classification in Germany_1 A Fusion Dataset for Crop Type Classification in Germany MLHUB STAC Catalog 2020-01-01 2023-01-01 13.6339485, 52.4179888, 14.3529903, 52.8494418 https://cmr.earthdata.nasa.gov/search/concepts/C2781412484-MLHUB.umm_json This dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Brandenburg, Germany. There are nine crop types in this dataset from years 2018 and 2019: Wheat, Rye, Barley, Oats, Corn, Oil Seeds, Root Crops, Meadows, Forage Crops. The 2018 labels from one of the tiles are provided for training, and the 2019 labels from a neighboring tile will be used for scoring in the competition. Input imagery consist of time series of Sentinel-2, Sentinel-1 and Planet Fusion (daily and 5-day composite) data. You can access each source from a different collection. The Planet fusion data are made available under a CC-BY-SA license. As an exception to the AI4EO Terms and Conditions published on the competition website, you confirm, by participating in it, that you agree that your results will be made public under the same, open-source license. proprietary
+A Fusion Dataset for Crop Type Classification in Germany_1 A Fusion Dataset for Crop Type Classification in Germany ALL STAC Catalog 2020-01-01 2023-01-01 13.6339485, 52.4179888, 14.3529903, 52.8494418 https://cmr.earthdata.nasa.gov/search/concepts/C2781412484-MLHUB.umm_json This dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Brandenburg, Germany. There are nine crop types in this dataset from years 2018 and 2019: Wheat, Rye, Barley, Oats, Corn, Oil Seeds, Root Crops, Meadows, Forage Crops. The 2018 labels from one of the tiles are provided for training, and the 2019 labels from a neighboring tile will be used for scoring in the competition. Input imagery consist of time series of Sentinel-2, Sentinel-1 and Planet Fusion (daily and 5-day composite) data. You can access each source from a different collection. The Planet fusion data are made available under a CC-BY-SA license. As an exception to the AI4EO Terms and Conditions published on the competition website, you confirm, by participating in it, that you agree that your results will be made public under the same, open-source license. proprietary
A Fusion Dataset for Crop Type Classification in Western Cape, South Africa_1 A Fusion Dataset for Crop Type Classification in Western Cape, South Africa MLHUB STAC Catalog 2020-01-01 2023-01-01 20.5212157, -34.413256, 21.043415, -33.9796334 https://cmr.earthdata.nasa.gov/search/concepts/C2781412697-MLHUB.umm_json This dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Western Cape, South Africa. There are five crop types from the year 2017: Wheat, Barely, Canola, Lucerne/Medics, Small grain grazing. The AOI is split to three tiles. Two tiles are provided as training labels, and one tile will be used for scoring in the competition. Input imagery consist of time series of Sentinel-2, Sentinel-1 and Planet Fusion (daily and 5-day composite) data. You can access each source from a different collection. The Planet fusion data are made available under a CC-BY-SA license. As an exception to the AI4EO Terms and Conditions published on the competition website, you confirm, by participating in it, that you agree that your results will be made public under the same, open-source license. The Western Cape Department of Agriculture (WCDoA) vector data are supplied via Radiant Earth Foundation with limited distribution rights. Data supplied by the WCDoA may not be distributed further or used for commercial purposes. The vector data supplied are intended strictly for use within the scope of this remote sensing competition - for the purpose of academic research to our mutual benefit. The data is intended for research purposes only and the WCDoA cannot be held responsible for any errors or omissions which may occur in the data. proprietary
A Fusion Dataset for Crop Type Classification in Western Cape, South Africa_1 A Fusion Dataset for Crop Type Classification in Western Cape, South Africa ALL STAC Catalog 2020-01-01 2023-01-01 20.5212157, -34.413256, 21.043415, -33.9796334 https://cmr.earthdata.nasa.gov/search/concepts/C2781412697-MLHUB.umm_json This dataset contains ground reference crop type labels and multispectral and synthetic aperture radar (SAR) imagery from multiple satellites in an area located in Western Cape, South Africa. There are five crop types from the year 2017: Wheat, Barely, Canola, Lucerne/Medics, Small grain grazing. The AOI is split to three tiles. Two tiles are provided as training labels, and one tile will be used for scoring in the competition. Input imagery consist of time series of Sentinel-2, Sentinel-1 and Planet Fusion (daily and 5-day composite) data. You can access each source from a different collection. The Planet fusion data are made available under a CC-BY-SA license. As an exception to the AI4EO Terms and Conditions published on the competition website, you confirm, by participating in it, that you agree that your results will be made public under the same, open-source license. The Western Cape Department of Agriculture (WCDoA) vector data are supplied via Radiant Earth Foundation with limited distribution rights. Data supplied by the WCDoA may not be distributed further or used for commercial purposes. The vector data supplied are intended strictly for use within the scope of this remote sensing competition - for the purpose of academic research to our mutual benefit. The data is intended for research purposes only and the WCDoA cannot be held responsible for any errors or omissions which may occur in the data. proprietary
-A crop type dataset for consistent land cover classification in Central Asia_1 A crop type dataset for consistent land cover classification in Central Asia ALL STAC Catalog 2020-01-01 2023-01-01 60.2013297, 37.4241018, 72.3539419, 41.8252151 https://cmr.earthdata.nasa.gov/search/concepts/C2781412666-MLHUB.umm_json Land cover is a key variable in the context of climate change. In particular, crop type information is essential to understand the spatial distribution of water usage and anticipate the risk of water scarcity and the consequent danger of food insecurity. This applies to arid regions such as the Aral Sea Basin (ASB), Central Asia, where agriculture relies heavily on irrigation. Here, remote sensing is valuable to map crop types, but its quality depends on consistent ground-truth data. Yet, in the ASB, such data is missing. Addressing this issue, we collected thousands of polygons on crop types, 97.7% of which in Uzbekistan and the remaining in Tajikistan. We collected 8,196 samples between 2015 and 2018, 213 in 2011 and 26 in 2008. Our data compiles samples for 40 crop types and is dominated by “cotton” (40%) and “wheat”, (25%). These data were meticulously validated using expert knowledge and remote sensing data and relied on transferable, open-source workflows that will assure the consistency of future sampling campaigns. proprietary
A crop type dataset for consistent land cover classification in Central Asia_1 A crop type dataset for consistent land cover classification in Central Asia MLHUB STAC Catalog 2020-01-01 2023-01-01 60.2013297, 37.4241018, 72.3539419, 41.8252151 https://cmr.earthdata.nasa.gov/search/concepts/C2781412666-MLHUB.umm_json Land cover is a key variable in the context of climate change. In particular, crop type information is essential to understand the spatial distribution of water usage and anticipate the risk of water scarcity and the consequent danger of food insecurity. This applies to arid regions such as the Aral Sea Basin (ASB), Central Asia, where agriculture relies heavily on irrigation. Here, remote sensing is valuable to map crop types, but its quality depends on consistent ground-truth data. Yet, in the ASB, such data is missing. Addressing this issue, we collected thousands of polygons on crop types, 97.7% of which in Uzbekistan and the remaining in Tajikistan. We collected 8,196 samples between 2015 and 2018, 213 in 2011 and 26 in 2008. Our data compiles samples for 40 crop types and is dominated by “cotton” (40%) and “wheat”, (25%). These data were meticulously validated using expert knowledge and remote sensing data and relied on transferable, open-source workflows that will assure the consistency of future sampling campaigns. proprietary
+A crop type dataset for consistent land cover classification in Central Asia_1 A crop type dataset for consistent land cover classification in Central Asia ALL STAC Catalog 2020-01-01 2023-01-01 60.2013297, 37.4241018, 72.3539419, 41.8252151 https://cmr.earthdata.nasa.gov/search/concepts/C2781412666-MLHUB.umm_json Land cover is a key variable in the context of climate change. In particular, crop type information is essential to understand the spatial distribution of water usage and anticipate the risk of water scarcity and the consequent danger of food insecurity. This applies to arid regions such as the Aral Sea Basin (ASB), Central Asia, where agriculture relies heavily on irrigation. Here, remote sensing is valuable to map crop types, but its quality depends on consistent ground-truth data. Yet, in the ASB, such data is missing. Addressing this issue, we collected thousands of polygons on crop types, 97.7% of which in Uzbekistan and the remaining in Tajikistan. We collected 8,196 samples between 2015 and 2018, 213 in 2011 and 26 in 2008. Our data compiles samples for 40 crop types and is dominated by “cotton” (40%) and “wheat”, (25%). These data were meticulously validated using expert knowledge and remote sensing data and relied on transferable, open-source workflows that will assure the consistency of future sampling campaigns. proprietary
A6_Survey_1 Casey Blue Ice Runway Survey Report 2002 - 2003 AU_AADC STAC Catalog 2003-01-01 2003-03-15 110.334, -66.742, 111.726, -66.2 https://cmr.earthdata.nasa.gov/search/concepts/C1214305622-AU_AADC.umm_json Construction of a suitable runway near Casey was a major objective of the Air Link from Hobart to the Australian Antarctic Territory and the main focus of the 2003/03 season's fieldwork. The 2001/2002 summer season's Air Transport Study identified several 'Blue Ice' runway sites and undertook a detailed investigation at a site named R3 in the upper Peterson Glacier (UPG) region approximately 50 km south east of Casey Station. This season a six-member team was assembled to undertake additional investigation work and initial site and runway surface construction capable of supporting wheeled aircraft conducting inter-continental flights from Hobart. The Air Transport Project team members included: George Blaisdell - Civil Engineer Aaron Read - Surveyor Seane Hall - Mechanic Simon Larkman - Communications Technician Leigh Maclagan - Plant Operator Rob Sheers - Mechanical Engineer The objective for this season’s survey was to undertake further work on R3 and if it was not suited for constructing a runway surface, then locate and map a site that met the criteria for a blue ice runway. From Landsat 7 satellite imagery it was evident that larger and higher elevated patches of blue ice were visible south of the R3 and within these areas a potential runway site might be located. A suitable runway site was located about 65 km south east of Casey and this report documents the work undertaken in locating and mapping this site. Additional to the runway survey a number of other tasks were also carried out in support of science programs being undertaken at Casey. The report covers the survey field work undertaken by Air Transport Project (ATP) during the 2002/2003 ANARE Summer Field Season. Data collected in support of other scientific programs has been included in this report primarily as a record of work undertaken by the mapping program. These data have been supplied to the various scientists for inclusion in their studies. proprietary
AADC-00009_1 Antarctic Fur Seal Populations on Heard Island Summer 1987-1988 AU_AADC STAC Catalog 1987-11-25 1988-02-25 73, -53, 74, -52 https://cmr.earthdata.nasa.gov/search/concepts/C1214311500-AU_AADC.umm_json Abstract from ANARE Research Notes 72 The Antarctic fur seal Arctocephalus gazella has increased in numbers at Heard Island since the Australian National Antarctic Research Expeditions (ANARE) station was established in 1947. Increases have also been recorded at other breeding sites in the South Atlantic and South Indian Oceans this century, particularly at South Georgia. In the 1987-88 summer, fur seals at Heard Island were counted in several age and sex categories. The aims of the project were to determine the location of pupping sites, the extent of the pupping season and the size of the population, and to record the changes in numbers of animals ashore during the summer. Maps of the colonies and main haul-out areas, together with descriptions of census areas and tabulations of counts, provide a basis for future comparison. This dataset contains the results from surveys of Antarctic Fur Seals (Arctocephalus gazella) on Heard Island during the summer of 1987-1988. As well as habitat descriptions, age, sex, count of adults and pups were determined. The three major aims of the study include: to determine accurately the location of pupping sites; to determine the extent of the pupping season, the median date of birth and the number of pups born; and to census fur seals on as much of the island as possible in order to determine the number of animals ashore and to document changes in numbers during the summer. The results are listed in the document, which includes detailed tabulations of counts made at colonies and major haul-out sites on Macquarie Island during summer 1987-88, and descriptions and maps of these locations. Tagging, mainly of pups, was also undertaken, and a total of 234 pups, 8 under-yearlings, 9 yearlings, 2 juveniles and 1 sub-adult male were tagged. Counts at 3-day intervals (pups) were made between 25 November and 19 December 1987, and major censuses were made between 19 December 1987 and 25 February 1988. The fields in this dataset are: Locality Age Class Date Colony Bulls Cows Pups proprietary
AADC-00016_1 Macquarie Island Bibliography AU_AADC STAC Catalog 1950-01-01 1998-12-31 158, -55, 159, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214311471-AU_AADC.umm_json This metadata record describes a bibliography compiled by Dr Donald S. Horning of the Tumblegum Research Laboratory about Macquarie Island. The download file contains three word documents, all of which contain separate bibliographies (there is no duplication of entries between the word documents). One of the word documents also contains either abstracts, extracts from the referenced paper, or personal summaries by Dr Horning about each reference in the bibliography. proprietary
@@ -795,8 +795,8 @@ AAD_Hydroacoustics_data_1 AAD Hydroacoustics hard disks - data collected from So
AAD_Hydroacoustics_data_All_1 Hydroacoustic data collected from Southern Ocean Cruises by the Australian Antarctic Division AU_AADC STAC Catalog 1990-05-04 20, -70, 180, -38 https://cmr.earthdata.nasa.gov/search/concepts/C1214305624-AU_AADC.umm_json The Australian Antarctic Division (AAD) has been collecting hydroacoustic data from its ocean going vessels for a number of years. This collection represents all hydroacoustic data gathered since 1990. The data are stored on the AAD Storage Area Network (SAN), and as such are only directly accessible by AAD personnel. Currently a very large volume of data are stored (greater than 2 TB), hence distribution of these data are logistically feasible really only for people with access to the SAN. As well as data, a large amount of documentation is provided - including methods used to collect these data, as well as any products resulting from these data (e.g. papers, reports, etc). In the past, these data have been collected under several ASAC projects, ASAC 357 (Hydroacoustic Determination of the Abundance and Distribution of Krill in the Region of Prydz Bay, Antarctica) and ASAC 1250 (Krill flux, acoustic methodology and penguin foraging - an integrated study) - ASAC_357 and ASAC_1250. As of 2019-12-19 the folders present in the acoustics data directory are: 1990-05_Aurora-Australis_HIMS 1991-01_Aurora-Australis_AAMBER2 1991-10_Aurora-Australis_WOCE91 1992-01_Aurora-Australis_Calibration_Great-Taylors-Bay 1993-01_Aurora-Australis_Calibration_Port-Arthur 1993-01_Aurora-Australis_KROCK 1993-02_Aurora-Australis_Calibration_Mawson 1993-03_Aurora-Australis_WOES-WORSE 1993-08_Aurora-Australis_Calibration_Port-Arthur 1993-08_Aurora-Australis_THIRST 1994-01_Aurora-Australis_SHAM 1994-12_Aurora-Australis_WOCET 1995-02_Aurora-Australis_Calibration_Casey 1995-07_Aurora-Australis_HI-HO_HI-HO 1996-01_Aurora-Australis_BROKE 1996-01_Aurora-Australis_Calibration_Port-Arthur 1996-02_Aurora-Australis_Calibration_Casey 1996-08_Aurora-Australis_WASTE 1997-01_Aurora-Australis_BRAD 1997-09_Aurora-Australis_ON-ICE 1997-09_Aurora-Australis_WANDER 1997-11_Aurora-Australis_SEXY 1997-11_Aurora-Australis_V3 1997-98-050_V5 1998-02_Aurora-Australis_SNARK 1998-04_Aurora-Australis_PICCIES 1998-07_Aurora-Australis_FIRE-and-ICE 1998-09_Aurora-Australis_V2 1998-10_Aurora-Australis_SEXYII 1999-01_Aurora-Australis_V5 1999-03_Aurora-Australis_STAY 1999-07_Aurora-Australis_Calibration_Port-Arthur 1999-07_Aurora-Australis_IDIOTS 1999-10_Aurora-Australis_V2 1999-11_Aurora-Australis_V4 2000-01_Aurora-Australis_V5 2000-02_Aurora-Australis_V6 2000-10_Aurora-Australis_Calibration_Port-Arthur 2000-11_Aurora-Australis_V1 2000-12_Aurora-Australis_KACTAS 2001-01_Aurora-Australis_Calibration_Mawson 2001-02_Aurora-Australis_Calibration_Davis 2001-10_Aurora-Australis_CLIVAR 2002-01_Aurora-Australis_LOSS 2002-09_Aurora-Australis_V1 2002-10_Aurora-Australis_Calibration_Port-Arthur 2003-01_Aurora-Australis_KAOS 2003-02_Aurora-Australis_Calibration_Mawson 2003-03_Aurora-Australis_Off-charter 2003-09_Aurora-Australis_ARISE 2003-09_Aurora-Australis_Calibration_NW-Bay 2003-11_Aurora-Australis_V2 2003-12_Aurora-Australis_HIPPIES 2004-02_Aurora-Australis_V7 2004-05_AAD_Lab-testing 2004-06_Aurora-Australis_Off-charter 2004-10 2004-10_Aurora-Australis_Calibration_NW-Bay 2004-10_Aurora-Australis_V1 2004-11_Aurora-Australis_V2 2004-11_Howard-Burton_NW-Bay-testing 2004-12_Aurora-Australis_ORCKA 2004-12_Howard-Burton_NW-Bay-testing 2005-02_Aurora-Australis_V5 2005-04_Howard-Burton_Bruny-Island-testing 2005-11_Aurora-Australis_Calibration_Port-Arthur 2005-11_Aurora-Australis_V2 2006-01_Aurora-Australis_BROKE-West 2006-02_Aurora-Australis_Calibration_Mawson 2006-03_Aurora-Australis_V5 2006-09_Aurora-Australis_V1 2006-12_Aurora-Australis_V2 2007-01_Aurora-Australis_SAZ-SENSE 2007-04_Aurora-Australis_V5 2007-08_Aurora-Australis_SIPEX 2011_10_20_Aurora_Calibration 200910_Aurora-Australis_BathymetryProcessing 201803_tankExperiments 20150102_Tangaroa 200708030_Aurora-Australis_V3_CEAMARC 200708040_Aurora-Australis_V4 200708060_Aurora-Australis_V6_CASO 200809000_Aurora-Australis_VTrials 200809010_Aurora-Australis_V1 200809020_Aurora-Australis_V2 200809030_Aurora-Australis_V3 200809050_Aurora-Australis_V5 200910000_Aurora-Australis_VTrials 200910010_Aurora-Australis_V1 200910020_Aurora-Australis_V2 200910030_Aurora-Australis_V3 200910040_Aurora-Australis_V4 200910050_Aurora-Australis_V5 200910070_Aurora-Australis_VE1 201011000_Aurora-Australis_VTrials 201011002_Aurora-Australis_VE2 201011010_Aurora-Australis_V1 201011020_Aurora-Australis_V2 201011021_Aurora-Australis_VMS 201011030_Aurora-Australis_V3 201011040_Aurora-Australis_V4 201011050_Aurora-Australis_V5 201112000_Aurora-Australis_VTrials 201112001_Aurora-Australis_VE1 201112010_Aurora-Australis_V1 201112020_Aurora-Australis_V2 201112030_Aurora-Australis_V3 201112040_Aurora-Australis_V4 201112050_Aurora-Australis_V5 201112060_Aurora-Australis_V6 201213000_Aurora-Australis_VTrials 201213001_Aurora-Australis_VMS_SIPEX 201213010_Aurora-Australis_V1 201213020_Aurora-Australis_V2 201213020_Aurora-Australis_V3 201213040_Aurora-Australis_V4 201314010_Aurora-Australis_V1 201314020_Aurora-Australis_V2 201314040_Aurora-Australis_V4 201314060_Aurora-Australis_V6 201415000_AuroraAustralis-Trials 201415010-AuroraAustralis_V1 201415020_AuroraAustralis_V2 201415030_AuroraAustralis_V3 201415040_AuroraAustralis_V4 201516000-AuroraAustralis_VTrials 201516010_AuroraAustralis_V1 201516020_AuroraAustralis_V2 201516030-AuroraAustralis_V3 201617010-AuroraAustralis_V1 201617020-AuroraAustralis_V2 201617030-AuroraAustralis_V3 201617040-AuroraAustralis_V4 201718010-AuroraAustralis_V1 201718020-AuroraAustralis_V2 201718030-AuroraAustralis_V3 201718040-AuroraAustralis_V4 201819010-AuroraAustralis_V1 201819020-AuroraAustralis_V2 201819030-AuroraAustralis_V3 201819040-AuroraAustralis_V4 201920000-AuroraAustralis_VTrials 201920010-AuroraAustralis_V1 201920011-AuroraAustralis_VMI proprietary
AAD_voyage_soundings_1 Acoustic depth soundings collected on Australian Antarctic Division voyages ALL STAC Catalog 1985-01-01 30, -70, 170, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1273648215-AU_AADC.umm_json Acoustic depth soundings are routinely collected on Australian Antarctic Division voyages. This metadata record links to child records which describe processed soundings datasets from voyages since 1985. Documentation is included with the datasets. proprietary
AAD_voyage_soundings_1 Acoustic depth soundings collected on Australian Antarctic Division voyages AU_AADC STAC Catalog 1985-01-01 30, -70, 170, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1273648215-AU_AADC.umm_json Acoustic depth soundings are routinely collected on Australian Antarctic Division voyages. This metadata record links to child records which describe processed soundings datasets from voyages since 1985. Documentation is included with the datasets. proprietary
-AAD_voyage_soundings_HI513_1 Acoustic depth soundings collected on Australian Antarctic Division voyages, 1997/98, 1998/99 and 2003/04 to 2011/12 AU_AADC STAC Catalog 1997-09-23 2012-02-11 30, -70, 170, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1278277535-AU_AADC.umm_json The data processing was done by the Royal Australian Navy's (RAN) Deployable Geospatial Support Team (DGST) and was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office. The dataset is titled HI513. The data was processed was collected on the following voyages: 1997/98 V2, V4, V6 1998/99 V1, V4, V5 2003/04 V1, V3, V7, V9 2004/05 V4, V5 2005/06 V2, V5 2006/07 V1, V2 2007/08 V1, V2, V3, V5, V6 2010/11 V3, V4, V5 2011/12 V1, V2, V3, VE1 The data has not been through the verification process for use in charts. proprietary
AAD_voyage_soundings_HI513_1 Acoustic depth soundings collected on Australian Antarctic Division voyages, 1997/98, 1998/99 and 2003/04 to 2011/12 ALL STAC Catalog 1997-09-23 2012-02-11 30, -70, 170, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1278277535-AU_AADC.umm_json The data processing was done by the Royal Australian Navy's (RAN) Deployable Geospatial Support Team (DGST) and was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office. The dataset is titled HI513. The data was processed was collected on the following voyages: 1997/98 V2, V4, V6 1998/99 V1, V4, V5 2003/04 V1, V3, V7, V9 2004/05 V4, V5 2005/06 V2, V5 2006/07 V1, V2 2007/08 V1, V2, V3, V5, V6 2010/11 V3, V4, V5 2011/12 V1, V2, V3, VE1 The data has not been through the verification process for use in charts. proprietary
+AAD_voyage_soundings_HI513_1 Acoustic depth soundings collected on Australian Antarctic Division voyages, 1997/98, 1998/99 and 2003/04 to 2011/12 AU_AADC STAC Catalog 1997-09-23 2012-02-11 30, -70, 170, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1278277535-AU_AADC.umm_json The data processing was done by the Royal Australian Navy's (RAN) Deployable Geospatial Support Team (DGST) and was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office. The dataset is titled HI513. The data was processed was collected on the following voyages: 1997/98 V2, V4, V6 1998/99 V1, V4, V5 2003/04 V1, V3, V7, V9 2004/05 V4, V5 2005/06 V2, V5 2006/07 V1, V2 2007/08 V1, V2, V3, V5, V6 2010/11 V3, V4, V5 2011/12 V1, V2, V3, VE1 The data has not been through the verification process for use in charts. proprietary
AAD_voyage_soundings_HI534_1 Acoustic depth soundings collected on Australian Antarctic Division voyages, 2012/13 ALL STAC Catalog 2012-09-13 2013-02-21 62, -68, 147, -43 https://cmr.earthdata.nasa.gov/search/concepts/C1291622702-AU_AADC.umm_json The data processing was done by the Royal Australian Navy's (RAN) Deployable Geospatial Support Team (DGST) and was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office. The dataset is titled HI534. The data processed was collected on the following voyages: 2012/13 voyages MS, 1, 2 and 3 The data has not been through the verification process for use in charts. proprietary
AAD_voyage_soundings_HI534_1 Acoustic depth soundings collected on Australian Antarctic Division voyages, 2012/13 AU_AADC STAC Catalog 2012-09-13 2013-02-21 62, -68, 147, -43 https://cmr.earthdata.nasa.gov/search/concepts/C1291622702-AU_AADC.umm_json The data processing was done by the Royal Australian Navy's (RAN) Deployable Geospatial Support Team (DGST) and was provided to the Australian Antarctic Data Centre by the Australian Hydrographic Office. The dataset is titled HI534. The data processed was collected on the following voyages: 2012/13 voyages MS, 1, 2 and 3 The data has not been through the verification process for use in charts. proprietary
AAMBER_II_Chlorophyll_1 Chlorophyll a data collected on the AAMBER II cruise of the Aurora Australis AU_AADC STAC Catalog 1991-01-03 1991-03-19 70, -70, 78, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311627-AU_AADC.umm_json Chlorophyll a data collected on the AAMBER II cruise of the Aurora Australis from January to March of 1991. The voyage traveled to the Prydz Bay region, and data were collected en route and in the area. proprietary
@@ -822,8 +822,8 @@ AAS_3010_Sea_Spiders_Genetics_1 Genetic signature of Last Glacial Maximum region
AAS_3013_4077_4346_Ant_synthetic_bed_elevation_2016_1 HRES -- Synthetic high-resolution Antarctic bed elevation AU_AADC STAC Catalog 1951-01-01 2013-12-31 -180, -90, 180, -48.4651908 https://cmr.earthdata.nasa.gov/search/concepts/C1267618075-AU_AADC.umm_json HRES is a high-resolution (100m) synthetic bed elevation terrain for the whole Antarctic continent. The synthetic bed surface preserves topographic roughness characteristics of airborne and ground-based ice-penetrating radar data from the Bedmap1 compilation and the ICECAP consortium. Broad-scale features of the Antarctic landscape are incorporated from a lowpass filter of the Bedmap2 bed elevation data. The data are available in NetCDF classic format on a 100m resolution grid in a Polar Stereographic Projection (Central Meridian 0 degrees, Standard Parallel 71 degrees S) with respect to the WGS84 geoid. The 100m grid is 66661 rows by 66661 columns, where the corner of the lower left cell is located at a polar stereographic easting and northing of -3333000 m and -3333000 m, respectively. The value for missing data is -9999. proprietary
AAS_3016_Macquarie_Island_flora_models_1 Macquarie Island modelled flora species distributions and vegetation AU_AADC STAC Catalog 2007-01-01 2018-03-01 158.75244, -54.78802, 158.97491, -54.47323 https://cmr.earthdata.nasa.gov/search/concepts/C2102891792-AU_AADC.umm_json Species distribution models (SDMs) are developed for nine major vascular plant taxa native to Macquarie Island, based on field data (point locations with three categories for each taxon: presence/ less than 25% foliage cover / greater than 25% cover). Spatial models of total range and core range (where the taxon is a dominant feature of the vegetation) for each taxon were used to predict the distribution of vegetation communities. proprietary
AAS_3016_Macquarie_Island_lapserates_1 Macquarie Island air temperature lapse rates and cloud cover, 2014-2016 AU_AADC STAC Catalog 2014-08-01 2016-03-01 158.76892, -54.6643, 158.98315, -54.46685 https://cmr.earthdata.nasa.gov/search/concepts/C2102891778-AU_AADC.umm_json Air temperature lapse rates vary geographically and temporally. Sub-Antarctic Macquarie Island provides an opportunity to compare lapse rates between windward and leeward slopes in a hyper-oceanic climate. Development of orographic cloud is expected to modify lapse rates, given the theoretical shift between dry and saturated adiabatic lapse rates that occurs with condensation of water vapour. This dataset is part of a PhD project examining vegetation patterns and drivers on Macquarie Island. Data loggers were placed along an east-west altitudinal transect across the narrow axis of Macquarie Island to record air temperature from August 2014 to March 2016.A random sample of digital photographs from the AAD webcam at Macquarie Island Station was used to classify cloud base level as observed from the Station. This dataset includes air temperature data from LogTag loggers, analysis of near surface atmospheric lapse rates, observations of cloud cover from webcam images and relevant data supplied by Bureau of Meteorology used in analysis. Reference: Fitzgerald, N. B., and Kirkpatrick, J. B. (2020). Air temperature lapse rates and cloud cover in a hyper-oceanic climate. Antarctic Science, 14. https://doi.org/10.1017/S0954102020000309 proprietary
-AAS_3051_AbatusMicrosatellites_2 Abatus Microsatellites data set ALL STAC Catalog 2009-10-01 2013-03-31 77.987556, -68.584139, 77.94931, -68.565625 https://cmr.earthdata.nasa.gov/search/concepts/C1328897052-AU_AADC.umm_json The present data set corresponds to the genotypes for seven microsatellite markers for three Antarctic sea urchin species of the genus Abatus. Sea urchin individuals were collected in five sites separated by up to 5 km in the near-shore area surrounding Davis Station in the Vestfold Hills Region, East Antarctica. For each microsatellite loci, the size of each allele was scored (in base pairs) using the CEQ 8000 Genetic Analysis System software v.8.0. Fragments were separated on an automated sequencer (CEQ 8000, Beckman Coulter) in the Central Science Laboratory at University of Tasmania. proprietary
AAS_3051_AbatusMicrosatellites_2 Abatus Microsatellites data set AU_AADC STAC Catalog 2009-10-01 2013-03-31 77.987556, -68.584139, 77.94931, -68.565625 https://cmr.earthdata.nasa.gov/search/concepts/C1328897052-AU_AADC.umm_json The present data set corresponds to the genotypes for seven microsatellite markers for three Antarctic sea urchin species of the genus Abatus. Sea urchin individuals were collected in five sites separated by up to 5 km in the near-shore area surrounding Davis Station in the Vestfold Hills Region, East Antarctica. For each microsatellite loci, the size of each allele was scored (in base pairs) using the CEQ 8000 Genetic Analysis System software v.8.0. Fragments were separated on an automated sequencer (CEQ 8000, Beckman Coulter) in the Central Science Laboratory at University of Tasmania. proprietary
+AAS_3051_AbatusMicrosatellites_2 Abatus Microsatellites data set ALL STAC Catalog 2009-10-01 2013-03-31 77.987556, -68.584139, 77.94931, -68.565625 https://cmr.earthdata.nasa.gov/search/concepts/C1328897052-AU_AADC.umm_json The present data set corresponds to the genotypes for seven microsatellite markers for three Antarctic sea urchin species of the genus Abatus. Sea urchin individuals were collected in five sites separated by up to 5 km in the near-shore area surrounding Davis Station in the Vestfold Hills Region, East Antarctica. For each microsatellite loci, the size of each allele was scored (in base pairs) using the CEQ 8000 Genetic Analysis System software v.8.0. Fragments were separated on an automated sequencer (CEQ 8000, Beckman Coulter) in the Central Science Laboratory at University of Tasmania. proprietary
AAS_3054_09_10_ecotox_hydrocarbon_1 Hydrocarbon exposure concentrations for bioassays 09_10 - Davis Station AU_AADC STAC Catalog 2010-01-20 2010-06-02 77.9167, -68.7167, 77.9708, -68.5667 https://cmr.earthdata.nasa.gov/search/concepts/C1267713107-AU_AADC.umm_json This dataset contains the results of replicate experiments which measured the total hydrocarbon content (THC) in water accommodated fractions (WAFs) of three fuels; Special Antarctic Blend diesel, Marine Gas oil and intermediate fuel oil IFO 180. proprietary
AAS_3054_THC_WAF_integrated_conc_10_11_1 Integrated hydrocarbon exposure concentrations for toxicity tests 2010_11 AU_AADC STAC Catalog 2008-07-01 2012-06-30 147.31, -42.89, 147.33, -42.87 https://cmr.earthdata.nasa.gov/search/concepts/C1929062046-AU_AADC.umm_json Experiments were done to quantify the Total Hydrocarbon Content (THC) in water accommodated fractions (WAF) of three fuels; Special Antarctic Blend diesel (SAB), Marine Gas Oil diesel (MGO) and an intermediate grade of marine bunker Fuel Oil (IFO 180).These tests measured the hydrocarbon content in freshly decanted WAFs and the resulting loss of hydrocarbons over time when WAFs were exposed in temperature controlled cabinets at 0°C. These tests are detailed in Dataset AAS_3054_THC_WAF. The results of hydrocarbon WAF tests were used to calculate integrated concentration from measured hydrocarbon concentrations weighted to time to be used as the exposure concentrations for toxicity tests with Antarctic invertebrates. Exposure concentrations used to model sensitivity estimates were derived by calculating the time weighted mean THC between pairs of successive measurements in the 100% WAFs and dilutions to give overall exposure concentrations for each time point.These modelled concentrations integrated the loss of hydrocarbons over time, and renewal of test solutions at 4 d intervals Exposure concentrations of THC in µg/L are shown for endpoints from 24 h to 21 d proprietary
AAS_3121_1 Mass balance of the Totten basin in East Antarctica: Estimation and calibration from ground, air and space-based observations (TOT-Cal) AU_AADC STAC Catalog 2009-09-30 2012-03-31 109.5, -68.75, 118, -65.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214311635-AU_AADC.umm_json Linked to this record are a report providing further details about the project, as well as the data from the project. Public Summary Regions of Antarctica are undergoing significant change in response to the Earth's changing climate. This project will provide a state of the art contemporary insight into the changing behaviour of the Totten drainage basin in East Antarctica - an area of vital importance in understanding ice/ocean/atmosphere and climate interactions in the Australian region of Antarctica. We will estimate the contribution of the Totten Glacier drainage basin to present-day sea level rise and simultaneously provide a critical validation of the European Space Agency (ESA) CryoSat-2 satellite mission over this region. Project #3121 investigated the mass balance of the Totten basin and provided an Australian contribution to the validation of CryoSat-2 data over Law Dome and the Totten Glacier. With field seasons in 2010/11 and 2011/12, the project gathered a range of in situ data using field and airborne data collection techniques. These data include geodetic quality GPS observations from up to 6 quasi-permanent GPS sites from which ice velocity, tropospheric water vapour and in some cases, tidal motion are derived. These sites were equipped with temperature and atmospheric pressure sensors, and in some cases, acoustic snow accumulation sensors. GPS equipped skidoo surveys were undertaken over the survey region on Law Dome to facilitate the generation of a validation surface to compare against airborne LiDAR and ASIRAS based DEMs. In the 2011/12 season, AWI collaborators achieved 4 days of survey flights in Polar-6, obtaining LiDAR and ASIRAS data over specific flight lines spanning Law Dome and the Totten Glacier. Project objectives: This project will provide a state-of-the-art contemporary insight into the most recent changes in the surface elevation of the Totten drainage basin in East Antarctica, whilst simultaneously providing a critical and unique contribution to the calibration and validation of the new European Space Agency (ESA) CryoSat-2 satellite mission and the Australian Antarctic Division (AAD) LiDAR/RADAR system. The present-day mass balance change of Antarctica plays a key role in understanding the effects of global warming on the Earth system, in particular the contribution of melting Antarctic ice to present-day sea level rise. The Totten Glacier is known to be undergoing significant surface lowering and is perhaps the most significant basin in the East Antarctic (e.g., Shepherd and Wingham, 2007). The basin itself drains approximately 1/8th of the East Antarctic Ice Sheet (EAIS) and, as a marine-based system, is analogous to the West Antarctic Ice Sheet (WAIS) whose changing mass balance dominates the Antarctic contribution to global sea level rise(Lemke et al., 2007). The TOT-Cal project will independently lead Australian research in understanding the contribution of Antarctic ice to changing sea-levels by focusing new data on this key drainage basin of international scientific interest. Importantly, this region can be reached with relative ease by AAD logistics - it is located literally at the doorstep of the Australian Casey station, in close proximity to the Wilkins intercontinental airstrip. With international interest focused on this region, this project provides a showcase of AAD short-stay logistics in support of vital time-critical research and a major new ESA satellite mission that will undoubtedly play a major role in cryospheric science into the future. The TOT-Cal project will draw upon key resources and personnel within the University of Tasmania (UTAS), Australian National University (ANU), Laboratoire d'Etudes en Geophysique et Oceanographie Spatiales (LEGOS, France), Scripps Institution of Oceanography (SIO, USA) and the AAD, requiring the collection and analysis of field based, airborne and satellite data over a multi-season campaign. It builds upon and extends related past, existing and planned Australian Antarctic Science (AAS), Australian Research Council (ARC) and International Polar Year (IPY) projects, addressing three specific questions: 1) What is the present-day mass balance of the Totten drainage basin and what is its contribution to global sea level change? This will be assessed through a combination of airborne LiDAR/RADAR observations, satellite altimetry observations including Seasat (1978), Geosat (1985-1989), ERS-1 (1992-1996), ERS-2 (1995-2005), Envisat-RA2 (2002 to present), ICESat (2003-present) and CryoSat-2 (expected launch 2009), space gravity observations (GRACE), along with ground-based validation experiments. 2) What are the accuracies and uncertainty characteristics of the altimetry measurement systems? (In other words, what is the expected accuracy of the altimetry-derived mass balance estimates?) With an emphasis on the new CryoSat-2 and AAD LiDAR/RADAR systems, this will be assessed through repeated ground and airborne experiments, providing direct contribution to the CryoSat-2 international Calibration, Validation and Retrieval Team (CVRT), whilst also providing an important cross-calibration of synchronous ICESat, Envisat and CryoSat-2 data. Of particular focus will be the understanding of the different surface interactions between the incident radar and laser waveforms (both satellite and airborne) with the surface snow/ice characteristics (topography, firn, seasonal changes, etc). 3) What is the magnitude of the present-day Glacial Isostatic Adjustment (GIA) in the region that needs to be removed from the space-based geodetic observations in order to estimate mass balance using a space geodetic approach? Present uncertainty in the magnitude of GIA is a dominant error source in the mass balance error budget and requires an analysis of recent models and in-situ geodetic evidence in order to fully understand and minimise this error contribution. Each of the objectives set out above will be assessed with data acquired over the coming three summer seasons, leading into participating in the larger period of logistics support around the Totten Glacier in 2011/12. This also enables this project to provide state-of-the-art estimates of surface lowering to the Australian AAD/ACECRC modelling team (R.Warner et al) for integration into dynamic ice models in the subsequent years of this project. These estimates will be fundamental in improving conventional forward ice models which to date, are not able to predict the observed changes in the Totten Glacier (van der Veen et al. 2008). The timing of the work outlined in this proposal is critical given the CryoSat-2 launch (expected late 2009) and the impending conclusion of the GRACE mission, this research needs to be undertaken now for the field seasons indicated in order to maximise the scientific impact and provide the necessary complement to other planned AAS projects that will operate over the same future field seasons. Public summary of the season progress: 2010/11 was the first field season for this project. Valuable GPS field data were acquired in the Law Dome and Totten Glacier regions to assist with providing an Australian contribution to the validation of the CryoSat-2 ice monitoring satellite mission, and to further understand ice shelf/ocean interactions and climate change in this region. Planned airborne surveys by the German AWI Polar-5 aircraft were unable to be completed due to poor weather. Collaboration with the 'Investigating the Cryospheric Evolution of the Central Antarctic Plate' project (ICECAP - UTexas) yielded important airborne scanning laser altimeter elevation data over the Law Dome site. proprietary
@@ -834,8 +834,8 @@ AAS_3130_moss_beds_2010_1 High resolution mapping of moss beds at ASPA 135, Robi
AAS_3132_1 An assessment of variability in the influx of cosmic dust during the Holocene and the potential effect on iron concentrations in the Southern Ocean. AU_AADC STAC Catalog 2010-04-02 2010-04-12 158.883, -54.634, 158.884, -54.633 https://cmr.earthdata.nasa.gov/search/concepts/C1214311655-AU_AADC.umm_json Metadata record for data from AAS (ASAC) project 3132. Public This research will determine variability in the influx and mineralogy of cosmic dust to the Southern Ocean during the Holocene from peat bog cores. Cosmic dust contains significant quantities of soluble iron, a micronutrient required for photosynthesis. Therefore, variations in the deposition of cosmic dust could significantly affect primary production in the Southern Ocean. This may also play an important role in global climate due to its influence on carbon dioxide draw-down from, and emission of volatile sulphur compounds to, the atmosphere. The download file contain a csv spreadsheet of carbon dating from geochemical peat cores collected from Green Gorge on Macquarie Island. Project objectives: This project will sample peat bogs on Macquarie Island to: 1. Quantify and develop a high-temporal resolution record of the variability in cosmic dust deposition during the Holocene; 2. Determine the mineralogy and quantify the solubility of iron contained in the cosmic dust; Iron is a micronutrient required for photosynthetic reactions within chloroplasts. Martin [1990] proposed that many oceanic phytoplankton, especially those in the high nutrient - low chlorophyll (HNLC) regions of the world's oceans (such as the Southern Ocean) were limited by the availability of iron. Martin et al. [1991] demonstrated that nanomolar increases in dissolved iron stimulated phytoplankton blooms in the North and Equatorial Pacific and Southern Oceans. Several large-scale field experiments (see de Baar et al [2005] for a summary) demonstrated that the addition of iron stimulated phytoplankton productivity significantly. Eleven further experiments have confirmed these results in many other regions [Boyd, et al., 2007] and models of the cellular processes by which iron fertilisation stimulates phytoplankton blooms are now available [Fasham, et al., 2006]. The response of phytoplankton to iron fertilisation has attracted much research effort because phytoplankton blooms increase the draw-down of carbon from the atmosphere and ultimately export a fraction to the deep ocean where it is stored as particulate organic carbon [Watson, et al., 2000] and hence may play an important role in climate. Cosmic and terrestrial dust can both contain significant quantities of soluble, bio-available iron [Fung, et al., 2000; Plane, 2003]. The potential for iron contained in aeolian terrestrial dust to affect climate was recently assessed by Kohfeld et al. [2005], who concluded that dust-induced iron-fertilisation of ocean ecosystems might account for 30 - 50 ppm of atmospheric CO2 draw-down during the last glacial period. Satellite data provide support for these hypotheses at the regional scales at which terrestrial dust deposition events occur [Cropp, et al., 2003; Gabric, et al., 2002]. The influx of cosmic dust to the oceans could be significantly different to terrestrial dust inputs as it is likely to be uniformly distributed around the globe [Johnson, 2001], vary on longer time scales (although this is not well understood [Winckler and Fischer, 2006]), and is expected to be of finer particle-size and contrasting mineralogy [Plane, 2003]. Ice cores provide excellent long-term records of terrestrial and cosmic dust deposition, however, cores from ombrotrophic peat bogs, that receive their inputs exclusively from the atmosphere, can provide high temporal resolution records of cosmic and terrestrial dust during the Holocene [Cortizas and Gayoso, 2002]. Data from ice cores in Greenland and ocean sediment cores in the tropical Pacific have revealed variations in cosmic dust influx between glacial and inter-glacial periods, with increases in cosmic dust influx associated with cooler temperatures [Dalai, et al., 2006; Gabrielli, et al., 2004; Karner, et al., 2003]. Johnson [2001] calculated that the current background cosmic dust deposition of about 40,000 tonnes per annum delivered 30-300% of the aeolian iron flux due to terrestrial dust and about 20% of the upwelled iron flux in the Southern Ocean. Ombrotrophic peatlands, such as those found on Macquarie Island, which receive inputs of material solely from the atmosphere, provide especially useful records of cosmic dust deposition over the Holocene. Taken from the 2009-2010 Progress Report: Progress against objectives: Peat core samples were collected on Macquarie Island in April 2010. These samples will be analysed over the coming year. proprietary
AAS_3134_urchins_climate_change_2 Climate change and urchin fertilisation and the effect on the growth rate of juvenile Abatus sp. AU_AADC STAC Catalog 2011-12-01 2012-03-15 77.8, -68.8, 78, -68.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214305633-AU_AADC.umm_json The effect of pH, temperature and sperm concentration on the fertilisation of Sterechinus neumayeri was investigated. Adult Sterechinus neumayeri were collected from Ellis Fjord Narrows between December and January 2011-12 and held in the Ecotox Field Aquarium Module until used. Between 3-4 male and female individuals were spawned using 0.5M KCl and gametes were collected separately before being fertilised in treatment. The data set shows the percentage of fertilised and non-fertilised eggs of Sterechinus neumayeri scored at 20h post-fertilisation. Eggs were fertilised in various combinations of pH, temperature and sperm concentration treatments (pH: 8.0 (Control), 7.8 and 7.6; Temperature: 1 degrees C (Control), 3 degrees C and 5 degrees C; Sperm concentration (sperm:egg ratio): 1000:1 (Control), 750:1, 250: 1, 50:1 and 5:1). At 20h post fertilisation, 5 ml aliquot was removed from fertilisation vials and eggs were counted and determined if they were fertilised or not. Seawater parameters of treatments were measured at the start and end of the experiment. Detailed information of the spreadsheets are as follows: Seawater Parameters column headings: Temperature - measured in degrees C , shows the temperature treatments used pH - shows the pH levels used Subheading pH - pH level measured for the day using NIST certified buffers Subheading MV - pH level measured for the day in millivolts Subheading Total pH - total pH level in seawater obtained from MV measurements Subheading Temp - temperature of seawater measured for the day 1 deg C column headings: Experiment - number of experiments pH - shows the pH for each treatment Sperm Concentration - shows the sperm concentration used for each treatment in a egg:sperm ratio Rep - shows the number of replicates per experiment Unfertilised eggs - eggs without visible fertilisation envelope and no cleavage after 20h Fertilised eggs - eggs with visible fertilisation envelope and/or cleavage after 20h Fertilised deformed eggs - eggs with visible fertilisation envelope but deformed Total eggs - total eggs scored (whether fertilised or unfertilised) % Fertilised - fertilised eggs (deformed and non-deformed)/Total eggs 3 deg C and 5 deg C have the same column headings as 1 deg C. AAS3134 Abatus sp Growth Experiment Davis 2011-12: The effect of pH and temperature on the growth rate of juvenile Abatus ingens and Abatus shackletoni were investigated. Adult Abatus were collected off Airport Beach in waters 4-5m depth. Data set shows the growth rate of juveniles of Abatus ingens and Abatus shackletoni after a 4-week exposure to various combinations of pH and temperature. Juveniles of each species was removed from maternal pouches and photographed on the oral side before being exposed to combinations of pH (8.0 (Control), 7.8 and 7.6) and temperature (-1 degrees C (Control) and 1 degrees C) levels. They were incubated in treatments for 4 weeks before being removed and rephotographed. The lengths of 10 spines per juvenile were measured in the pre- and post-experiment photographs using ImageJ and the difference calculated to get a growth rate per juvenile. Seawater parameters of treatments were measured at the beginning of the experiment and subsequently once a day until the end of the experiment. Detailed information of the spreadsheets are as follows: A ingens (pre-exp) i.e. juvenile Abatus ingens spine lengths measured before exposure to experimental treatments. Column headings are: Spine number and length (mm): Length of each spine (1 - 10) measured per juvenile in mm. R1 - R12: Number of juveniles A ingens (post-exp) i.e. juvenile Abatus ingens spine lengths measured after 4-week exposure to experimental treatments. Column headings are identical to the above. A shackletoni (pre-exp) i.e. juvenile Abatus shackletoni spine lengths measured before exposure to experimental treatments. Column headings are identical to the above. A shackletoni (post-exp) i.e. juvenile Abatus shackletoni spine lengths measured after 4-week exposure to experimental treatments. Column headings are identical to the above. 2011-12 Aquarium pH and temp main headings show different treatment parameters. Column sub-headings are: Date - Date of measured seawater parameters Salinity - salinity of seawater measured Ppm - Amount of CO2 gas pumped into water recorded in parts per million pH - measured pH of seawater using NIST-certified buffers MV - pH of seawater recorded in millivolts Total pH - total pH of seawater derived from MV Temp - Temperature of seawater measured in degrees C. proprietary
AAS_3140_1 Dynamical Variability of the Lower Atmosphere AU_AADC STAC Catalog 2009-09-30 2012-03-31 77, -68, 78, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311613-AU_AADC.umm_json Metadata record for data from AAS (ASAC) Project 3140 See the link below for public details on this project. Public Summary A thorough understanding of the coupling and dynamics of the Antarctic lower atmosphere is critical for understanding how it will respond to climate change. However, this region of the atmosphere has not been studied in sufficient detail. Energy and momentum are redistributed in the atmosphere by large scale planetary waves and small scale gravity (buoyancy) waves. By combining the high-resolution instruments from Davis with global satellite observations, these waves and their effect on the atmosphere will be understood. Results from this project will be of value to modellers for improving global climate models. Project objectives: This project will study the variability, dynamics and coupling of the Antarctic lower atmosphere. The objective is to determine some of the most important and urgently needed information for global climate models by examining high-resolution observational datasets. Areas where understanding is limited and need to be improved include the effects of atmospheric gravity (buoyancy) waves on the lower atmosphere and their relation to the cold biases observed in the polar stratospheres of models (Sato and Yoshiki, 2008), determining critical wave parameter information (Alexander et al., 2008a), and studying troposphere - stratosphere coupling, particularly in relation to the polar night jet (e.g. Baumgaertner and McDonald 2007, Hei et al 2008). In order to achieve this, data which are collected at Davis as part of the current ASAC projects: a) the lidar - project 737 (Klekociuk et al. 2003) and b) the VHF MST radar - project 2325 (Morris et al. 2006) will be analysed. These results will be combined with data collected by the Bureau of Meteorology (radiosondes and ozonesondes launched at Davis) and various satellites including the CHAMP (Challenging Minisatellite Payload) and COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate) GPS radio occultation experiments (Alexander et al. 2008c). The multi-year ground-based observational records at Davis collected by the lidar and radar will be used to study the spatial and temporal variability of gravity waves in the troposphere and stratosphere over a wide range of scales. Waves and their sources will be identified and quantified. Such sources include the stratospheric polar night jet, orographic waves, tropospheric weather frontal systems and storms. The lidar and radar data will be combined with ozonesonde and radiosonde data from routine Bureau of Meteorology flights made at Davis for studies of stratosphere-troposphere interactions, dynamics, mixing, folding and mass transport across the tropopause. Satellite-based data, including those made by GPS radio occultation, will be used to set the Davis results into a regional and global scale context. The energy and momentum of small-scale gravity waves and large scale planetary waves will be examined. In particular, the stratospheric polar night jet will be studied to investigate wave generation and upward and downward propagation and understand how the downward propagating waves affect the troposphere. This project will establish a world-wide reputation for AAD as providing leading-edge studies, analysis and interpretation of the dynamic variability of the Antarctic lower atmosphere. Taken from the 2009-2010 Progress Report: Progress against objectives: Gravity wave activity associated with both the Antarctic and Arctic polar stratospheric vortices has been quantified using COSMIC GPS satellite data (Alexander et al. 2009). The high resolution nature of these data allowed information on regional scales and short duration wave processes to be identified and quantified. In particular, large intermittent bursts of orographic wave activity were identified above the Antarctic Peninsula. This has led to a continuing investigation of the effect of these waves on Polar Stratospheric Clouds (PSCs) by incorporation of CALIPSO satellite lidar data and MLS trace gas observations, both from the lower stratosphere. Foundations for this PSC / wave interaction were laid with work completed during the first year of project 3140, i.e. both the gravity wave analysis of Alexander et al (2009) and the planetary wave results of Alexander and Shepherd (2010). Lidar temperature data obtained in the upper troposphere - lower stratosphere (UTLS) region have been analysed and in particular one case study of a stratospheric intrusion during May 2008 has been identified and studied in detail. With the addition of satellite and radiosonde data, the lidar results are allowing quantification of small scale gravity wave parameters as the passage of a large scale planetary wave results in irreversible mixing of stratospheric air into the troposphere. Further UTLS experiments were run during winter 2009 by the chief investigator, thus allowing a statistical analysis of these events to be conducted in the future. A comparison between MST radar tropospheric winds and radiosonde winds revealed issues in the MST data which are still being addressed before these data become ready to use. proprietary
-AAS_3145_Advection_1 Advection shapes Southern Ocean microbial assemblages independent of distance and environment effects ALL STAC Catalog 2012-01-20 2012-02-07 113, -65, 115, -37 https://cmr.earthdata.nasa.gov/search/concepts/C1214311660-AU_AADC.umm_json See the referenced paper for additional details. Sampling. Sampling was conducted on board the RSV Aurora Australis during cruise V3 from 20 January to 7 February 2012. This cruise occupied a latitudinal transect from waters north of Cape Poinsett, Antarctica (65_ S) to south of Cape Leeuwin, Australia (37_ S) within a longitudinal range of 113-115_ E. Sampling was performed as described in ref. 29, with sites and depths selected to provide coverage of all major SO water masses. At each surface station, E250-560 l of seawater was pumped from E1.5 to 2.5m depth. At some surface stations, an additional sample was taken from the Deep Chlorophyll Maximum (DCM), as determined by chlorophyll fluorescence measurements taken from a conductivity, temperature and depth probe (CTD) cast at each sampling station. Samples of mesopelagic and deeper waters (E120-240 l) were also collected at some stations using Niskin bottles attached to the CTD. Sampling depths were selected based on temperature, salinity and dissolved oxygen profiles to capture water from the targeted water masses. Profiles were generated on the CTD descent, and samples were collected on the ascent at the selected depths. Deep water masses were identified by the following criteria: CDW 1/4 oxygen minimum (Upper Circumpolar Deep) or salinity maximum (Lower Circumpolar Deep); AABW 1/4 deep potential temperature minimum; AAIW 1/4 salinity minimum 18. The major fronts of the SO, which coincide with strong horizontal gradients in temperature and salinity 19,30, separate regions with similar surface water properties. The AZ lies south of the Polar Front (which was at 51_ S during sampling), whereas the PFZ lies between the Polar Front and the Subantarctic Front. In total, 25 samples from the AZ, PFZ, SAMW, AAIW, CDW and AABW were collected for this study (Fig. 1, Supplementary Data 1). Seawater samples were prefiltered through a 20-mm plankton net, biomass captured on sequential 3.0-, 0.8- and 0.1-mm 293-mm polyethersulphone membrane filters and filters immediately stored at _80 _C31,32. DNA extraction and sequencing. DNA was extracted with a modified version of the phenol-chloroform method 31. Tag pyrosequencing was performed by Research and Testing Laboratory (Lubbock, USA) on a GS FLXb platform (Roche, Branford, USA) using a modification of the standard 926F/1392R primers targeting the V6-V8 hypervariable regions of bacterial and archaeal 16S rRNA genes (926wF: 50-AAA-CTY-AAA-KGA-ATT-GRC-GG-30 , 1,392 R: 50-ACG-GGCGGT-GTG-TRC-30). Denoising, chimera removal and trimming of poor quality read ends were performed by the sequencing facility. proprietary
AAS_3145_Advection_1 Advection shapes Southern Ocean microbial assemblages independent of distance and environment effects AU_AADC STAC Catalog 2012-01-20 2012-02-07 113, -65, 115, -37 https://cmr.earthdata.nasa.gov/search/concepts/C1214311660-AU_AADC.umm_json See the referenced paper for additional details. Sampling. Sampling was conducted on board the RSV Aurora Australis during cruise V3 from 20 January to 7 February 2012. This cruise occupied a latitudinal transect from waters north of Cape Poinsett, Antarctica (65_ S) to south of Cape Leeuwin, Australia (37_ S) within a longitudinal range of 113-115_ E. Sampling was performed as described in ref. 29, with sites and depths selected to provide coverage of all major SO water masses. At each surface station, E250-560 l of seawater was pumped from E1.5 to 2.5m depth. At some surface stations, an additional sample was taken from the Deep Chlorophyll Maximum (DCM), as determined by chlorophyll fluorescence measurements taken from a conductivity, temperature and depth probe (CTD) cast at each sampling station. Samples of mesopelagic and deeper waters (E120-240 l) were also collected at some stations using Niskin bottles attached to the CTD. Sampling depths were selected based on temperature, salinity and dissolved oxygen profiles to capture water from the targeted water masses. Profiles were generated on the CTD descent, and samples were collected on the ascent at the selected depths. Deep water masses were identified by the following criteria: CDW 1/4 oxygen minimum (Upper Circumpolar Deep) or salinity maximum (Lower Circumpolar Deep); AABW 1/4 deep potential temperature minimum; AAIW 1/4 salinity minimum 18. The major fronts of the SO, which coincide with strong horizontal gradients in temperature and salinity 19,30, separate regions with similar surface water properties. The AZ lies south of the Polar Front (which was at 51_ S during sampling), whereas the PFZ lies between the Polar Front and the Subantarctic Front. In total, 25 samples from the AZ, PFZ, SAMW, AAIW, CDW and AABW were collected for this study (Fig. 1, Supplementary Data 1). Seawater samples were prefiltered through a 20-mm plankton net, biomass captured on sequential 3.0-, 0.8- and 0.1-mm 293-mm polyethersulphone membrane filters and filters immediately stored at _80 _C31,32. DNA extraction and sequencing. DNA was extracted with a modified version of the phenol-chloroform method 31. Tag pyrosequencing was performed by Research and Testing Laboratory (Lubbock, USA) on a GS FLXb platform (Roche, Branford, USA) using a modification of the standard 926F/1392R primers targeting the V6-V8 hypervariable regions of bacterial and archaeal 16S rRNA genes (926wF: 50-AAA-CTY-AAA-KGA-ATT-GRC-GG-30 , 1,392 R: 50-ACG-GGCGGT-GTG-TRC-30). Denoising, chimera removal and trimming of poor quality read ends were performed by the sequencing facility. proprietary
+AAS_3145_Advection_1 Advection shapes Southern Ocean microbial assemblages independent of distance and environment effects ALL STAC Catalog 2012-01-20 2012-02-07 113, -65, 115, -37 https://cmr.earthdata.nasa.gov/search/concepts/C1214311660-AU_AADC.umm_json See the referenced paper for additional details. Sampling. Sampling was conducted on board the RSV Aurora Australis during cruise V3 from 20 January to 7 February 2012. This cruise occupied a latitudinal transect from waters north of Cape Poinsett, Antarctica (65_ S) to south of Cape Leeuwin, Australia (37_ S) within a longitudinal range of 113-115_ E. Sampling was performed as described in ref. 29, with sites and depths selected to provide coverage of all major SO water masses. At each surface station, E250-560 l of seawater was pumped from E1.5 to 2.5m depth. At some surface stations, an additional sample was taken from the Deep Chlorophyll Maximum (DCM), as determined by chlorophyll fluorescence measurements taken from a conductivity, temperature and depth probe (CTD) cast at each sampling station. Samples of mesopelagic and deeper waters (E120-240 l) were also collected at some stations using Niskin bottles attached to the CTD. Sampling depths were selected based on temperature, salinity and dissolved oxygen profiles to capture water from the targeted water masses. Profiles were generated on the CTD descent, and samples were collected on the ascent at the selected depths. Deep water masses were identified by the following criteria: CDW 1/4 oxygen minimum (Upper Circumpolar Deep) or salinity maximum (Lower Circumpolar Deep); AABW 1/4 deep potential temperature minimum; AAIW 1/4 salinity minimum 18. The major fronts of the SO, which coincide with strong horizontal gradients in temperature and salinity 19,30, separate regions with similar surface water properties. The AZ lies south of the Polar Front (which was at 51_ S during sampling), whereas the PFZ lies between the Polar Front and the Subantarctic Front. In total, 25 samples from the AZ, PFZ, SAMW, AAIW, CDW and AABW were collected for this study (Fig. 1, Supplementary Data 1). Seawater samples were prefiltered through a 20-mm plankton net, biomass captured on sequential 3.0-, 0.8- and 0.1-mm 293-mm polyethersulphone membrane filters and filters immediately stored at _80 _C31,32. DNA extraction and sequencing. DNA was extracted with a modified version of the phenol-chloroform method 31. Tag pyrosequencing was performed by Research and Testing Laboratory (Lubbock, USA) on a GS FLXb platform (Roche, Branford, USA) using a modification of the standard 926F/1392R primers targeting the V6-V8 hypervariable regions of bacterial and archaeal 16S rRNA genes (926wF: 50-AAA-CTY-AAA-KGA-ATT-GRC-GG-30 , 1,392 R: 50-ACG-GGCGGT-GTG-TRC-30). Denoising, chimera removal and trimming of poor quality read ends were performed by the sequencing facility. proprietary
AAS_3214_1 Diatom identification and counts from 7 multicore locations on Kerguelen Plateau, Southern Ocean AU_AADC STAC Catalog 2011-10-01 2012-03-31 66.69944, -50.65333, 74.81222, -48.11667 https://cmr.earthdata.nasa.gov/search/concepts/C1214311614-AU_AADC.umm_json "This dataset was collected as part of an honours project by Jessica Wilks at Macquarie University (submitted May 2012). The samples analysed were taken from an expedition conducted by Dr Leanne Armand in 2011 as part of the KEOPS2 mission (KErguelen: compared study of the Ocean and the Plateau in Surface water). During this mission 7 locations (A3-1, A3-2, E1-3, E14W2, NPF-L, R2 and TEW) around the Kerguelen Plateau were sampled for seafloor sediment. Each attached spreadsheet represents the data from one of these locations. Three tubes of sediment were taken for each location. The data within each spreadsheet is separate for the three tubes. After the tubes of seafloor sediment were processed to remove organic material and carbonates (leaving nothing but siliceous material, primarily diatoms) slides were made with a small amount of material, three slides per tube of sediment. Diatoms were identified using a light microscope at 40x magnification. Approximately 400 frustules were counter per tube (ie per set of 3 slides) in order to represent the diversity of the species present. The number of each species or subspecies of diatom are tallied in the spreadsheets attached. Species identifications follow Armand et al 2008. Other information in the attached spreadsheets includes the seafloor depth at the point of sampling, the distance from the Kerguelen shoreline at the point of sampling, the amount of suspended material used on each slide, the number of field of view (at 40X) viewed to count the quota of 400 diatom frustules, and the calculated number of frustules/ gram of dry sediment weight. Counting protocol: centric frustules were counted only when 1) more than half of the frustule was intact; and 2) the frustule was clearly identifiable. If 1) but not 2) then the frustule was counted as ""unidentified centric"". For Rhizosolenia spp, frustules were couned if the apex was present and identifiable, otherwise it was counted as ""R. unknown"". Thalassiothrix and Tricotoxon were only counted if one end was present and identifiable. The number was later divided by 2, to give the number of complete frustules. Abbreviations: A. spp= Actinocyclus As. spp= Asteromphalus Az. spp= Azpeita Ch. spp= Chaetoceros Co. spp= Coscinodiscus C. spp= Cocconeis D. spp= Dactyliosen E. spp= Eucampia F. spp= Fragilariopsis O. spp= Odontella P. spp= Paralia Po. spp= Porosira R. spp= Rhizosolenia Th. spp= Thalassionema T. spp= Thalassiosira Locations A3-1, Kerguelen Plateau: -50.65333 S, 72.04 E A3-2, Kerguelen Plateau: -50.64722 S, 72.07 E E1-3, Kerguelen Plateau: -48.11667 S, 71.96667 E E14W2, Kerguelen Plateau: -48.7775 S, 71.43833 E NPF-L, Kerguelen Plateau: -48.62417 S, 74.81222 E R2, Kerguelen Plateau: -50.39389 S, 66.69944 E TEW, Kerguelen Plateau: -49.16083 S, 69.83389 E" proprietary
AAS_3214_Photos_1 Kerguelen Plateau (Southern Ocean) diatom photographs taken using light microscopy AU_AADC STAC Catalog 2011-10-01 2012-03-31 72, -50, 72, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214305635-AU_AADC.umm_json This dataset was collected as part of an honours project by Jessica Wilks at Macquarie University (submitted May 2012). The samples analysed were taken from an expedition conducted by Dr Leanne Armand in 2011 as part of the KEOPS2 mission (KErguelen: compared study of the Ocean and the Plateau in Surface water). During this mission 7 locations (A3-1, A3-2, E1-3, E14W2, NPF-L, R2 and TEW) around the Kerguelen Plateau were sampled for seafloor sediment. This study involved identification of over 50 species of diatoms as part of a species assemblage/ distribution study. A photograph of each diatom encountered in this study is included in the attached plates. proprietary
AAS_3217_Davis_CurrentMetersDispersalModelling_1 Davis STP current meter data and effluent dispersal modelling report AU_AADC STAC Catalog 2010-01-23 2010-03-12 77.77962, -68.78812, 78.84338, -68.41121 https://cmr.earthdata.nasa.gov/search/concepts/C1455695367-AU_AADC.umm_json This metadata record contains an Excel workbook of current meter data and a report derived from this data detailing an analysis of the mean and variability of the longshore component of the current using observations from four current meters, and, simple modelling of the effluent outfall using a model originally developed for shoreline discharges from the oil industry. The Excel workbook contains data from 4 of the 6 analogue Anderra current that meters were deployed in the area in front of Davis Station in early 2010. Data was not retrievable from meters CM4 and CM6. The meters were deployed at approximately 5 m below the surface. Refer to the Davis STP reports lodged under metadata record Davis_STP for current meter locations and deployment and retrieval details. Background of the Davis STP project - Refer to the Davis STP reports lodged under metadata record Davis_STP. proprietary
@@ -843,8 +843,8 @@ AAS_3227_predicted_habitat_1 Important marine habitat off east Antarctica reveal
AAS_3229_1 Impact of Black Saturday bushfire plume and other pyrocarbon emissions on stratospheric ozone above Antarctica AU_AADC STAC Catalog 2009-02-01 2012-03-31 -180, -70, 180, -35 https://cmr.earthdata.nasa.gov/search/concepts/C1214311658-AU_AADC.umm_json Metadata record for data from AAS (ASAC) project 3229. Public Summary: We investigate the impact of Black Saturday Australian bushfire in 2009 on the atmosphere above Australia and in the southern hemisphere in general, including Antarctica. Using high quality measurements collected by modern satellite and ground-based instruments, we study vertical and horizontal motion of the smoke plume, chemical composition of this plume, and chemical reactions between various molecules in the plume and other atmospheric gases. We want to answer an important question on how the bushfire plume may interact with the ozone molecules and whether it adds to the depletion of the protective ozone layer above Australia and above Antarctica. Project Objectives: - Using satellite and ground-based measurements, investigate the horizontal and vertical transport of the plume that resulted from Black Saturday Australian bushfire in February 2009. This includes analysis of the short-term (within one month) and long-term (up to several years) transport of plume material. Perform this analysis for other significant bushfire events that may occur in the southern hemisphere throughout the duration of this project and result in the injection of plume material into the stratosphere. - Study the evolution in chemical composition of stratospheric aerosols associated with Black Saturday bushfire and other significant pyrocarbon events. - Analyse the short- and long-term effects of Black Saturday bushfire and other significant pyrocarbon events in the southern hemisphere on the stratospheric ozone concentration at various locations and in particular on the Antarctic ozone hole. - Analyse the climate impact of bushfire plume material injected into the stratosphere. Taken from the 2010-2011 Progress Report: - We used the Odin/OSIRIS and CALIPSO satellite data and investigated the horizontal and vertical transport of the Australian-2009 Black Saturday bushfire smoke plume in the stratosphere in February-June 2009. - We identified the enhanced water absorption bands in the OSIRIS spectra of smoke plume. We are currently studying this smoke hydration in the stratosphere using multiple satellite instruments. A paper for Geophysical Research letters is currently in preparation. - We are currently investigating the horizontal spread of bushfire smoke material to all locations in the Northern and Southern hemispheres, up to the polar regions. 2012-11-12 Update The data are from the OSIRIS (Optical Spectrograph and Infrared Imager System) instrument on the Odin satellite. The exact data used in project 3229 are: Level 1 spectral solar irradiances measured by OSIRIS in February - June 2009. The detailed description of the wavelengths used and the approach to data analysis are given in the paper: Siddaway, J. M. and S. V. Petelina (2011), Transport and evolution of the 2009 Australian Black Saturday bushfire smoke in the lower stratosphere observed by OSIRIS on Odin, J. Geophys. Res., 116, D06203, doi:10.1029/2010JD015162. proprietary
AAS_3289_1 Cape Denison Geological Sampling - 2010-2011 AU_AADC STAC Catalog 2010-10-31 2011-02-28 142.655, -67.008, 142.657, -67.008 https://cmr.earthdata.nasa.gov/search/concepts/C1214305636-AU_AADC.umm_json Two samples were collected at Cape Denison (Permit no. ATEP 10-11-3289), both by Dr David Tingay (2010 - 2011 Mawson's Huts Foundation Expedition). Both samples were cleaned with water prior to RTA and cleared in Hobart by AQIS (permit attached) The first was a sample of the Cape Denison Orthogneiss (GA sample_no 2122491; Lat 67.008 S; long 142.655E). The sample was taken from loose material on the surface on the west side of Memorial Hill out of sight of Mawson's Hut. The location of the sample site is shown in the attached image. The second was a sample of the Cape Denison Amphibolite (GA sample_no 2122492; Lat 67.008 S; long 142.657E). The sample was taken from loose material adjacent to Granholm Hut (see attached image). The results appears in a brief format in AusGeo News (a GA publication) for the 100th AAE celebrations (AusGeo News 104 'Dec 2011' can downloaded at http://www.ga.gov.au/ausgeonews/download.jsp ). Also SHRIMP results are discussed in the Cape Denison Map (available at https://www.ga.gov.au/products/servlet/controller?event=GEOCAT_DETAILS&catno=72710 ). Both rock types are approved Geoscience Australia lithological names The sample sites are shown in the attached images Rock samples are stored at Geoscience Australia. proprietary
AAS_3313_V2_2014_15_Deep_Ocean_Camera_Observations_1 Deep Ocean Camera Observations, Dalton Polynya and Mertz Glacier region, V2 2014/15 AU_AADC STAC Catalog 2014-12-05 2015-01-25 111.7871, -67.2721, 146.7871, -60.3495 https://cmr.earthdata.nasa.gov/search/concepts/C1929062057-AU_AADC.umm_json The RSV Aurora Australis V2 – Casey Resupply and Marine Science Voyage took place from 5 December 2014 to 25 January 2015. The voyage code is v2_201415020. The principal objective of the voyage was to undertake the Casey Resupply and then conduct marine science in the Dalton Polynya and near the Mertz Glacier. A downwards looking video camera system was fitted to the CTD and operated during most casts. The system was remotely controlled and typically operated only while the CTD was near the bottom although some videos show the complete descent through the water column. The video footage for each deployment was labelled as follows: VOYAGE_DATE_TIME_SITE.MTS Where: VOYAGE = v2_201415020 DATE = YYYY-MM-DD TIME = HHMMUTC (in 24 hr time) SITE = the CTD site name (e.g. SiteA5) Details on each site, including geographic coordinates and depth, are available in the Marine Data Voyage Report. The underway data from the voyage is available here: https://data.aad.gov.au/metadata/records/201415020 proprietary
-AAS_3326_bathymetric_grid_casey_2013-2015_1 A high resolution bathymetric grid of the nearshore area at Casey station, Antarctica AU_AADC STAC Catalog 2013-12-23 2015-01-30 110.3633, -66.3122, 110.5703, -66.2311 https://cmr.earthdata.nasa.gov/search/concepts/C1333031752-AU_AADC.umm_json A high resolution bathymetric grid of the nearshore area at Casey station, Antarctica was produced by Geoscience Australia by combining data from two multibeam hydrographic surveys: 1) A survey conducted by the Royal Australian Navy in 2013/14. Refer to the metadata record 'Hydrographic survey HI545 by the RAN Australian Hydrographic Service at Casey, December 2013 to January 2014' with ID HI545_hydrographic_survey. 2) A survey conducted by Geoscience Australia and the Royal Australian Navy in 2014/15. Refer to the metadata record 'Hydrographic survey HI560 by the RAN Australian Hydrographic Service at Casey, December 2014 to February 2015' with ID HI560_hydrographic_survey and the metadata record 'Seafloor Mapping Survey, Windmill Islands and Casey region, Antarctica, December 2014 - February 2015' with ID AAS_3326_seafloor_mapping_casey_2014_15. The grid has a cell size of one metre and is stored in a UTM Zone 49S projection, based on WGS84. Further information is available from the Geoscience Australia website (see a Related URL). proprietary
AAS_3326_bathymetric_grid_casey_2013-2015_1 A high resolution bathymetric grid of the nearshore area at Casey station, Antarctica ALL STAC Catalog 2013-12-23 2015-01-30 110.3633, -66.3122, 110.5703, -66.2311 https://cmr.earthdata.nasa.gov/search/concepts/C1333031752-AU_AADC.umm_json A high resolution bathymetric grid of the nearshore area at Casey station, Antarctica was produced by Geoscience Australia by combining data from two multibeam hydrographic surveys: 1) A survey conducted by the Royal Australian Navy in 2013/14. Refer to the metadata record 'Hydrographic survey HI545 by the RAN Australian Hydrographic Service at Casey, December 2013 to January 2014' with ID HI545_hydrographic_survey. 2) A survey conducted by Geoscience Australia and the Royal Australian Navy in 2014/15. Refer to the metadata record 'Hydrographic survey HI560 by the RAN Australian Hydrographic Service at Casey, December 2014 to February 2015' with ID HI560_hydrographic_survey and the metadata record 'Seafloor Mapping Survey, Windmill Islands and Casey region, Antarctica, December 2014 - February 2015' with ID AAS_3326_seafloor_mapping_casey_2014_15. The grid has a cell size of one metre and is stored in a UTM Zone 49S projection, based on WGS84. Further information is available from the Geoscience Australia website (see a Related URL). proprietary
+AAS_3326_bathymetric_grid_casey_2013-2015_1 A high resolution bathymetric grid of the nearshore area at Casey station, Antarctica AU_AADC STAC Catalog 2013-12-23 2015-01-30 110.3633, -66.3122, 110.5703, -66.2311 https://cmr.earthdata.nasa.gov/search/concepts/C1333031752-AU_AADC.umm_json A high resolution bathymetric grid of the nearshore area at Casey station, Antarctica was produced by Geoscience Australia by combining data from two multibeam hydrographic surveys: 1) A survey conducted by the Royal Australian Navy in 2013/14. Refer to the metadata record 'Hydrographic survey HI545 by the RAN Australian Hydrographic Service at Casey, December 2013 to January 2014' with ID HI545_hydrographic_survey. 2) A survey conducted by Geoscience Australia and the Royal Australian Navy in 2014/15. Refer to the metadata record 'Hydrographic survey HI560 by the RAN Australian Hydrographic Service at Casey, December 2014 to February 2015' with ID HI560_hydrographic_survey and the metadata record 'Seafloor Mapping Survey, Windmill Islands and Casey region, Antarctica, December 2014 - February 2015' with ID AAS_3326_seafloor_mapping_casey_2014_15. The grid has a cell size of one metre and is stored in a UTM Zone 49S projection, based on WGS84. Further information is available from the Geoscience Australia website (see a Related URL). proprietary
AAS_3338_Davis_Gravel_Runway_2 Geotechnical and Environmental data for Potential Gravel Runway at Davis AU_AADC STAC Catalog 2012-12-21 2018-03-31 77.3877, -68.78414, 78.61816, -68.39918 https://cmr.earthdata.nasa.gov/search/concepts/C1273648982-AU_AADC.umm_json Project 3338 (2012-13), 3372 (2013-14), 5007 (2014-17) During the 2012/13 field season geotechnical and environmental investigations were undertaken at Davis Station in order to investigate the viability of the 'Coastal Site' as a potential gravel or hard surface runway through standard site investigation and environmental sampling techniques (Project 3338). The study area was referred to as 'Adams' Flat' for project purposes. Ongoing data acquisition was managed through Project 3372. For Project 5007 the sites of interest include Heidemann Valley. Both areas are in close proximity to Davis on Broad Peninsula. proprietary
AAS_339_geomor_1 Geomorphological data relating to the Windmill Islands AU_AADC STAC Catalog 1989-01-01 1989-12-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1625715062-AU_AADC.umm_json This dataset contains geomorphological data relating to the Windmill Islands, Wilkes Land, Antarctica. The dataset was captured at 1:10,000 and comprises of - glacial sediments, collected and identified from documented field observations (point data) - the locations of raised beach sequences (polygon data) - the Holocene Marine Incursion limits. The marine incursion limits are represented by a height in meters (line data). The attribute table contains an attribute hlmi_height_m field. - glacial sediments, collected and identified from documented field observations (point data) - of a Jokulhlaup event near Casey Station winter 1985 (ie outburst of water from beneath a cold ice-cap terminus on Law Dome) (point data). This event was observed and documented by Dr Ian D Goodwin. The attribute tables contains additional information. From the results of oxygen-isotope and solute analysis, the water was found to have originated as basal melt water. It contained a high total solute load with a dominant enrichment in alkalis, indicating that it has been squeezed through subglacial sediments for an extensive time period. This event represents one of the first known recordings in Antarctica and provides further insight into determining the subglacial hydrological regime beneath the Law Dome ice cap. - contour boundaries defining the marine incursion limits (line data). Interpretation of these boundaries can be carried out using sediment samples collected predominantly along the shoreline of these lakes. They represent a chronology of glacial events/influences on these lakes. Sediment samples taken as a profiled sequence or core can be dated using radiocarbon dating to provide a chronological picture and age occurance of deglaciation and glaciation cycles within the Windmill Islands. - the locations of Windmill Islands lichenometric observations. Attribute data contains the maximum thallus size recorded at each location. It includes observations of lichens growing on nunataks for which the time of deglaciation is known and observations of lichens growing on supraglacial moraine ridges for which the time of formation is not known. This dataset thus provides the basis for a relative chronology of moraine development based on the assumption that the growth of the lichen thallus has been constant since the time of deglaciation. - lakes and ponds identified from documented field observations (polygon and point data). Each lake/pond is represented as a polygon with a central point label which is linked to a separate an attribute table containing additional information. Sediment samples collected predominantly along the shoreline of these lakes represent a chronology of glacial events/influences on these lakes. Sediment samples taken as a profiled sequence or core can be dated using radiocarbon dating to provide a chronological picture and age occurance of deglaciation and glaciation cycles within the Windmill Islands. - Point data assigned to topographic profiles and transects and to the respective samples represented along these profiles. The point and line data contains attribute tables profile.aat and profile.pat assigned with the following items respectively : profile_name, descript, descript1, descript2, descript3 and profile.pat : profile_name, site, s_elev, br_elev, s_elev_source, br_elev_source, s_elev_qual, br_elev_qual. Data does not conform to Geoscience Australia's Data Dictionary as the data is too detailed. Glacial sediments were collected and identified from documented field observations compiled by Dr Ian D Goodwin from his own field notes and from the records of other workers, as well as topographic and surface features identified and interpreted on aerial photographs taken by then AUSLIG in the 1993-94 field season. Sediment samples collected predominantly along the shoreline of these lakes represent a chronology of glacial events/influences on these lakes. Sediment samples taken as a profiled sequence or core can be dated using radiocarbon dating to provide a chronological picture and age occurance of deglaciation and glaciation cycles within the Windmill Islands. proprietary
AAS_4011_PLATO_CSTAR_1 CSTAR photometry of 21845 stars around the South Celestial Pole observed from Kunlun Station at Dome A AU_AADC STAC Catalog 2008-01-01 2008-12-31 77.1, -80.4, 77.1, -80.4 https://cmr.earthdata.nasa.gov/search/concepts/C1667373832-AU_AADC.umm_json "The original CSTAR (Chinese Small Telescope ARray) consisted of four identical f/1.2 Schmidt telescopes on a common mount, located at Kunlun Station at Dome A, Antarctica. The CSTAR mount was fixed to look at the South Celestial Pole, with no tracking, in order to simplify the instrument. Each telescope had an entrance aperture of 145 mm and a 4.5 x 4.5 degree field-of-view. The telescopes used Andor 1k x 1k CCDs with a pixel size of 13 microns, giving 15 arcseconds per pixel. Each telescope observed through a different filter: either g, r, i or open. CSTAR was developed by Purple Mountain Observatory, the Nanjing Institute of Astronomical Optics and Technology, and the National Astronomical Observatories of China. CSTAR was supported by UNSW's PLATO observatory. The original CSTAR operated from 2008 to 2011 inclusive, producing about 3 TB of image data. The star catalog and photometry from 2008 is available here: http://casdc.china-vo.org/archive/cstar/ and duplicated in the AAD data archive. The data are freely available. You are invited to cite the relevant papers in the ""papers/"" directory. The data are described in the following two papers: Wang, L., Macri, L. M., Krisciunas, K., Wang, L., Ashley, M. C. B., Cui, X., Feng, L.-L., Gong, X., Lawrence, J. S., Liu, Q., Luong-Van, D., Pennypacker, C. R., Shang, Z., Storey, J. W. V., Yang, H., Yang, J., Yuan, X., York, D. G., Zhou, X., Zhu, Z., 2011, Photometry of Variable Stars from Dome A, Antarctica, The Astronomical Journal, 142, 155. Zhou, X., Fan, Z., Jiang, Z., Ashley, M. C. B., Cui, X., Feng, L., Gong, X., Hu, J., Kulesa, C. A., Lawrence, J. S., Liu, G., Luong-Van, D. M., Ma, J., Moore, A. M., Qin, W., Shang, Z., Storey, J. W. V., Sun, B., Travouillon, T., Walker, C. K., Wang, J., Wang, L., Wu, J., Wu, Z., Xia, L., Yan, J., Yang, J., Yang, H., Yuan, X., York, D., Zhang, Z., Zhu, Z., 2010, The First Release of the CSTAR Point Source Catalogue from Dome A, Antarctica, Publications of the Astronomical Society of the Pacific, 122, 347–353. The files/directories here are: README.txt A description of the data. papers/ Published papers from CSTAR (listed below). catalog.dat The coordinates of the 21845 stars in this study. catalog.fits The catalogue in FITS format. png/ Light curves for each of the 21845 stars. fits/ The photometric data in FITS format. In January 2015 a new CSTAR instrument was installed. This consists of two of the original CSTAR telescopes, placed on a tracking mount. For more information visit the website of the Chinese Center for Antarctic Astronomy (CCAA)." proprietary
@@ -879,8 +879,8 @@ AAS_4036_Casey_Assessments_1 Casey Station Site Assessments - 2016, 2018 AU_AADC
AAS_4036_Fuel-MarineRiskAssessment-MacquarieIsland-2017_4 Environmental risk assessment of groundwater from remediated fuel spill sites on land into the marine environment at subantarctic Macquarie Island AU_AADC STAC Catalog 2017-11-23 2018-01-13 158.93372, -54.50108, 158.94307, -54.49637 https://cmr.earthdata.nasa.gov/search/concepts/C1929062054-AU_AADC.umm_json " An ecotoxicological risk assessment of groundwater from two Macquarie Island fuel spill sites was conducted to assess the level of risk posed by the sites to the adjacent marine receiving environment. Experiments were conducted on Macquarie Island during the summer season of 2017/18. The two fuel spill sites (known as: Fuel Farm and Power House, see file: Map-macquarie_building_and_structures_14676.pdf) within the vicinity of the Macquarie Island research station had undergone intensive in situ remediation by the Australian Antarctic Division over the previous decade. Despite remediation efforts, groundwater leaching from the sites continued to contain some residual fuel contamination, with sheen observed at several shoreline seeps and chemical analysis of groundwater samples confirmed some hydrocarbon contamination remained. This study aimed to assess the level of residual risk posed by groundwater from these sites as it enters the adjacent marine environment. We ran a series of toxicity tests using composited samples of salinity-adjusted groundwater discharge, as an exposure medium to test the sensitivity of 11 locally collected marine invertebrate species to the groundwater. Groundwater sampling was conducted over two periods: 23-29/11/17 and 18-20/12/17, for use in two rounds of toxicity testing (referred to as test round 1 (A and B) and test round 2). Groundwater samples were collected from 22 groundwater monitoring points; 12 surface seeps and 7 previously installed piezometers. These monitoring points were located along the coastal margin of the of the fuel spill sites, at their boundary with the adjacent marine environment (see: Locations-Fuel Farm-groundwater monitoring.pdf and Locations-Powerhouse-groundwater monitoring.pdf). The 22 groundwater samples were used to prepare seven salinity-adjusted composite test solutions (TS), each composed of equal volumes of up to nine groundwater samples. Salinity adjustment was to approximately that of ambient seawater (34 ppt), using hypersaline brine (prepared from locally collected clean seawater, which was frozen, then partially defrosted to collect concentrated brine). A total of approximately 6 L of was prepared for each of the seven TSs. See file: MI Ecotox-2017-18_TestSolutions_v03.xlsx for TS details (including: collection, preparation and physicochemical analysis results). Eleven locally collected marine invertebrate species were used in the tests. Biota were collected from two sites on Macquarie Island, both within the vicinity of the research station but away from areas of known fuel contamination: 1). Garden Bay on the East Coast (54° 29' 56.9"" S, 158° 56' 28.8"" E) and 2). Hasselborough Bay on the West Coast (54° 29' 45.6"" S, 158° 55' 55.8"" E). See: Map-macquarie_building_and_structures_14676.pdf. Dates of collection of test biota were 1/12/2017 (for test round 1A), 6/12/2017 (for test round 1B) and 20 and 22/12/17 (for test round 2). The 11 test taxa were from six broad taxonomic groups: 2 amphipods (Paramoera sp., Parawaldeckia kidderi), 2 flatworms (Obrimoposthia wandeli, Obrimoposthia ohlini), 2 copepods (Tigriopus angulatus, Harpacticus sp.), 2 gastropods (Laevilitorina caliginosa, Macquariella hamiltoni), 2 bivalves (Gaimardia trapesina, Lasaea hinemoa) and 1 isopod (Exosphaeroma gigas). Test biota were observed for 14 or 21 days and survival observed periodically. Full details of toxicity test conditions are provided in the file: MI Ecotox-2017-18_RawTestObs v02.xlsx (worksheets: TestSummary, Species and Endpoints). This file also contains, on subsequent worksheets, the raw toxicity test observations for each text taxa. These raw result data are compiled in the file: MI Ecotox-2017-18_Test-DATA.xlsx, worksheet: Survival-ALL contains survival data for all tests and taxa. Subsequent worksheets provide data for each test taxa separately and also include any sublethal observations that were made. All data associated with test solution collection, composition and chemistry are provided in the file: MI Ecotox-2017-18_TestSolutions.xlsx. The following (A. – I.) provides a description for the files provided with this record: A. MI Ecotox-2017-18_A-Map-Groundwater monitoring sites.png Images of study sites. A.) Overall Macquarie Island station environment, with Fuel Farm (red) and Power House (blue) indicated and showing the close proximity of the two land based sites to the adjacent high energy marine receiving environment. B.) Line map indicating relative location sites; Power House (blue) and Fuel Farm (red) sites, within the Macquarie Island station area. C.) and D.) Aerial images of the two sites, showing groundwater monitoring point locations (piezometers and seeps) used to prepare the seven test solutions (TS) as per key; Power House (TS4 and TS5) and Fuel Farm (TS1, TS2, TS3, TS6 and TS7), respectively. Monitoring point labels correspond with those provided in the file: MI Ecotox-2017-18_D-TestSolutions.xlsx / TS-Collection. B. MI Ecotox-2017-18_B-Map-macquarie_building_and_structures_14676.pdf Map of overall Macquarie Island station area, showing locations referred to in this study relative to other station infrastructure; Fuel Farm and Power House (land based fuel contaminated sites) and Hasselborough Bay and Garden Bay (clean marine areas for collection of test biota). Produced by the Australian Antarctic Data Centre, July 2018. Map available at: https://data.aad.gov.au/aadc/mapcat/. Map Catalogue No. 14676. © Commonwealth of Australia 2018. C. MI Ecotox-2017-18_C-RawTestObs.xlsx Toxicity test condition details (in worksheets named: TestSummary, Species, Endpoints) and raw toxicity test observations for each text taxa (in subsequent worksheets). D. MI Ecotox-2017-18_D-TestSolutions.xlsx Details of test solutions, including collection, composition and chemistry. E. MI Ecotox-2017-18_E-Test-DATA.xlsx Compiled raw toxicity test results in long format. Worksheet: Survival-ALL contains survival data for all tests and taxa. Subsequent worksheets provide data for each test taxa separately and includes sublethal observations if made). F. MI Ecotox-2017-18_F-ScanLabBook.pdf Scanned copy of the laboratory notebook associated with these tests. Notes were recorded by Cath King and Jessica Holan during the 17/18 Macquarie Island field season. G. MI Ecotox-2017-18_G-ScanObservationSheets.pdf Scanned copy of the handwritten raw observation sheets used to record test observations (observations scored by: Cath King and Jessica Holan). H. MI Ecotox-2017-18_H-ChemicalAnalysis-ALS-COA.pdf Certificate of Analysis for chemistry results for samples analysed by Australian Laboratory Services (ALS) Environmental, Melbourne. Includes Total Recoverable Hydrocarbons (TRH; with and without silica gel clean up), nutrients (nitrogen) and a standard toxicity test (Microtox). Client sample ID with “Ecotox TS” prefix are those relevant to this study (other samples are associated with broader site remediation monitoring for the 17/18 season). I. MI Ecotox-2017-18_I-ChemicalAnalysis-ALS-QAQC.pdf Quality Assurance (QA) and Quality Control (QC) report provided by ALS, in association with the Certificate of Analysis. As previous, Client sample ID with “Ecotox TS” prefix are relevant to this study. J. MI Ecotox-2017-18_J-size measurements.zip Measures of specimen body lengths (mm). The .zip file contains a text file named: SizeMeasurements-README.txt, providing a description of the content associated with these data. " proprietary
AAS_4036_GPS_Survey_1 Macquarie Island Station GPS survey 2014, for the Hydrocarbon Risk and Remediation Program AU_AADC STAC Catalog 2014-12-02 2014-12-06 158.93595, -54.49973, 158.93595, -54.49973 https://cmr.earthdata.nasa.gov/search/concepts/C1214311667-AU_AADC.umm_json "The data was collected by Lisa Meyer with a Leica1200 RTK dGPS unit loaned from the Australian Antarctic Divisions Science Branch. Point data was collected within the Macquarie Island station limits to enable accurate mapping of the existing and newly installed infrastructure, as well as sampling sites, associated with the Remediation Program. Data included: 1. Infrastructure - the water sampling sites (mini/piezometers and seeps), soil sampling sites for the 2014-15 season (Annual pits, Environment Protection Authority sampling sites), aeration manifolds, the Permeable Reactive Barriers and drainage channels installed at the Main Power House (MPH) remediation site; 2. Building boundaries, footpaths, fence lines around the Fuel Farm and MPH, and any other permanent features close to, or closely associated with the Remediation infrastructure. 3. External position of the newly built Machinery Shed (a.k.a. the helicopter shed during station resupply) for mapping by the AADC. 4. Height data for the isthmus area relating to the remediation work. These are referred to as 'spot heights' and are not useful to generate contour maps, but rather give some general idea as to the terrain around sampling sites and potential transport pathways from the isthmus area to the adjacent ocean. 5. State Permanent Markers (10708, 10709, AUS211-RM3). 6. Base station data collected at permanent survey marker NMX1. 7. Surveying points used by Parks and Wildlife Service Tasmania, to assess the location of the shoreline around the isthmus. Data available include: 1. GPS raw data downloaded from the Leica GPS unit. There are three field survey files (one for each day's surveying). There are also three separate base station data files, associated with each field survey. This data requires the program Leica Geo Office to visualise the data and export it (LGO has a free download that can be used). Note: see comments in Q.7. 2. Updated raw data files - Three CSV files of the updated raw data, created by Scott Strong (DPIPWE) using the full version of Leica Geo Office. The datum is ITRF2000@2000, GRS1980 ellipsoid. Coordinates used were projected - Universal Transverse Mercator Zone 57. 3. A PDF file showing duplicate Point ID's that were changed in the three LGO projects. 4. Scott Strong shapefiles - Three ArcGIS shapefiles of the field data. These files were used to create individual shapefiles for separate features e.g. 'PRB infrastructure', because each of the field data files contains data from the entire days surveying. 5. Updated shapefiles - (i) The Scott Strong shapefiles copied and renamed with ""_ITRF2000"" in the name. (ii) The Scott Strong shapefiles copied and the data shifted to match the station WGS84 datum and shapefiles renamed with ""_WGS84"" in the name. See further details below. Scott Strong generated three shapefiles of the original field data, which were named: Macca02122014FieldSurvey Macca05122014FieldData Macca06122014FieldData These three files are in 'AAD Macquarie Island Dec 2014.zip'. The datum is ITRF2000@2000, GRS1980 ellipsoid. Coordinates used were projected UTM Zone 57. These shapefiles were renamed to: Macca02122014FieldData_ITRF2000 Macca05122014FieldData_ITRF2000 Macca06122014FieldData_ITRF2000 Note: The AADC uses the WGS84 datum from the mid-1990s for previously surveyed data of Macquarie Island. To transform data surveyed on ITRF2000@2000 to WGS84 apply ""The coordinate difference between ITRF 2000 and Auslig WGS84 values, based on coordinate values for NMX/1, is -1.40 E and -0.20 N."" given on page 3 of the survey report ""Macquarie Island OSG Survey Campaign, Voyage 8 Round Trip, March 2002"" by John VanderNiet and Nick Bowden. i.e. the eastings of the WGS84 data will be 1.40 metres greater than the ITRF2000@2000 data and the northings of the WGS84 data will be 0.20 metres greater than the ITRF2000@2000 data. A copy of the ITRF2000 shapefiles was created and edited in ArcMap to shift the data points to match the station WGS84 reference frame (i.e. by subtracting -1.4 m from the eastings and -0.2 m from the northings). The projection data is also changed to WGS84, rather than ITRF2000. These shapefiles are named: Macca02122014FieldData_WGS84station Macca05122014FieldData_WGS84station Macca06122014FieldData_WGS84station The data point names are not changed in the WGS84 shapefiles - the points that were averaged in the raw data, which Scott changed to his codes. e.g. NW1SS1 (point is called NW1 Scott Strong1). NW1 (i.e. north west 1) is a corner on the FF bund and also a point up in the Power House area somewhere (PRB mini cage). A Point Description (Point_Desc) field was added to the attribute tables of the WGS84 shapefiles to explain what the point codes are for each of the data points. The Macca06122014FieldData_WGS84station shapefile includes points collected in a survey of the isthmus shoreline at the request of Chris Howard of the Tasmanian Parks and Wildlife Service. These include points on the beach and up on the road along several transects. They have been labelled with codes as specified by Chris and have 'Parks and Wildlife isthmus survey mark' in the Point_desc field in the attribute table. The Australian Antarctic Data Centre used data from the WGS84 shapefiles to create data in its GIS database representing the Machinery Shed and some fences, gates and foot paths at the station." proprietary
AAS_4036_RemediationProject_2009-16_RawData_AccessDB_1 Historical remediation data from 2009-2016 from Macquarie Island, Casey Station, Davis Station and Lake Dingle AU_AADC STAC Catalog 2009-01-01 2017-03-15 77.9611, -68.5804, 158.9472, -54.4917 https://cmr.earthdata.nasa.gov/search/concepts/C1380160960-AU_AADC.umm_json The data is all contained within an Access database. This is the historical data for the Remediation Project from 2009 up until the end of 2016.The database contains some unprocessed data from the 2016-17 field season so this will likely be updated in future revisions/submissions of this database. In terms of locality, this covers Macquarie Island, Casey Station (biopiles, fuel spill sites), Davis and Lake Dingle. Tables/Data included: Sampling metadata (barcodes, collector, season, location, etc.) Laboratory batch information (details of analysis events in lab) Hydrocarbon (TPH) in soils Hydrocarbon (TPH) in water Nutrients in soils Nutrients in water Volatile compounds (VOC) in soils Volatile compounds (VOC) in water Sample weights and volumes Sample moisture levels Soil grain size QAQC analysis of the above data Minor analysis on invertebrates The database includes some data collected for another project in addition to AAS 4036. For example, AAS 4029. The database includes barcodes which were allocated to the data. Each barcode is linked to an AAS project. However, only one barcode could be allocated to any given data even if it was collected for more than one project. proprietary
-AAS_4036_aerial_mosaic_macquarie_jan2015_1 Aerial photograph mosaic of Macquarie Island isthmus, 31 January 2015 ALL STAC Catalog 2015-01-31 2015-01-31 158.9332, -54.5013, 158.9414, -54.4972 https://cmr.earthdata.nasa.gov/search/concepts/C1214311666-AU_AADC.umm_json A custom built flying wing (FX79 airframe) Unmanned Aerial Vehicle (UAV) was flown at approximately 120 metres above surface level over the isthmus at Macquarie Island on 31 January 2015. The isthmus is where the station is located. Attached to the UAV were a Canon EOS M camera and a GPS. High resolution aerial photographs were taken and a georeferenced mosiac of the photographs was later created using Pix4D software. The mosaic was not orthorectified. The data includes the original georeferenced mosaic as a geotiff and a version that has been further georeferenced to the station WGS84 horizontal datum. The pixel size is 1.3 centimetres. proprietary
AAS_4036_aerial_mosaic_macquarie_jan2015_1 Aerial photograph mosaic of Macquarie Island isthmus, 31 January 2015 AU_AADC STAC Catalog 2015-01-31 2015-01-31 158.9332, -54.5013, 158.9414, -54.4972 https://cmr.earthdata.nasa.gov/search/concepts/C1214311666-AU_AADC.umm_json A custom built flying wing (FX79 airframe) Unmanned Aerial Vehicle (UAV) was flown at approximately 120 metres above surface level over the isthmus at Macquarie Island on 31 January 2015. The isthmus is where the station is located. Attached to the UAV were a Canon EOS M camera and a GPS. High resolution aerial photographs were taken and a georeferenced mosiac of the photographs was later created using Pix4D software. The mosaic was not orthorectified. The data includes the original georeferenced mosaic as a geotiff and a version that has been further georeferenced to the station WGS84 horizontal datum. The pixel size is 1.3 centimetres. proprietary
+AAS_4036_aerial_mosaic_macquarie_jan2015_1 Aerial photograph mosaic of Macquarie Island isthmus, 31 January 2015 ALL STAC Catalog 2015-01-31 2015-01-31 158.9332, -54.5013, 158.9414, -54.4972 https://cmr.earthdata.nasa.gov/search/concepts/C1214311666-AU_AADC.umm_json A custom built flying wing (FX79 airframe) Unmanned Aerial Vehicle (UAV) was flown at approximately 120 metres above surface level over the isthmus at Macquarie Island on 31 January 2015. The isthmus is where the station is located. Attached to the UAV were a Canon EOS M camera and a GPS. High resolution aerial photographs were taken and a georeferenced mosiac of the photographs was later created using Pix4D software. The mosaic was not orthorectified. The data includes the original georeferenced mosaic as a geotiff and a version that has been further georeferenced to the station WGS84 horizontal datum. The pixel size is 1.3 centimetres. proprietary
AAS_4037_4050_Krill_Microscopy_1 Microscopy imaging of live Antarctic krill (Euphausia superba) AU_AADC STAC Catalog 2014-09-01 2014-09-30 147.308, 42.976, 147.308, 42.976 https://cmr.earthdata.nasa.gov/search/concepts/C1214311668-AU_AADC.umm_json Microscopy imaging of live Antarctic krill using a Leica M205C dissecting stereo-microscope with a Leica DFC 450 camera and Leica LAS V4.0 software. Krill were held in a custom made 'krill trap', details provided in manuscript in section eight of this form. The data are available as a single video file. These data are part of Australian Antarctic Science (AAS) projects 4037 and 4050. Project 4037 - Experimental krill biology: Response of krill to environmental change The experimental krill research project is designed to focus on obtaining life history information of use in managing the krill fishery - the largest Antarctic fishery. In particular, the project will concentrate on studies into impacts of climate change on key aspects of krill biology and ecology. Project 4050 - Assessing change in krill distribution and abundance in Eastern Antarctica Antarctic krill is the key species of the Southern Ocean ecosystem. Its fishery is rapidly expanding and it is vulnerable to changes in climate. Australia has over a decade of krill abundance and distribution data collected off Eastern Antarctica. This project will analyse these datasets and investigate if krill abundance and distribution has altered over time. The results are important for the future management of the fishery, as well as understanding broader ecological consequences of change in this important species. proprietary
AAS_4037_Krill_Modelling_1 Modelling growth and reproduction of Antarctic krill, Euphausia superba, based on temperature, food and resource allocation amongst life history functions AU_AADC STAC Catalog 2012-07-01 2017-05-01 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1559902849-AU_AADC.umm_json This model was produced as part of Australian Antarctic Science project 4037 - Experimental krill biology: Response of krill to environmental change - The experimental krill research project is designed to focus on obtaining life history information of use in managing the krill fishery - the largest Antarctic fishery. In particular, the project will concentrate on studies into impacts of climate change on key aspects of krill biology and ecology. This metadata record is to reference the paper that describes the model. There is no archived data output from this data product. Taken from the abstract of the referenced paper: Estimates of productivity of Antarctic krill, Euphausia superba, are dependent on accurate models of growth and reproduction. Incorrect growth models, specifically those giving unrealistically high production, could lead to over-exploitation of the krill population if those models are used in setting catch limits. Here we review available approaches to modelling productivity and note that existing models do not account for the interactions between growth and reproduction and variable environmental conditions. We develop a new energetics moult-cycle (EMC) model which combines energetics and the constraints on growth of the moult-cycle. This model flexibly accounts for regional, inter- and intra-annual variation in temperature, food supply, and day length. The EMC model provides results consistent with the general expectations for krill growth in length and mass, including having thin krill, as well as providing insights into the effects that increasing temperature may have on growth and reproduction. We recommend that this new model be incorporated into assessments of catch limits for Antarctic krill. proprietary
AAS_4037_Long-term_Krill-CO2_1 Impacts of increased CO2 on Antarctic krill AU_AADC STAC Catalog 2015-01-25 2016-02-01 -180, -65, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1553718877-AU_AADC.umm_json Long-term experiment on increased CO2 level on krill physiology. Krill were exposed to a range of CO2 conditions 400-4000ppm over a year, and their growth, mortality, and physiology were monitored. -List of files- Ericson Krill Ocean Acidification Study Raw Data_for data centre.xlsx: This file contains data on krill growth, mortality, physiology, and biochemistry, as well as information on water chemistry throughout 1 year period of the experiment. Ericson et al. Adult krill OA MS final submission.pdf: Unpublished manuscript of the experiment including all methods of the experiment. proprietary
@@ -912,8 +912,8 @@ AAS_4075_ABN1314_BoreholeTemperature_1 ABN1314 borehole temperature profile for
AAS_4075_ABN1314_BoreholeTemperature_1 ABN1314 borehole temperature profile for the ABN1314 main ice core drill hole ALL STAC Catalog 2013-12-01 2014-01-31 111.366531, -71.166889, 111.366531, -71.166889 https://cmr.earthdata.nasa.gov/search/concepts/C1351116005-AU_AADC.umm_json The ABN1314 borehole temperature profile was completed on the 13/1/2014 using the CIC borehole temperature logger. Measurements were completed by Simon Sheldon. proprietary
AAS_4075_ABN1314_Glacial_isotopic_composition_2 Aurora Basin North, glacial isotopic composition data from the 2013-2014 season AU_AADC STAC Catalog 2013-12-24 2014-01-14 111.366531, -71.166889, 111.366531, -71.166889 https://cmr.earthdata.nasa.gov/search/concepts/C1339057422-AU_AADC.umm_json The ABN1314_Glacial_isotopic_composition is the measured oxygen and deuterium isotopic data for the 'ABN' (Aurora Basin North) ice core (ABN1314) collected during the Antarctic 13/14 season. The glacial isotopic composition was measured on melted ice samples using a Picarro water isotope analyser (L2130-i). A key output for the ABN project will be the time series of water isotopes (d18O and dD), which provides a temperature proxy record. In addition, deuterium excess can be computed, to explore moisture source conditions, including its applicability as a source temperature. In addition, the time series of water isotopes (d18O and dD) will also contribute to the multi proxy approach for the chronological control of the ABN ice core. An extra isotope spreadsheet was added to the dataset in June, 2020. proprietary
AAS_4075_ABN1314_chem_data_1 Chemical concentrations of trace ions from the Aurora Basin North ice core (ABN1314 main) drilled as part of AAS#4075 AU_AADC STAC Catalog 2013-12-01 2014-01-31 111.366531, -71.166889, 111.366531, -71.166889 https://cmr.earthdata.nasa.gov/search/concepts/C1351116139-AU_AADC.umm_json Chemical concentrations of trace ions from the Aurora Basin North ice core (ABN1314 main) drilled as part of AAS#4075. The ABN1314 ice core extends from 3.9m to 303m. Chemical concentrations of trace ions are given in micro equivalents per litre (uEq/L). Ions measured are MSA, Nitrate, Sulphate, Sodium, Potassium, Magnesium and Calcium. proprietary
-AAS_4075_ABN_continuousGas-CFA_1 ABN continuous gas CFA - methane (CH4) and carbon monoxide (CO) AU_AADC STAC Catalog 2013-12-01 2014-01-31 111.366531, -71.166889, 111.366531, -71.166889 https://cmr.earthdata.nasa.gov/search/concepts/C1380160790-AU_AADC.umm_json The ABN_continuousGas-CFA is the measured methane (CH4) and carbon monoxide (CO) from the ABN (Aurora Basin North) ice core (ABN1314) collected during the Antarctic 13/14 season. proprietary
AAS_4075_ABN_continuousGas-CFA_1 ABN continuous gas CFA - methane (CH4) and carbon monoxide (CO) ALL STAC Catalog 2013-12-01 2014-01-31 111.366531, -71.166889, 111.366531, -71.166889 https://cmr.earthdata.nasa.gov/search/concepts/C1380160790-AU_AADC.umm_json The ABN_continuousGas-CFA is the measured methane (CH4) and carbon monoxide (CO) from the ABN (Aurora Basin North) ice core (ABN1314) collected during the Antarctic 13/14 season. proprietary
+AAS_4075_ABN_continuousGas-CFA_1 ABN continuous gas CFA - methane (CH4) and carbon monoxide (CO) AU_AADC STAC Catalog 2013-12-01 2014-01-31 111.366531, -71.166889, 111.366531, -71.166889 https://cmr.earthdata.nasa.gov/search/concepts/C1380160790-AU_AADC.umm_json The ABN_continuousGas-CFA is the measured methane (CH4) and carbon monoxide (CO) from the ABN (Aurora Basin North) ice core (ABN1314) collected during the Antarctic 13/14 season. proprietary
AAS_4077_ELEV_1 Glacier Elevation/Ice Sheet Elevation AU_AADC STAC Catalog 2008-12-29 2013-01-22 -180, -90, 180, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1366863846-AU_AADC.umm_json The IceBridge Riegl Laser Altimeter L2 Geolocated Surface Elevation Triplets (ILUTP2) data set contains surface range values for Antarctica and Greenland derived from measurements captured using the Riegl Laser Altimeter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, proprietary
AAS_4077_GRAV_1 Free air gravity disturbances derived from measurements taken over Antarctica AU_AADC STAC Catalog 2012-11-22 2013-01-22 -180, -90, 180, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1366863791-AU_AADC.umm_json This data set contains geolocated free air gravity disturbances derived from measurements taken over Antarctica using the GT-1A gravity meter S-019. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project proprietary
AAS_4077_ICE_THICKNESS_1 Ice thickness, surface and bed elevation, and echo strength measurements taken over Antarctica AU_AADC STAC Catalog 2010-10-22 2013-01-25 -180, -90, 180, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1366863839-AU_AADC.umm_json This data set contains ice thickness, surface and bed elevation, and echo strength measurements taken over Antarctica using the Hi-Capability Airborne Radar Sounder (HiCARS) instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project proprietary
@@ -923,8 +923,8 @@ AAS_4078_Wilks_SAZ_47S_sediment_trap_dataset_1 Biogeochemical flux and phytoplan
AAS_4078_diatoms_biogenic_flux_1 Diatom species and biogenic particle fluxes in the Australian sector of the southern Antarctic Zone AU_AADC STAC Catalog 2001-11-30 2002-09-29 139.89, -61.75, 139.9, -61.74 https://cmr.earthdata.nasa.gov/search/concepts/C1214305661-AU_AADC.umm_json Diatom and biogenic particle fluxes were investigated over a one-year period (2001-02) at the southern Antarctic Zone in the Australian Sector of the Southern Ocean. Two vertically moored sediment traps were deployed at 60 degrees 44.43'S 139 degrees 53.97' E at 2000 and 3800 m below sea-level. In these data sets we present the results on the temporal and vertical variability of total diatom flux, species composition and biogenic particle fluxes during a year. A detailed description of the field experiment, sample processing and counting methods can be found in Rigual-Hernandez et al. (2015). Total fluxes of particulates at both traps were highly seasonal, with maxima registered during the austral summer (up to 1151 mg m-2 d-1 at 2000 m and 1157 mg m-2 d-1 at 3700 m) and almost negligible fluxes during winter (up to 42 mg m-2 d-1 at 2000 m and below detection limits at 3700 m). Particulate fluxes were slightly higher at 2000 m than at 3700 m (deployment average = 261 and 216 mg m-2 d-1, respectively). Biogenic silica (SiO2) was the dominant bulk component, regardless of the sampling period or depth (deployment average = 76% at 2000 and 78% at 3700 m). Highest relative contribution of opal was registered from the end of summer through early-autumn at both depths. Secondary contributors were carbonate (CaCO3) (7% at 2000 m and 9% at 3700 m) and particulate organic carbon (POC) (1.4% at 2000 m and 1.2% at 3700 m). The relative concentration of carbonate and POC was at its highest in austral spring and summer. Diatom frustules from 61 taxa were identified over the entire experiment. The dominant species of the diatom assemblage was Fragilariopsis kerguelensis with a mean flux between 53 x 106 and 60 x 106 valves m-2 day-1 at 2000 m (annualized mean and deployment average, respectively). Secondary contributors to the diatom assemblage at 2000 and 3700 m were Thalassiosira lentiginosa, Thalassiosira gracilis var. gracilis, Fragilariopsis separanda, Fragilariopsis pseudonana, Fragilariopsis rhombica, Fragilariopsis curta and Azpeitia tabularis. Data available: two excel files containing sampling dates and depths, raw counts, relative abundance and fluxes (valves m-2 d-1) of the diatom species, and biogenic particle fluxes found at 2000 m and 3700 m depth. Each file contains four spreadsheets: raw diatom valve counts, relative abundance of diatom species and valve flux of diatom species and biogenic particle composition and fluxes. Detailed information of the column headings is provided below. Cup - Cup (=sample) number Depth - vertical location of the sediment trap in meters below the surface Mid-point date - Mid date of the sampling interval Length (days) - number of days the cup was open Girdle bands instead of valves were counted for Dactyliosolen antarcticus Castracane. Therefore, D. antarcticus girdles counts were not included in relative abundance calculations proprietary
AAS_4078_diatoms_biogenic_flux_subantarctic_1 Diatom species and biogenic particle fluxes in the Australian sector of the Subantarctic and Polar Frontal Zones at ~ 1 km depth AU_AADC STAC Catalog 1997-09-01 2007-10-31 141.75, -53.75, 142.06, -46.76 https://cmr.earthdata.nasa.gov/search/concepts/C1214311684-AU_AADC.umm_json Diatom and biogenic particle fluxes were investigated over a two-year and six-year periods at the Subantarctic and Polar Frontal Zones, respectively, in the Australian Sector of the Southern Ocean. Both sites were located along ~ 140 degrees E: station 47 degrees S was set on the abyssal plain of the central SAZ whereas station 54 degrees S was placed on a bathymetric high of the Southeast Indian Ridge in the PFZ. The data sets contain diatom species and biogeochemical flux data measured at 1000 m at the 47 degrees S site between 1999-2001 and at 800 m at the 54 degrees S site during six selected years between 1997-2007. All traps were MacLane Parflux sediment traps: conical in shape with a 0.5 m2 opening area and equipped with a carousel of 13 or 21 sampling cups. Shortest intervals corresponded with the austral summer and autumn ranging typically between 4.25 and 10 days, whereas the longest intervals were 60 days and corresponded with winter. Total fluxes of particulates at both traps were highly seasonal, with maxima registered during the austral spring and summer and very low fluxes during winter. Seasonality was more pronounced in the 54 degrees S site. Biogenic silica (SiO2) was the dominant bulk component in the PFZ while carbonate (CaCO3) dominated the particle fluxes at the SAZ. POC export was relatively similar between sites despite significant differences in the total diatom flux. Diatom frustules from 94 taxa were identified over the entire experiment. The dominant species of the diatom assemblage was Fragilariopsis kerguelensis at both sites, representing 43% and 59% of the integrated diatom assemblage at the 47 degrees S and 54 degrees S sites, respectively. Secondary contributors to the diatom assemblage at the 47 degrees S were Azpeitia tabularis, Thalassiosira sp. 1, Nitzschia bicapitata, resting spores of Chaetoceros spp., Thalassiosira oestrupii var. oestrupii, Hemidiscus cuneiformis and Roperia tesselata. Subordinate contributions to the diatom assemblage correspond to Pseudo-nitzschia lineola cf. lineola, Pseudo-nitzschia heimii, Thalassiosira gracilis group and Fragilariopsis pseudonana, Fragilariopsis rhombica and Thalassiosira lentiginosa. Data available: two excel files containing sampling dates and depths, raw counts, relative abundance and fluxes (valves m-2 d-1) of the diatom species, and biogenic particle fluxes measured at 1000 m and 800 m depth at the 47 degrees S and 54 degrees S sites, respectively. Each file contains four spreadsheets: raw diatom valve counts, relative abundance of diatom species and valve flux of diatom species and biogenic particle composition and fluxes. Detailed information of the column headings is provided below. Cup - Cup (=sample) number Depth - vertical location of the sediment trap in meters below the surface Mid-point date - Mid date of the sampling interval Length (days) - number of days the cup was open Girdle bands instead of valves were counted for Dactyliosolen antarcticus Castracane. Therefore, D. antarcticus girdles counts were not included in relative abundance calculations. Dates of data collection: 47 degrees S site: July 1999 - October 2001 (two-year record) 54 degrees S site: September 1997 - February 1998, July 1999 - August 2000, November 2002 - October 2004 and December 2005 - October 2007 (six-year record). proprietary
AAS_4086_Weighbridge_1 Data from the Bechervaise Island Adelie penguin weighbridge, 2006 onwards AU_AADC STAC Catalog 2006-01-01 62.78961, -67.62282, 62.88849, -67.582 https://cmr.earthdata.nasa.gov/search/concepts/C2102891796-AU_AADC.umm_json The dataset comprises records of crossings by Adelie penguins of a weighbridge and gateway established on Bechervaise Island. The weighbridge and gateway are positioned so that most or all of the penguins breeding in a set of sub-colonies on the island cross the weighbridge when they leave the colony to forage and when they return from foraging. The gateway records the time of each crossing, the dynamic weight of the penguin as it crosses, and the identity of penguins that have been sub-cutaneously tagged. The weighbridge and gateway operate continuously throughout the austral breeding season. The data are currently in an unprocessed form. proprietary
-AAS_4087_Fulmarine_petrel_tracking_study_Hop_Island_2015_16_1 AAS 4087 Fulmarine petrel tracking study, Hop Island, 2015/16 ALL STAC Catalog 2015-11-01 2016-03-31 68.55469, -69.225, 81.91406, -64.62388 https://cmr.earthdata.nasa.gov/search/concepts/C1625715004-AU_AADC.umm_json The foraging ecology of three fulmarine petrels including Cape petrels, Southern fulmars and Antarctic petrels were investigated at Hop Island during the 2015/16 austral summer. Two datasets were generated: 1) tracking data from Fulmarine petrels, and 2) stable isotope analysis of blood, feathers and egg shells. Tracking data were collected using Ecotone GPS trackers attached to the birds back feathers with tape. Location data has been interpolated using great circle distance to a time step of 15 minutes and include a record of whether the bird dived during that time period or not. Each location point was assigned a breeding stage (incubation or chick rearing) based on individual nest activities. Stable isotope ratios of carbon (13C/12C) and nitrogen (15N/14N) were determined by analysing 1 mg aliquots through continuous flow - elemental analysis - isotope ratio mass spectrometry (CF-EA-IRMS). Isotopic values of blood reflect approximately the last 52 days before sampling and thus the incubation period of all three species. Egg membranes and feathers remain metabolically inert after formation, and hence reflect the trophic niche during the pre-laying and moult period, respectively. We collected moult feathers during the chick-rearing period and therefore assumed that these were formed one year prior to the collection date and thus represent the trophic niche of the chick-rearing period one year earlier (austral summer 2014-15). proprietary
AAS_4087_Fulmarine_petrel_tracking_study_Hop_Island_2015_16_1 AAS 4087 Fulmarine petrel tracking study, Hop Island, 2015/16 AU_AADC STAC Catalog 2015-11-01 2016-03-31 68.55469, -69.225, 81.91406, -64.62388 https://cmr.earthdata.nasa.gov/search/concepts/C1625715004-AU_AADC.umm_json The foraging ecology of three fulmarine petrels including Cape petrels, Southern fulmars and Antarctic petrels were investigated at Hop Island during the 2015/16 austral summer. Two datasets were generated: 1) tracking data from Fulmarine petrels, and 2) stable isotope analysis of blood, feathers and egg shells. Tracking data were collected using Ecotone GPS trackers attached to the birds back feathers with tape. Location data has been interpolated using great circle distance to a time step of 15 minutes and include a record of whether the bird dived during that time period or not. Each location point was assigned a breeding stage (incubation or chick rearing) based on individual nest activities. Stable isotope ratios of carbon (13C/12C) and nitrogen (15N/14N) were determined by analysing 1 mg aliquots through continuous flow - elemental analysis - isotope ratio mass spectrometry (CF-EA-IRMS). Isotopic values of blood reflect approximately the last 52 days before sampling and thus the incubation period of all three species. Egg membranes and feathers remain metabolically inert after formation, and hence reflect the trophic niche during the pre-laying and moult period, respectively. We collected moult feathers during the chick-rearing period and therefore assumed that these were formed one year prior to the collection date and thus represent the trophic niche of the chick-rearing period one year earlier (austral summer 2014-15). proprietary
+AAS_4087_Fulmarine_petrel_tracking_study_Hop_Island_2015_16_1 AAS 4087 Fulmarine petrel tracking study, Hop Island, 2015/16 ALL STAC Catalog 2015-11-01 2016-03-31 68.55469, -69.225, 81.91406, -64.62388 https://cmr.earthdata.nasa.gov/search/concepts/C1625715004-AU_AADC.umm_json The foraging ecology of three fulmarine petrels including Cape petrels, Southern fulmars and Antarctic petrels were investigated at Hop Island during the 2015/16 austral summer. Two datasets were generated: 1) tracking data from Fulmarine petrels, and 2) stable isotope analysis of blood, feathers and egg shells. Tracking data were collected using Ecotone GPS trackers attached to the birds back feathers with tape. Location data has been interpolated using great circle distance to a time step of 15 minutes and include a record of whether the bird dived during that time period or not. Each location point was assigned a breeding stage (incubation or chick rearing) based on individual nest activities. Stable isotope ratios of carbon (13C/12C) and nitrogen (15N/14N) were determined by analysing 1 mg aliquots through continuous flow - elemental analysis - isotope ratio mass spectrometry (CF-EA-IRMS). Isotopic values of blood reflect approximately the last 52 days before sampling and thus the incubation period of all three species. Egg membranes and feathers remain metabolically inert after formation, and hence reflect the trophic niche during the pre-laying and moult period, respectively. We collected moult feathers during the chick-rearing period and therefore assumed that these were formed one year prior to the collection date and thus represent the trophic niche of the chick-rearing period one year earlier (austral summer 2014-15). proprietary
AAS_4087_adelie_penguin_foraging_hop_island_2012_13_1 Foraging ecology of Adelie penguins at Hop Island, Rauer Group 2012/13 AU_AADC STAC Catalog 2012-12-06 2013-01-14 72.6041, -69.0623, 78.0234, -66.1287 https://cmr.earthdata.nasa.gov/search/concepts/C1403324995-AU_AADC.umm_json At Hop Island in the Rauer Group during the 2012/13 field season combinations of data loggers were deployed on different adelie penguins. The data loggers were GPS (two types), time-depth recorders and accelerometers. The accelerometer records head movement to identify when the bird captures prey. The units were later retrieved and the data downloaded. A document included with the data has further information about the data. The data were collected following protocols approved by the Australian Antarctic Animal Ethics Committee and supported through the Australian Antarctic program through Australian Antarctic Science project 4087. Data from GPS units deployed at Hop Island in 2011/12 is described by the metadata record with ID AAS_4087_adelie_penguin_tracking_hop_island_2011_12. proprietary
AAS_4087_adelie_penguin_tracking_hop_island_2011_12_1 GPS location data for Adelie penguins at Hop Island, Rauer Group 2011/12 AU_AADC STAC Catalog 2011-11-27 2012-01-22 73.5746, -68.9945, 79.0037, -65.6911 https://cmr.earthdata.nasa.gov/search/concepts/C1380161446-AU_AADC.umm_json GPS units were deployed on Adelie penguins at Hop Island in the Rauer Group during the 2011/12 field season. Deployments were made during the incubation, guard and creche periods. The units were later retrieved and the data downloaded. The data were collected following protocols approved by the Australian Antarctic Animal Ethics Committee and supported through the Australian Antarctic program through Australian Antarctic Science project 4087. The GPS units were supplied by Louise Emmerson of the Australian Antarctic Division through the AAS project 4087 budget and deployed and retrieved by Nobuo Kokubun of the National Institute of Polar Research, Japan with field assistance from Barbara Wienecke of the Australian Antarctic Division. Further information is available with the data. proprietary
AAS_4088_Adelie_Counts_Cameras_1 Attendance counts of Adelie penguins from remotely operating cameras in East Antarctica AU_AADC STAC Catalog 2013-10-01 2017-02-28 60, -70, 140, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1517284097-AU_AADC.umm_json This dataset comprises counts of Adelie penguins attending breeding sites from images obtained with 20 remotely operating cameras across East Antarctica. Counts were made of adults, occupied nests and chicks every few days throughout the breeding season from October through to February. Locations of cameras are given in an associated dataset (Photographic images of seabird nesting sites in the Antarctic, collected by remote camera) which also provides the images obtained from the cameras. proprietary
@@ -932,50 +932,50 @@ AAS_4088_Adelie_Diet_2 Diet results from Adelie penguins at Bechervaise Island a
AAS_4088_Adelie_breeding_colony_boundaries_1 Boundaries of Adelie penguin breeding colonies at numerous breeding sites across east Antarctica AU_AADC STAC Catalog 1981-09-01 2017-03-31 60, -70, 140, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1384657154-AU_AADC.umm_json The dataset contains boundaries of Adelie penguin breeding colonies at numerous breeding sites across east Antarctica. The boundary data were obtained using a range of methods which are detailed in separate spatial group-season accounts. The database of potential Adelie penguin breeding habitat in Southwell et al. (2016a) was used to associate colony boundaries to a particular breeding site and structure how the boundaries are stored. The breeding site database has a unique identifying code of every site of potential breeding habitat in East Antarctica, and the sites are aggregated into spatial sub-groups and then spatial groups. The file structure in which the boundaries are stored has a combination of 'group' and 'split-year breeding season' at the top level (eg VES 2015-16 contains all boundaries in spatial group VES (Vestfold Hills and islands) taken in the 2015-16 breeding season). Within each group-year folder are sub-folders for each breeding site where photos were taken (eg IS_72276 is Gardner Island in the VES group). proprietary
AAS_4088_Adelie_occupancy_Balaena_1 Adelie penguin occupancy survey of the Balaena Islands, 2012 ALL STAC Catalog 2012-01-26 2012-01-26 111, -65.1, 111.2, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1388926438-AU_AADC.umm_json An occupancy survey on 26 January 2012 found 1 island (70166) along the coast between 111 degrees 00'E - 111 degrees 10'E had populations of breeding Adelie penguins. The survey was conducted from a fixed wing aircraft and oblique aerial photographs were taken of the occupied site. The aerial photographs were geo-referenced to the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E158) and the boundaries of penguin colonies were digitised from the geo-referenced photos with not intentional buffer. Note the quality of the aerial photos was poor and so the resultant boundary mapping will not be very accurate. Also in the Balaena Islands there is a historic record from the 50s of penguins nesting on Thompson Islet (70166). When aerial photos were taken of this island penguins could not be detected. proprietary
AAS_4088_Adelie_occupancy_Balaena_1 Adelie penguin occupancy survey of the Balaena Islands, 2012 AU_AADC STAC Catalog 2012-01-26 2012-01-26 111, -65.1, 111.2, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1388926438-AU_AADC.umm_json An occupancy survey on 26 January 2012 found 1 island (70166) along the coast between 111 degrees 00'E - 111 degrees 10'E had populations of breeding Adelie penguins. The survey was conducted from a fixed wing aircraft and oblique aerial photographs were taken of the occupied site. The aerial photographs were geo-referenced to the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E158) and the boundaries of penguin colonies were digitised from the geo-referenced photos with not intentional buffer. Note the quality of the aerial photos was poor and so the resultant boundary mapping will not be very accurate. Also in the Balaena Islands there is a historic record from the 50s of penguins nesting on Thompson Islet (70166). When aerial photos were taken of this island penguins could not be detected. proprietary
-AAS_4088_Adelie_occupancy_Bechervaise_2013_1 Adelie penguin occupancy survey of Bechervaise Island, 2013 AU_AADC STAC Catalog 2013-01-09 2013-01-09 62.806, -67.588, 62.808, -67.586 https://cmr.earthdata.nasa.gov/search/concepts/C1384657571-AU_AADC.umm_json All subcolonies on Bechervaise Island were mapped with a hand held GPS (Garmin Legend) on the 9th of January 2013). The mapping was undertaken by Julie McInnes and Helen Achurch. The colonies were mapped at a constant 2m buffer. If subcolonies were less than 2m apart they were mapped in the same outline, colonies greater than 2m apart were mapped separately. The final layer has a 2m buffer around the colony included in the layer. proprietary
AAS_4088_Adelie_occupancy_Bechervaise_2013_1 Adelie penguin occupancy survey of Bechervaise Island, 2013 ALL STAC Catalog 2013-01-09 2013-01-09 62.806, -67.588, 62.808, -67.586 https://cmr.earthdata.nasa.gov/search/concepts/C1384657571-AU_AADC.umm_json All subcolonies on Bechervaise Island were mapped with a hand held GPS (Garmin Legend) on the 9th of January 2013). The mapping was undertaken by Julie McInnes and Helen Achurch. The colonies were mapped at a constant 2m buffer. If subcolonies were less than 2m apart they were mapped in the same outline, colonies greater than 2m apart were mapped separately. The final layer has a 2m buffer around the colony included in the layer. proprietary
-AAS_4088_Adelie_occupancy_Bechervaise_2016_1 Adelie penguin occupancy survey of Bechervaise Island, 2016 AU_AADC STAC Catalog 2016-12-21 2016-12-21 62.806, -67.588, 62.808, -67.586 https://cmr.earthdata.nasa.gov/search/concepts/C1384657597-AU_AADC.umm_json Adelie colony boundaries at Bechervaise Island were mapped by Matthew Pauza on the 21 Dec 2016. Subcolonies were mapped by circumnavigating the perimeter on foot while carrying a Garmin GPS (Etrex30) to record the track. When mapping the perimeter of the subcolonies a buffer distance of approximately 2.5 meters was maintained between the mapper and the breeding birds. This buffer distance was reduced by .5m to between 2m in the final shapefiles. proprietary
+AAS_4088_Adelie_occupancy_Bechervaise_2013_1 Adelie penguin occupancy survey of Bechervaise Island, 2013 AU_AADC STAC Catalog 2013-01-09 2013-01-09 62.806, -67.588, 62.808, -67.586 https://cmr.earthdata.nasa.gov/search/concepts/C1384657571-AU_AADC.umm_json All subcolonies on Bechervaise Island were mapped with a hand held GPS (Garmin Legend) on the 9th of January 2013). The mapping was undertaken by Julie McInnes and Helen Achurch. The colonies were mapped at a constant 2m buffer. If subcolonies were less than 2m apart they were mapped in the same outline, colonies greater than 2m apart were mapped separately. The final layer has a 2m buffer around the colony included in the layer. proprietary
AAS_4088_Adelie_occupancy_Bechervaise_2016_1 Adelie penguin occupancy survey of Bechervaise Island, 2016 ALL STAC Catalog 2016-12-21 2016-12-21 62.806, -67.588, 62.808, -67.586 https://cmr.earthdata.nasa.gov/search/concepts/C1384657597-AU_AADC.umm_json Adelie colony boundaries at Bechervaise Island were mapped by Matthew Pauza on the 21 Dec 2016. Subcolonies were mapped by circumnavigating the perimeter on foot while carrying a Garmin GPS (Etrex30) to record the track. When mapping the perimeter of the subcolonies a buffer distance of approximately 2.5 meters was maintained between the mapper and the breeding birds. This buffer distance was reduced by .5m to between 2m in the final shapefiles. proprietary
+AAS_4088_Adelie_occupancy_Bechervaise_2016_1 Adelie penguin occupancy survey of Bechervaise Island, 2016 AU_AADC STAC Catalog 2016-12-21 2016-12-21 62.806, -67.588, 62.808, -67.586 https://cmr.earthdata.nasa.gov/search/concepts/C1384657597-AU_AADC.umm_json Adelie colony boundaries at Bechervaise Island were mapped by Matthew Pauza on the 21 Dec 2016. Subcolonies were mapped by circumnavigating the perimeter on foot while carrying a Garmin GPS (Etrex30) to record the track. When mapping the perimeter of the subcolonies a buffer distance of approximately 2.5 meters was maintained between the mapper and the breeding birds. This buffer distance was reduced by .5m to between 2m in the final shapefiles. proprietary
AAS_4088_Adelie_occupancy_Bechervaise_Kista_2013_1 Adelie penguin occupancy survey of Bechervaise Island and Kista Rock, 2013 AU_AADC STAC Catalog 2013-12-01 2013-12-03 62.806, -69.7327, 74.3798, -67.586 https://cmr.earthdata.nasa.gov/search/concepts/C1384657574-AU_AADC.umm_json Six colonies with breeding Adelie colonies were mapped this season on Kista Island. On Bechervaise Island subcolonies C and R were not mapped and so are missing from the final layer, but birds were present in these subcolonies. Subcolonies were mapped by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx) to log the track taken. The person walking the perimeter of the sub-colonies maintained a buffer distance of approximately 2.5m between themselves and the breeding birds along the sub-colony boundary. This buffer distance was reduced to approximately 2m in the final shapefiles. proprietary
AAS_4088_Adelie_occupancy_Bechervaise_Kista_2013_1 Adelie penguin occupancy survey of Bechervaise Island and Kista Rock, 2013 ALL STAC Catalog 2013-12-01 2013-12-03 62.806, -69.7327, 74.3798, -67.586 https://cmr.earthdata.nasa.gov/search/concepts/C1384657574-AU_AADC.umm_json Six colonies with breeding Adelie colonies were mapped this season on Kista Island. On Bechervaise Island subcolonies C and R were not mapped and so are missing from the final layer, but birds were present in these subcolonies. Subcolonies were mapped by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx) to log the track taken. The person walking the perimeter of the sub-colonies maintained a buffer distance of approximately 2.5m between themselves and the breeding birds along the sub-colony boundary. This buffer distance was reduced to approximately 2m in the final shapefiles. proprietary
AAS_4088_Adelie_occupancy_Biscoe_1 Adelie penguin occupancy survey of Mount Biscoe, 1985 AU_AADC STAC Catalog 1985-10-29 1985-10-29 51.293, -66.24, 51.358, -66.215 https://cmr.earthdata.nasa.gov/search/concepts/C1384657167-AU_AADC.umm_json Aerial and ground photos taken during a visit to Mount Biscoe in 1985 were used to map the extent of old guano and unoccupied pebble nests found in the area. The guano extended from the beach up the northern slope of the massif to an altitude of approximately 200m. Very few birds were present when the site was visited. The map was hand drawn and put into the paper documented below. With the aid of satellite imagery, the diagram was converted into a shapefile for the purposes of mapping the potential colony extent in this location. proprietary
AAS_4088_Adelie_occupancy_Biscoe_1 Adelie penguin occupancy survey of Mount Biscoe, 1985 ALL STAC Catalog 1985-10-29 1985-10-29 51.293, -66.24, 51.358, -66.215 https://cmr.earthdata.nasa.gov/search/concepts/C1384657167-AU_AADC.umm_json Aerial and ground photos taken during a visit to Mount Biscoe in 1985 were used to map the extent of old guano and unoccupied pebble nests found in the area. The guano extended from the beach up the northern slope of the massif to an altitude of approximately 200m. Very few birds were present when the site was visited. The map was hand drawn and put into the paper documented below. With the aid of satellite imagery, the diagram was converted into a shapefile for the purposes of mapping the potential colony extent in this location. proprietary
AAS_4088_Adelie_occupancy_Bolingen_1 Adelie penguin occupancy survey of the Bolingen Island group, 2010 AU_AADC STAC Catalog 2010-11-20 2010-12-06 75.333, -69.5, 75.912, -69.46 https://cmr.earthdata.nasa.gov/search/concepts/C1384657195-AU_AADC.umm_json Occupancy surveys in November 2009 and December 2010 (Southwell and Emmerson 2013) found a total of 2 Adelie penguin breeding sites in the Bolingen Island group between longitudes 75.333oE-75.912oE. The boundaries of breeding sub-colonies at 1 of these sites (Lichen Island, 73030) were subsequently mapped from vertical aerial photographs taken for abundance surveys on 20 November 2010 (for details of aerial photography see Southwell et al. 2013). The boundaries were mapped with a buffer distance of approximately 1-3 m from the perimeter of penguin sub-colonies. The other breeding site (73156) was photographed obliquely from a helicopter using a hand-held camera on 6 December 2010. Colony boundaries for this site were drawn and digitised by eye. proprietary
AAS_4088_Adelie_occupancy_Bolingen_1 Adelie penguin occupancy survey of the Bolingen Island group, 2010 ALL STAC Catalog 2010-11-20 2010-12-06 75.333, -69.5, 75.912, -69.46 https://cmr.earthdata.nasa.gov/search/concepts/C1384657195-AU_AADC.umm_json Occupancy surveys in November 2009 and December 2010 (Southwell and Emmerson 2013) found a total of 2 Adelie penguin breeding sites in the Bolingen Island group between longitudes 75.333oE-75.912oE. The boundaries of breeding sub-colonies at 1 of these sites (Lichen Island, 73030) were subsequently mapped from vertical aerial photographs taken for abundance surveys on 20 November 2010 (for details of aerial photography see Southwell et al. 2013). The boundaries were mapped with a buffer distance of approximately 1-3 m from the perimeter of penguin sub-colonies. The other breeding site (73156) was photographed obliquely from a helicopter using a hand-held camera on 6 December 2010. Colony boundaries for this site were drawn and digitised by eye. proprietary
-AAS_4088_Adelie_occupancy_Chick_Henry_2012_1 Adelie penguin occupancy survey of Chick and Henry Islands, 2012 AU_AADC STAC Catalog 2012-01-26 2012-01-26 120.5, -66.876, 121.03, -66.789 https://cmr.earthdata.nasa.gov/search/concepts/C1384657659-AU_AADC.umm_json An occupancy survey in 26 January 2012 found a total of 2 islands along the coast between 120o30’E - 121o02’E had populations of breeding Adelie penguins. The survey was conducted from a fixed wing aircraft and oblique aerial photographs were taken of each occupied site. The aerial photographs were geo-referenced to the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E159) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are: Chick: Photographs taken on 26 January 2012 and geo-referenced to LIMA tile E159 Henry 1: Photographs taken on 26 January 2012 and geo-referenced to LIMA tile E159 proprietary
AAS_4088_Adelie_occupancy_Chick_Henry_2012_1 Adelie penguin occupancy survey of Chick and Henry Islands, 2012 ALL STAC Catalog 2012-01-26 2012-01-26 120.5, -66.876, 121.03, -66.789 https://cmr.earthdata.nasa.gov/search/concepts/C1384657659-AU_AADC.umm_json An occupancy survey in 26 January 2012 found a total of 2 islands along the coast between 120o30’E - 121o02’E had populations of breeding Adelie penguins. The survey was conducted from a fixed wing aircraft and oblique aerial photographs were taken of each occupied site. The aerial photographs were geo-referenced to the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E159) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are: Chick: Photographs taken on 26 January 2012 and geo-referenced to LIMA tile E159 Henry 1: Photographs taken on 26 January 2012 and geo-referenced to LIMA tile E159 proprietary
+AAS_4088_Adelie_occupancy_Chick_Henry_2012_1 Adelie penguin occupancy survey of Chick and Henry Islands, 2012 AU_AADC STAC Catalog 2012-01-26 2012-01-26 120.5, -66.876, 121.03, -66.789 https://cmr.earthdata.nasa.gov/search/concepts/C1384657659-AU_AADC.umm_json An occupancy survey in 26 January 2012 found a total of 2 islands along the coast between 120o30’E - 121o02’E had populations of breeding Adelie penguins. The survey was conducted from a fixed wing aircraft and oblique aerial photographs were taken of each occupied site. The aerial photographs were geo-referenced to the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E159) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are: Chick: Photographs taken on 26 January 2012 and geo-referenced to LIMA tile E159 Henry 1: Photographs taken on 26 January 2012 and geo-referenced to LIMA tile E159 proprietary
AAS_4088_Adelie_occupancy_Kista_2015_1 Adelie penguin occupancy survey of the Kista Island Group, 2015 AU_AADC STAC Catalog 2015-11-17 2015-11-27 62.96, -67.56, 62.98, -67.54 https://cmr.earthdata.nasa.gov/search/concepts/C1384657598-AU_AADC.umm_json Seven colonies with breeding Adelie colonies were mapped this season in the Kista Island group between the 17th and 27th of November 2015. Subcolonies were mapped by circumnavigating the perimeter on foot while carrying a Garmin GPS (Etrex30) to record the track. When mapping the perimeter of the subcolonies, generally an average buffer distance of 2.5 meters was maintained between the mapper and breeding birds. However on Klung Island one of the mappers was mapping at a distance between 3 and 5m. Buffer distances were reduced accordingly for the varying tracks to produce a combined average buffer distance of 2m in the final layer. Given this the boundary mapping for these two islands may vary in accuracy. Note when mapping was undertaken at Peterson Island (74507) two subcolonies were not mapped when compared to mapping in the 13/14 season. The larger of these colonies was missed but the smaller colony did not exist in the 15/16 season. proprietary
AAS_4088_Adelie_occupancy_Kista_2015_1 Adelie penguin occupancy survey of the Kista Island Group, 2015 ALL STAC Catalog 2015-11-17 2015-11-27 62.96, -67.56, 62.98, -67.54 https://cmr.earthdata.nasa.gov/search/concepts/C1384657598-AU_AADC.umm_json Seven colonies with breeding Adelie colonies were mapped this season in the Kista Island group between the 17th and 27th of November 2015. Subcolonies were mapped by circumnavigating the perimeter on foot while carrying a Garmin GPS (Etrex30) to record the track. When mapping the perimeter of the subcolonies, generally an average buffer distance of 2.5 meters was maintained between the mapper and breeding birds. However on Klung Island one of the mappers was mapping at a distance between 3 and 5m. Buffer distances were reduced accordingly for the varying tracks to produce a combined average buffer distance of 2m in the final layer. Given this the boundary mapping for these two islands may vary in accuracy. Note when mapping was undertaken at Peterson Island (74507) two subcolonies were not mapped when compared to mapping in the 13/14 season. The larger of these colonies was missed but the smaller colony did not exist in the 15/16 season. proprietary
AAS_4088_Adelie_occupancy_Knox_2009-2010_1 Adelie penguin occupancy survey of islands along the Knox Coast, 2009-2010 ALL STAC Catalog 2009-12-01 2010-02-28 107.08, -66.55, 109.33, -66.45 https://cmr.earthdata.nasa.gov/search/concepts/C1388926523-AU_AADC.umm_json An occupancy survey in December 2009-February 2010 and January 2011 found a total of 6 islands along the Knox coast had populations of breeding Adelie penguins. The survey in 2009/10 was conducted from a fixed wing aircraft and oblique aerial photographs were taken of occupied sites. The aerial photographs were geo-referenced to satellite images or the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E157) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are: Merrit: Photographs taken on 1 February 2010 and geo-referenced to LIMA tile E157 Cape Nutt: Photographs taken on 5 January 2010 and geo-referenced to a Quickbird satellite image taken on 17 February 2011 Ivanoff Head: Photographs taken on 27 December 2009 and geo-referenced to LIMA tile E157 proprietary
AAS_4088_Adelie_occupancy_Knox_2009-2010_1 Adelie penguin occupancy survey of islands along the Knox Coast, 2009-2010 AU_AADC STAC Catalog 2009-12-01 2010-02-28 107.08, -66.55, 109.33, -66.45 https://cmr.earthdata.nasa.gov/search/concepts/C1388926523-AU_AADC.umm_json An occupancy survey in December 2009-February 2010 and January 2011 found a total of 6 islands along the Knox coast had populations of breeding Adelie penguins. The survey in 2009/10 was conducted from a fixed wing aircraft and oblique aerial photographs were taken of occupied sites. The aerial photographs were geo-referenced to satellite images or the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E157) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are: Merrit: Photographs taken on 1 February 2010 and geo-referenced to LIMA tile E157 Cape Nutt: Photographs taken on 5 January 2010 and geo-referenced to a Quickbird satellite image taken on 17 February 2011 Ivanoff Head: Photographs taken on 27 December 2009 and geo-referenced to LIMA tile E157 proprietary
-AAS_4088_Adelie_occupancy_Knox_2011_1 Adelie penguin occupancy survey of islands along the Knox Coast, 2011 ALL STAC Catalog 2011-01-01 2011-01-31 107.08, -66.55, 109.33, -66.45 https://cmr.earthdata.nasa.gov/search/concepts/C1388926597-AU_AADC.umm_json An occupancy survey in December 2009-February 2010 and January 2011 found a total of 6 islands along the Knox coast had populations of breeding Adelie penguins. The survey in 2009/10 was conducted from a fixed wing aircraft and oblique aerial photographs were taken of occupied sites. The aerial photographs were geo-referenced to satellite images or the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E157) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are: Merrit: Photographs taken on 1 February 2010 and geo-referenced to LIMA tile E157 Cape Nutt: Photographs taken on 5 January 2010 and geo-referenced to a Quickbird satellite image taken on 17 February 2011 Ivanoff Head: Photographs taken on 27 December 2009 and geo-referenced to LIMA tile E157 proprietary
AAS_4088_Adelie_occupancy_Knox_2011_1 Adelie penguin occupancy survey of islands along the Knox Coast, 2011 AU_AADC STAC Catalog 2011-01-01 2011-01-31 107.08, -66.55, 109.33, -66.45 https://cmr.earthdata.nasa.gov/search/concepts/C1388926597-AU_AADC.umm_json An occupancy survey in December 2009-February 2010 and January 2011 found a total of 6 islands along the Knox coast had populations of breeding Adelie penguins. The survey in 2009/10 was conducted from a fixed wing aircraft and oblique aerial photographs were taken of occupied sites. The aerial photographs were geo-referenced to satellite images or the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E157) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are: Merrit: Photographs taken on 1 February 2010 and geo-referenced to LIMA tile E157 Cape Nutt: Photographs taken on 5 January 2010 and geo-referenced to a Quickbird satellite image taken on 17 February 2011 Ivanoff Head: Photographs taken on 27 December 2009 and geo-referenced to LIMA tile E157 proprietary
+AAS_4088_Adelie_occupancy_Knox_2011_1 Adelie penguin occupancy survey of islands along the Knox Coast, 2011 ALL STAC Catalog 2011-01-01 2011-01-31 107.08, -66.55, 109.33, -66.45 https://cmr.earthdata.nasa.gov/search/concepts/C1388926597-AU_AADC.umm_json An occupancy survey in December 2009-February 2010 and January 2011 found a total of 6 islands along the Knox coast had populations of breeding Adelie penguins. The survey in 2009/10 was conducted from a fixed wing aircraft and oblique aerial photographs were taken of occupied sites. The aerial photographs were geo-referenced to satellite images or the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E157) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are: Merrit: Photographs taken on 1 February 2010 and geo-referenced to LIMA tile E157 Cape Nutt: Photographs taken on 5 January 2010 and geo-referenced to a Quickbird satellite image taken on 17 February 2011 Ivanoff Head: Photographs taken on 27 December 2009 and geo-referenced to LIMA tile E157 proprietary
AAS_4088_Adelie_occupancy_Lewis_2012_1 Adelie penguin occupancy survey of the Lewis Islands, 2012 ALL STAC Catalog 2012-01-26 2012-01-26 107.08, -66.55, 109.33, -66.45 https://cmr.earthdata.nasa.gov/search/concepts/C1388926613-AU_AADC.umm_json An occupancy survey in December 2009-February 2010 and January 2011 found a total of 6 islands along the Knox coast had populations of breeding Adelie penguins. The survey in 2009/10 was conducted from a fixed wing aircraft and oblique aerial photographs were taken of occupied sites. The aerial photographs were geo-referenced to satellite images or the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E157) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are: Merrit: Photographs taken on 1 February 2010 and geo-referenced to LIMA tile E157 Cape Nutt: Photographs taken on 5 January 2010 and geo-referenced to a Quickbird satellite image taken on 17 February 2011 Ivanoff Head: Photographs taken on 27 December 2009 and geo-referenced to LIMA tile E157 proprietary
AAS_4088_Adelie_occupancy_Lewis_2012_1 Adelie penguin occupancy survey of the Lewis Islands, 2012 AU_AADC STAC Catalog 2012-01-26 2012-01-26 107.08, -66.55, 109.33, -66.45 https://cmr.earthdata.nasa.gov/search/concepts/C1388926613-AU_AADC.umm_json An occupancy survey in December 2009-February 2010 and January 2011 found a total of 6 islands along the Knox coast had populations of breeding Adelie penguins. The survey in 2009/10 was conducted from a fixed wing aircraft and oblique aerial photographs were taken of occupied sites. The aerial photographs were geo-referenced to satellite images or the coastline shapefile from the Landsat Image Mosaic of Antarctica (LIMA, tile E157) and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are: Merrit: Photographs taken on 1 February 2010 and geo-referenced to LIMA tile E157 Cape Nutt: Photographs taken on 5 January 2010 and geo-referenced to a Quickbird satellite image taken on 17 February 2011 Ivanoff Head: Photographs taken on 27 December 2009 and geo-referenced to LIMA tile E157 proprietary
AAS_4088_Adelie_occupancy_Low_Tongue_2015_1 Adelie penguin occupancy survey of Low Tongue, 2015 AU_AADC STAC Catalog 2015-02-15 2015-02-15 61.989, -67.552, 61.99, -67.551 https://cmr.earthdata.nasa.gov/search/concepts/C1384658075-AU_AADC.umm_json The dataset comprises Adelie penguin colony boundaries derived from oblique aerial photographs. The aerial photographs were geo-referenced to AAT coastline polygon data and the boundaries of Adelie penguin colonies were digitised. proprietary
AAS_4088_Adelie_occupancy_Low_Tongue_2015_1 Adelie penguin occupancy survey of Low Tongue, 2015 ALL STAC Catalog 2015-02-15 2015-02-15 61.989, -67.552, 61.99, -67.551 https://cmr.earthdata.nasa.gov/search/concepts/C1384658075-AU_AADC.umm_json The dataset comprises Adelie penguin colony boundaries derived from oblique aerial photographs. The aerial photographs were geo-referenced to AAT coastline polygon data and the boundaries of Adelie penguin colonies were digitised. proprietary
AAS_4088_Adelie_occupancy_Mawson_Taylor_1 Adelie penguin occupancy survey between Mawson and Taylor Glacier, 2015 AU_AADC STAC Catalog 2015-02-15 2015-02-15 60.612, -67, 61.338, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1384657585-AU_AADC.umm_json The dataset comprises Adelie penguin colony boundaries derived from oblique aerial photographs taken towards the end of the 2014/15 summer between Mawson and Taylor Glacier. The aerial photographs were geo-referenced to AAT coastline polygon data and the boundaries of Adelie penguin colonies were digitised. proprietary
AAS_4088_Adelie_occupancy_Mawson_Taylor_1 Adelie penguin occupancy survey between Mawson and Taylor Glacier, 2015 ALL STAC Catalog 2015-02-15 2015-02-15 60.612, -67, 61.338, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1384657585-AU_AADC.umm_json The dataset comprises Adelie penguin colony boundaries derived from oblique aerial photographs taken towards the end of the 2014/15 summer between Mawson and Taylor Glacier. The aerial photographs were geo-referenced to AAT coastline polygon data and the boundaries of Adelie penguin colonies were digitised. proprietary
-AAS_4088_Adelie_occupancy_Murray_2010_1 Adelie penguin occupancy survey of Murray Monolith, 2010 ALL STAC Catalog 2010-12-10 2010-12-10 66.8874, -67.7847, 66.8884, -67.7837 https://cmr.earthdata.nasa.gov/search/concepts/C1384658088-AU_AADC.umm_json Oblique hand-held photographs were taken of all Adelie penguin breeding colonies at Murray Monolith from a fixed wing aircraft on 10 December 2010. These photographs were geo-referenced to a Worldview 2 satellite image of both monoliths taken on 26 January 2011 and the colony boundaries in the geo-referenced photos were digitised as shapefiles. Some sections of the digitised Murray Monolith colonies near the crescent shaped moraine were moved so they were contained within the shapefile ‘rock_exposed_for_modelling_Scullin_Murray’) proprietary
AAS_4088_Adelie_occupancy_Murray_2010_1 Adelie penguin occupancy survey of Murray Monolith, 2010 AU_AADC STAC Catalog 2010-12-10 2010-12-10 66.8874, -67.7847, 66.8884, -67.7837 https://cmr.earthdata.nasa.gov/search/concepts/C1384658088-AU_AADC.umm_json Oblique hand-held photographs were taken of all Adelie penguin breeding colonies at Murray Monolith from a fixed wing aircraft on 10 December 2010. These photographs were geo-referenced to a Worldview 2 satellite image of both monoliths taken on 26 January 2011 and the colony boundaries in the geo-referenced photos were digitised as shapefiles. Some sections of the digitised Murray Monolith colonies near the crescent shaped moraine were moved so they were contained within the shapefile ‘rock_exposed_for_modelling_Scullin_Murray’) proprietary
+AAS_4088_Adelie_occupancy_Murray_2010_1 Adelie penguin occupancy survey of Murray Monolith, 2010 ALL STAC Catalog 2010-12-10 2010-12-10 66.8874, -67.7847, 66.8884, -67.7837 https://cmr.earthdata.nasa.gov/search/concepts/C1384658088-AU_AADC.umm_json Oblique hand-held photographs were taken of all Adelie penguin breeding colonies at Murray Monolith from a fixed wing aircraft on 10 December 2010. These photographs were geo-referenced to a Worldview 2 satellite image of both monoliths taken on 26 January 2011 and the colony boundaries in the geo-referenced photos were digitised as shapefiles. Some sections of the digitised Murray Monolith colonies near the crescent shaped moraine were moved so they were contained within the shapefile ‘rock_exposed_for_modelling_Scullin_Murray’) proprietary
AAS_4088_Adelie_occupancy_Rauer_2009_1 Adelie penguin occupancy survey of the Rauer Group, 2009 ALL STAC Catalog 2009-11-21 2009-11-23 77.825, -68.86, 77.84, -68.84 https://cmr.earthdata.nasa.gov/search/concepts/C1384659288-AU_AADC.umm_json Occupancy surveys in November 2008 (Southwell and Emmerson 2013) found a total of 13 Adelie penguin breeding sites in the Rauer Group. The boundaries of breeding sub-colonies at 12 of these sites were subsequently mapped from vertical aerial photographs taken for abundance surveys on 21-23 November 2009 (for details of aerial photography see Southwell et al. 2013). The boundaries were mapped with a buffer distance of approximately 1-3 m from the perimeter of penguin sub-colonies. proprietary
AAS_4088_Adelie_occupancy_Rauer_2009_1 Adelie penguin occupancy survey of the Rauer Group, 2009 AU_AADC STAC Catalog 2009-11-21 2009-11-23 77.825, -68.86, 77.84, -68.84 https://cmr.earthdata.nasa.gov/search/concepts/C1384659288-AU_AADC.umm_json Occupancy surveys in November 2008 (Southwell and Emmerson 2013) found a total of 13 Adelie penguin breeding sites in the Rauer Group. The boundaries of breeding sub-colonies at 12 of these sites were subsequently mapped from vertical aerial photographs taken for abundance surveys on 21-23 November 2009 (for details of aerial photography see Southwell et al. 2013). The boundaries were mapped with a buffer distance of approximately 1-3 m from the perimeter of penguin sub-colonies. proprietary
AAS_4088_Adelie_occupancy_Rauer_2010_1 Adelie penguin occupancy survey of the Rauer Group, 2010 ALL STAC Catalog 2010-12-20 2010-12-20 77.825, -68.86, 77.84, -68.84 https://cmr.earthdata.nasa.gov/search/concepts/C1384659297-AU_AADC.umm_json Occupancy surveys in November 2008 (Southwell and Emmerson 2013) found a total of 13 Adelie penguin breeding sites in the Rauer Group. The boundaries of breeding sub-colonies at 12 of these sites were subsequently mapped from vertical aerial photographs taken for abundance surveys on 21-23 November 2009. The remaining breeding site (IS_72922) was photographed obliquely from a helicopter using a hand-held camera on 20 December 2010. Colony boundaries for this site were drawn and digitised by eye. proprietary
AAS_4088_Adelie_occupancy_Rauer_2010_1 Adelie penguin occupancy survey of the Rauer Group, 2010 AU_AADC STAC Catalog 2010-12-20 2010-12-20 77.825, -68.86, 77.84, -68.84 https://cmr.earthdata.nasa.gov/search/concepts/C1384659297-AU_AADC.umm_json Occupancy surveys in November 2008 (Southwell and Emmerson 2013) found a total of 13 Adelie penguin breeding sites in the Rauer Group. The boundaries of breeding sub-colonies at 12 of these sites were subsequently mapped from vertical aerial photographs taken for abundance surveys on 21-23 November 2009. The remaining breeding site (IS_72922) was photographed obliquely from a helicopter using a hand-held camera on 20 December 2010. Colony boundaries for this site were drawn and digitised by eye. proprietary
-AAS_4088_Adelie_occupancy_Robinson_2006_1 Adelie penguin occupancy survey of the Robinson Group, 2006 ALL STAC Catalog 2006-11-01 2006-11-30 63.435, -67.445, 63.443, -67.435 https://cmr.earthdata.nasa.gov/search/concepts/C1384659478-AU_AADC.umm_json An occupancy survey in November 2006 found a total of 29 islands in the Robinson Group of islands had populations of breeding Adelie penguins. The boundaries of breeding colonies at 27 of these islands with larger populations were subsequently mapped for abundance surveys by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx or Vista C) to log the track taken. The person walking around the sub-colonies maintained a buffer distance of 2-5m between themselves and the penguins at the sub-colony boundary. This buffer distance was reduced to between 1 and 4m in the final shapefiles. proprietary
AAS_4088_Adelie_occupancy_Robinson_2006_1 Adelie penguin occupancy survey of the Robinson Group, 2006 AU_AADC STAC Catalog 2006-11-01 2006-11-30 63.435, -67.445, 63.443, -67.435 https://cmr.earthdata.nasa.gov/search/concepts/C1384659478-AU_AADC.umm_json An occupancy survey in November 2006 found a total of 29 islands in the Robinson Group of islands had populations of breeding Adelie penguins. The boundaries of breeding colonies at 27 of these islands with larger populations were subsequently mapped for abundance surveys by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx or Vista C) to log the track taken. The person walking around the sub-colonies maintained a buffer distance of 2-5m between themselves and the penguins at the sub-colony boundary. This buffer distance was reduced to between 1 and 4m in the final shapefiles. proprietary
-AAS_4088_Adelie_occupancy_Robinson_2013_1 Adelie penguin occupancy survey of the Robinson Group, 2013 AU_AADC STAC Catalog 2013-11-29 2013-11-29 63.435, -67.445, 63.443, -67.435 https://cmr.earthdata.nasa.gov/search/concepts/C1625714088-AU_AADC.umm_json An occupancy survey in November 2006 found a total of 29 islands in the Robinson Group of islands had populations of breeding Adelie penguins. The boundaries of breeding colonies at 27 of these were mapped in Nov 2006 for abundance surveys. Nine of these breeding sites were remapped on the 29th of November 2013 in conjunction with colony counts. Subcolonies were mapped by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx) to log the track taken. The person walking around the sub-colonies maintained a buffer distance of approximately 2.5m between themselves and the breeding birds along the sub-colony boundary. This buffer distance was reduced to approximately 2m in the final shapefiles. proprietary
+AAS_4088_Adelie_occupancy_Robinson_2006_1 Adelie penguin occupancy survey of the Robinson Group, 2006 ALL STAC Catalog 2006-11-01 2006-11-30 63.435, -67.445, 63.443, -67.435 https://cmr.earthdata.nasa.gov/search/concepts/C1384659478-AU_AADC.umm_json An occupancy survey in November 2006 found a total of 29 islands in the Robinson Group of islands had populations of breeding Adelie penguins. The boundaries of breeding colonies at 27 of these islands with larger populations were subsequently mapped for abundance surveys by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx or Vista C) to log the track taken. The person walking around the sub-colonies maintained a buffer distance of 2-5m between themselves and the penguins at the sub-colony boundary. This buffer distance was reduced to between 1 and 4m in the final shapefiles. proprietary
AAS_4088_Adelie_occupancy_Robinson_2013_1 Adelie penguin occupancy survey of the Robinson Group, 2013 ALL STAC Catalog 2013-11-29 2013-11-29 63.435, -67.445, 63.443, -67.435 https://cmr.earthdata.nasa.gov/search/concepts/C1625714088-AU_AADC.umm_json An occupancy survey in November 2006 found a total of 29 islands in the Robinson Group of islands had populations of breeding Adelie penguins. The boundaries of breeding colonies at 27 of these were mapped in Nov 2006 for abundance surveys. Nine of these breeding sites were remapped on the 29th of November 2013 in conjunction with colony counts. Subcolonies were mapped by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx) to log the track taken. The person walking around the sub-colonies maintained a buffer distance of approximately 2.5m between themselves and the breeding birds along the sub-colony boundary. This buffer distance was reduced to approximately 2m in the final shapefiles. proprietary
+AAS_4088_Adelie_occupancy_Robinson_2013_1 Adelie penguin occupancy survey of the Robinson Group, 2013 AU_AADC STAC Catalog 2013-11-29 2013-11-29 63.435, -67.445, 63.443, -67.435 https://cmr.earthdata.nasa.gov/search/concepts/C1625714088-AU_AADC.umm_json An occupancy survey in November 2006 found a total of 29 islands in the Robinson Group of islands had populations of breeding Adelie penguins. The boundaries of breeding colonies at 27 of these were mapped in Nov 2006 for abundance surveys. Nine of these breeding sites were remapped on the 29th of November 2013 in conjunction with colony counts. Subcolonies were mapped by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx) to log the track taken. The person walking around the sub-colonies maintained a buffer distance of approximately 2.5m between themselves and the breeding birds along the sub-colony boundary. This buffer distance was reduced to approximately 2m in the final shapefiles. proprietary
AAS_4088_Adelie_occupancy_Rookery_2013_1 Adelie penguin occupancy survey of the Rookery Island Group, 2013 ALL STAC Catalog 2013-12-04 2013-12-04 62.51, -67.61, 62.52, -67.59 https://cmr.earthdata.nasa.gov/search/concepts/C1384657609-AU_AADC.umm_json Six colonies with breeding Adelie colonies were mapped this season in the Rookery Island group in conjunction with colony counts. Islands 74814 and the main Rookery Island 74721 were not mapped this season. Subcolonies were mapped by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx) to log the track taken. The person walking the perimeter of the sub-colonies maintained a buffer distance of approximately 2.5m between themselves and the breeding birds along the sub-colony boundary. This buffer distance was reduced to approximately 2m in the final shapefiles. proprietary
AAS_4088_Adelie_occupancy_Rookery_2013_1 Adelie penguin occupancy survey of the Rookery Island Group, 2013 AU_AADC STAC Catalog 2013-12-04 2013-12-04 62.51, -67.61, 62.52, -67.59 https://cmr.earthdata.nasa.gov/search/concepts/C1384657609-AU_AADC.umm_json Six colonies with breeding Adelie colonies were mapped this season in the Rookery Island group in conjunction with colony counts. Islands 74814 and the main Rookery Island 74721 were not mapped this season. Subcolonies were mapped by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx) to log the track taken. The person walking the perimeter of the sub-colonies maintained a buffer distance of approximately 2.5m between themselves and the breeding birds along the sub-colony boundary. This buffer distance was reduced to approximately 2m in the final shapefiles. proprietary
-AAS_4088_Adelie_occupancy_Rookery_2014_1 Adelie penguin occupancy survey of the Rookery Island Group, 2014 ALL STAC Catalog 2014-12-04 2014-12-04 62.51, -67.61, 62.52, -67.59 https://cmr.earthdata.nasa.gov/search/concepts/C1384657636-AU_AADC.umm_json Two colonies with breeding Adelie colonies were mapped this season in the Rookery Island group in conjunction with colony counts. Subcolonies were mapped by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx) to log the track taken. The person walking the perimeter of the sub-colonies maintained a buffer distance of approximately 2.5m between themselves and the breeding birds along the sub-colony boundary. This buffer distance was reduced to approximately 2m in the final shapefiles. proprietary
AAS_4088_Adelie_occupancy_Rookery_2014_1 Adelie penguin occupancy survey of the Rookery Island Group, 2014 AU_AADC STAC Catalog 2014-12-04 2014-12-04 62.51, -67.61, 62.52, -67.59 https://cmr.earthdata.nasa.gov/search/concepts/C1384657636-AU_AADC.umm_json Two colonies with breeding Adelie colonies were mapped this season in the Rookery Island group in conjunction with colony counts. Subcolonies were mapped by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx) to log the track taken. The person walking the perimeter of the sub-colonies maintained a buffer distance of approximately 2.5m between themselves and the breeding birds along the sub-colony boundary. This buffer distance was reduced to approximately 2m in the final shapefiles. proprietary
+AAS_4088_Adelie_occupancy_Rookery_2014_1 Adelie penguin occupancy survey of the Rookery Island Group, 2014 ALL STAC Catalog 2014-12-04 2014-12-04 62.51, -67.61, 62.52, -67.59 https://cmr.earthdata.nasa.gov/search/concepts/C1384657636-AU_AADC.umm_json Two colonies with breeding Adelie colonies were mapped this season in the Rookery Island group in conjunction with colony counts. Subcolonies were mapped by circumnavigating the perimeter of sub-colonies on foot while carrying a Garmin GPS (Legend Cx) to log the track taken. The person walking the perimeter of the sub-colonies maintained a buffer distance of approximately 2.5m between themselves and the breeding birds along the sub-colony boundary. This buffer distance was reduced to approximately 2m in the final shapefiles. proprietary
AAS_4088_Adelie_occupancy_Rookery_2015_1 Adelie penguin occupancy survey of the Rookery Island Group, 2015 AU_AADC STAC Catalog 2015-11-29 2015-12-14 62.51, -67.61, 62.52, -67.59 https://cmr.earthdata.nasa.gov/search/concepts/C1384658089-AU_AADC.umm_json Fourteen colonies with breeding Adelie colonies were mapped this season in the Rookery Island group between the 29th November and 14th of December 2015. Subcolonies were mapped by circumnavigating the perimeter on foot while carrying a Garmin GPS (Etrex30) to record the track. When mapping the perimeter of the subcolonies, generally an average buffer distance of 2.5 meters was maintained between the mapper and breeding birds. However on Gibbney and Rookery Island one of the mappers was mapping at a distance between 3 and 5m. Buffer distances were reduced accordingly for the varying tracks to produce a combined average buffer distance of 2m in the final layer. Given this the boundary mapping for these two islands may vary in accuracy. Note on Gibbney and Giganteus there were at least two subcolonies on both islands that were mapped but the density of breeding birds in these mapped sections was much less than that in the surrounding colonies. Subcolonies were tagged with L at the end of their name in the track files. This will not be shown in the final layer and if information on this is needed then the subcolonies can be identified from the original track data or created shapefiles for the individual subcolonies on the island. proprietary
AAS_4088_Adelie_occupancy_Rookery_2015_1 Adelie penguin occupancy survey of the Rookery Island Group, 2015 ALL STAC Catalog 2015-11-29 2015-12-14 62.51, -67.61, 62.52, -67.59 https://cmr.earthdata.nasa.gov/search/concepts/C1384658089-AU_AADC.umm_json Fourteen colonies with breeding Adelie colonies were mapped this season in the Rookery Island group between the 29th November and 14th of December 2015. Subcolonies were mapped by circumnavigating the perimeter on foot while carrying a Garmin GPS (Etrex30) to record the track. When mapping the perimeter of the subcolonies, generally an average buffer distance of 2.5 meters was maintained between the mapper and breeding birds. However on Gibbney and Rookery Island one of the mappers was mapping at a distance between 3 and 5m. Buffer distances were reduced accordingly for the varying tracks to produce a combined average buffer distance of 2m in the final layer. Given this the boundary mapping for these two islands may vary in accuracy. Note on Gibbney and Giganteus there were at least two subcolonies on both islands that were mapped but the density of breeding birds in these mapped sections was much less than that in the surrounding colonies. Subcolonies were tagged with L at the end of their name in the track files. This will not be shown in the final layer and if information on this is needed then the subcolonies can be identified from the original track data or created shapefiles for the individual subcolonies on the island. proprietary
AAS_4088_Adelie_occupancy_Scullin_2010_1 Adelie penguin occupancy survey of Scullin Monolith, 2010 ALL STAC Catalog 2010-12-10 2010-12-10 66.7183, -67.794, 66.7193, -67.793 https://cmr.earthdata.nasa.gov/search/concepts/C1384658092-AU_AADC.umm_json Oblique hand-held photographs were taken of all Adelie penguin breeding colonies at Scullin Monolith from a fixed wing aircraft on 10 December 2010. These photographs were geo-referenced to a Worldview 2 satellite image of both monoliths taken on 26 January 2011 and the colony boundaries in the geo-referenced photos were digitised as shapefiles. proprietary
AAS_4088_Adelie_occupancy_Scullin_2010_1 Adelie penguin occupancy survey of Scullin Monolith, 2010 AU_AADC STAC Catalog 2010-12-10 2010-12-10 66.7183, -67.794, 66.7193, -67.793 https://cmr.earthdata.nasa.gov/search/concepts/C1384658092-AU_AADC.umm_json Oblique hand-held photographs were taken of all Adelie penguin breeding colonies at Scullin Monolith from a fixed wing aircraft on 10 December 2010. These photographs were geo-referenced to a Worldview 2 satellite image of both monoliths taken on 26 January 2011 and the colony boundaries in the geo-referenced photos were digitised as shapefiles. proprietary
-AAS_4088_Adelie_occupancy_Stanton_2015_1 Adelie penguin occupancy survey of the Stanton Group, 2015 ALL STAC Catalog 2015-02-15 2015-02-15 61.608, -67.527, 61.618, -67.517 https://cmr.earthdata.nasa.gov/search/concepts/C1625714090-AU_AADC.umm_json The dataset comprises Adelie penguin colony boundaries at three sites in the vicinity of Stanton Island. Boundaries were derived from oblique aerial photographs taken in the Stanton Island group. The aerial photographs were geo-referenced to AAT coastline polygon data and the boundaries of Adelie penguin colonies were digitised. proprietary
AAS_4088_Adelie_occupancy_Stanton_2015_1 Adelie penguin occupancy survey of the Stanton Group, 2015 AU_AADC STAC Catalog 2015-02-15 2015-02-15 61.608, -67.527, 61.618, -67.517 https://cmr.earthdata.nasa.gov/search/concepts/C1625714090-AU_AADC.umm_json The dataset comprises Adelie penguin colony boundaries at three sites in the vicinity of Stanton Island. Boundaries were derived from oblique aerial photographs taken in the Stanton Island group. The aerial photographs were geo-referenced to AAT coastline polygon data and the boundaries of Adelie penguin colonies were digitised. proprietary
+AAS_4088_Adelie_occupancy_Stanton_2015_1 Adelie penguin occupancy survey of the Stanton Group, 2015 ALL STAC Catalog 2015-02-15 2015-02-15 61.608, -67.527, 61.618, -67.517 https://cmr.earthdata.nasa.gov/search/concepts/C1625714090-AU_AADC.umm_json The dataset comprises Adelie penguin colony boundaries at three sites in the vicinity of Stanton Island. Boundaries were derived from oblique aerial photographs taken in the Stanton Island group. The aerial photographs were geo-referenced to AAT coastline polygon data and the boundaries of Adelie penguin colonies were digitised. proprietary
AAS_4088_Adelie_occupancy_Stillwell_2015_1 Adelie penguin occupancy survey of Stillwell Island, 2015 AU_AADC STAC Catalog 2015-02-15 2015-02-15 143.918, -66.916, 143.92, -66.914 https://cmr.earthdata.nasa.gov/search/concepts/C1384659954-AU_AADC.umm_json The dataset comprises Adelie penguin colony boundaries on one island in the Stillwell Island group. Boundaries were derived from oblique aerial photographs. The aerial photographs were geo-referenced to AAT coastline polygon data and the boundaries of Adelie penguin colonies were digitised. proprietary
AAS_4088_Adelie_occupancy_Stillwell_2015_1 Adelie penguin occupancy survey of Stillwell Island, 2015 ALL STAC Catalog 2015-02-15 2015-02-15 143.918, -66.916, 143.92, -66.914 https://cmr.earthdata.nasa.gov/search/concepts/C1384659954-AU_AADC.umm_json The dataset comprises Adelie penguin colony boundaries on one island in the Stillwell Island group. Boundaries were derived from oblique aerial photographs. The aerial photographs were geo-referenced to AAT coastline polygon data and the boundaries of Adelie penguin colonies were digitised. proprietary
AAS_4088_Adelie_occupancy_Svenner_2010_1 Adelie penguin occupancy survey of the Svenner Islands, 2010 AU_AADC STAC Catalog 2010-11-20 2010-11-20 76.337, -68.863, 76.347, -68.853 https://cmr.earthdata.nasa.gov/search/concepts/C1384660014-AU_AADC.umm_json Occupancy surveys in November 2009 and December 2010 (Southwell and Emmerson 2013) found a total of 15 Adelie penguin breeding sites in the Svenner Islands between longitudes 76.50oE to 77.50oE. The boundaries of breeding sub-colonies were subsequently mapped from vertical aerial photographs taken for abundance surveys on 20 November 2010 (for details of aerial photography see Southwell et al. 2013). The boundaries were mapped with a buffer distance of approximately 1-3 m from the perimeter of penguin sub-colonies. When photos of Island 73036 were viewed there was no colony to map so only 14 islands were mapped. proprietary
@@ -984,8 +984,8 @@ AAS_4088_Adelie_occupancy_Vestfold_2009_1 Adelie penguin occupancy survey of the
AAS_4088_Adelie_occupancy_Vestfold_2009_1 Adelie penguin occupancy survey of the Vestfold Hills, 2009 ALL STAC Catalog 2009-11-18 2009-11-21 78.15, -68.6, 78.35, -68.4 https://cmr.earthdata.nasa.gov/search/concepts/C1384660020-AU_AADC.umm_json Occupancy surveys in November 2008 (Southwell and Emmerson 2013) found a total of 31 Adelie penguin breeding sites off the Vestfold Hills. The boundaries of breeding sub-colonies at 26 of these sites were subsequently mapped from vertical aerial photographs taken for abundance surveys on 18-21 November 2009 (for details of aerial photography see Southwell et al. 2013). These boundaries were mapped with a buffer distance of approximately 1-3 m from the perimeter of penguin sub-colonies. proprietary
AAS_4088_Adelie_occupancy_Vestfold_2011_1 Adelie penguin occupancy survey of the Vestfold Hills, 2011 ALL STAC Catalog 2011-01-10 2011-01-10 78.15, -68.6, 78.35, -68.4 https://cmr.earthdata.nasa.gov/search/concepts/C1384660027-AU_AADC.umm_json Occupancy surveys in November 2008 (Southwell and Emmerson 2013) found a total of 31 Adelie penguin breeding sites off the Vestfold Hills. The boundaries of breeding sub-colonies at 26 of these sites were subsequently mapped from vertical aerial photographs A further two breeding sites (IS_72295 and McCallie Rocks_72205) were photographed obliquely from a helicopter using a hand-held camera on 10 January. Colony boundaries for 72295 were drawn and digitised by eye. Colony boundaries for 72295 were sketched onto a rough island polygon from the oblique photo without being rectified. proprietary
AAS_4088_Adelie_occupancy_Vestfold_2011_1 Adelie penguin occupancy survey of the Vestfold Hills, 2011 AU_AADC STAC Catalog 2011-01-10 2011-01-10 78.15, -68.6, 78.35, -68.4 https://cmr.earthdata.nasa.gov/search/concepts/C1384660027-AU_AADC.umm_json Occupancy surveys in November 2008 (Southwell and Emmerson 2013) found a total of 31 Adelie penguin breeding sites off the Vestfold Hills. The boundaries of breeding sub-colonies at 26 of these sites were subsequently mapped from vertical aerial photographs A further two breeding sites (IS_72295 and McCallie Rocks_72205) were photographed obliquely from a helicopter using a hand-held camera on 10 January. Colony boundaries for 72295 were drawn and digitised by eye. Colony boundaries for 72295 were sketched onto a rough island polygon from the oblique photo without being rectified. proprietary
-AAS_4088_Adelie_occupancy_Vestfold_2012_1 Adelie penguin occupancy survey of the Vestfold Hills, 2012 ALL STAC Catalog 2012-12-13 2012-12-13 78.15, -68.6, 78.35, -68.4 https://cmr.earthdata.nasa.gov/search/concepts/C1384660028-AU_AADC.umm_json Occupancy surveys in November 2008 (Southwell and Emmerson 2013) found a total of 31 Adelie penguin breeding sites off the Vestfold Hills. The boundaries of breeding sub-colonies at 26 of these sites were subsequently mapped from vertical aerial photographs taken for abundance surveys on 18-21 November 2009. Two breeding sites were photographed obliquely from a helicopter using a hand-held camera on the 13 December 2012. Colony boundaries for these 2 sites were drawn and digitised by eye. proprietary
AAS_4088_Adelie_occupancy_Vestfold_2012_1 Adelie penguin occupancy survey of the Vestfold Hills, 2012 AU_AADC STAC Catalog 2012-12-13 2012-12-13 78.15, -68.6, 78.35, -68.4 https://cmr.earthdata.nasa.gov/search/concepts/C1384660028-AU_AADC.umm_json Occupancy surveys in November 2008 (Southwell and Emmerson 2013) found a total of 31 Adelie penguin breeding sites off the Vestfold Hills. The boundaries of breeding sub-colonies at 26 of these sites were subsequently mapped from vertical aerial photographs taken for abundance surveys on 18-21 November 2009. Two breeding sites were photographed obliquely from a helicopter using a hand-held camera on the 13 December 2012. Colony boundaries for these 2 sites were drawn and digitised by eye. proprietary
+AAS_4088_Adelie_occupancy_Vestfold_2012_1 Adelie penguin occupancy survey of the Vestfold Hills, 2012 ALL STAC Catalog 2012-12-13 2012-12-13 78.15, -68.6, 78.35, -68.4 https://cmr.earthdata.nasa.gov/search/concepts/C1384660028-AU_AADC.umm_json Occupancy surveys in November 2008 (Southwell and Emmerson 2013) found a total of 31 Adelie penguin breeding sites off the Vestfold Hills. The boundaries of breeding sub-colonies at 26 of these sites were subsequently mapped from vertical aerial photographs taken for abundance surveys on 18-21 November 2009. Two breeding sites were photographed obliquely from a helicopter using a hand-held camera on the 13 December 2012. Colony boundaries for these 2 sites were drawn and digitised by eye. proprietary
AAS_4088_Adelie_occupancy_Welch_2014_1 Adelie penguin occupancy survey of Welch Island 2014 ALL STAC Catalog 2014-11-30 2014-11-30 62.927, -67.561, 62.929, -67.559 https://cmr.earthdata.nasa.gov/search/concepts/C1384657647-AU_AADC.umm_json Adelie colony boundaries at Welch Island were mapped on the 30 Nov 2014 to provide a boundary for the pole camera survey. Subcolonies were mapped by circumnavigating the perimeter on foot while carrying a Garmin GPS (Legend and Etrex30) to record the track. When mapping the perimeter of the subcolonies a buffer distance of approximately 2.5 meters was maintained between the mapper and the breeding birds. This buffer distance was reduced by .5m to between 2m in the final shapefiles. proprietary
AAS_4088_Adelie_occupancy_Welch_2014_1 Adelie penguin occupancy survey of Welch Island 2014 AU_AADC STAC Catalog 2014-11-30 2014-11-30 62.927, -67.561, 62.929, -67.559 https://cmr.earthdata.nasa.gov/search/concepts/C1384657647-AU_AADC.umm_json Adelie colony boundaries at Welch Island were mapped on the 30 Nov 2014 to provide a boundary for the pole camera survey. Subcolonies were mapped by circumnavigating the perimeter on foot while carrying a Garmin GPS (Legend and Etrex30) to record the track. When mapping the perimeter of the subcolonies a buffer distance of approximately 2.5 meters was maintained between the mapper and the breeding birds. This buffer distance was reduced by .5m to between 2m in the final shapefiles. proprietary
AAS_4088_Adelie_occupancy_Wilkes_2011_1 Adelie penguin occupancy survey of the Wilkes Land Coastline, 2011 ALL STAC Catalog 2011-01-21 2011-01-21 89, -67, 93.5, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1384660074-AU_AADC.umm_json An occupancy survey on 21 January 2011 found a total of 7 islands along the Wilkes Land coastline had populations of breeding Adelie penguins. The survey was conducted from a fixed wing aircraft and oblique aerial photographs were taken of each occupied site except Haswell Island. The aerial photographs were geo-referenced to a satellite image and the boundaries of penguin colonies were digitised from the geo-referenced photos. Details for each island are: Adams: Photographs taken on 21 January 2011 and geo-referenced to a Quickbird satellite image taken on 30 January 2009 Fulmar: Photographs taken on 21 January 2011 and geo-referenced to a WorldView2 satellite image taken on 6 February 2011 Zykov: Photographs taken on 21 January 2011 and geo-referenced to a WorldView2 satellite image taken on 6 February 2011 Buromskiy: Photographs taken on 21 January 2011 and geo-referenced to a WorldView2 satellite image taken on 6 February 2011 Stroitley: Photographs taken on 21 January 2011 and geo-referenced to a WorldView2 satellite image taken on 6 February 2011 Tokarev: Photographs taken on 21 January 2011 and geo-referenced to a WorldView2 satellite image taken on 6 February 2011 Haswell: No photographs taken, no penguin colonies were digitised Note there are two colony boundary layers in each folder except Adams. One is the original layer mapped as above. The second is an adjusted layer that was created so that the mapped boundaries would land on the exposed rock layer. Mapping of some of the islands contained within the coast layer had been coarsely done using imagery available at the time. Now with more accurate satellite imagery the island mapping could potentially be updated which would more accurately locate these islands. If this occurred, the original colony boundary mapping may be a more appropriate fit. proprietary
@@ -1023,13 +1023,13 @@ AAS_4102_4104_4050_EchoviewR_1 EchoviewR supplementary data from the KAOS survey
AAS_4102_AcousticEventLog2013_1 Acoustic event log of the 2013 Antarctic Blue Whale Voyage to the Southern Ocean ALL STAC Catalog 2013-01-31 2013-03-16 140, -70, -170, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1420786752-AU_AADC.umm_json "During the 2013 Antarctic Blue Whale Voyage Acousticians noted all whale calls and other acoustic events that were detected during real-time monitoring in a Sonobuoy Event Log. The acoustic tracking software, difarBSM, stored processed bearings from acoustic events and cross bearings in tab delimited text files. Each event was assigned a classification by the acoustician, and events for each classification were stored in separate text files. The first row in each file contains the column headers, and the content of each column is as follows: buoyID: Buoy ID number is the number of the sonobuoy on which this event was detected. This can be used as a foreign key to link to the sonobuoy deployment log. timeStamp_matlabDatenum: Date and time (UTC) at the start of the event represented as a Matlab datenum (i.e. number of days since Jan 0 0000). Latitude: Latitude of the sonobuoy deployment in decimal degrees. Southern hemisphere latitudes should be negative. Longitude: Longitude of sonobuoy deployment in decimal degrees. Western hemisphere longitudes should be negative. Altitude: Depth of the sonobuoy deployment in metres. For DIFAR sonobuoys either 30, 120 or 300. magneticVariation_degrees: The estimated magnetic variation of the sonobuoy in degrees at the time of the event. Positive declination is East, negative is West. At the start of a recording this will be entered from a chart. As the recording progresses, this should be updated by measuring the bearing to the vessel. bearing_degreesMagnetic: Magnetic bearing in degrees from the sonobuoy to the acoustic event. Magnetic bearings were selected by the acoustician by choosing a single point on the bearing-frequency surface (AKA DIFARGram) produced by the analysis software difarBSM. frequency_Hz: The frequency in Hz of the magnetic bearing that the acoustician selected from the bearing-frequency surface (DIFARGram). logDifarPower: The base 10 logarithm of the height of the point on the DIFARGram receiveLevel_dB: This column contains an estimate of the The RMS receive level (dB SPL re 1 micro Pa) of the event. Received levels were estimated by applying a correction for the shaped sonobuoy frequency response, the receiver’s frequency response, and were calculated over only the frequency band specified in each classification (see below). soundType: soundType is the classification assigned to the event by the acoustician. Analysis parameters for each classification are included in the csv file classificationParameters.txt. The columns of this file are as follows: outFile: The name of the tab-separated text file that contains events for this classification. analysisType: A super-class describing the broad category of analysis parameters soundType: The name of the classification sampleRate: When events are processed, they are downsampled to this sample rate (in Hz) in order to make directional processing more efficient and precise FFTLength: The duration (in seconds) used for determining the size of the FFT during difar beamforming (i.e. creation of the DIFARGram). numFreqs: Not used during this voyage targetFreq: The midpoint of the frequency axis (in Hz) displayed in the DIFARGram Bandwidth: This describes the half-bandwidth (Hz) of the frequency axis of the DIFARGram. The frequency axis of the DIFARGram starts at targetFreq-bandwidth and ends at targetFreq + bandwidth frequencyBands_1: The lower frequency (Hz) used for determining RMS received level. frequencyBands_2: The upper frequency (Hz) used for determining RMS received level. preDetect: Duration of audio (in seconds) that will be loaded before the start of the event. The processed audio includes the time-bounds of the event marked by the acoustician as well as preDetect seconds before the start of the event. postDetect: Duration of audio (in seconds) that will be loaded after the end of the event. The processed audio includes the time-bounds of the event marked by the acoustician + postDetect seconds." proprietary
AAS_4102_AcousticEventLog2013_1 Acoustic event log of the 2013 Antarctic Blue Whale Voyage to the Southern Ocean AU_AADC STAC Catalog 2013-01-31 2013-03-16 140, -70, -170, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1420786752-AU_AADC.umm_json "During the 2013 Antarctic Blue Whale Voyage Acousticians noted all whale calls and other acoustic events that were detected during real-time monitoring in a Sonobuoy Event Log. The acoustic tracking software, difarBSM, stored processed bearings from acoustic events and cross bearings in tab delimited text files. Each event was assigned a classification by the acoustician, and events for each classification were stored in separate text files. The first row in each file contains the column headers, and the content of each column is as follows: buoyID: Buoy ID number is the number of the sonobuoy on which this event was detected. This can be used as a foreign key to link to the sonobuoy deployment log. timeStamp_matlabDatenum: Date and time (UTC) at the start of the event represented as a Matlab datenum (i.e. number of days since Jan 0 0000). Latitude: Latitude of the sonobuoy deployment in decimal degrees. Southern hemisphere latitudes should be negative. Longitude: Longitude of sonobuoy deployment in decimal degrees. Western hemisphere longitudes should be negative. Altitude: Depth of the sonobuoy deployment in metres. For DIFAR sonobuoys either 30, 120 or 300. magneticVariation_degrees: The estimated magnetic variation of the sonobuoy in degrees at the time of the event. Positive declination is East, negative is West. At the start of a recording this will be entered from a chart. As the recording progresses, this should be updated by measuring the bearing to the vessel. bearing_degreesMagnetic: Magnetic bearing in degrees from the sonobuoy to the acoustic event. Magnetic bearings were selected by the acoustician by choosing a single point on the bearing-frequency surface (AKA DIFARGram) produced by the analysis software difarBSM. frequency_Hz: The frequency in Hz of the magnetic bearing that the acoustician selected from the bearing-frequency surface (DIFARGram). logDifarPower: The base 10 logarithm of the height of the point on the DIFARGram receiveLevel_dB: This column contains an estimate of the The RMS receive level (dB SPL re 1 micro Pa) of the event. Received levels were estimated by applying a correction for the shaped sonobuoy frequency response, the receiver’s frequency response, and were calculated over only the frequency band specified in each classification (see below). soundType: soundType is the classification assigned to the event by the acoustician. Analysis parameters for each classification are included in the csv file classificationParameters.txt. The columns of this file are as follows: outFile: The name of the tab-separated text file that contains events for this classification. analysisType: A super-class describing the broad category of analysis parameters soundType: The name of the classification sampleRate: When events are processed, they are downsampled to this sample rate (in Hz) in order to make directional processing more efficient and precise FFTLength: The duration (in seconds) used for determining the size of the FFT during difar beamforming (i.e. creation of the DIFARGram). numFreqs: Not used during this voyage targetFreq: The midpoint of the frequency axis (in Hz) displayed in the DIFARGram Bandwidth: This describes the half-bandwidth (Hz) of the frequency axis of the DIFARGram. The frequency axis of the DIFARGram starts at targetFreq-bandwidth and ends at targetFreq + bandwidth frequencyBands_1: The lower frequency (Hz) used for determining RMS received level. frequencyBands_2: The upper frequency (Hz) used for determining RMS received level. preDetect: Duration of audio (in seconds) that will be loaded before the start of the event. The processed audio includes the time-bounds of the event marked by the acoustician as well as preDetect seconds before the start of the event. postDetect: Duration of audio (in seconds) that will be loaded after the end of the event. The processed audio includes the time-bounds of the event marked by the acoustician + postDetect seconds." proprietary
AAS_4102_AcousticGPSData2013_1 GPS data recorded on the sonobuoy workstation 2013 AU_AADC STAC Catalog 2013-01-31 2013-03-16 140, -70, -170, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1420786989-AU_AADC.umm_json GPS data were recorded on the Sonobuoy Workstation as daily text files containing the raw NMEA 0183 sentences from an independent Garmin GPS receiver located at the acoustic workstation. proprietary
-AAS_4102_AcousticTrackingLog2013_1 Acoustic whale tracking log of the 2013 Antarctic Blue Whale Voyage to the Southern Ocean AU_AADC STAC Catalog 2013-01-31 2013-03-16 140, -70, -170, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1532636994-AU_AADC.umm_json "During the 2013 Antarctic Blue Whale Voyage Acousticians noted all whale calls and other acoustic events that were detected during real-time monitoring in a Sonobuoy Event Log. A written summary of the event log was recorded during data collection at approximately 15 minute intervals, and this summary comprises the Whale Tracking Log. - The acoustician on-duty recorded the average bearings or locations of each calling whale/group every 15 minutes in the written Whale Tracking Log. - Entries in the written Sonobuoy Tracking Log (on the bench in the acoustics workstation) also include total number of different whales heard in that 15 minute interval. - If multiple whales/groups were detected, then the acoustician on-duty, in consultation with the lead acoustician and/or voyage management designateded one of the whales the 'target' whale, and attempted to encounter this target first. - When targeting a whale/group, the acoustician on-duty continued to track all other whales/groups in the area as these tracked whales/groups may become the next target after obtaining concluding with the current target. Date: (UTC) written only at top of datasheet Time: (UTC) on the hour, 15 past, half past, and 15 to. Track: Unique identifier for each whale/group tracked in the past 15 minutes. Each track will have: Location: Either an average bearing from a sonobuoy (eg 220 degrees from SB18) or a Lat/Lon from the most recent triangulation Notes: What is the vessel action with respect to this tracked whale/group? (eg. Is this the current or previous 'target'? Are we presently photographing this whale? Did we finish photographing the whale?) Has the whale gone silent? Has this track crossed paths with another?" proprietary
AAS_4102_AcousticTrackingLog2013_1 Acoustic whale tracking log of the 2013 Antarctic Blue Whale Voyage to the Southern Ocean ALL STAC Catalog 2013-01-31 2013-03-16 140, -70, -170, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1532636994-AU_AADC.umm_json "During the 2013 Antarctic Blue Whale Voyage Acousticians noted all whale calls and other acoustic events that were detected during real-time monitoring in a Sonobuoy Event Log. A written summary of the event log was recorded during data collection at approximately 15 minute intervals, and this summary comprises the Whale Tracking Log. - The acoustician on-duty recorded the average bearings or locations of each calling whale/group every 15 minutes in the written Whale Tracking Log. - Entries in the written Sonobuoy Tracking Log (on the bench in the acoustics workstation) also include total number of different whales heard in that 15 minute interval. - If multiple whales/groups were detected, then the acoustician on-duty, in consultation with the lead acoustician and/or voyage management designateded one of the whales the 'target' whale, and attempted to encounter this target first. - When targeting a whale/group, the acoustician on-duty continued to track all other whales/groups in the area as these tracked whales/groups may become the next target after obtaining concluding with the current target. Date: (UTC) written only at top of datasheet Time: (UTC) on the hour, 15 past, half past, and 15 to. Track: Unique identifier for each whale/group tracked in the past 15 minutes. Each track will have: Location: Either an average bearing from a sonobuoy (eg 220 degrees from SB18) or a Lat/Lon from the most recent triangulation Notes: What is the vessel action with respect to this tracked whale/group? (eg. Is this the current or previous 'target'? Are we presently photographing this whale? Did we finish photographing the whale?) Has the whale gone silent? Has this track crossed paths with another?" proprietary
+AAS_4102_AcousticTrackingLog2013_1 Acoustic whale tracking log of the 2013 Antarctic Blue Whale Voyage to the Southern Ocean AU_AADC STAC Catalog 2013-01-31 2013-03-16 140, -70, -170, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1532636994-AU_AADC.umm_json "During the 2013 Antarctic Blue Whale Voyage Acousticians noted all whale calls and other acoustic events that were detected during real-time monitoring in a Sonobuoy Event Log. A written summary of the event log was recorded during data collection at approximately 15 minute intervals, and this summary comprises the Whale Tracking Log. - The acoustician on-duty recorded the average bearings or locations of each calling whale/group every 15 minutes in the written Whale Tracking Log. - Entries in the written Sonobuoy Tracking Log (on the bench in the acoustics workstation) also include total number of different whales heard in that 15 minute interval. - If multiple whales/groups were detected, then the acoustician on-duty, in consultation with the lead acoustician and/or voyage management designateded one of the whales the 'target' whale, and attempted to encounter this target first. - When targeting a whale/group, the acoustician on-duty continued to track all other whales/groups in the area as these tracked whales/groups may become the next target after obtaining concluding with the current target. Date: (UTC) written only at top of datasheet Time: (UTC) on the hour, 15 past, half past, and 15 to. Track: Unique identifier for each whale/group tracked in the past 15 minutes. Each track will have: Location: Either an average bearing from a sonobuoy (eg 220 degrees from SB18) or a Lat/Lon from the most recent triangulation Notes: What is the vessel action with respect to this tracked whale/group? (eg. Is this the current or previous 'target'? Are we presently photographing this whale? Did we finish photographing the whale?) Has the whale gone silent? Has this track crossed paths with another?" proprietary
AAS_4102_KrillAcoustics_2015_1 Echosounder data to assess patterns in krill density - voyage of the RV Tangaroa, 2015 AU_AADC STAC Catalog 2015-02-02 2015-02-28 165, -75, 180, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1667372744-AU_AADC.umm_json Echosounder data were collected on a multidisciplinary research voyage conducted from the RV Tangaroa, operated by New Zealand’s National Institute of Water and Atmospheric Research Limited (NIWA). The voyage lasted 42 days, departing from Wellington, New Zealand on January 29th , 2015 and returning to the same port on 11th March 2015. Active acoustic data were obtained continuously using a calibrated scientific echosounder (Simrad EK60, Horten, Norway). The echosounder operated at 38 and 120 kHz for the duration of the voyage with a pulse duration of 1.024 ms, a pulse repetition rate of one ping per second and a 7° beam width. The echosounder data here are a subset of that collected throughout the voyage and include only data from south of 65°S. This subset of data focuses on research questions pertaining to Antarctic blue whales and krill. proprietary
-AAS_4102_all_photo_ID_images_2012_1 All identification photos taken of whales during the two blue whale voyages in the Bonney Upwelling, Januray and March 2012 AU_AADC STAC Catalog 2012-01-12 2012-03-30 141, -39.5, 143, -38 https://cmr.earthdata.nasa.gov/search/concepts/C1420798388-AU_AADC.umm_json All photos taken during the two Blue whale voyages undertaken in January and March 2012 in an attempt to get a best photo identification image of pygmy blue whales. Whales from the January voyage are numbered sequentially beginning with 1; whales from the March voyage are numbered sequentially beginning with 101. The folder contains a best left side and a best right side photo of each whale (if available). Identification photos of whales where a dorsal fin was not visible are included only if there was a dorsal fin visible in a good identification photo of the other side of the whale. Photo filenames include the photographer’s initials: CJ = Catriona Johnson DD = Dave Donnelly MD = Mike Double JS = Josh Smith NS = Nat Schmitt PE = Paul Ensor PO = Paula Olson RS = Rob Slade VAG = Virginia Andrews-Goff proprietary
AAS_4102_all_photo_ID_images_2012_1 All identification photos taken of whales during the two blue whale voyages in the Bonney Upwelling, Januray and March 2012 ALL STAC Catalog 2012-01-12 2012-03-30 141, -39.5, 143, -38 https://cmr.earthdata.nasa.gov/search/concepts/C1420798388-AU_AADC.umm_json All photos taken during the two Blue whale voyages undertaken in January and March 2012 in an attempt to get a best photo identification image of pygmy blue whales. Whales from the January voyage are numbered sequentially beginning with 1; whales from the March voyage are numbered sequentially beginning with 101. The folder contains a best left side and a best right side photo of each whale (if available). Identification photos of whales where a dorsal fin was not visible are included only if there was a dorsal fin visible in a good identification photo of the other side of the whale. Photo filenames include the photographer’s initials: CJ = Catriona Johnson DD = Dave Donnelly MD = Mike Double JS = Josh Smith NS = Nat Schmitt PE = Paul Ensor PO = Paula Olson RS = Rob Slade VAG = Virginia Andrews-Goff proprietary
-AAS_4102_all_photo_ID_images_2013_1 All identification photos taken of Antarctic blue whales during the Antarctic blue whale voyage 2013 AU_AADC STAC Catalog 2013-01-31 2013-03-16 140, -70, -170, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1428211288-AU_AADC.umm_json All photos taken during the Antarctic blue whale voyage in an attempt to get a best photo identification image of Antarctic blue whales, pygmy blue whales, killer whales, right whales and humpback whales. Image collection location and other details such as photographer, species, date (UTC) can be found in excel spreadsheet. proprietary
+AAS_4102_all_photo_ID_images_2012_1 All identification photos taken of whales during the two blue whale voyages in the Bonney Upwelling, Januray and March 2012 AU_AADC STAC Catalog 2012-01-12 2012-03-30 141, -39.5, 143, -38 https://cmr.earthdata.nasa.gov/search/concepts/C1420798388-AU_AADC.umm_json All photos taken during the two Blue whale voyages undertaken in January and March 2012 in an attempt to get a best photo identification image of pygmy blue whales. Whales from the January voyage are numbered sequentially beginning with 1; whales from the March voyage are numbered sequentially beginning with 101. The folder contains a best left side and a best right side photo of each whale (if available). Identification photos of whales where a dorsal fin was not visible are included only if there was a dorsal fin visible in a good identification photo of the other side of the whale. Photo filenames include the photographer’s initials: CJ = Catriona Johnson DD = Dave Donnelly MD = Mike Double JS = Josh Smith NS = Nat Schmitt PE = Paul Ensor PO = Paula Olson RS = Rob Slade VAG = Virginia Andrews-Goff proprietary
AAS_4102_all_photo_ID_images_2013_1 All identification photos taken of Antarctic blue whales during the Antarctic blue whale voyage 2013 ALL STAC Catalog 2013-01-31 2013-03-16 140, -70, -170, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1428211288-AU_AADC.umm_json All photos taken during the Antarctic blue whale voyage in an attempt to get a best photo identification image of Antarctic blue whales, pygmy blue whales, killer whales, right whales and humpback whales. Image collection location and other details such as photographer, species, date (UTC) can be found in excel spreadsheet. proprietary
+AAS_4102_all_photo_ID_images_2013_1 All identification photos taken of Antarctic blue whales during the Antarctic blue whale voyage 2013 AU_AADC STAC Catalog 2013-01-31 2013-03-16 140, -70, -170, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1428211288-AU_AADC.umm_json All photos taken during the Antarctic blue whale voyage in an attempt to get a best photo identification image of Antarctic blue whales, pygmy blue whales, killer whales, right whales and humpback whales. Image collection location and other details such as photographer, species, date (UTC) can be found in excel spreadsheet. proprietary
AAS_4102_all_photo_ID_images_2015_1 All identification photos taken of whales during the NZ-Australia Antarctic Ecosystems Voyage to the Ross Sea 2015 AU_AADC STAC Catalog 2015-01-29 2015-03-11 160, -75, -175, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1425887998-AU_AADC.umm_json All photos taken during the NZ-Australia Antarctic Ecosystems Voyage to the Ross Sea 2015 in an attempt to get a best photo identification image of blue whales, killer whales, humpback whales and minke whales. Image collection location and other details such as photographer, species, date (UTC) can be found in excel spreadsheet. proprietary
AAS_4102_all_photo_ID_images_2015_1 All identification photos taken of whales during the NZ-Australia Antarctic Ecosystems Voyage to the Ross Sea 2015 ALL STAC Catalog 2015-01-29 2015-03-11 160, -75, -175, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1425887998-AU_AADC.umm_json All photos taken during the NZ-Australia Antarctic Ecosystems Voyage to the Ross Sea 2015 in an attempt to get a best photo identification image of blue whales, killer whales, humpback whales and minke whales. Image collection location and other details such as photographer, species, date (UTC) can be found in excel spreadsheet. proprietary
AAS_4102_longTermAcousticRecordings_3 Long-term underwater acoustic recordings 2013-2019 AU_AADC STAC Catalog 2013-01-23 2018-12-31 62, -70, 150, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1420798568-AU_AADC.umm_json This dataset contains long-term underwater acoustic recordings made under Australian Antarctic Science Projects 4101 and 4102, and the International Whaling Commission’s Southern Ocean Research Partnership (IWC-SORP) Southern Ocean Hydrophone Network (SOHN). Calibrated measurements of sound pressure were made at several sites across several years using custom moored acoustic recorders (MARs) designed and manufactured by the Science Technical Support group of the Australian Antarctic Division. These moored acoustic recorders were designed to operate for year-long, deep-water, Antarctic deployments. Each moored acoustic recorder included a factory calibrated HTI 90-U hydrophone and workshop-calibrated frontend electronics (hydrophone preamplifier, bandpass filter, and analog-digital converter), and used solid state digital storage (SDHC) to reduce power consumption and mechanical self-noise (e.g. from hard-drives with motors and rotating disks). Electronics were placed in a glass instrumentation sphere rated to a depth of 6000 m, and the sphere was attached to a short mooring with nylon straps to decouple recorder and hydrophone from sea-bed. The hydrophone was mounted above the glass sphere with elastic connections to the mooring frame to reduce mechanical self-noise from movement of the hydrophone. The target noise floor of each recorder was below that expected for a quiet ocean at sea state zero. The analog-digital converter, based on an AD7683B chip, provides 100 dB of spurious free dynamic range, but a total signal-to-noise and distortion of 86 dB which yields 14 effective bits of dynamic range at a 1 kHz input frequency. The data for each recording site comprise a folder of 16-bit WAV audio files recorded at a nominal sample rate of 12 kHz. The names of each WAV file correspond to a deployment code followed by the start time (in UTC) of the file as determined by the microprocessor’s real-time clock e.g. 201_2013-12-25_13-00-00.wav would correspond to a wav file with deployment code 201 that starts at 1 pm on December 25th 2013 (UTC). Recording locations were chosen to correspond to sites used during AAS Project 2683. These sites were along the resupply routes for Australia’s Antarctic stations, and typically there was only one opportunity to recover and redeploy MARs each year. proprietary
@@ -1043,16 +1043,16 @@ AAS_4116_Iceberg_Distribution_1 Iceberg Distribution around the Antarctic contin
AAS_4116_Sea-Ice-Seasonality-East-Antarctic_1 Change and variability in East Antarctic sea ice seasonality 1979/80-2009/10 AU_AADC STAC Catalog 1979-02-15 2011-02-15 30, -74, 170, -46 https://cmr.earthdata.nasa.gov/search/concepts/C1667374128-AU_AADC.umm_json This dataset relates to long-term change and variability in annual timings of sea ice advance, retreat and resultant ice season duration in East Antarctica derived from the satellite passive-microwave time series dating back to Nimbus 7. These were calculated from satellite-derived ice concentration data for the period 1979/80 to 2009/10. The dataset includes more detailed analysis of change and variability in sea ice conditions along meridional transects i.e., 110 degrees E and 140 degrees E relating to sea ice concentration and extent, and along 90 deg E, 100 deg E, 110 deg E and 140 deg E for trends in sea ice concentration for the period 1979-2010. Also included are monthly sea-surface temperature (SST) trends mapped north of the East Antarctic sea-ice zone for the period 1982-2010. The SST data are from the Reynolds and Smith OLv2 dataset. These data form the basis of the publication: Massom, R.A., P. Reid, S. Stammerjohn, B. Raymond, A. Fraser and S. Ushio. 2013. Change and variability in East Antarctic sea ice seasonality, 1979/80-2009/10. PloS ONE, 8(5), e64756, doi:10.1371/journal.pone.0064756 proprietary
AAS_4121_Ecosystem_Model_Parameters_1 Ecosystem model parameter set for a near-shore Antarctic food web AU_AADC STAC Catalog 2012-01-01 2016-12-31 -180, -70, 180, -64 https://cmr.earthdata.nasa.gov/search/concepts/C1297573046-AU_AADC.umm_json This parameter set was developed to provide a plausible implementation for the ecological model described in Bates, M., S Bengtson Nash, D.W. Hawker, J. Norbury, J.S. Stark and R. A. Cropp. 2015. Construction of a trophically complex near-shore Antarctic food web model using the Conservative Normal framework with structural coexistence. Journal of Marine Systems. 145: 1-14. The ecosystem model used in this paper was designed to have the property of structural coexistence. This means that the functional forms used to describe population interactions in the equations were chosen to ensure that the boundary eigenvalues of every population were all always positive, ensuring that no population in the model can ever become extinct. This property is appropriate for models such as this that are implemented to model typical seasonal variations rather than changes over time. The actual parameter values were determined by searching a parameter space for parameter sets that resulted in a plausible distribution of biomass among the trophic levels. The search was implemented using the Boundary Eigenvalue Nudging - Genetic Algorithm (BENGA) method and was constrained by measured values where these were available. This parameter set is provided as an indicative set that is appropriate for studying the partitioning of Persistent Organic Pollutants in coastal Antarctic ecosystems. It should not be used for predictive population modelling without independent calibration and validation. proprietary
AAS_4123_model_comparisons_1 Comparison of theoretical and laboratory models of ocean wave transmission by a group of ice floes AU_AADC STAC Catalog 2013-07-01 2013-07-31 -180, -75, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214305666-AU_AADC.umm_json Although the floating sea ice surrounding the Antarctic damps ocean waves, they may still be detected hundreds of kilometres from the ice edge. Over this distance the waves leave an imprint of broken ice, which is susceptible to winds, currents, and lateral melting. The important omission of wave-ice interactions in ice/ocean models is now being addressed, which has prompted campaigns for experimental data. These exciting developments must be matched by innovative modelling techniques to create a true representation of the phenomenon that will enhance forecasting capabilities. This metadata record details laboratory wave basin experiments that were conducted to determine: (i) the wave induced motion of an isolated wooden floe; (ii) the proportion of wave energy transmitted by an array of 40 floes; and (iii) the proportion of wave energy transmitted by an array of 80 floes. Monochromatic incident waves were used, with different wave periods and wave amplitudes. The dataset provides: (i) response amplitude operators for the rigid-body motions of the isolated floe; and (ii) transmission coefficients for the multiple-floe arrays, extracted from raw experimental data using spectral methods. The dataset also contains codes required to produce theoretical predictions for comparison with the experimental data. The models are based on linear potential flow theory. These data models were developed to be applicable to Southern Ocean conditions. proprietary
-AAS_4124_CEAMARC200708_BenthicStills_1 Abundances of broad benthic functional groups in the CEAMARC region 2007/08 AU_AADC STAC Catalog 2007-12-22 2008-01-20 138.86719, -67.23806, 146.20605, -64.94216 https://cmr.earthdata.nasa.gov/search/concepts/C1437175330-AU_AADC.umm_json Derived dataset from the forward facing still-images collected during the benthic trawls of the 2007/08 CEAMARC voyage (raw data-set here: https://data.aad.gov.au/metadata/records/CEAMARC_CASO_200708_V3_IMAGES). All fauna in the bottom third of each image was scored to the lowest taxonomic resolution possible, and operational taxonomic units (OTUs) were aggregated by feeding type afterwards. The images originate from 32 transects, but were split by their lon-lat-position within a spatial grid of environmental variables into 41 sites. This dataset contains: - the mean longitude of all images aggregated per site. - the mean latitude of all images aggregated per site. - the number of images scored per site - the aggregated abundances (given in %-cover) for three main benthic groups (SF=Suspension Feeder, DF=Deposit Feeder, PR=Predator). - number of OTUs observed per benthic group per site. - the total number of OTUs observed per site. proprietary
AAS_4124_CEAMARC200708_BenthicStills_1 Abundances of broad benthic functional groups in the CEAMARC region 2007/08 ALL STAC Catalog 2007-12-22 2008-01-20 138.86719, -67.23806, 146.20605, -64.94216 https://cmr.earthdata.nasa.gov/search/concepts/C1437175330-AU_AADC.umm_json Derived dataset from the forward facing still-images collected during the benthic trawls of the 2007/08 CEAMARC voyage (raw data-set here: https://data.aad.gov.au/metadata/records/CEAMARC_CASO_200708_V3_IMAGES). All fauna in the bottom third of each image was scored to the lowest taxonomic resolution possible, and operational taxonomic units (OTUs) were aggregated by feeding type afterwards. The images originate from 32 transects, but were split by their lon-lat-position within a spatial grid of environmental variables into 41 sites. This dataset contains: - the mean longitude of all images aggregated per site. - the mean latitude of all images aggregated per site. - the number of images scored per site - the aggregated abundances (given in %-cover) for three main benthic groups (SF=Suspension Feeder, DF=Deposit Feeder, PR=Predator). - number of OTUs observed per benthic group per site. - the total number of OTUs observed per site. proprietary
+AAS_4124_CEAMARC200708_BenthicStills_1 Abundances of broad benthic functional groups in the CEAMARC region 2007/08 AU_AADC STAC Catalog 2007-12-22 2008-01-20 138.86719, -67.23806, 146.20605, -64.94216 https://cmr.earthdata.nasa.gov/search/concepts/C1437175330-AU_AADC.umm_json Derived dataset from the forward facing still-images collected during the benthic trawls of the 2007/08 CEAMARC voyage (raw data-set here: https://data.aad.gov.au/metadata/records/CEAMARC_CASO_200708_V3_IMAGES). All fauna in the bottom third of each image was scored to the lowest taxonomic resolution possible, and operational taxonomic units (OTUs) were aggregated by feeding type afterwards. The images originate from 32 transects, but were split by their lon-lat-position within a spatial grid of environmental variables into 41 sites. This dataset contains: - the mean longitude of all images aggregated per site. - the mean latitude of all images aggregated per site. - the number of images scored per site - the aggregated abundances (given in %-cover) for three main benthic groups (SF=Suspension Feeder, DF=Deposit Feeder, PR=Predator). - number of OTUs observed per benthic group per site. - the total number of OTUs observed per site. proprietary
AAS_4124_CEAMARC200708_BenthicStills_InvertebrateAbundances_2 Abundances of benthic invertebrate species in the CEAMARC region 2007/08 AU_AADC STAC Catalog 2007-12-22 2008-01-19 136.62598, -67.3737, 147.17285, -64.88627 https://cmr.earthdata.nasa.gov/search/concepts/C1517284100-AU_AADC.umm_json Percent-cover estimates from forward facing still-images collected during the benthic trawls of the 2007/08 CEAMARC voyage (raw data-set here: https://data.aad.gov.au/metadata/records/CEAMARC_CASO_200708_V3_IMAGES). All fauna in the bottom third of each image was scored to the lowest taxonomic resolution possible. The images originate from 32 transects, but were split by their lon-lat-position within a spatial grid of environmental variables into 41 sites. This dataset contains: (1) - species/ morphotypes identified to the highest taxonomic resolution possible - broader taxonomic classification (phylum/class) - each species mobility, feeding-type and body-shape if possible - average abundances in percent-cover at each site (2) - the mean longitude of all images aggregated per site - the mean latitude of all images aggregated per site - the number of images scored per site proprietary
AAS_4124_CEAMARC200708_BenthicStills_InvertebrateAbundances_2 Abundances of benthic invertebrate species in the CEAMARC region 2007/08 ALL STAC Catalog 2007-12-22 2008-01-19 136.62598, -67.3737, 147.17285, -64.88627 https://cmr.earthdata.nasa.gov/search/concepts/C1517284100-AU_AADC.umm_json Percent-cover estimates from forward facing still-images collected during the benthic trawls of the 2007/08 CEAMARC voyage (raw data-set here: https://data.aad.gov.au/metadata/records/CEAMARC_CASO_200708_V3_IMAGES). All fauna in the bottom third of each image was scored to the lowest taxonomic resolution possible. The images originate from 32 transects, but were split by their lon-lat-position within a spatial grid of environmental variables into 41 sites. This dataset contains: (1) - species/ morphotypes identified to the highest taxonomic resolution possible - broader taxonomic classification (phylum/class) - each species mobility, feeding-type and body-shape if possible - average abundances in percent-cover at each site (2) - the mean longitude of all images aggregated per site - the mean latitude of all images aggregated per site - the number of images scored per site proprietary
AAS_4124_CEAMARC_FoodAvailabilityMertzGlacierTongue_1 Environmental data layers for a 5 year period before and after the calving of the Mertz Glacier Tongue AU_AADC STAC Catalog 2005-01-01 2016-12-31 138, -67.5, 147, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1496920770-AU_AADC.umm_json The dataset contains raster files (.grd) for food-availability and predicted distribution of suspension feeder abundances averaged across a five year time-period before (2005-2009) and after (2011-2016) the calving of the Mertz Glacier Tongue in 2010. The following data are included: - sinking, settling and horizontal flux of food-particles along the seafloor - suspension feeder abundances and standard deviation of the predicted distribution All data has been generated as part of the paper: Jansen et al. (2018) Mapping Antarctic suspension feeder abundances and seafloor-food availability, and modelling their change after a major glacier calving. Frontiers in Ecology and Evolution proprietary
AAS_4124_Extract_Kerguelen_Plateau_Environmental_Layers_1 Extract_Kerguelen_Plateau_Environmental_Layers AU_AADC STAC Catalog 1982-01-01 2014-12-31 70, -54, 78, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1929062044-AU_AADC.umm_json "This dataset contains environmental layers used to model the predicted distribution of demersal fish bioregions for the paper: Hill et al. (2020) Determining Marine Bioregions: A comparison of quantitative approaches, Methods in Ecology and Evolution. It contains climatological variables from satellite and modelled data that represent sea floor and sea surface conditions likely to affect the distribution of demersal fish including: depth, slope, seafloor temperatures, seafloor current, seafloor nitrate, sea surface temperature, chlorophyll-a standard deviation and sea surface height standard deviation. Layers are presented at 0.1 degree resolution. ""prediction_space"" is a Rda file for R that consists of two objects: env_raster: a raster stack of the environmental layers pred_sp: a data.frame version of the env_raster where some variables have been transformed for statistical analysis and bioregion prediction. ""Env_data_sources.xlsx"" contains a description of each environmental variable and it's source." proprietary
AAS_4124_Kerguelen_Plateau_demersal_fish_assemblages_1 Kerguelen Plateau demersal fish assemblages- Regions of Common Profile Analysis Products AU_AADC STAC Catalog 2007-01-01 2013-12-31 60, -56, 80, -44 https://cmr.earthdata.nasa.gov/search/concepts/C1297567600-AU_AADC.umm_json Demersal fish form an important component of sub-Antarctic ecosystems. While understanding the distribution of key commercial species is the subject of much current research, patterns in the distribution of benthic fish assemblages as a whole and associated diversity has received less attention. Here we combine Australian (source: AAD Random Stratified Trawl Surveys) and French (source: POKER 2006, 2010, 2013) demersal fish datasets with synoptic environmental data to quantify and predict the distribution of fish assemblages across the Kerguelen Plateau. We achieve this by applying a recently developed method, called Regions of Common Profile (RCP), which quantifies distinct environmental regions containing a similar profile of species. The RCP method directly models species simultaneously (rather than dissimilarities or single species at a time) and offers advantages over previous methods in the areas of model diagnostics, the interpretability of model outputs, and providing estimates of uncertainty. We define the contents, environmental correlates and spatial extent of several assemblages across the plateau. The files provided here are the outputs of the RCP analyses. Files KP_RCP_Predictions.csv: Region of Common Profile (RCP) spatial predictions for entire Kerguelen Plateau. The resolution of the grid is 0.1 x 0.1 degrees (Long, Lat, WGS84) and predictions were restricted to depths shallower than 1200 m. The probability of each grid cell belonging to each RCP is reported (RCP_1 - RCP_7) as well as the most likely RCP (HClass) and the most likely RCP's probability of occurrence (HClass_prob) RCP_Species_Composition_Average.xls: Average (standard deviation) of probability of occurrence for each species in each RCP. Statistics calculated by taking 500 bootstrap samples of model parameters, generating expected probability of occurrences for each species in each RCP for each level of the sampling factor Year/Season/Gear and summarising over the 3500 (7 levels of sampling effect x 500 bootstraps) values. RCP_Species_Composition_SampEff.xls: Average (Standard deviation) probability of occurrence of species for each RCP for each level of the sampling factor (Year/Season/Gear). Marginal_env_plots (Folder): Marginal plots of the response of each RCP to depth (m), chl-a yearly mean (mg/m3) and surface temperature yearly mean (degrees Celsius). Plots were generated by predicting RCP membership for each trawl site based on its environmental covariates only and plotting. Interactive maps showing the predicted spatial distribution of the RCP groups, as well as the species profile and environmental conditions characterising each group, and the coverage of the HIMI Marine reserve can be found at doi: 10.4225/15/58169d06ee8fc. Contains the above results in an interactive map with the following layers: 1) Assemblage maps: Species Profile: Map of the most likely RCP group. The pop up graphic shows pictures of the four most likely species to occur in this assemblage as well as the expected occurrence of all species (the species profile). 2) Assemblage maps: Environment Characteristics: Map of the most likely RCP group. The pop up graphic shows the response of each assemblage to depth, surface temperature yearly mean and chl-a yearly mean. These inform us of the environmental characteristics of each RCP group. Plots were generated by predicting RCP group membership for each trawl site based only on its environmental covariates. 3) Group Membership: Map of the most likely RCP group and the uncertainty associated with this group. 4) HIMI Reserve Coverage: Location of Heard and McDonald Islands Marine Reserve with pop-up table of the proportion of each RCP group contained within the reserve. Proportion calculated within the Australian EEZ only. proprietary
AAS_4124_cephalopod_habitat_suitability_1 Habitat suitability predictions for 15 species of cephalopods in the Southern Ocean AU_AADC STAC Catalog 2012-07-01 2016-06-30 -180, -90, 180, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1214311694-AU_AADC.umm_json "Our understanding of how environmental change in the Southern Ocean will affect marine diversity,habitats and distribution remain limited. The habitats and distributions of Southern Ocean cephalopods are generally poorly understood, and yet such knowledge is necessary for research and conservation management purposes, as well as for assessing the potential impacts of environmental change. We used net-catch data to develop habitat suitability models for 15 of the most common cephalopods in the Southern Ocean. Full details of the methodology are provided in the paper (Xavier et al. (2015)). Briefly, occurrence data were taken from the SCAR Biogeographic Atlas of the Southern Ocean. This compilation was based upon Xavier et al. (1999), with additional data drawn from the Ocean Biogeographic Information System, biodiversity.aq, the Australian Antarctic Data Centre, and the National Institute of Water and Atmospheric Research. The habitat suitability modelling was conducted using the Maxent software package (v3.3.3k, Phillips et al., 2006). Maxent allows for nonlinear model terms by formulating a series of features from the predictor variables. Due to relatively limited sample sizes, we constrained the complexity of most models by considering only linear, quadratic, and product features. A multiplier of 3.0 was used on automatic regularization parameters to discourage overfitting; otherwise, default Maxent settings were used. Predictor variables were chosen from a collection of Southern Ocean layers. These variables were selected as indicators of ecosystem structure and processes including water mass properties, sea ice dynamics, and productivity. A 10-fold cross-validation procedure was used to assess model performance (using the area under the receiver-operating curve) and variable permutation importance, with values averaged over the 10 fitted models. The final predicted distribution for each species was based on a single model fitted using all data: these are the predictions included in this data set. The individual habitat suitability models were overlaid to generate a 'hotspot' index of species richness. The predicted habitat suitability for each species was converted to a binary presence/absence layer by applying a threshold, such that habitat suitability values above the threshold were converted to presences. The threshold used for each species was the average of the thresholds (for each of the 10 training models) chosen to maximize the test area under the receiver-operating curve. The binary layers were then summed to give the number of species estimated to be present in each pixel in the study region." proprietary
-AAS_4124_pelagic_regionalisation_1 A circumpolar pelagic regionalisation of the Southern Ocean AU_AADC STAC Catalog 2012-10-01 2016-03-31 -180, -80, 180, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1384659305-AU_AADC.umm_json This layer is a circumpolar, pelagic regionalisation of the Southern Ocean south of 40 degrees S, based on sea surface temperature, depth, and sea ice information. The results show a series of latitudinal bands in open ocean areas, consistent with the oceanic fronts. Around islands and continents, the spatial scale of the patterns is finer, and is driven by variations in depth and sea ice. The processing methods follow those of Grant et al. (2006) and the CCAMLR Bioregionalisation Workshop (SC-CAMLR-XXVI 2007). Briefly, a non-hierarchical clustering algorithm was used to reduce the full set of grid cells to 250 clusters. These 250 clusters were then further refined using a hierarchical (UPGMA) clustering algorithm. The first, non-hierarchical, clustering step is an efficient way of reducing the large number of grid cells, so that the subsequent hierarchical clustering step is tractable. The hierarchical clustering algorithm produces a dendrogram, which can be used to guide the clustering process (e.g. choices of data layers and number of clusters) but is difficult to use with large data sets. Analyses were conducted in Matlab (Mathworks, Natick MA, 2011) and R (R Foundation for Statistical Computing, Vienna 2009). Three variables were used for the pelagic regionalisation: sea surface temperature (SST), depth, and sea ice cover. Sea surface temperature was used as a general indicator of water masses and of Southern Ocean fronts (Moore et al. 1999, Kostianoy et al. 2004). Sea surface height (SSH) from satellite altimetry is also commonly used for this purpose (e.g. Sokolov and Rintoul 2009), and may give front positions that better match those from subsurface hydrography than does SST. However, SSH data has incomplete coverage in some near-coastal areas (particularly in the Weddell and Ross seas) and so in the interests of completeness, SST was used here. During the hierarchical clustering step, singleton clusters (clusters comprised of only one datum) were merged back into their parent cluster (5 instances, in cluster groups 2, 3, 8, and 13). Additionally, two branches of the dendrogram relating to temperate shelf areas (around South America, New Zealand, and Tasmania) were merged to reduce detail in these areas (since such detail is largely irrelevant in the broader Southern Ocean context). proprietary
AAS_4124_pelagic_regionalisation_1 A circumpolar pelagic regionalisation of the Southern Ocean ALL STAC Catalog 2012-10-01 2016-03-31 -180, -80, 180, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1384659305-AU_AADC.umm_json This layer is a circumpolar, pelagic regionalisation of the Southern Ocean south of 40 degrees S, based on sea surface temperature, depth, and sea ice information. The results show a series of latitudinal bands in open ocean areas, consistent with the oceanic fronts. Around islands and continents, the spatial scale of the patterns is finer, and is driven by variations in depth and sea ice. The processing methods follow those of Grant et al. (2006) and the CCAMLR Bioregionalisation Workshop (SC-CAMLR-XXVI 2007). Briefly, a non-hierarchical clustering algorithm was used to reduce the full set of grid cells to 250 clusters. These 250 clusters were then further refined using a hierarchical (UPGMA) clustering algorithm. The first, non-hierarchical, clustering step is an efficient way of reducing the large number of grid cells, so that the subsequent hierarchical clustering step is tractable. The hierarchical clustering algorithm produces a dendrogram, which can be used to guide the clustering process (e.g. choices of data layers and number of clusters) but is difficult to use with large data sets. Analyses were conducted in Matlab (Mathworks, Natick MA, 2011) and R (R Foundation for Statistical Computing, Vienna 2009). Three variables were used for the pelagic regionalisation: sea surface temperature (SST), depth, and sea ice cover. Sea surface temperature was used as a general indicator of water masses and of Southern Ocean fronts (Moore et al. 1999, Kostianoy et al. 2004). Sea surface height (SSH) from satellite altimetry is also commonly used for this purpose (e.g. Sokolov and Rintoul 2009), and may give front positions that better match those from subsurface hydrography than does SST. However, SSH data has incomplete coverage in some near-coastal areas (particularly in the Weddell and Ross seas) and so in the interests of completeness, SST was used here. During the hierarchical clustering step, singleton clusters (clusters comprised of only one datum) were merged back into their parent cluster (5 instances, in cluster groups 2, 3, 8, and 13). Additionally, two branches of the dendrogram relating to temperate shelf areas (around South America, New Zealand, and Tasmania) were merged to reduce detail in these areas (since such detail is largely irrelevant in the broader Southern Ocean context). proprietary
+AAS_4124_pelagic_regionalisation_1 A circumpolar pelagic regionalisation of the Southern Ocean AU_AADC STAC Catalog 2012-10-01 2016-03-31 -180, -80, 180, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1384659305-AU_AADC.umm_json This layer is a circumpolar, pelagic regionalisation of the Southern Ocean south of 40 degrees S, based on sea surface temperature, depth, and sea ice information. The results show a series of latitudinal bands in open ocean areas, consistent with the oceanic fronts. Around islands and continents, the spatial scale of the patterns is finer, and is driven by variations in depth and sea ice. The processing methods follow those of Grant et al. (2006) and the CCAMLR Bioregionalisation Workshop (SC-CAMLR-XXVI 2007). Briefly, a non-hierarchical clustering algorithm was used to reduce the full set of grid cells to 250 clusters. These 250 clusters were then further refined using a hierarchical (UPGMA) clustering algorithm. The first, non-hierarchical, clustering step is an efficient way of reducing the large number of grid cells, so that the subsequent hierarchical clustering step is tractable. The hierarchical clustering algorithm produces a dendrogram, which can be used to guide the clustering process (e.g. choices of data layers and number of clusters) but is difficult to use with large data sets. Analyses were conducted in Matlab (Mathworks, Natick MA, 2011) and R (R Foundation for Statistical Computing, Vienna 2009). Three variables were used for the pelagic regionalisation: sea surface temperature (SST), depth, and sea ice cover. Sea surface temperature was used as a general indicator of water masses and of Southern Ocean fronts (Moore et al. 1999, Kostianoy et al. 2004). Sea surface height (SSH) from satellite altimetry is also commonly used for this purpose (e.g. Sokolov and Rintoul 2009), and may give front positions that better match those from subsurface hydrography than does SST. However, SSH data has incomplete coverage in some near-coastal areas (particularly in the Weddell and Ross seas) and so in the interests of completeness, SST was used here. During the hierarchical clustering step, singleton clusters (clusters comprised of only one datum) were merged back into their parent cluster (5 instances, in cluster groups 2, 3, 8, and 13). Additionally, two branches of the dendrogram relating to temperate shelf areas (around South America, New Zealand, and Tasmania) were merged to reduce detail in these areas (since such detail is largely irrelevant in the broader Southern Ocean context). proprietary
AAS_4127_antFOCE_AmbientSeawaterTemperature_1 antFOCE Ambient Seawater Temperature AU_AADC STAC Catalog 2015-01-03 2015-03-02 110.30151, -66.37372, 110.69946, -66.17768 https://cmr.earthdata.nasa.gov/search/concepts/C1444708896-AU_AADC.umm_json "Refer to antFOCE report section 2.3 for deployment, sampling and analysis details. https://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127 The download file contains an Excel workbook with a series of data spreadsheets - one for each of the Onset Hoboware Tidbit v2 (UTBI-001) temperature loggers that were attached to the outside of various pieces of the underwater experimental infrastructure across the antFOCE site. A Notes spreadsheet is also included with information relevant to the data. Background The antFOCE experimental system was deployed in O'Brien Bay, approximately 5 kilometres south of Casey station, East Antarctica, in the austral summer of 2014/15. Surface and sub-surface (in water below the sea ice) infrastructure allowed controlled manipulation of seawater pH levels (reduced by 0.4 pH units below ambient) in 2 chambers placed on the sea floor over natural benthic communities. Two control chambers (no pH manipulation) and two open plots (no chambers, no pH manipulation) were also sampled to compare to the pH manipulated (acidified) treatment chambers. Details of the antFOCE experiment can be found in the report – ""antFOCE 2014/15 – Experimental System, Deployment, Sampling and Analysis"". This report and a diagram indicating how the various antFOCE data sets relate to each other are available at: https://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127 " proprietary
AAS_4127_antFOCE_ArtificialSubstrateUnits_1 Artificial Substrate Units from the antFOCE (Antarctic Free Ocean Carbon Enrichment) experiment at Casey Station AU_AADC STAC Catalog 2015-01-01 2015-03-02 109.66431, -66.59929, 111.29272, -65.91673 https://cmr.earthdata.nasa.gov/search/concepts/C1443629977-AU_AADC.umm_json Refer to antFOCE report section 4.5.2 for deployment, sampling and analysis details. https://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127 The download file contains an Excel workbook with one data spreadsheet and one of notes relevant to the data. The data are the total number of each organism collected from artificial substrate units (plastic pot scourers) deployed in chambers or open plots during the antFOCE experiment (Data = Number of Individuals). Analysis methods are detailed in the Notes spreadsheet. Background The antFOCE experimental system was deployed in O’Brien Bay, approximately 5 kilometres south of Casey station, East Antarctica, in the austral summer of 2014/15. Surface and sub-surface (in water below the sea ice) infrastructure allowed controlled manipulation of seawater pH levels (reduced by 0.4 pH units below ambient) in 2 chambers placed on the sea floor over natural benthic communities. Two control chambers (no pH manipulation) and two open plots (no chambers, no pH manipulation) were also sampled to compare to the pH manipulated (acidified) treatment chambers. Details of the antFOCE experiment can be found in the report – “antFOCE 2014/15 – Experimental System, Deployment, Sampling and Analysis”. This report and a diagram indicating how the various antFOCE data sets relate to each other are available at: https://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127 proprietary
AAS_4127_antFOCE_Biofilms_Eukaryotes_2 Eukaryotic 18S rDNA PCR amplification and high-throughput sequencing of antFOCE Biofilms AU_AADC STAC Catalog 2014-12-28 2015-03-04 109.62036, -66.68817, 111.38062, -65.89858 https://cmr.earthdata.nasa.gov/search/concepts/C1625714132-AU_AADC.umm_json "This metadata record contains an Excel spreadsheet with Operational Taxonomic Units (OTUs) gained from Eukaryotic 18S rDNA PCR amplification and high-throughput sequencing of samples from Biofilm slides deployed as part of the antFOCE experiment in the austral summer of 2014/15 at Casey station, East Antarctica. Refer to antFOCE report section 4.5.3 for deployment, sampling and analysis details. https://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127 Sampling design 2 trays of 8 horizontal standard glass microscope slides (72 x 25 mm) per chamber. Four of the glass slides were scored with a diamond pencil approximately 18 mm from the right hand end of the slide and deployed scored side up. The remaining four slides were unmodified. Slides were sampled at: - Tmid - one tray per chamber / open plot. The sampled try was repopulated with fresh slides and redeployed - Tend – 2 slides trays per chamber / open plot. Sampling procedure After 31 days deployment, 1 slide tray per chamber / open plot was sampled. At Tend both trays in each chamber / open plot were sampled. To minimize disturbance while being raised to the surface, each tray was removed from the tray holder by divers and placed in a seawater filled container with a lid. On the surface, slides were removed from the tray using ethanol sterilized forceps. The four unscoured slides per chamber / open plot were placed in a plastic microscope slide holder with a sealable lid. The scoured slides were placed individually in 70 ml plastic sample jars. Lab procedure - Casey The slide holder (4 unscoured slides) from each chamber / open plot was frozen at -20C immediately upon return to the lab. The scoured slides were preserved in sea water containing 1% final concentration glutaraldehyde in separate jars. Preservation Issue: Scoured slides were not refrigerated, either at Casey, during RTA or in Kingston before the 26th Nov 2015, when they were transferred to the 4C Cold Store. antFOCE Background The antFOCE experimental system was deployed in O’Brien Bay, approximately 5 kilometres south of Casey station, East Antarctica, in the austral summer of 2014/15. Surface and sub-surface (in water below the sea ice) infrastructure allowed controlled manipulation of seawater pH levels (reduced by 0.4 pH units below ambient) in 2 chambers placed on the sea floor over natural benthic communities. Two control chambers (no pH manipulation) and two open plots (no chambers, no pH manipulation) were also sampled to compare to the pH manipulated (acidified) treatment chambers. Details of the antFOCE experiment can be found in the report – ""antFOCE 2014/15 – Experimental System, Deployment, Sampling and Analysis"". This report and a diagram indicating how the various antFOCE data sets relate to each other are available at: https://data.aad.gov.au/metadata/records/AAS_4127_antFOCE_Project4127 AntFOCE biofilm DNA methods Laurence Clarke, Shane Powell, Bruce Deagle DNA extraction The biofilm was removed from the top of each slide with a cotton swab and DNA extracted directly from the swab using the MoBio PowerBiofilm DNA isolation kit following the manufacturer’s protocol. Extraction blanks were extracted in parallel to detect contamination. Eukaryotic 18S rDNA PCR amplification and high-throughput sequencing DNA extracts were PCR-amplified in triplicate with primers designed to amplify 140-170 bp of eukaryotic 18S ribosomal DNA (Jarman et al. 2013). The forward primer was modified to improve amplification of protists. Table 1. First and second round primers, including MID tags (Xs). ILF_ProSSU3'F_X TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG XXXXXX CACCGCCCGTCGCWMCTACCG ILR_SSU3'R_Y GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG XXXXXX GGTTCACCTACGGAAACCTTGTTACG msqFX AATGATACGGCGACCACCGAGATCTACAC XXXXXXXXXX TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG msqRY CAAGCAGAAGACGGCATACGAGAT XXXXXXXXXX GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG PCR amplifications were performed in two rounds, the first to amplify the 18S region and add sample-specific multiplex-identifier (MID) tags and Illumina sequencing primers, the second to add the P5 and P7 sequencing adapters and additional MIDs. Each reaction mix for the first PCR contained 0.1 µM each of forward and reverse primer, 0.2 µg/µL BSA, 0.2 U Phusion DNA polymerase in 1 x Phusion Master Mix (New England Biolabs, Ipswich, MA, USA) and 1 micro L DNA extract in a total reaction volume of 10 micro L. PCR thermal cycling conditions were initial denaturation at 98 degrees C for 30 secs, followed by 25 cycles of 98 degrees C for 5 secs, 67 degrees C for 20 secs and 72 degrees C for 20 secs, with a final extension at 72 degrees C for 5 min. Replicate PCR products were pooled then diluted 1:10 and Illumina sequencing adapters added in a second round of PCR using the same reaction mix and thermal cycling conditions as the first round, except the concentration of BSA was halved (0.1 micro g/micro L), and the number of cycles was reduced to 10 with an annealing temperature of 55 degrees C. Products from each round of PCR were visualized on 2% agarose gels. Second round PCR products were pooled in equimolar ratios based on band intensity. The pooled products were purified using Agencourt AMPure XP beads (Beckman Coulter, Brea, CA, USA) and the concentration of the library measured using the Qubit dsDNA HS assay on a QUBIT 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA). The pool was diluted to 2 nM and paired-end reads generated on a MiSeq (Illumina, San Diego, CA, USA) with MiSeq Reagent Nano kit vs (300-cycles). Bacterial 16S rDNA PCR amplification and high-throughput sequencing Bioinformatics Reads were sorted by sample-specific MIDs added in the second round PCR using the MiSeq Reporter software. Fastq reads were merged using the -fastq_mergepairs command in USEARCH v8.0.1623 (Edgar 2010). Merged reads were sorted by ""internal"" 6 bp MID tags, and locus-specific primers trimmed with custom R scripts using the ShortRead package (Morgan et al. 2009), with only reads containing perfect matches to the expected MIDs and primers retained. Reads for all samples were dereplicated and global singletons discarded (-derep_fulllength -minuniquesize 2), and clustered into OTUs with the UPARSE algorithm (Edgar 2013) using the '-cluster_otus' command. Potentially chimeric reads were also discarded during this step. Reads for each sample were then assigned to OTUs (-usearch_global -id .97), and an OTU table generated using a custom R script. Taxonomy was assigned to each OTU using MEGAN version 5.10.5 (Huson et al. 2011) based on 50 hits per OTU generated by BLASTN searches against the NCBI 'nt' database (downloaded August 2015). Default LCA parameters were used, except Min support = 1, Min score = 100, Top percent = 10. Alpha and beta-diversity analyses were performed based on a rarefied OTU table with QIIME v1.8.0 (alpha_rarefaction.py, beta_diversity_through_plots.py, Caporaso et al. 2010). References Caporaso JG, Kuczynski J, Stombaugh J, et al. (2010) QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7, 335-336. Huson DH, Mitra S, Ruscheweyh HJ, Weber N, Schuster SC (2011) Integrative analysis of environmental sequences using MEGAN4. Genome Research 21, 1552-1560. Jarman SN, McInnes JC, Faux C, et al. (2013) Adelie penguin population diet monitoring by analysis of food DNA in scats. PLoS One 8, e82227." proprietary
@@ -1077,8 +1077,8 @@ AAS_4140_Zooplankton_lengths_1 Lengths of key zooplankton species in the Indian
AAS_4156_Campbell_Diatoms_1 Environmental and diatom data obtained from a survey of Campbell Island's lakes and ponds 2010-2011 AU_AADC STAC Catalog 2010-01-01 2010-02-28 169.12, -52.54, 169.15, -52.52 https://cmr.earthdata.nasa.gov/search/concepts/C1214311703-AU_AADC.umm_json Public Summary of AAS project 4156 - High resolution reconstructions of climate and ecosystem variability in the sub-Antarctic during the last two millennia Our understanding of global climate and ability to predict future changes is limited by a lack of long-term (palaeoclimate) data from the Southern Hemisphere (SH). Sub-Antarctic islands are the only landmasses between Antarctica and the mid latitudes where terrestrial palaeoclimate records exist, making them crucial locations for linking data from the mid and high latitudes. Using lake sediments from sub-Antarctic islands, we will examine how the climate and ecosystems have changed over the last 2000 years. This will contribute vital information to understand SH climate and ecosystem variability Taken from the abstract of the referenced paper: Sub-Antarctic islands are ideally placed to reconstruct past changes in Southern Hemisphere westerly wind behaviour. They lie within their core belt (50-60 degrees South) and the strong winds deliver sea salt ions to the islands resulting in a west to east conductivity gradient in their water bodies. This means that the stronger (or weaker) the winds, the higher (or lower) the conductivity values measured in the water bodies. A survey of the water chemistry and diatom assemblages of lakes and ponds on sub-Antarctic Campbell Island (52 degrees 32 minutes S, 169 degrees 8 minutes E) revealed that, similar to other sub-Antarctic islands, conductivity was the most important, statistically significant ecological variable explaining turnover in diatom community structure. Based on this, a diatom-conductivity transfer function was developed (simple weighted averaging with inverse deshrinking). This transfer function will be applied to lake sediment cores from the western edge of the Campbell Island plateau to reconstruct past conductivity/sea spray and therefore directly reconstruct changes in Southern Hemisphere westerly wind strength within their core belt. proprietary
AAS_4156_Macquarie_Island_Emerald_Lake_1 12,000 year record of sea spray and minerogenic input from Emerald Lake, Macquarie Island AU_AADC STAC Catalog 2012-07-01 2019-06-30 158.77441, -54.77772, 158.94951, -54.4828 https://cmr.earthdata.nasa.gov/search/concepts/C2102891784-AU_AADC.umm_json Reconstructed sea spray and minerogenic data for a 12,000 year lake sediment record from Emerald Lake, Macquarie Island. Proxies are based on biological (diatoms) and geochemical (micro x-ray fluorescence and hyperspectral imaging) indicators. Data correspond to the figures in: Saunders et al. 2018 Holocene dynamics of the Southern Hemisphere westerly winds and possible links to CO2 outgassing. Nature Geoscience 11:650-655. doi.org/10.1038/s41561-018-0186-5. Detailed supplementary information: https://static-content.springer.com/esm/art%3A10.1038%2Fs41561-018-0186-5/MediaObjects/41561_2018_186_MOESM1_ESM.pdf Abstract: The Southern Hemisphere westerly winds (SHW) play an important role in regulating the capacity of the Southern Ocean carbon sink. They modulate upwelling of carbon-rich deep water and, with sea ice, determine the ocean surface area available for air–sea gas exchange. Some models indicate that the current strengthening and poleward shift of these winds will weaken the carbon sink. If correct, centennial- to millennial-scale reconstructions of the SHW intensity should be linked with past changes in atmospheric CO2, temperature and sea ice. Here we present a 12,300-year reconstruction of wind strength based on three independent proxies that track inputs of sea-salt aerosols and minerogenic particles accumulating in lake sediments on sub-Antarctic Macquarie Island. Between about 12.1 thousand years ago (ka) and 11.2 ka, and since about 7 ka, the wind intensities were above their long-term mean and corresponded with increasing atmospheric CO2. Conversely, from about 11.2 to 7.2 ka, the wind intensities were below their long-term mean and corresponded with decreasing atmospheric CO2. These observations are consistent with model inferences of enhanced SHW contributing to the long-term outgassing of CO2 from the Southern Ocean. proprietary
AAS_4156_Macquarie_Island_Emerald_Lake_1 12,000 year record of sea spray and minerogenic input from Emerald Lake, Macquarie Island ALL STAC Catalog 2012-07-01 2019-06-30 158.77441, -54.77772, 158.94951, -54.4828 https://cmr.earthdata.nasa.gov/search/concepts/C2102891784-AU_AADC.umm_json Reconstructed sea spray and minerogenic data for a 12,000 year lake sediment record from Emerald Lake, Macquarie Island. Proxies are based on biological (diatoms) and geochemical (micro x-ray fluorescence and hyperspectral imaging) indicators. Data correspond to the figures in: Saunders et al. 2018 Holocene dynamics of the Southern Hemisphere westerly winds and possible links to CO2 outgassing. Nature Geoscience 11:650-655. doi.org/10.1038/s41561-018-0186-5. Detailed supplementary information: https://static-content.springer.com/esm/art%3A10.1038%2Fs41561-018-0186-5/MediaObjects/41561_2018_186_MOESM1_ESM.pdf Abstract: The Southern Hemisphere westerly winds (SHW) play an important role in regulating the capacity of the Southern Ocean carbon sink. They modulate upwelling of carbon-rich deep water and, with sea ice, determine the ocean surface area available for air–sea gas exchange. Some models indicate that the current strengthening and poleward shift of these winds will weaken the carbon sink. If correct, centennial- to millennial-scale reconstructions of the SHW intensity should be linked with past changes in atmospheric CO2, temperature and sea ice. Here we present a 12,300-year reconstruction of wind strength based on three independent proxies that track inputs of sea-salt aerosols and minerogenic particles accumulating in lake sediments on sub-Antarctic Macquarie Island. Between about 12.1 thousand years ago (ka) and 11.2 ka, and since about 7 ka, the wind intensities were above their long-term mean and corresponded with increasing atmospheric CO2. Conversely, from about 11.2 to 7.2 ka, the wind intensities were below their long-term mean and corresponded with decreasing atmospheric CO2. These observations are consistent with model inferences of enhanced SHW contributing to the long-term outgassing of CO2 from the Southern Ocean. proprietary
-AAS_4156_Macquarie_Island_unnamed_lake_1 2000 year record of environmental change from an unnamed lake on Macquarie Island AU_AADC STAC Catalog 2012-07-01 2019-06-30 158.74969, -54.78485, 158.96118, -54.47004 https://cmr.earthdata.nasa.gov/search/concepts/C2102891849-AU_AADC.umm_json Age-depth and geochemical data for a 2000 year lake sediment record from an unnamed lake on Macquarie Island. The lake is the small lake to the west of Major Lake, on the edge of the Macquarie Island plateau. The chronology is based on lead-210 (last ca. 100 years) and radiocarbon (extending to ca. 2000 years). Geochemistry is based on micro x-ray fluroescence, and carbon, nitrogen and sulphur contents. Grain size and water content were also measured. Data correspond to the publication: Saunders et al. in prep.Southern Hemisphere westerly wind variability in the sub-Antarctic and relationships to mid-latitude precipitation for the last 2000 years proprietary
AAS_4156_Macquarie_Island_unnamed_lake_1 2000 year record of environmental change from an unnamed lake on Macquarie Island ALL STAC Catalog 2012-07-01 2019-06-30 158.74969, -54.78485, 158.96118, -54.47004 https://cmr.earthdata.nasa.gov/search/concepts/C2102891849-AU_AADC.umm_json Age-depth and geochemical data for a 2000 year lake sediment record from an unnamed lake on Macquarie Island. The lake is the small lake to the west of Major Lake, on the edge of the Macquarie Island plateau. The chronology is based on lead-210 (last ca. 100 years) and radiocarbon (extending to ca. 2000 years). Geochemistry is based on micro x-ray fluroescence, and carbon, nitrogen and sulphur contents. Grain size and water content were also measured. Data correspond to the publication: Saunders et al. in prep.Southern Hemisphere westerly wind variability in the sub-Antarctic and relationships to mid-latitude precipitation for the last 2000 years proprietary
+AAS_4156_Macquarie_Island_unnamed_lake_1 2000 year record of environmental change from an unnamed lake on Macquarie Island AU_AADC STAC Catalog 2012-07-01 2019-06-30 158.74969, -54.78485, 158.96118, -54.47004 https://cmr.earthdata.nasa.gov/search/concepts/C2102891849-AU_AADC.umm_json Age-depth and geochemical data for a 2000 year lake sediment record from an unnamed lake on Macquarie Island. The lake is the small lake to the west of Major Lake, on the edge of the Macquarie Island plateau. The chronology is based on lead-210 (last ca. 100 years) and radiocarbon (extending to ca. 2000 years). Geochemistry is based on micro x-ray fluroescence, and carbon, nitrogen and sulphur contents. Grain size and water content were also measured. Data correspond to the publication: Saunders et al. in prep.Southern Hemisphere westerly wind variability in the sub-Antarctic and relationships to mid-latitude precipitation for the last 2000 years proprietary
AAS_4157_Clouds_1 Cloud Detector measurements made at Davis Station, Antarctica AU_AADC STAC Catalog 2002-11-01 77.972, -68.57612, 77.972, -68.57612 https://cmr.earthdata.nasa.gov/search/concepts/C1214305668-AU_AADC.umm_json "It had been shown that remote cloud detection can be performed with the use of new generation Thermopile detectors. The detection method is based on the fact that a cloudy sky will be warmer than a clear sky. An ideal cloud detection system would also need to account for the effects of relative humidity and barometric pressure, however good performance can still be obtained by ignoring these effects. AAD Thermopile Detector ===================== A Thermopile detector is used to remotely measure the temperature of the sky. The TPS 534 Thermopile detector chosen is fitted with a 5.5um Longpass (standard) IR filter, which allows precise remote temperature measurement of an ideal black body source. The TPS 534 Thermopile detector produces an output voltage that is positive when the temperature of the scene it is viewing is higher than the temperature of itself, and a negative output voltage when the temperature of the scene it is viewing is lower than the temperature of itself. For this reason it is necessary to compensate for the temperature of the detector. The TPS 534 Thermopile detector has an internal NTC Thermistor which can be used for temperature compensation. This Cloud Detector design implements a very simple analogue form of temperature compensation. The main drawback of an analogue temperature compensation system is that the NTC Thermistor has a very non-linear response with temperature which can only be partially corrected using a linearization resistance network. The other main drawback of an analogue temperature compensation system is that the system gains and voltage levels must be precisely adjusted by trial and error to guarantee correct operation over the desired operational temperature range. The Cloud Detector is designed for an operational temperature range of -30 degrees to +25 degrees Celsius. Operation outside of this range may cause internal signal saturation, and incorrect temperature compensation performance. The Cloud Detector optical field of view has been constrained to a 30 degrees full angle with the use of a cylindrical baffle assembly fitted directly to the Thermopile detector. The dimensions of the cylindrical baffle assembly could in theory be defined such that any field of view up to 80 degrees could be achieved. The Cloud Detector provides three plus or minus 10V output voltage signals to the data logging hardware : - Uncompensated Sensor Output Signal : Thermopile detector output signal without any analogue temperature compensation. The output voltage is proportional to the amount of cloud detected within the field of view of the instrument. - Compensated Sensor Output Signal : Thermopile detector output signal with analogue temperature compensation. The output voltage is proportional to the amount of cloud detected within the field of view of the instrument. - Temperature Output Signal : Linearised NTC Thermistor output signal used to apply analogue temperature compensation to the Thermopile detector output signal. The output voltage is proportional to the temperature of the Thermopile detector. The output voltage is uncalibrated, however the temperature verses output voltage could easily be measured. Boltwood Cloud Sensor =================== This is a commercial cloud sensor unit manufactured by diffraction limited." proprietary
AAS_4158_POA_ANNUA_Herbicide_1 Herbicide movement and persistence in Macquarie Island soils AU_AADC STAC Catalog 2013-01-01 2015-04-30 158.90625, -54.97761, 158.90625, -54.97761 https://cmr.earthdata.nasa.gov/search/concepts/C1292611409-AU_AADC.umm_json This data set describes the persistence and movement of herbicides in 2 soil types from Macquarie Island. The soil characterization spreadsheet provides physical and chemical analyses of several Macquarie Island soils. A column leaching experiment was then used to assess the leaching and persistence in two Macquarie Island soils. Details of this experimental set up, and collection of leachate samples is provided in the Core leaching data spreadsheet. These samples were then analysed using LCMS to determine the concentration of glyphosate and AMPA in the leachate (Leachate samples_analysis) proprietary
AAS_4158_POA_ANNUA_Management_1 Management of Poa annua in the sub-Antarctic AU_AADC STAC Catalog 2013-01-01 2015-04-30 158.77029, -54.78247, 158.96393, -54.48041 https://cmr.earthdata.nasa.gov/search/concepts/C1292611416-AU_AADC.umm_json This data set describes several experiments undertaken to determine the efficacy of various control methods on Poa annua on Macquarie Island. The Management Trials spreadsheet quantifies the efficacy of several physical control methods on Poa annua in situ on Macquarie Island, and their impact on species richness. The herbicide efficacy_1 rate spreadsheet quantifies the efficacy and selectivity of 12 herbicide treatments on Poa annua grown ex situ under sub-Antarctic temperatures. The herbicide efficacy_several rates spreadsheet quantifies the efficacy and selectivity of the 3 herbicides deemed to be most effective and selective on Poa annua in the above dataset, at different rates and using different application methods ex situ at sub-Antarctic temperatures The sites datasheet describes the study sites used in the Management Trials spreadsheet. proprietary
@@ -1190,8 +1190,8 @@ AAS_4344_K-Axis_Chlorophyll_2 Chlorophyll K-Axis Voyage V3 2015/16 AU_AADC STAC
AAS_4344_KAXIS_Microscopy_1 Light microscopy images taken onboard KAXIS V3 2015/2016 AU_AADC STAC Catalog 2016-01-11 2016-03-12 33.04688, -70.14036, 95.625, -44.08759 https://cmr.earthdata.nasa.gov/search/concepts/C1703260555-AU_AADC.umm_json Samples were collected using a prototype basket sampler that concentrated phytoplankton from the underway water supply in the OG lab onboard Aurora Australis. The sampler filtered water during transit, and the distance travelled and the approximate volume of water sampled was recorded. A phytoplankton net tow was collected at each station. The majority of imaging was undertaken using a Leica DMLB2 microscope with phase contrastand Leica ICC50 digital in body camera. Samples were preserved with either glutaraldhyde or Lugols iodine for later examination as well. Details of sample collected are included in the Voyage sample log. proprietary
AAS_4344_au1603_CTD_version27sep2017_3 Aurora Australis Marine Science Cruise AU1603 - Oceanographic Field Measurements and Analysis AU_AADC STAC Catalog 2016-01-22 2016-02-16 71.1692, -65.1882, 93.5617, -57.586 https://cmr.earthdata.nasa.gov/search/concepts/C1517284149-AU_AADC.umm_json "Oceanographic measurements were collected aboard Aurora Australis cruise au1603, voyage 3 2015/2016, from 11th January to ~24th February 2016. The cruise commenced with the K-AXIS project, the major marine science component of the cruise. This was the Australian component (P.I.’s Andrew Constable, Steve Rintoul and others) of a combined biological and oceanographic study in the vicinity of the Kerguelen Axis. After conclusion of marine science work the ship went to Mawson for a resupply. During a storm on 24th February the ship broke free of its mooring lines and ran aground on the rocks at West Arm in Horseshoe Harbour, thus ending the cruise. Expeditioners were eventually taken to Casey on the Shirase, then flown home. Meanwhile the Aurora Australis was refloated and sailed to Fremantle, then on to Singapore for repairs. This report discusses the oceanographic data from CTD operations on the cruise. A total of 47 CTD vertical profile stations were taken on the cruise (Table 1). Over 850 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite and silicate), dissolved inorganic carbon (i.e. TCO2), alkalinity, POC and PN, and biological parameters, using a 24 bottle rosette sampler. A UVP particle counter/camera system was attached to the CTD package (P.I. Emmanuel Laurenceau). A separate trace metal rosette system was deployed from the trawl deck (P.I. Andrew Bowie). Upper water column current profile data were collected by a ship mounted ADCP, and meteorological and water property data were collected by the array of ship's underway sensors. Eight drifting floats were deployed over the course of the cruise. Processing/calibration and data quality for the main CTD data are described in this report. Underway sea surface temperature and salinity data are compared to near surface CTD data. CTD station positions are shown in Figure 1, while CTD station information is summarised in Table 1. Float deployments (5 x Argo/Apex, 2 x SOCCOM and 1 x Provor) are summarised in Table 10. Further cruise itinerary/summary details can be found in the voyage leader report (Australian Antarctic Division unpublished report: Voyage 3 2015-2016, RSV Aurora Australis, Voyage Leader’s report - see the metadata record ""Aurora Australis Voyage 3 2015/16 Track and Underway Data"" for access to the Voyage Report)." proprietary
AAS_4344_dFe_1 K-axis dissolved iron (dFe) data from the Kerguelen-Axis region of the Southern Ocean AU_AADC STAC Catalog 2017-01-10 2018-02-28 70, -66, 95, -57 https://cmr.earthdata.nasa.gov/search/concepts/C1625715202-AU_AADC.umm_json "Sampling was conducted according to GEOTRACES protocols. Samples for trace element analyses, including dissolved iron (dFe), were filtered through acid-cleaned 0.2 um cartridge filters (Pall Acropak) under constant airflow from several ISO class 5 HEPA units. All plastic ware was acid-cleaned prior to use, following GEOTRACES protocols. Samples were collected into low-density polyethylene (LDPE) bottles, acidified immediately to pH 1.7 with Seastar Baseline hydrochloric acid (HCl), double-bagged and stored at room temperature until analysis on shore. Samples for dFe analysis were pre-concentrated offline (factor 40) on a SeaFAST S2 pico (ESI, Elemental Scientific, USA) flow injection system with a Nobias Chelate-PA1 column. Samples were eluted from the column in 10% distilled nitric acid (HNO3), with calibration based on the method of standard additions in seawater (made using multi-element standards in a 10% HNO3 matrix, rather than an HCl matrix). Pre-concentrated samples were analysed using Sector Field Inductively Coupled Plasma Mass Spectrometry (SF-ICP-MS, Thermo Fisher Scientific, Inc.). Data were blank-corrected by subtracting an average acidified milli-Q blank that was treated similarly to the samples. The dFe detection limit for a given analysis run on the SeaFAST/SF-ICP-MS was calculated as 3 x standard deviation of the milli-Q blank on that run. Detection limits ranged from 0.016 to 0.067 nmol kg-1, with a median of 0.026 nmol kg-1 (n=12). GEOTRACES reference materials were analyzed along with samples and results were in good agreement with consensus values: SAFe D1 was measured at 0.69 +/- 0.05 nmol kg-1 (n=7; consensus value = 0.67 +/- 0.04 nmol kg-1) and GD was measured at 1.02 +/- 0.01 nmol kg-1 (n=6; consensus value = 1.00 +/- 0.1 nmol kg-1). Comments regarding the data spreadsheet: NaN = no sample dFe QC flags: 1 = high confidence in data quality 2 = detection limit 3 = low confidence in data quality detection limits: dFe data that were below the daily detection limit were replaced with the respective detection limit. They are flagged with the number 2 in the dFe QC flag column." proprietary
-AAS_4346_Airborne_Ocean_Sensors_2 Airborne-deployed ocean sensors in the Southern Ocean, 2016-2018, Level 0 data ALL STAC Catalog 2016-11-01 2020-01-31 99, -66.8, 121, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1709216509-AU_AADC.umm_json Extracted Level 0 data are provided as audio files recorded in flight with a Sony PX470 voice recorder. These files were processed to generate the associated Level 2 products. Project 4346 demonstrated the use of Airborne eXpendable Bathy-Thermograph (AXBT) and Airborne eXpendable Conductivity, Temperature, Depth (AXCTD) sensors from a BT-67 Basler aircraft in East Antarctica. The primary objective was to use AXBT and AXCTD sensors to infer seafloor depth where no previous measurements had been made by ship, often by deploying sensors into narrow gaps in sea ice. Inferring a snapshot of the ocean state by detecting major thermoclines was a secondary objective. Although several sensors were purchased with external funds, the efforts to develop operational and subsequent data analysis approaches were unfunded as this was an add-on, target of opportunity. The effort is best described as a prototype demonstration project to test whether the seafloor depth could be inferred beneath narrow sea ice leads from a rapidly flying aircraft. All but eight AXBT sensors were donated to the University of Texas Institute for Geophysics (UTIG); AXCTDs were purchased by the Antarctic Gateway Partnership. Receiver and data processing equipment were loaned to UTIG. proprietary
AAS_4346_Airborne_Ocean_Sensors_2 Airborne-deployed ocean sensors in the Southern Ocean, 2016-2018, Level 0 data AU_AADC STAC Catalog 2016-11-01 2020-01-31 99, -66.8, 121, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1709216509-AU_AADC.umm_json Extracted Level 0 data are provided as audio files recorded in flight with a Sony PX470 voice recorder. These files were processed to generate the associated Level 2 products. Project 4346 demonstrated the use of Airborne eXpendable Bathy-Thermograph (AXBT) and Airborne eXpendable Conductivity, Temperature, Depth (AXCTD) sensors from a BT-67 Basler aircraft in East Antarctica. The primary objective was to use AXBT and AXCTD sensors to infer seafloor depth where no previous measurements had been made by ship, often by deploying sensors into narrow gaps in sea ice. Inferring a snapshot of the ocean state by detecting major thermoclines was a secondary objective. Although several sensors were purchased with external funds, the efforts to develop operational and subsequent data analysis approaches were unfunded as this was an add-on, target of opportunity. The effort is best described as a prototype demonstration project to test whether the seafloor depth could be inferred beneath narrow sea ice leads from a rapidly flying aircraft. All but eight AXBT sensors were donated to the University of Texas Institute for Geophysics (UTIG); AXCTDs were purchased by the Antarctic Gateway Partnership. Receiver and data processing equipment were loaned to UTIG. proprietary
+AAS_4346_Airborne_Ocean_Sensors_2 Airborne-deployed ocean sensors in the Southern Ocean, 2016-2018, Level 0 data ALL STAC Catalog 2016-11-01 2020-01-31 99, -66.8, 121, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1709216509-AU_AADC.umm_json Extracted Level 0 data are provided as audio files recorded in flight with a Sony PX470 voice recorder. These files were processed to generate the associated Level 2 products. Project 4346 demonstrated the use of Airborne eXpendable Bathy-Thermograph (AXBT) and Airborne eXpendable Conductivity, Temperature, Depth (AXCTD) sensors from a BT-67 Basler aircraft in East Antarctica. The primary objective was to use AXBT and AXCTD sensors to infer seafloor depth where no previous measurements had been made by ship, often by deploying sensors into narrow gaps in sea ice. Inferring a snapshot of the ocean state by detecting major thermoclines was a secondary objective. Although several sensors were purchased with external funds, the efforts to develop operational and subsequent data analysis approaches were unfunded as this was an add-on, target of opportunity. The effort is best described as a prototype demonstration project to test whether the seafloor depth could be inferred beneath narrow sea ice leads from a rapidly flying aircraft. All but eight AXBT sensors were donated to the University of Texas Institute for Geophysics (UTIG); AXCTDs were purchased by the Antarctic Gateway Partnership. Receiver and data processing equipment were loaned to UTIG. proprietary
AAS_4346_Airborne_Ocean_Sensors_Level_2_1 Airborne-deployed ocean sensors in the Southern Ocean, 2016-2018, Level 2 data ALL STAC Catalog 2016-10-01 2018-03-31 99, -66.8, 121, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1929062055-AU_AADC.umm_json "Extracted Level 2 data include three data types: 1) Position data are included in .GPX files organized by campaign where “ICP8” refers to the 2016-2017 ICECAP2 field season and “ICP9” refers to the 2017-2018 field season. We recommend opening these files in QGIS or on similar platform. Metadata for each sonobuoy deployment include the unique identifier for each profile as well as the date, time, and aircraft longitude, latitude, elevation, and speed (in East, North, Up coordinates) at the time of deployment. Season identifier, flight number, and unique profile identifier are also displayed. In QGIS, for example, clicking on the drop locations using the “Identify Features” tool is a convenient way of investigating the metadata. 2) Profile data are released as Exportable Data Files (EDF), an ASCII format with a metadata header followed by the profile data. 3) Profile data are also released as Hierarchical Data Format (HDF) files using a .h5 extension. This format is provided so users can take advantage of numerous and freely available Python and MATLAB resources simplifying importing and investigating the profiles. Project 4346 demonstrated the use of Airborne eXpendable Bathy-Thermograph (AXBT) and Airborne eXpendable Conductivity, Temperature, Depth (AXCTD) sensors from a BT-67 Basler aircraft in East Antarctica. The primary objective was to use AXBT and AXCTD sensors to infer seafloor depth where no previous measurements had been made by ship, often by deploying sensors into narrow gaps in sea ice. Inferring a snapshot of the ocean state by detecting major thermoclines was a secondary objective. Although several sensors were purchased with external funds, the efforts to develop operational and subsequent data analysis approaches were unfunded as this was an add-on, target of opportunity. The effort is best described as a prototype demonstration project to test whether the seafloor depth could be inferred beneath narrow sea ice leads from a rapidly flying aircraft. All but eight AXBT sensors were donated to the University of Texas Institute for Geophysics (UTIG); AXCTDs were purchased by the Antarctic Gateway Partnership. Receiver and data processing equipment were loaned to UTIG." proprietary
AAS_4346_Airborne_Ocean_Sensors_Level_2_1 Airborne-deployed ocean sensors in the Southern Ocean, 2016-2018, Level 2 data AU_AADC STAC Catalog 2016-10-01 2018-03-31 99, -66.8, 121, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1929062055-AU_AADC.umm_json "Extracted Level 2 data include three data types: 1) Position data are included in .GPX files organized by campaign where “ICP8” refers to the 2016-2017 ICECAP2 field season and “ICP9” refers to the 2017-2018 field season. We recommend opening these files in QGIS or on similar platform. Metadata for each sonobuoy deployment include the unique identifier for each profile as well as the date, time, and aircraft longitude, latitude, elevation, and speed (in East, North, Up coordinates) at the time of deployment. Season identifier, flight number, and unique profile identifier are also displayed. In QGIS, for example, clicking on the drop locations using the “Identify Features” tool is a convenient way of investigating the metadata. 2) Profile data are released as Exportable Data Files (EDF), an ASCII format with a metadata header followed by the profile data. 3) Profile data are also released as Hierarchical Data Format (HDF) files using a .h5 extension. This format is provided so users can take advantage of numerous and freely available Python and MATLAB resources simplifying importing and investigating the profiles. Project 4346 demonstrated the use of Airborne eXpendable Bathy-Thermograph (AXBT) and Airborne eXpendable Conductivity, Temperature, Depth (AXCTD) sensors from a BT-67 Basler aircraft in East Antarctica. The primary objective was to use AXBT and AXCTD sensors to infer seafloor depth where no previous measurements had been made by ship, often by deploying sensors into narrow gaps in sea ice. Inferring a snapshot of the ocean state by detecting major thermoclines was a secondary objective. Although several sensors were purchased with external funds, the efforts to develop operational and subsequent data analysis approaches were unfunded as this was an add-on, target of opportunity. The effort is best described as a prototype demonstration project to test whether the seafloor depth could be inferred beneath narrow sea ice leads from a rapidly flying aircraft. All but eight AXBT sensors were donated to the University of Texas Institute for Geophysics (UTIG); AXCTDs were purchased by the Antarctic Gateway Partnership. Receiver and data processing equipment were loaned to UTIG." proprietary
AAS_4346_EAGLE_ICECAP_LEVEL0_RAW_DATA_1 EAGLE/ICECAP II Raw data (gps, raw serial packet data, raw radar records, gravimeter data and camera images) AU_AADC STAC Catalog 2015-12-31 2016-02-15 80.85937, -70.08056, 154.51172, -63 https://cmr.earthdata.nasa.gov/search/concepts/C1559903364-AU_AADC.umm_json These aerogeophysical data were collected as part of the ICECAP (International Collaborative Exploration of the Cryosphere through Airborne Profiling) collaboration in 2015/16 (ICP7) and 2016/17 (ICP8). These data were in part funded by the US National Science Foundation (grant PLR-1543452 to UTIG), Antarctic Gateway, ACE-CRC the G. Unger Vetlesen Foundation, and supported by the Australian Antarctic Division through project AAS-4346. This data collection represents geolocated, time registered geophysical observations (L2 data). These data are derived from L0 and L1B data published as separate datasets. The data format are space delimited ASCII files, following the formats used for UTIG/AAD/NASA's predecessor ICECAP/OIB project at NASA's NSIDC DAAC. Fields are described in the # delimited detailed header for each granule. proprietary
@@ -1228,8 +1228,8 @@ AAS_4444_MRC_Rock_Elemental_Geochemistry_1 Macquarie Ridge Complex basalt major
AAS_4444_MRC_Rock_Geochronology_1 Macquarie Ridge Complex basalt 40Ar/39Ar geochronology data AU_AADC STAC Catalog 2017-07-01 2019-06-30 156.00586, -60.32695, 167.51953, -44.71551 https://cmr.earthdata.nasa.gov/search/concepts/C1667374509-AU_AADC.umm_json 40Ar/39Ar geochronology data of basalt samples from the Macquarie Ridge Complex (MRC). The MRC samples include basalts collected from Macquarie Island, the only exposed portion of the submarine Macquarie Ridge, by R. Merle and K. Evans during a field trip in November 2017, and dredge samples collected by the National Institute of Water and Atmospheric Research, New Zealand, during the TAN0803 voyage in 2008. Analytical methods of the 40Ar/39Ar geochronology data: Samples were crushed and minerals/groundmass were separated using a Frantz magnetic separator. Plagioclase, pyroxene, amphibole, sericite, and basaltic glass crystals and groundmass were separated from either the 125–212 μm or the 212–355 μm size fractions using a Frantz isodynamic magnetic separator. Minerals and groundmass were subsequently hand-picked grain-by-grain under a binocular stereomicroscope. Plagioclase and groundmass were further leached using diluted HF (2N) for 5 minutes and thoroughly rinsed in distilled water. Samples were loaded into several large wells of 1.9cm diameter and 0.3 cm depth aluminium discs. The discs were Cd-shielded to minimise undesirable nuclear interference re-actions and irradiated for 40 hours in the Oregon State University nuclear reactor (USA) in the central position. The samples were irradiated alongside FCs and GA1550 standards, for which ages of 28.294 ± 0.037 Ma and 99.738 ± 0.100 Ma were used, respectively. The 40Ar/39Ar analyses were performed at the Western Australian Argon Isotope Facility at Curtin University. The samples were step-heated using a continuous 100 W PhotonMachine© CO2 (IR, 10.4 µm) laser fired on the crystals during 60 seconds. Each of the standard crystals was fused in a single step. The gas was purified in an extra low-volume stainless steel extraction line of 240cc and using one SAES AP10 and one GP50 getter. Ar isotopes were measured in static mode using a low volume (600 cc) ARGUS VI mass spectrometer from Thermofisher© set with a permanent resolution of ~200. Measurements were carried out in multi-collection mode using four faradays to measure mass 40 to 37 and a 0-background compact discrete dynode ion counter to measure mass 36. We measured the relative abundance of each mass simultaneously using 10 cycles of peak-hopping and 33 seconds of integration time for each mass. Detectors were calibrated to each other electronically and using air shot beam signals. The raw data were processed using the ArArCALC software. The criteria for the determination of plateau are as follows: plateaus must include at least 70% of 39Ar released. The plateau should be distributed over a minimum of 3 consecutive steps agreeing at 95% confidence level and satisfying a probability of fit (P) of at least 0.05. Plateau ages are given at the 2σ level and are calculated using the mean of all the plateau steps, each weighted by the inverse variance of their individual analytical error. Uncertainties include analytical and J-value errors. proprietary
AAS_4444_MRC_Rock_Isotope_Geochem_1 Macquarie Ridge Complex basalt isotope geochemical data AU_AADC STAC Catalog 2017-11-01 2019-05-11 155.74219, -62.02153, 166.99219, -45.83578 https://cmr.earthdata.nasa.gov/search/concepts/C1625715006-AU_AADC.umm_json Strontium, Nd and Pb isotopic data of basalt samples from the Macquarie Ridge Complex (MRC). The MRC samples include basalts collected from Macquarie Island, the only exposed portion of the submarine Macquarie Ridge, by R. Merle and K. Evans during a field trip in November 2017, and dredge samples collected by the National Institute of Water and Atmospheric Research, New Zealand, during the TAN0803 voyage in 2008. Analytical methods of the isotopes: samples were first crushed and pulverized into powders using a zirconia vessel at Genalysis Laboratory Services, Perth. Sample chemistry and radiogenic isotope ratios of Sr (87Sr/86Sr), Nd (143Nd/144Nd) and Pb (206Pb/204Pb, 207Pb/204Pb, and 208Pb/204Pb) measurements by mass spectrometry were measured at the University of Geneva, Switzerland. The whole rock powders were dissolved in Savillex® Teflon vials using HF and HNO3 in ultrasonic bath for 30 minutes twice a day at 140°C, and then dried and re-dissolved in HNO3 for 3 days and dried again. Purification and elution of Sr, Nd and Pb were performed using cascade columns with Sr-Spec, TRU-Spec and Ln-Spec resins. The materials were then re-dissolved in a 2% HNO3 solution and ratios were measured using a Thermo Neptune PLUS Multi-Collector ICP–MS in static mode. Ratios used to monitor internal fractionation were as follows: 88Sr/86Sr = 8.375209 for the 87Sr/86Sr ratio, 146Nd/144Nd = 0.7219 for the 143Nd/144Nd ratio, and 203Tl/205Tl = 0.418922 for the three Pb ratios (a Tl standard was added to the solution). External standards were used to monitor the long-term external reproducibility: SRM987 (87Sr/86Sr = 0.710248: McArthur et al., 2001; long-term external reproducibility: 10 ppm, 1σ), JNdi-1 (143Nd/144Nd = 0.512115; long-term external reproducibility: 10 ppm, 1σ), and SRM981 for Pb (long-term 1σ external reproducibility: 0.0048% for 206Pb/204Pb, 0.0049% for 207Pb/204Pb and 0.0062% for 208Pb/204Pb). Due to a systematic difference between measured and accepted standard ratios, 87Sr/86Sr, 143Nd/144Nd and Pb isotope ratios were corrected for external fractionation by a value of -0.021‰, +0.051‰ and +0.36‰ a.m.u., respectively. Interferences at masses 84 (84Kr), 86 (86Kr) and 87 (87Rb) were corrected by monitoring 83Kr and 85Rb, the 144Sm interference on 144Nd was monitored on the mass 147Sm and corrected using a 144Sm/147Sm value of 0.206700 and the 204Hg interference on 204Pb was corrected by monitoring 202Hg. Total procedural blanks were <500 pg for Pb and <100 pg for Sr and Nd; these values are insignificant compared to the amounts of these elements measured in the investigated samples. proprietary
AAS_4444_MRC_Rock_ThinSection_Images_1 Macquarie Ridge Complex Basalt Thin Section Images AU_AADC STAC Catalog 2017-11-01 2019-05-11 157.06055, -59.57885, 166.9043, -45.46013 https://cmr.earthdata.nasa.gov/search/concepts/C1625715007-AU_AADC.umm_json Plane-polarized and cross-polarized thin section images of basalt samples from Macquarie Ridge Complex. The MRC samples include basalts collected from Macquarie Island, the only exposed portion of the submarine Macquarie Ridge, by R. Merle and K. Evans during a field trip in November 2017, and dredge samples collected by the National Institute of Water and Atmospheric Research, New Zealand, during the TAN0803 voyage in 2008. The thin sections were made by Spectrum Petrographics. The images were taken by a Zeiss Axio microscope at Curtin University. proprietary
-AAS_4446_Kerguelen_Geochronology_1 40Ar/39Ar geochronology data of basalt samples from the Kerguelen Plateau and Broken Ridge AU_AADC STAC Catalog 2017-07-01 2019-06-30 59.76563, -64.32087, 103.71094, -23.88584 https://cmr.earthdata.nasa.gov/search/concepts/C1667374514-AU_AADC.umm_json 40Ar/39Ar geochronology data of basalt samples from the Kerguelen Plateau and Broken Ridge The samples include basalts from ODP drilling cores and dredge sites. The drilling core samples were stored in the Kochi Core Centre, Japan and the dredged samples were stored in the National Museum of Natural History, France. Analytical methods of the 40Ar/39Ar geochronology data: Samples were crushed and minerals/groundmass were separated using a Frantz magnetic separator. Plagioclase, pyroxene, amphibole, sericite, and basaltic glass crystals and groundmass were separated from either the 125–212 μm or the 212–355 μm size fractions using a Frantz isodynamic magnetic separator. Minerals and groundmass were subsequently hand-picked grain-by-grain under a binocular stereomicroscope. Plagioclase and groundmass were further leached using diluted HF (2N) for 5 minutes and thoroughly rinsed in distilled water. Samples were loaded into several large wells of 1.9cm diameter and 0.3 cm depth aluminium discs. The discs were Cd-shielded to minimise undesirable nuclear interference re-actions and irradiated for 40 hours in the Oregon State University nuclear reactor (USA) in the central position. The samples were irradiated alongside FCs and GA1550 standards, for which ages of 28.294 ± 0.037 Ma and 99.738 ± 0.100 Ma were used, respectively. The 40Ar/39Ar analyses were performed at the Western Australian Argon Isotope Facility at Curtin University. The samples were step-heated using a continuous 100 W PhotonMachine© CO2 (IR, 10.4 µm) laser fired on the crystals during 60 seconds. Each of the standard crystals was fused in a single step. The gas was purified in an extra low-volume stainless steel extraction line of 240cc and using one SAES AP10 and one GP50 getter. Ar isotopes were measured in static mode using a low volume (600 cc) ARGUS VI mass spectrometer from Thermofisher© set with a permanent resolution of ~200. Measurements were carried out in multi-collection mode using four faradays to measure mass 40 to 37 and a 0-background compact discrete dynode ion counter to measure mass 36. We measured the relative abundance of each mass simultaneously using 10 cycles of peak-hopping and 33 seconds of integration time for each mass. Detectors were calibrated to each other electronically and using air shot beam signals. The raw data were processed using the ArArCALC software. The criteria for the determination of plateau are as follows: plateaus must include at least 70% of 39Ar released. The plateau should be distributed over a minimum of 3 consecutive steps agreeing at 95% confidence level and satisfying a probability of fit (P) of at least 0.05. Plateau ages are given at the 2σ level and are calculated using the mean of all the plateau steps, each weighted by the inverse variance of their individual analytical error. Uncertainties include analytical and J-value errors. proprietary
AAS_4446_Kerguelen_Geochronology_1 40Ar/39Ar geochronology data of basalt samples from the Kerguelen Plateau and Broken Ridge ALL STAC Catalog 2017-07-01 2019-06-30 59.76563, -64.32087, 103.71094, -23.88584 https://cmr.earthdata.nasa.gov/search/concepts/C1667374514-AU_AADC.umm_json 40Ar/39Ar geochronology data of basalt samples from the Kerguelen Plateau and Broken Ridge The samples include basalts from ODP drilling cores and dredge sites. The drilling core samples were stored in the Kochi Core Centre, Japan and the dredged samples were stored in the National Museum of Natural History, France. Analytical methods of the 40Ar/39Ar geochronology data: Samples were crushed and minerals/groundmass were separated using a Frantz magnetic separator. Plagioclase, pyroxene, amphibole, sericite, and basaltic glass crystals and groundmass were separated from either the 125–212 μm or the 212–355 μm size fractions using a Frantz isodynamic magnetic separator. Minerals and groundmass were subsequently hand-picked grain-by-grain under a binocular stereomicroscope. Plagioclase and groundmass were further leached using diluted HF (2N) for 5 minutes and thoroughly rinsed in distilled water. Samples were loaded into several large wells of 1.9cm diameter and 0.3 cm depth aluminium discs. The discs were Cd-shielded to minimise undesirable nuclear interference re-actions and irradiated for 40 hours in the Oregon State University nuclear reactor (USA) in the central position. The samples were irradiated alongside FCs and GA1550 standards, for which ages of 28.294 ± 0.037 Ma and 99.738 ± 0.100 Ma were used, respectively. The 40Ar/39Ar analyses were performed at the Western Australian Argon Isotope Facility at Curtin University. The samples were step-heated using a continuous 100 W PhotonMachine© CO2 (IR, 10.4 µm) laser fired on the crystals during 60 seconds. Each of the standard crystals was fused in a single step. The gas was purified in an extra low-volume stainless steel extraction line of 240cc and using one SAES AP10 and one GP50 getter. Ar isotopes were measured in static mode using a low volume (600 cc) ARGUS VI mass spectrometer from Thermofisher© set with a permanent resolution of ~200. Measurements were carried out in multi-collection mode using four faradays to measure mass 40 to 37 and a 0-background compact discrete dynode ion counter to measure mass 36. We measured the relative abundance of each mass simultaneously using 10 cycles of peak-hopping and 33 seconds of integration time for each mass. Detectors were calibrated to each other electronically and using air shot beam signals. The raw data were processed using the ArArCALC software. The criteria for the determination of plateau are as follows: plateaus must include at least 70% of 39Ar released. The plateau should be distributed over a minimum of 3 consecutive steps agreeing at 95% confidence level and satisfying a probability of fit (P) of at least 0.05. Plateau ages are given at the 2σ level and are calculated using the mean of all the plateau steps, each weighted by the inverse variance of their individual analytical error. Uncertainties include analytical and J-value errors. proprietary
+AAS_4446_Kerguelen_Geochronology_1 40Ar/39Ar geochronology data of basalt samples from the Kerguelen Plateau and Broken Ridge AU_AADC STAC Catalog 2017-07-01 2019-06-30 59.76563, -64.32087, 103.71094, -23.88584 https://cmr.earthdata.nasa.gov/search/concepts/C1667374514-AU_AADC.umm_json 40Ar/39Ar geochronology data of basalt samples from the Kerguelen Plateau and Broken Ridge The samples include basalts from ODP drilling cores and dredge sites. The drilling core samples were stored in the Kochi Core Centre, Japan and the dredged samples were stored in the National Museum of Natural History, France. Analytical methods of the 40Ar/39Ar geochronology data: Samples were crushed and minerals/groundmass were separated using a Frantz magnetic separator. Plagioclase, pyroxene, amphibole, sericite, and basaltic glass crystals and groundmass were separated from either the 125–212 μm or the 212–355 μm size fractions using a Frantz isodynamic magnetic separator. Minerals and groundmass were subsequently hand-picked grain-by-grain under a binocular stereomicroscope. Plagioclase and groundmass were further leached using diluted HF (2N) for 5 minutes and thoroughly rinsed in distilled water. Samples were loaded into several large wells of 1.9cm diameter and 0.3 cm depth aluminium discs. The discs were Cd-shielded to minimise undesirable nuclear interference re-actions and irradiated for 40 hours in the Oregon State University nuclear reactor (USA) in the central position. The samples were irradiated alongside FCs and GA1550 standards, for which ages of 28.294 ± 0.037 Ma and 99.738 ± 0.100 Ma were used, respectively. The 40Ar/39Ar analyses were performed at the Western Australian Argon Isotope Facility at Curtin University. The samples were step-heated using a continuous 100 W PhotonMachine© CO2 (IR, 10.4 µm) laser fired on the crystals during 60 seconds. Each of the standard crystals was fused in a single step. The gas was purified in an extra low-volume stainless steel extraction line of 240cc and using one SAES AP10 and one GP50 getter. Ar isotopes were measured in static mode using a low volume (600 cc) ARGUS VI mass spectrometer from Thermofisher© set with a permanent resolution of ~200. Measurements were carried out in multi-collection mode using four faradays to measure mass 40 to 37 and a 0-background compact discrete dynode ion counter to measure mass 36. We measured the relative abundance of each mass simultaneously using 10 cycles of peak-hopping and 33 seconds of integration time for each mass. Detectors were calibrated to each other electronically and using air shot beam signals. The raw data were processed using the ArArCALC software. The criteria for the determination of plateau are as follows: plateaus must include at least 70% of 39Ar released. The plateau should be distributed over a minimum of 3 consecutive steps agreeing at 95% confidence level and satisfying a probability of fit (P) of at least 0.05. Plateau ages are given at the 2σ level and are calculated using the mean of all the plateau steps, each weighted by the inverse variance of their individual analytical error. Uncertainties include analytical and J-value errors. proprietary
AAS_4460_Thermochronology_1 Low temperature thermochronology from Knox Rift region AU_AADC STAC Catalog 2018-02-23 2019-07-19 96.3, -67.5, 101.2, -65.9 https://cmr.earthdata.nasa.gov/search/concepts/C1667374517-AU_AADC.umm_json This dataset includes (U-Th)/He analyses on zircon and apatite grains from samples in the Knox Rift region. Samples were analysed at the John de Laeter Centre (Curtin University), using conventional single grain (U-Th)/He dating techniques. This dataset includes (U-Th)/He analyses on zircon and apatite grains from samples in the Knox Rift region. Samples were analysed at the John de Laeter Centre (Curtin University), using conventional single grain (U-Th)/He dating techniques. The file format is Microsoft Excel. The first sheet provides machine-readable summary of the data while the second worksheet includes the processed analytical data for each sample. Sample locations (Latitude, Longitude, Elevation - WGS84), location names, sample lithologies and the source collections of the original sample are included. -9999.9 indicates missing data. proprietary
AAS_4512_Antarctic_krill_growth_potential_projections_1 Circumpolar Projections of Antarctic krill (Euphausia superba) growth potential AU_AADC STAC Catalog 1960-01-01 2099-12-31 -180, -90, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C2102891775-AU_AADC.umm_json "These data represent the results of the first study to use Earth System Model (ESM) outputs of SST and chlorophyll-a to simulate circumpolar krill growth potential for the recent past (1960-1989) and future climate change scenarios (2070-2099). Growth potential is obtained using an empirically-derived krill growth model (Atkinson et al. 2006, Limnol. Oceanogr.), where growth is modeled as a function of SST and chlorophyll-a. It serves as an approximation of habitat quality, as areas that support high growth rates are assumed to be good habitat (see Murphy et al., 2017, Sci Rep). To increase confidence in the future projections, ESMs were selected and weighted for each season based on their skill at reproducing observation-based krill growth potential for the recent past. First, eleven ESMs which provided SST and chlorophyll-a outputs were obtained from the Coupled Model Inter-comparison Project 5 archive. These included: CanESM2, CMCC-CESM, CNRM-CM5, GFL-ESM2G, GFDL-ESM2M, GISS-E2-H-CC, HadGEM2-CC, IPSL-CM5A-LR, MPI-ESM-MR, MRI-ESM1 and NorESM1-ME. For each ESM, seasonal surface averages of SST and chlorophyll-a were used to calculate growth potential for the historical scenario (1960-1989), which was then bilinearly interpolated on to the same 1°x1° grid. Satellite observation-based datasets for SST and chlorophyll-a were used to calculate observation-based growth potential for the recent past (1997-2010). These comprised seasonal surface averages of SST (from the OISST v2 daily dataset, 1/4⁰ horizontal resolution) and chlorophyll-a (the mean of the SeaWiFS and Johnson et al. (2013) corrected estimate of SeaWiFS daily datasets, 1/12⁰ horizontal resolution). Observation-based growth potential was then bilinearly interpolated onto the same grid as the ESMs. ESM skill for each season was subsequently assessed against observation-based growth potential using a Taylor Diagram. The ESMs were selected and weighted according to their performance to produce a weighted subset (see ""ESM_weighting_method.pdf"" file). Of the netcdfs provided, ""hist_mean_ensemble.nc"" represents the unweighted mean of seasonal growth potential, calculated from the initial ensemble of eleven ESMs for the historical scenario. The ""hist_mean_subset.nc"" file represents the analogous output of the weighted subset. Future projections of seasonal growth potential for Representative Concentration Pathways (RCPs) 4.5 and 8.5 were obtained using the weighted subset for the period of 2070-2099. These projected seasonal surface averages are provided in the ""rcp45_mean_subset.nc"" and ""rcp85_mean_subset.nc"" files. RCPs represent standard climate change scenarios developed by the Intergovernmental Panel on Climate Change, with 4.5 reflecting some mitigation of carbon emissions, and 8.5 being the ""business as usual"" scenario. Analogous netcdfs for the weighted subset outputs of chlorophyll-a (chl) and SST (tos) for the historical and RCP scenarios are also provided in the ""chl_tos_netcdfs.zip"" file so that the driving environmental variables underlying growth potential can be examined." proprietary
AAS_4537_z500_SynopticTyping_SouthernIndianOcean_1 Daily synoptic weather types of southern Indian Ocean: January 1979-October 2018 AU_AADC STAC Catalog 1979-01-01 2018-10-31 40, -75, 180, -30 https://cmr.earthdata.nasa.gov/search/concepts/C1968847762-AU_AADC.umm_json "Daily synoptic typing dataset for the southern Indian Ocean (30°-75°S, 40°-180° E) for the period January 1979 to October 2018 developed using self-organising maps (SOMs). The nine synoptic types represented by the nodes defined in this study included four meridional (SOM1, SOM2, SOM6 and SOM9), three mixed (SOM4, SOM7 and SOM8), a zonal (SOM3) and a transitional pattern (SOM5). Refer to Udy et al. Links between large scale modes of climate variability and synoptic weather patterns in the southern Indian Ocean. J.Climate in review. Included datasets: SOM_daily_z500_anomaly_3_3.nc includes the composite z500 daily anomaly patterns for each of the 9 SOM nodes - lat, lon, z500 anomaly, node. SOM_daily_z500_anomaly_3_3.txt includes the daily 'winning' node between 1st January 1979 and 31st October 2018. Each day is assigned to a 'winning' node between 1-9. e.g. 19790101 is assigned to SOM3. SOM code: ""Kohonen"" Package in R https://cran.r-project.org/web/packages/kohonen/index.html Study domain: 30°-75°S, 40°-180° E Time step / period: daily / January 1979-October 2018 Input data: ERA-Interim (https://apps.ecmwf.int/datasets/) 500hPa geopotential height (z500) daily anomalies. Climate Data Operators (CDO) was used to calculate the daily anomaly (https://code.mpimet.mpg.de/projects/cdo/) SOM algorithm parameters: refer to kohonen documentation for more information. dist.fcts (performance evaluation distance) = euclidean, grid = rect, neighbourhood function = gaussian, Nodes (number of SOM nodes) = 9, rlen (number of iterations) = 1000, alp (learning rate) = 0.05 to 0.01 and rad (radius) = 4 to 0. In the Kohonen R-package, a radius value less than or equal to one corresponds to the point where only the 'winning' node is updated by each iteration, making the SOM algorithm similar to clustering techniques (e.g. k-means). The SOM algorithm used in this study was a hybrid between SOM/clustering (75% SOM, 25% clustering)." proprietary
@@ -1248,8 +1248,8 @@ AAS_859_Fur_Seals_MI_1997_1999_1 ARGOS Tracking Data of Fur Seals from Macquarie
AAS_926_Bunger_Hills_Biogeography_1 Biogeography of the Bunger Hills - plant and bird locations 1995-2000 AU_AADC STAC Catalog 1995-12-01 2000-03-01 100.460683, -66.343254, 101.09032, -66.1849967 https://cmr.earthdata.nasa.gov/search/concepts/C1693329237-AU_AADC.umm_json Biogeography of Bunger Hills Bunger Hills Locations of plants (moss, Lichens - Buellia frigida, Physcia caesia, Rhizocarpon flavum, Usnea antarctica, Umbilicaria decussata) Locations of birds (Wilsons Storm Petrels (Oceanites oceanicus), Snow Petrels (Pagodroma nivea), Skua (Catharacta mccormicki)) sightings and, more rarely, nests. Data consist of grid references. proprietary
AAS_926_Bunger_Hills_Human_Impacts_1 Human occupation, impacts and environmental management of Bunger Hills AU_AADC STAC Catalog 1995-12-12 2000-03-01 100.45, -66.35, 101.1, -66.17 https://cmr.earthdata.nasa.gov/search/concepts/C1709216449-AU_AADC.umm_json A summary of human occupation, the distribution of physical and chemical impacts, and recommendations for environmental management. The emphasis was on documenting the distribution of in situ and ex situ (mainly wind dispersed) rubbish. proprietary
AAS_926_Bunger_Hills_Salt_Sediment_Weathering_1 Bunger Hills Salt mineralogy, sediment extract chemistry, glacial sediment grain size, surface weathering characteristics AU_AADC STAC Catalog 1995-11-01 1996-03-01 100.464826, -66.347407, 101.331648, -66.081501 https://cmr.earthdata.nasa.gov/search/concepts/C1693329238-AU_AADC.umm_json The data consists of an excel workbook with five sheets; 1. Location and mineralogy of samples of salt taken from the land surface. 2. Location and elemental composition of aqueous extracts taken from surface sediments, on a 1 x 1 km grid. 3. Weathering criteria (frost cracks, glacial polish, tafoni, wind pits, sand accumulations) on a 1 x 1 km grid. 4. Glacial sediment grain size statistics, from samples collected on a 2 x 2 km grid. 5. Antarctic seawater chemistry from the literature. Five supplemental tables associated with the referenced publication will be found at; https://doi.org/10.1017/S0954102020000073. and http://data.aad.gov.au/metadata/records/AAS_926_Bunger_Hills_Salt_Sediment_Weathering (10.26179/5e1fa087f185c). proprietary
-AAS_974_Concordia_2009to2011_1min_1 Absolute vertical electric field data raw and selected data - Concordia from 2006-2011; processed 1-minute averages AU_AADC STAC Catalog 2009-01-01 2011-12-31 123.4, -71.15, 123.5, -71.05 https://cmr.earthdata.nasa.gov/search/concepts/C1380158056-AU_AADC.umm_json The vertical electric field data were collected using an electric field mill (EFM) developed and deployed under the approval of AAS_974 (Principal Investigator: Gary Burns). The Concordia EFM deployment and data collection was approved by IPEV (France)/PNRA(Italy) via 'Electrocite Atmospherique DC 33N'. 1-minute absolute vertical electric field averages (positive down; derived from 10 sec resolution data available at AAS_974_Concordia) for raw (=all available 1-min averages) and selected nvr, mvr and svr data which are described, tested and discussed in Burns et al. (2017). The values here-in have not been corrected for the solar-wind-imposed-potential (SWIP)-above-the-station. SWIP correction values are only derived for 20-minute averages and are applied to the nvr, mvr and svr 20-min averages (AAS_974_Concordia_2009to2011_20min) and described in Burns et al. (2017). In January 2009 an electric field mill (EFM) was deployed at Concordia and operated until December 2011. This electric field mill is mounted on an all metal post ~3m above the snow surface. We use all metal coverings on our instrumentation as near-by insulators can retain a charge which could be slowly released and influence the electric field measurements over significant intervals. Values are positive for a downward-directed electric field. This EFM is similar to one deployed at Vostok in January 2006. The Concordia EFM 'compression factor' is taken to be equivalent to the similar instrument calibrated at Vostok and was determined by stepping voltages between +5 and -5kV through a wire above the EFM at Vostok. Linkage to the Concordia EFM was determined using a Faraday-shielded box containing parallel plates placed over the rotating dipole to which a range of stepped voltages were applied. A single calibration factor has been applied for the entire (2009-2011) Concordia data set and absolute values (V/m) at ~3m above the snow surface are provided. The three separate data selections are as described in Burns et al. (2017). An initial rejection of the minute-averaged data is made for electric fields with two hour prior to and after exceeding 333 V/m for all three data selections. This rejects measurements generally influenced by local falling, wind-blown or lifted snow or ice which result in high electric field values. The extended time intervals are a conservative allowance for the local influences prior to the cut-off electric field value being reached and after lower values are again recorded. For two of the three selected data sets an additional criteria with different levels of severity was used to reject rapid variations below the cut-off threshold based on jumps in the electric field within a five-minute interval. Due to the time constant (~15 minutes) associated with the atmospheric circuit, rapid variations in the electric field are more likely to be associated with local influences. This is confirmed in Burns et al. (2017) by the relative association of the selected data sets with the SWIP-above-Concordia and independently by comparison with simultaneous electric field measurements at Vostok. Strong variation rejection (svr) data selections additionally reject minute-averaged data within 30 minutes of a jump of 33 V/m (with 5 minutes). Medium variation rejection (mvr) data selections additionally reject minute-averaged data within 10 minutes of a jump of 57 V/m (within 5 minutes). No variation rejection (nvr) data selections made no rejection on the basis of rapid variations (within 5 minutes). The fourth electric field data set listed herein is all the raw 1-minute averages. This includes the high values (which are constrained by the electronics to an extreme upper value). Earlier in the deployment, this may include 1-min averages associated with instrument calibrations. The raw 10-sec data associated with these calibrations were used to match the Concordia instrument to the similar Vostok instrument to determine absolute values. The 1 minute averages should not be used for calibration purposes as the 1-min averages may include transition intervals. Tests and changes of instrumentation resulted in the earliest selected (nvr) 1-minute-averaged electric field measurements at Concordia commencing at 0710UT, 9th January, 2009. Raw 1-minute-averaged values are intermittent from 0744UT 5th January, 2009. The data provided consists of up to 12 fields. The first eight columns are date-time related fields. The first field is an Excel derived date-time to the mid-point of the 1-minute average. The second field is the 'year', followed by the 'day-of-year', 'month', 'day-of-month', 'UT-hour', UT-min' and 'UT-second' of the averaged data. The ninth column is the raw minute-averaged absolute value (V/m downward) of the measured vertical electric field. The tenth to twelfth columns list the nvr, mvr and svr Concordia 1-minute electric field averages, without SWIP corrections, but otherwise as described and tested in Burns et al. (2017). Missing data are presented as blanks. Suggested acknowledgements for the utilization of these data are: 'The Concordia electric field data were collected by collaboration between AAD (Australia), IPEV (France) and PNRA (Italy). Australian involvement was approved by the Australian Antarctic Advisory Committee (AAS 974). Deployment and data collection at Concordia was approved by IPEV/PNRA via 'Electricite Atmospherique DC 33N'. Concordia meteoroloical data were provided by IPEV/PNRA project 'Routine Meteorological Observations at Station Concordia,' which is financially supported by ENEA (Italy).' References: Burns, G.B., A.V. Frank-Kamenetsky, B.A. Tinsley, W.J.R. French, and P. Grigioni, G. Camporeale, and E.A. Bering, 2017: Atmospheric global circuit variations from Vostok and Concordia electric field measurements. J. Atmos. Sci., 74, 783-800, doi:10.1175/JAS-D-16-0159-1. proprietary
AAS_974_Concordia_2009to2011_1min_1 Absolute vertical electric field data raw and selected data - Concordia from 2006-2011; processed 1-minute averages ALL STAC Catalog 2009-01-01 2011-12-31 123.4, -71.15, 123.5, -71.05 https://cmr.earthdata.nasa.gov/search/concepts/C1380158056-AU_AADC.umm_json The vertical electric field data were collected using an electric field mill (EFM) developed and deployed under the approval of AAS_974 (Principal Investigator: Gary Burns). The Concordia EFM deployment and data collection was approved by IPEV (France)/PNRA(Italy) via 'Electrocite Atmospherique DC 33N'. 1-minute absolute vertical electric field averages (positive down; derived from 10 sec resolution data available at AAS_974_Concordia) for raw (=all available 1-min averages) and selected nvr, mvr and svr data which are described, tested and discussed in Burns et al. (2017). The values here-in have not been corrected for the solar-wind-imposed-potential (SWIP)-above-the-station. SWIP correction values are only derived for 20-minute averages and are applied to the nvr, mvr and svr 20-min averages (AAS_974_Concordia_2009to2011_20min) and described in Burns et al. (2017). In January 2009 an electric field mill (EFM) was deployed at Concordia and operated until December 2011. This electric field mill is mounted on an all metal post ~3m above the snow surface. We use all metal coverings on our instrumentation as near-by insulators can retain a charge which could be slowly released and influence the electric field measurements over significant intervals. Values are positive for a downward-directed electric field. This EFM is similar to one deployed at Vostok in January 2006. The Concordia EFM 'compression factor' is taken to be equivalent to the similar instrument calibrated at Vostok and was determined by stepping voltages between +5 and -5kV through a wire above the EFM at Vostok. Linkage to the Concordia EFM was determined using a Faraday-shielded box containing parallel plates placed over the rotating dipole to which a range of stepped voltages were applied. A single calibration factor has been applied for the entire (2009-2011) Concordia data set and absolute values (V/m) at ~3m above the snow surface are provided. The three separate data selections are as described in Burns et al. (2017). An initial rejection of the minute-averaged data is made for electric fields with two hour prior to and after exceeding 333 V/m for all three data selections. This rejects measurements generally influenced by local falling, wind-blown or lifted snow or ice which result in high electric field values. The extended time intervals are a conservative allowance for the local influences prior to the cut-off electric field value being reached and after lower values are again recorded. For two of the three selected data sets an additional criteria with different levels of severity was used to reject rapid variations below the cut-off threshold based on jumps in the electric field within a five-minute interval. Due to the time constant (~15 minutes) associated with the atmospheric circuit, rapid variations in the electric field are more likely to be associated with local influences. This is confirmed in Burns et al. (2017) by the relative association of the selected data sets with the SWIP-above-Concordia and independently by comparison with simultaneous electric field measurements at Vostok. Strong variation rejection (svr) data selections additionally reject minute-averaged data within 30 minutes of a jump of 33 V/m (with 5 minutes). Medium variation rejection (mvr) data selections additionally reject minute-averaged data within 10 minutes of a jump of 57 V/m (within 5 minutes). No variation rejection (nvr) data selections made no rejection on the basis of rapid variations (within 5 minutes). The fourth electric field data set listed herein is all the raw 1-minute averages. This includes the high values (which are constrained by the electronics to an extreme upper value). Earlier in the deployment, this may include 1-min averages associated with instrument calibrations. The raw 10-sec data associated with these calibrations were used to match the Concordia instrument to the similar Vostok instrument to determine absolute values. The 1 minute averages should not be used for calibration purposes as the 1-min averages may include transition intervals. Tests and changes of instrumentation resulted in the earliest selected (nvr) 1-minute-averaged electric field measurements at Concordia commencing at 0710UT, 9th January, 2009. Raw 1-minute-averaged values are intermittent from 0744UT 5th January, 2009. The data provided consists of up to 12 fields. The first eight columns are date-time related fields. The first field is an Excel derived date-time to the mid-point of the 1-minute average. The second field is the 'year', followed by the 'day-of-year', 'month', 'day-of-month', 'UT-hour', UT-min' and 'UT-second' of the averaged data. The ninth column is the raw minute-averaged absolute value (V/m downward) of the measured vertical electric field. The tenth to twelfth columns list the nvr, mvr and svr Concordia 1-minute electric field averages, without SWIP corrections, but otherwise as described and tested in Burns et al. (2017). Missing data are presented as blanks. Suggested acknowledgements for the utilization of these data are: 'The Concordia electric field data were collected by collaboration between AAD (Australia), IPEV (France) and PNRA (Italy). Australian involvement was approved by the Australian Antarctic Advisory Committee (AAS 974). Deployment and data collection at Concordia was approved by IPEV/PNRA via 'Electricite Atmospherique DC 33N'. Concordia meteoroloical data were provided by IPEV/PNRA project 'Routine Meteorological Observations at Station Concordia,' which is financially supported by ENEA (Italy).' References: Burns, G.B., A.V. Frank-Kamenetsky, B.A. Tinsley, W.J.R. French, and P. Grigioni, G. Camporeale, and E.A. Bering, 2017: Atmospheric global circuit variations from Vostok and Concordia electric field measurements. J. Atmos. Sci., 74, 783-800, doi:10.1175/JAS-D-16-0159-1. proprietary
+AAS_974_Concordia_2009to2011_1min_1 Absolute vertical electric field data raw and selected data - Concordia from 2006-2011; processed 1-minute averages AU_AADC STAC Catalog 2009-01-01 2011-12-31 123.4, -71.15, 123.5, -71.05 https://cmr.earthdata.nasa.gov/search/concepts/C1380158056-AU_AADC.umm_json The vertical electric field data were collected using an electric field mill (EFM) developed and deployed under the approval of AAS_974 (Principal Investigator: Gary Burns). The Concordia EFM deployment and data collection was approved by IPEV (France)/PNRA(Italy) via 'Electrocite Atmospherique DC 33N'. 1-minute absolute vertical electric field averages (positive down; derived from 10 sec resolution data available at AAS_974_Concordia) for raw (=all available 1-min averages) and selected nvr, mvr and svr data which are described, tested and discussed in Burns et al. (2017). The values here-in have not been corrected for the solar-wind-imposed-potential (SWIP)-above-the-station. SWIP correction values are only derived for 20-minute averages and are applied to the nvr, mvr and svr 20-min averages (AAS_974_Concordia_2009to2011_20min) and described in Burns et al. (2017). In January 2009 an electric field mill (EFM) was deployed at Concordia and operated until December 2011. This electric field mill is mounted on an all metal post ~3m above the snow surface. We use all metal coverings on our instrumentation as near-by insulators can retain a charge which could be slowly released and influence the electric field measurements over significant intervals. Values are positive for a downward-directed electric field. This EFM is similar to one deployed at Vostok in January 2006. The Concordia EFM 'compression factor' is taken to be equivalent to the similar instrument calibrated at Vostok and was determined by stepping voltages between +5 and -5kV through a wire above the EFM at Vostok. Linkage to the Concordia EFM was determined using a Faraday-shielded box containing parallel plates placed over the rotating dipole to which a range of stepped voltages were applied. A single calibration factor has been applied for the entire (2009-2011) Concordia data set and absolute values (V/m) at ~3m above the snow surface are provided. The three separate data selections are as described in Burns et al. (2017). An initial rejection of the minute-averaged data is made for electric fields with two hour prior to and after exceeding 333 V/m for all three data selections. This rejects measurements generally influenced by local falling, wind-blown or lifted snow or ice which result in high electric field values. The extended time intervals are a conservative allowance for the local influences prior to the cut-off electric field value being reached and after lower values are again recorded. For two of the three selected data sets an additional criteria with different levels of severity was used to reject rapid variations below the cut-off threshold based on jumps in the electric field within a five-minute interval. Due to the time constant (~15 minutes) associated with the atmospheric circuit, rapid variations in the electric field are more likely to be associated with local influences. This is confirmed in Burns et al. (2017) by the relative association of the selected data sets with the SWIP-above-Concordia and independently by comparison with simultaneous electric field measurements at Vostok. Strong variation rejection (svr) data selections additionally reject minute-averaged data within 30 minutes of a jump of 33 V/m (with 5 minutes). Medium variation rejection (mvr) data selections additionally reject minute-averaged data within 10 minutes of a jump of 57 V/m (within 5 minutes). No variation rejection (nvr) data selections made no rejection on the basis of rapid variations (within 5 minutes). The fourth electric field data set listed herein is all the raw 1-minute averages. This includes the high values (which are constrained by the electronics to an extreme upper value). Earlier in the deployment, this may include 1-min averages associated with instrument calibrations. The raw 10-sec data associated with these calibrations were used to match the Concordia instrument to the similar Vostok instrument to determine absolute values. The 1 minute averages should not be used for calibration purposes as the 1-min averages may include transition intervals. Tests and changes of instrumentation resulted in the earliest selected (nvr) 1-minute-averaged electric field measurements at Concordia commencing at 0710UT, 9th January, 2009. Raw 1-minute-averaged values are intermittent from 0744UT 5th January, 2009. The data provided consists of up to 12 fields. The first eight columns are date-time related fields. The first field is an Excel derived date-time to the mid-point of the 1-minute average. The second field is the 'year', followed by the 'day-of-year', 'month', 'day-of-month', 'UT-hour', UT-min' and 'UT-second' of the averaged data. The ninth column is the raw minute-averaged absolute value (V/m downward) of the measured vertical electric field. The tenth to twelfth columns list the nvr, mvr and svr Concordia 1-minute electric field averages, without SWIP corrections, but otherwise as described and tested in Burns et al. (2017). Missing data are presented as blanks. Suggested acknowledgements for the utilization of these data are: 'The Concordia electric field data were collected by collaboration between AAD (Australia), IPEV (France) and PNRA (Italy). Australian involvement was approved by the Australian Antarctic Advisory Committee (AAS 974). Deployment and data collection at Concordia was approved by IPEV/PNRA via 'Electricite Atmospherique DC 33N'. Concordia meteoroloical data were provided by IPEV/PNRA project 'Routine Meteorological Observations at Station Concordia,' which is financially supported by ENEA (Italy).' References: Burns, G.B., A.V. Frank-Kamenetsky, B.A. Tinsley, W.J.R. French, and P. Grigioni, G. Camporeale, and E.A. Bering, 2017: Atmospheric global circuit variations from Vostok and Concordia electric field measurements. J. Atmos. Sci., 74, 783-800, doi:10.1175/JAS-D-16-0159-1. proprietary
AAS_974_DataForSolarWindInfluencesOnSurfaceLevelPressure_1 Modified NCEP surface pressure data, key-dates for superposed epoch analyses, and videos of surface pressure responses AU_AADC STAC Catalog 1995-01-01 2017-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1693329240-AU_AADC.umm_json The data interval is 1995 to 2017, excluding end-points imposed by averaging. NCEP/NCAR reanalysis surface pressure data was obtained from https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.surface.html. Surface pressure residuals are produced consisting of 1-day, 3-day and 5-day averages from which long-term variations are removed by subtracting a pressure average of the remaining, centred, 27-days. These data are in NCEP format (2.5°lat. x 2.5°long.) ASCII csv files. Selected daily-averaged solar wind and auroral activity indices, and solar sector boundary crossing dates are derived from data obtained at GSFC/NASA OMNIWeb data at https://omniweb.gsfc.nasa.gov. These data are listed in an EXCEL xlsx file. Videos of +/-13 day lags of four superposed epoch analyses of surface pressure responses to selected solar sector boundary crossings. The video are designated NH_MAMJJA_3dpc, SH_JJASON_3dpc_ave-of-3,NH_DJFMAM_6dpc and Equ_Year_8dpc, indicating the region (Northern Hemisphere, Southern Hemisphere, and Equatorial), the interval (acronym of first letter of each month or the entire Year) and the days of IMF By phase consistency (dpc) used to select the solar sector boundary crossing key-dates. The 'ave-of-3' designation indicates the averaging of initial superposed epoch responses across the original lags at -27 days, the key-date and +27 days. proprietary
AAS_974_Vostok_2006to2011_1min_1 Absolute vertical electric field data raw and selected data - Vostok from 2006-2011; processed 1-minute averages AU_AADC STAC Catalog 2006-01-01 2011-12-31 107.05, -78.55, 107.15, -78.45 https://cmr.earthdata.nasa.gov/search/concepts/C1380158481-AU_AADC.umm_json These data are collected under a collaborative arrangement between the Australian Antarctic Division (Principal Investigator: Gary Burns) and the Russian Antarctic Expeditions (Most-recent contact: Alexandr Frank-Kamenetsky, Institute of Arctic and Antarctic Studies, St Petersburg) In 2006 a new electric field mill (EFM) commenced operation at Vostok. This electric field mill is mounted on an all metal post ~3m above the snow surface. Values are positive for a downward-directed electric field. This EFM is different in operation, deployment and calibration from an earlier instrument that operated between 1998-2002 and in 2004 which was mounted on a 1.5m metal pole at deployment. The ASAC_974 project formally concluded in June 2011 (however processed data here-in is up to the end of December 2011), but the Russians (contact: Alexandr Frank-Kamenetsky, Institute of Arctic and Antarctic Studies, St Petersburg) have continued data collection at Vostok after this time, under an agreement to utilize the Australian developed equipment. 1-minute absolute vertical electric field averages (positive down) for raw (=all available 1-min averages; derived from 10 sec resolution data available at ASAC_974_2) and selected nvr, mvr and svr data which are described, tested and discussed in Burns et al. (2017). The values here-in have not been corrected for the solar-wind-imposed-potential (SWIP)-above-the-station. SWIP correction values are only derived for 20-minute averages and are applied to the nvr, mvr and svr 20-min averages (AAS_974_Vostok_2006to2011_20min) and described in Burns et al. (2017). In January 2006 a new electric field mill (EFM) was deployed at Vostok. This electric field mill is mounted on an all metal post ~3m above the snow surface. We use all metal coverings on our instrumentation as near-by insulators can retain a charge which could be slowly released and influence the electric field measurements over significant intervals. Values are positive for a downward-directed electric field. The 'compression factor' (3.0) was determined by stepping voltages between +5 and -5kV through a wire above the EFM at Vostok. Absolute values (V/m) at ~3m above the snow surface are provided. The three separate data selections are as described in Burns et al. (2017). An initial rejection of the minute-averaged data is made for electric fields with two hour prior to and after exceeding 300 V/m for all three data selections. This rejects measurements generally influenced by local falling, wind-blown or lifted snow or ice which result in high electric field values. The extended time intervals are a conservative allowance for the local influences prior to the cut-off electric field value being reached and after lower values are again recorded. For two of the three selected data sets an additional criteria with different levels of severity was used to reject rapid variations below the cut-off threshold based on jumps in the electric field within a five-minute interval. Due to the time constant (~15 minutes) associated with the atmospheric circuit, rapid variations in the electric field are more likely to be associated with local influences. This is confirmed in Burns et al. (2017) by the relative association of the selected data sets with the SWIP-above-Concordia and independently by comparison with simultaneous electric field measurements at Vostok. Strong variation rejection (svr) data selections additionally reject minute-averaged data within 30 minutes of a jump of 30 V/m (with 5 minutes). Medium variation rejection (mvr) data selections additionally reject minute-averaged data within 10 minutes of a jump of 50 V/m (within 5 minutes). No variation rejection (nvr) data selections made no rejection on the basis of rapid variations (within 5 minutes). The fourth electric field data set listed herein is all the raw 1-minute averages. This includes the high values (which are constrained by the electronics to an extreme upper value). This includes 1-min averages associated with monthly instrument calibrations conducted by placing a Faraday box over the rotating dipole and steeping through a range of voltages applied to parallel plates within. This does not allow for absolute calibration, but is used to determine relative calibrations. For brief intervals within the 2006 to 2011 interval a different EFM was used for measurements. The absolute values listed herein have been adjusted using the relative calibrations associated with the Faraday measurements. Monthly calibrations are linearly interpolated between calibrations. The EFMs deployed at Vostok (2006 on) and Concordia (2006 to 2009) are stable within the estimated uncertainties associated with the relative Faraday box calibrations but are linearly interpolated. Instrument changes are significant and have been allowed for. The 1 minute averages should not be used for calibration purposes as the 1-min averages may include transition intervals; it is necessary to evaluate the raw 10 sec data. Tests and changes of instrumentation resulted in the earliest selected (nvr) 1-minute-averaged electric field measurements at Vostok commencing at 1409UT, 5th January, 2006. Raw 1-minute-averaged values are intermittent from 00UT, 2nd January, 2009. The data provided consists of up to 12 fields. The first eight columns are date-time related fields. The first field is an Excel derived date-time to the mid-point of the 1-minute average. The second field is the 'year', followed by the 'day-of-year', 'month', 'day-of-month', 'UT-hour', UT-min' and 'UT-second' of the averaged data. The ninth column is the raw minute-averaged absolute value (V/m downward) of the measured vertical electric field. The tenth to twelfth columns list the nvr, mvr and svr Vostok 1-minute electric field averages, without SWIP corrections, but otherwise as described and tested in Burns et al. (2017). Missing data are presented as blanks. Suggested acknowledgements for the utilization of these data are: ‘These Vostok electric field data were collected by collaboration between the Australian Antarctic Division and the Russian Antarctic Expeditions. Australian involvement was approved by the Australian Antarctic Advisory Committee (AAS 974). Russian involvement was supported under the Russian Federal Program: World Ocean: Study and Research in Antarctica: Determination of Changes in the Antarctic Environment: Environmental Monitoring operated by the Arctic and Antarctic Research Institute, St. Petersburg.’ References: Burns, G.B., A.V. Frank-Kamenetsky, B.A. Tinsley, W.J.R. French, and P. Grigioni, G. Camporeale, and E.A. Bering, 2017: Atmospheric global circuit variations from Vostok and Concordia electric field measurements. J. Atmos. Sci., 74, 783-800, doi:10.1175/JAS-D-16-0159-1. proprietary
AAS_974_Vostok_2006to2011_1min_1 Absolute vertical electric field data raw and selected data - Vostok from 2006-2011; processed 1-minute averages ALL STAC Catalog 2006-01-01 2011-12-31 107.05, -78.55, 107.15, -78.45 https://cmr.earthdata.nasa.gov/search/concepts/C1380158481-AU_AADC.umm_json These data are collected under a collaborative arrangement between the Australian Antarctic Division (Principal Investigator: Gary Burns) and the Russian Antarctic Expeditions (Most-recent contact: Alexandr Frank-Kamenetsky, Institute of Arctic and Antarctic Studies, St Petersburg) In 2006 a new electric field mill (EFM) commenced operation at Vostok. This electric field mill is mounted on an all metal post ~3m above the snow surface. Values are positive for a downward-directed electric field. This EFM is different in operation, deployment and calibration from an earlier instrument that operated between 1998-2002 and in 2004 which was mounted on a 1.5m metal pole at deployment. The ASAC_974 project formally concluded in June 2011 (however processed data here-in is up to the end of December 2011), but the Russians (contact: Alexandr Frank-Kamenetsky, Institute of Arctic and Antarctic Studies, St Petersburg) have continued data collection at Vostok after this time, under an agreement to utilize the Australian developed equipment. 1-minute absolute vertical electric field averages (positive down) for raw (=all available 1-min averages; derived from 10 sec resolution data available at ASAC_974_2) and selected nvr, mvr and svr data which are described, tested and discussed in Burns et al. (2017). The values here-in have not been corrected for the solar-wind-imposed-potential (SWIP)-above-the-station. SWIP correction values are only derived for 20-minute averages and are applied to the nvr, mvr and svr 20-min averages (AAS_974_Vostok_2006to2011_20min) and described in Burns et al. (2017). In January 2006 a new electric field mill (EFM) was deployed at Vostok. This electric field mill is mounted on an all metal post ~3m above the snow surface. We use all metal coverings on our instrumentation as near-by insulators can retain a charge which could be slowly released and influence the electric field measurements over significant intervals. Values are positive for a downward-directed electric field. The 'compression factor' (3.0) was determined by stepping voltages between +5 and -5kV through a wire above the EFM at Vostok. Absolute values (V/m) at ~3m above the snow surface are provided. The three separate data selections are as described in Burns et al. (2017). An initial rejection of the minute-averaged data is made for electric fields with two hour prior to and after exceeding 300 V/m for all three data selections. This rejects measurements generally influenced by local falling, wind-blown or lifted snow or ice which result in high electric field values. The extended time intervals are a conservative allowance for the local influences prior to the cut-off electric field value being reached and after lower values are again recorded. For two of the three selected data sets an additional criteria with different levels of severity was used to reject rapid variations below the cut-off threshold based on jumps in the electric field within a five-minute interval. Due to the time constant (~15 minutes) associated with the atmospheric circuit, rapid variations in the electric field are more likely to be associated with local influences. This is confirmed in Burns et al. (2017) by the relative association of the selected data sets with the SWIP-above-Concordia and independently by comparison with simultaneous electric field measurements at Vostok. Strong variation rejection (svr) data selections additionally reject minute-averaged data within 30 minutes of a jump of 30 V/m (with 5 minutes). Medium variation rejection (mvr) data selections additionally reject minute-averaged data within 10 minutes of a jump of 50 V/m (within 5 minutes). No variation rejection (nvr) data selections made no rejection on the basis of rapid variations (within 5 minutes). The fourth electric field data set listed herein is all the raw 1-minute averages. This includes the high values (which are constrained by the electronics to an extreme upper value). This includes 1-min averages associated with monthly instrument calibrations conducted by placing a Faraday box over the rotating dipole and steeping through a range of voltages applied to parallel plates within. This does not allow for absolute calibration, but is used to determine relative calibrations. For brief intervals within the 2006 to 2011 interval a different EFM was used for measurements. The absolute values listed herein have been adjusted using the relative calibrations associated with the Faraday measurements. Monthly calibrations are linearly interpolated between calibrations. The EFMs deployed at Vostok (2006 on) and Concordia (2006 to 2009) are stable within the estimated uncertainties associated with the relative Faraday box calibrations but are linearly interpolated. Instrument changes are significant and have been allowed for. The 1 minute averages should not be used for calibration purposes as the 1-min averages may include transition intervals; it is necessary to evaluate the raw 10 sec data. Tests and changes of instrumentation resulted in the earliest selected (nvr) 1-minute-averaged electric field measurements at Vostok commencing at 1409UT, 5th January, 2006. Raw 1-minute-averaged values are intermittent from 00UT, 2nd January, 2009. The data provided consists of up to 12 fields. The first eight columns are date-time related fields. The first field is an Excel derived date-time to the mid-point of the 1-minute average. The second field is the 'year', followed by the 'day-of-year', 'month', 'day-of-month', 'UT-hour', UT-min' and 'UT-second' of the averaged data. The ninth column is the raw minute-averaged absolute value (V/m downward) of the measured vertical electric field. The tenth to twelfth columns list the nvr, mvr and svr Vostok 1-minute electric field averages, without SWIP corrections, but otherwise as described and tested in Burns et al. (2017). Missing data are presented as blanks. Suggested acknowledgements for the utilization of these data are: ‘These Vostok electric field data were collected by collaboration between the Australian Antarctic Division and the Russian Antarctic Expeditions. Australian involvement was approved by the Australian Antarctic Advisory Committee (AAS 974). Russian involvement was supported under the Russian Federal Program: World Ocean: Study and Research in Antarctica: Determination of Changes in the Antarctic Environment: Environmental Monitoring operated by the Arctic and Antarctic Research Institute, St. Petersburg.’ References: Burns, G.B., A.V. Frank-Kamenetsky, B.A. Tinsley, W.J.R. French, and P. Grigioni, G. Camporeale, and E.A. Bering, 2017: Atmospheric global circuit variations from Vostok and Concordia electric field measurements. J. Atmos. Sci., 74, 783-800, doi:10.1175/JAS-D-16-0159-1. proprietary
@@ -1264,49 +1264,49 @@ ABI_G16-STAR-L3C-v2.70_2.70 GHRSST NOAA/STAR GOES-16 ABI L3C America Region SST
ABI_G17-STAR-L2P-v2.71_2.71 GHRSST NOAA/STAR GOES-17 ABI L2P America Region SST v2.71 dataset in GDS2 POCLOUD STAC Catalog 2019-10-16 2023-01-10 163, -60, -77, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2036877626-POCLOUD.umm_json GOES-17 (G17) is the second satellite in the US NOAA's GOES-R series. It was launched on 1 Mar 2018 in an interim position at 89.5-deg W for initial Cal/Val, moved to its nominal position at 137.2-deg W in Nov 2018, and declared NOAA operational GOES-West satellite on 12 Feb 2019. Advanced Baseline Imager (ABI) is a 16 channel sensor, of which five (3.9, 8.4, 10.3, 11.2, 12.3 um) are suitable for SST. From altitude 35,800km, G17/ABI maps SST in a Full Disk (FD) area from 163E-77W and 60S-60N, with spatial resolution 2km/nadir to 15km/VZA 67-deg, and 10-min temporal sampling. The ABI L2P SST is derived at the native sensor resolution using NOAA ACSPO system. ACSPO processes every 10-min FD, identifies good-quality ocean pixels (Petrenko et al., 2010) and derives SST using Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014). Unfortunately, the G17 ABI loop heat pipe (LHP) that should maintain the ABI at its intended temperature, is not operating at its designed capacity, which required mitigations to the ACSPO algorithms and releasing an updated ACSPO version 2.71 (Pennybacker et al, 2019). In particular, band 11.2um, most subject to calibration problems, is not used leading to a 3-band (8.4, 10.3, and 12.3um) NLSST, and increased calibration problems prevent SST retrievals at night. As a result, the G17 SST is only reported for 13 out of 24hrs/day, from 20UTC to 08UTC. The 10-min FD data are subsequently collated in time, to produce 1-hr product, with improved coverage and reduced cloud leakages and image noise. The collation algorithm also reduces G17 excessive sensor noise and striping to levels similar to G16. The collated SSTs are only reported over clear-sky water pixels. All pixels with valid SSTs are recommended for use. The L2P is reported in NetCDF4 GDS2 format, 13 granules per day, with a total data volume 0.3GB/day. ACSPO files also report sun-sensor geometry, wind speed and l2p_flags (day/night, land, ice, twilight, glint flags). Per GDS2 specifications, two Sensor-Specific Error Statistics (bias and standard deviation) are reported in each pixel (Petrenko et al., 2016). Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script (see Documentation page). The ACSPO G17 ABI SSTs are continuously validated in SQUAM (Dash et al, 2010). A reduced size (0.1GB/day), 0.02-deg equal-angle gridded L3C product is available at https://podaac.jpl.nasa.gov/dataset/ABI_G17-STAR-L3C-v2.71. proprietary
ABI_G17-STAR-L3C-v2.71_2.71 GHRSST NOAA/STAR GOES-17 ABI L3C America Region SST v2.71 dataset in GDS2 POCLOUD STAC Catalog 2019-10-16 2023-01-10 163, -60, -77, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2036877645-POCLOUD.umm_json The ACSPO G17/ABI L3C (Level 3 Collated) product is a gridded version of the ACSPO G17/ABI L2P product available at https://podaac.jpl.nasa.gov/dataset/ABI_G17-STAR-L2P-v2.71. The L3C output files are 1hr granules in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). Due to the loop heat pipe (LHP) issue on G17 ABI, there are only 13 granules available per 24hr interval, from 20UTC to 08UTC, followed by a break from 09UTC to 19UTC, with a total data volume of 0.1GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. The following additional layers are also reported: SST, ACSPO clear-sky mask (ACSM; provided in each grid as part of l2p_flags, which also includes day/night, land, ice, twilight, and glint flags), NCEP wind speed and ACSPO SST minus reference (Canadian Met Centre 0.1deg L4 SST; available at https://podaac.jpl.nasa.gov/dataset/CMC0.1deg-CMC-L4-GLOB-v3.0 ). All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST. The ACSPO VIIRS L3U product is monitored and validated against iQuam in situ data (Xu and Ignatov, 2014) in SQUAM (Dash et al, 2010). proprietary
ABLE_897_1 Pre-LBA ABLE-2A and ABLE-2B Expedition Data ORNL_CLOUD STAC Catalog 1985-07-11 1987-05-13 -70, -10, -50, 0 https://cmr.earthdata.nasa.gov/search/concepts/C2777402194-ORNL_CLOUD.umm_json The ABLE 2A and 2B (Atmospheric Boundary Layer Experiments) data consists of estimates of the rate of exchange of a wide variety of aerosols and gases between the Amazon Basin and its atmospheric boundary layer, and the processes by which these aerosols and gases are moved between the boundary layer and the free troposphere. The data are presented in gzipped ASCII text files in Global Tropospheric Experiment (GTE) format.The ABLE-2 project consisted of two expeditions: the first in the Amazonian dry season (ABLE-2A, July-August 1985); and the second in the wet season (ABLE-2B, April-May 1987). The ABLE-2 core research data were gathered by NASA Electra aircraft flights that stretched from Belem, at the mouth of the Amazon River, west to Tabatinga, on the Brazil-Colombia border, from a base at Manaus in the heart of the forest. See Figure 1. These observations were supplemented by ground based chemical and meteorological measurements in the dry forest, the Amazon floodplain, and the tributary rivers through use of enclosures, an instrumented tower in the jungle, a large tethered balloon, and weather and ozone sondes.This study showed air above the Amazon jungle to be extremely clean during the wet season but air quality deteriorated dramatically during the dry season as the result of biomass burning, performed mostly at the edges of the forest. Biomass burning is also a source of greenhouse gases carbon dioxide and methane, as well as other pollutants (carbon monoxide and oxides of nitrogen). Amazonian ozone deposition rates were found to be 5 to 50 times higher than those previously measured over pine forests and water surfaces. The Amazon River floodplain is a globally significant source of methane, supplying about 12% of the estimated worldwide total from all wetlands sources. Over Amazonia, carbon monoxide is enhanced by factors ranging from 1.2 to 2.7 by comparison with adjacent regions due to isoprene oxidation and biomass burning. Over the rainforest individual convective storms transport 200 megatons of air per hour, of which 3 megatons is water vapor that releases 100,000 megawatts of energy into the atmosphere through condensation into rain.The ABLE was a collaboration of U.S. and Brazilian scientists sponsored by NASA and Instituto Nacional de Pesquisas Espaciais (INPE) and supported by the Global Tropospheric Experiment (GTE) component of the NASA Tropospheric Chemistry Program. proprietary
-ABLVIS1B_1 ABoVE LVIS L1B Geolocated Return Energy Waveforms V001 NSIDC_ECS STAC Catalog 2017-06-29 2017-07-17 -158, 48, -104, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1513105920-NSIDC_ECS.umm_json This data set contains return energy waveform data over Alaska and Western Canada measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE). proprietary
ABLVIS1B_1 ABoVE LVIS L1B Geolocated Return Energy Waveforms V001 ALL STAC Catalog 2017-06-29 2017-07-17 -158, 48, -104, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1513105920-NSIDC_ECS.umm_json This data set contains return energy waveform data over Alaska and Western Canada measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE). proprietary
+ABLVIS1B_1 ABoVE LVIS L1B Geolocated Return Energy Waveforms V001 NSIDC_ECS STAC Catalog 2017-06-29 2017-07-17 -158, 48, -104, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1513105920-NSIDC_ECS.umm_json This data set contains return energy waveform data over Alaska and Western Canada measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE). proprietary
ABLVIS2_1 ABoVE LVIS L2 Geolocated Surface Elevation Product V001 NSIDC_ECS STAC Catalog 2017-06-29 2017-07-17 -158, 48, -104, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1513105984-NSIDC_ECS.umm_json This data set contains surface elevation data over Alaska and Western Canada measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE). proprietary
ABLVIS2_1 ABoVE LVIS L2 Geolocated Surface Elevation Product V001 ALL STAC Catalog 2017-06-29 2017-07-17 -158, 48, -104, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1513105984-NSIDC_ECS.umm_json This data set contains surface elevation data over Alaska and Western Canada measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE). proprietary
ABOA_Absolute_Gravity_1 Absolute gravity measurements at Aboa station ALL STAC Catalog 2005-01-01 2017-01-31 13.42, -73.05, 13.42, -73.05 https://cmr.earthdata.nasa.gov/search/concepts/C1609650237-SCIOPS.umm_json Absolute gravity measurements at the Finnish Antarctic Station Aboa. Measurements have been performed with the FGI FG-5 absolute gravimeter, which was upgraded into FG5-X for the 2017 campaign. Data is not available online but is available upon request. proprietary
ABOA_Absolute_Gravity_1 Absolute gravity measurements at Aboa station SCIOPS STAC Catalog 2005-01-01 2017-01-31 13.42, -73.05, 13.42, -73.05 https://cmr.earthdata.nasa.gov/search/concepts/C1609650237-SCIOPS.umm_json Absolute gravity measurements at the Finnish Antarctic Station Aboa. Measurements have been performed with the FGI FG-5 absolute gravimeter, which was upgraded into FG5-X for the 2017 campaign. Data is not available online but is available upon request. proprietary
-ABOA_bb ABOA seismic broad band station ALL STAC Catalog 1970-01-01 -13.41, -73.04, -13.41, -73.04 https://cmr.earthdata.nasa.gov/search/concepts/C1214592914-SCIOPS.umm_json Continuous seismic broad band data in the vicinity of Aboa station at 73S, 13W. proprietary
ABOA_bb ABOA seismic broad band station SCIOPS STAC Catalog 1970-01-01 -13.41, -73.04, -13.41, -73.04 https://cmr.earthdata.nasa.gov/search/concepts/C1214592914-SCIOPS.umm_json Continuous seismic broad band data in the vicinity of Aboa station at 73S, 13W. proprietary
-ABOLVIS1A_1 ABoVE LVIS L1A Geotagged Images V001 NSIDC_ECS STAC Catalog 2017-06-29 2017-07-17 -158, 48, -104, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1673546369-NSIDC_ECS.umm_json This data set contains geotagged images collected over Alaska and Western Canada. The images were taken by the NASA Digital Mapping Camera, paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE). proprietary
+ABOA_bb ABOA seismic broad band station ALL STAC Catalog 1970-01-01 -13.41, -73.04, -13.41, -73.04 https://cmr.earthdata.nasa.gov/search/concepts/C1214592914-SCIOPS.umm_json Continuous seismic broad band data in the vicinity of Aboa station at 73S, 13W. proprietary
ABOLVIS1A_1 ABoVE LVIS L1A Geotagged Images V001 ALL STAC Catalog 2017-06-29 2017-07-17 -158, 48, -104, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1673546369-NSIDC_ECS.umm_json This data set contains geotagged images collected over Alaska and Western Canada. The images were taken by the NASA Digital Mapping Camera, paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE). proprietary
-ABoVE_ASCENDS_Backscatter_2051_1 ABoVE/ASCENDS: Atmospheric Backscattering Coefficient Profiles from CO2 Sounder, 2017 ALL STAC Catalog 2017-07-20 2017-08-08 -165.68, 34.59, -98.15, 71.27 https://cmr.earthdata.nasa.gov/search/concepts/C2264344759-ORNL_CLOUD.umm_json This dataset provides atmospheric backscattering coefficient profiles collected during Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) deployments from 2017-07-20 to 2017-08-08 over Alaska, U.S., and the Yukon and Northwest Territories of Canada. These profiles were measured by the CO2 Sounder Lidar instrument carried on a DC-8 aircraft. The airborne CO2 Sounder is a pulsed, multi-wavelength Integrated Path Differential Absorption lidar that estimates column-averaged dry-air CO2 mixing ratio (XCO2) in the nadir path from the aircraft to the scattering surface. In addition to XCO2, the lidar receiver recorded the time-resolved atmospheric backscatter signal strength as the laser pulses propagated through the atmosphere. Raw lidar data were converted to the atmospheric backscatter cross-section product and the two-way atmosphere transmission, also known as attenuated backscatter profiles. These ASCENDS flights were coordinated with the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) campaign and are provided in ICARTT format. proprietary
+ABOLVIS1A_1 ABoVE LVIS L1A Geotagged Images V001 NSIDC_ECS STAC Catalog 2017-06-29 2017-07-17 -158, 48, -104, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1673546369-NSIDC_ECS.umm_json This data set contains geotagged images collected over Alaska and Western Canada. The images were taken by the NASA Digital Mapping Camera, paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE). proprietary
ABoVE_ASCENDS_Backscatter_2051_1 ABoVE/ASCENDS: Atmospheric Backscattering Coefficient Profiles from CO2 Sounder, 2017 ORNL_CLOUD STAC Catalog 2017-07-20 2017-08-08 -165.68, 34.59, -98.15, 71.27 https://cmr.earthdata.nasa.gov/search/concepts/C2264344759-ORNL_CLOUD.umm_json This dataset provides atmospheric backscattering coefficient profiles collected during Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) deployments from 2017-07-20 to 2017-08-08 over Alaska, U.S., and the Yukon and Northwest Territories of Canada. These profiles were measured by the CO2 Sounder Lidar instrument carried on a DC-8 aircraft. The airborne CO2 Sounder is a pulsed, multi-wavelength Integrated Path Differential Absorption lidar that estimates column-averaged dry-air CO2 mixing ratio (XCO2) in the nadir path from the aircraft to the scattering surface. In addition to XCO2, the lidar receiver recorded the time-resolved atmospheric backscatter signal strength as the laser pulses propagated through the atmosphere. Raw lidar data were converted to the atmospheric backscatter cross-section product and the two-way atmosphere transmission, also known as attenuated backscatter profiles. These ASCENDS flights were coordinated with the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) campaign and are provided in ICARTT format. proprietary
+ABoVE_ASCENDS_Backscatter_2051_1 ABoVE/ASCENDS: Atmospheric Backscattering Coefficient Profiles from CO2 Sounder, 2017 ALL STAC Catalog 2017-07-20 2017-08-08 -165.68, 34.59, -98.15, 71.27 https://cmr.earthdata.nasa.gov/search/concepts/C2264344759-ORNL_CLOUD.umm_json This dataset provides atmospheric backscattering coefficient profiles collected during Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) deployments from 2017-07-20 to 2017-08-08 over Alaska, U.S., and the Yukon and Northwest Territories of Canada. These profiles were measured by the CO2 Sounder Lidar instrument carried on a DC-8 aircraft. The airborne CO2 Sounder is a pulsed, multi-wavelength Integrated Path Differential Absorption lidar that estimates column-averaged dry-air CO2 mixing ratio (XCO2) in the nadir path from the aircraft to the scattering surface. In addition to XCO2, the lidar receiver recorded the time-resolved atmospheric backscatter signal strength as the laser pulses propagated through the atmosphere. Raw lidar data were converted to the atmospheric backscatter cross-section product and the two-way atmosphere transmission, also known as attenuated backscatter profiles. These ASCENDS flights were coordinated with the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) campaign and are provided in ICARTT format. proprietary
ABoVE_ASCENDS_Merge_2114_1 ABoVE/ASCENDS: Merged Atmospheric CO2, CH4, and Meteorological Data, 2017 ALL STAC Catalog 2017-07-20 2017-08-09 -165.68, 34.59, -98.15, 71.27 https://cmr.earthdata.nasa.gov/search/concepts/C2575399701-ORNL_CLOUD.umm_json This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2), methane (CH4), water vapor concentrations, air temperature, pressure, and wind speed and direction as well as airborne remote sensing measurements of column average CO2 collected during Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) deployments from 2017-07-20 to 2017-08-08 over Alaska, US, and the Yukon and Northwest Territories of Canada. CO2 and CH4 were measured with NASA's Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument. Water vapor and relative humidity were measured with Diode Laser Hydrometer. Measurements were taken onboard a DC-8 aircraft. The ASCENDS flights were coordinated with the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) campaign. The data are provided in ICARTT format along with an archive of flight videos. proprietary
ABoVE_ASCENDS_Merge_2114_1 ABoVE/ASCENDS: Merged Atmospheric CO2, CH4, and Meteorological Data, 2017 ORNL_CLOUD STAC Catalog 2017-07-20 2017-08-09 -165.68, 34.59, -98.15, 71.27 https://cmr.earthdata.nasa.gov/search/concepts/C2575399701-ORNL_CLOUD.umm_json This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2), methane (CH4), water vapor concentrations, air temperature, pressure, and wind speed and direction as well as airborne remote sensing measurements of column average CO2 collected during Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) deployments from 2017-07-20 to 2017-08-08 over Alaska, US, and the Yukon and Northwest Territories of Canada. CO2 and CH4 were measured with NASA's Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument. Water vapor and relative humidity were measured with Diode Laser Hydrometer. Measurements were taken onboard a DC-8 aircraft. The ASCENDS flights were coordinated with the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) campaign. The data are provided in ICARTT format along with an archive of flight videos. proprietary
-ABoVE_ASCENDS_XCO2_2050_1 ABoVE/ASCENDS: Active Sensing of CO2, CH4, and Water Vapor, Alaska and Canada, 2017 ORNL_CLOUD STAC Catalog 2017-07-20 2017-08-08 -165.68, 34.59, -98.1, 71.28 https://cmr.earthdata.nasa.gov/search/concepts/C2264340976-ORNL_CLOUD.umm_json This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2), methane (CH4), and water vapor concentrations, plus air temperature, pressure, relative humidity, and wind speed values over Alaska and the Yukon and Northwest Territories of Canada during 2017-07-20 to 2017-08-08. Measurements were taken onboard a DC-8 aircraft during this Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) airborne deployment over portions of the Arctic-Boreal Vulnerability Experiment (ABoVE) domain. CO2 and CH4 were measured with NASA's Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument. Water vapor and relative humidity were measured with Diode Laser Hydrometer. Measurements of column-averaged dry-air mixing ratio CO2 measurements (XCO2) were taken with the CO2 Sounder Lidar instrument. The airborne CO2 Sounder is a pulsed, multi-wavelength Integrated Path Differential Absorption lidar. It estimates XCO2 in the nadir path from the aircraft to the scattering surface by measuring the shape of the 1572.33 nm CO2 absorption line. The data were collected in order to capture the spatial and temporal dynamics of the northern high latitude carbon cycle as part of ABoVE and are provided in ICARTT file format. proprietary
ABoVE_ASCENDS_XCO2_2050_1 ABoVE/ASCENDS: Active Sensing of CO2, CH4, and Water Vapor, Alaska and Canada, 2017 ALL STAC Catalog 2017-07-20 2017-08-08 -165.68, 34.59, -98.1, 71.28 https://cmr.earthdata.nasa.gov/search/concepts/C2264340976-ORNL_CLOUD.umm_json This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2), methane (CH4), and water vapor concentrations, plus air temperature, pressure, relative humidity, and wind speed values over Alaska and the Yukon and Northwest Territories of Canada during 2017-07-20 to 2017-08-08. Measurements were taken onboard a DC-8 aircraft during this Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) airborne deployment over portions of the Arctic-Boreal Vulnerability Experiment (ABoVE) domain. CO2 and CH4 were measured with NASA's Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument. Water vapor and relative humidity were measured with Diode Laser Hydrometer. Measurements of column-averaged dry-air mixing ratio CO2 measurements (XCO2) were taken with the CO2 Sounder Lidar instrument. The airborne CO2 Sounder is a pulsed, multi-wavelength Integrated Path Differential Absorption lidar. It estimates XCO2 in the nadir path from the aircraft to the scattering surface by measuring the shape of the 1572.33 nm CO2 absorption line. The data were collected in order to capture the spatial and temporal dynamics of the northern high latitude carbon cycle as part of ABoVE and are provided in ICARTT file format. proprietary
+ABoVE_ASCENDS_XCO2_2050_1 ABoVE/ASCENDS: Active Sensing of CO2, CH4, and Water Vapor, Alaska and Canada, 2017 ORNL_CLOUD STAC Catalog 2017-07-20 2017-08-08 -165.68, 34.59, -98.1, 71.28 https://cmr.earthdata.nasa.gov/search/concepts/C2264340976-ORNL_CLOUD.umm_json This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2), methane (CH4), and water vapor concentrations, plus air temperature, pressure, relative humidity, and wind speed values over Alaska and the Yukon and Northwest Territories of Canada during 2017-07-20 to 2017-08-08. Measurements were taken onboard a DC-8 aircraft during this Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) airborne deployment over portions of the Arctic-Boreal Vulnerability Experiment (ABoVE) domain. CO2 and CH4 were measured with NASA's Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument. Water vapor and relative humidity were measured with Diode Laser Hydrometer. Measurements of column-averaged dry-air mixing ratio CO2 measurements (XCO2) were taken with the CO2 Sounder Lidar instrument. The airborne CO2 Sounder is a pulsed, multi-wavelength Integrated Path Differential Absorption lidar. It estimates XCO2 in the nadir path from the aircraft to the scattering surface by measuring the shape of the 1572.33 nm CO2 absorption line. The data were collected in order to capture the spatial and temporal dynamics of the northern high latitude carbon cycle as part of ABoVE and are provided in ICARTT file format. proprietary
ABoVE_AirSWOT_Radar_Data_1646_1 ABoVE: AirSWOT Ka-band Radar over Surface Waters of Alaska and Canada, 2017 ALL STAC Catalog 2017-07-08 2017-08-17 -149.83, 46.85, -98.63, 70.49 https://cmr.earthdata.nasa.gov/search/concepts/C2111827036-ORNL_CLOUD.umm_json AirSWOT is an airborne calibration and validation instrument for the upcoming Surface Water Topography Mission (SWOT) satellite. AirSWOT is capable of producing high resolution digital elevation models over land and water bodies. This dataset provides AirSWOT Ka-band (35.75 GHz) radar data products collected from an airborne platform over parts of Alaska and Canada during the period 2017-07-09 to 2017-08-17. Flights targeted specific surface water features, including rivers, lakes, ponds, and wetlands in the ABoVE domain. The radar data include six products: elevation (above the WGS84 ellipsoid), incidence angle, magnitude (backscatter), interferometric correlation (coherence), DHDPHI (incidence angle dependent height sensitivity), and error (estimated height random error, 1-sigma standard deviation). The flight lines were selected to span a full spectrum of permafrost conditions (permafrost-free to continuous permafrost, low to high ground ice content), ecosystems, climatic regions, topographic relief, and geological substrates across the ABoVE domain to investigate surface water responses to thawing permafrost and spatial and temporal variability in terrestrial water storage by measuring elevation and extent of surface waters. The data are provided in two forms: 1) the original output (outer-swath products only) at 3.6 m2 resolution in UTM coordinates from the AirSWOT processing group at the Jet Propulsion Laboratory (JPL), and 2) the ABoVE Projection at 3.6 m2 resolution, clipped to the ABoVE reference grid tiles using the C grid. proprietary
ABoVE_AirSWOT_Radar_Data_1646_1 ABoVE: AirSWOT Ka-band Radar over Surface Waters of Alaska and Canada, 2017 ORNL_CLOUD STAC Catalog 2017-07-08 2017-08-17 -149.83, 46.85, -98.63, 70.49 https://cmr.earthdata.nasa.gov/search/concepts/C2111827036-ORNL_CLOUD.umm_json AirSWOT is an airborne calibration and validation instrument for the upcoming Surface Water Topography Mission (SWOT) satellite. AirSWOT is capable of producing high resolution digital elevation models over land and water bodies. This dataset provides AirSWOT Ka-band (35.75 GHz) radar data products collected from an airborne platform over parts of Alaska and Canada during the period 2017-07-09 to 2017-08-17. Flights targeted specific surface water features, including rivers, lakes, ponds, and wetlands in the ABoVE domain. The radar data include six products: elevation (above the WGS84 ellipsoid), incidence angle, magnitude (backscatter), interferometric correlation (coherence), DHDPHI (incidence angle dependent height sensitivity), and error (estimated height random error, 1-sigma standard deviation). The flight lines were selected to span a full spectrum of permafrost conditions (permafrost-free to continuous permafrost, low to high ground ice content), ecosystems, climatic regions, topographic relief, and geological substrates across the ABoVE domain to investigate surface water responses to thawing permafrost and spatial and temporal variability in terrestrial water storage by measuring elevation and extent of surface waters. The data are provided in two forms: 1) the original output (outer-swath products only) at 3.6 m2 resolution in UTM coordinates from the AirSWOT processing group at the Jet Propulsion Laboratory (JPL), and 2) the ABoVE Projection at 3.6 m2 resolution, clipped to the ABoVE reference grid tiles using the C grid. proprietary
-ABoVE_AirSWOT_Water_Mask_1707_1 ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017 ALL STAC Catalog 2017-07-09 2017-08-17 -152.18, 43.27, -98.64, 76.28 https://cmr.earthdata.nasa.gov/search/concepts/C2143402575-ORNL_CLOUD.umm_json This dataset provides 1) a conservative open water mask for future water surface elevation (WSE) extraction from the co-registered AirSWOT Ka-band interferometry data, and 2) high-resolution (1 m) water body distribution maps for water bodies greater than 40 m2 along the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) foundational flight lines. The masks and maps were derived from georeferenced three-band orthomosaics generated from individual images collected during the flights and a semi-automated water classification algorithm based on the Normalized Difference Water Index (NDWI). In total, 3,167 km2 of open water were mapped from 23,380 km2 of flight lines spanning 23 degrees of latitude. Detected water body sizes range from 40 m2 to 15 km2. The image tiles were georeferenced using manually selected ground control points (GCPs). Comparison with manually digitized open water boundaries yields an overall open-water classification accuracy of 98.0%. proprietary
ABoVE_AirSWOT_Water_Mask_1707_1 ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017 ORNL_CLOUD STAC Catalog 2017-07-09 2017-08-17 -152.18, 43.27, -98.64, 76.28 https://cmr.earthdata.nasa.gov/search/concepts/C2143402575-ORNL_CLOUD.umm_json This dataset provides 1) a conservative open water mask for future water surface elevation (WSE) extraction from the co-registered AirSWOT Ka-band interferometry data, and 2) high-resolution (1 m) water body distribution maps for water bodies greater than 40 m2 along the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) foundational flight lines. The masks and maps were derived from georeferenced three-band orthomosaics generated from individual images collected during the flights and a semi-automated water classification algorithm based on the Normalized Difference Water Index (NDWI). In total, 3,167 km2 of open water were mapped from 23,380 km2 of flight lines spanning 23 degrees of latitude. Detected water body sizes range from 40 m2 to 15 km2. The image tiles were georeferenced using manually selected ground control points (GCPs). Comparison with manually digitized open water boundaries yields an overall open-water classification accuracy of 98.0%. proprietary
-ABoVE_Airborne_AVIRIS_NG_V3_2362_3 ABoVE: AVIRIS-NG Imaging Spectroscopy for Alaska, Canada, and Iceland, 2017-2022, V3 ALL STAC Catalog 2017-06-24 2022-08-19 -166.65, 52.16, 28.22, 71.38 https://cmr.earthdata.nasa.gov/search/concepts/C3253178409-ORNL_CLOUD.umm_json This dataset supersedes the previously published ABoVE AVIRIS-NG Level 2 surface reflectance files for 2017-2019 surveys of Alaska and northwestern Canada. It also includes previously unpublished L1 radiance and L2 reflectance for the 2021 surveys in Iceland when COVID-era policies prevented normal ABoVE flights, and the 2022 surveys, which returned to the ABoVE domain. The dataset comprises ~1700 individual flight lines covering ~120,000 km2 with a nominal spatial resolution of 5 m. Sampling includes individual transects to capture key gradients like the tundra-taiga ecotone and raster maps of key study areas like the CHARS Greiner watershed, the Mackenzie Delta, and the Utqiagvik/Point Barrow area. AVIRIS-NG measures reflected radiance in 425 bands at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Measurements were radiometrically and geometrically calibrated. This dataset represents one part of a multi-sensor airborne sampling campaign conducted by eleven different aircraft teams for ABoVE. The imagery data are provided in ENVI format along with a RGB composite image for each flight line and shapefiles showing imagery boundaries. proprietary
+ABoVE_AirSWOT_Water_Mask_1707_1 ABoVE: AirSWOT Water Masks from Color-Infrared Imagery over Alaska and Canada, 2017 ALL STAC Catalog 2017-07-09 2017-08-17 -152.18, 43.27, -98.64, 76.28 https://cmr.earthdata.nasa.gov/search/concepts/C2143402575-ORNL_CLOUD.umm_json This dataset provides 1) a conservative open water mask for future water surface elevation (WSE) extraction from the co-registered AirSWOT Ka-band interferometry data, and 2) high-resolution (1 m) water body distribution maps for water bodies greater than 40 m2 along the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) foundational flight lines. The masks and maps were derived from georeferenced three-band orthomosaics generated from individual images collected during the flights and a semi-automated water classification algorithm based on the Normalized Difference Water Index (NDWI). In total, 3,167 km2 of open water were mapped from 23,380 km2 of flight lines spanning 23 degrees of latitude. Detected water body sizes range from 40 m2 to 15 km2. The image tiles were georeferenced using manually selected ground control points (GCPs). Comparison with manually digitized open water boundaries yields an overall open-water classification accuracy of 98.0%. proprietary
ABoVE_Airborne_AVIRIS_NG_V3_2362_3 ABoVE: AVIRIS-NG Imaging Spectroscopy for Alaska, Canada, and Iceland, 2017-2022, V3 ORNL_CLOUD STAC Catalog 2017-06-24 2022-08-19 -166.65, 52.16, 28.22, 71.38 https://cmr.earthdata.nasa.gov/search/concepts/C3253178409-ORNL_CLOUD.umm_json This dataset supersedes the previously published ABoVE AVIRIS-NG Level 2 surface reflectance files for 2017-2019 surveys of Alaska and northwestern Canada. It also includes previously unpublished L1 radiance and L2 reflectance for the 2021 surveys in Iceland when COVID-era policies prevented normal ABoVE flights, and the 2022 surveys, which returned to the ABoVE domain. The dataset comprises ~1700 individual flight lines covering ~120,000 km2 with a nominal spatial resolution of 5 m. Sampling includes individual transects to capture key gradients like the tundra-taiga ecotone and raster maps of key study areas like the CHARS Greiner watershed, the Mackenzie Delta, and the Utqiagvik/Point Barrow area. AVIRIS-NG measures reflected radiance in 425 bands at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Measurements were radiometrically and geometrically calibrated. This dataset represents one part of a multi-sensor airborne sampling campaign conducted by eleven different aircraft teams for ABoVE. The imagery data are provided in ENVI format along with a RGB composite image for each flight line and shapefiles showing imagery boundaries. proprietary
+ABoVE_Airborne_AVIRIS_NG_V3_2362_3 ABoVE: AVIRIS-NG Imaging Spectroscopy for Alaska, Canada, and Iceland, 2017-2022, V3 ALL STAC Catalog 2017-06-24 2022-08-19 -166.65, 52.16, 28.22, 71.38 https://cmr.earthdata.nasa.gov/search/concepts/C3253178409-ORNL_CLOUD.umm_json This dataset supersedes the previously published ABoVE AVIRIS-NG Level 2 surface reflectance files for 2017-2019 surveys of Alaska and northwestern Canada. It also includes previously unpublished L1 radiance and L2 reflectance for the 2021 surveys in Iceland when COVID-era policies prevented normal ABoVE flights, and the 2022 surveys, which returned to the ABoVE domain. The dataset comprises ~1700 individual flight lines covering ~120,000 km2 with a nominal spatial resolution of 5 m. Sampling includes individual transects to capture key gradients like the tundra-taiga ecotone and raster maps of key study areas like the CHARS Greiner watershed, the Mackenzie Delta, and the Utqiagvik/Point Barrow area. AVIRIS-NG measures reflected radiance in 425 bands at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Measurements were radiometrically and geometrically calibrated. This dataset represents one part of a multi-sensor airborne sampling campaign conducted by eleven different aircraft teams for ABoVE. The imagery data are provided in ENVI format along with a RGB composite image for each flight line and shapefiles showing imagery boundaries. proprietary
ABoVE_Annual_Veg_Resilience_2374_1 MODIS-derived Annual Vegetation Resilience, 2000-2019 ORNL_CLOUD STAC Catalog 2000-01-01 2019-12-31 -170.01, 50.26, -98.97, 76.23 https://cmr.earthdata.nasa.gov/search/concepts/C3255176825-ORNL_CLOUD.umm_json This dataset provides estimates of vegetation resilience in the Arctic Boreal Vulnerability Experiment (ABoVE) core domain at annual time steps for 2000-2019 and at 300-m spatial resolution. Vegetation resilience is defined as the recovery rate from deviations, due to climate perturbations or disturbances, to the equilibrium state. It is quantified as the negative temporal lag-1 autocorrelation of Enhanced Vegetation Index (EVI). Using a time series of MODIS EVI, the vegetation resilience was estimated using a Bayesian dynamic linear model. The mapped vegetation resilience was derived from Terra EVI products (MOD13Q1v061) across 175 ABoVE B grid tiles over the ABoVE core domain. The estimated mean resilience, upper boundary, and lower boundary results are provided for each tile in cloud optimized GeoTIFF (COG) format. proprietary
ABoVE_Arctic_CAP_1658_1 ABoVE: Atmospheric Profiles of CO, CO2 and CH4 Concentrations from Arctic-CAP, 2017 ALL STAC Catalog 2017-04-26 2017-11-05 -166.04, 40.04, -104.11, 71.29 https://cmr.earthdata.nasa.gov/search/concepts/C2162185379-ORNL_CLOUD.umm_json This dataset provides in situ airborne measurements of atmospheric carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), and water vapor concentrations, plus air temperature, pressure, relative humidity, and wind speed values over Alaska and the Yukon and Northwest Territories of Canada during the Arctic Carbon Aircraft Profile (Arctic-CAP) monthly sampling campaigns from April-November 2017. Observations have been averaged to a 10-second interval and are reported with the number of samples (N) and standard deviation. During each of the six monthly campaigns, flights over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain included 25 vertical profiles, from the surface up to 6 km altitude, at locations selected to complement regular long-term vertical profiles, remote sensing data, and ground-based flux tower measurements. proprietary
ABoVE_Arctic_CAP_1658_1 ABoVE: Atmospheric Profiles of CO, CO2 and CH4 Concentrations from Arctic-CAP, 2017 ORNL_CLOUD STAC Catalog 2017-04-26 2017-11-05 -166.04, 40.04, -104.11, 71.29 https://cmr.earthdata.nasa.gov/search/concepts/C2162185379-ORNL_CLOUD.umm_json This dataset provides in situ airborne measurements of atmospheric carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), and water vapor concentrations, plus air temperature, pressure, relative humidity, and wind speed values over Alaska and the Yukon and Northwest Territories of Canada during the Arctic Carbon Aircraft Profile (Arctic-CAP) monthly sampling campaigns from April-November 2017. Observations have been averaged to a 10-second interval and are reported with the number of samples (N) and standard deviation. During each of the six monthly campaigns, flights over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain included 25 vertical profiles, from the surface up to 6 km altitude, at locations selected to complement regular long-term vertical profiles, remote sensing data, and ground-based flux tower measurements. proprietary
-ABoVE_Atmospheric_Flask_Data_1717_1 ABoVE: Atmospheric Gas Concentrations from Airborne Flasks, Arctic-CAP, 2017 ALL STAC Catalog 2017-04-27 2017-11-04 -165.48, 58.08, -111.57, 71.27 https://cmr.earthdata.nasa.gov/search/concepts/C2143812247-ORNL_CLOUD.umm_json "This dataset provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e. ""concentrations"") from flights over Alaska and the Yukon and Northwest Territories of Canada during the Arctic Carbon Aircraft Profile (Arctic-CAP) monthly sampling campaigns from April-November 2017. The data were derived from laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard the aircraft. During each of the six monthly campaigns, flights over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain included 25 vertical profiles, from the surface up to 6 km altitude, at locations selected to complement regular long-term vertical profiles, remote sensing data, and ground-based flux tower measurements. Measurements were initiated by the aircraft pilot at predetermined locations within each profile in order to evenly distribute flask sampling points throughout each flight. A total of 408 flask samples were collected during 55 individual flights. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost." proprietary
ABoVE_Atmospheric_Flask_Data_1717_1 ABoVE: Atmospheric Gas Concentrations from Airborne Flasks, Arctic-CAP, 2017 ORNL_CLOUD STAC Catalog 2017-04-27 2017-11-04 -165.48, 58.08, -111.57, 71.27 https://cmr.earthdata.nasa.gov/search/concepts/C2143812247-ORNL_CLOUD.umm_json "This dataset provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e. ""concentrations"") from flights over Alaska and the Yukon and Northwest Territories of Canada during the Arctic Carbon Aircraft Profile (Arctic-CAP) monthly sampling campaigns from April-November 2017. The data were derived from laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard the aircraft. During each of the six monthly campaigns, flights over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain included 25 vertical profiles, from the surface up to 6 km altitude, at locations selected to complement regular long-term vertical profiles, remote sensing data, and ground-based flux tower measurements. Measurements were initiated by the aircraft pilot at predetermined locations within each profile in order to evenly distribute flask sampling points throughout each flight. A total of 408 flask samples were collected during 55 individual flights. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost." proprietary
+ABoVE_Atmospheric_Flask_Data_1717_1 ABoVE: Atmospheric Gas Concentrations from Airborne Flasks, Arctic-CAP, 2017 ALL STAC Catalog 2017-04-27 2017-11-04 -165.48, 58.08, -111.57, 71.27 https://cmr.earthdata.nasa.gov/search/concepts/C2143812247-ORNL_CLOUD.umm_json "This dataset provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e. ""concentrations"") from flights over Alaska and the Yukon and Northwest Territories of Canada during the Arctic Carbon Aircraft Profile (Arctic-CAP) monthly sampling campaigns from April-November 2017. The data were derived from laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard the aircraft. During each of the six monthly campaigns, flights over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain included 25 vertical profiles, from the surface up to 6 km altitude, at locations selected to complement regular long-term vertical profiles, remote sensing data, and ground-based flux tower measurements. Measurements were initiated by the aircraft pilot at predetermined locations within each profile in order to evenly distribute flask sampling points throughout each flight. A total of 408 flask samples were collected during 55 individual flights. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost." proprietary
ABoVE_CO2_CH4_Flux_Estimates_2121_1 Gridded CO2 and CH4 Flux Estimates for pan-Arctic and Boreal Regions, 2003-2015 ORNL_CLOUD STAC Catalog 2003-01-01 2015-12-31 -180, 49, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2909734357-ORNL_CLOUD.umm_json This dataset provides gridded estimates of gross primary productivity (GPP), ecosystem respiration (Reco), net ecosystem CO2 exchange (NEE = Reco - GPP), and methane (CH4) emissions from tundra and boreal wetland soils, across the pan-Arctic and Boreal zone (>49 degrees north) at 1-km spatial resolution. The data were produced through simulations of the Arctic Terrestrial Carbon Flux Model (TCFM-Arctic) and are provided at the daily time step for the years 2003-2015. TCFM-Arctic uses a light-use efficiency approach driven by satellite estimates of FPAR (fraction of absorbed photosynthetically active radiation) to estimate GPP, and autotrophic respiration (Rauto) is estimated as a fraction of GPP. Heterotrophic respiration (Rhetero) is estimated using decomposition rates with environmental constraints applied to three near-surface soil organic carbon (SOC) pools, and Reco is determined as the sum of Ra and Rh. Methane production is estimated using optimal CH4 production rates with environmental constraints applied to the labile carbon pool, and transfer of CH4 from the soil to the atmosphere is modeled through vegetation, soil diffusion, and water ebullition pathways. The model estimates were calibrated and evaluated using >60 tower eddy covariance (EC) sites. Baseline carbon pools were initialized by continuously cycling (spinning-up) the model for 1,000 model years using recent climatology from 1985 to 2002 to reach a dynamic steady-state between estimated net primary productivity (NPP = GPP - Rauto) and near-surface SOC pools. The TCFM-Arctic simulations were extended to the full Arctic-boreal domain at a 1-km spatial resolution using land cover maps representing high latitude vegetation communities. The data are provided in NetCDF and comma-separated values (CSV) formats. proprietary
-ABoVE_Concise_Experiment_Plan_1617_1.1 A Concise Experiment Plan for the Arctic-Boreal Vulnerability Experiment ALL STAC Catalog 2014-01-01 2021-12-31 -176.12, 39.42, -66.92, 81.61 https://cmr.earthdata.nasa.gov/search/concepts/C2162145735-ORNL_CLOUD.umm_json This document presents the Concise Experiment Plan for NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) to serve as a guide to the Program as it identifies the research to be conducted under this study. Research for ABoVE will link field-based, process-level studies with geospatial data products derived from airborne and satellite remote sensing, providing a foundation for improving the analysis and modeling capabilities needed to understand and predict ecosystem responses and societal implications. The ABoVE Concise Experiment Plan (ACEP) outlines the conceptual basis for the Field Campaign and expresses the compelling rationale explaining the scientific and societal importance of the study. It presents both the science questions driving ABoVE research as well as the top-level requirements for a study design to address them. proprietary
ABoVE_Concise_Experiment_Plan_1617_1.1 A Concise Experiment Plan for the Arctic-Boreal Vulnerability Experiment ORNL_CLOUD STAC Catalog 2014-01-01 2021-12-31 -176.12, 39.42, -66.92, 81.61 https://cmr.earthdata.nasa.gov/search/concepts/C2162145735-ORNL_CLOUD.umm_json This document presents the Concise Experiment Plan for NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) to serve as a guide to the Program as it identifies the research to be conducted under this study. Research for ABoVE will link field-based, process-level studies with geospatial data products derived from airborne and satellite remote sensing, providing a foundation for improving the analysis and modeling capabilities needed to understand and predict ecosystem responses and societal implications. The ABoVE Concise Experiment Plan (ACEP) outlines the conceptual basis for the Field Campaign and expresses the compelling rationale explaining the scientific and societal importance of the study. It presents both the science questions driving ABoVE research as well as the top-level requirements for a study design to address them. proprietary
+ABoVE_Concise_Experiment_Plan_1617_1.1 A Concise Experiment Plan for the Arctic-Boreal Vulnerability Experiment ALL STAC Catalog 2014-01-01 2021-12-31 -176.12, 39.42, -66.92, 81.61 https://cmr.earthdata.nasa.gov/search/concepts/C2162145735-ORNL_CLOUD.umm_json This document presents the Concise Experiment Plan for NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) to serve as a guide to the Program as it identifies the research to be conducted under this study. Research for ABoVE will link field-based, process-level studies with geospatial data products derived from airborne and satellite remote sensing, providing a foundation for improving the analysis and modeling capabilities needed to understand and predict ecosystem responses and societal implications. The ABoVE Concise Experiment Plan (ACEP) outlines the conceptual basis for the Field Campaign and expresses the compelling rationale explaining the scientific and societal importance of the study. It presents both the science questions driving ABoVE research as well as the top-level requirements for a study design to address them. proprietary
ABoVE_Domain_Projected_LULC_2353_1 Land Use and Land Cover Change Projection in the ABoVE Domain ORNL_CLOUD STAC Catalog 2015-01-01 2100-12-31 -169, 49, -81, 80 https://cmr.earthdata.nasa.gov/search/concepts/C3255116494-ORNL_CLOUD.umm_json This dataset provides projections of land use and land cover (LULC) change within the Arctic Boreal Vulnerability Experiment (ABoVE) domain, spanning from 2015 to 2100 with a spatial resolution of 0.25 degrees. It includes LULC change under two Shared Socioeconomic Pathways (SSP126 and SSP585) derived from Global Change Analysis Model (GCAM) at an annual scale. The specific land types include: needleleaf evergreen tree-temperate, needleleaf evergreen tree-boreal, needleleaf deciduous tree-boreal, broadleaf evergreen tree-tropical, broadleaf evergreen tree-temperate, broadleaf deciduous tree-tropical, broadleaf deciduous tree-temperate, broadleaf deciduous tree-boreal, broadleaf evergreen shrub-temperate, broadleaf deciduous shrub-temperate, broadleaf deciduous shrub-boreal, C3 arctic grass, C3 grass, C4 grass, and C3 unmanaged rainfed crop. The data were generated by integrating regional LULC projections from GCAM with high-resolution MODIS land cover data and applying two alternative spatial downscaling models: FLUS and Demeter. Data are provided in NetCDF format. proprietary
-ABoVE_Fire_Severity_dNBR_1564_1 ABoVE: Landsat-derived Burn Scar dNBR across Alaska and Canada, 1985-2015 ORNL_CLOUD STAC Catalog 1985-01-01 2015-12-31 -168.42, 50.25, -101.74, 71.36 https://cmr.earthdata.nasa.gov/search/concepts/C2111787144-ORNL_CLOUD.umm_json This dataset contains differenced Normalized Burned Ratio (dNBR) at 30-m resolution calculated for burn scars from fires that occurred within the Arctic Boreal and Vulnerability Experiment (ABoVE) Project domain in Alaska and Canada during 1985-2015. The fire perimeters were obtained from the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) fire occurrence datasets. Only burns with an area larger than 200-ha were included. The dNBR for each burn scar at 30-m pixel resolution was derived from pre- and post-burn Landsat 5, 7, and 8 scenes within a 5-km buffered area surrounding each burn scar using Landsat LEDAPS surface reflection image pairs. proprietary
ABoVE_Fire_Severity_dNBR_1564_1 ABoVE: Landsat-derived Burn Scar dNBR across Alaska and Canada, 1985-2015 ALL STAC Catalog 1985-01-01 2015-12-31 -168.42, 50.25, -101.74, 71.36 https://cmr.earthdata.nasa.gov/search/concepts/C2111787144-ORNL_CLOUD.umm_json This dataset contains differenced Normalized Burned Ratio (dNBR) at 30-m resolution calculated for burn scars from fires that occurred within the Arctic Boreal and Vulnerability Experiment (ABoVE) Project domain in Alaska and Canada during 1985-2015. The fire perimeters were obtained from the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) fire occurrence datasets. Only burns with an area larger than 200-ha were included. The dNBR for each burn scar at 30-m pixel resolution was derived from pre- and post-burn Landsat 5, 7, and 8 scenes within a 5-km buffered area surrounding each burn scar using Landsat LEDAPS surface reflection image pairs. proprietary
+ABoVE_Fire_Severity_dNBR_1564_1 ABoVE: Landsat-derived Burn Scar dNBR across Alaska and Canada, 1985-2015 ORNL_CLOUD STAC Catalog 1985-01-01 2015-12-31 -168.42, 50.25, -101.74, 71.36 https://cmr.earthdata.nasa.gov/search/concepts/C2111787144-ORNL_CLOUD.umm_json This dataset contains differenced Normalized Burned Ratio (dNBR) at 30-m resolution calculated for burn scars from fires that occurred within the Arctic Boreal and Vulnerability Experiment (ABoVE) Project domain in Alaska and Canada during 1985-2015. The fire perimeters were obtained from the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) fire occurrence datasets. Only burns with an area larger than 200-ha were included. The dNBR for each burn scar at 30-m pixel resolution was derived from pre- and post-burn Landsat 5, 7, and 8 scenes within a 5-km buffered area surrounding each burn scar using Landsat LEDAPS surface reflection image pairs. proprietary
ABoVE_Footprints_WRF_AK_NWCa_1896_1 ABoVE: Level-4 WRF-STILT Footprint Files for Circumpolar Receptors, 2016-2019 ALL STAC Catalog 2016-07-24 2019-12-31 -180, 30, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2181255288-ORNL_CLOUD.umm_json This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) Footprint data products for receptors (observations) located at positions along flight paths and at various fixed observing sites at circumpolar locations at northern latitudes during 2016-2019. Each aircraft and station position is treated as an independent receptor in the WRF-STILT model in order to simulate the land surface influence on observed atmospheric constituents. The footprints are independent of chemical species and can be applied to different flux models and incorporated into formal inversion frameworks. The particle trajectories that determine the footprint field are constrained only by the outer edges of the WRF modeling domain. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by the thawing of Arctic permafrost. proprietary
ABoVE_Footprints_WRF_AK_NWCa_1896_1 ABoVE: Level-4 WRF-STILT Footprint Files for Circumpolar Receptors, 2016-2019 ORNL_CLOUD STAC Catalog 2016-07-24 2019-12-31 -180, 30, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2181255288-ORNL_CLOUD.umm_json This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) Footprint data products for receptors (observations) located at positions along flight paths and at various fixed observing sites at circumpolar locations at northern latitudes during 2016-2019. Each aircraft and station position is treated as an independent receptor in the WRF-STILT model in order to simulate the land surface influence on observed atmospheric constituents. The footprints are independent of chemical species and can be applied to different flux models and incorporated into formal inversion frameworks. The particle trajectories that determine the footprint field are constrained only by the outer edges of the WRF modeling domain. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by the thawing of Arctic permafrost. proprietary
-ABoVE_Forage_Lichen_Maps_1867_1 ABoVE: Lichen Forage Cover over Fortymile Caribou Range, Alaska and Yukon, 2000-2015 ALL STAC Catalog 2000-01-01 2017-08-01 -153.86, 58.61, -128.26, 70.09 https://cmr.earthdata.nasa.gov/search/concepts/C2143401709-ORNL_CLOUD.umm_json This dataset provides modeled estimates of lichen ground cover at 30 m resolution across the Fortymile study area in interior eastern Alaska, U.S., and the Yukon Territory, Canada, for the nominal year 2015. The mapped lichens are important winter forage for the nine resident caribou (Rangifer tarandus) herds in the region. A random forest modeling approach with vegetation inputs and environmental and spectral predictors was used to estimate lichen cover for 2015. Input data for the model were aggregated from historical in-situ vegetation plots, visual aerial surveys, and recent unmanned aerial system (UAS) imagery to align with 30 m resolution Landsat pixels over the 583,200 km2 study area. The model was also used to estimate lichen cover for the year 2000 by applying the trained model to historical Landsat imagery. An estimate of lichen volume in 2015, based on a published algorithm, is also provided. In addition, site-level presence-absence maps at <1 m resolution and site-level lichen cover maps at both 2 m and 30 resolution are provided. Site-level data were derived from high-resolution RGB imagery collected in summer 2017 from UASs at 22 forested and alpine sites across interior Alaska and western Yukon. Due to the use of two unique UAS imagers at 7 sites, there are 29 data collections across the 22 sites. Each UAS data collection is associated with three data files. These landscape-scale maps could be useful for understanding trends in lichen abundance and distribution, as well as for caribou research, management, and conservation. proprietary
ABoVE_Forage_Lichen_Maps_1867_1 ABoVE: Lichen Forage Cover over Fortymile Caribou Range, Alaska and Yukon, 2000-2015 ORNL_CLOUD STAC Catalog 2000-01-01 2017-08-01 -153.86, 58.61, -128.26, 70.09 https://cmr.earthdata.nasa.gov/search/concepts/C2143401709-ORNL_CLOUD.umm_json This dataset provides modeled estimates of lichen ground cover at 30 m resolution across the Fortymile study area in interior eastern Alaska, U.S., and the Yukon Territory, Canada, for the nominal year 2015. The mapped lichens are important winter forage for the nine resident caribou (Rangifer tarandus) herds in the region. A random forest modeling approach with vegetation inputs and environmental and spectral predictors was used to estimate lichen cover for 2015. Input data for the model were aggregated from historical in-situ vegetation plots, visual aerial surveys, and recent unmanned aerial system (UAS) imagery to align with 30 m resolution Landsat pixels over the 583,200 km2 study area. The model was also used to estimate lichen cover for the year 2000 by applying the trained model to historical Landsat imagery. An estimate of lichen volume in 2015, based on a published algorithm, is also provided. In addition, site-level presence-absence maps at <1 m resolution and site-level lichen cover maps at both 2 m and 30 resolution are provided. Site-level data were derived from high-resolution RGB imagery collected in summer 2017 from UASs at 22 forested and alpine sites across interior Alaska and western Yukon. Due to the use of two unique UAS imagers at 7 sites, there are 29 data collections across the 22 sites. Each UAS data collection is associated with three data files. These landscape-scale maps could be useful for understanding trends in lichen abundance and distribution, as well as for caribou research, management, and conservation. proprietary
+ABoVE_Forage_Lichen_Maps_1867_1 ABoVE: Lichen Forage Cover over Fortymile Caribou Range, Alaska and Yukon, 2000-2015 ALL STAC Catalog 2000-01-01 2017-08-01 -153.86, 58.61, -128.26, 70.09 https://cmr.earthdata.nasa.gov/search/concepts/C2143401709-ORNL_CLOUD.umm_json This dataset provides modeled estimates of lichen ground cover at 30 m resolution across the Fortymile study area in interior eastern Alaska, U.S., and the Yukon Territory, Canada, for the nominal year 2015. The mapped lichens are important winter forage for the nine resident caribou (Rangifer tarandus) herds in the region. A random forest modeling approach with vegetation inputs and environmental and spectral predictors was used to estimate lichen cover for 2015. Input data for the model were aggregated from historical in-situ vegetation plots, visual aerial surveys, and recent unmanned aerial system (UAS) imagery to align with 30 m resolution Landsat pixels over the 583,200 km2 study area. The model was also used to estimate lichen cover for the year 2000 by applying the trained model to historical Landsat imagery. An estimate of lichen volume in 2015, based on a published algorithm, is also provided. In addition, site-level presence-absence maps at <1 m resolution and site-level lichen cover maps at both 2 m and 30 resolution are provided. Site-level data were derived from high-resolution RGB imagery collected in summer 2017 from UASs at 22 forested and alpine sites across interior Alaska and western Yukon. Due to the use of two unique UAS imagers at 7 sites, there are 29 data collections across the 22 sites. Each UAS data collection is associated with three data files. These landscape-scale maps could be useful for understanding trends in lichen abundance and distribution, as well as for caribou research, management, and conservation. proprietary
ABoVE_ForestDisturbance_Agents_1924_1 ABoVE: Landsat-derived Annual Disturbance Agents Across ABoVE Core Domain, 1987-2012 ALL STAC Catalog 1985-01-01 2012-12-31 -169.96, 50.26, -98.97, 75.69 https://cmr.earthdata.nasa.gov/search/concepts/C2226005584-ORNL_CLOUD.umm_json This dataset provides spatial data on disturbance agents of fire, insects, and logging in the Arctic Boreal Vulnerability Experiment (ABoVE) core domain at an annual time step from 1987-2012 and 30 m resolution. Using a time-series of Landsat data, the three disturbance types were identified by abrupt changes in Tasseled Cap (dTC) indices of brightness, greenness, and wetness. Disturbances were detected by a Continuous Change Detection and Classification (CCDC) harmonic regression model applied to the time series. The dTC indices and disturbance results are provided. proprietary
ABoVE_ForestDisturbance_Agents_1924_1 ABoVE: Landsat-derived Annual Disturbance Agents Across ABoVE Core Domain, 1987-2012 ORNL_CLOUD STAC Catalog 1985-01-01 2012-12-31 -169.96, 50.26, -98.97, 75.69 https://cmr.earthdata.nasa.gov/search/concepts/C2226005584-ORNL_CLOUD.umm_json This dataset provides spatial data on disturbance agents of fire, insects, and logging in the Arctic Boreal Vulnerability Experiment (ABoVE) core domain at an annual time step from 1987-2012 and 30 m resolution. Using a time-series of Landsat data, the three disturbance types were identified by abrupt changes in Tasseled Cap (dTC) indices of brightness, greenness, and wetness. Disturbances were detected by a Continuous Change Detection and Classification (CCDC) harmonic regression model applied to the time series. The dTC indices and disturbance results are provided. proprietary
-ABoVE_Frac_Open_Water_1362_1 ABoVE: Fractional Open Water Cover for Pan-Arctic and ABoVE-Domain Regions, 2002-2015 ALL STAC Catalog 2002-06-20 2015-12-31 -180, 39.38, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2111722183-ORNL_CLOUD.umm_json This data set provides land surface fractional open water cover maps for two overlapping regions: the entire pan-Arctic region (latitude > 45 degrees) and the Arctic-Boreal Vulnerability Experiment (ABoVE) domain across Alaska and Canada. The data are a 10-day averaged time step at 5-km spatial resolution for the period 2002-2015. Data represent the aerial portion of a grid cell covered by open water. The data were produced using high frequency (89 GHz) brightness temperatures from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), with other ancillary inputs from AMSR-E/AMSR2 25-km products and the Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting data record for fractional water is suitable for documenting open water patterns and inundation dynamics in boreal-Arctic ecosystems experiencing rapid climate change. proprietary
ABoVE_Frac_Open_Water_1362_1 ABoVE: Fractional Open Water Cover for Pan-Arctic and ABoVE-Domain Regions, 2002-2015 ORNL_CLOUD STAC Catalog 2002-06-20 2015-12-31 -180, 39.38, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2111722183-ORNL_CLOUD.umm_json This data set provides land surface fractional open water cover maps for two overlapping regions: the entire pan-Arctic region (latitude > 45 degrees) and the Arctic-Boreal Vulnerability Experiment (ABoVE) domain across Alaska and Canada. The data are a 10-day averaged time step at 5-km spatial resolution for the period 2002-2015. Data represent the aerial portion of a grid cell covered by open water. The data were produced using high frequency (89 GHz) brightness temperatures from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), with other ancillary inputs from AMSR-E/AMSR2 25-km products and the Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting data record for fractional water is suitable for documenting open water patterns and inundation dynamics in boreal-Arctic ecosystems experiencing rapid climate change. proprietary
-ABoVE_GrowingSeason_Lake_Color_1866_1 ABoVE: Lake Growing Season Green Surface Reflectance Trends, AK and Canada, 1984-2019 ORNL_CLOUD STAC Catalog 1984-07-01 2019-09-01 -168.1, 49.54, -81.23, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2143401725-ORNL_CLOUD.umm_json This dataset provides an annual time series of Landsat green surface reflectance and the derived annual trend during the growing season (June and July) for 472,890 lakes across the ABoVE Extended Study Domain from 1984 to 2019. The reflectance data are from Landsat-5, Landsat-7, and Landsat-8 sensors for the green band (center wavelength 560 nm). Over 270,000 Landsat scenes were evaluated and quality assured to be cloud-free and over water. Lakes were selected from HydroLAKES, a global database of lakes of at least 10 ha. Lake surface reflectance was extracted from a 3-by-3-pixel area centered on each lake centroid from the selected Landsat scenes determined from lake polygons. This dataset demonstrates changes in lake color over time in the arctic and boreal regions of North America. Color is relevant for understanding physical, ecological, and biogeochemical processes in some of the world’s highest concentrations of lakes where climate change may have significant impacts. proprietary
+ABoVE_Frac_Open_Water_1362_1 ABoVE: Fractional Open Water Cover for Pan-Arctic and ABoVE-Domain Regions, 2002-2015 ALL STAC Catalog 2002-06-20 2015-12-31 -180, 39.38, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2111722183-ORNL_CLOUD.umm_json This data set provides land surface fractional open water cover maps for two overlapping regions: the entire pan-Arctic region (latitude > 45 degrees) and the Arctic-Boreal Vulnerability Experiment (ABoVE) domain across Alaska and Canada. The data are a 10-day averaged time step at 5-km spatial resolution for the period 2002-2015. Data represent the aerial portion of a grid cell covered by open water. The data were produced using high frequency (89 GHz) brightness temperatures from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), with other ancillary inputs from AMSR-E/AMSR2 25-km products and the Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting data record for fractional water is suitable for documenting open water patterns and inundation dynamics in boreal-Arctic ecosystems experiencing rapid climate change. proprietary
ABoVE_GrowingSeason_Lake_Color_1866_1 ABoVE: Lake Growing Season Green Surface Reflectance Trends, AK and Canada, 1984-2019 ALL STAC Catalog 1984-07-01 2019-09-01 -168.1, 49.54, -81.23, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2143401725-ORNL_CLOUD.umm_json This dataset provides an annual time series of Landsat green surface reflectance and the derived annual trend during the growing season (June and July) for 472,890 lakes across the ABoVE Extended Study Domain from 1984 to 2019. The reflectance data are from Landsat-5, Landsat-7, and Landsat-8 sensors for the green band (center wavelength 560 nm). Over 270,000 Landsat scenes were evaluated and quality assured to be cloud-free and over water. Lakes were selected from HydroLAKES, a global database of lakes of at least 10 ha. Lake surface reflectance was extracted from a 3-by-3-pixel area centered on each lake centroid from the selected Landsat scenes determined from lake polygons. This dataset demonstrates changes in lake color over time in the arctic and boreal regions of North America. Color is relevant for understanding physical, ecological, and biogeochemical processes in some of the world’s highest concentrations of lakes where climate change may have significant impacts. proprietary
+ABoVE_GrowingSeason_Lake_Color_1866_1 ABoVE: Lake Growing Season Green Surface Reflectance Trends, AK and Canada, 1984-2019 ORNL_CLOUD STAC Catalog 1984-07-01 2019-09-01 -168.1, 49.54, -81.23, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2143401725-ORNL_CLOUD.umm_json This dataset provides an annual time series of Landsat green surface reflectance and the derived annual trend during the growing season (June and July) for 472,890 lakes across the ABoVE Extended Study Domain from 1984 to 2019. The reflectance data are from Landsat-5, Landsat-7, and Landsat-8 sensors for the green band (center wavelength 560 nm). Over 270,000 Landsat scenes were evaluated and quality assured to be cloud-free and over water. Lakes were selected from HydroLAKES, a global database of lakes of at least 10 ha. Lake surface reflectance was extracted from a 3-by-3-pixel area centered on each lake centroid from the selected Landsat scenes determined from lake polygons. This dataset demonstrates changes in lake color over time in the arctic and boreal regions of North America. Color is relevant for understanding physical, ecological, and biogeochemical processes in some of the world’s highest concentrations of lakes where climate change may have significant impacts. proprietary
ABoVE_Izaviknek_Field_Data_1772_1 ABoVE: Vegetation Composition across Fire History Gradients on the Y-K Delta, Alaska ALL STAC Catalog 2017-07-20 2018-07-16 -164.69, 60.36, -160.94, 62.09 https://cmr.earthdata.nasa.gov/search/concepts/C2143402545-ORNL_CLOUD.umm_json This dataset provides ecological field data that were collected during July 2017 and July 2018 from 43 plots spanning gradients of fire history in the upland tundra of the Yukon-Kuskokwim (Y-K) Delta, Alaska. Plot-level data include vegetation species composition and structure, leaf area index (LAI), topography, thaw-depth, and soil characteristics collected at plots burned in 1971-1972, 1985, 2006-2007, 2015, or unburned controls. Vegetation species were sampled along transects using the vegetation point-intercept (VPI) sampling approach and summarized by three metrics of vegetation cover: (1) top-hit cover, (2) any-hit cover, and (3) multi-hit cover. Each metric is the total number of hits for a species divided by the total number of sample points. The VPI any-hit cover metric data were combined with Landsat imagery to develop fractional maps of any-hit cover for four aggregated plant functional types (PFTs); bryophytes, herbs, lichen, and shrubs for the upland tundra area. Photographs of vegetation transects and soil pits are included as companion files. proprietary
ABoVE_Izaviknek_Field_Data_1772_1 ABoVE: Vegetation Composition across Fire History Gradients on the Y-K Delta, Alaska ORNL_CLOUD STAC Catalog 2017-07-20 2018-07-16 -164.69, 60.36, -160.94, 62.09 https://cmr.earthdata.nasa.gov/search/concepts/C2143402545-ORNL_CLOUD.umm_json This dataset provides ecological field data that were collected during July 2017 and July 2018 from 43 plots spanning gradients of fire history in the upland tundra of the Yukon-Kuskokwim (Y-K) Delta, Alaska. Plot-level data include vegetation species composition and structure, leaf area index (LAI), topography, thaw-depth, and soil characteristics collected at plots burned in 1971-1972, 1985, 2006-2007, 2015, or unburned controls. Vegetation species were sampled along transects using the vegetation point-intercept (VPI) sampling approach and summarized by three metrics of vegetation cover: (1) top-hit cover, (2) any-hit cover, and (3) multi-hit cover. Each metric is the total number of hits for a species divided by the total number of sample points. The VPI any-hit cover metric data were combined with Landsat imagery to develop fractional maps of any-hit cover for four aggregated plant functional types (PFTs); bryophytes, herbs, lichen, and shrubs for the upland tundra area. Photographs of vegetation transects and soil pits are included as companion files. proprietary
ABoVE_L1_P_SAR_1800_1 ABoVE: L1 S-0 Polarimetric Data from UAVSAR P-band SAR, Alaska and Canada, 2017 ALL STAC Catalog 2017-05-22 2017-08-18 -166.61, 52.08, -104.18, 71.46 https://cmr.earthdata.nasa.gov/search/concepts/C2143401773-ORNL_CLOUD.umm_json This dataset provides Level 1 (L1) polarimetric radar backscattering coefficient (Sigma-0 or S-0), multi-look complex, polarimetrically calibrated, and georeferenced data products from the UAVSAR P-band SAR radar instrument collected over 74 study sites across Alaska, USA, and western Canada. The radar instrument is a fully polarimetric P-band (ultra-high frequency) SAR operating in the 420-440 MHz band. The flight campaigns took place periodically in May-August 2017 onboard a NASA Gulfstream-III aircraft. Each set of products was produced from a data take (i.e., acquisition) of the UAVSAR P-band SAR radar instrument, where one data take is equivalent to one flight line over a site. Two to four data takes were sought for each site, although for some sites as few as one or as many as six are provided. There were a total of 139 data takes over the 74 sites. proprietary
@@ -1317,16 +1317,16 @@ ABoVE_MODIS_MAIAC_Reflectance_1858_1 ABoVE: Angular-corrected MODIS MAIAC Reflec
ABoVE_MODIS_MAIAC_Reflectance_1858_1 ABoVE: Angular-corrected MODIS MAIAC Reflectance across Alaska and Canada, 2000-2017 ORNL_CLOUD STAC Catalog 2000-02-24 2017-12-31 -180, 44.12, 180, 80.81 https://cmr.earthdata.nasa.gov/search/concepts/C2192631093-ORNL_CLOUD.umm_json This dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances across the ABoVE domain in Alaska and western Canada from 2000 to 2017. Using random forests (RF), a machine-learning approach, the original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) to reduce artifacts and variability due to angular effects. The original MAIAC data's sub-daily temporal resolution and 1 km spatial resolution with seven land bands (bands 1-7) and five ocean bands (bands 8-12) were preserved. The resulting surface reflectance data are suitable for long-term studies on patterns, processes, and dynamics of surface phenomena. The data cover 11 different Terra and Aqua satellite MODIS MAIAC tiles. proprietary
ABoVE_NWT_2017_Field_Data_1771_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2017 ALL STAC Catalog 2015-07-13 2017-08-10 -117.38, 60.52, -111.37, 62.58 https://cmr.earthdata.nasa.gov/search/concepts/C2308231345-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2017 from 11 study sites in the ABoVE Study area. The 11 study areas contained 28 sites that were burned by wildfires in 2014 and 2015, and 10 unburned sites in the Northwest Territories (NWT), Canada. Burned sites included peatland and upland. These field data include assessment of burn severity, vegetation inventories, ground cover, diameter and height for trees and shrubs, seedling and sprouting cover, soil moisture, and depth of unfrozen soil. Plot sizes were 10 m x 10 m with smaller subplots for selected measurements. Similar data were collected for these sites in the years 2015-2019 and are available in related separate datasets. Field data are provided in CSV format. The dataset includes digital photographs (in JPEG format) of vegetation conditions at sampling sites. proprietary
ABoVE_NWT_2017_Field_Data_1771_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2017 ORNL_CLOUD STAC Catalog 2015-07-13 2017-08-10 -117.38, 60.52, -111.37, 62.58 https://cmr.earthdata.nasa.gov/search/concepts/C2308231345-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2017 from 11 study sites in the ABoVE Study area. The 11 study areas contained 28 sites that were burned by wildfires in 2014 and 2015, and 10 unburned sites in the Northwest Territories (NWT), Canada. Burned sites included peatland and upland. These field data include assessment of burn severity, vegetation inventories, ground cover, diameter and height for trees and shrubs, seedling and sprouting cover, soil moisture, and depth of unfrozen soil. Plot sizes were 10 m x 10 m with smaller subplots for selected measurements. Similar data were collected for these sites in the years 2015-2019 and are available in related separate datasets. Field data are provided in CSV format. The dataset includes digital photographs (in JPEG format) of vegetation conditions at sampling sites. proprietary
-ABoVE_Open_Water_Map_1643_1 ABoVE: AirSWOT Color-Infrared Imagery Over Alaska and Canada, 2017 ALL STAC Catalog 2017-07-09 2017-08-17 -149.26, 46.85, -98.64, 69.47 https://cmr.earthdata.nasa.gov/search/concepts/C2162145875-ORNL_CLOUD.umm_json This dataset contains georeferenced three-band orthomosaics of green, red, and near-infrared (NIR) digital imagery at 1m resolution collected over selected surface waters across Alaska and Canada between July 9 and August 17, 2017. The orthomosaics were generated from individual images collected by a Cirrus Designs Digital Camera System (DCS) mounted on a Beechcraft Super King Air B200 aircraft from approximately 8-11 km altitude. Flights were over the following areas: Saskatchewan River, Saskatoon, Inuvik, Yukon River including Yukon Flats, Sagavanirktok River, Arctic Coastal Plain, Old Crow Flats, Peace-Athabasca Delta, Slave River, Athabasca River, Yellowknife, Great Slave Lake, Mackenzie River and Delta, Daring Lake, and other selected locations. Most locations were imaged twice during two flight campaigns in Canada and Alaska extending roughly SE-NW then NW-SE up to a month apart. The data were georeferenced using 303 ground control points (GCPs) across the study region. proprietary
ABoVE_Open_Water_Map_1643_1 ABoVE: AirSWOT Color-Infrared Imagery Over Alaska and Canada, 2017 ORNL_CLOUD STAC Catalog 2017-07-09 2017-08-17 -149.26, 46.85, -98.64, 69.47 https://cmr.earthdata.nasa.gov/search/concepts/C2162145875-ORNL_CLOUD.umm_json This dataset contains georeferenced three-band orthomosaics of green, red, and near-infrared (NIR) digital imagery at 1m resolution collected over selected surface waters across Alaska and Canada between July 9 and August 17, 2017. The orthomosaics were generated from individual images collected by a Cirrus Designs Digital Camera System (DCS) mounted on a Beechcraft Super King Air B200 aircraft from approximately 8-11 km altitude. Flights were over the following areas: Saskatchewan River, Saskatoon, Inuvik, Yukon River including Yukon Flats, Sagavanirktok River, Arctic Coastal Plain, Old Crow Flats, Peace-Athabasca Delta, Slave River, Athabasca River, Yellowknife, Great Slave Lake, Mackenzie River and Delta, Daring Lake, and other selected locations. Most locations were imaged twice during two flight campaigns in Canada and Alaska extending roughly SE-NW then NW-SE up to a month apart. The data were georeferenced using 303 ground control points (GCPs) across the study region. proprietary
+ABoVE_Open_Water_Map_1643_1 ABoVE: AirSWOT Color-Infrared Imagery Over Alaska and Canada, 2017 ALL STAC Catalog 2017-07-09 2017-08-17 -149.26, 46.85, -98.64, 69.47 https://cmr.earthdata.nasa.gov/search/concepts/C2162145875-ORNL_CLOUD.umm_json This dataset contains georeferenced three-band orthomosaics of green, red, and near-infrared (NIR) digital imagery at 1m resolution collected over selected surface waters across Alaska and Canada between July 9 and August 17, 2017. The orthomosaics were generated from individual images collected by a Cirrus Designs Digital Camera System (DCS) mounted on a Beechcraft Super King Air B200 aircraft from approximately 8-11 km altitude. Flights were over the following areas: Saskatchewan River, Saskatoon, Inuvik, Yukon River including Yukon Flats, Sagavanirktok River, Arctic Coastal Plain, Old Crow Flats, Peace-Athabasca Delta, Slave River, Athabasca River, Yellowknife, Great Slave Lake, Mackenzie River and Delta, Daring Lake, and other selected locations. Most locations were imaged twice during two flight campaigns in Canada and Alaska extending roughly SE-NW then NW-SE up to a month apart. The data were georeferenced using 303 ground control points (GCPs) across the study region. proprietary
ABoVE_PBand_SAR_1657_1 ABoVE: Active Layer and Soil Moisture Properties from AirMOSS P-band SAR in Alaska ORNL_CLOUD STAC Catalog 2014-08-16 2017-10-10 -167.94, 64.71, -150.25, 70.88 https://cmr.earthdata.nasa.gov/search/concepts/C2170972048-ORNL_CLOUD.umm_json This dataset provides estimates of soil geophysical properties derived from Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) P-band polarimetric synthetic aperture radar (PolSAR) data collected in August and October of 2014, 2015, and 2017 over 12 study sites (with some exceptions) across Northern Alaska. Soil properties reported include the active layer thickness (ALT), dielectric constant, soil moisture profile, surface roughness, and their respective uncertainty estimates at 30-m spatial resolution over the 12 flight transects. Most of the study sites are located within the continuous permafrost zone and where the aboveground vegetation consisting mainly of dwarf shrub and tussock/sedge/moss tundra has a minimal impact on P-band radar backscatter. proprietary
ABoVE_PBand_SAR_1657_1 ABoVE: Active Layer and Soil Moisture Properties from AirMOSS P-band SAR in Alaska ALL STAC Catalog 2014-08-16 2017-10-10 -167.94, 64.71, -150.25, 70.88 https://cmr.earthdata.nasa.gov/search/concepts/C2170972048-ORNL_CLOUD.umm_json This dataset provides estimates of soil geophysical properties derived from Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) P-band polarimetric synthetic aperture radar (PolSAR) data collected in August and October of 2014, 2015, and 2017 over 12 study sites (with some exceptions) across Northern Alaska. Soil properties reported include the active layer thickness (ALT), dielectric constant, soil moisture profile, surface roughness, and their respective uncertainty estimates at 30-m spatial resolution over the 12 flight transects. Most of the study sites are located within the continuous permafrost zone and where the aboveground vegetation consisting mainly of dwarf shrub and tussock/sedge/moss tundra has a minimal impact on P-band radar backscatter. proprietary
-ABoVE_Particles_WRF_AK_NWCa_1895_1 ABoVE: Level-4 WRF-STILT Particle Trajectories for Circumpolar Receptors, 2016-2019 ORNL_CLOUD STAC Catalog 2016-07-24 2019-12-31 -180, 10, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2180373101-ORNL_CLOUD.umm_json This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory files for receptors located at positions along flight paths and at various fixed observing sites at circumpolar locations above 45 degrees North during 2016-2019. The particle files describe the motion of particles released backward in time over a 10-day period. The particle files are separated into archives by platform type (some platforms are combined) and can be characterized as either low resolution or high resolution depending on whether the subsequent footprint fields were generated on a circumpolar 0.5-degree grid (low-resolution) or both 0.5-degree and 0.1-degree grids (high-resolution). The platforms include flux towers at fixed sites, laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard aircraft, and observations by NASA's Orbiting Carbon Observatory-2 satellite. These particle files were thinned to retain particle location information only when the particles have non-zero contributions to the corresponding footprint field. These particle files are used to compute the footprint fields available in a companion dataset. The particle trajectories that determine the footprint field are constrained only by the outer edges of the WRF modeling domain. Likewise, the companion footprint files are provided on a regular latitude-longitude grid. This dataset extends previous research on the atmospheric transport of land-surface emissions of greenhouse gases by the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) project. In particular, the content of the low-resolution particle files is similar to those for the CARVE dataset. proprietary
ABoVE_Particles_WRF_AK_NWCa_1895_1 ABoVE: Level-4 WRF-STILT Particle Trajectories for Circumpolar Receptors, 2016-2019 ALL STAC Catalog 2016-07-24 2019-12-31 -180, 10, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2180373101-ORNL_CLOUD.umm_json This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory files for receptors located at positions along flight paths and at various fixed observing sites at circumpolar locations above 45 degrees North during 2016-2019. The particle files describe the motion of particles released backward in time over a 10-day period. The particle files are separated into archives by platform type (some platforms are combined) and can be characterized as either low resolution or high resolution depending on whether the subsequent footprint fields were generated on a circumpolar 0.5-degree grid (low-resolution) or both 0.5-degree and 0.1-degree grids (high-resolution). The platforms include flux towers at fixed sites, laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard aircraft, and observations by NASA's Orbiting Carbon Observatory-2 satellite. These particle files were thinned to retain particle location information only when the particles have non-zero contributions to the corresponding footprint field. These particle files are used to compute the footprint fields available in a companion dataset. The particle trajectories that determine the footprint field are constrained only by the outer edges of the WRF modeling domain. Likewise, the companion footprint files are provided on a regular latitude-longitude grid. This dataset extends previous research on the atmospheric transport of land-surface emissions of greenhouse gases by the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) project. In particular, the content of the low-resolution particle files is similar to those for the CARVE dataset. proprietary
-ABoVE_Planning_Field_Sites_1582_1 ABoVE: Directory of Field Sites Associated with 2017 ABoVE Airborne Campaign ALL STAC Catalog 2017-04-01 2017-04-01 -166.01, 52.71, -103.6, 71.33 https://cmr.earthdata.nasa.gov/search/concepts/C2162139992-ORNL_CLOUD.umm_json This dataset provides a listing of the ~6,700 field sites used in planning the ABoVE Airborne Campaign (AAC) for 2017. The sites included point, polygon, and line locations that were used in determining the 2017 AAC flight paths. We intend this compilation to assist investigators in understanding the flight line choices and as a method for investigators to identify ground locations used in the airborne campaign. Data users may also search for the underlying data available at each of these locations. Site descriptors include name, coordinates, principal investigators with emails, data types, long-term archive locations, and links to project descriptions. proprietary
+ABoVE_Particles_WRF_AK_NWCa_1895_1 ABoVE: Level-4 WRF-STILT Particle Trajectories for Circumpolar Receptors, 2016-2019 ORNL_CLOUD STAC Catalog 2016-07-24 2019-12-31 -180, 10, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2180373101-ORNL_CLOUD.umm_json This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory files for receptors located at positions along flight paths and at various fixed observing sites at circumpolar locations above 45 degrees North during 2016-2019. The particle files describe the motion of particles released backward in time over a 10-day period. The particle files are separated into archives by platform type (some platforms are combined) and can be characterized as either low resolution or high resolution depending on whether the subsequent footprint fields were generated on a circumpolar 0.5-degree grid (low-resolution) or both 0.5-degree and 0.1-degree grids (high-resolution). The platforms include flux towers at fixed sites, laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard aircraft, and observations by NASA's Orbiting Carbon Observatory-2 satellite. These particle files were thinned to retain particle location information only when the particles have non-zero contributions to the corresponding footprint field. These particle files are used to compute the footprint fields available in a companion dataset. The particle trajectories that determine the footprint field are constrained only by the outer edges of the WRF modeling domain. Likewise, the companion footprint files are provided on a regular latitude-longitude grid. This dataset extends previous research on the atmospheric transport of land-surface emissions of greenhouse gases by the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) project. In particular, the content of the low-resolution particle files is similar to those for the CARVE dataset. proprietary
ABoVE_Planning_Field_Sites_1582_1 ABoVE: Directory of Field Sites Associated with 2017 ABoVE Airborne Campaign ORNL_CLOUD STAC Catalog 2017-04-01 2017-04-01 -166.01, 52.71, -103.6, 71.33 https://cmr.earthdata.nasa.gov/search/concepts/C2162139992-ORNL_CLOUD.umm_json This dataset provides a listing of the ~6,700 field sites used in planning the ABoVE Airborne Campaign (AAC) for 2017. The sites included point, polygon, and line locations that were used in determining the 2017 AAC flight paths. We intend this compilation to assist investigators in understanding the flight line choices and as a method for investigators to identify ground locations used in the airborne campaign. Data users may also search for the underlying data available at each of these locations. Site descriptors include name, coordinates, principal investigators with emails, data types, long-term archive locations, and links to project descriptions. proprietary
-ABoVE_Plot_Data_Burned_Sites_1744_1 ABoVE: Synthesis of Burned and Unburned Forest Site Data, AK and Canada, 1983-2016 ALL STAC Catalog 1983-01-01 2016-08-08 -150.9, 53.19, -88.61, 67.23 https://cmr.earthdata.nasa.gov/search/concepts/C2143402559-ORNL_CLOUD.umm_json This dataset is a synthesis of field plot characterization data, derived above-ground and below-ground combusted carbon, and acquired Fire Weather Index (FWI) System components for burned boreal forest sites across Alaska, USA, the Northwest Territories, and Saskatchewan, Canada from 1983-2016. Unburned plot data are also included. Compiled plot-level characterization data include stand age, disturbance history, tree density, and tree biophysical measurements for calculation of the above-ground (ag) and below-ground (bg) biomass/carbon pools, pre-fire and residual post-fire soil organic layer (SOL) depths and estimates of combustion of tree structural classes. The measured slope and aspect for each site and an assigned moisture class based on topography are also provided. Data from 1019 burned and 152 unburned sites are included. From the estimates of combusted ag and bg carbon pools and SOL losses, the total carbon combusted, the proportion of pre-fire carbon combusted, and the proportion of total carbon combusted were calculated for each plot. FWI System components including moisture and drought codes and indices of fire danger were obtained for each plot from existing data sources based on the plot location, year of burn, and a dynamic start-up date (day of burn, DOB) from the global fire weather database. Data for soil characteristics are included in a separate file. proprietary
+ABoVE_Planning_Field_Sites_1582_1 ABoVE: Directory of Field Sites Associated with 2017 ABoVE Airborne Campaign ALL STAC Catalog 2017-04-01 2017-04-01 -166.01, 52.71, -103.6, 71.33 https://cmr.earthdata.nasa.gov/search/concepts/C2162139992-ORNL_CLOUD.umm_json This dataset provides a listing of the ~6,700 field sites used in planning the ABoVE Airborne Campaign (AAC) for 2017. The sites included point, polygon, and line locations that were used in determining the 2017 AAC flight paths. We intend this compilation to assist investigators in understanding the flight line choices and as a method for investigators to identify ground locations used in the airborne campaign. Data users may also search for the underlying data available at each of these locations. Site descriptors include name, coordinates, principal investigators with emails, data types, long-term archive locations, and links to project descriptions. proprietary
ABoVE_Plot_Data_Burned_Sites_1744_1 ABoVE: Synthesis of Burned and Unburned Forest Site Data, AK and Canada, 1983-2016 ORNL_CLOUD STAC Catalog 1983-01-01 2016-08-08 -150.9, 53.19, -88.61, 67.23 https://cmr.earthdata.nasa.gov/search/concepts/C2143402559-ORNL_CLOUD.umm_json This dataset is a synthesis of field plot characterization data, derived above-ground and below-ground combusted carbon, and acquired Fire Weather Index (FWI) System components for burned boreal forest sites across Alaska, USA, the Northwest Territories, and Saskatchewan, Canada from 1983-2016. Unburned plot data are also included. Compiled plot-level characterization data include stand age, disturbance history, tree density, and tree biophysical measurements for calculation of the above-ground (ag) and below-ground (bg) biomass/carbon pools, pre-fire and residual post-fire soil organic layer (SOL) depths and estimates of combustion of tree structural classes. The measured slope and aspect for each site and an assigned moisture class based on topography are also provided. Data from 1019 burned and 152 unburned sites are included. From the estimates of combusted ag and bg carbon pools and SOL losses, the total carbon combusted, the proportion of pre-fire carbon combusted, and the proportion of total carbon combusted were calculated for each plot. FWI System components including moisture and drought codes and indices of fire danger were obtained for each plot from existing data sources based on the plot location, year of burn, and a dynamic start-up date (day of burn, DOB) from the global fire weather database. Data for soil characteristics are included in a separate file. proprietary
+ABoVE_Plot_Data_Burned_Sites_1744_1 ABoVE: Synthesis of Burned and Unburned Forest Site Data, AK and Canada, 1983-2016 ALL STAC Catalog 1983-01-01 2016-08-08 -150.9, 53.19, -88.61, 67.23 https://cmr.earthdata.nasa.gov/search/concepts/C2143402559-ORNL_CLOUD.umm_json This dataset is a synthesis of field plot characterization data, derived above-ground and below-ground combusted carbon, and acquired Fire Weather Index (FWI) System components for burned boreal forest sites across Alaska, USA, the Northwest Territories, and Saskatchewan, Canada from 1983-2016. Unburned plot data are also included. Compiled plot-level characterization data include stand age, disturbance history, tree density, and tree biophysical measurements for calculation of the above-ground (ag) and below-ground (bg) biomass/carbon pools, pre-fire and residual post-fire soil organic layer (SOL) depths and estimates of combustion of tree structural classes. The measured slope and aspect for each site and an assigned moisture class based on topography are also provided. Data from 1019 burned and 152 unburned sites are included. From the estimates of combusted ag and bg carbon pools and SOL losses, the total carbon combusted, the proportion of pre-fire carbon combusted, and the proportion of total carbon combusted were calculated for each plot. FWI System components including moisture and drought codes and indices of fire danger were obtained for each plot from existing data sources based on the plot location, year of burn, and a dynamic start-up date (day of burn, DOB) from the global fire weather database. Data for soil characteristics are included in a separate file. proprietary
ABoVE_ReSALT_InSAR_PolSAR_V3_2004_3 ABoVE: Active Layer Thickness from Airborne L- and P- band SAR, Alaska, 2017, Ver. 3 ALL STAC Catalog 2017-06-19 2017-09-16 -166.73, 57.83, -110.42, 71.52 https://cmr.earthdata.nasa.gov/search/concepts/C2432584227-ORNL_CLOUD.umm_json This dataset provides estimates of seasonal subsidence, active layer thickness (ALT), the vertical soil moisture profile, and uncertainties at a 30 m resolution for 51 sites across the ABoVE domain, including 39 sites in Alaska and 12 sites in Northwest Canada. The ALT and soil moisture profile retrievals simultaneously use L- and P-band synthetic aperture radar (SAR) data acquired by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instruments during the 2017 Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign. The data are provided in NetCDF Version 4 format along with a python script for estimating soil volumetric water content from data. proprietary
ABoVE_ReSALT_InSAR_PolSAR_V3_2004_3 ABoVE: Active Layer Thickness from Airborne L- and P- band SAR, Alaska, 2017, Ver. 3 ORNL_CLOUD STAC Catalog 2017-06-19 2017-09-16 -166.73, 57.83, -110.42, 71.52 https://cmr.earthdata.nasa.gov/search/concepts/C2432584227-ORNL_CLOUD.umm_json This dataset provides estimates of seasonal subsidence, active layer thickness (ALT), the vertical soil moisture profile, and uncertainties at a 30 m resolution for 51 sites across the ABoVE domain, including 39 sites in Alaska and 12 sites in Northwest Canada. The ALT and soil moisture profile retrievals simultaneously use L- and P-band synthetic aperture radar (SAR) data acquired by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instruments during the 2017 Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign. The data are provided in NetCDF Version 4 format along with a python script for estimating soil volumetric water content from data. proprietary
ABoVE_SAR_Surveys_2150_1 Summary of the ABoVE L-band and P-band Airborne SAR Surveys, 2012-2022 ORNL_CLOUD STAC Catalog 2012-01-01 2022-12-31 -169, 50, -102, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2787699093-ORNL_CLOUD.umm_json This dataset contains tables containing Airborne flight metadata from synthetic aperture radar (SAR) surveys from 2012 to 2022 in Alaska and Canada. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne SAR surveys of over 120,000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. Legacy lines acquired between 2012 and 2015 by other projects are included for completeness and to enable longer times series creation. The data files and companion file contain L-band and P-band airborne SAR metadata acquired during the ABoVE airborne campaigns. Included are detailed descriptions of ~80 SAR flight lines and how each fits into the ABoVE experimental design. Extensive maps, tables, and hyperlinks give direct access to every flight plan as well as individual flight lines. This entry is a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data. proprietary
@@ -1334,32 +1334,32 @@ ABoVE_SnowModel_Data_2105_1 Daily SnowModel Outputs Covering the ABoVE Core Doma
ABoVE_Soil_Radiocarbon_NWT_1664_1 ABoVE: Characterization of Carbon Dynamics in Burned Forest Plots, NWT, Canada, 2014 ORNL_CLOUD STAC Catalog 2015-06-14 2015-06-14 -136.12, 56.25, -102, 71.69 https://cmr.earthdata.nasa.gov/search/concepts/C2170972694-ORNL_CLOUD.umm_json "This dataset provides field data from boreal forests in the Northwest Territories (NWT), Canada, that were burned by wildfires in 2014. During fieldwork in 2015, 211 burned plots were established. From these plots, thirty-two forest plots were selected that were dominated by black spruce and were representative of the full moisture gradient across the landscape, ranging from xeric to sub-hygric. Plot observations included slope, aspect, and moisture. At each plot, one intact organic soil profile associated with a specific burn depth was selected and analyzed for carbon content and radiocarbon (14C) values at specific profile depth increments to assess legacy carbon presence and combustion. Vegetation observations included tree density. Stand age at the time of the fire was determined from tree-ring counts. Estimates of pre-fire below and aboveground carbon pools were derived. The percent of total NWT wildfire burned area comprising of ""young"" stands (less than 60 years old at time of fire) was estimated." proprietary
ABoVE_Soil_Radiocarbon_NWT_1664_1 ABoVE: Characterization of Carbon Dynamics in Burned Forest Plots, NWT, Canada, 2014 ALL STAC Catalog 2015-06-14 2015-06-14 -136.12, 56.25, -102, 71.69 https://cmr.earthdata.nasa.gov/search/concepts/C2170972694-ORNL_CLOUD.umm_json "This dataset provides field data from boreal forests in the Northwest Territories (NWT), Canada, that were burned by wildfires in 2014. During fieldwork in 2015, 211 burned plots were established. From these plots, thirty-two forest plots were selected that were dominated by black spruce and were representative of the full moisture gradient across the landscape, ranging from xeric to sub-hygric. Plot observations included slope, aspect, and moisture. At each plot, one intact organic soil profile associated with a specific burn depth was selected and analyzed for carbon content and radiocarbon (14C) values at specific profile depth increments to assess legacy carbon presence and combustion. Vegetation observations included tree density. Stand age at the time of the fire was determined from tree-ring counts. Estimates of pre-fire below and aboveground carbon pools were derived. The percent of total NWT wildfire burned area comprising of ""young"" stands (less than 60 years old at time of fire) was estimated." proprietary
ABoVE_Soil_Respiration_Maps_1935_1 Soil Respiration Maps for the ABoVE Domain, 2016-2017 ORNL_CLOUD STAC Catalog 2016-08-18 2018-09-12 -169.51, 55.81, -98.74, 76.69 https://cmr.earthdata.nasa.gov/search/concepts/C2254714725-ORNL_CLOUD.umm_json This dataset provides gridded estimates of carbon dioxide (CO2) emissions from soil respiration occurring within permafrost-affected tundra and boreal ecosystems of Alaska and Northwest Canada at a 300 m spatial resolution for the period 2016-08-18 to 2018-09-12. The estimates include monthly average CO2 flux (gCO2 C m-2 d-1), daily average CO2 flux and error estimates by season (Autumn, Winter, Spring, Summer), estimates of annual offset of CO2 uptake (i.e., vegetation GPP), annual budgets of vegetation gross primary productivity (GPP; gCO2 C m-2 yr-1), and the fraction of open (non-vegetated) water within each 300 m grid cell. Belowground sources of respiration (i.e., root and microbial) are included. The gridded soil CO2 estimates were obtained using seasonal Random Forest models, information from remote sensing, and a new compilation of in-situ soil CO2 flux from Soil Respiration Stations and eddy covariance towers. The flux tower data are provided along with daily gap-filled flux observations for each Soil Respiration station forced diffusion (FD) chamber record. The data cover the NASA ABoVE Domain. proprietary
-ABoVE_Soil_ThawDepth_Moisture_1903_1 ABoVE: Soil Moisture and Active Layer Thickness in Alaska and NWT, Canada, 2008-2020 ALL STAC Catalog 2008-06-22 2020-08-15 -165.97, 60.45, -111.37, 71.32 https://cmr.earthdata.nasa.gov/search/concepts/C2162189255-ORNL_CLOUD.umm_json This dataset provides soil thaw depth and moisture (STDM) measurements and dielectric properties measured by different research teams at sites in Alaska, U.S., and the Northwest Territories, Canada. There are multiple observations per site and 352,719 total observations. The dataset includes 206,000 observations of active layer thickness measured by mechanical probing (6.0%) or ground penetrating radar (GPR) (94.0%). Approximately 16,000 volumetric water content measurements were collected using GPR (22.1%), Hydrosense I and II probes (75.3%), and DualEM (2.6%). Metadata includes the location, time, geospatial coordinates, technique, measurement teams. Measurements were typically collected in August and September near the end of the thaw season and cover the period 2008-06-22 to 2020-08-15. proprietary
ABoVE_Soil_ThawDepth_Moisture_1903_1 ABoVE: Soil Moisture and Active Layer Thickness in Alaska and NWT, Canada, 2008-2020 ORNL_CLOUD STAC Catalog 2008-06-22 2020-08-15 -165.97, 60.45, -111.37, 71.32 https://cmr.earthdata.nasa.gov/search/concepts/C2162189255-ORNL_CLOUD.umm_json This dataset provides soil thaw depth and moisture (STDM) measurements and dielectric properties measured by different research teams at sites in Alaska, U.S., and the Northwest Territories, Canada. There are multiple observations per site and 352,719 total observations. The dataset includes 206,000 observations of active layer thickness measured by mechanical probing (6.0%) or ground penetrating radar (GPR) (94.0%). Approximately 16,000 volumetric water content measurements were collected using GPR (22.1%), Hydrosense I and II probes (75.3%), and DualEM (2.6%). Metadata includes the location, time, geospatial coordinates, technique, measurement teams. Measurements were typically collected in August and September near the end of the thaw season and cover the period 2008-06-22 to 2020-08-15. proprietary
+ABoVE_Soil_ThawDepth_Moisture_1903_1 ABoVE: Soil Moisture and Active Layer Thickness in Alaska and NWT, Canada, 2008-2020 ALL STAC Catalog 2008-06-22 2020-08-15 -165.97, 60.45, -111.37, 71.32 https://cmr.earthdata.nasa.gov/search/concepts/C2162189255-ORNL_CLOUD.umm_json This dataset provides soil thaw depth and moisture (STDM) measurements and dielectric properties measured by different research teams at sites in Alaska, U.S., and the Northwest Territories, Canada. There are multiple observations per site and 352,719 total observations. The dataset includes 206,000 observations of active layer thickness measured by mechanical probing (6.0%) or ground penetrating radar (GPR) (94.0%). Approximately 16,000 volumetric water content measurements were collected using GPR (22.1%), Hydrosense I and II probes (75.3%), and DualEM (2.6%). Metadata includes the location, time, geospatial coordinates, technique, measurement teams. Measurements were typically collected in August and September near the end of the thaw season and cover the period 2008-06-22 to 2020-08-15. proprietary
ABoVE_Thaw_Depth_1579_1.0 ABoVE: Thaw Depth at Selected Unburned and Burned Sites Across Alaska ALL STAC Catalog 2016-08-09 2018-08-28 -163.24, 61.27, -146.56, 68.99 https://cmr.earthdata.nasa.gov/search/concepts/C2162139721-ORNL_CLOUD.umm_json This dataset provides thaw depth measurements made at seven locations across Alaska, during August 2016, June and September 2017, and July-August 2018. Three of the locations are paired unburned-burned sites. At each site, three 30-meter transects were established and thaw depth was measured at 1-meter increments along each transect using a 1.15 m T-shaped thaw depth probe. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma-separated values (CSV) format. proprietary
ABoVE_Thaw_Depth_1579_1.0 ABoVE: Thaw Depth at Selected Unburned and Burned Sites Across Alaska ORNL_CLOUD STAC Catalog 2016-08-09 2018-08-28 -163.24, 61.27, -146.56, 68.99 https://cmr.earthdata.nasa.gov/search/concepts/C2162139721-ORNL_CLOUD.umm_json This dataset provides thaw depth measurements made at seven locations across Alaska, during August 2016, June and September 2017, and July-August 2018. Three of the locations are paired unburned-burned sites. At each site, three 30-meter transects were established and thaw depth was measured at 1-meter increments along each transect using a 1.15 m T-shaped thaw depth probe. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma-separated values (CSV) format. proprietary
ABoVE_Uncertainty_Maps_1652_1 ABoVE: Multi-model Uncertainty of Carbon Stocks and Fluxes across ABoVE Domain, 2003 ORNL_CLOUD STAC Catalog 2003-01-01 2003-12-31 -176.12, 39.41, -67.12, 81.41 https://cmr.earthdata.nasa.gov/search/concepts/C2170971555-ORNL_CLOUD.umm_json This dataset provides estimates of the uncertainty in components of the carbon cycle including: soil carbon stock, autotrophic respiration (Ra), heterotrophic respiration (Rh), net ecosystem exchange (NEE), net primary production (NPP), and gross primary productivity (GPP) across the entire ABoVE Study Domain at 0.5-degree resolution for the reference year 2003. The uncertainties were calculated from the multi-model (n = 20) disagreement, i.e. standard deviation, from the Trends in Net Land Atmosphere Carbon Exchanges program (TRENDY) and the North American Carbon Program (NACP) regional synthesis model outputs averaged to annual means. This total uncertainty integrates both structural uncertainty of land-surface physics among models as well as inherent parametric uncertainty introduced within models, and uncertainty from forcing data. proprietary
ABoVE_Uncertainty_Maps_1652_1 ABoVE: Multi-model Uncertainty of Carbon Stocks and Fluxes across ABoVE Domain, 2003 ALL STAC Catalog 2003-01-01 2003-12-31 -176.12, 39.41, -67.12, 81.41 https://cmr.earthdata.nasa.gov/search/concepts/C2170971555-ORNL_CLOUD.umm_json This dataset provides estimates of the uncertainty in components of the carbon cycle including: soil carbon stock, autotrophic respiration (Ra), heterotrophic respiration (Rh), net ecosystem exchange (NEE), net primary production (NPP), and gross primary productivity (GPP) across the entire ABoVE Study Domain at 0.5-degree resolution for the reference year 2003. The uncertainties were calculated from the multi-model (n = 20) disagreement, i.e. standard deviation, from the Trends in Net Land Atmosphere Carbon Exchanges program (TRENDY) and the North American Carbon Program (NACP) regional synthesis model outputs averaged to annual means. This total uncertainty integrates both structural uncertainty of land-surface physics among models as well as inherent parametric uncertainty introduced within models, and uncertainty from forcing data. proprietary
-ABoVE_reference_grid_v2_1527_2.1 ABoVE: Study Domain and Standard Reference Grids, Version 2 ALL STAC Catalog 2014-01-01 2023-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2111709298-ORNL_CLOUD.umm_json The Arctic - Boreal Vulnerability Experiment (ABoVE) has developed two standardized spatial data products to expedite coordination of research activities and to facilitate data interoperability. The ABoVE Study Domain encompasses the Arctic and boreal regions of Alaska, USA, and the western provinces of Canada, North America. Core and Extended study regions have been designated within this Domain and are provided in a vector representation (Shapefile), a raster representation (GeoTIFF at 1,000-meter spatial resolution), and a NetCDF file. A standard Reference Grid System has been developed to cover the entire Study Domain and extends to the eastern portion of North America. This Reference Grid is provided as nested polygon grids at scales of 240, 30, and 5-meter spatial resolution. The 5-meter grid is new in Version 2. Note that the designated standard projection for all ABoVE products is the Canadian Albers Equal Area projection. proprietary
ABoVE_reference_grid_v2_1527_2.1 ABoVE: Study Domain and Standard Reference Grids, Version 2 ORNL_CLOUD STAC Catalog 2014-01-01 2023-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2111709298-ORNL_CLOUD.umm_json The Arctic - Boreal Vulnerability Experiment (ABoVE) has developed two standardized spatial data products to expedite coordination of research activities and to facilitate data interoperability. The ABoVE Study Domain encompasses the Arctic and boreal regions of Alaska, USA, and the western provinces of Canada, North America. Core and Extended study regions have been designated within this Domain and are provided in a vector representation (Shapefile), a raster representation (GeoTIFF at 1,000-meter spatial resolution), and a NetCDF file. A standard Reference Grid System has been developed to cover the entire Study Domain and extends to the eastern portion of North America. This Reference Grid is provided as nested polygon grids at scales of 240, 30, and 5-meter spatial resolution. The 5-meter grid is new in Version 2. Note that the designated standard projection for all ABoVE products is the Canadian Albers Equal Area projection. proprietary
-ACCLIP_AerosolCloud_AircraftRemoteSensing_WB57_Data_1 ACCLIP WB-57 Aerosol and Cloud Remotely Sensed Data ALL STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2655162569-LARC_ASDC.umm_json ACCLIP_AerosolCloud_AircraftRemoteSensing_WB57_Data is the cloud and aerosol remote sensing data from the Roscoe lidar collected during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
+ABoVE_reference_grid_v2_1527_2.1 ABoVE: Study Domain and Standard Reference Grids, Version 2 ALL STAC Catalog 2014-01-01 2023-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2111709298-ORNL_CLOUD.umm_json The Arctic - Boreal Vulnerability Experiment (ABoVE) has developed two standardized spatial data products to expedite coordination of research activities and to facilitate data interoperability. The ABoVE Study Domain encompasses the Arctic and boreal regions of Alaska, USA, and the western provinces of Canada, North America. Core and Extended study regions have been designated within this Domain and are provided in a vector representation (Shapefile), a raster representation (GeoTIFF at 1,000-meter spatial resolution), and a NetCDF file. A standard Reference Grid System has been developed to cover the entire Study Domain and extends to the eastern portion of North America. This Reference Grid is provided as nested polygon grids at scales of 240, 30, and 5-meter spatial resolution. The 5-meter grid is new in Version 2. Note that the designated standard projection for all ABoVE products is the Canadian Albers Equal Area projection. proprietary
ACCLIP_AerosolCloud_AircraftRemoteSensing_WB57_Data_1 ACCLIP WB-57 Aerosol and Cloud Remotely Sensed Data LARC_ASDC STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2655162569-LARC_ASDC.umm_json ACCLIP_AerosolCloud_AircraftRemoteSensing_WB57_Data is the cloud and aerosol remote sensing data from the Roscoe lidar collected during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
+ACCLIP_AerosolCloud_AircraftRemoteSensing_WB57_Data_1 ACCLIP WB-57 Aerosol and Cloud Remotely Sensed Data ALL STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2655162569-LARC_ASDC.umm_json ACCLIP_AerosolCloud_AircraftRemoteSensing_WB57_Data is the cloud and aerosol remote sensing data from the Roscoe lidar collected during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
ACCLIP_Aerosol_AircraftInSitu_WB57_Data_1 ACCLIP WB-57 Aircraft In-Situ Aerosol Data ALL STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2609962127-LARC_ASDC.umm_json ACCLIP_Aerosol_AircraftInSitu_WB57_Data is the in-situ aerosol data collected during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Particle Analysis by Laser Mass Spectrometry - Next Generation (PALMS-NG), Single Particle Soot Photometer (SP2), Nucleation-Mode Aerosol Size Spectrometer (N-MASS), Printed Optical Particle Spectrometer (POPS), and the Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) is featured in this collection. Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
ACCLIP_Aerosol_AircraftInSitu_WB57_Data_1 ACCLIP WB-57 Aircraft In-Situ Aerosol Data LARC_ASDC STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2609962127-LARC_ASDC.umm_json ACCLIP_Aerosol_AircraftInSitu_WB57_Data is the in-situ aerosol data collected during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Particle Analysis by Laser Mass Spectrometry - Next Generation (PALMS-NG), Single Particle Soot Photometer (SP2), Nucleation-Mode Aerosol Size Spectrometer (N-MASS), Printed Optical Particle Spectrometer (POPS), and the Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) is featured in this collection. Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
-ACCLIP_AircraftInSitu_WB57_Water_Data_1 ACCLIP WB-57 Aircraft Water In-situ Data LARC_ASDC STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2609920136-LARC_ASDC.umm_json ACCLIP_AircraftInSitu_WB57_Water_Data is the in-situ water data collection during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Chicago Water Isotope Spectrometer (ChiWIS) is featured in this collection. Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
ACCLIP_AircraftInSitu_WB57_Water_Data_1 ACCLIP WB-57 Aircraft Water In-situ Data ALL STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2609920136-LARC_ASDC.umm_json ACCLIP_AircraftInSitu_WB57_Water_Data is the in-situ water data collection during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Chicago Water Isotope Spectrometer (ChiWIS) is featured in this collection. Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
-ACCLIP_Cloud_AircraftInSitu_WB57_Data_1 ACCLIP WB-57 Aircraft In-situ Cloud Data ALL STAC Catalog 2022-07-14 2022-09-15 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2609947245-LARC_ASDC.umm_json ACCLIP_Cloud_AircraftInSitu_WB57_Data is the in-situ cloud data collection during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Cloud, Aerosol, and Precipitation Spectrometer (CAPS) is featured in this collection. Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
+ACCLIP_AircraftInSitu_WB57_Water_Data_1 ACCLIP WB-57 Aircraft Water In-situ Data LARC_ASDC STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2609920136-LARC_ASDC.umm_json ACCLIP_AircraftInSitu_WB57_Water_Data is the in-situ water data collection during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Chicago Water Isotope Spectrometer (ChiWIS) is featured in this collection. Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
ACCLIP_Cloud_AircraftInSitu_WB57_Data_1 ACCLIP WB-57 Aircraft In-situ Cloud Data LARC_ASDC STAC Catalog 2022-07-14 2022-09-15 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2609947245-LARC_ASDC.umm_json ACCLIP_Cloud_AircraftInSitu_WB57_Data is the in-situ cloud data collection during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Cloud, Aerosol, and Precipitation Spectrometer (CAPS) is featured in this collection. Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
+ACCLIP_Cloud_AircraftInSitu_WB57_Data_1 ACCLIP WB-57 Aircraft In-situ Cloud Data ALL STAC Catalog 2022-07-14 2022-09-15 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2609947245-LARC_ASDC.umm_json ACCLIP_Cloud_AircraftInSitu_WB57_Data is the in-situ cloud data collection during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Cloud, Aerosol, and Precipitation Spectrometer (CAPS) is featured in this collection. Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
ACCLIP_Merge_WB57-Aircraft_Data_1 ACCLIP WB-57 Aircraft Merge Data ALL STAC Catalog 2022-07-16 2022-09-14 -180, 16.6, 180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2609887645-LARC_ASDC.umm_json ACCLIP_Merge_WB57-Aircraft_Data is the pre-generated merge files created from a variety of in-situ instrumentation collecting measurements onboard the WB-57 aircraft during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
ACCLIP_Merge_WB57-Aircraft_Data_1 ACCLIP WB-57 Aircraft Merge Data LARC_ASDC STAC Catalog 2022-07-16 2022-09-14 -180, 16.6, 180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2609887645-LARC_ASDC.umm_json ACCLIP_Merge_WB57-Aircraft_Data is the pre-generated merge files created from a variety of in-situ instrumentation collecting measurements onboard the WB-57 aircraft during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
-ACCLIP_MetNav_AircraftInSitu_WB57_Data_1 ACCLIP WB-57 Meteorological and Navigational Data ALL STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2566338281-LARC_ASDC.umm_json ACCLIP_MetNav_AircraftInSitu_WB57_Data is the in-situ meteorology and navigational data collection during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Meteorological Measurement System (MMS) and Diode Laser Hygrometer (DLH) is featured in this collection. Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
ACCLIP_MetNav_AircraftInSitu_WB57_Data_1 ACCLIP WB-57 Meteorological and Navigational Data LARC_ASDC STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2566338281-LARC_ASDC.umm_json ACCLIP_MetNav_AircraftInSitu_WB57_Data is the in-situ meteorology and navigational data collection during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Meteorological Measurement System (MMS) and Diode Laser Hygrometer (DLH) is featured in this collection. Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
-ACCLIP_Model_WB57_Data_1 ACCLIP WB-57 Aircraft Model Data ALL STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2609869612-LARC_ASDC.umm_json ACCLIP_Model_WB57_Data contains modeled meteorological, chemical, and aerosol data along the flight tracks of the WB-57 aircraft during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
+ACCLIP_MetNav_AircraftInSitu_WB57_Data_1 ACCLIP WB-57 Meteorological and Navigational Data ALL STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2566338281-LARC_ASDC.umm_json ACCLIP_MetNav_AircraftInSitu_WB57_Data is the in-situ meteorology and navigational data collection during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Meteorological Measurement System (MMS) and Diode Laser Hygrometer (DLH) is featured in this collection. Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
ACCLIP_Model_WB57_Data_1 ACCLIP WB-57 Aircraft Model Data LARC_ASDC STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2609869612-LARC_ASDC.umm_json ACCLIP_Model_WB57_Data contains modeled meteorological, chemical, and aerosol data along the flight tracks of the WB-57 aircraft during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
+ACCLIP_Model_WB57_Data_1 ACCLIP WB-57 Aircraft Model Data ALL STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2609869612-LARC_ASDC.umm_json ACCLIP_Model_WB57_Data contains modeled meteorological, chemical, and aerosol data along the flight tracks of the WB-57 aircraft during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
ACCLIP_TraceGas_AircraftInSitu_WB57_Data_1 ACCLIP WB-57 Aircraft In-situ Trace Gas Data ALL STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2566342407-LARC_ASDC.umm_json ACCLIP_TraceGas_AircraftInSitu_WB57_Data is the in-situ trace gas data collection during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Airborne Carbon Oxide Sulfide Spectrometer (ACOS), Carbon monOxide Measurement from Ames (COMA), Laser Induced Fluorescence - Nitrogen Oxide (LIF-NO), In Situ Airborne Formaldehyde (ISAF), Carbon Oxide Laser Detector 2 (COLD 2), and the NOAA UAS O3 Photometer (UASO3) is featured in this collection. Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
ACCLIP_TraceGas_AircraftInSitu_WB57_Data_1 ACCLIP WB-57 Aircraft In-situ Trace Gas Data LARC_ASDC STAC Catalog 2022-07-14 2022-09-14 180, 16.6, -180, 61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2566342407-LARC_ASDC.umm_json ACCLIP_TraceGas_AircraftInSitu_WB57_Data is the in-situ trace gas data collection during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data from the Airborne Carbon Oxide Sulfide Spectrometer (ACOS), Carbon monOxide Measurement from Ames (COMA), Laser Induced Fluorescence - Nitrogen Oxide (LIF-NO), In Situ Airborne Formaldehyde (ISAF), Carbon Oxide Laser Detector 2 (COLD 2), and the NOAA UAS O3 Photometer (UASO3) is featured in this collection. Data collection for this product is complete. ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate. The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere. proprietary
-ACE-ASIA_0 Aerosol Characterization Experiment (ACE) - Asia ALL STAC Catalog 2001-03-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360087-OB_DAAC.umm_json Measurements taken during the Aerosol Characterization Experiment (ACE) off the coast of Asia in the East China Sea, Sea of Japan, and Pacific Ocean. proprietary
ACE-ASIA_0 Aerosol Characterization Experiment (ACE) - Asia OB_DAAC STAC Catalog 2001-03-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360087-OB_DAAC.umm_json Measurements taken during the Aerosol Characterization Experiment (ACE) off the coast of Asia in the East China Sea, Sea of Japan, and Pacific Ocean. proprietary
+ACE-ASIA_0 Aerosol Characterization Experiment (ACE) - Asia ALL STAC Catalog 2001-03-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360087-OB_DAAC.umm_json Measurements taken during the Aerosol Characterization Experiment (ACE) off the coast of Asia in the East China Sea, Sea of Japan, and Pacific Ocean. proprietary
ACE-INC_0 Aerosol Characterization Experiment (ACE) for the in-land Chesapeake Bay region ALL STAC Catalog 2002-04-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360089-OB_DAAC.umm_json Measurements made as a part of the Aerosol Characterization Experiment (ACE) for the in-land Chesapeake Bay region between 2002 and 2003. proprietary
ACE-INC_0 Aerosol Characterization Experiment (ACE) for the in-land Chesapeake Bay region OB_DAAC STAC Catalog 2002-04-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360089-OB_DAAC.umm_json Measurements made as a part of the Aerosol Characterization Experiment (ACE) for the in-land Chesapeake Bay region between 2002 and 2003. proprietary
ACEPOL_AircraftRemoteSensing_AirHARP_Data_1 ACEPOL Airborne Hyper Angular Rainbow Polarimeter (AirHARP) Remotely Sensed Data Version 1 ALL STAC Catalog 2017-10-18 2020-11-20 -130, 25, -100, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1758588261-LARC_ASDC.umm_json ACEPOL Airborne Hyper Angular Rainbow Polarimeter (AirHARP) Remotely Sensed Data (ACEPOL_AircraftRemoteSensing_AirHARP_Data) are remotely sensed measurements collected by the Airborne Hyper Angular Rainbow Polarimeter (AirHARP) onboard the ER-2 during ACEPOL. In order to improve our understanding of the effect of aerosols on climate and air quality, measurements of aerosol chemical composition, size distribution, height profile, and optical properties are of crucial importance. In terms of remotely sensed instrumentation, the most extensive set of aerosol properties can be obtained by combining passive multi-angle, multi-spectral measurements of intensity and polarization with active measurements performed by a High Spectral Resolution Lidar. During Fall 2017, the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign, jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON), performed aerosol and cloud measurements over the United States from the NASA high altitude ER-2 aircraft. Six instruments were deployed on the aircraft. Four of these instruments were multi-angle polarimeters: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary Exploration (SPEX Airborne) and the Research Scanning Polarimeter (RSP). The other two instruments were lidars: the High Spectral Resolution Lidar 2 (HSRL-2) and the Cloud Physics Lidar (CPL). The ACEPOL operation was based at NASA’s Armstrong Flight Research Center in Palmdale California, which enabled observations of a wide variety of scene types, including urban, desert, forest, coastal ocean and agricultural areas, with clear, cloudy, polluted and pristine atmospheric conditions. The primary goal of ACEPOL was to assess the capabilities of the different polarimeters for retrieval of aerosol and cloud microphysical and optical parameters, as well as their capabilities to derive aerosol layer height (near-UV polarimetry, O2 A-band). ACEPOL also focused on the development and evaluation of aerosol retrieval algorithms that combine data from both active (lidar) and passive (polarimeter) instruments. ACEPOL data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion, which is a valuable resource for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions. proprietary
@@ -1374,88 +1374,88 @@ ACEPOL_AircraftRemoteSensing_RSP_Data_1 ACEPOL Research Scanning Polarimeter (RS
ACEPOL_AircraftRemoteSensing_RSP_Data_1 ACEPOL Research Scanning Polarimeter (RSP) Remotely Sensed Data Version 1 LARC_ASDC STAC Catalog 2017-10-23 2017-11-09 -130, 25, -100, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1758588354-LARC_ASDC.umm_json ACEPOL Research Scanning Polarimeter (RSP) Remotely Sensed Data (ACEPOL_AircraftRemoteSensing_RSP_Data) are remotely sensed measurements collected by the Research Scanning Polarimeter (RSP) onboard the ER-2 during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. In order to improve our understanding of the effect of aerosols on climate and air quality, measurements of aerosol chemical composition, size distribution, height profile, and optical properties are of crucial importance. In terms of remotely sensed instrumentation, the most extensive set of aerosol properties can be obtained by combining passive multi-angle, multi-spectral measurements of intensity and polarization with active measurements performed by a High Spectral Resolution Lidar. During Fall 2017, the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign, jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON), performed aerosol and cloud measurements over the United States from the NASA high altitude ER-2 aircraft. Six instruments were deployed on the aircraft. Four of these instruments were multi-angle polarimeters: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary Exploration (SPEX Airborne) and the Research Scanning Polarimeter (RSP). The other two instruments were lidars: the High Spectral Resolution Lidar 2 (HSRL-2) and the Cloud Physics Lidar (CPL). The ACEPOL operation was based at NASA’s Armstrong Flight Research Center in Palmdale California, which enabled observations of a wide variety of scene types, including urban, desert, forest, coastal ocean and agricultural areas, with clear, cloudy, polluted and pristine atmospheric conditions. The primary goal of ACEPOL was to assess the capabilities of the different polarimeters for retrieval of aerosol and cloud microphysical and optical parameters, as well as their capabilities to derive aerosol layer height (near-UV polarimetry, O2 A-band). ACEPOL also focused on the development and evaluation of aerosol retrieval algorithms that combine data from both active (lidar) and passive (polarimeter) instruments. ACEPOL data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion, which is a valuable resource for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions. proprietary
ACEPOL_MetNav_AircraftInSitu_Data_1 ACEPOL ER-2 Meteorological and Navigational Data Version 1 LARC_ASDC STAC Catalog 2017-10-19 2017-11-09 -130, 25, -100, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1758588825-LARC_ASDC.umm_json ACEPOL_MetNav_AircraftInSitu_Data are in situ meteorological and navigational measurements collected onboard the ER-2 during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. In order to improve our understanding of the effect of aerosols on climate and air quality, measurements of aerosol chemical composition, size distribution, height profile, and optical properties are of crucial importance. In terms of remotely sensed instrumentation, the most extensive set of aerosol properties can be obtained by combining passive multi-angle, multi-spectral measurements of intensity and polarization with active measurements performed by a High Spectral Resolution Lidar. During Fall 2017, the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign, jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON), performed aerosol and cloud measurements over the United States from the NASA high altitude ER-2 aircraft. Six instruments were deployed on the aircraft. Four of these instruments were multi-angle polarimeters: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary Exploration (SPEX Airborne) and the Research Scanning Polarimeter (RSP). The other two instruments were lidars: the High Spectral Resolution Lidar 2 (HSRL-2) and the Cloud Physics Lidar (CPL). The ACEPOL operation was based at NASA’s Armstrong Flight Research Center in Palmdale California, which enabled observations of a wide variety of scene types, including urban, desert, forest, coastal ocean and agricultural areas, with clear, cloudy, polluted and pristine atmospheric conditions. The primary goal of ACEPOL was to assess the capabilities of the different polarimeters for retrieval of aerosol and cloud microphysical and optical parameters, as well as their capabilities to derive aerosol layer height (near-UV polarimetry, O2 A-band). ACEPOL also focused on the development and evaluation of aerosol retrieval algorithms that combine data from both active (lidar) and passive (polarimeter) instruments. ACEPOL data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion, which make them valuable resources for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions. proprietary
ACEPOL_MetNav_AircraftInSitu_Data_1 ACEPOL ER-2 Meteorological and Navigational Data Version 1 ALL STAC Catalog 2017-10-19 2017-11-09 -130, 25, -100, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1758588825-LARC_ASDC.umm_json ACEPOL_MetNav_AircraftInSitu_Data are in situ meteorological and navigational measurements collected onboard the ER-2 during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. In order to improve our understanding of the effect of aerosols on climate and air quality, measurements of aerosol chemical composition, size distribution, height profile, and optical properties are of crucial importance. In terms of remotely sensed instrumentation, the most extensive set of aerosol properties can be obtained by combining passive multi-angle, multi-spectral measurements of intensity and polarization with active measurements performed by a High Spectral Resolution Lidar. During Fall 2017, the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign, jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON), performed aerosol and cloud measurements over the United States from the NASA high altitude ER-2 aircraft. Six instruments were deployed on the aircraft. Four of these instruments were multi-angle polarimeters: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary Exploration (SPEX Airborne) and the Research Scanning Polarimeter (RSP). The other two instruments were lidars: the High Spectral Resolution Lidar 2 (HSRL-2) and the Cloud Physics Lidar (CPL). The ACEPOL operation was based at NASA’s Armstrong Flight Research Center in Palmdale California, which enabled observations of a wide variety of scene types, including urban, desert, forest, coastal ocean and agricultural areas, with clear, cloudy, polluted and pristine atmospheric conditions. The primary goal of ACEPOL was to assess the capabilities of the different polarimeters for retrieval of aerosol and cloud microphysical and optical parameters, as well as their capabilities to derive aerosol layer height (near-UV polarimetry, O2 A-band). ACEPOL also focused on the development and evaluation of aerosol retrieval algorithms that combine data from both active (lidar) and passive (polarimeter) instruments. ACEPOL data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion, which make them valuable resources for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions. proprietary
-ACE_0 Aerosol Characterization Experiment (ACE) OB_DAAC STAC Catalog 1997-06-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360090-OB_DAAC.umm_json Measurements taken during the Aerosol Characterization Experiment (ACE) off the coast of Spain, Portugal, and Northern Africa in the Atlantic Ocean. proprietary
ACE_0 Aerosol Characterization Experiment (ACE) ALL STAC Catalog 1997-06-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360090-OB_DAAC.umm_json Measurements taken during the Aerosol Characterization Experiment (ACE) off the coast of Spain, Portugal, and Northern Africa in the Atlantic Ocean. proprietary
+ACE_0 Aerosol Characterization Experiment (ACE) OB_DAAC STAC Catalog 1997-06-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360090-OB_DAAC.umm_json Measurements taken during the Aerosol Characterization Experiment (ACE) off the coast of Spain, Portugal, and Northern Africa in the Atlantic Ocean. proprietary
ACE_AME_Bibliography_1 Bibliography of papers relevant to the ACE-CRC's Antarctic Marine Ecosystem programme AU_AADC STAC Catalog 1959-01-01 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311743-AU_AADC.umm_json This bibliography is a selected list of scientific papers collected by scientists in the ACE-CRC's Antarctic Marine Ecosystem research programme. proprietary
-ACE_EPAM_LEVEL2 Advanced Composition Explorer (ACE) Electron, Proton, and Alpha Monitor (EPAM) Level 2 Data SCIOPS STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614862-SCIOPS.umm_json The Electron, Proton, and Alpha Monitor (EPAM) is composed of five telescope apertures of three different types. Two Low Energy Foil Spectrometers (LEFS) measure the flux and direction of electrons above 30 keV (geometry factor = 0.397 cm2*sr), two Low Energy Magnetic Spectrometers (LEMS) measure the flux and direction of ions greater than 50 keV (geometry factor = 0.48 cm2*sr), and the Composition Aperture (CA) measures the elemental composition of the ions (geometry factor = 0.24 cm2*sr). The telescopes use the spin of the spacecraft to sweep the full sky. Solid-state detectors are used to measure the energy and composition of the incoming particles. EPAM Level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the Level 2 data contains time averages of energetic charged particle fluxes over the following time periods: - hourly - daily - 27 days (1 Bartels rotation) The DE30 detector, (Deflected Electrons), measures electrons at 30 degrees from the spacecraft spin axis. Electrons entering the LEMS30 detector are swept out by a rare-earth magnet and are deflected into the B detector. The 4 DE channels are pure electron channels. The geometrical factor for the DE30 channels is 0.14 (cm2*sr). The CA60 telescope, (Composition Aperture) measures ion composition. It's look-direction is oriented 60 degrees from the spacecraft spin-axis. The CA telescope is capable of determining ion composition using a dE X E detection scheme. Although the principal responsibility of EPAM is to monitor electrons, protons, and alphas, the CA provides an unambiguous determination of ion composition, unlike the LEMS detectors. The CA60 telescope is comprised of three solid state detectors, a thin, ~5 micron epitaxial silicon detector referred to as the D detector, and two thick (200 micron) totally depleted surface barrier silicon detectors known as C and B. The B detector, as measures deflected electrons from the LEMS30 head, but also acts as the anti-coincidence detector for the CA. The CA system uses log amplifiers to extend the dynamic range of the detector. These amplifiers are extremely temperature sensitive, and therefore are thermally regulated with heaters to maintain calibration. The logic used in the CA depends on slanted discriminators to define each species group. The eight Ca rate channels, denoted by the symbols W1 - W8, count all particles in a given energy/nucleon range. Multiple species may therefore be associated with a single Ca rate channel. As a result, a species group is identified by the dominant species in that group. See: http://www.srl.caltech.edu/ACE/ASC/level2/epam_l2desc.html proprietary
ACE_EPAM_LEVEL2 Advanced Composition Explorer (ACE) Electron, Proton, and Alpha Monitor (EPAM) Level 2 Data ALL STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614862-SCIOPS.umm_json The Electron, Proton, and Alpha Monitor (EPAM) is composed of five telescope apertures of three different types. Two Low Energy Foil Spectrometers (LEFS) measure the flux and direction of electrons above 30 keV (geometry factor = 0.397 cm2*sr), two Low Energy Magnetic Spectrometers (LEMS) measure the flux and direction of ions greater than 50 keV (geometry factor = 0.48 cm2*sr), and the Composition Aperture (CA) measures the elemental composition of the ions (geometry factor = 0.24 cm2*sr). The telescopes use the spin of the spacecraft to sweep the full sky. Solid-state detectors are used to measure the energy and composition of the incoming particles. EPAM Level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the Level 2 data contains time averages of energetic charged particle fluxes over the following time periods: - hourly - daily - 27 days (1 Bartels rotation) The DE30 detector, (Deflected Electrons), measures electrons at 30 degrees from the spacecraft spin axis. Electrons entering the LEMS30 detector are swept out by a rare-earth magnet and are deflected into the B detector. The 4 DE channels are pure electron channels. The geometrical factor for the DE30 channels is 0.14 (cm2*sr). The CA60 telescope, (Composition Aperture) measures ion composition. It's look-direction is oriented 60 degrees from the spacecraft spin-axis. The CA telescope is capable of determining ion composition using a dE X E detection scheme. Although the principal responsibility of EPAM is to monitor electrons, protons, and alphas, the CA provides an unambiguous determination of ion composition, unlike the LEMS detectors. The CA60 telescope is comprised of three solid state detectors, a thin, ~5 micron epitaxial silicon detector referred to as the D detector, and two thick (200 micron) totally depleted surface barrier silicon detectors known as C and B. The B detector, as measures deflected electrons from the LEMS30 head, but also acts as the anti-coincidence detector for the CA. The CA system uses log amplifiers to extend the dynamic range of the detector. These amplifiers are extremely temperature sensitive, and therefore are thermally regulated with heaters to maintain calibration. The logic used in the CA depends on slanted discriminators to define each species group. The eight Ca rate channels, denoted by the symbols W1 - W8, count all particles in a given energy/nucleon range. Multiple species may therefore be associated with a single Ca rate channel. As a result, a species group is identified by the dominant species in that group. See: http://www.srl.caltech.edu/ACE/ASC/level2/epam_l2desc.html proprietary
-ACE_LEVEL2 Advanced Composition Explorer (ACE) CRIS Level 2 Data ALL STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614848-SCIOPS.umm_json The Cosmic Ray Isotope Spectrometer (CRIS) on the Advanced Composition Explorer (ACE) spacecraft is intended to be a major step in ascertaining the isotopic composition of the Galactic Cosmic Rays and hence a major step in determining their origin. The GCRs (Galactic Cosmic Rays) consist, by number, primarily of hydrogen nuclei (~92%) and He nuclei (~7%). The heavier nuclei (1%) provide most of the information about cosmic-ray origin through their elemental and isotopic composition. The intensities of these heavy cosmic rays are very low and progress in the past has been impeded by limited particle collection power, particularly regarding individual isotopes. CRIS is designed to have far greater collection power (~250 cm2*sr) than previous satellite instruments (< 10 cm2*sr) while still maintaining excellent isotopic resolution through Z=30 (Zinc) and beyond. CRIS level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of energetic charged particle fluxes over the following time periods: - hourly - daily - 27 days (1 Bartels rotation) Currently, flux data are available for 24 elements, in units of particles/(cm2*sr*sec*Mev/nucleon), in seven energy ranges. The energy ranges are different for each element. The elements for which data are available are: - B, C, N, O, F, Ne, Na, Mg, Al, Si, P, S, Cl, Ar, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni See: http://www.srl.caltech.edu/ACE/ASC/level2/cris_l2desc.html proprietary
+ACE_EPAM_LEVEL2 Advanced Composition Explorer (ACE) Electron, Proton, and Alpha Monitor (EPAM) Level 2 Data SCIOPS STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614862-SCIOPS.umm_json The Electron, Proton, and Alpha Monitor (EPAM) is composed of five telescope apertures of three different types. Two Low Energy Foil Spectrometers (LEFS) measure the flux and direction of electrons above 30 keV (geometry factor = 0.397 cm2*sr), two Low Energy Magnetic Spectrometers (LEMS) measure the flux and direction of ions greater than 50 keV (geometry factor = 0.48 cm2*sr), and the Composition Aperture (CA) measures the elemental composition of the ions (geometry factor = 0.24 cm2*sr). The telescopes use the spin of the spacecraft to sweep the full sky. Solid-state detectors are used to measure the energy and composition of the incoming particles. EPAM Level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the Level 2 data contains time averages of energetic charged particle fluxes over the following time periods: - hourly - daily - 27 days (1 Bartels rotation) The DE30 detector, (Deflected Electrons), measures electrons at 30 degrees from the spacecraft spin axis. Electrons entering the LEMS30 detector are swept out by a rare-earth magnet and are deflected into the B detector. The 4 DE channels are pure electron channels. The geometrical factor for the DE30 channels is 0.14 (cm2*sr). The CA60 telescope, (Composition Aperture) measures ion composition. It's look-direction is oriented 60 degrees from the spacecraft spin-axis. The CA telescope is capable of determining ion composition using a dE X E detection scheme. Although the principal responsibility of EPAM is to monitor electrons, protons, and alphas, the CA provides an unambiguous determination of ion composition, unlike the LEMS detectors. The CA60 telescope is comprised of three solid state detectors, a thin, ~5 micron epitaxial silicon detector referred to as the D detector, and two thick (200 micron) totally depleted surface barrier silicon detectors known as C and B. The B detector, as measures deflected electrons from the LEMS30 head, but also acts as the anti-coincidence detector for the CA. The CA system uses log amplifiers to extend the dynamic range of the detector. These amplifiers are extremely temperature sensitive, and therefore are thermally regulated with heaters to maintain calibration. The logic used in the CA depends on slanted discriminators to define each species group. The eight Ca rate channels, denoted by the symbols W1 - W8, count all particles in a given energy/nucleon range. Multiple species may therefore be associated with a single Ca rate channel. As a result, a species group is identified by the dominant species in that group. See: http://www.srl.caltech.edu/ACE/ASC/level2/epam_l2desc.html proprietary
ACE_LEVEL2 Advanced Composition Explorer (ACE) CRIS Level 2 Data SCIOPS STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614848-SCIOPS.umm_json The Cosmic Ray Isotope Spectrometer (CRIS) on the Advanced Composition Explorer (ACE) spacecraft is intended to be a major step in ascertaining the isotopic composition of the Galactic Cosmic Rays and hence a major step in determining their origin. The GCRs (Galactic Cosmic Rays) consist, by number, primarily of hydrogen nuclei (~92%) and He nuclei (~7%). The heavier nuclei (1%) provide most of the information about cosmic-ray origin through their elemental and isotopic composition. The intensities of these heavy cosmic rays are very low and progress in the past has been impeded by limited particle collection power, particularly regarding individual isotopes. CRIS is designed to have far greater collection power (~250 cm2*sr) than previous satellite instruments (< 10 cm2*sr) while still maintaining excellent isotopic resolution through Z=30 (Zinc) and beyond. CRIS level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of energetic charged particle fluxes over the following time periods: - hourly - daily - 27 days (1 Bartels rotation) Currently, flux data are available for 24 elements, in units of particles/(cm2*sr*sec*Mev/nucleon), in seven energy ranges. The energy ranges are different for each element. The elements for which data are available are: - B, C, N, O, F, Ne, Na, Mg, Al, Si, P, S, Cl, Ar, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni See: http://www.srl.caltech.edu/ACE/ASC/level2/cris_l2desc.html proprietary
-ACE_MAG_LEVEL2 Advanced Composition Explorer (ACE) Magnetic Field Experiment (MAG) Level 2 Data SCIOPS STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614850-SCIOPS.umm_json The Magnetic Field Experiment (MAG) consists of twin vector fluxgate magnetometers controlled by a common CPU. The sensors are mounted on booms extending 4.19 meters from the center of the spacecraft at opposite sides along the +/-Y axes of the spacecraft. The instrument returns 6 magnetic field vector measurements each second, divided between the two sensors, with onboard snapshot and FFT buffers to enhance the high-frequency resolution. MAG level 2 data is organized into 27 day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of the magnetic field data over the following time periods: - 16 seconds - 4 minutes - hourly - daily - 27 days (1 Bartels rotation) See: http://www.srl.caltech.edu/ACE/ASC/level2/mag_l2desc.html proprietary
+ACE_LEVEL2 Advanced Composition Explorer (ACE) CRIS Level 2 Data ALL STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614848-SCIOPS.umm_json The Cosmic Ray Isotope Spectrometer (CRIS) on the Advanced Composition Explorer (ACE) spacecraft is intended to be a major step in ascertaining the isotopic composition of the Galactic Cosmic Rays and hence a major step in determining their origin. The GCRs (Galactic Cosmic Rays) consist, by number, primarily of hydrogen nuclei (~92%) and He nuclei (~7%). The heavier nuclei (1%) provide most of the information about cosmic-ray origin through their elemental and isotopic composition. The intensities of these heavy cosmic rays are very low and progress in the past has been impeded by limited particle collection power, particularly regarding individual isotopes. CRIS is designed to have far greater collection power (~250 cm2*sr) than previous satellite instruments (< 10 cm2*sr) while still maintaining excellent isotopic resolution through Z=30 (Zinc) and beyond. CRIS level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of energetic charged particle fluxes over the following time periods: - hourly - daily - 27 days (1 Bartels rotation) Currently, flux data are available for 24 elements, in units of particles/(cm2*sr*sec*Mev/nucleon), in seven energy ranges. The energy ranges are different for each element. The elements for which data are available are: - B, C, N, O, F, Ne, Na, Mg, Al, Si, P, S, Cl, Ar, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni See: http://www.srl.caltech.edu/ACE/ASC/level2/cris_l2desc.html proprietary
ACE_MAG_LEVEL2 Advanced Composition Explorer (ACE) Magnetic Field Experiment (MAG) Level 2 Data ALL STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614850-SCIOPS.umm_json The Magnetic Field Experiment (MAG) consists of twin vector fluxgate magnetometers controlled by a common CPU. The sensors are mounted on booms extending 4.19 meters from the center of the spacecraft at opposite sides along the +/-Y axes of the spacecraft. The instrument returns 6 magnetic field vector measurements each second, divided between the two sensors, with onboard snapshot and FFT buffers to enhance the high-frequency resolution. MAG level 2 data is organized into 27 day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of the magnetic field data over the following time periods: - 16 seconds - 4 minutes - hourly - daily - 27 days (1 Bartels rotation) See: http://www.srl.caltech.edu/ACE/ASC/level2/mag_l2desc.html proprietary
+ACE_MAG_LEVEL2 Advanced Composition Explorer (ACE) Magnetic Field Experiment (MAG) Level 2 Data SCIOPS STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614850-SCIOPS.umm_json The Magnetic Field Experiment (MAG) consists of twin vector fluxgate magnetometers controlled by a common CPU. The sensors are mounted on booms extending 4.19 meters from the center of the spacecraft at opposite sides along the +/-Y axes of the spacecraft. The instrument returns 6 magnetic field vector measurements each second, divided between the two sensors, with onboard snapshot and FFT buffers to enhance the high-frequency resolution. MAG level 2 data is organized into 27 day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of the magnetic field data over the following time periods: - 16 seconds - 4 minutes - hourly - daily - 27 days (1 Bartels rotation) See: http://www.srl.caltech.edu/ACE/ASC/level2/mag_l2desc.html proprietary
ACE_PARTCLE_FLUXES Advanced Composition Explorer (ACE) Particle Composition and Flux Browse Data SCIOPS STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214603832-SCIOPS.umm_json The Advanced Composition Explorer (ACE) is an Explorer mission that is being managed by the Office of Space Science Mission and Payload Development Division of the National Aeronautics and Space Administration (NASA). The primary purpose of ACE is to determine and compare the isotopic and elemental composition of several distinct samples of matter, including the solar corona, the interplanetary medium, the local interstellar medium, and Galactic matter. The ACE spacecraft measures the flux of charged particles from solar wind energies (300 km/sec) up through galactic cosmic rays (500 MeV/nucleon) and the interplanetary magnetic field upstream of earth. The ACE data includes energetic particles from solar wind cosmic ray energies. In addition, this data set covers both atomic and isotopic composition data for most energy ranges. This pace data is at L1 (approx. 1.5 million km upstream along earth-sun line). ACE browse data are designed for monitoring large scale particle and field behavior and for selecting interesting time periods. The data are automatically generated from the spacecraft data stream using simple algorithms provided by the instrument investigators. They are not routinely checked for accuracy and are subject to revision. Use these data at your own risk, and consult with the appropriate instrument investigators about citing them. Browse parameters are a subset of measurements by the ACE instruments which are created at the Science Center during level one processing. They are delivered to the public domain as soon as possible. Their purpose is to allow monitoring of the solar wind and large-scale particle and magnetic field behavior, and selection of interesting time periods for more intensive study. Interesting time periods might include solar energetic particle events, or the passage of an interplanetary shock. An additional use of the browse parameters is to investigate relationships between the data from the various ACE instruments, and between ACE data and data from other sources. The browse parameters include unsectored fluxes of ions at many different energies and electrons at a few energies. They also include the interplanetary magnetic field, and solar wind parameters such as proton speed and temperature. They therefore furnish a very abbreviated description of what is being observed by the ACE instruments, without the relatively high cost of storing and analyzing all the level one data. Eventually they may be supplemented with event data from the particle detectors, but experience with the flight data is a prerequisite for delivering useful products of that type. See: http://www.srl.caltech.edu/ACE/ASC/browse/browse_info.html for more information. proprietary
ACE_PARTCLE_FLUXES Advanced Composition Explorer (ACE) Particle Composition and Flux Browse Data ALL STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214603832-SCIOPS.umm_json The Advanced Composition Explorer (ACE) is an Explorer mission that is being managed by the Office of Space Science Mission and Payload Development Division of the National Aeronautics and Space Administration (NASA). The primary purpose of ACE is to determine and compare the isotopic and elemental composition of several distinct samples of matter, including the solar corona, the interplanetary medium, the local interstellar medium, and Galactic matter. The ACE spacecraft measures the flux of charged particles from solar wind energies (300 km/sec) up through galactic cosmic rays (500 MeV/nucleon) and the interplanetary magnetic field upstream of earth. The ACE data includes energetic particles from solar wind cosmic ray energies. In addition, this data set covers both atomic and isotopic composition data for most energy ranges. This pace data is at L1 (approx. 1.5 million km upstream along earth-sun line). ACE browse data are designed for monitoring large scale particle and field behavior and for selecting interesting time periods. The data are automatically generated from the spacecraft data stream using simple algorithms provided by the instrument investigators. They are not routinely checked for accuracy and are subject to revision. Use these data at your own risk, and consult with the appropriate instrument investigators about citing them. Browse parameters are a subset of measurements by the ACE instruments which are created at the Science Center during level one processing. They are delivered to the public domain as soon as possible. Their purpose is to allow monitoring of the solar wind and large-scale particle and magnetic field behavior, and selection of interesting time periods for more intensive study. Interesting time periods might include solar energetic particle events, or the passage of an interplanetary shock. An additional use of the browse parameters is to investigate relationships between the data from the various ACE instruments, and between ACE data and data from other sources. The browse parameters include unsectored fluxes of ions at many different energies and electrons at a few energies. They also include the interplanetary magnetic field, and solar wind parameters such as proton speed and temperature. They therefore furnish a very abbreviated description of what is being observed by the ACE instruments, without the relatively high cost of storing and analyzing all the level one data. Eventually they may be supplemented with event data from the particle detectors, but experience with the flight data is a prerequisite for delivering useful products of that type. See: http://www.srl.caltech.edu/ACE/ASC/browse/browse_info.html for more information. proprietary
-ACE_SEPICA_LEVEL2 Advanced Composition Explorer (ACE) Solar Energetic Particle Charge Analyser (SEPICA) Level 2 Data SCIOPS STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614851-SCIOPS.umm_json The Solar Energetic Particle Charge Analyser (SEPICA) is used to determine the charge state distribution of energetic particle distributions. SEPICA is designed to measure the ionic charge state, Q, the kinetic energy, E, and the nuclear charge, Z, of energetic ions above 0.2 MeV/Nuc. This includes ions accelerated in solar flares as well as in interplanetary space during energetic storm particle (ESP) and co-rotating interaction region (CIR) events. For low mass numbers SEPICA will also separate isotopes -- for example, 3He and 4He. During solar quiet times, SEPICA should also be able to directly measure the charge states of anomalous cosmic ray nuclei, including H, N, O, and Ne, which are presumed to be singly-charged. With the capability to differentiate the charge states of ions, the instrument will also be able to separate neutral atoms (Q = 0) from ions. Thus it may be able to identify energetic neutrals created through charge exchange. SEPICA level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of solar energetic particle fluxes over the following time periods: - 120-second (H and He only) - hourly - (all elements) daily - (all elements) 27 days (1 Bartels rotation) (all elements) Currently, spin-averaged flux data are available for 8 elements, in units of particles/(cm2*Sr*sec*MeV/nucleon), in a number of energy ranges. The energy ranges are different for each element. The elements for which data are available are: - H, He, C, O, Ne, Mg, Si and Fe. See: http://www.srl.caltech.edu/ACE/ASC/level2/sepica_l2desc.html proprietary
ACE_SEPICA_LEVEL2 Advanced Composition Explorer (ACE) Solar Energetic Particle Charge Analyser (SEPICA) Level 2 Data ALL STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614851-SCIOPS.umm_json The Solar Energetic Particle Charge Analyser (SEPICA) is used to determine the charge state distribution of energetic particle distributions. SEPICA is designed to measure the ionic charge state, Q, the kinetic energy, E, and the nuclear charge, Z, of energetic ions above 0.2 MeV/Nuc. This includes ions accelerated in solar flares as well as in interplanetary space during energetic storm particle (ESP) and co-rotating interaction region (CIR) events. For low mass numbers SEPICA will also separate isotopes -- for example, 3He and 4He. During solar quiet times, SEPICA should also be able to directly measure the charge states of anomalous cosmic ray nuclei, including H, N, O, and Ne, which are presumed to be singly-charged. With the capability to differentiate the charge states of ions, the instrument will also be able to separate neutral atoms (Q = 0) from ions. Thus it may be able to identify energetic neutrals created through charge exchange. SEPICA level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of solar energetic particle fluxes over the following time periods: - 120-second (H and He only) - hourly - (all elements) daily - (all elements) 27 days (1 Bartels rotation) (all elements) Currently, spin-averaged flux data are available for 8 elements, in units of particles/(cm2*Sr*sec*MeV/nucleon), in a number of energy ranges. The energy ranges are different for each element. The elements for which data are available are: - H, He, C, O, Ne, Mg, Si and Fe. See: http://www.srl.caltech.edu/ACE/ASC/level2/sepica_l2desc.html proprietary
+ACE_SEPICA_LEVEL2 Advanced Composition Explorer (ACE) Solar Energetic Particle Charge Analyser (SEPICA) Level 2 Data SCIOPS STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614851-SCIOPS.umm_json The Solar Energetic Particle Charge Analyser (SEPICA) is used to determine the charge state distribution of energetic particle distributions. SEPICA is designed to measure the ionic charge state, Q, the kinetic energy, E, and the nuclear charge, Z, of energetic ions above 0.2 MeV/Nuc. This includes ions accelerated in solar flares as well as in interplanetary space during energetic storm particle (ESP) and co-rotating interaction region (CIR) events. For low mass numbers SEPICA will also separate isotopes -- for example, 3He and 4He. During solar quiet times, SEPICA should also be able to directly measure the charge states of anomalous cosmic ray nuclei, including H, N, O, and Ne, which are presumed to be singly-charged. With the capability to differentiate the charge states of ions, the instrument will also be able to separate neutral atoms (Q = 0) from ions. Thus it may be able to identify energetic neutrals created through charge exchange. SEPICA level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of solar energetic particle fluxes over the following time periods: - 120-second (H and He only) - hourly - (all elements) daily - (all elements) 27 days (1 Bartels rotation) (all elements) Currently, spin-averaged flux data are available for 8 elements, in units of particles/(cm2*Sr*sec*MeV/nucleon), in a number of energy ranges. The energy ranges are different for each element. The elements for which data are available are: - H, He, C, O, Ne, Mg, Si and Fe. See: http://www.srl.caltech.edu/ACE/ASC/level2/sepica_l2desc.html proprietary
ACE_SIS_LEVEL2 Advanced Composition Explorer (ACE) Solar Isotope Spectrometer (SIS) Level 2 Data ALL STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614863-SCIOPS.umm_json The Solar Isotope Spectrometer (SIS) is designed to provide high resolution measurements of the isotopic composition of energetic nuclei from He to Ni (Z=2 to 28) over the energy range from ~10 to ~100 MeV/nucleon. During large solar events, when particle fluxes can increase over quiet-time values by factors of up to 10000, SIS measures the isotopic composition of the solar corona, while during solar quiet times SIS measures the isotopes of low-energy Galactic cosmic rays and the composition of the anomalous cosmic rays which are thought to originate in the nearby interstellar medium. The solar energetic particle measurements are useful to further our understanding of the Sun, while also providing a baseline for comparison with the Galactic cosmic ray measurements carried out by CRIS. SIS has a geometry factor of ~40 cm2--sr, which is significantly larger than previous satellite solar particle isotope spectrometers. It is also designed to provide excellent mass resolution during the extremely high particle flux conditions which occur during large solar particle events. SIS level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of energetic charged particle fluxes over the following time periods: - 256 seconds - hourly - daily - 27 days (1 Bartels rotation) Currently, flux data are available for 8 elements, in units of particles/(cm2 Sr sec MeV/nucleon), in eight energy ranges. The energy ranges are different for each element. The elements for which data are available are: - He, C, N, O, Ne, Mg, Si, S, and Fe. See: http://www.srl.caltech.edu/ACE/ASC/level2/sis_l2desc.html proprietary
ACE_SIS_LEVEL2 Advanced Composition Explorer (ACE) Solar Isotope Spectrometer (SIS) Level 2 Data SCIOPS STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614863-SCIOPS.umm_json The Solar Isotope Spectrometer (SIS) is designed to provide high resolution measurements of the isotopic composition of energetic nuclei from He to Ni (Z=2 to 28) over the energy range from ~10 to ~100 MeV/nucleon. During large solar events, when particle fluxes can increase over quiet-time values by factors of up to 10000, SIS measures the isotopic composition of the solar corona, while during solar quiet times SIS measures the isotopes of low-energy Galactic cosmic rays and the composition of the anomalous cosmic rays which are thought to originate in the nearby interstellar medium. The solar energetic particle measurements are useful to further our understanding of the Sun, while also providing a baseline for comparison with the Galactic cosmic ray measurements carried out by CRIS. SIS has a geometry factor of ~40 cm2--sr, which is significantly larger than previous satellite solar particle isotope spectrometers. It is also designed to provide excellent mass resolution during the extremely high particle flux conditions which occur during large solar particle events. SIS level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of energetic charged particle fluxes over the following time periods: - 256 seconds - hourly - daily - 27 days (1 Bartels rotation) Currently, flux data are available for 8 elements, in units of particles/(cm2 Sr sec MeV/nucleon), in eight energy ranges. The energy ranges are different for each element. The elements for which data are available are: - He, C, N, O, Ne, Mg, Si, S, and Fe. See: http://www.srl.caltech.edu/ACE/ASC/level2/sis_l2desc.html proprietary
-ACE_SWEPAM_LEVEL2 Advanced Composition Explorer (ACE) Solar Wind Electron, Proton, and Alpha Monitor (SWEPAM) Level 2 Data SCIOPS STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614864-SCIOPS.umm_json The Solar Wind Electron, Proton, and Alpha Monitor (SWEPAM) measures the solar wind plasma electron and ion fluxes (rates of particle flow) as functions of direction and energy. These data provide detailed knowledge of the solar wind conditions and internal state every minute. SWEPAM also provides real-time solar wind observations which are continuously telemetered to the ground for space weather purposes. Electron and ion measurements are made with separate sensors. The ion sensor measures particle energies between about 0.26 and 36 KeV, and the electron sensor's energy range is between 1 and 1350 eV. Both sensors use electrostatic analyzers with fan-shaped fields-of-view. The electrostatic analyzers measure the energy per charge of each particle by bending their flight paths through the system. The fields-of-view are swept across all solar wind directions by the rotation of the spacecraft. WEPAM level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of solar wind parameters over the following time periods: - 64 seconds (ion data only) - 128 seconds (electron data only) - hourly - (all data) daily - (all data) 27 days (1 Bartels rotation) (all data) SWEPAM level 2 data consists of the following data items: - Ion data o Proton Density (np in cm -3) o Radial Component of the Proton Temperature (TP,rr in o Kelvin) o Ratio of Alpha Density to proton Density (nHe/nP) o Proton Speed (VP in km/s) o Proton Velocity Vector in GSE coordinates (in km/s) o Proton Velocity Vector in RTN coordinates (in km/s) o Proton Velocity Vector in GSM coordinates (in km/s) - Electron data o Electron Temperature (in o Kelvin) (not yet available) See: http://www.srl.caltech.edu/ACE/ASC/level2/swepam_l2desc.html proprietary
ACE_SWEPAM_LEVEL2 Advanced Composition Explorer (ACE) Solar Wind Electron, Proton, and Alpha Monitor (SWEPAM) Level 2 Data ALL STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614864-SCIOPS.umm_json The Solar Wind Electron, Proton, and Alpha Monitor (SWEPAM) measures the solar wind plasma electron and ion fluxes (rates of particle flow) as functions of direction and energy. These data provide detailed knowledge of the solar wind conditions and internal state every minute. SWEPAM also provides real-time solar wind observations which are continuously telemetered to the ground for space weather purposes. Electron and ion measurements are made with separate sensors. The ion sensor measures particle energies between about 0.26 and 36 KeV, and the electron sensor's energy range is between 1 and 1350 eV. Both sensors use electrostatic analyzers with fan-shaped fields-of-view. The electrostatic analyzers measure the energy per charge of each particle by bending their flight paths through the system. The fields-of-view are swept across all solar wind directions by the rotation of the spacecraft. WEPAM level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of solar wind parameters over the following time periods: - 64 seconds (ion data only) - 128 seconds (electron data only) - hourly - (all data) daily - (all data) 27 days (1 Bartels rotation) (all data) SWEPAM level 2 data consists of the following data items: - Ion data o Proton Density (np in cm -3) o Radial Component of the Proton Temperature (TP,rr in o Kelvin) o Ratio of Alpha Density to proton Density (nHe/nP) o Proton Speed (VP in km/s) o Proton Velocity Vector in GSE coordinates (in km/s) o Proton Velocity Vector in RTN coordinates (in km/s) o Proton Velocity Vector in GSM coordinates (in km/s) - Electron data o Electron Temperature (in o Kelvin) (not yet available) See: http://www.srl.caltech.edu/ACE/ASC/level2/swepam_l2desc.html proprietary
-ACE_SWICS_SWIMS_LEVEL2 Advanced Composition Explorer (ACE) Solar Wind Ion Composition Spectrometer (SWICS) and Solar Wind Ion Mass Spectrometer (SWIMS) Level 2 Data ALL STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614852-SCIOPS.umm_json The Solar Wind Ion Composition Spectrometer (SWICS) and the Solar Wind Ion Mass Spectrometer (SWIMS) on ACE are instruments optimized for measurements of the chemical and isotopic composition of solar and interstellar matter. SWICS determines uniquely the chemical and ionic-charge composition of the solar wind, the temperatures and mean speeds of all major solar-wind ions, from H through Fe, at all solar wind speeds above 300 km/s (protons) and 170 km/s (Fe+16), and resolves H and He isotopes of both solar and interstellar sources. SWICS will measure the distribution functions of both the interstellar cloud and dust cloud pickup ions up to energies of 100 keV/e. SWIMS will measure the chemical and isotopic and charge state composition of the solar wind for every element between He and Ni. Each of the two instruments uses electrostatic analysis followed by a time-of-flight and, as required, an energy measurement. The observations made with SWICS and SWIMS will make valuable contributions to the ISTP objectives by providing information regarding the composition and energy distribution of matter entering the magnetosphere. SWICS level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of solar wind parameters over the following time periods: - hourly - daily - 27 days (1 Bartels rotation) SWICS level 2 data consists of the following solar wind data items: - Bulk and Thermal ion Speeds (km/s) => H+, He+2, O+6, Mg+10, and Fe+11 - Ratio of Elements => 4He+2/O, 20Ne+8/O, 24Mg+10/O, and 56Fe+(7 to 12)/O - Ratio of Charge States of the Same Element => C+5/C+6, O+7/O+6, and Fe+11/Fe+9 - Isotope ratios => 3He/4He, 22Ne/20Ne, 24Mg/26Mg See: http://www.srl.caltech.edu/ACE/ASC/level2/swics_swims_l2desc.html proprietary
+ACE_SWEPAM_LEVEL2 Advanced Composition Explorer (ACE) Solar Wind Electron, Proton, and Alpha Monitor (SWEPAM) Level 2 Data SCIOPS STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614864-SCIOPS.umm_json The Solar Wind Electron, Proton, and Alpha Monitor (SWEPAM) measures the solar wind plasma electron and ion fluxes (rates of particle flow) as functions of direction and energy. These data provide detailed knowledge of the solar wind conditions and internal state every minute. SWEPAM also provides real-time solar wind observations which are continuously telemetered to the ground for space weather purposes. Electron and ion measurements are made with separate sensors. The ion sensor measures particle energies between about 0.26 and 36 KeV, and the electron sensor's energy range is between 1 and 1350 eV. Both sensors use electrostatic analyzers with fan-shaped fields-of-view. The electrostatic analyzers measure the energy per charge of each particle by bending their flight paths through the system. The fields-of-view are swept across all solar wind directions by the rotation of the spacecraft. WEPAM level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of solar wind parameters over the following time periods: - 64 seconds (ion data only) - 128 seconds (electron data only) - hourly - (all data) daily - (all data) 27 days (1 Bartels rotation) (all data) SWEPAM level 2 data consists of the following data items: - Ion data o Proton Density (np in cm -3) o Radial Component of the Proton Temperature (TP,rr in o Kelvin) o Ratio of Alpha Density to proton Density (nHe/nP) o Proton Speed (VP in km/s) o Proton Velocity Vector in GSE coordinates (in km/s) o Proton Velocity Vector in RTN coordinates (in km/s) o Proton Velocity Vector in GSM coordinates (in km/s) - Electron data o Electron Temperature (in o Kelvin) (not yet available) See: http://www.srl.caltech.edu/ACE/ASC/level2/swepam_l2desc.html proprietary
ACE_SWICS_SWIMS_LEVEL2 Advanced Composition Explorer (ACE) Solar Wind Ion Composition Spectrometer (SWICS) and Solar Wind Ion Mass Spectrometer (SWIMS) Level 2 Data SCIOPS STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614852-SCIOPS.umm_json The Solar Wind Ion Composition Spectrometer (SWICS) and the Solar Wind Ion Mass Spectrometer (SWIMS) on ACE are instruments optimized for measurements of the chemical and isotopic composition of solar and interstellar matter. SWICS determines uniquely the chemical and ionic-charge composition of the solar wind, the temperatures and mean speeds of all major solar-wind ions, from H through Fe, at all solar wind speeds above 300 km/s (protons) and 170 km/s (Fe+16), and resolves H and He isotopes of both solar and interstellar sources. SWICS will measure the distribution functions of both the interstellar cloud and dust cloud pickup ions up to energies of 100 keV/e. SWIMS will measure the chemical and isotopic and charge state composition of the solar wind for every element between He and Ni. Each of the two instruments uses electrostatic analysis followed by a time-of-flight and, as required, an energy measurement. The observations made with SWICS and SWIMS will make valuable contributions to the ISTP objectives by providing information regarding the composition and energy distribution of matter entering the magnetosphere. SWICS level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of solar wind parameters over the following time periods: - hourly - daily - 27 days (1 Bartels rotation) SWICS level 2 data consists of the following solar wind data items: - Bulk and Thermal ion Speeds (km/s) => H+, He+2, O+6, Mg+10, and Fe+11 - Ratio of Elements => 4He+2/O, 20Ne+8/O, 24Mg+10/O, and 56Fe+(7 to 12)/O - Ratio of Charge States of the Same Element => C+5/C+6, O+7/O+6, and Fe+11/Fe+9 - Isotope ratios => 3He/4He, 22Ne/20Ne, 24Mg/26Mg See: http://www.srl.caltech.edu/ACE/ASC/level2/swics_swims_l2desc.html proprietary
-ACE_ULEIS_LEVEL2 Advanced Composition Explorer (ACE) Ultra Low Energy Isotope Spectrometer (ULEIS) Level 2 Data ALL STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614832-SCIOPS.umm_json The Ultra Low Energy Isotope Spectrometer (ULEIS) measures ion fluxes over the charge range from H through Ni from about 20 keV/nucleon to 10 MeV/nucleon, thus covering both suprathermal and energetic particle energy ranges. Exploratory measurements of ultra-heavy species (mass range above Ni) will also be performed in a more limited energy range near 0.5 MeV/nucleon. ULEIS will be studying the elemental and isotopic composition of solar energetic particles, and the mechanisms by which these particles are energized in the solar corona. ULEIS will also investigate mechanisms by which supersonic interplanetary shock waves energize ions. ULEIS level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of energetic charged particle fluxes over the following time periods: - hourly - daily - 27 days (1 Bartels rotation) Currently, flux data are available for 7 species, in several energy intervals for each species. Flux data are in units of particles/(cm2 Sr sec MeV/nucleon). The species for which data are available are: - H, 3He, 4He, C, O, Ne-S and Fe. See: http://www.srl.caltech.edu/ACE/ASC/level2/uleis_l2desc.html proprietary
+ACE_SWICS_SWIMS_LEVEL2 Advanced Composition Explorer (ACE) Solar Wind Ion Composition Spectrometer (SWICS) and Solar Wind Ion Mass Spectrometer (SWIMS) Level 2 Data ALL STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614852-SCIOPS.umm_json The Solar Wind Ion Composition Spectrometer (SWICS) and the Solar Wind Ion Mass Spectrometer (SWIMS) on ACE are instruments optimized for measurements of the chemical and isotopic composition of solar and interstellar matter. SWICS determines uniquely the chemical and ionic-charge composition of the solar wind, the temperatures and mean speeds of all major solar-wind ions, from H through Fe, at all solar wind speeds above 300 km/s (protons) and 170 km/s (Fe+16), and resolves H and He isotopes of both solar and interstellar sources. SWICS will measure the distribution functions of both the interstellar cloud and dust cloud pickup ions up to energies of 100 keV/e. SWIMS will measure the chemical and isotopic and charge state composition of the solar wind for every element between He and Ni. Each of the two instruments uses electrostatic analysis followed by a time-of-flight and, as required, an energy measurement. The observations made with SWICS and SWIMS will make valuable contributions to the ISTP objectives by providing information regarding the composition and energy distribution of matter entering the magnetosphere. SWICS level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of solar wind parameters over the following time periods: - hourly - daily - 27 days (1 Bartels rotation) SWICS level 2 data consists of the following solar wind data items: - Bulk and Thermal ion Speeds (km/s) => H+, He+2, O+6, Mg+10, and Fe+11 - Ratio of Elements => 4He+2/O, 20Ne+8/O, 24Mg+10/O, and 56Fe+(7 to 12)/O - Ratio of Charge States of the Same Element => C+5/C+6, O+7/O+6, and Fe+11/Fe+9 - Isotope ratios => 3He/4He, 22Ne/20Ne, 24Mg/26Mg See: http://www.srl.caltech.edu/ACE/ASC/level2/swics_swims_l2desc.html proprietary
ACE_ULEIS_LEVEL2 Advanced Composition Explorer (ACE) Ultra Low Energy Isotope Spectrometer (ULEIS) Level 2 Data SCIOPS STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614832-SCIOPS.umm_json The Ultra Low Energy Isotope Spectrometer (ULEIS) measures ion fluxes over the charge range from H through Ni from about 20 keV/nucleon to 10 MeV/nucleon, thus covering both suprathermal and energetic particle energy ranges. Exploratory measurements of ultra-heavy species (mass range above Ni) will also be performed in a more limited energy range near 0.5 MeV/nucleon. ULEIS will be studying the elemental and isotopic composition of solar energetic particles, and the mechanisms by which these particles are energized in the solar corona. ULEIS will also investigate mechanisms by which supersonic interplanetary shock waves energize ions. ULEIS level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of energetic charged particle fluxes over the following time periods: - hourly - daily - 27 days (1 Bartels rotation) Currently, flux data are available for 7 species, in several energy intervals for each species. Flux data are in units of particles/(cm2 Sr sec MeV/nucleon). The species for which data are available are: - H, 3He, 4He, C, O, Ne-S and Fe. See: http://www.srl.caltech.edu/ACE/ASC/level2/uleis_l2desc.html proprietary
+ACE_ULEIS_LEVEL2 Advanced Composition Explorer (ACE) Ultra Low Energy Isotope Spectrometer (ULEIS) Level 2 Data ALL STAC Catalog 1997-08-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214614832-SCIOPS.umm_json The Ultra Low Energy Isotope Spectrometer (ULEIS) measures ion fluxes over the charge range from H through Ni from about 20 keV/nucleon to 10 MeV/nucleon, thus covering both suprathermal and energetic particle energy ranges. Exploratory measurements of ultra-heavy species (mass range above Ni) will also be performed in a more limited energy range near 0.5 MeV/nucleon. ULEIS will be studying the elemental and isotopic composition of solar energetic particles, and the mechanisms by which these particles are energized in the solar corona. ULEIS will also investigate mechanisms by which supersonic interplanetary shock waves energize ions. ULEIS level 2 data is organized into 27-day time periods (Bartels Rotations - roughly one solar rotation period). For each Bartels Rotation, the level 2 data contains time averages of energetic charged particle fluxes over the following time periods: - hourly - daily - 27 days (1 Bartels rotation) Currently, flux data are available for 7 species, in several energy intervals for each species. Flux data are in units of particles/(cm2 Sr sec MeV/nucleon). The species for which data are available are: - H, 3He, 4He, C, O, Ne-S and Fe. See: http://www.srl.caltech.edu/ACE/ASC/level2/uleis_l2desc.html proprietary
ACIDD_0 Across the Channel Investigating Diel Dynamics project ALL STAC Catalog 2017-12-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360091-OB_DAAC.umm_json The ACIDD (Across the Channel Investigating Diel Dynamics) project, in the Santa Barbara Channel, was initially designed to characterize daily variations in phytoplankton populations, but with the Thomas Fire in the Santa Barbara Hills December 2017, this project evolved into a study to characterize the effects of smoke and ash on the mixed layer in the Santa Barbara Channel. proprietary
ACIDD_0 Across the Channel Investigating Diel Dynamics project OB_DAAC STAC Catalog 2017-12-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360091-OB_DAAC.umm_json The ACIDD (Across the Channel Investigating Diel Dynamics) project, in the Santa Barbara Channel, was initially designed to characterize daily variations in phytoplankton populations, but with the Thomas Fire in the Santa Barbara Hills December 2017, this project evolved into a study to characterize the effects of smoke and ash on the mixed layer in the Santa Barbara Channel. proprietary
-ACIDRAINSENDAI Acid Precipitation Survey ALL STAC Catalog 1975-01-01 140, 38, 140, 38 https://cmr.earthdata.nasa.gov/search/concepts/C1214584951-SCIOPS.umm_json The pH, EC and 8 chemical compositions (e.g. NO3, SO4, NH4, Ca etc..) in acid rain were surveyed from 1975 through 1991 in Sendai, Japan. The input data capacity of latitude and longitude values are limited only to degrees. The exact values are as follows: Min. Latitude: 38deg.15min. N Max. Latitude: 38deg.15min. N Min. Longitude: 140deg.52min. E Max. Longitude: 140deg.52min. E proprietary
ACIDRAINSENDAI Acid Precipitation Survey SCIOPS STAC Catalog 1975-01-01 140, 38, 140, 38 https://cmr.earthdata.nasa.gov/search/concepts/C1214584951-SCIOPS.umm_json The pH, EC and 8 chemical compositions (e.g. NO3, SO4, NH4, Ca etc..) in acid rain were surveyed from 1975 through 1991 in Sendai, Japan. The input data capacity of latitude and longitude values are limited only to degrees. The exact values are as follows: Min. Latitude: 38deg.15min. N Max. Latitude: 38deg.15min. N Min. Longitude: 140deg.52min. E Max. Longitude: 140deg.52min. E proprietary
+ACIDRAINSENDAI Acid Precipitation Survey ALL STAC Catalog 1975-01-01 140, 38, 140, 38 https://cmr.earthdata.nasa.gov/search/concepts/C1214584951-SCIOPS.umm_json The pH, EC and 8 chemical compositions (e.g. NO3, SO4, NH4, Ca etc..) in acid rain were surveyed from 1975 through 1991 in Sendai, Japan. The input data capacity of latitude and longitude values are limited only to degrees. The exact values are as follows: Min. Latitude: 38deg.15min. N Max. Latitude: 38deg.15min. N Min. Longitude: 140deg.52min. E Max. Longitude: 140deg.52min. E proprietary
ACOSMonthlyGriddedXCO2_3 Monthly Gridded Level 4 bias-corrected XCO2 and other select fields aggregated from ACOS as Level 4 monthly files V3 (ACOSMonthlyGriddedXCO2) GES_DISC STAC Catalog 2014-09-07 2020-06-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2219374316-GES_DISC.umm_json Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Greenhouse Gases Observing Satellite (GOSAT) bias corrected data. This is the latest version of this collection. proprietary
ACOS_L2S_7.3 ACOS GOSAT/TANSO-FTS Level 2 Full Physics Standard Product V7.3 (ACOS_L2S) at GES DISC GES_DISC STAC Catalog 2009-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1339230297-GES_DISC.umm_json "Version 7.3 is the current version of the data set. Version 3.5 is no longer available and has been superseded by Version 7.3. This data set is currently provided by the OCO (Orbiting Carbon Observatory) Project. In expectation of the OCO-2 launch, the algorithm was developed by the Atmospheric CO2 Observations from Space (ACOS) Task as a preparatory project, using GOSAT TANSO-FTS spectra. After the OCO-2 launch, ""ACOS"" data are still produced and improved, using approaches applied to the OCO-2 spectra. The ""ACOS"" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances, and algorithm build version 7.3. The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the ""ACOS"" Level 2 production process. Even though the GES DISC is not publicly distributing Level 1B ACOS products, it should be known that changes in this version are affecting both Level 1B and Level 2 data. An important enhancement in Level1B will address the degradation in the number of quality-passed soundings. Elimination of many systematic biases, and better agreement with TCCON (Total Carbon Column Observing Network), is expected in Level 2 retrievals. The key changes to the L2 algorithm include scaling the O2-A band spectroscopy (reducing XCO2 bias by 4 or 5 ppm); using interpolation with the instrument lineshape [ ILS ] (reducing XCO2 bias by 1.5 ppm); and fitting a zero level offset to the A-band. Users have to also carefully familiarize themselves with the disclaimer in the new documentation. An important element to note are the updates on data screening. Although a Master Quality Flag is provided in the data product, further analysis of a larger set of data has allowed the science team to provide an updated set of screening criteria. These are listed in the data user's guide, and are recommended instead of the Master Quality Flag. Lastly, users should continue to carefully observe and weigh information from three important flags: ""warn_level"" - Provides a value that summarizes each sounding's acceptability to a larger set of quality filters. A high warn level predicts that the sounding would fail most data filters applied to it. A low warn level suggests that the sounding would pass most quality filters that might be applied. ""sounding_qual_flag"" - quality of input data provided to the retrieval processing ""outcome_flag"" - retrieval quality based upon certain internal thresholds (not thoroughly evaluated) ""master_quality_flag"" - four possible values: ""Good"", ""Caution"" and ""Bad"", and ""Failed"", as determined from other flags in the L2 productThe short name for this data type is ACOS_L2S." proprietary
ACOS_L2S_7.3 ACOS GOSAT/TANSO-FTS Level 2 Full Physics Standard Product V7.3 (ACOS_L2S) at GES DISC ALL STAC Catalog 2009-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1339230297-GES_DISC.umm_json "Version 7.3 is the current version of the data set. Version 3.5 is no longer available and has been superseded by Version 7.3. This data set is currently provided by the OCO (Orbiting Carbon Observatory) Project. In expectation of the OCO-2 launch, the algorithm was developed by the Atmospheric CO2 Observations from Space (ACOS) Task as a preparatory project, using GOSAT TANSO-FTS spectra. After the OCO-2 launch, ""ACOS"" data are still produced and improved, using approaches applied to the OCO-2 spectra. The ""ACOS"" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances, and algorithm build version 7.3. The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the ""ACOS"" Level 2 production process. Even though the GES DISC is not publicly distributing Level 1B ACOS products, it should be known that changes in this version are affecting both Level 1B and Level 2 data. An important enhancement in Level1B will address the degradation in the number of quality-passed soundings. Elimination of many systematic biases, and better agreement with TCCON (Total Carbon Column Observing Network), is expected in Level 2 retrievals. The key changes to the L2 algorithm include scaling the O2-A band spectroscopy (reducing XCO2 bias by 4 or 5 ppm); using interpolation with the instrument lineshape [ ILS ] (reducing XCO2 bias by 1.5 ppm); and fitting a zero level offset to the A-band. Users have to also carefully familiarize themselves with the disclaimer in the new documentation. An important element to note are the updates on data screening. Although a Master Quality Flag is provided in the data product, further analysis of a larger set of data has allowed the science team to provide an updated set of screening criteria. These are listed in the data user's guide, and are recommended instead of the Master Quality Flag. Lastly, users should continue to carefully observe and weigh information from three important flags: ""warn_level"" - Provides a value that summarizes each sounding's acceptability to a larger set of quality filters. A high warn level predicts that the sounding would fail most data filters applied to it. A low warn level suggests that the sounding would pass most quality filters that might be applied. ""sounding_qual_flag"" - quality of input data provided to the retrieval processing ""outcome_flag"" - retrieval quality based upon certain internal thresholds (not thoroughly evaluated) ""master_quality_flag"" - four possible values: ""Good"", ""Caution"" and ""Bad"", and ""Failed"", as determined from other flags in the L2 productThe short name for this data type is ACOS_L2S." proprietary
-ACOS_L2S_9r ACOS GOSAT/TANSO-FTS Level 2 Full Physics Standard Product V9r (ACOS_L2S) at GES DISC ALL STAC Catalog 2009-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633158704-GES_DISC.umm_json "Version 9r is the current version of the data set. Older versions will no longer be available and are superseded by Version 9r. This data set is currently provided by the OCO (Orbiting Carbon Observatory) Project. In expectation of the OCO-2 launch, the algorithm was developed by the Atmospheric CO2 Observations from Space (ACOS) Task as a preparatory project, using GOSAT TANSO-FTS spectra. After the OCO-2 launch, ""ACOS"" data are still produced and improved, using approaches applied to the OCO-2 spectra. The ""ACOS"" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances, and algorithm build version 7.3. The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the ""ACOS"" Level 2 production process. Even though the GES DISC is not publicly distributing Level 1B ACOS products, it should be known that changes in this version are affecting both Level 1B and Level 2 data. An important enhancement in Level1B will address the degradation in the number of quality-passed soundings. Elimination of many systematic biases, and better agreement with TCCON (Total Carbon Column Observing Network), is expected in Level 2 retrievals. The key changes to the L2 algorithm include scaling the O2-A band spectroscopy (reducing XCO2 bias by 4 or 5 ppm); using interpolation with the instrument lineshape [ ILS ] (reducing XCO2 bias by 1.5 ppm); and fitting a zero level offset to the A-band. Users have to also carefully familiarize themselves with the disclaimer in the new documentation. An important element to note are the updates on data screening. Although a Master Quality Flag is provided in the data product, further analysis of a larger set of data has allowed the science team to provide an updated set of screening criteria. These are listed in the data user's guide, and are recommended instead of the Master Quality Flag. Lastly, users should continue to carefully observe and weigh information from three important flags: ""sounding_qual_flag"" - quality of input data provided to the retrieval processing ""outcome_flag"" - retrieval quality based upon certain internal thresholds (not thoroughly evaluated) " proprietary
ACOS_L2S_9r ACOS GOSAT/TANSO-FTS Level 2 Full Physics Standard Product V9r (ACOS_L2S) at GES DISC GES_DISC STAC Catalog 2009-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633158704-GES_DISC.umm_json "Version 9r is the current version of the data set. Older versions will no longer be available and are superseded by Version 9r. This data set is currently provided by the OCO (Orbiting Carbon Observatory) Project. In expectation of the OCO-2 launch, the algorithm was developed by the Atmospheric CO2 Observations from Space (ACOS) Task as a preparatory project, using GOSAT TANSO-FTS spectra. After the OCO-2 launch, ""ACOS"" data are still produced and improved, using approaches applied to the OCO-2 spectra. The ""ACOS"" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances, and algorithm build version 7.3. The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the ""ACOS"" Level 2 production process. Even though the GES DISC is not publicly distributing Level 1B ACOS products, it should be known that changes in this version are affecting both Level 1B and Level 2 data. An important enhancement in Level1B will address the degradation in the number of quality-passed soundings. Elimination of many systematic biases, and better agreement with TCCON (Total Carbon Column Observing Network), is expected in Level 2 retrievals. The key changes to the L2 algorithm include scaling the O2-A band spectroscopy (reducing XCO2 bias by 4 or 5 ppm); using interpolation with the instrument lineshape [ ILS ] (reducing XCO2 bias by 1.5 ppm); and fitting a zero level offset to the A-band. Users have to also carefully familiarize themselves with the disclaimer in the new documentation. An important element to note are the updates on data screening. Although a Master Quality Flag is provided in the data product, further analysis of a larger set of data has allowed the science team to provide an updated set of screening criteria. These are listed in the data user's guide, and are recommended instead of the Master Quality Flag. Lastly, users should continue to carefully observe and weigh information from three important flags: ""sounding_qual_flag"" - quality of input data provided to the retrieval processing ""outcome_flag"" - retrieval quality based upon certain internal thresholds (not thoroughly evaluated) " proprietary
+ACOS_L2S_9r ACOS GOSAT/TANSO-FTS Level 2 Full Physics Standard Product V9r (ACOS_L2S) at GES DISC ALL STAC Catalog 2009-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633158704-GES_DISC.umm_json "Version 9r is the current version of the data set. Older versions will no longer be available and are superseded by Version 9r. This data set is currently provided by the OCO (Orbiting Carbon Observatory) Project. In expectation of the OCO-2 launch, the algorithm was developed by the Atmospheric CO2 Observations from Space (ACOS) Task as a preparatory project, using GOSAT TANSO-FTS spectra. After the OCO-2 launch, ""ACOS"" data are still produced and improved, using approaches applied to the OCO-2 spectra. The ""ACOS"" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances, and algorithm build version 7.3. The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the ""ACOS"" Level 2 production process. Even though the GES DISC is not publicly distributing Level 1B ACOS products, it should be known that changes in this version are affecting both Level 1B and Level 2 data. An important enhancement in Level1B will address the degradation in the number of quality-passed soundings. Elimination of many systematic biases, and better agreement with TCCON (Total Carbon Column Observing Network), is expected in Level 2 retrievals. The key changes to the L2 algorithm include scaling the O2-A band spectroscopy (reducing XCO2 bias by 4 or 5 ppm); using interpolation with the instrument lineshape [ ILS ] (reducing XCO2 bias by 1.5 ppm); and fitting a zero level offset to the A-band. Users have to also carefully familiarize themselves with the disclaimer in the new documentation. An important element to note are the updates on data screening. Although a Master Quality Flag is provided in the data product, further analysis of a larger set of data has allowed the science team to provide an updated set of screening criteria. These are listed in the data user's guide, and are recommended instead of the Master Quality Flag. Lastly, users should continue to carefully observe and weigh information from three important flags: ""sounding_qual_flag"" - quality of input data provided to the retrieval processing ""outcome_flag"" - retrieval quality based upon certain internal thresholds (not thoroughly evaluated) " proprietary
ACOS_L2_Lite_FP_7.3 ACOS GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files V7.3 (ACOS_L2_Lite_FP) at GES DISC GES_DISC STAC Catalog 2009-04-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1339230298-GES_DISC.umm_json "The ACOS Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files. Orbital granules of the ACOS Level 2 standard product (ACOS_L2S) are used as input. The ""ACOS"" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances. The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the ""ACOS"" Level 2 production process." proprietary
ACOS_L2_Lite_FP_7.3 ACOS GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files V7.3 (ACOS_L2_Lite_FP) at GES DISC ALL STAC Catalog 2009-04-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1339230298-GES_DISC.umm_json "The ACOS Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files. Orbital granules of the ACOS Level 2 standard product (ACOS_L2S) are used as input. The ""ACOS"" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances. The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the ""ACOS"" Level 2 production process." proprietary
-ACOS_L2_Lite_FP_9r ACOS GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files V9r (ACOS_L2_Lite_FP) at GES DISC GES_DISC STAC Catalog 2009-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1720416694-GES_DISC.umm_json "Version 9r is the current version of the data set. Older versions will no longer be available and are superseded by Version 9r. The ACOS Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files. Orbital granules of the ACOS Level 2 standard product (ACOS_L2S) are used as input. The ""ACOS"" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances. The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the ""ACOS"" Level 2 production process." proprietary
ACOS_L2_Lite_FP_9r ACOS GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files V9r (ACOS_L2_Lite_FP) at GES DISC ALL STAC Catalog 2009-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1720416694-GES_DISC.umm_json "Version 9r is the current version of the data set. Older versions will no longer be available and are superseded by Version 9r. The ACOS Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files. Orbital granules of the ACOS Level 2 standard product (ACOS_L2S) are used as input. The ""ACOS"" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances. The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the ""ACOS"" Level 2 production process." proprietary
-ACR3L2DM_1 ACRIM III Level 2 Daily Mean Data V001 ALL STAC Catalog 2000-04-05 2013-11-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179031504-LARC.umm_json ACR3L2DM_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Daily Mean Data version 1 product consists of Level 2 total solar irradiance in the form of daily means gathered by the ACRIM III instrument on the ACRIMSAT satellite. The daily means are constructed from the shutter cycle results for each day. proprietary
+ACOS_L2_Lite_FP_9r ACOS GOSAT/TANSO-FTS Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files V9r (ACOS_L2_Lite_FP) at GES DISC GES_DISC STAC Catalog 2009-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1720416694-GES_DISC.umm_json "Version 9r is the current version of the data set. Older versions will no longer be available and are superseded by Version 9r. The ACOS Lite files contain bias-corrected XCO2 along with other select fields aggregated as daily files. Orbital granules of the ACOS Level 2 standard product (ACOS_L2S) are used as input. The ""ACOS"" data set contains Carbon Dioxide (CO2) column averaged dry air mole fraction for all soundings for which retrieval was attempted. These are the highest-level products made available by the OCO Project, using TANSO-FTS spectral radiances. The GOSAT team at JAXA produces GOSAT TANSO-FTS Level 1B (L1B) data products for internal use and for distribution to collaborative partners, such as ESA and NASA. These calibrated products are augmented by the OCO Project with additional geolocation information and further corrections. Thus produced Level 1B products (with calibrated radiances and geolocation) are the input to the ""ACOS"" Level 2 production process." proprietary
ACR3L2DM_1 ACRIM III Level 2 Daily Mean Data V001 LARC STAC Catalog 2000-04-05 2013-11-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179031504-LARC.umm_json ACR3L2DM_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Daily Mean Data version 1 product consists of Level 2 total solar irradiance in the form of daily means gathered by the ACRIM III instrument on the ACRIMSAT satellite. The daily means are constructed from the shutter cycle results for each day. proprietary
+ACR3L2DM_1 ACRIM III Level 2 Daily Mean Data V001 ALL STAC Catalog 2000-04-05 2013-11-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179031504-LARC.umm_json ACR3L2DM_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Daily Mean Data version 1 product consists of Level 2 total solar irradiance in the form of daily means gathered by the ACRIM III instrument on the ACRIMSAT satellite. The daily means are constructed from the shutter cycle results for each day. proprietary
ACR3L2SC_1 ACRIM III Level 2 Shutter Cycle Data V001 LARC STAC Catalog 2000-04-05 2013-11-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C61787524-LARC.umm_json ACR3L2SC_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Shutter Cycle Data version 1 product contains Level 2 total solar irradiance in the form of shutter cycles gathered by the ACRIM instrument on the ACRIMSAT satellite. proprietary
ACR3L2SC_1 ACRIM III Level 2 Shutter Cycle Data V001 ALL STAC Catalog 2000-04-05 2013-11-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C61787524-LARC.umm_json ACR3L2SC_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Shutter Cycle Data version 1 product contains Level 2 total solar irradiance in the form of shutter cycles gathered by the ACRIM instrument on the ACRIMSAT satellite. proprietary
-ACRIMII_TSI_UARS_NAT_1 Active Cavity Radiometer Irradiance Monitor (ACRIM) II Total Solar Irradiance (TSI) aboard UARS in Native format ALL STAC Catalog 1991-10-04 2001-11-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000700-LARC_ASDC.umm_json ACRIMII_TSI_UARS_NAT data are Active Cavity Radiometer Irradiance Monitor II (ACRIM II) Total Solar Irradiance (TSI) aboard the Upper Atmosphere Research Satellite (UARS) Data in Native (NAT) format. The ACRIMII_TSI_UARS_NAT data product consists of the Level 2 total solar irradiance in the form of daily means gathered by the ACRIM II instrument on the UARS satellite. The daily means are constructed from the shutter cycle results for each day. This data set is considered Version 2. proprietary
ACRIMII_TSI_UARS_NAT_1 Active Cavity Radiometer Irradiance Monitor (ACRIM) II Total Solar Irradiance (TSI) aboard UARS in Native format LARC_ASDC STAC Catalog 1991-10-04 2001-11-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000700-LARC_ASDC.umm_json ACRIMII_TSI_UARS_NAT data are Active Cavity Radiometer Irradiance Monitor II (ACRIM II) Total Solar Irradiance (TSI) aboard the Upper Atmosphere Research Satellite (UARS) Data in Native (NAT) format. The ACRIMII_TSI_UARS_NAT data product consists of the Level 2 total solar irradiance in the form of daily means gathered by the ACRIM II instrument on the UARS satellite. The daily means are constructed from the shutter cycle results for each day. This data set is considered Version 2. proprietary
+ACRIMII_TSI_UARS_NAT_1 Active Cavity Radiometer Irradiance Monitor (ACRIM) II Total Solar Irradiance (TSI) aboard UARS in Native format ALL STAC Catalog 1991-10-04 2001-11-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000700-LARC_ASDC.umm_json ACRIMII_TSI_UARS_NAT data are Active Cavity Radiometer Irradiance Monitor II (ACRIM II) Total Solar Irradiance (TSI) aboard the Upper Atmosphere Research Satellite (UARS) Data in Native (NAT) format. The ACRIMII_TSI_UARS_NAT data product consists of the Level 2 total solar irradiance in the form of daily means gathered by the ACRIM II instrument on the UARS satellite. The daily means are constructed from the shutter cycle results for each day. This data set is considered Version 2. proprietary
ACTAMERICA-PICARRO_Ground_1568_1.1 ACT-America: L2 In Situ CO2, CO, and CH4 Concentrations from Towers, Eastern USA ALL STAC Catalog 2015-01-01 2017-12-31 -98.59, 30.2, -76.42, 44.05 https://cmr.earthdata.nasa.gov/search/concepts/C2706398349-ORNL_CLOUD.umm_json This dataset provides atmospheric carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4) concentrations as measured on a network of instrumented communications towers operated by the Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spans five years and includes five 6-week intensive field campaigns covering all 4 seasons and 3 regions of the central and eastern United States. Tower-based measurements began in early 2015 and are continuously collecting CO2, CO, and CH4 data to characterize ground-level (>100 m) carbon background conditions to support the periodic airborne measurement campaigns and transport modeling conducted by ACT-America. The towers are instrumented with infrared cavity ring-down spectrometer systems (CRDS; Picarro Inc.). Data are reported for the highest sampling port on each tower. The averaging interval standard deviation and uncertainty derived from periodic flask sample to in-situ measurement comparisons are provided. Complete tower location, elevation, instrument height, and date/time information are also provided. proprietary
ACTAMERICA-PICARRO_Ground_1568_1.1 ACT-America: L2 In Situ CO2, CO, and CH4 Concentrations from Towers, Eastern USA ORNL_CLOUD STAC Catalog 2015-01-01 2017-12-31 -98.59, 30.2, -76.42, 44.05 https://cmr.earthdata.nasa.gov/search/concepts/C2706398349-ORNL_CLOUD.umm_json This dataset provides atmospheric carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4) concentrations as measured on a network of instrumented communications towers operated by the Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spans five years and includes five 6-week intensive field campaigns covering all 4 seasons and 3 regions of the central and eastern United States. Tower-based measurements began in early 2015 and are continuously collecting CO2, CO, and CH4 data to characterize ground-level (>100 m) carbon background conditions to support the periodic airborne measurement campaigns and transport modeling conducted by ACT-America. The towers are instrumented with infrared cavity ring-down spectrometer systems (CRDS; Picarro Inc.). Data are reported for the highest sampling port on each tower. The averaging interval standard deviation and uncertainty derived from periodic flask sample to in-situ measurement comparisons are provided. Complete tower location, elevation, instrument height, and date/time information are also provided. proprietary
-ACTAMERICA_Hskping_1574_1.1 ACT-America: L1 Meteorological and Aircraft Navigational Data ALL STAC Catalog 2016-05-27 2019-07-27 -106.49, 27.23, -71.91, 50.55 https://cmr.earthdata.nasa.gov/search/concepts/C2706344412-ORNL_CLOUD.umm_json "This dataset provides aircraft navigational parameters and related meteorological data (often referred to as ""housekeeping"" data) in support of the research activities for the two aircrafts that flew for the NASA Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spans five years and includes five 6-week intensive field campaigns covering all 4 seasons and 3 regions of the central and eastern United States. Two instrumented aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. During these flights, aircraft positional, meteorological, and environmental data are recorded by a variety of instruments. For this dataset, measurements include, but are not limited to: latitude, longitude, altitude, ground speed, air temperature, and wind speed and direction. These data are incorporated into related ACT-America flight-instrumented datasets to provide geotrajectory file information for position, attitude, and altitude awareness of instrumented sampling." proprietary
ACTAMERICA_Hskping_1574_1.1 ACT-America: L1 Meteorological and Aircraft Navigational Data ORNL_CLOUD STAC Catalog 2016-05-27 2019-07-27 -106.49, 27.23, -71.91, 50.55 https://cmr.earthdata.nasa.gov/search/concepts/C2706344412-ORNL_CLOUD.umm_json "This dataset provides aircraft navigational parameters and related meteorological data (often referred to as ""housekeeping"" data) in support of the research activities for the two aircrafts that flew for the NASA Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spans five years and includes five 6-week intensive field campaigns covering all 4 seasons and 3 regions of the central and eastern United States. Two instrumented aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. During these flights, aircraft positional, meteorological, and environmental data are recorded by a variety of instruments. For this dataset, measurements include, but are not limited to: latitude, longitude, altitude, ground speed, air temperature, and wind speed and direction. These data are incorporated into related ACT-America flight-instrumented datasets to provide geotrajectory file information for position, attitude, and altitude awareness of instrumented sampling." proprietary
+ACTAMERICA_Hskping_1574_1.1 ACT-America: L1 Meteorological and Aircraft Navigational Data ALL STAC Catalog 2016-05-27 2019-07-27 -106.49, 27.23, -71.91, 50.55 https://cmr.earthdata.nasa.gov/search/concepts/C2706344412-ORNL_CLOUD.umm_json "This dataset provides aircraft navigational parameters and related meteorological data (often referred to as ""housekeeping"" data) in support of the research activities for the two aircrafts that flew for the NASA Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spans five years and includes five 6-week intensive field campaigns covering all 4 seasons and 3 regions of the central and eastern United States. Two instrumented aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. During these flights, aircraft positional, meteorological, and environmental data are recorded by a variety of instruments. For this dataset, measurements include, but are not limited to: latitude, longitude, altitude, ground speed, air temperature, and wind speed and direction. These data are incorporated into related ACT-America flight-instrumented datasets to provide geotrajectory file information for position, attitude, and altitude awareness of instrumented sampling." proprietary
ACTAMERICA_MFFLL_1649_1.1 ACT-America: L2 Remotely Sensed Column-average CO2 by Airborne Lidar, Eastern USA ORNL_CLOUD STAC Catalog 2016-05-27 2018-05-20 -106.05, 27.23, -71.91, 49.11 https://cmr.earthdata.nasa.gov/search/concepts/C2706335063-ORNL_CLOUD.umm_json This dataset provides Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the United States for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at 0.1 second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1 second column CO2 reporting frequency, are included, but not limited to, latitude, longitude, altitude, and attitude. Processing for this Level 2 (L2) product included additional processing and calibration procedures described in this document as applied to retrieval of column CO2 from L1 MFLL data. Data users should use this L2 data unless different CO2 retrieval criteria are preferred. proprietary
ACTAMERICA_MFFLL_1649_1.1 ACT-America: L2 Remotely Sensed Column-average CO2 by Airborne Lidar, Eastern USA ALL STAC Catalog 2016-05-27 2018-05-20 -106.05, 27.23, -71.91, 49.11 https://cmr.earthdata.nasa.gov/search/concepts/C2706335063-ORNL_CLOUD.umm_json This dataset provides Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the United States for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at 0.1 second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1 second column CO2 reporting frequency, are included, but not limited to, latitude, longitude, altitude, and attitude. Processing for this Level 2 (L2) product included additional processing and calibration procedures described in this document as applied to retrieval of column CO2 from L1 MFLL data. Data users should use this L2 data unless different CO2 retrieval criteria are preferred. proprietary
ACTAMERICA_MFLL_L1_1817_1 ACT-America: L1 DAOD Measurements by Airborne CO2 Lidar, Eastern USA ALL STAC Catalog 2016-05-27 2018-05-20 -106.05, 27.23, -71.91, 49.11 https://cmr.earthdata.nasa.gov/search/concepts/C2705731187-ORNL_CLOUD.umm_json This dataset provides Level 1 (L1) remotely sensed differential absorption optical depth (DAOD) measurements made through the Multi-Functional Fiber Laser Lidar (MFLL; Harris Corporation) during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the United States for the Atmospheric Carbon and Transport (ACT-America) project. DAOD were measured at 0.1 second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with MFLL. The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1 second column CO2 reporting frequency, are included, but not limited to, latitude, longitude, altitude, and attitude. Data users should note that a Level 2 (L2) MFLL data product is available (related dataset) that contains all data variables (plus the column-average CO2) included in this L1 MFLL data product but has undergone additional processing and calibrations and is recommended for most use cases. proprietary
ACTAMERICA_MFLL_L1_1817_1 ACT-America: L1 DAOD Measurements by Airborne CO2 Lidar, Eastern USA ORNL_CLOUD STAC Catalog 2016-05-27 2018-05-20 -106.05, 27.23, -71.91, 49.11 https://cmr.earthdata.nasa.gov/search/concepts/C2705731187-ORNL_CLOUD.umm_json This dataset provides Level 1 (L1) remotely sensed differential absorption optical depth (DAOD) measurements made through the Multi-Functional Fiber Laser Lidar (MFLL; Harris Corporation) during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the United States for the Atmospheric Carbon and Transport (ACT-America) project. DAOD were measured at 0.1 second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with MFLL. The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1 second column CO2 reporting frequency, are included, but not limited to, latitude, longitude, altitude, and attitude. Data users should note that a Level 2 (L2) MFLL data product is available (related dataset) that contains all data variables (plus the column-average CO2) included in this L1 MFLL data product but has undergone additional processing and calibrations and is recommended for most use cases. proprietary
-ACTAMERICA_Merge_1593_1.2 ACT-America: L3 Merged In Situ Atmospheric Trace Gases and Flask Data, Eastern USA ALL STAC Catalog 2016-07-11 2019-07-27 -106.49, 27.23, -72.66, 50.55 https://cmr.earthdata.nasa.gov/search/concepts/C2367023363-ORNL_CLOUD.umm_json This dataset provides merged data products acquired during flights over the central and eastern United States as part of the Atmospheric Carbon and Transport - America (ACT-America) project. Two aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. The merged data products are composed of continuous in situ measurements of atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), ozone (O3), and ethane (C2H6, B200 aircraft only) that were averaged to uniform intervals and merged with aircraft navigation and meteorological variables as well as trace gas concentrations from discrete flask samples collected with the Programmable Flask Package (PFP). These merged data products provide integrated measurements at intervals useful to the modeling community for studying the transport and fluxes of atmospheric carbon dioxide and methane across North America. proprietary
ACTAMERICA_Merge_1593_1.2 ACT-America: L3 Merged In Situ Atmospheric Trace Gases and Flask Data, Eastern USA ORNL_CLOUD STAC Catalog 2016-07-11 2019-07-27 -106.49, 27.23, -72.66, 50.55 https://cmr.earthdata.nasa.gov/search/concepts/C2367023363-ORNL_CLOUD.umm_json This dataset provides merged data products acquired during flights over the central and eastern United States as part of the Atmospheric Carbon and Transport - America (ACT-America) project. Two aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. The merged data products are composed of continuous in situ measurements of atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), ozone (O3), and ethane (C2H6, B200 aircraft only) that were averaged to uniform intervals and merged with aircraft navigation and meteorological variables as well as trace gas concentrations from discrete flask samples collected with the Programmable Flask Package (PFP). These merged data products provide integrated measurements at intervals useful to the modeling community for studying the transport and fluxes of atmospheric carbon dioxide and methane across North America. proprietary
-ACTAMERICA_PFP_1575_1.2 ACT-America: L2 In Situ Atmospheric Gas Concentrations from Flasks, Eastern USA ORNL_CLOUD STAC Catalog 2016-05-27 2019-07-27 -105.89, 27.79, -72.94, 49.4 https://cmr.earthdata.nasa.gov/search/concepts/C2706340483-ORNL_CLOUD.umm_json This dataset provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e., concentrations) from airborne campaigns over North America for the NASA Atmospheric Carbon and Transport - America (ACT-America) project. ACT-America's mission spanned five years and included five six-week field campaigns covering all four seasons and three regions of the central and eastern United States. Two instrumented aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. The data were derived from laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard the two ACT-America aircraft. Approximately 10 - 12 discrete flask samples were captured during each of the 195 flights. This dataset provides results from all five campaigns, including Summer 2016, Winter 2017, Fall 2017, Spring 2018, and Summer 2019. proprietary
+ACTAMERICA_Merge_1593_1.2 ACT-America: L3 Merged In Situ Atmospheric Trace Gases and Flask Data, Eastern USA ALL STAC Catalog 2016-07-11 2019-07-27 -106.49, 27.23, -72.66, 50.55 https://cmr.earthdata.nasa.gov/search/concepts/C2367023363-ORNL_CLOUD.umm_json This dataset provides merged data products acquired during flights over the central and eastern United States as part of the Atmospheric Carbon and Transport - America (ACT-America) project. Two aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. The merged data products are composed of continuous in situ measurements of atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), ozone (O3), and ethane (C2H6, B200 aircraft only) that were averaged to uniform intervals and merged with aircraft navigation and meteorological variables as well as trace gas concentrations from discrete flask samples collected with the Programmable Flask Package (PFP). These merged data products provide integrated measurements at intervals useful to the modeling community for studying the transport and fluxes of atmospheric carbon dioxide and methane across North America. proprietary
ACTAMERICA_PFP_1575_1.2 ACT-America: L2 In Situ Atmospheric Gas Concentrations from Flasks, Eastern USA ALL STAC Catalog 2016-05-27 2019-07-27 -105.89, 27.79, -72.94, 49.4 https://cmr.earthdata.nasa.gov/search/concepts/C2706340483-ORNL_CLOUD.umm_json This dataset provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e., concentrations) from airborne campaigns over North America for the NASA Atmospheric Carbon and Transport - America (ACT-America) project. ACT-America's mission spanned five years and included five six-week field campaigns covering all four seasons and three regions of the central and eastern United States. Two instrumented aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. The data were derived from laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard the two ACT-America aircraft. Approximately 10 - 12 discrete flask samples were captured during each of the 195 flights. This dataset provides results from all five campaigns, including Summer 2016, Winter 2017, Fall 2017, Spring 2018, and Summer 2019. proprietary
-ACTAMERICA_PICARRO_1556_1.2 ACT-America: L2 In Situ Atmospheric CO2, CO, CH4, and O3 Concentrations, Eastern USA ALL STAC Catalog 2016-07-11 2019-07-27 -110, 25, -70, 50.55 https://cmr.earthdata.nasa.gov/search/concepts/C2706347267-ORNL_CLOUD.umm_json This dataset provides atmospheric carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), water vapor (H2O), and ozone (O3) concentrations collected during airborne campaigns conducted by the Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spanned 4 years and included five 6-week airborne campaigns covering all 4 seasons and 3 regions of the central and eastern United States. This dataset provides results from all five campaigns, including Summer 2016, Winter 2017, Fall 2017, Spring 2018, and Summer 2019. Two instrumented aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. CO2, CO, CH4, and H2O were collected with an infrared cavity ring-down spectrometer system (CRDS; Picarro Inc.). Ozone data were collected with a dual beam differential UV absorption ozone monitor (Model 205; 2B Technologies). Both aircraft hosted identical arrays of in situ sensors. Complete aircraft flight information including, but not limited to, latitude, longitude, altitude, and meteorological conditions are also provided. proprietary
+ACTAMERICA_PFP_1575_1.2 ACT-America: L2 In Situ Atmospheric Gas Concentrations from Flasks, Eastern USA ORNL_CLOUD STAC Catalog 2016-05-27 2019-07-27 -105.89, 27.79, -72.94, 49.4 https://cmr.earthdata.nasa.gov/search/concepts/C2706340483-ORNL_CLOUD.umm_json This dataset provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e., concentrations) from airborne campaigns over North America for the NASA Atmospheric Carbon and Transport - America (ACT-America) project. ACT-America's mission spanned five years and included five six-week field campaigns covering all four seasons and three regions of the central and eastern United States. Two instrumented aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. The data were derived from laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard the two ACT-America aircraft. Approximately 10 - 12 discrete flask samples were captured during each of the 195 flights. This dataset provides results from all five campaigns, including Summer 2016, Winter 2017, Fall 2017, Spring 2018, and Summer 2019. proprietary
ACTAMERICA_PICARRO_1556_1.2 ACT-America: L2 In Situ Atmospheric CO2, CO, CH4, and O3 Concentrations, Eastern USA ORNL_CLOUD STAC Catalog 2016-07-11 2019-07-27 -110, 25, -70, 50.55 https://cmr.earthdata.nasa.gov/search/concepts/C2706347267-ORNL_CLOUD.umm_json This dataset provides atmospheric carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), water vapor (H2O), and ozone (O3) concentrations collected during airborne campaigns conducted by the Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spanned 4 years and included five 6-week airborne campaigns covering all 4 seasons and 3 regions of the central and eastern United States. This dataset provides results from all five campaigns, including Summer 2016, Winter 2017, Fall 2017, Spring 2018, and Summer 2019. Two instrumented aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. CO2, CO, CH4, and H2O were collected with an infrared cavity ring-down spectrometer system (CRDS; Picarro Inc.). Ozone data were collected with a dual beam differential UV absorption ozone monitor (Model 205; 2B Technologies). Both aircraft hosted identical arrays of in situ sensors. Complete aircraft flight information including, but not limited to, latitude, longitude, altitude, and meteorological conditions are also provided. proprietary
-ACTAMERICA_WRF_Chem_Output_1884_1 ACT-America: WRF-Chem Baseline Simulations for North America, 2016-2019 ORNL_CLOUD STAC Catalog 2016-06-29 2019-07-31 -150.39, 12.99, -41.61, 62.84 https://cmr.earthdata.nasa.gov/search/concepts/C2704985393-ORNL_CLOUD.umm_json This dataset includes hourly output from the WRF-Chem simulation model for North America at a resolution of 27 km for 2016-06-29 through 2019-07-31. WRF-Chem is the Weather Research and Forecasting (WRF) model coupled with Chemistry. The output provides baseline conditions for comparison to data from ACT-America airborne campaigns conducted to study atmospheric CO2 and CH4 from 2016 to 2019. The WRF-Chem (v. 3.6.1) model was driven by meteorological conditions and sea-surface temperatures. The output includes 50 vertical layers up to atmospheric pressure of 50 hPa with 20 levels in the lowest 1 km. It provides information for understanding the fluxes and atmospheric transport of carbon dioxide (CO2), methane (CH4), and ethane (C2H6). proprietary
+ACTAMERICA_PICARRO_1556_1.2 ACT-America: L2 In Situ Atmospheric CO2, CO, CH4, and O3 Concentrations, Eastern USA ALL STAC Catalog 2016-07-11 2019-07-27 -110, 25, -70, 50.55 https://cmr.earthdata.nasa.gov/search/concepts/C2706347267-ORNL_CLOUD.umm_json This dataset provides atmospheric carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), water vapor (H2O), and ozone (O3) concentrations collected during airborne campaigns conducted by the Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spanned 4 years and included five 6-week airborne campaigns covering all 4 seasons and 3 regions of the central and eastern United States. This dataset provides results from all five campaigns, including Summer 2016, Winter 2017, Fall 2017, Spring 2018, and Summer 2019. Two instrumented aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. CO2, CO, CH4, and H2O were collected with an infrared cavity ring-down spectrometer system (CRDS; Picarro Inc.). Ozone data were collected with a dual beam differential UV absorption ozone monitor (Model 205; 2B Technologies). Both aircraft hosted identical arrays of in situ sensors. Complete aircraft flight information including, but not limited to, latitude, longitude, altitude, and meteorological conditions are also provided. proprietary
ACTAMERICA_WRF_Chem_Output_1884_1 ACT-America: WRF-Chem Baseline Simulations for North America, 2016-2019 ALL STAC Catalog 2016-06-29 2019-07-31 -150.39, 12.99, -41.61, 62.84 https://cmr.earthdata.nasa.gov/search/concepts/C2704985393-ORNL_CLOUD.umm_json This dataset includes hourly output from the WRF-Chem simulation model for North America at a resolution of 27 km for 2016-06-29 through 2019-07-31. WRF-Chem is the Weather Research and Forecasting (WRF) model coupled with Chemistry. The output provides baseline conditions for comparison to data from ACT-America airborne campaigns conducted to study atmospheric CO2 and CH4 from 2016 to 2019. The WRF-Chem (v. 3.6.1) model was driven by meteorological conditions and sea-surface temperatures. The output includes 50 vertical layers up to atmospheric pressure of 50 hPa with 20 levels in the lowest 1 km. It provides information for understanding the fluxes and atmospheric transport of carbon dioxide (CO2), methane (CH4), and ethane (C2H6). proprietary
-ACTIVATE-FLEXPART_1 ACTIVATE FLEXible PARTicle (FLEXPART) Dispersion Model Back-trajectories LARC_ASDC STAC Catalog 2020-02-14 2022-06-30 180, 0, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2647129204-LARC_ASDC.umm_json ACTIVATE-FLEXPART is the FLEXible PARTicle dispersion model back-trajectories ending at the HU-25 Falcon locations. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
+ACTAMERICA_WRF_Chem_Output_1884_1 ACT-America: WRF-Chem Baseline Simulations for North America, 2016-2019 ORNL_CLOUD STAC Catalog 2016-06-29 2019-07-31 -150.39, 12.99, -41.61, 62.84 https://cmr.earthdata.nasa.gov/search/concepts/C2704985393-ORNL_CLOUD.umm_json This dataset includes hourly output from the WRF-Chem simulation model for North America at a resolution of 27 km for 2016-06-29 through 2019-07-31. WRF-Chem is the Weather Research and Forecasting (WRF) model coupled with Chemistry. The output provides baseline conditions for comparison to data from ACT-America airborne campaigns conducted to study atmospheric CO2 and CH4 from 2016 to 2019. The WRF-Chem (v. 3.6.1) model was driven by meteorological conditions and sea-surface temperatures. The output includes 50 vertical layers up to atmospheric pressure of 50 hPa with 20 levels in the lowest 1 km. It provides information for understanding the fluxes and atmospheric transport of carbon dioxide (CO2), methane (CH4), and ethane (C2H6). proprietary
ACTIVATE-FLEXPART_1 ACTIVATE FLEXible PARTicle (FLEXPART) Dispersion Model Back-trajectories ALL STAC Catalog 2020-02-14 2022-06-30 180, 0, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2647129204-LARC_ASDC.umm_json ACTIVATE-FLEXPART is the FLEXible PARTicle dispersion model back-trajectories ending at the HU-25 Falcon locations. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
+ACTIVATE-FLEXPART_1 ACTIVATE FLEXible PARTicle (FLEXPART) Dispersion Model Back-trajectories LARC_ASDC STAC Catalog 2020-02-14 2022-06-30 180, 0, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2647129204-LARC_ASDC.umm_json ACTIVATE-FLEXPART is the FLEXible PARTicle dispersion model back-trajectories ending at the HU-25 Falcon locations. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE-MODIS-MERRA2_1 ACTIVATE Merged MODIS and MERRA-2 Dataset ALL STAC Catalog 2013-01-01 2022-06-30 -85, 0, -25, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2647007347-LARC_ASDC.umm_json ACTIVATE-MODIS-MERRA2 is the merged CERES MODIS and MERRA-2 dataset (pixel-level geostationary cloud products) produced by SatCORPS group at NASA Langley Research Center in support of ACTIVATE. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE-MODIS-MERRA2_1 ACTIVATE Merged MODIS and MERRA-2 Dataset LARC_ASDC STAC Catalog 2013-01-01 2022-06-30 -85, 0, -25, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2647007347-LARC_ASDC.umm_json ACTIVATE-MODIS-MERRA2 is the merged CERES MODIS and MERRA-2 dataset (pixel-level geostationary cloud products) produced by SatCORPS group at NASA Langley Research Center in support of ACTIVATE. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE-Satellite_1 ACTIVATE GOES-16 Supplementary Data Products LARC_ASDC STAC Catalog 2020-02-14 2022-10-31 -95, 0, -25, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2647017088-LARC_ASDC.umm_json ACTIVATE_Satellite_Data_1 is the GOES-16 satellite data supporting the ACTIVATE suborbital campaign. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE-Satellite_1 ACTIVATE GOES-16 Supplementary Data Products ALL STAC Catalog 2020-02-14 2022-10-31 -95, 0, -25, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2647017088-LARC_ASDC.umm_json ACTIVATE_Satellite_Data_1 is the GOES-16 satellite data supporting the ACTIVATE suborbital campaign. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE_AerosolCloud_AircraftRemoteSensing_KingAir_Data_1 ACTIVATE King Air Aerosol and Cloud Remotely Sensed Data ALL STAC Catalog 2020-02-10 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994461250-LARC_ASDC.umm_json ACTIVATE_AerosolCloud_AircraftRemoteSensing_KingAir_Data is the aerosol and cloud data collected onboard the B-200 King Air aircraft via remote sensing instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project is a five-year project (January 2019-December 2023) that will provide important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studies the atmosphere over the western North Atlantic and samples its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air will primarily be used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements will also be onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic are planned through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy is implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE_AerosolCloud_AircraftRemoteSensing_KingAir_Data_1 ACTIVATE King Air Aerosol and Cloud Remotely Sensed Data LARC_ASDC STAC Catalog 2020-02-10 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994461250-LARC_ASDC.umm_json ACTIVATE_AerosolCloud_AircraftRemoteSensing_KingAir_Data is the aerosol and cloud data collected onboard the B-200 King Air aircraft via remote sensing instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project is a five-year project (January 2019-December 2023) that will provide important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studies the atmosphere over the western North Atlantic and samples its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air will primarily be used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements will also be onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic are planned through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy is implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
-ACTIVATE_Aerosol_AircraftInSitu_Falcon_Data_1 ACTIVATE Falcon In Situ Aerosol Data LARC_ASDC STAC Catalog 2020-02-14 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994460846-LARC_ASDC.umm_json ACTIVATE_Aerosol_AircraftInSitu_Falcon_Data is the aerosol data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE_Aerosol_AircraftInSitu_Falcon_Data_1 ACTIVATE Falcon In Situ Aerosol Data ALL STAC Catalog 2020-02-14 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994460846-LARC_ASDC.umm_json ACTIVATE_Aerosol_AircraftInSitu_Falcon_Data is the aerosol data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
+ACTIVATE_Aerosol_AircraftInSitu_Falcon_Data_1 ACTIVATE Falcon In Situ Aerosol Data LARC_ASDC STAC Catalog 2020-02-14 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994460846-LARC_ASDC.umm_json ACTIVATE_Aerosol_AircraftInSitu_Falcon_Data is the aerosol data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE_Cloud_AircraftInSitu_Falcon_Data_1 ACTIVATE Falcon In Situ Cloud Data ALL STAC Catalog 2020-02-14 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994461088-LARC_ASDC.umm_json ACTIVATE_Cloud_AircraftInSitu_Falcon_Data is the cloud data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE_Cloud_AircraftInSitu_Falcon_Data_1 ACTIVATE Falcon In Situ Cloud Data LARC_ASDC STAC Catalog 2020-02-14 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994461088-LARC_ASDC.umm_json ACTIVATE_Cloud_AircraftInSitu_Falcon_Data is the cloud data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
-ACTIVATE_Merge_Data_1 ACTIVATE Falcon Aircraft Merge Data Files ALL STAC Catalog 2020-02-14 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2119361908-LARC_ASDC.umm_json ACTIVATE_Merge_Data is the pre-generated merge data files created from data collected onboard the HU-25 Falcon aircraft during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project is a five-year project (January 2019-December 2023) that will provide important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studies the atmosphere over the western North Atlantic and samples its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air will primarily be used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements will also be onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic are planned through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy is implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE_Merge_Data_1 ACTIVATE Falcon Aircraft Merge Data Files LARC_ASDC STAC Catalog 2020-02-14 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2119361908-LARC_ASDC.umm_json ACTIVATE_Merge_Data is the pre-generated merge data files created from data collected onboard the HU-25 Falcon aircraft during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project is a five-year project (January 2019-December 2023) that will provide important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studies the atmosphere over the western North Atlantic and samples its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air will primarily be used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements will also be onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic are planned through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy is implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
-ACTIVATE_MetNav_AircraftInSitu_Falcon_Data_1 ACTIVATE Falcon In-Situ Meteorological and Navigational Data ALL STAC Catalog 2020-02-10 2022-06-20 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994460739-LARC_ASDC.umm_json ACTIVATE_MetNav_AircraftInSitu_Falcon_Data is the meteorological and navigational data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
+ACTIVATE_Merge_Data_1 ACTIVATE Falcon Aircraft Merge Data Files ALL STAC Catalog 2020-02-14 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2119361908-LARC_ASDC.umm_json ACTIVATE_Merge_Data is the pre-generated merge data files created from data collected onboard the HU-25 Falcon aircraft during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project is a five-year project (January 2019-December 2023) that will provide important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studies the atmosphere over the western North Atlantic and samples its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air will primarily be used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements will also be onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic are planned through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy is implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE_MetNav_AircraftInSitu_Falcon_Data_1 ACTIVATE Falcon In-Situ Meteorological and Navigational Data LARC_ASDC STAC Catalog 2020-02-10 2022-06-20 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994460739-LARC_ASDC.umm_json ACTIVATE_MetNav_AircraftInSitu_Falcon_Data is the meteorological and navigational data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
-ACTIVATE_MetNav_AircraftInSitu_KingAir_Data_1 ACTIVATE King Air Meteorological and Navigational Data LARC_ASDC STAC Catalog 2019-12-16 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994460996-LARC_ASDC.umm_json ACTIVATE_MetNav_AircraftInSitu_KingAir_Data is the meteorological and navigational data collected onboard the B-200 King Air aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project is a five-year project (January 2019-December 2023) that will provide important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studies the atmosphere over the western North Atlantic and samples its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air will primarily be used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements will also be onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic are planned through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy is implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
+ACTIVATE_MetNav_AircraftInSitu_Falcon_Data_1 ACTIVATE Falcon In-Situ Meteorological and Navigational Data ALL STAC Catalog 2020-02-10 2022-06-20 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994460739-LARC_ASDC.umm_json ACTIVATE_MetNav_AircraftInSitu_Falcon_Data is the meteorological and navigational data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE_MetNav_AircraftInSitu_KingAir_Data_1 ACTIVATE King Air Meteorological and Navigational Data ALL STAC Catalog 2019-12-16 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994460996-LARC_ASDC.umm_json ACTIVATE_MetNav_AircraftInSitu_KingAir_Data is the meteorological and navigational data collected onboard the B-200 King Air aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project is a five-year project (January 2019-December 2023) that will provide important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studies the atmosphere over the western North Atlantic and samples its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air will primarily be used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements will also be onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic are planned through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy is implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
-ACTIVATE_Miscellaneous_Data_1 ACTIVATE Miscellaneous and Ancillary Data LARC_ASDC STAC Catalog 2020-02-10 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2132326850-LARC_ASDC.umm_json ACTIVATE_Miscellaneous_Data is the supplementary miscellaneous data collected and utilized during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
+ACTIVATE_MetNav_AircraftInSitu_KingAir_Data_1 ACTIVATE King Air Meteorological and Navigational Data LARC_ASDC STAC Catalog 2019-12-16 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994460996-LARC_ASDC.umm_json ACTIVATE_MetNav_AircraftInSitu_KingAir_Data is the meteorological and navigational data collected onboard the B-200 King Air aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project is a five-year project (January 2019-December 2023) that will provide important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studies the atmosphere over the western North Atlantic and samples its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air will primarily be used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements will also be onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic are planned through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy is implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE_Miscellaneous_Data_1 ACTIVATE Miscellaneous and Ancillary Data ALL STAC Catalog 2020-02-10 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2132326850-LARC_ASDC.umm_json ACTIVATE_Miscellaneous_Data is the supplementary miscellaneous data collected and utilized during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
+ACTIVATE_Miscellaneous_Data_1 ACTIVATE Miscellaneous and Ancillary Data LARC_ASDC STAC Catalog 2020-02-10 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2132326850-LARC_ASDC.umm_json ACTIVATE_Miscellaneous_Data is the supplementary miscellaneous data collected and utilized during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE_Model_Data_1 ACTIVATE Supplementary Model Data LARC_ASDC STAC Catalog 2020-02-14 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2163554174-LARC_ASDC.umm_json ACTIVATE_Model_Data is the MERRA-2 variables sampled along the HU-25 flight tracks during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE_Model_Data_1 ACTIVATE Supplementary Model Data ALL STAC Catalog 2020-02-14 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2163554174-LARC_ASDC.umm_json ACTIVATE_Model_Data is the MERRA-2 variables sampled along the HU-25 flight tracks during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
-ACTIVATE_TraceGas_AircraftInSitu_Falcon_Data_1 ACTIVATE Falcon In Situ Trace Gas Data ALL STAC Catalog 2020-02-14 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994460919-LARC_ASDC.umm_json ACTIVATE_TraceGas_AircraftInSitu_Falcon_Data is the trace gas data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACTIVATE_TraceGas_AircraftInSitu_Falcon_Data_1 ACTIVATE Falcon In Situ Trace Gas Data LARC_ASDC STAC Catalog 2020-02-14 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994460919-LARC_ASDC.umm_json ACTIVATE_TraceGas_AircraftInSitu_Falcon_Data is the trace gas data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
-ACT_CASA_Ensemble_Prior_Fluxes_1675_1.1 ACT-America: Gridded Ensembles of Surface Biogenic Carbon Fluxes, 2003-2019 ORNL_CLOUD STAC Catalog 2003-01-01 2019-12-31 -176, 0.5, -24.5, 70.5 https://cmr.earthdata.nasa.gov/search/concepts/C2705715010-ORNL_CLOUD.umm_json This data set provides gridded, model-derived gross primary productivity (GPP), ecosystem respiration (RECO), and net ecosystem exchange (NEE) of CO2 biogenic fluxes and their uncertainties at monthly and 3-hourly time scales over 2003-2019 on a 463-m spatial resolution grid for the conterminous United States (CONUS) and on both 5-km and half-degree spatial resolution grids for North America (NA). The biogeochemical model Carnegie Ames Stanford Approach (CASA) was used. proprietary
+ACTIVATE_TraceGas_AircraftInSitu_Falcon_Data_1 ACTIVATE Falcon In Situ Trace Gas Data ALL STAC Catalog 2020-02-14 2022-06-30 -85, 25, -58.5, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1994460919-LARC_ASDC.umm_json ACTIVATE_TraceGas_AircraftInSitu_Falcon_Data is the trace gas data collected onboard the HU-25 Falcon aircraft via in-situ instrumentation during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions. proprietary
ACT_CASA_Ensemble_Prior_Fluxes_1675_1.1 ACT-America: Gridded Ensembles of Surface Biogenic Carbon Fluxes, 2003-2019 ALL STAC Catalog 2003-01-01 2019-12-31 -176, 0.5, -24.5, 70.5 https://cmr.earthdata.nasa.gov/search/concepts/C2705715010-ORNL_CLOUD.umm_json This data set provides gridded, model-derived gross primary productivity (GPP), ecosystem respiration (RECO), and net ecosystem exchange (NEE) of CO2 biogenic fluxes and their uncertainties at monthly and 3-hourly time scales over 2003-2019 on a 463-m spatial resolution grid for the conterminous United States (CONUS) and on both 5-km and half-degree spatial resolution grids for North America (NA). The biogeochemical model Carnegie Ames Stanford Approach (CASA) was used. proprietary
+ACT_CASA_Ensemble_Prior_Fluxes_1675_1.1 ACT-America: Gridded Ensembles of Surface Biogenic Carbon Fluxes, 2003-2019 ORNL_CLOUD STAC Catalog 2003-01-01 2019-12-31 -176, 0.5, -24.5, 70.5 https://cmr.earthdata.nasa.gov/search/concepts/C2705715010-ORNL_CLOUD.umm_json This data set provides gridded, model-derived gross primary productivity (GPP), ecosystem respiration (RECO), and net ecosystem exchange (NEE) of CO2 biogenic fluxes and their uncertainties at monthly and 3-hourly time scales over 2003-2019 on a 463-m spatial resolution grid for the conterminous United States (CONUS) and on both 5-km and half-degree spatial resolution grids for North America (NA). The biogeochemical model Carnegie Ames Stanford Approach (CASA) was used. proprietary
ADAM.Surface.Reflectance.Database_3.0 ADAM Surface Reflectance Database v4.0 ALL STAC Catalog 2005-01-01 2005-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336812-ESA.umm_json ADAM enables generating typical monthly variations of the global Earth surface reflectance at 0.1° spatial resolution (Plate Carree projection) and over the spectral range 240-4000nm. The ADAM product is made of gridded monthly mean climatologies over land and ocean surfaces, and of a companion API toolkit that enables the calculation of hyperspectral (at 1 nm resolution over the whole 240-4000 nm spectral range) and multidirectional reflectances (i.e. in any illumination/viewing geometry) depending on user choices. The ADAM climatologies that feed the ADAM calculation tools are: For ocean: monthly chlorophyll concentration derived from SeaWiFS-OrbView-2 (1999-2009); it is used to compute the water column reflectance (which shows large spectral variations in the visible, but is insignificant in the near and mid infrared). monthly wind speed derived from SeaWinds-QuikSCAT-(1999-2009); it is used to calculate the ocean glint reflectance. For land: monthly normalized surface reflectances in the 7 MODIS narrow spectral bands derived from FondsdeSol processing chain of MOD09A1 products (derived from Aqua and Terra observations), on which relies the modelling of the hyperspectral/multidirectional surface (soil/vegetation/snow) reflectance. uncertainty variance-covariance matrix for the 7 spectral bands associated to the normalized surface reflectance. For sea-ice: Sea ice pixels (masked in the original MOD09A1 products) have been accounted for by a gap-filling approach relying on the spatial-temporal distribution of sea ice coverage provided by the CryoClim climatology for year 2005. proprietary
ADAM.Surface.Reflectance.Database_3.0 ADAM Surface Reflectance Database v4.0 ESA STAC Catalog 2005-01-01 2005-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336812-ESA.umm_json ADAM enables generating typical monthly variations of the global Earth surface reflectance at 0.1° spatial resolution (Plate Carree projection) and over the spectral range 240-4000nm. The ADAM product is made of gridded monthly mean climatologies over land and ocean surfaces, and of a companion API toolkit that enables the calculation of hyperspectral (at 1 nm resolution over the whole 240-4000 nm spectral range) and multidirectional reflectances (i.e. in any illumination/viewing geometry) depending on user choices. The ADAM climatologies that feed the ADAM calculation tools are: For ocean: monthly chlorophyll concentration derived from SeaWiFS-OrbView-2 (1999-2009); it is used to compute the water column reflectance (which shows large spectral variations in the visible, but is insignificant in the near and mid infrared). monthly wind speed derived from SeaWinds-QuikSCAT-(1999-2009); it is used to calculate the ocean glint reflectance. For land: monthly normalized surface reflectances in the 7 MODIS narrow spectral bands derived from FondsdeSol processing chain of MOD09A1 products (derived from Aqua and Terra observations), on which relies the modelling of the hyperspectral/multidirectional surface (soil/vegetation/snow) reflectance. uncertainty variance-covariance matrix for the 7 spectral bands associated to the normalized surface reflectance. For sea-ice: Sea ice pixels (masked in the original MOD09A1 products) have been accounted for by a gap-filling approach relying on the spatial-temporal distribution of sea ice coverage provided by the CryoClim climatology for year 2005. proprietary
ADBEX_III_density_1 ADBEX III Water Density Results AU_AADC STAC Catalog 1985-10-09 1985-11-09 49, -66, 70, -55 https://cmr.earthdata.nasa.gov/search/concepts/C1214305676-AU_AADC.umm_json During the ADBEX III voyage, many samples were taken of the sea ice and snow. These samples were analysed to determine water density, with the results recorded in a physical note book that is archived at the Australian Antarctic Division. Logbook(s): - Glaciology ADBEX III Water Density Results - Glaciology ADBEX III Oxygen Isotope Sample Record proprietary
@@ -1468,142 +1468,142 @@ ADBEX_III_strain_grid_1 ADBEX III Sea Ice Strain Grid Measurements AU_AADC STAC
ADBEX_III_strain_grid_1 ADBEX III Sea Ice Strain Grid Measurements ALL STAC Catalog 1985-10-29 1985-11-09 50.21, -66.1, 50.43, -65.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214305700-AU_AADC.umm_json Details of the setup and (re)measurements taken of the strain grid laid out on the sea ice during the ADBEX III voyage of the Nella Dan. The grid was made up of six canes (plus the bridge, used as one of the measurement points). Physical log book is archived at the Australian Antarctic Division. Logbook(s): Glaciology ADBEX III Sea Ice Strain Grid Measurements proprietary
ADBEX_I_nutrient_1 ADBEX I cruise to the Prydz Bay region, 1982: nutrient data AU_AADC STAC Catalog 1982-11-19 1982-12-17 62.68, -69.033, 89.9016, -61.37 https://cmr.earthdata.nasa.gov/search/concepts/C1214305675-AU_AADC.umm_json From the abstract and introduction of ANARE Research Notes 44 - ADBEX I cruise to the Prydz Bay region, 1982: nutrient data. Nitrate, phosphate and silicate concentrations obtained during the ADBEX I cruise to the Prydz Bay region in November and December 1982 are plotted with depth and the raw data are tabulated. Location of the sampling stations and the average concentration of each nutrient in the top 100 m of the water column is mapped. The ADBEX I (Antarctic Division BIOMASS Experiment) cruise is part of a long-term, national program of field surveys aimed at fulfilling the objectives of the BIOMASS (Biological Investigation of Marine Antarctic Systems and Stocks) program. The ADBEX I cruise on MV Nella Dan to the Prydz Bay region between 19 November and 17 December 1982, is the second Antarctic Division cruise to contribute to BIOMASS, the first being FIBEX (First International Biomass Experiment) in 1981. Nutrient data were collected at twenty-eight of the seventy-nine hydrographic stations to provide information for the interpretation of phytoplankton distribution and abundance. The sampling locations and depths were not selected, therefore, on the basis of nutrient-related considerations. The concentration of nitrate, phosphate and silicate is plotted to 600 m for each station and where casts were much deeper or much shallower, a second plot is shown. To show water column structure at the time of sampling, sigma-t values were also plotted, unless data for a cast were unavailable. In addition to the depth profiles, the average concentration to 100 m of each nutrient species is mapped to give a first-order approximation of the horizontal pattern of nutrient distribution in the upper layers. proprietary
ADBEX_I_nutrient_1 ADBEX I cruise to the Prydz Bay region, 1982: nutrient data ALL STAC Catalog 1982-11-19 1982-12-17 62.68, -69.033, 89.9016, -61.37 https://cmr.earthdata.nasa.gov/search/concepts/C1214305675-AU_AADC.umm_json From the abstract and introduction of ANARE Research Notes 44 - ADBEX I cruise to the Prydz Bay region, 1982: nutrient data. Nitrate, phosphate and silicate concentrations obtained during the ADBEX I cruise to the Prydz Bay region in November and December 1982 are plotted with depth and the raw data are tabulated. Location of the sampling stations and the average concentration of each nutrient in the top 100 m of the water column is mapped. The ADBEX I (Antarctic Division BIOMASS Experiment) cruise is part of a long-term, national program of field surveys aimed at fulfilling the objectives of the BIOMASS (Biological Investigation of Marine Antarctic Systems and Stocks) program. The ADBEX I cruise on MV Nella Dan to the Prydz Bay region between 19 November and 17 December 1982, is the second Antarctic Division cruise to contribute to BIOMASS, the first being FIBEX (First International Biomass Experiment) in 1981. Nutrient data were collected at twenty-eight of the seventy-nine hydrographic stations to provide information for the interpretation of phytoplankton distribution and abundance. The sampling locations and depths were not selected, therefore, on the basis of nutrient-related considerations. The concentration of nitrate, phosphate and silicate is plotted to 600 m for each station and where casts were much deeper or much shallower, a second plot is shown. To show water column structure at the time of sampling, sigma-t values were also plotted, unless data for a cast were unavailable. In addition to the depth profiles, the average concentration to 100 m of each nutrient species is mapped to give a first-order approximation of the horizontal pattern of nutrient distribution in the upper layers. proprietary
-ADCP_5MINUTE_SO ACDP Data, 5min. ensemble avrgs. of ocean current velocities, Mar-Sept 2001-2002, Drake Passage and Continental Margin off Western Antarctic Peninsula, GLOBEC ALL STAC Catalog 2001-03-19 2002-09-17 -78, -71, -60, -52 https://cmr.earthdata.nasa.gov/search/concepts/C1214155091-SCIOPS.umm_json Data from a ship-mounted Acoustic Doppler Current Profiler (ADCP) are reported from 7 ship cruises to the Antarctic, March - September 2001 and 2002. The survey area includes the continental margin off the Western Antarctic Peninsula and the adjacent inshore water bodies of Marguerite Bay and Crystal Sound. Ancillary north/south sections across the Drake Passage are reported for transects from Punta Arenas, Chile to the study area and return. Data reported: five minute ensemble averaged values of the U (east-west) and V (north-south) components of ocean currents, for 8 meter depth bins between 26 and ~350 meters, along the ships track. Ships/cruises/dates: AESV Laurence M. Gould / LMG0103 / Mar 19-Apr 12 2001 AESV Laurence M. Gould / LMG0104 / Apr 21-Jun 4 2001 AESV Laurence M. Gould / LMG0106 / Jul 22-Aug 30 2001 RVIB Nathaniel B. Palmer / NBP0103 / Apr 25-Jun 5 2001 RVIB Nathaniel B. Palmer / NBP0104 / Jul 23-Aug 30 2001 RVIB Nathaniel B. Palmer / NBP0202 / Apr 9-May 20 2002 RVIB Nathaniel B. Palmer / NBP0204 / Aug 1-Sep 17 2002 Related data set: file: ADCP_hourly. Hourly averaged data derived from the 5 minute ensemble values are available for each cruise at the above referenced web site. proprietary
ADCP_5MINUTE_SO ACDP Data, 5min. ensemble avrgs. of ocean current velocities, Mar-Sept 2001-2002, Drake Passage and Continental Margin off Western Antarctic Peninsula, GLOBEC SCIOPS STAC Catalog 2001-03-19 2002-09-17 -78, -71, -60, -52 https://cmr.earthdata.nasa.gov/search/concepts/C1214155091-SCIOPS.umm_json Data from a ship-mounted Acoustic Doppler Current Profiler (ADCP) are reported from 7 ship cruises to the Antarctic, March - September 2001 and 2002. The survey area includes the continental margin off the Western Antarctic Peninsula and the adjacent inshore water bodies of Marguerite Bay and Crystal Sound. Ancillary north/south sections across the Drake Passage are reported for transects from Punta Arenas, Chile to the study area and return. Data reported: five minute ensemble averaged values of the U (east-west) and V (north-south) components of ocean currents, for 8 meter depth bins between 26 and ~350 meters, along the ships track. Ships/cruises/dates: AESV Laurence M. Gould / LMG0103 / Mar 19-Apr 12 2001 AESV Laurence M. Gould / LMG0104 / Apr 21-Jun 4 2001 AESV Laurence M. Gould / LMG0106 / Jul 22-Aug 30 2001 RVIB Nathaniel B. Palmer / NBP0103 / Apr 25-Jun 5 2001 RVIB Nathaniel B. Palmer / NBP0104 / Jul 23-Aug 30 2001 RVIB Nathaniel B. Palmer / NBP0202 / Apr 9-May 20 2002 RVIB Nathaniel B. Palmer / NBP0204 / Aug 1-Sep 17 2002 Related data set: file: ADCP_hourly. Hourly averaged data derived from the 5 minute ensemble values are available for each cruise at the above referenced web site. proprietary
+ADCP_5MINUTE_SO ACDP Data, 5min. ensemble avrgs. of ocean current velocities, Mar-Sept 2001-2002, Drake Passage and Continental Margin off Western Antarctic Peninsula, GLOBEC ALL STAC Catalog 2001-03-19 2002-09-17 -78, -71, -60, -52 https://cmr.earthdata.nasa.gov/search/concepts/C1214155091-SCIOPS.umm_json Data from a ship-mounted Acoustic Doppler Current Profiler (ADCP) are reported from 7 ship cruises to the Antarctic, March - September 2001 and 2002. The survey area includes the continental margin off the Western Antarctic Peninsula and the adjacent inshore water bodies of Marguerite Bay and Crystal Sound. Ancillary north/south sections across the Drake Passage are reported for transects from Punta Arenas, Chile to the study area and return. Data reported: five minute ensemble averaged values of the U (east-west) and V (north-south) components of ocean currents, for 8 meter depth bins between 26 and ~350 meters, along the ships track. Ships/cruises/dates: AESV Laurence M. Gould / LMG0103 / Mar 19-Apr 12 2001 AESV Laurence M. Gould / LMG0104 / Apr 21-Jun 4 2001 AESV Laurence M. Gould / LMG0106 / Jul 22-Aug 30 2001 RVIB Nathaniel B. Palmer / NBP0103 / Apr 25-Jun 5 2001 RVIB Nathaniel B. Palmer / NBP0104 / Jul 23-Aug 30 2001 RVIB Nathaniel B. Palmer / NBP0202 / Apr 9-May 20 2002 RVIB Nathaniel B. Palmer / NBP0204 / Aug 1-Sep 17 2002 Related data set: file: ADCP_hourly. Hourly averaged data derived from the 5 minute ensemble values are available for each cruise at the above referenced web site. proprietary
ADCP_HOURLY_SO ACDP Data, hourly ocean current velocities, Mar-Sept 2001-2002, Drake Passage and Continental Margin off Western Antarctic Peninsula, GLOBEC SCIOPS STAC Catalog 2001-03-19 2002-09-17 -78, -71, -60, -52 https://cmr.earthdata.nasa.gov/search/concepts/C1214155112-SCIOPS.umm_json Data from a ship-mounted Acoustic Doppler Current Profiler (ADCP) are reported from 7 cruises to the Antarctic, March - September 2001 and 2002. The survey area includes the continental margin off the Western Antarctic Peninsula and the adjacent inshore water of Marguerite Bay and Crystal Sound. Ancillary north/south sections across the Drake Passage are reported for transects from Punta Arenas, Chile to the study area and return. Data reported: hourly averaged values of the U (east-west) and V (north-south) components of ocean currents, for 8 meter depth bins between 26 and ~350 meters, along the ships' tracks. Ships/cruises/dates: AESV Laurence M. Gould / LMG0103 / Mar 19-Apr 12 2001 AESV Laurence M. Gould / LMG0104 / Apr 21-Jun 4 2001 AESV Laurence M. Gould / LMG0106 / Jul 22-Aug 30 2001 RVIB Nathaniel B. Palmer / NBP0103 / Apr 25-Jun 5 2001 RVIB Nathaniel B. Palmer / NBP0104 / Jul 23-Aug 30 2001 RVIB Nathaniel B. Palmer / NBP0202 / Apr 9-May 20 2002 RVIB Nathaniel B. Palmer / NBP0204 / Aug 1-Sep 17 2002 Related data set: file: ADCP_5minute. The original ADCP 5 minute averaged ensemble data set for each cruise is found at the above referenced web site. proprietary
ADCP_HOURLY_SO ACDP Data, hourly ocean current velocities, Mar-Sept 2001-2002, Drake Passage and Continental Margin off Western Antarctic Peninsula, GLOBEC ALL STAC Catalog 2001-03-19 2002-09-17 -78, -71, -60, -52 https://cmr.earthdata.nasa.gov/search/concepts/C1214155112-SCIOPS.umm_json Data from a ship-mounted Acoustic Doppler Current Profiler (ADCP) are reported from 7 cruises to the Antarctic, March - September 2001 and 2002. The survey area includes the continental margin off the Western Antarctic Peninsula and the adjacent inshore water of Marguerite Bay and Crystal Sound. Ancillary north/south sections across the Drake Passage are reported for transects from Punta Arenas, Chile to the study area and return. Data reported: hourly averaged values of the U (east-west) and V (north-south) components of ocean currents, for 8 meter depth bins between 26 and ~350 meters, along the ships' tracks. Ships/cruises/dates: AESV Laurence M. Gould / LMG0103 / Mar 19-Apr 12 2001 AESV Laurence M. Gould / LMG0104 / Apr 21-Jun 4 2001 AESV Laurence M. Gould / LMG0106 / Jul 22-Aug 30 2001 RVIB Nathaniel B. Palmer / NBP0103 / Apr 25-Jun 5 2001 RVIB Nathaniel B. Palmer / NBP0104 / Jul 23-Aug 30 2001 RVIB Nathaniel B. Palmer / NBP0202 / Apr 9-May 20 2002 RVIB Nathaniel B. Palmer / NBP0204 / Aug 1-Sep 17 2002 Related data set: file: ADCP_5minute. The original ADCP 5 minute averaged ensemble data set for each cruise is found at the above referenced web site. proprietary
ADEOS-II_AMSR_L1A_NA ADEOS-II/AMSR L1A JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130570-JAXA.umm_json "ADEOS-II/AMSR L1A dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.The Level 1A product is extracted data in range of a half orbit between the South Pole and North Pole from level 0 data and stores the value of observed microwave radiation from the earth surface.This dataset includes digital count value (raw data) with the missing values filled with dummy data. Quality information and Land/Ocean flag are appended. For AMSR/AMSR-E, they correspond to digital numbers (DN) converted from instrument output voltages. Other necessary information for higher-level processing, including satellite attitudes and the instrument condition, is also included. Data are not map-projected, but stored in the swath format. (Not open to public)The provided format is HDF4. The current version of the product is ""Version 3"". The generation unit is scene (defined as a half orbit)." proprietary
ADEOS-II_AMSR_L1A_NA ADEOS-II/AMSR L1A ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130570-JAXA.umm_json "ADEOS-II/AMSR L1A dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.The Level 1A product is extracted data in range of a half orbit between the South Pole and North Pole from level 0 data and stores the value of observed microwave radiation from the earth surface.This dataset includes digital count value (raw data) with the missing values filled with dummy data. Quality information and Land/Ocean flag are appended. For AMSR/AMSR-E, they correspond to digital numbers (DN) converted from instrument output voltages. Other necessary information for higher-level processing, including satellite attitudes and the instrument condition, is also included. Data are not map-projected, but stored in the swath format. (Not open to public)The provided format is HDF4. The current version of the product is ""Version 3"". The generation unit is scene (defined as a half orbit)." proprietary
ADEOS-II_AMSR_L1B_NA ADEOS-II/AMSR_L1B JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130446-JAXA.umm_json "ADEOS-II/AMSR L1B dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.This dataset includes the brightness temperature converted by the radiometric correction coefficients from observed sensor data of level 1A. It also contains the ancillary data stored in level 1A product. The physical quantity unit is Kelvin.For AMSR/AMSR-E, they correspond to brightness temperatures. Data location and quality information are also included. Data are not map-projected, but stored in the swath format.The provided format is HDF4. The current version of the product is ""Version 3"". The generation unit is scene(defined as a half orbit)." proprietary
ADEOS-II_AMSR_L1B_NA ADEOS-II/AMSR_L1B ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130446-JAXA.umm_json "ADEOS-II/AMSR L1B dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.This dataset includes the brightness temperature converted by the radiometric correction coefficients from observed sensor data of level 1A. It also contains the ancillary data stored in level 1A product. The physical quantity unit is Kelvin.For AMSR/AMSR-E, they correspond to brightness temperatures. Data location and quality information are also included. Data are not map-projected, but stored in the swath format.The provided format is HDF4. The current version of the product is ""Version 3"". The generation unit is scene(defined as a half orbit)." proprietary
-ADEOS-II_AMSR_L2_AP_NA ADEOS-II/AMSR L2 Amount of Precipitation ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129938-JAXA.umm_json "ADEOS-II/AMSR L2 Amount of Precipitation dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Amount of Precipitation. Combinations of both emission and scattering signatures are used in retrieval algorithm. The algorithm retrieves rainfall over ocean and land areas except for the following surfaces: coastal (~25 km from coastal line), sea ice, snow-covered land, and desert areas. Separate algorithms are applied for over ocean and over land regions. Generally, retrievals over ocean have better quality than those over land. The sea ice flag is based on sea ice concentration retrievals from AMSR provided by the EOC integrated retrieval system. Snow-covered land and desert surface detection is based on AMSR brightness temperatures and embedded in the precipitation retrieval algorithm. The physical quantity unit is mm/h.The provided format is HDF4. The current version of the product is ""Version 3"". The generation unit is scene(defined as a half orbit)." proprietary
ADEOS-II_AMSR_L2_AP_NA ADEOS-II/AMSR L2 Amount of Precipitation JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129938-JAXA.umm_json "ADEOS-II/AMSR L2 Amount of Precipitation dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Amount of Precipitation. Combinations of both emission and scattering signatures are used in retrieval algorithm. The algorithm retrieves rainfall over ocean and land areas except for the following surfaces: coastal (~25 km from coastal line), sea ice, snow-covered land, and desert areas. Separate algorithms are applied for over ocean and over land regions. Generally, retrievals over ocean have better quality than those over land. The sea ice flag is based on sea ice concentration retrievals from AMSR provided by the EOC integrated retrieval system. Snow-covered land and desert surface detection is based on AMSR brightness temperatures and embedded in the precipitation retrieval algorithm. The physical quantity unit is mm/h.The provided format is HDF4. The current version of the product is ""Version 3"". The generation unit is scene(defined as a half orbit)." proprietary
-ADEOS-II_AMSR_L2_CLW_NA ADEOS-II/AMSR L2 Cloud Liquid Water ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129635-JAXA.umm_json "ADEOS-II/AMSR L2 Cloud Liquid Water dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. In this processing, a combination of coefficients is utilized and these are specified in accordance with a simulation in which brightness temperatures for a wide variety of ocean scenes (sea surface temperature, wind speed, water vapor and cloud liquid water) are computed by the Radiative Transfer Model (RTM). These coefficients were found such that the rms difference between estimated value and the true value for the specified environmental scene was minimized If the value of cloud liquid water is above 0.18 mm, it flags the observation as having rain. The physical quantity unit is kg/m^2.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene (defined as a half orbit)." proprietary
+ADEOS-II_AMSR_L2_AP_NA ADEOS-II/AMSR L2 Amount of Precipitation ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129938-JAXA.umm_json "ADEOS-II/AMSR L2 Amount of Precipitation dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Amount of Precipitation. Combinations of both emission and scattering signatures are used in retrieval algorithm. The algorithm retrieves rainfall over ocean and land areas except for the following surfaces: coastal (~25 km from coastal line), sea ice, snow-covered land, and desert areas. Separate algorithms are applied for over ocean and over land regions. Generally, retrievals over ocean have better quality than those over land. The sea ice flag is based on sea ice concentration retrievals from AMSR provided by the EOC integrated retrieval system. Snow-covered land and desert surface detection is based on AMSR brightness temperatures and embedded in the precipitation retrieval algorithm. The physical quantity unit is mm/h.The provided format is HDF4. The current version of the product is ""Version 3"". The generation unit is scene(defined as a half orbit)." proprietary
ADEOS-II_AMSR_L2_CLW_NA ADEOS-II/AMSR L2 Cloud Liquid Water JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129635-JAXA.umm_json "ADEOS-II/AMSR L2 Cloud Liquid Water dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. In this processing, a combination of coefficients is utilized and these are specified in accordance with a simulation in which brightness temperatures for a wide variety of ocean scenes (sea surface temperature, wind speed, water vapor and cloud liquid water) are computed by the Radiative Transfer Model (RTM). These coefficients were found such that the rms difference between estimated value and the true value for the specified environmental scene was minimized If the value of cloud liquid water is above 0.18 mm, it flags the observation as having rain. The physical quantity unit is kg/m^2.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene (defined as a half orbit)." proprietary
-ADEOS-II_AMSR_L2_IC_NA ADEOS-II/AMSR L2 Ice Concentration ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133375-JAXA.umm_json "ADEOS-II/AMSR L2 Ice Concentration dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes Ice Concentration (IC). The technique uses data from the 6 GHz and 37 GHz channels at vertical polarization to obtain an initial estimate of sea ice concentration and ice temperature. The derived ice temperature is then utilized to estimate the emissivity for the corresponding observations at all the other channels. Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %. The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene(defined as a half orbit)." proprietary
+ADEOS-II_AMSR_L2_CLW_NA ADEOS-II/AMSR L2 Cloud Liquid Water ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129635-JAXA.umm_json "ADEOS-II/AMSR L2 Cloud Liquid Water dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. In this processing, a combination of coefficients is utilized and these are specified in accordance with a simulation in which brightness temperatures for a wide variety of ocean scenes (sea surface temperature, wind speed, water vapor and cloud liquid water) are computed by the Radiative Transfer Model (RTM). These coefficients were found such that the rms difference between estimated value and the true value for the specified environmental scene was minimized If the value of cloud liquid water is above 0.18 mm, it flags the observation as having rain. The physical quantity unit is kg/m^2.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene (defined as a half orbit)." proprietary
ADEOS-II_AMSR_L2_IC_NA ADEOS-II/AMSR L2 Ice Concentration JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133375-JAXA.umm_json "ADEOS-II/AMSR L2 Ice Concentration dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes Ice Concentration (IC). The technique uses data from the 6 GHz and 37 GHz channels at vertical polarization to obtain an initial estimate of sea ice concentration and ice temperature. The derived ice temperature is then utilized to estimate the emissivity for the corresponding observations at all the other channels. Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %. The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene(defined as a half orbit)." proprietary
-ADEOS-II_AMSR_L2_SM_NA ADEOS-II/AMSR L2 Soil Moisture JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130123-JAXA.umm_json "ADEOS-II/AMSR L2 Soil Moisture dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Soil Moisture (SM). In general, at a smooth interface between two semi-infinite media, the emissivity is equal to one minus the Fresnel power reflectivity, which is calculated by using dielectric constant of the media and incident angle. Among the water surface emissivity at AMSR observing frequencies, 6.9; l0.6, 18.7, 36.5 and 89 GHz, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene (defined as a half orbit)." proprietary
+ADEOS-II_AMSR_L2_IC_NA ADEOS-II/AMSR L2 Ice Concentration ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133375-JAXA.umm_json "ADEOS-II/AMSR L2 Ice Concentration dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes Ice Concentration (IC). The technique uses data from the 6 GHz and 37 GHz channels at vertical polarization to obtain an initial estimate of sea ice concentration and ice temperature. The derived ice temperature is then utilized to estimate the emissivity for the corresponding observations at all the other channels. Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %. The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene(defined as a half orbit)." proprietary
ADEOS-II_AMSR_L2_SM_NA ADEOS-II/AMSR L2 Soil Moisture ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130123-JAXA.umm_json "ADEOS-II/AMSR L2 Soil Moisture dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Soil Moisture (SM). In general, at a smooth interface between two semi-infinite media, the emissivity is equal to one minus the Fresnel power reflectivity, which is calculated by using dielectric constant of the media and incident angle. Among the water surface emissivity at AMSR observing frequencies, 6.9; l0.6, 18.7, 36.5 and 89 GHz, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene (defined as a half orbit)." proprietary
-ADEOS-II_AMSR_L2_SST_NA ADEOS-II/AMSR L2 Sea Surface Temperature ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129787-JAXA.umm_json "ADEOS-II/AMSR L2 Sea Surface Temperature dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes Sea Surface Temperature (SST). The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene(defined as a half orbit)." proprietary
+ADEOS-II_AMSR_L2_SM_NA ADEOS-II/AMSR L2 Soil Moisture JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130123-JAXA.umm_json "ADEOS-II/AMSR L2 Soil Moisture dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Soil Moisture (SM). In general, at a smooth interface between two semi-infinite media, the emissivity is equal to one minus the Fresnel power reflectivity, which is calculated by using dielectric constant of the media and incident angle. Among the water surface emissivity at AMSR observing frequencies, 6.9; l0.6, 18.7, 36.5 and 89 GHz, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene (defined as a half orbit)." proprietary
ADEOS-II_AMSR_L2_SST_NA ADEOS-II/AMSR L2 Sea Surface Temperature JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129787-JAXA.umm_json "ADEOS-II/AMSR L2 Sea Surface Temperature dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes Sea Surface Temperature (SST). The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene(defined as a half orbit)." proprietary
-ADEOS-II_AMSR_L2_SSW_NA ADEOS-II/AMSR L2 Sea Surface Wind ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131061-JAXA.umm_json "ADEOS-II/AMSR L2 Sea Surface Wind dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The retrieval is restricted to no rain condition since the brightness temperature of 36.5 GHz is saturated under rainy condition, SSW obtained only from 36.5 GHz has a large anisotropic feature depending on an angle between antenna direction and wind direction. Its anisotropic feature is corrected by using two data from 36.5 and 10.65 GHz, since 10.65 GHz data are less anisotropic. Even under rainy condition, 10.65 and 6.925 GHZ data are not saturated, so wind speed is retrieved by using those H data. Retrieval accuracy of wind speed using 10.65 and 6.925 GHz becomes worse than using 36.5 GHz, since a sensitivity of 10.65 and 6.925 GHz to wind speed is not so strong. 36.5 GHz data is used for the algorithm of standard products processing. 6.925 GHz and 10.65 GHz data are used for research product, which is provided from EORC. The physical quantity unit is m/s.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene (defined as a half orbit)." proprietary
+ADEOS-II_AMSR_L2_SST_NA ADEOS-II/AMSR L2 Sea Surface Temperature ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129787-JAXA.umm_json "ADEOS-II/AMSR L2 Sea Surface Temperature dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product.This product includes Sea Surface Temperature (SST). The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene(defined as a half orbit)." proprietary
ADEOS-II_AMSR_L2_SSW_NA ADEOS-II/AMSR L2 Sea Surface Wind JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131061-JAXA.umm_json "ADEOS-II/AMSR L2 Sea Surface Wind dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The retrieval is restricted to no rain condition since the brightness temperature of 36.5 GHz is saturated under rainy condition, SSW obtained only from 36.5 GHz has a large anisotropic feature depending on an angle between antenna direction and wind direction. Its anisotropic feature is corrected by using two data from 36.5 and 10.65 GHz, since 10.65 GHz data are less anisotropic. Even under rainy condition, 10.65 and 6.925 GHZ data are not saturated, so wind speed is retrieved by using those H data. Retrieval accuracy of wind speed using 10.65 and 6.925 GHz becomes worse than using 36.5 GHz, since a sensitivity of 10.65 and 6.925 GHz to wind speed is not so strong. 36.5 GHz data is used for the algorithm of standard products processing. 6.925 GHz and 10.65 GHz data are used for research product, which is provided from EORC. The physical quantity unit is m/s.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene (defined as a half orbit)." proprietary
+ADEOS-II_AMSR_L2_SSW_NA ADEOS-II/AMSR L2 Sea Surface Wind ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131061-JAXA.umm_json "ADEOS-II/AMSR L2 Sea Surface Wind dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The retrieval is restricted to no rain condition since the brightness temperature of 36.5 GHz is saturated under rainy condition, SSW obtained only from 36.5 GHz has a large anisotropic feature depending on an angle between antenna direction and wind direction. Its anisotropic feature is corrected by using two data from 36.5 and 10.65 GHz, since 10.65 GHz data are less anisotropic. Even under rainy condition, 10.65 and 6.925 GHZ data are not saturated, so wind speed is retrieved by using those H data. Retrieval accuracy of wind speed using 10.65 and 6.925 GHz becomes worse than using 36.5 GHz, since a sensitivity of 10.65 and 6.925 GHz to wind speed is not so strong. 36.5 GHz data is used for the algorithm of standard products processing. 6.925 GHz and 10.65 GHz data are used for research product, which is provided from EORC. The physical quantity unit is m/s.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene (defined as a half orbit)." proprietary
ADEOS-II_AMSR_L2_SWE_NA ADEOS-II/AMSR L2 Snow Water Equivalent JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128759-JAXA.umm_json "ADEOS-II/AMSR L2 Snow Water Equivalent dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Snow Water Equivalent (SWE). Compared with non-snow surfaces, therefore, a snowpack has a distinctive electromagnetic signature at frequencies above 25 GHz. When viewed using passive microwave radiometers from above the snowpack, the scattering of upwelling radiation depresses the brightness temperature of the snow at increasingly high frequencies. This scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene (defined as a half orbit)." proprietary
ADEOS-II_AMSR_L2_SWE_NA ADEOS-II/AMSR L2 Snow Water Equivalent ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128759-JAXA.umm_json "ADEOS-II/AMSR L2 Snow Water Equivalent dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy. Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Snow Water Equivalent (SWE). Compared with non-snow surfaces, therefore, a snowpack has a distinctive electromagnetic signature at frequencies above 25 GHz. When viewed using passive microwave radiometers from above the snowpack, the scattering of upwelling radiation depresses the brightness temperature of the snow at increasingly high frequencies. This scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene (defined as a half orbit)." proprietary
ADEOS-II_AMSR_L2_WV_NA ADEOS-II/AMSR L2 Water Vapor ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129870-JAXA.umm_json "ADEOS-II/AMSR L2 Water Vapor dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Water Vapor (WV). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radio sonde. If PWI is out of range of look-up table, the flag 'low accuracy' is added. The physical quantity unit is kg/m^2.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene (defined as a half orbit)." proprietary
ADEOS-II_AMSR_L2_WV_NA ADEOS-II/AMSR L2 Water Vapor JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129870-JAXA.umm_json "ADEOS-II/AMSR L2 Water Vapor dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 2 product stores the Geophysical quantity from the brightness temperature of level 1 product. This product includes Water Vapor (WV). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radio sonde. If PWI is out of range of look-up table, the flag 'low accuracy' is added. The physical quantity unit is kg/m^2.The provided format is HDF4. Spatial resolution is 10 km. The current version of the product is ""Version 7"". The generation unit is scene (defined as a half orbit)." proprietary
ADEOS-II_AMSR_L3_AP_1day_0.25deg_NA ADEOS-II/AMSR L3 Amount of Precipitation (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129198-JAXA.umm_json "ADEOS-II/AMSR L3 Amount of Precipitation (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Amount of Precipitation (AP). Combinations of both emission and scattering signatures. Separate algorithms are applied for over ocean and over land regions. The physical quantity unit is mm/hr.The provided format is HDF4. The current version of the product is ""Version 3"". The statistical period is 1 day.The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_AP_1day_0.25deg_NA ADEOS-II/AMSR L3 Amount of Precipitation (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129198-JAXA.umm_json "ADEOS-II/AMSR L3 Amount of Precipitation (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Amount of Precipitation (AP). Combinations of both emission and scattering signatures. Separate algorithms are applied for over ocean and over land regions. The physical quantity unit is mm/hr.The provided format is HDF4. The current version of the product is ""Version 3"". The statistical period is 1 day.The projection method is EQR. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_AP_1month_0.25deg_NA ADEOS-II/AMSR L3 Amount of Precipitation (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130166-JAXA.umm_json "ADEOS-II/AMSR L3 Amount of Precipitation (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Amount of Precipitation (AP). Combinations of both emission and scattering signatures. Separate algorithms are applied for over ocean and over land regions. The physical quantity unit is mm/hr.The provided format is HDF4. The current version of the product is ""Version 3"". The statistical period is 1 month.The projection method is EQR. The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_AP_1month_0.25deg_NA ADEOS-II/AMSR L3 Amount of Precipitation (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130166-JAXA.umm_json "ADEOS-II/AMSR L3 Amount of Precipitation (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Amount of Precipitation (AP). Combinations of both emission and scattering signatures. Separate algorithms are applied for over ocean and over land regions. The physical quantity unit is mm/hr.The provided format is HDF4. The current version of the product is ""Version 3"". The statistical period is 1 month.The projection method is EQR. The projection method is EQR. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_CLW_1day_0.25deg_NA ADEOS-II/AMSR L3 Cloud Liquid Water (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129458-JAXA.umm_json "ADEOS-II/AMSR L3 Cloud Liquid Water (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Integrated cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. The physical quantity unit is kg/m^2.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR. The projection method is EQR. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_AP_1month_0.25deg_NA ADEOS-II/AMSR L3 Amount of Precipitation (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130166-JAXA.umm_json "ADEOS-II/AMSR L3 Amount of Precipitation (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Amount of Precipitation (AP). Combinations of both emission and scattering signatures. Separate algorithms are applied for over ocean and over land regions. The physical quantity unit is mm/hr.The provided format is HDF4. The current version of the product is ""Version 3"". The statistical period is 1 month.The projection method is EQR. The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_CLW_1day_0.25deg_NA ADEOS-II/AMSR L3 Cloud Liquid Water (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129458-JAXA.umm_json "ADEOS-II/AMSR L3 Cloud Liquid Water (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Integrated cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. The physical quantity unit is kg/m^2.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR. The projection method is EQR. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_CLW_1day_0.25deg_NA ADEOS-II/AMSR L3 Cloud Liquid Water (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129458-JAXA.umm_json "ADEOS-II/AMSR L3 Cloud Liquid Water (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Integrated cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. The physical quantity unit is kg/m^2.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR. The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_CLW_1month_0.25deg_NA ADEOS-II/AMSR L3 Cloud Liquid Water (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129152-JAXA.umm_json "ADEOS-II/AMSR L3 Cloud Liquid Water (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Integrated cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. The physical quantity unit is kg/m^2.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month. The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_CLW_1month_0.25deg_NA ADEOS-II/AMSR L3 Cloud Liquid Water (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129152-JAXA.umm_json "ADEOS-II/AMSR L3 Cloud Liquid Water (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Integrated cloud liquid water (CLW). CLW is calculated from brightness temperature of 10 channels (5frequencies X 2 polarization) by using a Linear Statistical Regression (LSR) algorithm. The physical quantity unit is kg/m^2.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month. The projection method is EQR. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_IC_1day_0.25deg_NA ADEOS-II/AMSR L3 Ice Concentration (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129905-JAXA.umm_json "ADEOS-II/AMSR L3 Ice Concentration (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes averaged Ice Concentration (IC). Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_IC_1day_0.25deg_NA ADEOS-II/AMSR L3 Ice Concentration (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129905-JAXA.umm_json "ADEOS-II/AMSR L3 Ice Concentration (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes averaged Ice Concentration (IC). Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_IC_1day_0.25deg_NA ADEOS-II/AMSR L3 Ice Concentration (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129905-JAXA.umm_json "ADEOS-II/AMSR L3 Ice Concentration (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes averaged Ice Concentration (IC). Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_IC_1month_0.25deg_NA ADEOS-II/AMSR L3 Ice Concentration (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130518-JAXA.umm_json "ADEOS-II/AMSR L3 Ice Concentration (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Ice Concentration (IC). Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_IC_1month_0.25deg_NA ADEOS-II/AMSR L3 Ice Concentration (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130518-JAXA.umm_json "ADEOS-II/AMSR L3 Ice Concentration (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes monthly mean Ice Concentration (IC). Ice concentrations are derived mainly from 37 GHz and 19 GHz channels, as in the Bootstrap technique, but makes use of emissivity instead of brightness temperatures to minimizes errors associated with spatial changes in sea ice temperatures. The ice temperature is in the end normalized using the derived ice concentration value, for it to represent temperature only of the sea ice part of the satellite observational area. The physical quantity unit is %.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_SM_1day_0.25deg_NA ADEOS-II/AMSR L3 Soil Moisture (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129592-JAXA.umm_json "ADEOS-II/AMSR L3 Soil Moisture (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographi (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Soil Moisture (SM). Among the water surface emissivity at AMSR observing frequencies, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_SM_1day_0.25deg_NA ADEOS-II/AMSR L3 Soil Moisture (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129592-JAXA.umm_json "ADEOS-II/AMSR L3 Soil Moisture (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographi (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Soil Moisture (SM). Among the water surface emissivity at AMSR observing frequencies, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_SM_1month_0.25deg_NA ADEOS-II/AMSR L3 Soil Moisture (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129988-JAXA.umm_json "ADEOS-II/AMSR L3 Soil Moisture (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular(EQR) or Polar Stereographic(PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Soil Moisture (SM). Among the water surface emissivity at AMSR observing frequencies, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_SM_1month_0.25deg_NA ADEOS-II/AMSR L3 Soil Moisture (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129988-JAXA.umm_json "ADEOS-II/AMSR L3 Soil Moisture (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular(EQR) or Polar Stereographic(PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Soil Moisture (SM). Among the water surface emissivity at AMSR observing frequencies, the emissivity is larger at the higher frequency than at the lower one for both polarizations. The index, the discrepancy between the brightness temperatures at two frequencies divided by one at lower frequency, can be used as an index for surface wetness. The physical quantity unit is %.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_SST_1day_0.25deg_NA ADEOS-II/AMSR L3 Sea Surface Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132553-JAXA.umm_json "ADEOS-II/AMSR L3 Sea Surface Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Sea Surface Temperature (SST). The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_SST_1day_0.25deg_NA ADEOS-II/AMSR L3 Sea Surface Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132553-JAXA.umm_json "ADEOS-II/AMSR L3 Sea Surface Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Sea Surface Temperature (SST). The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_SST_1day_0.25deg_NA ADEOS-II/AMSR L3 Sea Surface Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132553-JAXA.umm_json "ADEOS-II/AMSR L3 Sea Surface Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Sea Surface Temperature (SST). The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_SST_1month_0.25deg_NA ADEOS-II/AMSR L3 Sea Surface Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129941-JAXA.umm_json "ADEOS-II/AMSR L3 Sea Surface Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Sea Surface Temperature (SST). The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_SST_1month_0.25deg_NA ADEOS-II/AMSR L3 Sea Surface Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129941-JAXA.umm_json "ADEOS-II/AMSR L3 Sea Surface Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Sea Surface Temperature (SST). The relationship between 6V (or 10V) and SST is calculated by using the complex relative dielectric constant. The physical quantity unit is degree.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_SSW_1day_0.25deg_NA ADEOS-II/AMSR L3 Sea Surface Wind (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129793-JAXA.umm_json "ADEOS-II/AMSR L3 Sea Surface Wind (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The physical quantity unit is m/s.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_SSW_1day_0.25deg_NA ADEOS-II/AMSR L3 Sea Surface Wind (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129793-JAXA.umm_json "ADEOS-II/AMSR L3 Sea Surface Wind (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The physical quantity unit is m/s.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_SSW_1month_0.25deg_NA ADEOS-II/AMSR L3 Sea Surface Wind (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129059-JAXA.umm_json "ADEOS-II/AMSR L3 Sea Surface Wind (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The physical quantity unit is m/s.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_SSW_1day_0.25deg_NA ADEOS-II/AMSR L3 Sea Surface Wind (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129793-JAXA.umm_json "ADEOS-II/AMSR L3 Sea Surface Wind (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The physical quantity unit is m/s.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_SSW_1month_0.25deg_NA ADEOS-II/AMSR L3 Sea Surface Wind (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129059-JAXA.umm_json "ADEOS-II/AMSR L3 Sea Surface Wind (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The physical quantity unit is m/s.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_SWE_1day_0.25deg_NA ADEOS-II/AMSR L3 Snow Water Equivalent (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129488-JAXA.umm_json "ADEOS-II/AMSR L3 Snow Water Equivalent (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Snow Water Equivalent (SWE). The scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_SSW_1month_0.25deg_NA ADEOS-II/AMSR L3 Sea Surface Wind (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129059-JAXA.umm_json "ADEOS-II/AMSR L3 Sea Surface Wind (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Sea Surface Wind (SSW). SSW is retrieved mainly from 36.5 GHz vertical (V) and horizontal (H) brightness temperature of AMSR by a graphical method. The physical quantity unit is m/s.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_SWE_1day_0.25deg_NA ADEOS-II/AMSR L3 Snow Water Equivalent (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129488-JAXA.umm_json "ADEOS-II/AMSR L3 Snow Water Equivalent (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Snow Water Equivalent (SWE). The scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_SWE_1month_0.25deg_NA ADEOS-II/AMSR L3 Snow Water Equivalent (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133156-JAXA.umm_json "ADEOS-II/AMSR L3 Snow Water Equivalent (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Snow Water Equivalent (SWE). The scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_SWE_1day_0.25deg_NA ADEOS-II/AMSR L3 Snow Water Equivalent (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129488-JAXA.umm_json "ADEOS-II/AMSR L3 Snow Water Equivalent (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Snow Water Equivalent (SWE). The scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_SWE_1month_0.25deg_NA ADEOS-II/AMSR L3 Snow Water Equivalent (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133156-JAXA.umm_json "ADEOS-II/AMSR L3 Snow Water Equivalent (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Snow Water Equivalent (SWE). The scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_SWE_1month_0.25deg_NA ADEOS-II/AMSR L3 Snow Water Equivalent (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133156-JAXA.umm_json "ADEOS-II/AMSR L3 Snow Water Equivalent (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Snow Water Equivalent (SWE). The scattering behavior of snow can be exploited to detect the presence of snow on the ground. Having detected the snow, it is then possible to estimate the snow depth of the pack using the degree of scattering. The physical quantity unit is cm.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_10.65GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 10.65GHz-V Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698134000-JAXA.umm_json "ADEOS-II/AMSR L3 10.65GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes Brightness Temperature at 10.65GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_10.65GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 10.65GHz-V Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698134000-JAXA.umm_json "ADEOS-II/AMSR L3 10.65GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes Brightness Temperature at 10.65GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin. Horizontal polarized wave and vertical polarized wave are stored separately.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_10.65GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 10.65GHz-V Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130486-JAXA.umm_json "ADEOS-II/AMSR L3 10.65GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 10.65GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_10.65GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 10.65GHz-V Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130486-JAXA.umm_json "ADEOS-II/AMSR L3 10.65GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 10.65GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_10.65GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130365-JAXA.umm_json "ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 10.65GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_10.65GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 10.65GHz-V Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130486-JAXA.umm_json "ADEOS-II/AMSR L3 10.65GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 10.65GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_10.65GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130365-JAXA.umm_json "ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 10.65GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_10.65GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128885-JAXA.umm_json "ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 10.65GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_10.65GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130365-JAXA.umm_json "ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 10.65GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_10.65GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128885-JAXA.umm_json "ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 10.65GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_10.65GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128885-JAXA.umm_json "ADEOS-II/AMSR L3 10.65GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 10.65GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_18.7GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 18.7GHz-H Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131224-JAXA.umm_json "ADEOS-II/AMSR L3 18.7GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 18.7GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_18.7GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 18.7GHz-H Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131224-JAXA.umm_json "ADEOS-II/AMSR L3 18.7GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 18.7GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_18.7GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 18.7GHz-H Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129192-JAXA.umm_json "ADEOS-II/AMSR L3 18.7GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 18.7GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_18.7GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 18.7GHz-H Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129192-JAXA.umm_json "ADEOS-II/AMSR L3 18.7GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 18.7GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_18.7GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 18.7GHz-V Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130061-JAXA.umm_json "ADEOS-II/AMSR L3 18.7GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 18.7GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_18.7GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 18.7GHz-H Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129192-JAXA.umm_json "ADEOS-II/AMSR L3 18.7GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 18.7GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_18.7GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 18.7GHz-V Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130061-JAXA.umm_json "ADEOS-II/AMSR L3 18.7GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 18.7GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_18.7GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 18.7GHz-V Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130061-JAXA.umm_json "ADEOS-II/AMSR L3 18.7GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 18.7GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_18.7GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 18.7GHz-V Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130086-JAXA.umm_json "ADEOS-II/AMSR L3 18.7GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 18.7GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_18.7GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 18.7GHz-V Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130086-JAXA.umm_json "ADEOS-II/AMSR L3 18.7GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 18.7GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_23.8GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 23.8GHz-H Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130471-JAXA.umm_json "ADEOS-II/AMSR L3 23.8GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 23.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_23.8GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 23.8GHz-H Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130471-JAXA.umm_json "ADEOS-II/AMSR L3 23.8GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 23.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_23.8GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 23.8GHz-H Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130471-JAXA.umm_json "ADEOS-II/AMSR L3 23.8GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 23.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_23.8GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 23.8GHz-H Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130155-JAXA.umm_json "ADEOS-II/AMSR L3 23.8GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 23.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_23.8GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 23.8GHz-H Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130155-JAXA.umm_json "ADEOS-II/AMSR L3 23.8GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 23.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_23.8GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 23.8GHz-V Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129216-JAXA.umm_json "ADEOS-II/AMSR L3 23.8GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 23.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_23.8GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 23.8GHz-V Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129216-JAXA.umm_json "ADEOS-II/AMSR L3 23.8GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively. This product includes daily mean Brightness Temperature at 23.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_23.8GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 23.8GHz-V Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129603-JAXA.umm_json "ADEOS-II/AMSR L3 23.8GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 23.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_23.8GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 23.8GHz-V Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129603-JAXA.umm_json "ADEOS-II/AMSR L3 23.8GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 23.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_36.5GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 36.5GHz-H Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130162-JAXA.umm_json "ADEOS-II/AMSR L3 36.5GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 36.5GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_23.8GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 23.8GHz-V Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129603-JAXA.umm_json "ADEOS-II/AMSR L3 23.8GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 23.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_36.5GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 36.5GHz-H Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130162-JAXA.umm_json "ADEOS-II/AMSR L3 36.5GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 36.5GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_36.5GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 36.5GHz-H Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130162-JAXA.umm_json "ADEOS-II/AMSR L3 36.5GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 36.5GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_36.5GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 36.5GHz-H Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130048-JAXA.umm_json "ADEOS-II/AMSR L3 36.5GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 36.5GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_36.5GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 36.5GHz-H Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130048-JAXA.umm_json "ADEOS-II/AMSR L3 36.5GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 36.5GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_36.5GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130331-JAXA.umm_json "ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 36.5GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_36.5GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130331-JAXA.umm_json "ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 36.5GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_36.5GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129211-JAXA.umm_json "ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 36.5GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_36.5GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130331-JAXA.umm_json "ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 36.5GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_36.5GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129211-JAXA.umm_json "ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 36.5GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_36.5GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129211-JAXA.umm_json "ADEOS-II/AMSR L3 36.5GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 36.5GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_50.3GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 50.3GHz-H Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129497-JAXA.umm_json "ADEOS-II/AMSR L3 50.3GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 50.3GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_50.3GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 50.3GHz-H Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129497-JAXA.umm_json "ADEOS-II/AMSR L3 50.3GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 50.3GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_50.3GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 50.3GHz-H Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129325-JAXA.umm_json "ADEOS-II/AMSR L3 50.3GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 50.3GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_50.3GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 50.3GHz-H Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129325-JAXA.umm_json "ADEOS-II/AMSR L3 50.3GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 50.3GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_50.3GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 50.3GHz-H Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129325-JAXA.umm_json "ADEOS-II/AMSR L3 50.3GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 50.3GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_50.3GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 50.3GHz-V Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130102-JAXA.umm_json "ADEOS-II/AMSR L3 50.3GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 50.3GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_50.3GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 50.3GHz-V Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130102-JAXA.umm_json "ADEOS-II/AMSR L3 50.3GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 50.3GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_50.3GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 50.3GHz-V Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129918-JAXA.umm_json "ADEOS-II/AMSR L3 50.3GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 50.3GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_50.3GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 50.3GHz-V Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129918-JAXA.umm_json "ADEOS-II/AMSR L3 50.3GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 50.3GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_50.3GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 50.3GHz-V Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129918-JAXA.umm_json "ADEOS-II/AMSR L3 50.3GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 50.3GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_52.8GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 52.8GHz-H Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132048-JAXA.umm_json "ADEOS-II/AMSR L3 52.8GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 52.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_52.8GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 52.8GHz-H Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132048-JAXA.umm_json "ADEOS-II/AMSR L3 52.8GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 52.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_52.8GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 52.8GHz-H Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129453-JAXA.umm_json "ADEOS-II/AMSR L3 52.8GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 52.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_52.8GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 52.8GHz-H Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129453-JAXA.umm_json "ADEOS-II/AMSR L3 52.8GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 52.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_52.8GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 52.8GHz-V Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129143-JAXA.umm_json "ADEOS-II/AMSR L3 52.8GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 52.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_52.8GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 52.8GHz-H Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129453-JAXA.umm_json "ADEOS-II/AMSR L3 52.8GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 52.8GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_52.8GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 52.8GHz-V Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129143-JAXA.umm_json "ADEOS-II/AMSR L3 52.8GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 52.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_52.8GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 52.8GHz-V Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129143-JAXA.umm_json "ADEOS-II/AMSR L3 52.8GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 52.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_52.8GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 52.8GHz-V Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129590-JAXA.umm_json "ADEOS-II/AMSR L3 52.8GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 52.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_52.8GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 52.8GHz-V Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129590-JAXA.umm_json "ADEOS-II/AMSR L3 52.8GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 52.8GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_6GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130526-JAXA.umm_json "ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 6GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is PS and EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_6GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130526-JAXA.umm_json "ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 6GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is PS and EQR. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_6GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131216-JAXA.umm_json "ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 6GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is PS and EQR. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_6GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130526-JAXA.umm_json "ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 6GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is PS and EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_6GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131216-JAXA.umm_json "ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 6GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is PS and EQR. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_6GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 6GHz-V Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133592-JAXA.umm_json "ADEOS-II/AMSR L3 6GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes averaged Brightness Temperature at 6GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is PS and EQR. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_6GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131216-JAXA.umm_json "ADEOS-II/AMSR L3 6GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 6GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is PS and EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_6GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 6GHz-V Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133592-JAXA.umm_json "ADEOS-II/AMSR L3 6GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes averaged Brightness Temperature at 6GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is PS and EQR. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_6GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 6GHz-V Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133592-JAXA.umm_json "ADEOS-II/AMSR L3 6GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes averaged Brightness Temperature at 6GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is PS and EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_6GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 6GHz-V Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130503-JAXA.umm_json "ADEOS-II/AMSR L3 6GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 6GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is PS and EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_6GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 6GHz-V Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130503-JAXA.umm_json "ADEOS-II/AMSR L3 6GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 6GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is PS and EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_89.0GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 89.0GHz-H Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129289-JAXA.umm_json "ADEOS-II/AMSR L3 89.0GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 89.0GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_89.0GHz-H_1day_0.25deg_NA ADEOS-II/AMSR L3 89.0GHz-H Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129289-JAXA.umm_json "ADEOS-II/AMSR L3 89.0GHz-H Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 89.0GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_89.0GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 89.0GHz-H Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133312-JAXA.umm_json "ADEOS-II/AMSR L3 89.0GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 89.0GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_89.0GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 89.0GHz-H Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133312-JAXA.umm_json "ADEOS-II/AMSR L3 89.0GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 89.0GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_89.0GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128842-JAXA.umm_json "ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 89.0GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_89.0GHz-H_1month_0.25deg_NA ADEOS-II/AMSR L3 89.0GHz-H Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133312-JAXA.umm_json "ADEOS-II/AMSR L3 89.0GHz-H Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 89.0GHz horizontal polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_89.0GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128842-JAXA.umm_json "ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 89.0GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
-ADEOS-II_AMSR_L3_TB_89.0GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128796-JAXA.umm_json "ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 89.0Hz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_89.0GHz-V_1day_0.25deg_NA ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128842-JAXA.umm_json "ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Brightness Temperature at 89.0GHz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_TB_89.0GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128796-JAXA.umm_json "ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 89.0Hz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
+ADEOS-II_AMSR_L3_TB_89.0GHz-V_1month_0.25deg_NA ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128796-JAXA.umm_json "ADEOS-II/AMSR L3 89.0GHz-V Mean for Brightness Temperature (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Brightness Temperature at 89.0Hz vertical polarized wave. Values that are calculated from sensor counts and are proportional to the power of observed electromagnetic wave. Unit is Kelvin.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR and PS. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_WV_1day_0.25deg_NA ADEOS-II/AMSR L3 Water Vapor (1day,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133069-JAXA.umm_json "ADEOS-II/AMSR L3 Water Vapor (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Water Vapor (WV). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radiosonde. The physical quantity unit is kg/m^2.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_WV_1day_0.25deg_NA ADEOS-II/AMSR L3 Water Vapor (1day,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133069-JAXA.umm_json "ADEOS-II/AMSR L3 Water Vapor (1day,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes daily mean Water Vapor (WV). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radiosonde. The physical quantity unit is kg/m^2.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 day.The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_WV_1month_0.25deg_NA ADEOS-II/AMSR L3 Water Vapor (1month,0.25deg) ALL STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129882-JAXA.umm_json "ADEOS-II/AMSR L3 Water Vapor (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Water Vapor (WV). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radiosonde. The physical quantity unit is kg/m^2.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR. The generation unit is global." proprietary
ADEOS-II_AMSR_L3_WV_1month_0.25deg_NA ADEOS-II/AMSR L3 Water Vapor (1month,0.25deg) JAXA STAC Catalog 2003-04-02 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129882-JAXA.umm_json "ADEOS-II/AMSR L3 Water Vapor (1month,0.25deg) dataset is obtained from the AMSR sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA).The Advanced Earth Observing Satellite-II (ADEOS-II) was launched on December 14, 2002 (Japanese Standard Time). The objective of ADEOS-II was to acquire data to contribute to international global climate change research, as well as for applications such as meteorology and fishery. ADEOS-II operation on orbit was given up on October 31 2003, because sufficient electric power was not available to maintain operation of the satellite.AMSR observes various physical contents concerning to water (H2O) by receiving weak microwaves naturally radiated from the Earth's surface and atmosphere and also regardless of day or night, the presence of clouds. Those sensors aim at collecting global data for mainly understanding the circulation of water and energy.Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then averaged temporally and spatially. These statistical values are computed for observation data on ascending orbit and descending orbit respectively.This product includes monthly mean Water Vapor (WV). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radiosonde. The physical quantity unit is kg/m^2.The provided format is HDF4. The current version of the product is ""Version 7"". The statistical period is 1 month.The projection method is EQR. The generation unit is global." proprietary
-ADEOS-II_GLI_L1A_250m_NA ADEOS/2GLI L1A 250m JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132887-JAXA.umm_json "ADEOS-II/GLI L1A Middle and thermal infrared is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The dataset resolution is 1 km. GLI-250m's uncorrected data (ch 20,21,22,23,28,29, wavelength 460, 545, 660, 825, 1640, 2210 nm) with missing frames filled with dummy data. Radiometric and geometric correction coefficients, missing data flag, and so forth are attached. The dataset resolution is 250 m. The provided format is HDF. Image data are grouped in bands. All pixel data of a band are arranged in lines forming a contiguous image scene. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L1A_250m_NA ADEOS/2GLI L1A 250m ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132887-JAXA.umm_json "ADEOS-II/GLI L1A Middle and thermal infrared is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The dataset resolution is 1 km. GLI-250m's uncorrected data (ch 20,21,22,23,28,29, wavelength 460, 545, 660, 825, 1640, 2210 nm) with missing frames filled with dummy data. Radiometric and geometric correction coefficients, missing data flag, and so forth are attached. The dataset resolution is 250 m. The provided format is HDF. Image data are grouped in bands. All pixel data of a band are arranged in lines forming a contiguous image scene. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L1A_250m_NA ADEOS/2GLI L1A 250m JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132887-JAXA.umm_json "ADEOS-II/GLI L1A Middle and thermal infrared is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The dataset resolution is 1 km. GLI-250m's uncorrected data (ch 20,21,22,23,28,29, wavelength 460, 545, 660, 825, 1640, 2210 nm) with missing frames filled with dummy data. Radiometric and geometric correction coefficients, missing data flag, and so forth are attached. The dataset resolution is 250 m. The provided format is HDF. Image data are grouped in bands. All pixel data of a band are arranged in lines forming a contiguous image scene. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L1A_MTIR_1km_NA ADEOS-II/GLI L1A Middle and thermal infrared (1km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130432-JAXA.umm_json "ADEOS-II/GLI L1A Middle and thermal infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's uncorrected MTIR (middle and thermal infrared, ch 30-36 3.715 - 12.0 micro meter) data with missing frames filled with dummy data. Radiometric and geometric correction coefficients, missing data flag, and piecewise linear flag are attached. The dataset resolution is 1 km. MTIR data is always acquired. The provided format is HDF. Image data are grouped in bands. All pixel data of a band are arranged in lines forming a contiguous image scene. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L1A_MTIR_1km_NA ADEOS-II/GLI L1A Middle and thermal infrared (1km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130432-JAXA.umm_json "ADEOS-II/GLI L1A Middle and thermal infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's uncorrected MTIR (middle and thermal infrared, ch 30-36 3.715 - 12.0 micro meter) data with missing frames filled with dummy data. Radiometric and geometric correction coefficients, missing data flag, and piecewise linear flag are attached. The dataset resolution is 1 km. MTIR data is always acquired. The provided format is HDF. Image data are grouped in bands. All pixel data of a band are arranged in lines forming a contiguous image scene. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L1A_SWIR_1km_NA ADEOS-II/GLI L1A Short-wavelength infrared (1km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132180-JAXA.umm_json "ADEOS-II/GLI L1A Short-wavelength infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's uncorrected SWIR (Short-wavelength infrared, ch 24-29, 1050 - 2210 nm) data with missing frames filled with dummy data. Radiometric and geometric correction coefficients, missing data flag, and piecewise linear flag are attached. The dataset resolution is 1 km. SWIR data is normally acquired during daytime. The provided format is HDF. Image data are grouped in bands. All pixel data of a band are arranged in lines forming a contiguous image scene. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L1A_SWIR_1km_NA ADEOS-II/GLI L1A Short-wavelength infrared (1km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132180-JAXA.umm_json "ADEOS-II/GLI L1A Short-wavelength infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's uncorrected SWIR (Short-wavelength infrared, ch 24-29, 1050 - 2210 nm) data with missing frames filled with dummy data. Radiometric and geometric correction coefficients, missing data flag, and piecewise linear flag are attached. The dataset resolution is 1 km. SWIR data is normally acquired during daytime. The provided format is HDF. Image data are grouped in bands. All pixel data of a band are arranged in lines forming a contiguous image scene. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L1A_VNIR_1km_NA ADEOS-II/GLI L1A Visible and near infrared (1km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129663-JAXA.umm_json "ADEOS-II/GLI L1A Visible and near infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's uncorrected VNIR (visible and near infrared, ch 01-19, 380- 895 nm) data with missing frames filled with dummy data. Radiometric and geometric correction coefficients, missing data flag, and piecewise linear flag are attached. The dataset resolution is 1 km. SWIR data is normally acquired during daytime. The provided format is HDF. Image data are grouped in bands. All pixel data of a band are arranged in lines forming a contiguous image scene. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L1A_SWIR_1km_NA ADEOS-II/GLI L1A Short-wavelength infrared (1km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132180-JAXA.umm_json "ADEOS-II/GLI L1A Short-wavelength infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's uncorrected SWIR (Short-wavelength infrared, ch 24-29, 1050 - 2210 nm) data with missing frames filled with dummy data. Radiometric and geometric correction coefficients, missing data flag, and piecewise linear flag are attached. The dataset resolution is 1 km. SWIR data is normally acquired during daytime. The provided format is HDF. Image data are grouped in bands. All pixel data of a band are arranged in lines forming a contiguous image scene. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L1A_VNIR_1km_NA ADEOS-II/GLI L1A Visible and near infrared (1km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129663-JAXA.umm_json "ADEOS-II/GLI L1A Visible and near infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's uncorrected VNIR (visible and near infrared, ch 01-19, 380- 895 nm) data with missing frames filled with dummy data. Radiometric and geometric correction coefficients, missing data flag, and piecewise linear flag are attached. The dataset resolution is 1 km. SWIR data is normally acquired during daytime. The provided format is HDF. Image data are grouped in bands. All pixel data of a band are arranged in lines forming a contiguous image scene. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L1A_VNIR_1km_NA ADEOS-II/GLI L1A Visible and near infrared (1km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129663-JAXA.umm_json "ADEOS-II/GLI L1A Visible and near infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's uncorrected VNIR (visible and near infrared, ch 01-19, 380- 895 nm) data with missing frames filled with dummy data. Radiometric and geometric correction coefficients, missing data flag, and piecewise linear flag are attached. The dataset resolution is 1 km. SWIR data is normally acquired during daytime. The provided format is HDF. Image data are grouped in bands. All pixel data of a band are arranged in lines forming a contiguous image scene. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L1B_250m_NA ADEOS/2GLI L1B 250m ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128835-JAXA.umm_json "ADEOS-II/GLI L1A Middle and thermal infrared is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. The dataset resolution is 1 km. GLI-250m's data (ch 20,21,22,23,28,29, wavelength 460, 545, 660, 825, 1640, 2210 nm) radiometric and geometric correction applied. Project coefficients and Ocean/Land flags are attached. The dataset resolution is 250 m. The provided format is HDF. Missing data/saturation /supersaturation flag, transient response flag and piecewise liner flag are attached. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L1B_250m_NA ADEOS/2GLI L1B 250m JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128835-JAXA.umm_json "ADEOS-II/GLI L1A Middle and thermal infrared is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. The dataset resolution is 1 km. GLI-250m's data (ch 20,21,22,23,28,29, wavelength 460, 545, 660, 825, 1640, 2210 nm) radiometric and geometric correction applied. Project coefficients and Ocean/Land flags are attached. The dataset resolution is 250 m. The provided format is HDF. Missing data/saturation /supersaturation flag, transient response flag and piecewise liner flag are attached. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L1B_MTIR_1km_NA ADEOS-II/GLI L1B Middle and thermal infrared (1km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130281-JAXA.umm_json "ADEOS-II/GLI L1B Middle and thermal infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's MTIR (middle and thermal infrared, ch 30-36 3.715 - 12.0 micro meter) data radiometric and geometric correction applied. Project coefficients and Ocean/Land flags are attached. The dataset resolution is 1 km. The provided format is HDF. Missing data/saturation /supersaturation flag, transient response flag and piecewise liner flag are attached. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L1B_MTIR_1km_NA ADEOS-II/GLI L1B Middle and thermal infrared (1km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130281-JAXA.umm_json "ADEOS-II/GLI L1B Middle and thermal infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's MTIR (middle and thermal infrared, ch 30-36 3.715 - 12.0 micro meter) data radiometric and geometric correction applied. Project coefficients and Ocean/Land flags are attached. The dataset resolution is 1 km. The provided format is HDF. Missing data/saturation /supersaturation flag, transient response flag and piecewise liner flag are attached. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L1B_SLPT_1km_NA ADEOS-II/GLI L1B Satellite Position (1km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130127-JAXA.umm_json "ADEOS-II/GLI L1B Satellite Position is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product satellite position product includes space craft information needed to calculate satellite position for each channel. The provided format if HDF. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L1B_SLPT_1km_NA ADEOS-II/GLI L1B Satellite Position (1km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130127-JAXA.umm_json "ADEOS-II/GLI L1B Satellite Position is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product satellite position product includes space craft information needed to calculate satellite position for each channel. The provided format if HDF. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L1B_SWIR_1km_NA ADEOS-II/GLI L1B Short-wavelength infrared (1km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129943-JAXA.umm_json "ADEOS-II/GLI L1B Short-wavelength infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km'SWIR (Short-wavelength infrared, ch 24-29, 1050 - 2210 nm) data radiometric and geometric correction applied. Project coefficients and Ocean/Land flags are attached. The dataset resolution is 1 km. The provided format is HDF. Missing data/saturation /supersaturation flag, transient response flag and piecewise liner flag are attached. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L1B_SLPT_1km_NA ADEOS-II/GLI L1B Satellite Position (1km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130127-JAXA.umm_json "ADEOS-II/GLI L1B Satellite Position is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product satellite position product includes space craft information needed to calculate satellite position for each channel. The provided format if HDF. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L1B_SWIR_1km_NA ADEOS-II/GLI L1B Short-wavelength infrared (1km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129943-JAXA.umm_json "ADEOS-II/GLI L1B Short-wavelength infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km'SWIR (Short-wavelength infrared, ch 24-29, 1050 - 2210 nm) data radiometric and geometric correction applied. Project coefficients and Ocean/Land flags are attached. The dataset resolution is 1 km. The provided format is HDF. Missing data/saturation /supersaturation flag, transient response flag and piecewise liner flag are attached. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L1B_SWIR_1km_NA ADEOS-II/GLI L1B Short-wavelength infrared (1km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129943-JAXA.umm_json "ADEOS-II/GLI L1B Short-wavelength infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km'SWIR (Short-wavelength infrared, ch 24-29, 1050 - 2210 nm) data radiometric and geometric correction applied. Project coefficients and Ocean/Land flags are attached. The dataset resolution is 1 km. The provided format is HDF. Missing data/saturation /supersaturation flag, transient response flag and piecewise liner flag are attached. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L1B_VNIR_1km_NA ADEOS-II/GLI L1B Visible and near infrared (1km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129597-JAXA.umm_json "ADEOS-II/GLI L1B Visible and near infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's VNIR (visible and near infrared, ch 01-19, 380- 895 nm) data radiometric and geometric correction applied. Projection coefficients and Ocean/Land flags are attached. The dataset resolution is 1 km. The provided format is HDF. Missing data/saturation /supersaturation flag, transient response flag and piecewise liner flag are attached. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L1B_VNIR_1km_NA ADEOS-II/GLI L1B Visible and near infrared (1km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129597-JAXA.umm_json "ADEOS-II/GLI L1B Visible and near infrared (1km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is GLI-1km's VNIR (visible and near infrared, ch 01-19, 380- 895 nm) data radiometric and geometric correction applied. Projection coefficients and Ocean/Land flags are attached. The dataset resolution is 1 km. The provided format is HDF. Missing data/saturation /supersaturation flag, transient response flag and piecewise liner flag are attached. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2A_LC_NA ADEOS-II/GLI L2A Land and Cryosphere ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129158-JAXA.umm_json "ADEOS-II/GLI L2 Land and Cryosphere is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is ADEOS-II/GLI L2 Land and Cryosphere data is map-projected global full resolution product. This data is generated each 16days and most cloud-free pixel is selected, mosaicking is performed. This product consists of 56 areas. Northern and southern 4 area is polar-stereographic projected. Middle latitude region is equi-rectangular grid and separated 48 areas (30deg. x 30deg.). The resolution is about 1km. The provided format is HDF. The channels (band: 1,5,8,13,15,17,19,24,26,27,28,29,30,31,34,35,36) necessary for land and cryosphere algorithms are included. Cloud flag and land-water flag are attached. The resolution is 1km. Each pixel has solar and satellite zenith/azimuth angle, observation date. In 16 days, there are 4 opportunities in one ground point at least because ADEOS-II recurrent period is 4 days. The time difference of adjacent pixels are 16 days in maximum. The observation condition of each pixel is different. The current version of the product is ""Version 2""." proprietary
@@ -1612,76 +1612,76 @@ ADEOS-II_GLI_L2A_OA_NA ADEOS-II/GLI L2A Ocean and Atmosphere ALL STAC Catalog 20
ADEOS-II_GLI_L2A_OA_NA ADEOS-II/GLI L2A Ocean and Atmosphere JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128826-JAXA.umm_json "ADEOS-II/GLI L2A Ocean and Atmosphere is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km.This product is a basic product for atmosphere and ocean level 2 products. Level 2A_OA consists of 4 pixel/4 line sampled all 1km GLI ch. Data, auxiliary data for atmosphere and ocean, cloud flag data and deviation table for removed data. The scene separated level 1B images are connected to tilt segment and eliminated overlapped scan lines.Map projection is not performed. MTIR ch. data are filled in path, but VNIR and SWIR data are filled in only half path because they are worked only in daytime. All GLI channels except 250m resolution ch. are included. (ch.1-19, 24-36: although ch.28, 29 are 250m resolution, 2km sampled data are also acquired) The provided format is HDF. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_ACLC_NA ADEOS-II/GLI L2 Atmospheric correction data for land and cryosphere JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129586-JAXA.umm_json "ADEOS-II/GLI L2 Atmospheric correction data for land and cryosphere is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is atmospheric correction data which is atmospherically correct the composited, normalized radiances for ""Rayleigh scattering and ozone absorption"". Rayleigh scattering and ozone absorption are corrected with the assistance of ancillary data, such as the TOMS data set and ETOPO 5. This product includes radiance data for channel 1, 5, 8, 13, 15, 17, 19, 24, 26, 27, 28, 29, 30, 31, 34, 35, 36. The physical quantity unit is W/m^2/micro-m/sr.This product also includes Satellite Zenith Angle, Solar Zenith Angle, Relative Azimuth Angle and Quality Control Flag. The provided format is HDF. Map projection is EQR and PS. Generation unit is area. The spatial resolution is 1 km and the statistical period is 16 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_ACLC_NA ADEOS-II/GLI L2 Atmospheric correction data for land and cryosphere ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129586-JAXA.umm_json "ADEOS-II/GLI L2 Atmospheric correction data for land and cryosphere is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is atmospheric correction data which is atmospherically correct the composited, normalized radiances for ""Rayleigh scattering and ozone absorption"". Rayleigh scattering and ozone absorption are corrected with the assistance of ancillary data, such as the TOMS data set and ETOPO 5. This product includes radiance data for channel 1, 5, 8, 13, 15, 17, 19, 24, 26, 27, 28, 29, 30, 31, 34, 35, 36. The physical quantity unit is W/m^2/micro-m/sr.This product also includes Satellite Zenith Angle, Solar Zenith Angle, Relative Azimuth Angle and Quality Control Flag. The provided format is HDF. Map projection is EQR and PS. Generation unit is area. The spatial resolution is 1 km and the statistical period is 16 days. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L2_ARAE_NA ADEOS-II/GLI L2 Aerosol Angstrom Exponent ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131090-JAXA.umm_json "ADEOS-II/GLI L2 Aerosol Angstrom Exponent is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_ARAE_NA ADEOS-II/GLI L2 Aerosol Angstrom Exponent JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131090-JAXA.umm_json "ADEOS-II/GLI L2 Aerosol Angstrom Exponent is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L2_AROP_NA ADEOS-II/GLI L2 Aerosol Optical Thickness JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129501-JAXA.umm_json "ADEOS-II/GLI L2 Aerosol Optical Thickness is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L2_ARAE_NA ADEOS-II/GLI L2 Aerosol Angstrom Exponent ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131090-JAXA.umm_json "ADEOS-II/GLI L2 Aerosol Angstrom Exponent is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_AROP_NA ADEOS-II/GLI L2 Aerosol Optical Thickness ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129501-JAXA.umm_json "ADEOS-II/GLI L2 Aerosol Optical Thickness is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L2_CLER_i_e_NA ADEOS-II/GLI L2 Cloud Effective Particle Radius of ice cloud by emission method ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133944-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Effective Particle Radius of ice cloud by emission method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. The provided format is HDF. The physical quantity unit is micrometer. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L2_AROP_NA ADEOS-II/GLI L2 Aerosol Optical Thickness JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129501-JAXA.umm_json "ADEOS-II/GLI L2 Aerosol Optical Thickness is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLER_i_e_NA ADEOS-II/GLI L2 Cloud Effective Particle Radius of ice cloud by emission method JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133944-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Effective Particle Radius of ice cloud by emission method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. The provided format is HDF. The physical quantity unit is micrometer. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L2_CLER_w_r_NA ADEOS-II/GLI L2 Cloud Effective Particle Radius of water cloud by reflection method JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129010-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Effective Particle Radius of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. The provided format is HDF. The physical quantity unit is micrometer. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L2_CLER_i_e_NA ADEOS-II/GLI L2 Cloud Effective Particle Radius of ice cloud by emission method ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133944-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Effective Particle Radius of ice cloud by emission method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. The provided format is HDF. The physical quantity unit is micrometer. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLER_w_r_NA ADEOS-II/GLI L2 Cloud Effective Particle Radius of water cloud by reflection method ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129010-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Effective Particle Radius of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. The provided format is HDF. The physical quantity unit is micrometer. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L2_CLER_w_r_NA ADEOS-II/GLI L2 Cloud Effective Particle Radius of water cloud by reflection method JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129010-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Effective Particle Radius of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. The provided format is HDF. The physical quantity unit is micrometer. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLFLG_p_NA ADEOS-II/GLI L2 Cloud flag JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132540-JAXA.umm_json "ADEOS-II/GLI L2 Cloud flag is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud mask data which indicate whether a given view of the earth surface is unobstructed by clouds or optically thick aerosol, and whether that clear scene is contaminated by a shadow, and L1B data is used as input data. The provided format is HDF. The physical quantity unit is None. The generation unit is global. Map projection is not done. The spatial resolution is 0.25 degree and the statistical period is 4 days. This product also includes Day/Night Flag, Sunlit Flag, Snow / Ice background Flag and Land/Water Flag. Note that this product has an error for ""L1B_bound"" data. L1B_bound is a parameter that decides whether or not the granule scene of L1B data crosses the boundary of latitude and/or longitude. As an alternative, CLFLG_P has attribute information that includes the latitude and longitude of four corners of the granule scene that can be used for the same decision. Hence, we do not plan to reprocess CLFLG_P to correct this error. Please use CLFLG_P to resolve this issue. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLFLG_p_NA ADEOS-II/GLI L2 Cloud flag ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132540-JAXA.umm_json "ADEOS-II/GLI L2 Cloud flag is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud mask data which indicate whether a given view of the earth surface is unobstructed by clouds or optically thick aerosol, and whether that clear scene is contaminated by a shadow, and L1B data is used as input data. The provided format is HDF. The physical quantity unit is None. The generation unit is global. Map projection is not done. The spatial resolution is 0.25 degree and the statistical period is 4 days. This product also includes Day/Night Flag, Sunlit Flag, Snow / Ice background Flag and Land/Water Flag. Note that this product has an error for ""L1B_bound"" data. L1B_bound is a parameter that decides whether or not the granule scene of L1B data crosses the boundary of latitude and/or longitude. As an alternative, CLFLG_P has attribute information that includes the latitude and longitude of four corners of the granule scene that can be used for the same decision. Hence, we do not plan to reprocess CLFLG_P to correct this error. Please use CLFLG_P to resolve this issue. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L2_CLFR_NA ADEOS-II/GLI L2 Cloud fraction JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129222-JAXA.umm_json "ADEOS-II/GLI L2 Cloud fraction is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 0.25 degreeï½ 0.25 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The cloud information can be used for estimation of surface radiation budget as a research product. The provided format is HDF. The physical quantity unit is None. The generation unit is global. Map projection is EQR. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLFR_NA ADEOS-II/GLI L2 Cloud fraction ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129222-JAXA.umm_json "ADEOS-II/GLI L2 Cloud fraction is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 0.25 degreeï½ 0.25 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The cloud information can be used for estimation of surface radiation budget as a research product. The provided format is HDF. The physical quantity unit is None. The generation unit is global. Map projection is EQR. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L2_CLFR_NA ADEOS-II/GLI L2 Cloud fraction JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129222-JAXA.umm_json "ADEOS-II/GLI L2 Cloud fraction is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 0.25 degreeï½ 0.25 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The cloud information can be used for estimation of surface radiation budget as a research product. The provided format is HDF. The physical quantity unit is None. The generation unit is global. Map projection is EQR. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLHT_w_r_NA ADEOS-II/GLI L2 Cloud Top Height of water cloud by reflection method ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128793-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Top Height of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. The provided format is HDF. The physical quantity unit is km. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLHT_w_r_NA ADEOS-II/GLI L2 Cloud Top Height of water cloud by reflection method JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128793-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Top Height of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. The provided format is HDF. The physical quantity unit is km. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLOP_i_e_NA ADEOS-II/GLI L2 Cloud Optical Thickness of ice cloud by emission method ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130189-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Optical Thickness of ice cloud by emission method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has an swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. The provided format is HDF. The physical quantity unit is none. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLOP_i_e_NA ADEOS-II/GLI L2 Cloud Optical Thickness of ice cloud by emission method JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130189-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Optical Thickness of ice cloud by emission method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has an swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. The provided format is HDF. The physical quantity unit is none. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L2_CLOP_i_r_NA ADEOS-II/GLI Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128808-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Optical Thickness of ice cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 μm) and 35 (10.8 μm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. The provided format is HDF. The physical quantity unit is none. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLOP_i_r_NA ADEOS-II/GLI Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128808-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Optical Thickness of ice cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 μm) and 35 (10.8 μm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. The provided format is HDF. The physical quantity unit is none. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L2_CLOP_i_r_NA ADEOS-II/GLI Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128808-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Optical Thickness of ice cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 μm) and 35 (10.8 μm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. The provided format is HDF. The physical quantity unit is none. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLOP_w_r_NA ADEOS-II/GLI L2 Cloud Optical Thickness of water cloud by reflection method JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133040-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Optical Thickness of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. The provided format is HDF. The physical quantity unit is none. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLOP_w_r_NA ADEOS-II/GLI L2 Cloud Optical Thickness of water cloud by reflection method ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133040-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Optical Thickness of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. The provided format is HDF. The physical quantity unit is none. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLTT_i_e_NA ADEOS-II/GLI L2 Cloud Top Temperature of ice cloud by emission method ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129893-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Top Temperature of ice cloud by emission method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Cloud Top Temperature of ice cloud is which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. The provided format is HDF. The physical quantity unit is Kelvin. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLTT_i_e_NA ADEOS-II/GLI L2 Cloud Top Temperature of ice cloud by emission method JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129893-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Top Temperature of ice cloud by emission method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Cloud Top Temperature of ice cloud is which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. The provided format is HDF. The physical quantity unit is Kelvin. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L2_CLTT_w_r_NA ADEOS-II/GLI L2 Cloud Top Temperature of water cloud by reflection method ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129873-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Top Temperature of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. The provided format is HDF. The physical quantity unit is Kelvin. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLTT_w_r_NA ADEOS-II/GLI L2 Cloud Top Temperature of water cloud by reflection method JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129873-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Top Temperature of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. The provided format is HDF. The physical quantity unit is Kelvin. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L2_CLWP_w_r_NA ADEOS-II/GLI L2 Cloud Liquid Water Path of water cloud by reflection method ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130181-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Liquid Water Path of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. The provided format is HDF. The physical quantity unit is g/m^2. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L2_CLTT_w_r_NA ADEOS-II/GLI L2 Cloud Top Temperature of water cloud by reflection method ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129873-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Top Temperature of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. The provided format is HDF. The physical quantity unit is Kelvin. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CLWP_w_r_NA ADEOS-II/GLI L2 Cloud Liquid Water Path of water cloud by reflection method JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130181-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Liquid Water Path of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. The provided format is HDF. The physical quantity unit is g/m^2. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L2_CLWP_w_r_NA ADEOS-II/GLI L2 Cloud Liquid Water Path of water cloud by reflection method ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130181-JAXA.umm_json "ADEOS-II/GLI L2 Cloud Liquid Water Path of water cloud by reflection method is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. The provided format is HDF. The physical quantity unit is g/m^2. Map projection is EQR and generation unit is global. The spatial resolution is 0.25 degree and the statistical period is 4 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CS_LR_NA ADEOS-II/GLI L2 Ocean color ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131205-JAXA.umm_json "ADEOS-II/GLI L2 Ocean color is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration, Attenuation at 490 nm, Suspended solid concentration, CDOM absorption at 440nm. They are derived from GLI_ADEOS-II_L2_NW data by using empirical relationships based on in-water NWLR and measurements of the products of interest. Each physical unit is mg/m^3, 1/m g/m^3 and 1/m. The provided format is HDF. Map projection is not done. Generation unit is path. The spatial resolution is approximately 4 km. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_CS_LR_NA ADEOS-II/GLI L2 Ocean color JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131205-JAXA.umm_json "ADEOS-II/GLI L2 Ocean color is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration, Attenuation at 490 nm, Suspended solid concentration, CDOM absorption at 440nm. They are derived from GLI_ADEOS-II_L2_NW data by using empirical relationships based on in-water NWLR and measurements of the products of interest. Each physical unit is mg/m^3, 1/m g/m^3 and 1/m. The provided format is HDF. Map projection is not done. Generation unit is path. The spatial resolution is approximately 4 km. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_NW_NA ADEOS-II/GLI L2 Normalized water leaving radiance ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129601-JAXA.umm_json "ADEOS-II/GLI L2 Normalized water leaving radiance is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680 nm, Normalized water-leaving radiance at 678, 865 nm by in-water model, Aerosol radiance at 865, 380 nm, Angstrom exponent derived from 520 and 865 nm, Aerosol optical thickness at 865 nm, Photosynthetically available radiation.They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. The unit of Normalized water-leaving radiance, Aerosol radiance and Aerosol albedo is mW cm^-2 um^-1 sr^-1. Photosynthetically available radiation is Ein m^-2 D^-1. The provided format is HDF. Map projection is not done. Generation unit is path. The spatial resolution is approximately 4 km. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_NW_NA ADEOS-II/GLI L2 Normalized water leaving radiance JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129601-JAXA.umm_json "ADEOS-II/GLI L2 Normalized water leaving radiance is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680 nm, Normalized water-leaving radiance at 678, 865 nm by in-water model, Aerosol radiance at 865, 380 nm, Angstrom exponent derived from 520 and 865 nm, Aerosol optical thickness at 865 nm, Photosynthetically available radiation.They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. The unit of Normalized water-leaving radiance, Aerosol radiance and Aerosol albedo is mW cm^-2 um^-1 sr^-1. Photosynthetically available radiation is Ein m^-2 D^-1. The provided format is HDF. Map projection is not done. Generation unit is path. The spatial resolution is approximately 4 km. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_PGCP_NA ADEOS-II/GLI L2 Precise Geometric Correction Parameter JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130201-JAXA.umm_json "ADEOS-II/GLI L2 Precise Geometric Correction Parameter is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Precise geometric correction parameter. This parameter is a parameter that combines with L1B, and obtains precise geometry correction image. The provided format is HDF. The physical quantity unit is none. Map projection is None and generation unit is scene. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_PGCP_NA ADEOS-II/GLI L2 Precise Geometric Correction Parameter ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130201-JAXA.umm_json "ADEOS-II/GLI L2 Precise Geometric Correction Parameter is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Precise geometric correction parameter. This parameter is a parameter that combines with L1B, and obtains precise geometry correction image. The provided format is HDF. The physical quantity unit is none. Map projection is None and generation unit is scene. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L2_SNGI_NA ADEOS-II/GLI L2 Snow Grain and Impurities ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130283-JAXA.umm_json "ADEOS-II/GLI L2 Snow Grain and Impurities is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm and 1640nm band, Snow impurities as soot, Snow surface temperature, Surface classification flag.Snow grain size retrieved with 865nm is using GLI channels 5 (0.46 μm) and 19 (0.865 μm), is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. It can be applied at high latitude (polar) as well as mid-latitude regions. The physical quantity is micro meter.Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 μm) independently to retrieve snow grain size at very top surface. The physical quantity is micro meter. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code.The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain size and mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. The physical quantity is ppmw. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Surface classification flag uses L2A_LC data in channels 8,13, 17, 19, 24, 27, 30, 31, 34, 35 and 36 is used as input to this product. The output of the cloudy/clear and snow/sea-ice discriminator algorithm will be an 8-bit word for each field of view. It includes information about whether a view of the surface is obstructed by cloud and the surface type for each pixel. There are four levels of confidence to indicate whether a pixel is judged to be cloudy or clear. The physical quantity is dimensionless. The provided format is HDF. Map projection is EQR and PS. Generation unit is zone. The spatial resolution is 1 km and the statistical period is 16 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_SNGI_NA ADEOS-II/GLI L2 Snow Grain and Impurities JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130283-JAXA.umm_json "ADEOS-II/GLI L2 Snow Grain and Impurities is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm and 1640nm band, Snow impurities as soot, Snow surface temperature, Surface classification flag.Snow grain size retrieved with 865nm is using GLI channels 5 (0.46 μm) and 19 (0.865 μm), is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. It can be applied at high latitude (polar) as well as mid-latitude regions. The physical quantity is micro meter.Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 μm) independently to retrieve snow grain size at very top surface. The physical quantity is micro meter. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code.The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain size and mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. The physical quantity is ppmw. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Surface classification flag uses L2A_LC data in channels 8,13, 17, 19, 24, 27, 30, 31, 34, 35 and 36 is used as input to this product. The output of the cloudy/clear and snow/sea-ice discriminator algorithm will be an 8-bit word for each field of view. It includes information about whether a view of the surface is obstructed by cloud and the surface type for each pixel. There are four levels of confidence to indicate whether a pixel is judged to be cloudy or clear. The physical quantity is dimensionless. The provided format is HDF. Map projection is EQR and PS. Generation unit is zone. The spatial resolution is 1 km and the statistical period is 16 days. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L2_SNGI_NA ADEOS-II/GLI L2 Snow Grain and Impurities ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130283-JAXA.umm_json "ADEOS-II/GLI L2 Snow Grain and Impurities is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm and 1640nm band, Snow impurities as soot, Snow surface temperature, Surface classification flag.Snow grain size retrieved with 865nm is using GLI channels 5 (0.46 μm) and 19 (0.865 μm), is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. It can be applied at high latitude (polar) as well as mid-latitude regions. The physical quantity is micro meter.Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 μm) independently to retrieve snow grain size at very top surface. The physical quantity is micro meter. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code.The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain size and mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. The physical quantity is ppmw. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Surface classification flag uses L2A_LC data in channels 8,13, 17, 19, 24, 27, 30, 31, 34, 35 and 36 is used as input to this product. The output of the cloudy/clear and snow/sea-ice discriminator algorithm will be an 8-bit word for each field of view. It includes information about whether a view of the surface is obstructed by cloud and the surface type for each pixel. There are four levels of confidence to indicate whether a pixel is judged to be cloudy or clear. The physical quantity is dimensionless. The provided format is HDF. Map projection is EQR and PS. Generation unit is zone. The spatial resolution is 1 km and the statistical period is 16 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_ST_LR_NA ADEOS-II/GLI L2 Sea surface temperature JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130175-JAXA.umm_json "ADEOS-II/GLI L2 Sea surface temperature is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes bulk sea surface temperature, which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. The Multi-Channel SST (MCSST) technique is used. This product is generated from Level-2A_OA product. The physical quantity is Kelvin. This product also includes Quality flag or mask, Satellite Zenith Angle, Satellite Azimuth Angle, Solar Zenith Angle, Solar Azimuth Angle, Tilt Angle Flag as supplement data. The provided format is HDF. Map projection is not done. Generation unit is path. The spatial resolution is approximately 4 km. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_ST_LR_NA ADEOS-II/GLI L2 Sea surface temperature ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130175-JAXA.umm_json "ADEOS-II/GLI L2 Sea surface temperature is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes bulk sea surface temperature, which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. The Multi-Channel SST (MCSST) technique is used. This product is generated from Level-2A_OA product. The physical quantity is Kelvin. This product also includes Quality flag or mask, Satellite Zenith Angle, Satellite Azimuth Angle, Solar Zenith Angle, Solar Azimuth Angle, Tilt Angle Flag as supplement data. The provided format is HDF. Map projection is not done. Generation unit is path. The spatial resolution is approximately 4 km. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_VGI_NA ADEOS-II/GLI L2 Vegetation Index JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129966-JAXA.umm_json "ADEOS-II/GLI L2 Vegetation Indices is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes the normalized difference vegetation index (NDVI) which is the ratio between the difference in the red and near- infrared and their sum the most widely used index in global vegetation studies and the enhanced vegetation index (EVI) for increased sensitivity over a wider range of vegetation conditions, removal of soil background influences, and removal of residual atmospheric contamination effects present in the NDVI. They use L2_ACLC data as input. The GLI VI products will be spatially and temporally re-sampled, and designed to provide cloud free vegetation index maps at nominal resolutions of 1 km. The composited surface reflectance data from each pixel is used to compute both the NDVI and the EVI gridded products. The bands used to compute the VI are as follows: Red band: Band 13, BIR band: band 19, Blue band: band 5. The gridded VIs is produced at 16-day (half- month) also Monthly gridded VI products based on temporal averaging of the 16 days products is available. The provided format is HDF. The physical quantity unit is dimensionless. Map projection is EQR and PS. Generation unit is zone. The spatial resolution is 0.25 degree and the statistical period is 16 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L2_VGI_NA ADEOS-II/GLI L2 Vegetation Index ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129966-JAXA.umm_json "ADEOS-II/GLI L2 Vegetation Indices is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes the normalized difference vegetation index (NDVI) which is the ratio between the difference in the red and near- infrared and their sum the most widely used index in global vegetation studies and the enhanced vegetation index (EVI) for increased sensitivity over a wider range of vegetation conditions, removal of soil background influences, and removal of residual atmospheric contamination effects present in the NDVI. They use L2_ACLC data as input. The GLI VI products will be spatially and temporally re-sampled, and designed to provide cloud free vegetation index maps at nominal resolutions of 1 km. The composited surface reflectance data from each pixel is used to compute both the NDVI and the EVI gridded products. The bands used to compute the VI are as follows: Red band: Band 13, BIR band: band 19, Blue band: band 5. The gridded VIs is produced at 16-day (half- month) also Monthly gridded VI products based on temporal averaging of the 16 days products is available. The provided format is HDF. The physical quantity unit is dimensionless. Map projection is EQR and PS. Generation unit is zone. The spatial resolution is 0.25 degree and the statistical period is 16 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_ARAE_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Aerosol Angstrom Exponent (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130583-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol Angstrom Exponent (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent and temporarily and spatially sampled level 2 data. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 1/4 degree and the statistical period is 16 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_ARAE_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Aerosol Angstrom Exponent (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130583-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol Angstrom Exponent (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent and temporarily and spatially sampled level 2 data. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 1/4 degree and the statistical period is 16 days. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_ARAE_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Aerosol Angstrom Exponent (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130148-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol Angstrom Exponent (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent and temporarily and spatially sampled level 2 data. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 1/4 degree and the statistical period is 1 month. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_ARAE_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Aerosol Angstrom Exponent (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130148-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol Angstrom Exponent (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent and temporarily and spatially sampled level 2 data. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 1/4 degree and the statistical period is 1 month. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_AROP_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133757-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 1/4 degree and the statistical period is 16 days. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_ARAE_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Aerosol Angstrom Exponent (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130148-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol Angstrom Exponent (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent and temporarily and spatially sampled level 2 data. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 1/4 degree and the statistical period is 1 month. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_AROP_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133757-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 1/4 degree and the statistical period is 16 days. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_AROP_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130239-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 1/4 degree and the statistical period is 1 month. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_AROP_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133757-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 1/4 degree and the statistical period is 16 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_AROP_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130239-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 1/4 degree and the statistical period is 1 month. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_AROP_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130239-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol Optical Thickness (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. Map projection is EQR and generation unit is global. The spatial resolution is 1/4 degree and the statistical period is 1 month. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLER_i_e_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of ice cloud by emission method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130470-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLER_i_e_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of ice cloud by emission method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130470-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_CLER_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128782-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLER_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128782-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_CLER_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of water cloud by reflection method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130279-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_CLER_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128782-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLER_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of water cloud by reflection method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130279-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_CLER_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of water cloud by reflection method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130279-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLER_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of water cloud by reflection method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133829-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLER_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of water cloud by reflection method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133829-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Effective Particle Radius of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLFR_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud fraction (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129845-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud fraction (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 1/4 degree x 1/4 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The cloud information can be used for estimation of surface radiation budget as a research product. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. The generation unit is global. Map projection is EQR. The spatial resolution is 1/4 degree and the statistical period is 16 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLFR_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud fraction (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129845-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud fraction (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 1/4 degree x 1/4 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The cloud information can be used for estimation of surface radiation budget as a research product. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. The generation unit is global. Map projection is EQR. The spatial resolution is 1/4 degree and the statistical period is 16 days. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLFR_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud fraction (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128790-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud fraction (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 1/4 degree x 1/4 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The cloud information can be used for estimation of surface radiation budget as a research product. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. The generation unit is global. Map projection is EQR. The spatial resolution is 1/4 degree and time resolution are 1month. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLFR_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud fraction (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128790-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud fraction (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 1/4 degree x 1/4 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The cloud information can be used for estimation of surface radiation budget as a research product. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is None. The generation unit is global. Map projection is EQR. The spatial resolution is 1/4 degree and time resolution are 1month. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_CLHT_w_e_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Top Height of water cloud by emission method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128877-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Top Height of water cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is km. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLHT_w_e_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Top Height of water cloud by emission method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128877-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Top Height of water cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is km. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_CLHT_w_e_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Top Height of water cloud by emission method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128877-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Top Height of water cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is km. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLHT_w_e_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Top Height of water cloud by emission method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130180-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Top Height of water cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is km. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLHT_w_e_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Top Height of water cloud by emission method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130180-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Top Height of water cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is km. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLOP_i_e_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by emission method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129566-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLOP_i_e_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by emission method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129566-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_CLOP_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130430-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period are 1month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLOP_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130430-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period are 1month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_CLOP_i_r_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128784-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 μm) and 35 (10.8 μm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_CLOP_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130430-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period are 1month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLOP_i_r_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128784-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 μm) and 35 (10.8 μm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_CLOP_i_r_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129877-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 μm) and 35 (10.8 μm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period are 1month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_CLOP_i_r_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128784-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 μm) and 35 (10.8 μm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLOP_i_r_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129877-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 μm) and 35 (10.8 μm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period are 1month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_CLOP_i_r_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129877-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of ice cloud by reflection method ( i r: ice cloud reflectance) (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 μm) and 35 (10.8 μm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period are 1month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLOP_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Optical Thickness of water cloud by reflection method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129483-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLOP_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Optical Thickness of water cloud by reflection method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129483-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLOP_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Optical Thickness of water cloud by reflection method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128909-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Optical Thickness of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
@@ -1690,238 +1690,238 @@ ADEOS-II_GLI_L3B_CLTT_i_e_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Top Temp
ADEOS-II_GLI_L3B_CLTT_i_e_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Top Temperature of ice cloud by emission method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130516-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Top Temperature of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Cloud Top Temperature of ice cloud is which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLTT_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Top Temperature of ice cloud by emission method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128786-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Top Temperature of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Cloud Top Temperature of ice cloud is which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLTT_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Top Temperature of ice cloud by emission method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128786-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Top Temperature of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Cloud Top Temperature of ice cloud is which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_CLTT_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Top Temperature of water cloud by reflection method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130714-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Top Temperature of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLTT_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Top Temperature of water cloud by reflection method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130714-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Top Temperature of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_CLTT_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Top Temperature of water cloud by reflection method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130714-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Top Temperature of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLTT_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Top Temperature of water cloud by reflection method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132707-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Top Temperature of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLTT_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Top Temperature of water cloud by reflection method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132707-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Top Temperature of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLWP_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Liquid Water Path of water cloud by reflection method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130285-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Liquid Water Path of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is g/m^2. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLWP_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Liquid Water Path of water cloud by reflection method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130285-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Liquid Water Path of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is g/m^2. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLWP_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Liquid Water Path of water cloud by reflection method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131734-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Liquid Water Path of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is g/m^2. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CLWP_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 Binned Cloud Liquid Water Path of water cloud by reflection method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131734-JAXA.umm_json "ADEOS-II/GLI L3 Binned Cloud Liquid Water Path of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical quantity unit is g/m^2. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_CS_1day_9km_NA ADEOS-II/GLI L3 Binned Ocean Color (1day,9 km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130122-JAXA.umm_json "ADEOS-II/GLI L3 Binned Ocean Color (1day, 9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration, Attenuation at 490 nm, Suspended solid concentration, CDOM absorption at 440nm. Each physical unit is mg/m^3, 1/m g/m^3 and 1/m. This product includes sum, square sum, max, min of each pixel is included. They are derived from 1km resolution ocean color product (CS_FR) data. The provided format is HDF. The spatial resolution is 9 km, and The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CS_1day_9km_NA ADEOS-II/GLI L3 Binned Ocean Color (1day,9 km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130122-JAXA.umm_json "ADEOS-II/GLI L3 Binned Ocean Color (1day, 9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration, Attenuation at 490 nm, Suspended solid concentration, CDOM absorption at 440nm. Each physical unit is mg/m^3, 1/m g/m^3 and 1/m. This product includes sum, square sum, max, min of each pixel is included. They are derived from 1km resolution ocean color product (CS_FR) data. The provided format is HDF. The spatial resolution is 9 km, and The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_CS_1day_9km_NA ADEOS-II/GLI L3 Binned Ocean Color (1day,9 km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130122-JAXA.umm_json "ADEOS-II/GLI L3 Binned Ocean Color (1day, 9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration, Attenuation at 490 nm, Suspended solid concentration, CDOM absorption at 440nm. Each physical unit is mg/m^3, 1/m g/m^3 and 1/m. This product includes sum, square sum, max, min of each pixel is included. They are derived from 1km resolution ocean color product (CS_FR) data. The provided format is HDF. The spatial resolution is 9 km, and The statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CS_1month_9km_NA ADEOS-II/GLI L3 Binned Ocean Color (1month,9 km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129044-JAXA.umm_json "ADEOS-II/GLI L3 Binned Ocean Color (1month,9 km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration, Attenuation at 490 nm, Suspended solid concentration, CDOM absorption at 440nm. Each physical unit is mg/m^3, 1/m g/m^3 and 1/m. This product includes sum, square sum, max, min of each pixel is included. They are derived from 1km resolution ocean color product (CS_FR) data. The provided format is HDF. The spatial resolution is 9 km and the statistical period is 1 month, also 1 day and 8 days statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CS_1month_9km_NA ADEOS-II/GLI L3 Binned Ocean Color (1month,9 km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129044-JAXA.umm_json "ADEOS-II/GLI L3 Binned Ocean Color (1month,9 km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration, Attenuation at 490 nm, Suspended solid concentration, CDOM absorption at 440nm. Each physical unit is mg/m^3, 1/m g/m^3 and 1/m. This product includes sum, square sum, max, min of each pixel is included. They are derived from 1km resolution ocean color product (CS_FR) data. The provided format is HDF. The spatial resolution is 9 km and the statistical period is 1 month, also 1 day and 8 days statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CS_8days_9km_NA ADEOS-II/GLI L3 Binned Ocean Color (8days,9 km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128798-JAXA.umm_json "ADEOS-II/GLI L3 Binned Ocean Color (8days, 9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration, Attenuation at 490 nm, Suspended solid concentration, CDOM absorption at 440nm. Each physical unit is mg/m^3, 1/m g/m^3 and 1/m. This product includes sum, square sum, max, min of each pixel is included. They are derived from 1km resolution ocean color product (CS_FR) data. The provided format is HDF. The spatial resolution is 9 km, and the statistical period is 8 days, also 1 day and 1 month statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_CS_8days_9km_NA ADEOS-II/GLI L3 Binned Ocean Color (8days,9 km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128798-JAXA.umm_json "ADEOS-II/GLI L3 Binned Ocean Color (8days, 9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration, Attenuation at 490 nm, Suspended solid concentration, CDOM absorption at 440nm. Each physical unit is mg/m^3, 1/m g/m^3 and 1/m. This product includes sum, square sum, max, min of each pixel is included. They are derived from 1km resolution ocean color product (CS_FR) data. The provided format is HDF. The spatial resolution is 9 km, and the statistical period is 8 days, also 1 day and 1 month statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_LA_1day_9km_NA ADEOS-II/GLI L3 Binned Aerosol radiance (1day,9 km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131938-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol radiance (1day, 9 km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_LA_1day_9km_NA ADEOS-II/GLI L3 Binned Aerosol radiance (1day,9 km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131938-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol radiance (1day, 9 km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_LA_1day_9km_NA ADEOS-II/GLI L3 Binned Aerosol radiance (1day,9 km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131938-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol radiance (1day, 9 km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_LA_1month_9km_NA ADEOS-II/GLI L3 Binned Aerosol radiance (1month,9 km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133413-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol radiance (1month, 9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 1 month, also 1 day and 8 days statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_LA_1month_9km_NA ADEOS-II/GLI L3 Binned Aerosol radiance (1month,9 km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133413-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol radiance (1month, 9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 1 month, also 1 day and 8 days statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_LA_8days_9km_NA ADEOS-II/GLI L3 Binned Aerosol radiance (8days,9 km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131854-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol radiance (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 8 days, also 1 day and 1 month statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_LA_8days_9km_NA ADEOS-II/GLI L3 Binned Aerosol radiance (8days,9 km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131854-JAXA.umm_json "ADEOS-II/GLI L3 Binned Aerosol radiance (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 8 days, also 1 day and 1 month statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_NW_1day_9km_NA ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (1day,9 km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129797-JAXA.umm_json "ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9km and the statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_NW_1day_9km_NA ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (1day,9 km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129797-JAXA.umm_json "ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9km and the statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_NW_1month_9km_NA ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (1month,9 km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129471-JAXA.umm_json "ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm.They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 1 month, also 1 day and 8 days statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_NW_1month_9km_NA ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (1month,9 km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129471-JAXA.umm_json "ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm.They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 1 month, also 1 day and 8 days statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_NW_1month_9km_NA ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (1month,9 km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129471-JAXA.umm_json "ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm.They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 1 month, also 1 day and 8 days statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_NW_8days_9km_NA ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (8days,9 km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128756-JAXA.umm_json "ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 8 days, also 1 day and 1 month statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_NW_8days_9km_NA ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (8days,9 km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128756-JAXA.umm_json "ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km and the statistical period is 8 days, also 1 day and 1 month statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_SNWGS_16days_1-12deg_NA ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (16days,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129073-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has an swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 μm) independently to retrieve snow grain size at very top surface. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_SNWGS_16days_1-12deg_NA ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (16days,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129073-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has an swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 μm) independently to retrieve snow grain size at very top surface. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_SNWGS_1month_1-12deg_NA ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (1month,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130255-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has an swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 μm) independently to retrieve snow grain size at very top surface. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_SNWGS_16days_1-12deg_NA ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (16days,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129073-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has an swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 μm) independently to retrieve snow grain size at very top surface. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1 month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_SNWGS_1month_1-12deg_NA ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (1month,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130255-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has an swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 μm) independently to retrieve snow grain size at very top surface. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_SNWGS_1month_1-12deg_NA ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (1month,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130255-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 1640nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has an swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 μm) independently to retrieve snow grain size at very top surface. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_SNWG_16days_1-12deg_NA ADEOS-II/GLI L3 Binned Snow grain size retrieved with 865nm band (16days,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130538-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 865nm band (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm is using GLI channels 5 (0.46 μm) and 19 (0.865 μm) which is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. It can be applied at high latitude (polar) as well as mid-latitude regions. The physical quantity is micro meter. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_SNWG_16days_1-12deg_NA ADEOS-II/GLI L3 Binned Snow grain size retrieved with 865nm band (16days,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130538-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 865nm band (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm is using GLI channels 5 (0.46 μm) and 19 (0.865 μm) which is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. It can be applied at high latitude (polar) as well as mid-latitude regions. The physical quantity is micro meter. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_SNWG_1month_1-12deg_NA ADEOS-II/GLI L3 Binned Snow grain size retrieved with 865nm band (1month,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129230-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 865nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm is using GLI channels 5 (0.46 μm) and 19 (0.865 μm) which is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. It can be applied at high latitude (polar) as well as mid-latitude regions. The physical quantity is micro meter. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_SNWG_1month_1-12deg_NA ADEOS-II/GLI L3 Binned Snow grain size retrieved with 865nm band (1month,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129230-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow grain size retrieved with 865nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm is using GLI channels 5 (0.46 μm) and 19 (0.865 μm) which is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. It can be applied at high latitude (polar) as well as mid-latitude regions. The physical quantity is micro meter. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_SNWI_16days_1-12deg_NA ADEOS-II/GLI L3 Binned Snow impurities (16days,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131106-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow impurities (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow impurities. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code. The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain size and mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is ppmw. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_SNWI_16days_1-12deg_NA ADEOS-II/GLI L3 Binned Snow impurities (16days,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131106-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow impurities (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow impurities. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code. The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain size and mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is ppmw. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_SNWI_1month_1-12deg_NA ADEOS-II/GLI L3 Binned Snow impurities (1month,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130726-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow impurities (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow impurities. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code. The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain sizeand mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is ppmw. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_SNWI_16days_1-12deg_NA ADEOS-II/GLI L3 Binned Snow impurities (16days,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131106-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow impurities (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow impurities. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code. The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain size and mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is ppmw. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_SNWI_1month_1-12deg_NA ADEOS-II/GLI L3 Binned Snow impurities (1month,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130726-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow impurities (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow impurities. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code. The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain sizeand mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is ppmw. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_SNWI_1month_1-12deg_NA ADEOS-II/GLI L3 Binned Snow impurities (1month,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130726-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow impurities (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow impurities. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code. The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain sizeand mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is ppmw. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_SNWTS_16days_1-12deg_NA ADEOS-II/GLI L3 Binned Snow surface temperature (16days,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130276-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow surface temperature (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow surface temperature. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_SNWTS_16days_1-12deg_NA ADEOS-II/GLI L3 Binned Snow surface temperature (16days,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130276-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow surface temperature (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow surface temperature. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_SNWTS_1month_1-12deg_NA ADEOS-II/GLI L3 Binned Snow surface temperature (1month,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130479-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow surface temperature (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow surface temperature. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_SNWTS_1month_1-12deg_NA ADEOS-II/GLI L3 Binned Snow surface temperature (1month,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130479-JAXA.umm_json "ADEOS-II/GLI L3 Binned Snow surface temperature (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow surface temperature. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_ST_1day_9km_NA ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1day,9 km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129902-JAXA.umm_json "ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes bulk sea surface temperature which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. Daytime and night time are separated. This product includes sum, square sum of each pixel is included.The provided format is HDF. The spatial resolution is 9 km and the statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_ST_1day_9km_NA ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1day,9 km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129902-JAXA.umm_json "ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes bulk sea surface temperature which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. Daytime and night time are separated. This product includes sum, square sum of each pixel is included.The provided format is HDF. The spatial resolution is 9 km and the statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3B_ST_1month_9km_NA ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1month,9 km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129829-JAXA.umm_json "ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes bulk sea surface temperature which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. Daytime and nighttime are separated. This product includes sum, square sum of each pixel is included.The provided format is HDF. The spatial resolution is 9 km and the statistical period is 1 month, also 1 day and 8 days statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_ST_1day_9km_NA ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1day,9 km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129902-JAXA.umm_json "ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes bulk sea surface temperature which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. Daytime and night time are separated. This product includes sum, square sum of each pixel is included.The provided format is HDF. The spatial resolution is 9 km and the statistical period is 1 day, also 8 days and 1 month statistics are available. The projection method is EQA. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_ST_1month_9km_NA ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1month,9 km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129829-JAXA.umm_json "ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes bulk sea surface temperature which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. Daytime and nighttime are separated. This product includes sum, square sum of each pixel is included.The provided format is HDF. The spatial resolution is 9 km and the statistical period is 1 month, also 1 day and 8 days statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3B_ST_1month_9km_NA ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1month,9 km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129829-JAXA.umm_json "ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes bulk sea surface temperature which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. Daytime and nighttime are separated. This product includes sum, square sum of each pixel is included.The provided format is HDF. The spatial resolution is 9 km and the statistical period is 1 month, also 1 day and 8 days statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_ST_8days_9km_NA ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (8days,9 km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132996-JAXA.umm_json "ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes bulk sea surface temperature which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. Daytime and nighttime are separated. This product includes sum, square sum of each pixel is included.The provided format is HDF. The spatial resolution is 9 km and the statistical period is 8 days, also 1 day and 1month statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3B_ST_8days_9km_NA ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (8days,9 km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132996-JAXA.umm_json "ADEOS-II/GLI L3 Binned Bulk Sea surface temperature (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes bulk sea surface temperature which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. Daytime and nighttime are separated. This product includes sum, square sum of each pixel is included.The provided format is HDF. The spatial resolution is 9 km and the statistical period is 8 days, also 1 day and 1month statistics is available. Map projection is EQA. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_ARAE_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Aerosol Angstrom Exponent (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128899-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol Angstrom Exponent (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. The physical quantity unit is None. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_ARAE_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Aerosol Angstrom Exponent (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128899-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol Angstrom Exponent (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. The physical quantity unit is None. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_ARAE_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Aerosol Angstrom Exponent (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128789-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol Angstrom Exponent (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. The physical quantity unit is None. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_ARAE_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Aerosol Angstrom Exponent (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128789-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol Angstrom Exponent (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. The physical quantity unit is None. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_ARAE_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Aerosol Angstrom Exponent (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128789-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol Angstrom Exponent (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Angstrom exponent data which is an index of aerosol size distribution over ocean surface. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve Angstrom exponent. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. The physical quantity unit is None. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_AROP_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Aerosol Optical Thickness (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129027-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol Optical Thickness (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The physical quantity unit is None.The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_AROP_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Aerosol Optical Thickness (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129027-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol Optical Thickness (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The physical quantity unit is None.The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_AROP_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Aerosol Optical Thickness (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698134106-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol Optical Thickness (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The physical quantity unit is None.The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_AROP_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Aerosol Optical Thickness (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698134106-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol Optical Thickness (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is Aerosol optical thickness at 0.5 micron. Visible (channel 13, 678nm) and near-IR (channel 19, 865nm) channels are used as input to retrieve aerosol optical thickness. For retrievals, ancillary data are needed, which include wind velocity at 10meter height, ozone and water vapor amount to correct radiance for surface reflectance, ozone and water vapor absorption. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The physical quantity unit is None.The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CDOM_1day_9km_NA ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (1day,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130694-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes CDOM absorption at 440nm. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CDOM_1day_9km_NA ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (1day,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130694-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes CDOM absorption at 440nm. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_CDOM_1month_9km_NA ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (1month,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131937-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes CDOM absorption at 440nm. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CDOM_1month_9km_NA ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (1month,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131937-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes CDOM absorption at 440nm. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_CDOM_1month_9km_NA ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (1month,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131937-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes CDOM absorption at 440nm. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CDOM_8days_9km_NA ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (8days,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129245-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes CDOM absorption at 440nm. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CDOM_8days_9km_NA ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (8days,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129245-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Absorption of colored dissolved organic matter (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes CDOM absorption at 440nm. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_CHLA_1day_9km_NA ADEOS-II/GLI L3 STA Map Chlorophyll-a (1day,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129194-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Chlorophyll-a (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration derived from GLI_ADEOS-II_L2_NW data by using empirical relationships based on in-water NWLR and measurements of the products of interest. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mg/m^3. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CHLA_1day_9km_NA ADEOS-II/GLI L3 STA Map Chlorophyll-a (1day,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129194-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Chlorophyll-a (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration derived from GLI_ADEOS-II_L2_NW data by using empirical relationships based on in-water NWLR and measurements of the products of interest. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mg/m^3. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_CHLA_1day_9km_NA ADEOS-II/GLI L3 STA Map Chlorophyll-a (1day,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129194-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Chlorophyll-a (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration derived from GLI_ADEOS-II_L2_NW data by using empirical relationships based on in-water NWLR and measurements of the products of interest. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mg/m^3. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CHLA_1month_9km_NA ADEOS-II/GLI L3 STA Map Chlorophyll-a (1month,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130317-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Chlorophyll-a (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration derived from GLI_ADEOS-II_L2_NW data by using empirical relationships based on in-water NWLR and measurements of the products of interest. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mg/m^3. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CHLA_1month_9km_NA ADEOS-II/GLI L3 STA Map Chlorophyll-a (1month,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130317-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Chlorophyll-a (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration derived from GLI_ADEOS-II_L2_NW data by using empirical relationships based on in-water NWLR and measurements of the products of interest. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mg/m^3. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CHLA_8days_9km_NA ADEOS-II/GLI L3 STA Map Chlorophyll-a (8days,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130858-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Chlorophyll-a (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration derived from GLI_ADEOS-II_L2_NW data by using empirical relationships based on in-water NWLR and measurements of the products of interest. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mg/m^3. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CHLA_8days_9km_NA ADEOS-II/GLI L3 STA Map Chlorophyll-a (8days,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130858-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Chlorophyll-a (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Chlorophyll_a concentration derived from GLI_ADEOS-II_L2_NW data by using empirical relationships based on in-water NWLR and measurements of the products of interest. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mg/m^3. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLER_i_e_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of ice cloud by emission method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128827-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLER_i_e_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of ice cloud by emission method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128827-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_CLER_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130853-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLER_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130853-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_CLER_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130853-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLER_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of water cloud by reflection method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129867-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLER_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of water cloud by reflection method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129867-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is micrometer. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLER_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of water cloud by reflection method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129224-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLER_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of water cloud by reflection method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129224-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Effective Particle Radius of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud effective particle radius of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_CLFR_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud fraction (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128872-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud fraction (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 1/4 degreeï½ 1/4 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The physical quantity unit is None. The cloud information can be used for estimation of surface radiation budget as a research product. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLFR_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud fraction (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128872-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud fraction (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 1/4 degreeï½ 1/4 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The physical quantity unit is None. The cloud information can be used for estimation of surface radiation budget as a research product. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_CLFR_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud fraction (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129226-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud fraction (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 1/4 degreeï½ 1/4 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The physical quantity unit is None. The cloud information can be used for estimation of surface radiation budget as a research product. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_CLFR_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud fraction (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128872-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud fraction (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 1/4 degreeï½ 1/4 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The physical quantity unit is None. The cloud information can be used for estimation of surface radiation budget as a research product. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLFR_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud fraction (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129226-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud fraction (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 1/4 degreeï½ 1/4 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The physical quantity unit is None. The cloud information can be used for estimation of surface radiation budget as a research product. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_CLFR_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud fraction (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129226-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud fraction (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud fraction data which classified by the ATSK16 algorithm and cloud property products are used as input. The cloud shape can be determined by sum of spatial differences between each pixel in an area of 1/4 degreeï½ 1/4 degree in Lat. and Lon., so a high difference means cumulus-type and a low one stratus-type. The physical quantity unit is None. The cloud information can be used for estimation of surface radiation budget as a research product. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLHT_w_e_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Top Height of water cloud by emission method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130220-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Top Height of water cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is km. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLHT_w_e_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Top Height of water cloud by emission method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130220-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Top Height of water cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is km. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_CLHT_w_e_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Top Height of water cloud by emission method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129891-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Top Height of water cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is km. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLHT_w_e_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Top Height of water cloud by emission method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129891-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Top Height of water cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is km. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_CLHT_w_e_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Top Height of water cloud by emission method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129891-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Top Height of water cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top height of water cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is km. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLOP_i_e_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by emission method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130524-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 25 km and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLOP_i_e_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by emission method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130524-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 25 km and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_CLOP_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129023-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLOP_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129023-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_CLOP_i_r_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128882-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 μm) and 35 (10.8 μm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 deg and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_CLOP_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129023-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLOP_i_r_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128882-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 μm) and 35 (10.8 μm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 deg and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_CLOP_i_r_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129606-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 μm) and 35 (10.8 μm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_CLOP_i_r_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128882-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 μm) and 35 (10.8 μm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 deg and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLOP_i_r_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129606-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 μm) and 35 (10.8 μm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_CLOP_i_r_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129606-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of ice cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of ice cloud applying emission method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13 or 19 (678 or 865 nm), 30 (3.715 μm) and 35 (10.8 μm) of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLOP_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of water cloud by reflection method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128950-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLOP_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of water cloud by reflection method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128950-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLOP_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of water cloud by reflection method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129995-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLOP_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of water cloud by reflection method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129995-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Optical Thickness of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud optical thickness of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is none. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLTT_i_e_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Top Temperature of ice cloud by emission method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130141-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Cloud Top Temperature of ice cloud is which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLTT_i_e_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Top Temperature of ice cloud by emission method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130141-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of ice cloud by emission method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Cloud Top Temperature of ice cloud is which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_CLTT_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Top Temperature of ice cloud by emission method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130458-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Cloud Top Temperature of ice cloud is which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLTT_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Top Temperature of ice cloud by emission method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130458-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Cloud Top Temperature of ice cloud is which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_CLTT_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Top Temperature of water cloud by reflection method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129577-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_CLTT_i_e_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Top Temperature of ice cloud by emission method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130458-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of ice cloud by emission method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product Cloud Top Temperature of ice cloud is which is retrieved from multi-channel radiance (channel 30, 35, 36) applying emission method. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLTT_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Top Temperature of water cloud by reflection method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129577-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_CLTT_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Top Temperature of water cloud by reflection method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129577-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLTT_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Top Temperature of water cloud by reflection method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131637-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLTT_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Top Temperature of water cloud by reflection method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131637-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Top Temperature of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud top temperature of water cloud which is retrieved from a non-absorption channel (channel 13), an absorption channel (channel 30), and a thermal channel (channel 35) are used to derive cloud effective particle radius. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is Kelvin. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_CLWP_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130034-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is g/m^2. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLWP_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (16days,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130034-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is g/m^2. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_CLWP_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128773-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is g/m^2. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_CLWP_w_r_16days_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (16days,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130034-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (16days,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is g/m^2. The spatial resolution is 1/4 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_CLWP_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (1month,1/4deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128773-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is g/m^2. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_K490_1day_9km_NA ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (1day,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128865-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Attenuation coefficient at 490 nm Each physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_CLWP_w_r_1month_1-4deg_NA ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (1month,1/4deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128773-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Cloud Liquid Water Path of water cloud by reflection method (1month,1/4deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product is cloud liquid water path of water cloud by reflection method. Undesirable radiation components such as ground-reflected solar radiation and thermal radiation are guessed from satellite-received radiances in channels 13, 30 and 35 of GLI and subtracted from radiances in channels 13 and 30 to derive the reflected solar radiation of a cloud layer which includes information about cloud microphysical properties. This method can be applied to a broad range of water clouds from semi-transparent to thick clouds. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity unit is g/m^2. The spatial resolution is 1/4 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_K490_1day_9km_NA ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (1day,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128865-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Attenuation coefficient at 490 nm Each physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_K490_1day_9km_NA ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (1day,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128865-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Attenuation coefficient at 490 nm Each physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_K490_1month_9km_NA ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (1month,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130457-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Attenuation coefficient at 490 nm Each physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_K490_1month_9km_NA ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (1month,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130457-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Attenuation coefficient at 490 nm Each physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_K490_8days_9km_NA ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (8days,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129185-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Attenuation coefficient at 490 nm Each physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_K490_8days_9km_NA ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (8days,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129185-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Attenuation coefficient at 490 nm Each physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_LA_1day_9km_NA ADEOS-II/GLI L3 STA Map Aerosol radiance (1day,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131101-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol radiance (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_K490_8days_9km_NA ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (8days,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129185-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Attenuation coefficient at 490nm (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Attenuation coefficient at 490 nm Each physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_LA_1day_9km_NA ADEOS-II/GLI L3 STA Map Aerosol radiance (1day,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131101-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol radiance (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_LA_1month_9km_NA ADEOS-II/GLI L3 STA Map Aerosol radiance (1month,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130232-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol radiance (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_LA_1day_9km_NA ADEOS-II/GLI L3 STA Map Aerosol radiance (1day,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131101-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol radiance (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_LA_1month_9km_NA ADEOS-II/GLI L3 STA Map Aerosol radiance (1month,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130232-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol radiance (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_LA_8days_9km_NA ADEOS-II/GLI L3 STA Map Aerosol radiance (8days,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128794-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol radiance (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_LA_1month_9km_NA ADEOS-II/GLI L3 STA Map Aerosol radiance (1month,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130232-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol radiance (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_LA_8days_9km_NA ADEOS-II/GLI L3 STA Map Aerosol radiance (8days,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128794-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol radiance (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_LA_8days_9km_NA ADEOS-II/GLI L3 STA Map Aerosol radiance (8days,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128794-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Aerosol radiance (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Aerosol radiance at 865, 380 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map.The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_NW_1day_9km_NA ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (1day,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130092-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_NW_1day_9km_NA ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (1day,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130092-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_NW_1month_9km_NA ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (1month,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129457-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_NW_1month_9km_NA ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (1month,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129457-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_NW_8days_9km_NA ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (8days,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132163-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (8days,1/12deg) and projected onto map. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_NW_8days_9km_NA ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (8days,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132163-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (8days,1/12deg) and projected onto map. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_SNWGS_16days_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (16days,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132946-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 μm) independently to retrieve snow grain size at very top surface. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_NW_8days_9km_NA ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (8days,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132163-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Normalized water-leaving radiance (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Normalized water-leaving radiance at 380, 400, 412, 443, 460, 490, 520, 545, 565, 625, 666, 680, 710 nm. They are derived from an extension of the OCTS atmospheric correction algorithm. It treated multiple scattering among the aerosol particles and gas molecules, as well as the effects of variable ozone concentration, surface pressure, surface wind speed, and water vapor amount. The atmospheric correction with iterative procedure was developed to avoid the black pixel assumption, and to consider absorptive aerosol. This product is the representative values, which are estimated from ADEOS-II/GLI L3 Binned Normalized water-leaving radiance (8days,1/12deg) and projected onto map. The provided format is HDF. The physical unit is mW cm^-2 um^-1 sr^-1. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_SNWGS_16days_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (16days,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132946-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 μm) independently to retrieve snow grain size at very top surface. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_SNWGS_1month_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (1month,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130258-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 μm) independently to retrieve snow grain size at very top surface. Level 2 snowimpurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_SNWGS_16days_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (16days,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132946-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 μm) independently to retrieve snow grain size at very top surface. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_SNWGS_1month_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (1month,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130258-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 μm) independently to retrieve snow grain size at very top surface. Level 2 snowimpurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_SNWG_16days_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (16days,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130513-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm is using GLI channels 5 (0.46 μm) and 19 (0.865 μm) which is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_SNWGS_1month_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (1month,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130258-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 1640nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. Snow grain size retrieved with 1640nm is using GLI channel 28 (1.64 μm) independently to retrieve snow grain size at very top surface. Level 2 snowimpurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_SNWG_16days_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (16days,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130513-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm is using GLI channels 5 (0.46 μm) and 19 (0.865 μm) which is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_SNWG_1month_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (1month,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129806-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm is using GLI channels 5 (0.46 μm) and 19 (0.865 μm) which is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_SNWG_16days_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (16days,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130513-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm is using GLI channels 5 (0.46 μm) and 19 (0.865 μm) which is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_SNWG_1month_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (1month,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129806-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm is using GLI channels 5 (0.46 μm) and 19 (0.865 μm) which is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_SNWI_16days_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow impurities (16days,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131626-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow impurities (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow impurities. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code. The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain size and mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is ppmw. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_SNWG_1month_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (1month,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129806-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow grain size retrieved with 865nm band (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow grain size retrieved with 865nm is using GLI channels 5 (0.46 μm) and 19 (0.865 μm) which is based on the principle that the reflectance of snow is known to be dependent on snow grain size in the near infra-red (NIR) range and pollution in the visible range. The physical quantity is micro meter. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_SNWI_16days_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow impurities (16days,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131626-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow impurities (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow impurities. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code. The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain size and mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is ppmw. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_SNWI_16days_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow impurities (16days,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131626-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow impurities (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow impurities. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code. The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain size and mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is ppmw. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_SNWI_1month_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow impurities (1month,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128893-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow impurities (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow impurities. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code. The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain size and mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is ppmw. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_SNWI_1month_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow impurities (1month,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128893-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow impurities (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow impurities. Snow impurities applies lookup tables have been constructed by using atmospheric optical properties obtained from MODTRAN in conjunction with the DISORT radiative transfer code. The bi-directional reflectance of snow is taken into account. In the lookup tables the radiances that would be measured by the satellite instrument are simulated as a function of snow grain size and mass fraction of soot mixed in the snow. The snow grain size and mass fraction of soot are obtained by requiring the simulated radiances to be consistent with the measured ones in both GLI channel 5 and 19. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. The physical quantity is ppmw. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_SNWTS_16days_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow surface temperature (16days,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130439-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow surface temperature (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow surface temperature. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_SNWTS_16days_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow surface temperature (16days,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130439-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow surface temperature (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow surface temperature. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_SNWTS_1month_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow surface temperature (1month,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130559-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow surface temperature (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow surface temperature. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_SNWTS_16days_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow surface temperature (16days,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130439-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow surface temperature (16days,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow surface temperature. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days, also 1month statistics is available. Map projection is EQA and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_SNWTS_1month_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow surface temperature (1month,1/12deg) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130559-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow surface temperature (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow surface temperature. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_SNWTS_1month_1-12deg_NA ADEOS-II/GLI L3 STA Map Snow surface temperature (1month,1/12deg) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130559-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Snow surface temperature (1month,1/12deg) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes snow surface temperature. Snow surface temperature is retrieving the sea surface temperature (SST) for an area consisting of a mixture of snow/ice and melt ponds, and the snow/ice surface temperature (IST) for ocean areas covered by snow/ice. This product is only for the polar regions and for the use with GLI channel 35 and 36. The physical quantity is Kelvin. Level 2 snow impurities, grain size and surface temperature product (SNGI_p) is used as input data. This product includes sum, square sum, max, min of each pixel is included.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 1 month, also 16 days statistics is available. Map projection is EQR and PS. The generation unit is Global, North and South Hemisphere. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_SS_1day_9km_NA ADEOS-II/GLI L3 STA Map Suspended solid weight (1day,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130209-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Suspended solid weight (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Suspended solid concentration. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_SS_1day_9km_NA ADEOS-II/GLI L3 STA Map Suspended solid weight (1day,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130209-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Suspended solid weight (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Suspended solid concentration. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_SS_1month_9km_NA ADEOS-II/GLI L3 STA Map Suspended solid weight (1month,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130961-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Suspended solid weight (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Suspended solid concentration. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_SS_1month_9km_NA ADEOS-II/GLI L3 STA Map Suspended solid weight (1month,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130961-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Suspended solid weight (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Suspended solid concentration. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_SS_1month_9km_NA ADEOS-II/GLI L3 STA Map Suspended solid weight (1month,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130961-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Suspended solid weight (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Suspended solid concentration. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_SS_8days_9km_NA ADEOS-II/GLI L3 STA Map Suspended solid weight (8days,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129114-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Suspended solid weight (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Suspended solid concentration. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_SS_8days_9km_NA ADEOS-II/GLI L3 STA Map Suspended solid weight (8days,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129114-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Suspended solid weight (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Suspended solid concentration. The physical unit is 1/m. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_ST_ALL_1day_9km_NA ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (1day,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132814-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (all data averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_ST_ALL_1day_9km_NA ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (1day,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132814-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (all data averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_ST_ALL_1day_9km_NA ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (1day,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132814-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (all data averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9 km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_ST_ALL_1month_9km_NA ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (1month,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130190-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (all data averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_ST_ALL_1month_9km_NA ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (1month,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130190-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (all data averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9 km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_ST_ALL_8days_9km_NA ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (8days,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130393-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (all data averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_ST_ALL_8days_9km_NA ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (8days,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130393-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (all data averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_ST_DayNight_1day_9km_NA ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1day,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129203-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (day/night separately averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_ST_ALL_8days_9km_NA ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (8days,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130393-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Bulk Sea surface temperature (all data averaged) (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (all data averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9 km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_ST_DayNight_1day_9km_NA ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1day,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129203-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (day/night separately averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
-ADEOS-II_GLI_L3STA_Map_ST_DayNight_1month_9km_NA ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1month,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130274-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (day/night separately averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_ST_DayNight_1day_9km_NA ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1day,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129203-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1day,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (day/night separately averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9km. The statistical period is 1 day, also 8 days and 1month statistics are available. The projection method is EQR. The generation unit is Global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_ST_DayNight_1month_9km_NA ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1month,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130274-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (day/night separately averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
+ADEOS-II_GLI_L3STA_Map_ST_DayNight_1month_9km_NA ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1month,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130274-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (1month,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (day/night separately averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9km. The statistical period is 1month, also 1 day and 8 days statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_ST_DayNight_8days_9km_NA ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (8days,9km) JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129219-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (day/night separately averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_ST_DayNight_8days_9km_NA ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (8days,9km) ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129219-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Sea surface temperature (day/night separately averaged) (8days,9km) is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes Sea surface temperature (day/night separately averaged) which applied the cloud detection and the atmospheric correction. The former is the process to find clear, or no cloud-contaminated, pixels in the image. The combination of the threshold tests is used to detect clouds. The latter is needed to obtain SST of clear pixels from the brightness temperatures observed by GLI. This product is the representative values, which are estimated from level 3 binned products and projected onto map. The provided format is HDF. The physical quantity is Kelvin. The spatial resolution is 9km. The statistical period is 8 days, also 1 day and 1month statistics are available. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_VGI_1-12deg_NA ADEOS-II/GLI L3 STA Map Vegetation Index product JAXA STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129956-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Vegetation Index (16days,1/12deg)is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes the normalized difference vegetation index (NDVI) which is the ratio between the difference in the red and near- infrared and their sum the most widely used index in global vegetation studies and the enhanced vegetation index (EVI) for increased sensitivity over a wider range of vegetation conditions, removal of soil background influences, and removal of residual atmospheric contamination effects present in the NDVI. This product is generated from Level-2 product. All zone of Level-2 product is connected and northern and southern polar stereographic region is projected to equi-rectangular. Level 3 STA Map product of land is the representative values, which are estimated from level 2 binned product and projected onto map. As for the estimation arithmetic mean method is applied.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
ADEOS-II_GLI_L3STA_Map_VGI_1-12deg_NA ADEOS-II/GLI L3 STA Map Vegetation Index product ALL STAC Catalog 2003-01-24 2003-10-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129956-JAXA.umm_json "ADEOS-II/GLI L3 STA Map Vegetation Index (16days,1/12deg)is obtained from the GLI (Global-Imager) sensor onboard ADEOS-II and produced by the Japan Aerospace Exploration Agency (JAXA). Global environment change has become a worldwide concern in recent years. In order to clarify these environmental changes, the Advanced Earth Observing Satellite (ADEOS-II, renamed ""Midori II"" after launch) has been developed for the purpose of monitoring the Earth environment using remote sensing technology from space. Midori II carries mission instruments that are particularly dedicated to clarify the water energy cycle and the carbon cycle, and is expected to be utilized for international climate change research programs. GLI an optical sensor that observes the reflected solar radiation from the Earth's surface, including land, oceans and clouds and/or infrared radiation with a multi-channel system for measuring the biological content. The SGLI has a swath of 1600 km. This product includes the normalized difference vegetation index (NDVI) which is the ratio between the difference in the red and near- infrared and their sum the most widely used index in global vegetation studies and the enhanced vegetation index (EVI) for increased sensitivity over a wider range of vegetation conditions, removal of soil background influences, and removal of residual atmospheric contamination effects present in the NDVI. This product is generated from Level-2 product. All zone of Level-2 product is connected and northern and southern polar stereographic region is projected to equi-rectangular. Level 3 STA Map product of land is the representative values, which are estimated from level 2 binned product and projected onto map. As for the estimation arithmetic mean method is applied.The provided format is HDF. The spatial resolution is 1/12 degree and the statistical period is 16 days. Map projection is EQR. The generation unit is global. The current version of the product is ""Version 2""." proprietary
-ADEOS_AVNIR_L1A_MU_NA ADEOS/AVNIR L1A Multispectral band JAXA STAC Catalog 1996-10-30 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133247-JAXA.umm_json ADEOS AVNIR L1A Multispectral band data set is obtained from AVNIR sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor. The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. AVNIR (Advanced Visible and Near-Infrared Radiometer) is a NASDA core sensor, an optoelectronic scanning radiometer using CCD detectors. Those sensors aim at collecting global data for mainly understanding land and coastal zone. The Level 1A product is uncorrected data, Level 0 data, divided by scene unit, announced with radiometric and geometric calibration coefficients. This product is ADEOS AVNIR L1A Multispectral band data. AVNIR has 5 bands from 0.42 - 0.89 µm (multispectral bands: 0.42-0.50, 0.52-0.60, 0.61-0.69 and 0.76-0.89 µm, panchromatic band (visible): 1 band 0.52-0.69 µm) and 80 km swath. A large line array CCD with multiple pixels of 10,000 pixels (multi-spectral band) and 5,000 pixels (panchromatic band) is adopted to achieve high resolution. The provided format is CEOS, 5403x5017 array tile. The spatial resolution is 16 m. Supplemental data include such as radiometric correction information and geometric correction information. proprietary
ADEOS_AVNIR_L1A_MU_NA ADEOS/AVNIR L1A Multispectral band ALL STAC Catalog 1996-10-30 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133247-JAXA.umm_json ADEOS AVNIR L1A Multispectral band data set is obtained from AVNIR sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor. The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. AVNIR (Advanced Visible and Near-Infrared Radiometer) is a NASDA core sensor, an optoelectronic scanning radiometer using CCD detectors. Those sensors aim at collecting global data for mainly understanding land and coastal zone. The Level 1A product is uncorrected data, Level 0 data, divided by scene unit, announced with radiometric and geometric calibration coefficients. This product is ADEOS AVNIR L1A Multispectral band data. AVNIR has 5 bands from 0.42 - 0.89 µm (multispectral bands: 0.42-0.50, 0.52-0.60, 0.61-0.69 and 0.76-0.89 µm, panchromatic band (visible): 1 band 0.52-0.69 µm) and 80 km swath. A large line array CCD with multiple pixels of 10,000 pixels (multi-spectral band) and 5,000 pixels (panchromatic band) is adopted to achieve high resolution. The provided format is CEOS, 5403x5017 array tile. The spatial resolution is 16 m. Supplemental data include such as radiometric correction information and geometric correction information. proprietary
+ADEOS_AVNIR_L1A_MU_NA ADEOS/AVNIR L1A Multispectral band JAXA STAC Catalog 1996-10-30 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133247-JAXA.umm_json ADEOS AVNIR L1A Multispectral band data set is obtained from AVNIR sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor. The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. AVNIR (Advanced Visible and Near-Infrared Radiometer) is a NASDA core sensor, an optoelectronic scanning radiometer using CCD detectors. Those sensors aim at collecting global data for mainly understanding land and coastal zone. The Level 1A product is uncorrected data, Level 0 data, divided by scene unit, announced with radiometric and geometric calibration coefficients. This product is ADEOS AVNIR L1A Multispectral band data. AVNIR has 5 bands from 0.42 - 0.89 µm (multispectral bands: 0.42-0.50, 0.52-0.60, 0.61-0.69 and 0.76-0.89 µm, panchromatic band (visible): 1 band 0.52-0.69 µm) and 80 km swath. A large line array CCD with multiple pixels of 10,000 pixels (multi-spectral band) and 5,000 pixels (panchromatic band) is adopted to achieve high resolution. The provided format is CEOS, 5403x5017 array tile. The spatial resolution is 16 m. Supplemental data include such as radiometric correction information and geometric correction information. proprietary
ADEOS_AVNIR_L1A_PAN_NA ADEOS/AVNIR L1A Panchromatic band JAXA STAC Catalog 1996-10-30 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128999-JAXA.umm_json ADEOS AVNIR L1A Panchromatic band data set is obtained from AVNIR sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor. The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. AVNIR (Advanced Visible and Near-Infrared Radiometer) is a NASDA core sensor, an optoelectronic scanning radiometer using CCD detectors. Those sensors aim at collecting global data for mainly understanding land and coastal zone. The Level 1A product is uncorrected data, Level 0 data, divided by scene unit, announced with radiometric and geometric calibration coefficients. This product is ADEOS AVNIR L1A Panchromatic band data. AVNIR has 5 bands from 0.42 - 0.89 µm (multispectral bands: 0.42-0.50, 0.52-0.60, 0.61-0.69 and 0.76-0.89 µm, panchromatic band (visible): 1 band 0.52-0.69 µm) and 80 km swath. A large line array CCD with multiple pixels of 10,000 pixels (multi-spectral band) and 5,000 pixels (panchromatic band) is adopted to achieve high resolution. The provided format is CEOS, 10660Ã10028 array tile. The spatial resolution is 8 m. Supplemental data include such as radiometric correction information and geometric correction information. proprietary
ADEOS_AVNIR_L1A_PAN_NA ADEOS/AVNIR L1A Panchromatic band ALL STAC Catalog 1996-10-30 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128999-JAXA.umm_json ADEOS AVNIR L1A Panchromatic band data set is obtained from AVNIR sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor. The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. AVNIR (Advanced Visible and Near-Infrared Radiometer) is a NASDA core sensor, an optoelectronic scanning radiometer using CCD detectors. Those sensors aim at collecting global data for mainly understanding land and coastal zone. The Level 1A product is uncorrected data, Level 0 data, divided by scene unit, announced with radiometric and geometric calibration coefficients. This product is ADEOS AVNIR L1A Panchromatic band data. AVNIR has 5 bands from 0.42 - 0.89 µm (multispectral bands: 0.42-0.50, 0.52-0.60, 0.61-0.69 and 0.76-0.89 µm, panchromatic band (visible): 1 band 0.52-0.69 µm) and 80 km swath. A large line array CCD with multiple pixels of 10,000 pixels (multi-spectral band) and 5,000 pixels (panchromatic band) is adopted to achieve high resolution. The provided format is CEOS, 10660Ã10028 array tile. The spatial resolution is 8 m. Supplemental data include such as radiometric correction information and geometric correction information. proprietary
-ADEOS_AVNIR_L1B2_MU_NA ADEOS/AVNIR L1B2 Multispectral band JAXA STAC Catalog 1996-10-30 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129220-JAXA.umm_json ADEOS AVNIR L1B2 Multispectral band data set is obtained from AVNIR sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. AVNIR (Advanced Visible and Near-Infrared Radiometer) is a NASDA core sensor, an optoelectronic scanning radiometer using CCD detectors. Those sensors aim at collecting global data for mainly understanding land and coastal zone.The Level L1B2 product is radiometically and geometrically corrected image from Level1B data, geometically corrected, projected on the map. This product is ADEOS AVNIR L1B2 Multispectral band data. AVNIR has 5 bands from 0.42 - 0.89 µm (multispectral bands: 0.42-0.50, 0.52-0.60, 0.61-0.69 and 0.76-0.89 µm, panchromatic band (visible): 1 band 0.52-0.69 µm) and 80 km swath. A large line array CCD with multiple pixels of 10,000 pixels (multi-spectral band) and 5,000 pixels (panchromatic band) is adopted to achieve high resolution.The provided format is CEOS, 5403Ã5017 array tile. The spatial resolution is 16 m. proprietary
ADEOS_AVNIR_L1B2_MU_NA ADEOS/AVNIR L1B2 Multispectral band ALL STAC Catalog 1996-10-30 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129220-JAXA.umm_json ADEOS AVNIR L1B2 Multispectral band data set is obtained from AVNIR sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. AVNIR (Advanced Visible and Near-Infrared Radiometer) is a NASDA core sensor, an optoelectronic scanning radiometer using CCD detectors. Those sensors aim at collecting global data for mainly understanding land and coastal zone.The Level L1B2 product is radiometically and geometrically corrected image from Level1B data, geometically corrected, projected on the map. This product is ADEOS AVNIR L1B2 Multispectral band data. AVNIR has 5 bands from 0.42 - 0.89 µm (multispectral bands: 0.42-0.50, 0.52-0.60, 0.61-0.69 and 0.76-0.89 µm, panchromatic band (visible): 1 band 0.52-0.69 µm) and 80 km swath. A large line array CCD with multiple pixels of 10,000 pixels (multi-spectral band) and 5,000 pixels (panchromatic band) is adopted to achieve high resolution.The provided format is CEOS, 5403Ã5017 array tile. The spatial resolution is 16 m. proprietary
+ADEOS_AVNIR_L1B2_MU_NA ADEOS/AVNIR L1B2 Multispectral band JAXA STAC Catalog 1996-10-30 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129220-JAXA.umm_json ADEOS AVNIR L1B2 Multispectral band data set is obtained from AVNIR sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. AVNIR (Advanced Visible and Near-Infrared Radiometer) is a NASDA core sensor, an optoelectronic scanning radiometer using CCD detectors. Those sensors aim at collecting global data for mainly understanding land and coastal zone.The Level L1B2 product is radiometically and geometrically corrected image from Level1B data, geometically corrected, projected on the map. This product is ADEOS AVNIR L1B2 Multispectral band data. AVNIR has 5 bands from 0.42 - 0.89 µm (multispectral bands: 0.42-0.50, 0.52-0.60, 0.61-0.69 and 0.76-0.89 µm, panchromatic band (visible): 1 band 0.52-0.69 µm) and 80 km swath. A large line array CCD with multiple pixels of 10,000 pixels (multi-spectral band) and 5,000 pixels (panchromatic band) is adopted to achieve high resolution.The provided format is CEOS, 5403Ã5017 array tile. The spatial resolution is 16 m. proprietary
ADEOS_AVNIR_L1B2_PAN_NA ADEOS/AVNIR L1B2 Panchromatic band JAXA STAC Catalog 1996-10-30 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698134145-JAXA.umm_json ADEOS AVNIR L1B2 Multispectral band data set is obtained from AVNIR sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor. The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. AVNIR (Advanced Visible and Near-Infrared Radiometer) is a NASDA core sensor, an optoelectronic scanning radiometer using CCD detectors. Those sensors aim at collecting global data for mainly understanding land and coastal zone. The Level L1B2 product is uncorrected data, Level 0 data, divided by scene unit, announced with radiometric and geometric calibration coefficients. This product is ADEOS AVNIR L1B2 Panchromatic band data. AVNIR has 4 bands from 0.42 - 0.89 µm (multispectral bands: 0.42-0.50, 0.52-0.60, 0.61-0.69 and 0.76-0.89 µm, panchromatic band (visible): 1 band 0.52-0.69 µm) and 80 km swath. A large line array CCD with multiple pixels of 10,000 pixels (multi-spectral band) and 5,000 pixels (panchromatic band) is adopted to achieve high resolution. The provided format is CEOS, 10660Ã10028 array tile. The spatial resolution is 8 m. proprietary
ADEOS_AVNIR_L1B2_PAN_NA ADEOS/AVNIR L1B2 Panchromatic band ALL STAC Catalog 1996-10-30 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698134145-JAXA.umm_json ADEOS AVNIR L1B2 Multispectral band data set is obtained from AVNIR sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor. The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. AVNIR (Advanced Visible and Near-Infrared Radiometer) is a NASDA core sensor, an optoelectronic scanning radiometer using CCD detectors. Those sensors aim at collecting global data for mainly understanding land and coastal zone. The Level L1B2 product is uncorrected data, Level 0 data, divided by scene unit, announced with radiometric and geometric calibration coefficients. This product is ADEOS AVNIR L1B2 Panchromatic band data. AVNIR has 4 bands from 0.42 - 0.89 µm (multispectral bands: 0.42-0.50, 0.52-0.60, 0.61-0.69 and 0.76-0.89 µm, panchromatic band (visible): 1 band 0.52-0.69 µm) and 80 km swath. A large line array CCD with multiple pixels of 10,000 pixels (multi-spectral band) and 5,000 pixels (panchromatic band) is adopted to achieve high resolution. The provided format is CEOS, 10660Ã10028 array tile. The spatial resolution is 8 m. proprietary
-ADEOS_OCTS_L1A_GAC_TI_NA ADEOS/OCTS L1A GAC Thermal infrared ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130195-JAXA.umm_json ADEOS OCTS L1A GAC TI dataset is obtained from OCTS (Ocean Color and Temperature Scanner) sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is GAC (Global Area Coverage) thermal infrared band (VI) data cut out into scenes from Level 0 data. GAC product contains one scene data that observed with the same tilt angle continuously within sun shining period: tilt segment. GAC data are subsampled from full-resolution data with about every sixth pixel of a scan line and every fifth line recorded. This product also contains radiometric correction coefficients required for data correction (each band, each pixel) and geometric correction information. Furthermore, supplemental information, such as the telemetry of OCTS, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L1A_GAC_TI_NA ADEOS/OCTS L1A GAC Thermal infrared JAXA STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130195-JAXA.umm_json ADEOS OCTS L1A GAC TI dataset is obtained from OCTS (Ocean Color and Temperature Scanner) sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is GAC (Global Area Coverage) thermal infrared band (VI) data cut out into scenes from Level 0 data. GAC product contains one scene data that observed with the same tilt angle continuously within sun shining period: tilt segment. GAC data are subsampled from full-resolution data with about every sixth pixel of a scan line and every fifth line recorded. This product also contains radiometric correction coefficients required for data correction (each band, each pixel) and geometric correction information. Furthermore, supplemental information, such as the telemetry of OCTS, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
+ADEOS_OCTS_L1A_GAC_TI_NA ADEOS/OCTS L1A GAC Thermal infrared ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130195-JAXA.umm_json ADEOS OCTS L1A GAC TI dataset is obtained from OCTS (Ocean Color and Temperature Scanner) sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is GAC (Global Area Coverage) thermal infrared band (VI) data cut out into scenes from Level 0 data. GAC product contains one scene data that observed with the same tilt angle continuously within sun shining period: tilt segment. GAC data are subsampled from full-resolution data with about every sixth pixel of a scan line and every fifth line recorded. This product also contains radiometric correction coefficients required for data correction (each band, each pixel) and geometric correction information. Furthermore, supplemental information, such as the telemetry of OCTS, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L1A_GAC_VNR_NA ADEOS/OCTS L1A GAC Visible and near infrared JAXA STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130080-JAXA.umm_json ADEOS OCTS L1A GAC VNR dataset is obtained from OCTS (Ocean Color and Temperature Scanner) sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is GAC (Global Area Coverage) visible and near Infrared band (VNR) data cut out into scenes from Level 0 data. GAC product contains one scene data that observed with the same tilt angle continuously within sun shining period: tilt segment. GAC data are subsampled from full-resolution data with about every sixth pixel of a scan line and every fifth line recorded. This product also contains radiometric correction coefficients required for data correction (each band, each pixel) and geometric correction information. Furthermore, supplemental information, such as the telemetry of OCTS, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L1A_GAC_VNR_NA ADEOS/OCTS L1A GAC Visible and near infrared ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130080-JAXA.umm_json ADEOS OCTS L1A GAC VNR dataset is obtained from OCTS (Ocean Color and Temperature Scanner) sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is GAC (Global Area Coverage) visible and near Infrared band (VNR) data cut out into scenes from Level 0 data. GAC product contains one scene data that observed with the same tilt angle continuously within sun shining period: tilt segment. GAC data are subsampled from full-resolution data with about every sixth pixel of a scan line and every fifth line recorded. This product also contains radiometric correction coefficients required for data correction (each band, each pixel) and geometric correction information. Furthermore, supplemental information, such as the telemetry of OCTS, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
-ADEOS_OCTS_L1A_RTC_TI_NA ADEOS/OCTS L1A RTC Thermal infrared ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129807-JAXA.umm_json ADEOS OCTS L1A RTC TI dataset is obtained from OCTS (Ocean Color and Temperature Scanner) sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is RTC (Real Time Coverage) thermal infrared band (TI) data cut out into scenes from Level 0 data. RTC data have a coverage at EOC, but if tilt angle is changed, the data is divided into two scenes (products). This product also contains radiometric correction coefficients required for data correction (each band, each pixel) and geometric correction information. Furthermore, supplemental information, such as the telemetry of OCTS, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L1A_RTC_TI_NA ADEOS/OCTS L1A RTC Thermal infrared JAXA STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129807-JAXA.umm_json ADEOS OCTS L1A RTC TI dataset is obtained from OCTS (Ocean Color and Temperature Scanner) sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is RTC (Real Time Coverage) thermal infrared band (TI) data cut out into scenes from Level 0 data. RTC data have a coverage at EOC, but if tilt angle is changed, the data is divided into two scenes (products). This product also contains radiometric correction coefficients required for data correction (each band, each pixel) and geometric correction information. Furthermore, supplemental information, such as the telemetry of OCTS, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
+ADEOS_OCTS_L1A_RTC_TI_NA ADEOS/OCTS L1A RTC Thermal infrared ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129807-JAXA.umm_json ADEOS OCTS L1A RTC TI dataset is obtained from OCTS (Ocean Color and Temperature Scanner) sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is RTC (Real Time Coverage) thermal infrared band (TI) data cut out into scenes from Level 0 data. RTC data have a coverage at EOC, but if tilt angle is changed, the data is divided into two scenes (products). This product also contains radiometric correction coefficients required for data correction (each band, each pixel) and geometric correction information. Furthermore, supplemental information, such as the telemetry of OCTS, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L1A_RTC_VNR_NA ADEOS/OCTS L1A RTC Visible and near infrared JAXA STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130113-JAXA.umm_json ADEOS OCTS L1A RTC VNR dataset is obtained from OCTS (Ocean Color and Temperature Scanner) sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is RTC (Real Time Coverage) visible and near Infrared band (VNR) data cut out into scenes from Level 0 data. RTC data have a coverage at EOC, but if tilt angle is changed, the data is divided into two scenes (products). This product also contains radiometric correction coefficients required for data correction (each band, each pixel) and geometric correction information. Furthermore, supplemental information, such as the telemetry of OCTS, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L1A_RTC_VNR_NA ADEOS/OCTS L1A RTC Visible and near infrared ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130113-JAXA.umm_json ADEOS OCTS L1A RTC VNR dataset is obtained from OCTS (Ocean Color and Temperature Scanner) sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is RTC (Real Time Coverage) visible and near Infrared band (VNR) data cut out into scenes from Level 0 data. RTC data have a coverage at EOC, but if tilt angle is changed, the data is divided into two scenes (products). This product also contains radiometric correction coefficients required for data correction (each band, each pixel) and geometric correction information. Furthermore, supplemental information, such as the telemetry of OCTS, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
-ADEOS_OCTS_L2_GAC_OC1_NA ADEOS/OCTS L2 GAC Ocean Color (OC1) JAXA STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129638-JAXA.umm_json ADEOS OCTS L2 GAC OC1 dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is GAC (Global Area Coverage) ocean color1 (OC1) product, transformed to geophysical parameters from level 1B data, includes Normalized water-leaving radiance at 412, 443, 490, 520, 565 nm, Aerosol radiance at 670, 765, 865 nm, Epsilon of aerosol correction at 670 and 865 nm and Aerosol optical thickness at 865 nm data. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L2_GAC_OC1_NA ADEOS/OCTS L2 GAC Ocean Color (OC1) ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129638-JAXA.umm_json ADEOS OCTS L2 GAC OC1 dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is GAC (Global Area Coverage) ocean color1 (OC1) product, transformed to geophysical parameters from level 1B data, includes Normalized water-leaving radiance at 412, 443, 490, 520, 565 nm, Aerosol radiance at 670, 765, 865 nm, Epsilon of aerosol correction at 670 and 865 nm and Aerosol optical thickness at 865 nm data. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
-ADEOS_OCTS_L2_GAC_OC2_NA ADEOS/OCTS L2 GAC Ocean Color (OC2) ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130013-JAXA.umm_json ADEOS OCTS L2 GAC OC2 dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan).Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is GAC (Global Area Coverage) ocean color2 (OC2) product, transformed to geophysical parameters from level 1B data, includes CZCS-like pigment concentration Chlorophyll a concentration Diffusion attenuation coefficient at 490 nm ,and Level 2 Quality flags. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
+ADEOS_OCTS_L2_GAC_OC1_NA ADEOS/OCTS L2 GAC Ocean Color (OC1) JAXA STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129638-JAXA.umm_json ADEOS OCTS L2 GAC OC1 dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is GAC (Global Area Coverage) ocean color1 (OC1) product, transformed to geophysical parameters from level 1B data, includes Normalized water-leaving radiance at 412, 443, 490, 520, 565 nm, Aerosol radiance at 670, 765, 865 nm, Epsilon of aerosol correction at 670 and 865 nm and Aerosol optical thickness at 865 nm data. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L2_GAC_OC2_NA ADEOS/OCTS L2 GAC Ocean Color (OC2) JAXA STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130013-JAXA.umm_json ADEOS OCTS L2 GAC OC2 dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan).Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is GAC (Global Area Coverage) ocean color2 (OC2) product, transformed to geophysical parameters from level 1B data, includes CZCS-like pigment concentration Chlorophyll a concentration Diffusion attenuation coefficient at 490 nm ,and Level 2 Quality flags. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
-ADEOS_OCTS_L2_GAC_SST_NA ADEOS/OCTS L2 GAC Sea Surface Temperature ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130226-JAXA.umm_json ADEOS OCTS L2 GAC SST dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is GAC (Global Area Coverage) sea surface temperature (SST) product, transformed to geophysical parameters from level 1B data, includes Sea surface temperature. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
+ADEOS_OCTS_L2_GAC_OC2_NA ADEOS/OCTS L2 GAC Ocean Color (OC2) ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130013-JAXA.umm_json ADEOS OCTS L2 GAC OC2 dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan).Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is GAC (Global Area Coverage) ocean color2 (OC2) product, transformed to geophysical parameters from level 1B data, includes CZCS-like pigment concentration Chlorophyll a concentration Diffusion attenuation coefficient at 490 nm ,and Level 2 Quality flags. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L2_GAC_SST_NA ADEOS/OCTS L2 GAC Sea Surface Temperature JAXA STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130226-JAXA.umm_json ADEOS OCTS L2 GAC SST dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is GAC (Global Area Coverage) sea surface temperature (SST) product, transformed to geophysical parameters from level 1B data, includes Sea surface temperature. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
+ADEOS_OCTS_L2_GAC_SST_NA ADEOS/OCTS L2 GAC Sea Surface Temperature ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130226-JAXA.umm_json ADEOS OCTS L2 GAC SST dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is GAC (Global Area Coverage) sea surface temperature (SST) product, transformed to geophysical parameters from level 1B data, includes Sea surface temperature. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L2_GAC_VI_NA ADEOS/OCTS L2 GAC Vegetation Indices ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129996-JAXA.umm_json ADEOS OCTS L2 GAC VI dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor. The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. OCTS has 12 bands and 1400 km swath.This product is vegetation indices GAC (Global Area Coverage) product (VI), transformed to vegetation index from level 1B data. The provided format is HDF4 format, with 700m ground resolution. Supplemental data files include such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others. ADEOS OCTS L2 GAC VI dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is GAC (Global Area Coverage) vegetation indices (VI) product, transformed to geophysical parameters from level 1B data, includes Sea surface temperature. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L2_GAC_VI_NA ADEOS/OCTS L2 GAC Vegetation Indices JAXA STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129996-JAXA.umm_json ADEOS OCTS L2 GAC VI dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor. The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. OCTS has 12 bands and 1400 km swath.This product is vegetation indices GAC (Global Area Coverage) product (VI), transformed to vegetation index from level 1B data. The provided format is HDF4 format, with 700m ground resolution. Supplemental data files include such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others. ADEOS OCTS L2 GAC VI dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is GAC (Global Area Coverage) vegetation indices (VI) product, transformed to geophysical parameters from level 1B data, includes Sea surface temperature. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
-ADEOS_OCTS_L2_RTC_OC1_NA ADEOS/OCTS L2 RTC Ocean Color (OC1) ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130171-JAXA.umm_json ADEOS OCTS L2 RTC OC1 dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is RTC (Real Time coverage) ocean color (OC1) product, transformed to geophysical parameters from level 1B data, includes Normalized water-leaving radiance at 412, 443, 490, 520, 565 nm, Aerosol radiance at 670, 765, 865 nm, Epsilon of aerosol correction at 670 and 865 nm and Aerosol optical thickness at 865 nm data. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L2_RTC_OC1_NA ADEOS/OCTS L2 RTC Ocean Color (OC1) JAXA STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130171-JAXA.umm_json ADEOS OCTS L2 RTC OC1 dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is RTC (Real Time coverage) ocean color (OC1) product, transformed to geophysical parameters from level 1B data, includes Normalized water-leaving radiance at 412, 443, 490, 520, 565 nm, Aerosol radiance at 670, 765, 865 nm, Epsilon of aerosol correction at 670 and 865 nm and Aerosol optical thickness at 865 nm data. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
+ADEOS_OCTS_L2_RTC_OC1_NA ADEOS/OCTS L2 RTC Ocean Color (OC1) ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130171-JAXA.umm_json ADEOS OCTS L2 RTC OC1 dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is RTC (Real Time coverage) ocean color (OC1) product, transformed to geophysical parameters from level 1B data, includes Normalized water-leaving radiance at 412, 443, 490, 520, 565 nm, Aerosol radiance at 670, 765, 865 nm, Epsilon of aerosol correction at 670 and 865 nm and Aerosol optical thickness at 865 nm data. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L2_RTC_OC2_NA ADEOS/OCTS L2 RTC Ocean Color (OC2) JAXA STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128834-JAXA.umm_json ADEOS OCTS L2 RTC OC2 dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is RTC (Real Time Coverage) OC2 (ocean color2) product, transformed to geophysical parameters from level 1B data, includes CZCS-like pigment concentration, Chlorophyll-a concentration, Diffuse attenuation coefficient, and quality information. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L2_RTC_OC2_NA ADEOS/OCTS L2 RTC Ocean Color (OC2) ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128834-JAXA.umm_json ADEOS OCTS L2 RTC OC2 dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is RTC (Real Time Coverage) OC2 (ocean color2) product, transformed to geophysical parameters from level 1B data, includes CZCS-like pigment concentration, Chlorophyll-a concentration, Diffuse attenuation coefficient, and quality information. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
-ADEOS_OCTS_L2_RTC_SST_NA ADEOS/OCTS L2 RTC Sea Surface Temperature (SST) ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132646-JAXA.umm_json ADEOS OCTS L2 RTC SST dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is RTC (real time coverage) sea surface temperature product (SST)product, transformed to geophysical parameters from level 1B data. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L2_RTC_SST_NA ADEOS/OCTS L2 RTC Sea Surface Temperature (SST) JAXA STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132646-JAXA.umm_json ADEOS OCTS L2 RTC SST dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is RTC (real time coverage) sea surface temperature product (SST)product, transformed to geophysical parameters from level 1B data. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
+ADEOS_OCTS_L2_RTC_SST_NA ADEOS/OCTS L2 RTC Sea Surface Temperature (SST) ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132646-JAXA.umm_json ADEOS OCTS L2 RTC SST dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is RTC (real time coverage) sea surface temperature product (SST)product, transformed to geophysical parameters from level 1B data. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L2_RTC_VI_NA ADEOS/OCTS L2 RTC Vegetation Indices ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130167-JAXA.umm_json ADEOS OCTS L2 RTC VI dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is RTC (Real Time Coverage) Vegetation Indices product (VI), transformed to geophysical parameters from level 1B data. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L2_RTC_VI_NA ADEOS/OCTS L2 RTC Vegetation Indices JAXA STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130167-JAXA.umm_json ADEOS OCTS L2 RTC VI dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is RTC (Real Time Coverage) Vegetation Indices product (VI), transformed to geophysical parameters from level 1B data. Furthermore, supplemental data, such as geometric correction information, the OCTS telemetry, quality information, the sun vector, scene information and others are included.The provided format is HDF4 format The Spatial resolution is 700 m. proprietary
ADEOS_OCTS_L3BM_GAC_OCC_1day_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCC) (1-Day) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128761-JAXA.umm_json "ADEOS OCTS L3BM GAC OCC 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is daily L3BM, Level 3 Binned map GAC (Global Area Coverage) OCC (Ocean Color-Chlorophyll-a concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCC product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are array of chlorophyll a concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCC_1day_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCC) (1-Day) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128761-JAXA.umm_json "ADEOS OCTS L3BM GAC OCC 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is daily L3BM, Level 3 Binned map GAC (Global Area Coverage) OCC (Ocean Color-Chlorophyll-a concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCC product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are array of chlorophyll a concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCC_1month_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCC) (1-Month) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129571-JAXA.umm_json "ADEOS OCTS L3BM GAC OCC 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is monthly L3BM, Level 3 Binned map GAC (Global Area Coverage) OCC (Ocean Color-Chlorophyll-a concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCC product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of chlorophyll a concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCC_1month_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCC) (1-Month) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129571-JAXA.umm_json "ADEOS OCTS L3BM GAC OCC 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is monthly L3BM, Level 3 Binned map GAC (Global Area Coverage) OCC (Ocean Color-Chlorophyll-a concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCC product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of chlorophyll a concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
-ADEOS_OCTS_L3BM_GAC_OCC_1week_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCC) (1-Week) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129911-JAXA.umm_json "ADEOS OCTS L3BM GAC OCC 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weekly L3BM, Level 3 Binned map GAC (Global Area Coverage) OCC (Ocean Color-Chlorophyll-a concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCC product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of chlorophyll a concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCC_1week_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCC) (1-Week) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129911-JAXA.umm_json "ADEOS OCTS L3BM GAC OCC 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weekly L3BM, Level 3 Binned map GAC (Global Area Coverage) OCC (Ocean Color-Chlorophyll-a concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCC product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of chlorophyll a concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
+ADEOS_OCTS_L3BM_GAC_OCC_1week_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCC) (1-Week) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129911-JAXA.umm_json "ADEOS OCTS L3BM GAC OCC 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weekly L3BM, Level 3 Binned map GAC (Global Area Coverage) OCC (Ocean Color-Chlorophyll-a concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCC product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of chlorophyll a concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCC_1year_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCC) (1-Year) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131479-JAXA.umm_json "ADEOS OCTS L3BM GAC OCC 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3BM, Level 3 Binned map GAC (Global Area Coverage) OCC (Ocean Color-Chlorophyll-a concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCC product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of chlorophyll a concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCC_1year_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCC) (1-Year) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131479-JAXA.umm_json "ADEOS OCTS L3BM GAC OCC 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3BM, Level 3 Binned map GAC (Global Area Coverage) OCC (Ocean Color-Chlorophyll-a concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCC product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of chlorophyll a concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCK_1day_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCK) (1-Day) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130444-JAXA.umm_json "ADEOS OCTS L3BM GAC OCK 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is daily L3BM, Level 3 Binned map GAC (Global Area Coverage) OCK (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCK product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are Array of diffuse attenuation coefficient at 490 nm and palette data. The unit of geophysical quantity in this product is ""m-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCK_1day_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCK) (1-Day) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130444-JAXA.umm_json "ADEOS OCTS L3BM GAC OCK 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is daily L3BM, Level 3 Binned map GAC (Global Area Coverage) OCK (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCK product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are Array of diffuse attenuation coefficient at 490 nm and palette data. The unit of geophysical quantity in this product is ""m-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCK_1month_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCK) (1-Month) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131472-JAXA.umm_json "ADEOS OCTS L3BM GAC OCK 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is monthly L3BM, Level 3 Binned map GAC (Global Area Coverage) OCK (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCK product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are Array of diffuse attenuation coefficient at 490 nm and palette data. The unit of geophysical quantity in this product is ""m-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCK_1month_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCK) (1-Month) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131472-JAXA.umm_json "ADEOS OCTS L3BM GAC OCK 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is monthly L3BM, Level 3 Binned map GAC (Global Area Coverage) OCK (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCK product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are Array of diffuse attenuation coefficient at 490 nm and palette data. The unit of geophysical quantity in this product is ""m-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
-ADEOS_OCTS_L3BM_GAC_OCK_1week_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCK) (1-Week) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132483-JAXA.umm_json "ADEOS OCTS L3BM GAC OCK 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weekly L3BM, Level 3 Binned map GAC (Global Area Coverage) OCK (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCK product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are Array of diffuse attenuation coefficient at 490 nm and palette data. The unit of geophysical quantity in this product is ""m-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCK_1week_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCK) (1-Week) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132483-JAXA.umm_json "ADEOS OCTS L3BM GAC OCK 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weekly L3BM, Level 3 Binned map GAC (Global Area Coverage) OCK (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCK product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are Array of diffuse attenuation coefficient at 490 nm and palette data. The unit of geophysical quantity in this product is ""m-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
-ADEOS_OCTS_L3BM_GAC_OCK_1year_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCK) (1-Year) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129238-JAXA.umm_json "ADEOS OCTS L3BM GAC OCK 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3BM, Level 3 Binned map GAC (Global Area Coverage) OCK (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCK product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are Array of diffuse attenuation coefficient at 490 nm and palette data. The unit of geophysical quantity in this product is ""m-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
+ADEOS_OCTS_L3BM_GAC_OCK_1week_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCK) (1-Week) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132483-JAXA.umm_json "ADEOS OCTS L3BM GAC OCK 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weekly L3BM, Level 3 Binned map GAC (Global Area Coverage) OCK (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCK product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are Array of diffuse attenuation coefficient at 490 nm and palette data. The unit of geophysical quantity in this product is ""m-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCK_1year_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCK) (1-Year) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129238-JAXA.umm_json "ADEOS OCTS L3BM GAC OCK 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3BM, Level 3 Binned map GAC (Global Area Coverage) OCK (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCK product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are Array of diffuse attenuation coefficient at 490 nm and palette data. The unit of geophysical quantity in this product is ""m-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
+ADEOS_OCTS_L3BM_GAC_OCK_1year_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCK) (1-Year) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129238-JAXA.umm_json "ADEOS OCTS L3BM GAC OCK 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3BM, Level 3 Binned map GAC (Global Area Coverage) OCK (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCK product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are Array of diffuse attenuation coefficient at 490 nm and palette data. The unit of geophysical quantity in this product is ""m-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCL_1day_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCL) (1-Day) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128801-JAXA.umm_json "ADEOS OCTS L3BM GAC OCL 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is daily L3BM, Level 3 binned map GAC (Global Area Coverage) OCL (Ocean Color) product includes Normalized water radiance at 412nm,443nm,490nm,520nm, and 565nm (nLw) and aerosol radiance at 670nm,765nm and 865nm. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are array of normalized water-leaving radiance and aerosol radiance and palette data. The unit of geophysical quantity is ""mW/cm-2/mm-1/sr-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCL_1day_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCL) (1-Day) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128801-JAXA.umm_json "ADEOS OCTS L3BM GAC OCL 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is daily L3BM, Level 3 binned map GAC (Global Area Coverage) OCL (Ocean Color) product includes Normalized water radiance at 412nm,443nm,490nm,520nm, and 565nm (nLw) and aerosol radiance at 670nm,765nm and 865nm. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are array of normalized water-leaving radiance and aerosol radiance and palette data. The unit of geophysical quantity is ""mW/cm-2/mm-1/sr-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCL_1month_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCL) (1-Month) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130286-JAXA.umm_json "ADEOS OCTS L3BM GAC OCL 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is monthly L3BM, Level 3 binned map GAC (Global Area Coverage) OCL (Ocean Color) product includes Normalized water radiance at 412nm,443nm,490nm,520nm, and 565nm (nLw) and aerosol radiance at 670nm,765nm and 865nm. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of normalized water-leaving radiance and aerosol radiance and palette data. The unit of geophysical quantity is ""mW/cm-2/mm-1/sr-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCL_1month_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCL) (1-Month) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130286-JAXA.umm_json "ADEOS OCTS L3BM GAC OCL 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is monthly L3BM, Level 3 binned map GAC (Global Area Coverage) OCL (Ocean Color) product includes Normalized water radiance at 412nm,443nm,490nm,520nm, and 565nm (nLw) and aerosol radiance at 670nm,765nm and 865nm. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of normalized water-leaving radiance and aerosol radiance and palette data. The unit of geophysical quantity is ""mW/cm-2/mm-1/sr-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCL_1week_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCL) (1-Week) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129155-JAXA.umm_json "ADEOS OCTS L3BM GAC OCL 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is weekly L3BM, Level 3 binned map GAC (Global Area Coverage) OCL (Ocean Color) product includes Normalized water radiance at 412nm,443nm,490nm,520nm, and 565nm (nLw) and aerosol radiance at 670nm,765nm and 865nm. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of normalized water-leaving radiance and aerosol radiance and palette data. The unit of geophysical quantity is ""mW/cm-2/mm-1/sr-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCL_1week_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCL) (1-Week) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129155-JAXA.umm_json "ADEOS OCTS L3BM GAC OCL 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is weekly L3BM, Level 3 binned map GAC (Global Area Coverage) OCL (Ocean Color) product includes Normalized water radiance at 412nm,443nm,490nm,520nm, and 565nm (nLw) and aerosol radiance at 670nm,765nm and 865nm. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of normalized water-leaving radiance and aerosol radiance and palette data. The unit of geophysical quantity is ""mW/cm-2/mm-1/sr-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
-ADEOS_OCTS_L3BM_GAC_OCL_1year_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCL) (1-Year) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129972-JAXA.umm_json "ADEOS OCTS L3BM GAC OCL 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is annually L3BM, Level 3 binned map GAC (Global Area Coverage) Ocean Color (OC) product includes Normalized water radiance at 412nm,443nm,490nm,520nm, and 565nm (nLw) and aerosol radiance at 670nm,765nm and 865nm. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of normalized water-leaving radiance and aerosol radiance and palette data. The unit of geophysical quantity is ""mW/cm-2/mm-1/sr-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCL_1year_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCL) (1-Year) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129972-JAXA.umm_json "ADEOS OCTS L3BM GAC OCL 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is annually L3BM, Level 3 binned map GAC (Global Area Coverage) Ocean Color (OC) product includes Normalized water radiance at 412nm,443nm,490nm,520nm, and 565nm (nLw) and aerosol radiance at 670nm,765nm and 865nm. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of normalized water-leaving radiance and aerosol radiance and palette data. The unit of geophysical quantity is ""mW/cm-2/mm-1/sr-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
+ADEOS_OCTS_L3BM_GAC_OCL_1year_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCL) (1-Year) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129972-JAXA.umm_json "ADEOS OCTS L3BM GAC OCL 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is annually L3BM, Level 3 binned map GAC (Global Area Coverage) Ocean Color (OC) product includes Normalized water radiance at 412nm,443nm,490nm,520nm, and 565nm (nLw) and aerosol radiance at 670nm,765nm and 865nm. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of normalized water-leaving radiance and aerosol radiance and palette data. The unit of geophysical quantity is ""mW/cm-2/mm-1/sr-1"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCP_1day_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCP) (1-Day) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129886-JAXA.umm_json "ADEOS OCTS L3BM GAC OCP 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is daily L3BM, Level 3 binned map GAC (Global Area Coverage) OCP (Ocean Color-CZCS like pigment concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of CZCS-like pigment concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe" proprietary
ADEOS_OCTS_L3BM_GAC_OCP_1day_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCP) (1-Day) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129886-JAXA.umm_json "ADEOS OCTS L3BM GAC OCP 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is daily L3BM, Level 3 binned map GAC (Global Area Coverage) OCP (Ocean Color-CZCS like pigment concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of CZCS-like pigment concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe" proprietary
ADEOS_OCTS_L3BM_GAC_OCP_1month_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCP) (1-Month) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133417-JAXA.umm_json "ADEOS OCTS L3BM GAC OCP 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is monthly L3BM, Level 3 binned map GAC (Global Area Coverage) OCP (Ocean Color-CZCS like pigment concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of CZCS-like pigment concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_OCP_1month_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCP) (1-Month) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133417-JAXA.umm_json "ADEOS OCTS L3BM GAC OCP 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is monthly L3BM, Level 3 binned map GAC (Global Area Coverage) OCP (Ocean Color-CZCS like pigment concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrate. This product is one of the Ocean Color product stores, and these parameters are array of CZCS-like pigment concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
-ADEOS_OCTS_L3BM_GAC_OCP_1week_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCP) (1-Week) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131253-JAXA.umm_json "ADEOS OCTS L3BM GAC OCP 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is weekly L3BM, Level 3 binned map GAC (Global Area Coverage) OCP (Ocean Color-CZCS like pigment concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are array of CZCS-like pigment concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe" proprietary
ADEOS_OCTS_L3BM_GAC_OCP_1week_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCP) (1-Week) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131253-JAXA.umm_json "ADEOS OCTS L3BM GAC OCP 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is weekly L3BM, Level 3 binned map GAC (Global Area Coverage) OCP (Ocean Color-CZCS like pigment concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are array of CZCS-like pigment concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe" proprietary
-ADEOS_OCTS_L3BM_GAC_OCP_1year_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCP) (1-Year) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130280-JAXA.umm_json "ADEOS OCTS L3BM GAC OCP 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is annually L3BM, Level 3 binned map GAC (Global Area Coverage) OCP (Ocean Color-CZCS like pigment concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are array of CZCS-like pigment concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
+ADEOS_OCTS_L3BM_GAC_OCP_1week_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCP) (1-Week) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131253-JAXA.umm_json "ADEOS OCTS L3BM GAC OCP 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is weekly L3BM, Level 3 binned map GAC (Global Area Coverage) OCP (Ocean Color-CZCS like pigment concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are array of CZCS-like pigment concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe" proprietary
ADEOS_OCTS_L3BM_GAC_OCP_1year_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCP) (1-Year) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130280-JAXA.umm_json "ADEOS OCTS L3BM GAC OCP 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is annually L3BM, Level 3 binned map GAC (Global Area Coverage) OCP (Ocean Color-CZCS like pigment concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are array of CZCS-like pigment concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
+ADEOS_OCTS_L3BM_GAC_OCP_1year_NA ADEOS OCTS L3 GAC Binned Map Ocean Color (OCP) (1-Year) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130280-JAXA.umm_json "ADEOS OCTS L3BM GAC OCP 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is annually L3BM, Level 3 binned map GAC (Global Area Coverage) OCP (Ocean Color-CZCS like pigment concentration) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG OCL product is daily or weekly, monthly, annually integrated. This product is one of the Ocean Color product stores, and these parameters are array of CZCS-like pigment concentration and palette data. The unit of geophysical quantity in this product is ""mg/m-3"". The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe." proprietary
ADEOS_OCTS_L3BM_GAC_SST_1day_NA ADEOS OCTS L3 GAC Binned Map Sea Surface Temperature (1-Day) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131349-JAXA.umm_json "ADEOS OCTS L3BM GAC SST 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is daily L3BM, Level 3 Binned map GAC (Global Area Coverage) SST (sea surface temperature). Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are array of sea surface temperature and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is ""Kelvin""." proprietary
ADEOS_OCTS_L3BM_GAC_SST_1day_NA ADEOS OCTS L3 GAC Binned Map Sea Surface Temperature (1-Day) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131349-JAXA.umm_json "ADEOS OCTS L3BM GAC SST 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is daily L3BM, Level 3 Binned map GAC (Global Area Coverage) SST (sea surface temperature). Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are array of sea surface temperature and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is ""Kelvin""." proprietary
-ADEOS_OCTS_L3BM_GAC_SST_1month_NA ADEOS OCTS L3 GAC Binned Map Sea Surface Temperature (1-Month) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128836-JAXA.umm_json "ADEOS OCTS L3BM GAC SST 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is monthly L3BM, Level 3 Binned map GAC (Global Area Coverage) SST (sea surface temperature). Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are array of sea surface temperature and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is ""Kelvin""." proprietary
ADEOS_OCTS_L3BM_GAC_SST_1month_NA ADEOS OCTS L3 GAC Binned Map Sea Surface Temperature (1-Month) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128836-JAXA.umm_json "ADEOS OCTS L3BM GAC SST 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is monthly L3BM, Level 3 Binned map GAC (Global Area Coverage) SST (sea surface temperature). Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are array of sea surface temperature and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is ""Kelvin""." proprietary
-ADEOS_OCTS_L3BM_GAC_SST_1week_NA ADEOS OCTS L3 GAC Binned Map Sea Surface Temperature (1-Week) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130133-JAXA.umm_json "ADEOS OCTS L3BM GAC SST 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is weekly L3BM, Level 3 Binned map GAC (Global Area Coverage) SST (sea surface temperature). Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are array of sea surface temperature and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is ""Kelvin""." proprietary
+ADEOS_OCTS_L3BM_GAC_SST_1month_NA ADEOS OCTS L3 GAC Binned Map Sea Surface Temperature (1-Month) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128836-JAXA.umm_json "ADEOS OCTS L3BM GAC SST 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is monthly L3BM, Level 3 Binned map GAC (Global Area Coverage) SST (sea surface temperature). Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are array of sea surface temperature and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is ""Kelvin""." proprietary
ADEOS_OCTS_L3BM_GAC_SST_1week_NA ADEOS OCTS L3 GAC Binned Map Sea Surface Temperature (1-Week) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130133-JAXA.umm_json "ADEOS OCTS L3BM GAC SST 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is weekly L3BM, Level 3 Binned map GAC (Global Area Coverage) SST (sea surface temperature). Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are array of sea surface temperature and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is ""Kelvin""." proprietary
+ADEOS_OCTS_L3BM_GAC_SST_1week_NA ADEOS OCTS L3 GAC Binned Map Sea Surface Temperature (1-Week) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130133-JAXA.umm_json "ADEOS OCTS L3BM GAC SST 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is weekly L3BM, Level 3 Binned map GAC (Global Area Coverage) SST (sea surface temperature). Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are array of sea surface temperature and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is ""Kelvin""." proprietary
ADEOS_OCTS_L3BM_GAC_SST_1year_NA ADEOS OCTS L3 GAC Binned Map Sea Surface Temperature (1-Year) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129834-JAXA.umm_json "ADEOS OCTS L3BM GAC SST 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is annually L3BM, Level 3 Binned map GAC (Global Area Coverage) SST (sea surface temperature). Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are array of sea surface temperature and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is ""Kelvin""." proprietary
ADEOS_OCTS_L3BM_GAC_SST_1year_NA ADEOS OCTS L3 GAC Binned Map Sea Surface Temperature (1-Year) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129834-JAXA.umm_json "ADEOS OCTS L3BM GAC SST 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is annually L3BM, Level 3 Binned map GAC (Global Area Coverage) SST (sea surface temperature). Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are array of sea surface temperature and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is ""Kelvin""." proprietary
ADEOS_OCTS_L3BM_GAC_VI_1day_NA ADEOS OCTS L3 GAC Binned Map Vegetation indices (1-Day) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129910-JAXA.umm_json "ADEOS OCTS L3BM GAC VI 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is daily L3BM, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are array of vegetation indices and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is ""dimensionless""." proprietary
@@ -1932,26 +1932,26 @@ ADEOS_OCTS_L3BM_GAC_VI_1week_NA ADEOS OCTS L3 GAC Binned Map Vegetation indices
ADEOS_OCTS_L3BM_GAC_VI_1week_NA ADEOS OCTS L3 GAC Binned Map Vegetation indices (1-Week) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133360-JAXA.umm_json "ADEOS OCTS L3BM GAC VI 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is weekly L3BM, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are array of vegetation indices and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is ""dimensionless""." proprietary
ADEOS_OCTS_L3BM_GAC_VI_1year_NA ADEOS OCTS L3 GAC Binned Map Vegetation indices (1-Year) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698134025-JAXA.umm_json "ADEOS OCTS L3BM GAC VI 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is annually L3BM, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are array of vegetation indices and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is ""dimensionless""." proprietary
ADEOS_OCTS_L3BM_GAC_VI_1year_NA ADEOS OCTS L3 GAC Binned Map Vegetation indices (1-Year) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698134025-JAXA.umm_json "ADEOS OCTS L3BM GAC VI 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is annually L3BM, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product. Level 3 Binned map products are generated from Level 3 Binned products and classified into three subproducts: ocean color, vegetation, and sea surface temperature. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are array of vegetation indices and palette data.The provided format is HDF4 format. The image data object, 13m-data, in each binned map product is a byte-valued, 4,096 * 2,048 array of an Equal-Area Rectangular projection of the globe. The unit of geophysical quantity in this product is ""dimensionless""." proprietary
-ADEOS_OCTS_L3B_GAC_OC_1day_NA ADEOS OCTS L3 GAC Binned Ocean Color (1-Day) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698134277-JAXA.umm_json "ADEOS OCTS L3B GAC OC 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is daily L3B, Level 3 binned GAC (Global Area Coverage) Ocean Color product (temporarily and spatially sampled) from level 2 data. GAG OC product is daily or weekly, monthly, annually integrated. Ocean color product stores Bin data, Normalized water-leaving radiance at 412nm, 443nm, 490nm, 520nm, 565nm, Aerosol radiance at 670nm, 765nm, 865nm, epsilon(670 : 865), and tau-a at 865nm.CZCS-like pigment, chlor_a, K_490, and chlor_a_K_490 data. These parameters are sum of natural logs of binned pixel values for corresponding and squares of natural logs of binned pixel values for corresponding each parameter.Normalized water-leaving radiance, Aerosol radiance, epsilon, and tau-a at 865nm data are stored in one subordinate file. The physical quantity unit except epsilon and tau_865 is ""mW/cm-2/mm-1/sr-1"". CZCS_pigment is CZCS-like pigment concentration data. The physical quantity unit is ""mg/m-3"". Chlor_a data is Chlorophyll a concentration data. The physical quantity unit is ""mg/m-3"". K_490 data is diffuse attenuation coefficient at 490 nm. The physical quantity unit is ""m-1"". Chlor_a_K_490 is integral chlorophyll, calculated using the Level 2 values chlorophyll a divided by K(490). The physical quantity unit is ""mg/m-2"". The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_OC_1day_NA ADEOS OCTS L3 GAC Binned Ocean Color (1-Day) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698134277-JAXA.umm_json "ADEOS OCTS L3B GAC OC 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is daily L3B, Level 3 binned GAC (Global Area Coverage) Ocean Color product (temporarily and spatially sampled) from level 2 data. GAG OC product is daily or weekly, monthly, annually integrated. Ocean color product stores Bin data, Normalized water-leaving radiance at 412nm, 443nm, 490nm, 520nm, 565nm, Aerosol radiance at 670nm, 765nm, 865nm, epsilon(670 : 865), and tau-a at 865nm.CZCS-like pigment, chlor_a, K_490, and chlor_a_K_490 data. These parameters are sum of natural logs of binned pixel values for corresponding and squares of natural logs of binned pixel values for corresponding each parameter.Normalized water-leaving radiance, Aerosol radiance, epsilon, and tau-a at 865nm data are stored in one subordinate file. The physical quantity unit except epsilon and tau_865 is ""mW/cm-2/mm-1/sr-1"". CZCS_pigment is CZCS-like pigment concentration data. The physical quantity unit is ""mg/m-3"". Chlor_a data is Chlorophyll a concentration data. The physical quantity unit is ""mg/m-3"". K_490 data is diffuse attenuation coefficient at 490 nm. The physical quantity unit is ""m-1"". Chlor_a_K_490 is integral chlorophyll, calculated using the Level 2 values chlorophyll a divided by K(490). The physical quantity unit is ""mg/m-2"". The provided format is HDF4 format." proprietary
-ADEOS_OCTS_L3B_GAC_OC_1month_NA ADEOS OCTS L3 GAC Binned Ocean Color (1-Month) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129108-JAXA.umm_json "ADEOS OCTS L3B GAC OC 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is monthly L3B, Level 3 binned GAC (Global Area Coverage) Ocean Color (OC) product (temporarily and spatially sampled) from level 2 data. GAG OC product is daily or weekly, monthly, annually integrated. Ocean color product stores Bin data, Normalized water-leaving radiance at 412nm, 443nm, 490nm, 520nm, 565nm, Aerosol radiance at 670nm, 765nm, 865nm, epsilon(670 : 865), and tau-a at 865nm. CZCS-like pigment, chlor_a, K_490, and chlor_a_K_490 data. These parameters are sum of natural logs of binned pixel values for corresponding and squares of natural logs of binned pixel values for corresponding each parameter.Normalized water-leaving radiance, Aerosol radiance, epsilon, and tau-a at 865nm data are stored in one subordinate file. The physical quantity unit except epsilon and tau_865 is âmW/cm-2/mm-1/sr-1â. CZCS_pigment is CZCS-like pigment concentration data. The physical quantity unit is ""mg/m-3"". Chlor_a data is Chlorophyll a concentration data. The physical quantity unit is ""mg/m-3"". K_490 data is diffuse attenuation coefficient at 490 nm. The physical quantity unit is ""m-1"". Chlor_a_K_490 is integral chlorophyll, calculated using the Level 2 values chlorophyll a divided by K(490). The physical quantity unit is ""mg/m-2"".The provided format is HDF4 format. The unit of geophysical quantity in this product is ""mg/m^3""." proprietary
+ADEOS_OCTS_L3B_GAC_OC_1day_NA ADEOS OCTS L3 GAC Binned Ocean Color (1-Day) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698134277-JAXA.umm_json "ADEOS OCTS L3B GAC OC 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is daily L3B, Level 3 binned GAC (Global Area Coverage) Ocean Color product (temporarily and spatially sampled) from level 2 data. GAG OC product is daily or weekly, monthly, annually integrated. Ocean color product stores Bin data, Normalized water-leaving radiance at 412nm, 443nm, 490nm, 520nm, 565nm, Aerosol radiance at 670nm, 765nm, 865nm, epsilon(670 : 865), and tau-a at 865nm.CZCS-like pigment, chlor_a, K_490, and chlor_a_K_490 data. These parameters are sum of natural logs of binned pixel values for corresponding and squares of natural logs of binned pixel values for corresponding each parameter.Normalized water-leaving radiance, Aerosol radiance, epsilon, and tau-a at 865nm data are stored in one subordinate file. The physical quantity unit except epsilon and tau_865 is ""mW/cm-2/mm-1/sr-1"". CZCS_pigment is CZCS-like pigment concentration data. The physical quantity unit is ""mg/m-3"". Chlor_a data is Chlorophyll a concentration data. The physical quantity unit is ""mg/m-3"". K_490 data is diffuse attenuation coefficient at 490 nm. The physical quantity unit is ""m-1"". Chlor_a_K_490 is integral chlorophyll, calculated using the Level 2 values chlorophyll a divided by K(490). The physical quantity unit is ""mg/m-2"". The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_OC_1month_NA ADEOS OCTS L3 GAC Binned Ocean Color (1-Month) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129108-JAXA.umm_json "ADEOS OCTS L3B GAC OC 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is monthly L3B, Level 3 binned GAC (Global Area Coverage) Ocean Color (OC) product (temporarily and spatially sampled) from level 2 data. GAG OC product is daily or weekly, monthly, annually integrated. Ocean color product stores Bin data, Normalized water-leaving radiance at 412nm, 443nm, 490nm, 520nm, 565nm, Aerosol radiance at 670nm, 765nm, 865nm, epsilon(670 : 865), and tau-a at 865nm. CZCS-like pigment, chlor_a, K_490, and chlor_a_K_490 data. These parameters are sum of natural logs of binned pixel values for corresponding and squares of natural logs of binned pixel values for corresponding each parameter.Normalized water-leaving radiance, Aerosol radiance, epsilon, and tau-a at 865nm data are stored in one subordinate file. The physical quantity unit except epsilon and tau_865 is âmW/cm-2/mm-1/sr-1â. CZCS_pigment is CZCS-like pigment concentration data. The physical quantity unit is ""mg/m-3"". Chlor_a data is Chlorophyll a concentration data. The physical quantity unit is ""mg/m-3"". K_490 data is diffuse attenuation coefficient at 490 nm. The physical quantity unit is ""m-1"". Chlor_a_K_490 is integral chlorophyll, calculated using the Level 2 values chlorophyll a divided by K(490). The physical quantity unit is ""mg/m-2"".The provided format is HDF4 format. The unit of geophysical quantity in this product is ""mg/m^3""." proprietary
-ADEOS_OCTS_L3B_GAC_OC_1week_NA ADEOS OCTS L3 GAC Binned Ocean Color (1-Week) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129892-JAXA.umm_json "ADEOS OCTS L3B GAC OC 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weely L3B, Level 3 binned GAC (Global Area Coverage) Ocean Color (OC) product (temporarily and spatially sampled) from level 2 data. GAG OC product is daily or weekly, monthly, annually integrated. Ocean color product stores Bin data, Normalized water-leaving radiance at 412nm, 443nm, 490nm, 520nm, 565nm, Aerosol radiance at 670nm, 765nm, 865nm, epsilon(670 : 865), and tau-a at 865nm.CZCS-like pigment, chlor_a, K_490, and chlor_a_K_490 data. These parameters are sum of natural logs of binned pixel values for corresponding and squares of natural logs of binned pixel values for corresponding each parameter.Normalized water-leaving radiance, Aerosol radiance, epsilon, and tau-a at 865nm data are stored in one subordinate file. The physical quantity unit except epsilon and tau_865 is âmW/cm-2/mm-1/sr-1â. CZCS_pigment is CZCS-like pigment concentration data. The physical quantity unit is ""mg/m-3"". Chlor_a data is Chlorophyll a concentration data. The physical quantity unit is ""mg/m-3"". K_490 data is diffuse attenuation coefficient at 490 nm. The physical quantity unit is ""m-1"". Chlor_a_K_490 is integral chlorophyll, calculated using the Level 2 values chlorophyll a divided by K(490). The physical quantity unit is ""mg/m-2"".The provided format is HDF4 format." proprietary
+ADEOS_OCTS_L3B_GAC_OC_1month_NA ADEOS OCTS L3 GAC Binned Ocean Color (1-Month) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129108-JAXA.umm_json "ADEOS OCTS L3B GAC OC 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is monthly L3B, Level 3 binned GAC (Global Area Coverage) Ocean Color (OC) product (temporarily and spatially sampled) from level 2 data. GAG OC product is daily or weekly, monthly, annually integrated. Ocean color product stores Bin data, Normalized water-leaving radiance at 412nm, 443nm, 490nm, 520nm, 565nm, Aerosol radiance at 670nm, 765nm, 865nm, epsilon(670 : 865), and tau-a at 865nm. CZCS-like pigment, chlor_a, K_490, and chlor_a_K_490 data. These parameters are sum of natural logs of binned pixel values for corresponding and squares of natural logs of binned pixel values for corresponding each parameter.Normalized water-leaving radiance, Aerosol radiance, epsilon, and tau-a at 865nm data are stored in one subordinate file. The physical quantity unit except epsilon and tau_865 is âmW/cm-2/mm-1/sr-1â. CZCS_pigment is CZCS-like pigment concentration data. The physical quantity unit is ""mg/m-3"". Chlor_a data is Chlorophyll a concentration data. The physical quantity unit is ""mg/m-3"". K_490 data is diffuse attenuation coefficient at 490 nm. The physical quantity unit is ""m-1"". Chlor_a_K_490 is integral chlorophyll, calculated using the Level 2 values chlorophyll a divided by K(490). The physical quantity unit is ""mg/m-2"".The provided format is HDF4 format. The unit of geophysical quantity in this product is ""mg/m^3""." proprietary
ADEOS_OCTS_L3B_GAC_OC_1week_NA ADEOS OCTS L3 GAC Binned Ocean Color (1-Week) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129892-JAXA.umm_json "ADEOS OCTS L3B GAC OC 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weely L3B, Level 3 binned GAC (Global Area Coverage) Ocean Color (OC) product (temporarily and spatially sampled) from level 2 data. GAG OC product is daily or weekly, monthly, annually integrated. Ocean color product stores Bin data, Normalized water-leaving radiance at 412nm, 443nm, 490nm, 520nm, 565nm, Aerosol radiance at 670nm, 765nm, 865nm, epsilon(670 : 865), and tau-a at 865nm.CZCS-like pigment, chlor_a, K_490, and chlor_a_K_490 data. These parameters are sum of natural logs of binned pixel values for corresponding and squares of natural logs of binned pixel values for corresponding each parameter.Normalized water-leaving radiance, Aerosol radiance, epsilon, and tau-a at 865nm data are stored in one subordinate file. The physical quantity unit except epsilon and tau_865 is âmW/cm-2/mm-1/sr-1â. CZCS_pigment is CZCS-like pigment concentration data. The physical quantity unit is ""mg/m-3"". Chlor_a data is Chlorophyll a concentration data. The physical quantity unit is ""mg/m-3"". K_490 data is diffuse attenuation coefficient at 490 nm. The physical quantity unit is ""m-1"". Chlor_a_K_490 is integral chlorophyll, calculated using the Level 2 values chlorophyll a divided by K(490). The physical quantity unit is ""mg/m-2"".The provided format is HDF4 format." proprietary
+ADEOS_OCTS_L3B_GAC_OC_1week_NA ADEOS OCTS L3 GAC Binned Ocean Color (1-Week) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129892-JAXA.umm_json "ADEOS OCTS L3B GAC OC 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weely L3B, Level 3 binned GAC (Global Area Coverage) Ocean Color (OC) product (temporarily and spatially sampled) from level 2 data. GAG OC product is daily or weekly, monthly, annually integrated. Ocean color product stores Bin data, Normalized water-leaving radiance at 412nm, 443nm, 490nm, 520nm, 565nm, Aerosol radiance at 670nm, 765nm, 865nm, epsilon(670 : 865), and tau-a at 865nm.CZCS-like pigment, chlor_a, K_490, and chlor_a_K_490 data. These parameters are sum of natural logs of binned pixel values for corresponding and squares of natural logs of binned pixel values for corresponding each parameter.Normalized water-leaving radiance, Aerosol radiance, epsilon, and tau-a at 865nm data are stored in one subordinate file. The physical quantity unit except epsilon and tau_865 is âmW/cm-2/mm-1/sr-1â. CZCS_pigment is CZCS-like pigment concentration data. The physical quantity unit is ""mg/m-3"". Chlor_a data is Chlorophyll a concentration data. The physical quantity unit is ""mg/m-3"". K_490 data is diffuse attenuation coefficient at 490 nm. The physical quantity unit is ""m-1"". Chlor_a_K_490 is integral chlorophyll, calculated using the Level 2 values chlorophyll a divided by K(490). The physical quantity unit is ""mg/m-2"".The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_OC_1year_NA ADEOS OCTS L3 GAC Binned Ocean Color (1-Year) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130979-JAXA.umm_json "ADEOS OCTS L3B GAC OC 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3B, Level 3 binned GAC (Global Area Coverage) Ocean Color (OC)product (temporarily and spatially sampled) from level 2 data. GAG OC product is daily or weekly, monthly, annually integrated. Ocean color product stores Bin data, Normalized water-leaving radiance at 412nm, 443nm, 490nm, 520nm, 565nm, Aerosol radiance at 670nm, 765nm, 865nm, epsilon(670 : 865), and tau-a at 865nm.CZCS-like pigment, chlor_a, K_490, and chlor_a_K_490 data. These parameters are sum of natural logs of binned pixel values for corresponding and squares of natural logs of binned pixel values for corresponding each parameter.Normalized water-leaving radiance, Aerosol radiance, epsilon, and tau-a at 865nm data are stored in one subordinate file. The physical quantity unit except epsilon and tau_865 is ""mW/cm-2/mm-1/sr-1"". CZCS_pigment is CZCS-like pigment concentration data. The physical quantity unit is ""mg/m-3"". Chlor_a data is Chlorophyll a concentration data. The physical quantity unit is ""mg/m-3"". K_490 data is diffuse attenuation coefficient at 490 nm. The physical quantity unit is ""m-1"". Chlor_a_K_490 is integral chlorophyll, calculated using the Level 2 values chlorophyll a divided by K(490). The physical quantity unit is ""mg/m-2"".The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_OC_1year_NA ADEOS OCTS L3 GAC Binned Ocean Color (1-Year) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130979-JAXA.umm_json "ADEOS OCTS L3B GAC OC 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3B, Level 3 binned GAC (Global Area Coverage) Ocean Color (OC)product (temporarily and spatially sampled) from level 2 data. GAG OC product is daily or weekly, monthly, annually integrated. Ocean color product stores Bin data, Normalized water-leaving radiance at 412nm, 443nm, 490nm, 520nm, 565nm, Aerosol radiance at 670nm, 765nm, 865nm, epsilon(670 : 865), and tau-a at 865nm.CZCS-like pigment, chlor_a, K_490, and chlor_a_K_490 data. These parameters are sum of natural logs of binned pixel values for corresponding and squares of natural logs of binned pixel values for corresponding each parameter.Normalized water-leaving radiance, Aerosol radiance, epsilon, and tau-a at 865nm data are stored in one subordinate file. The physical quantity unit except epsilon and tau_865 is ""mW/cm-2/mm-1/sr-1"". CZCS_pigment is CZCS-like pigment concentration data. The physical quantity unit is ""mg/m-3"". Chlor_a data is Chlorophyll a concentration data. The physical quantity unit is ""mg/m-3"". K_490 data is diffuse attenuation coefficient at 490 nm. The physical quantity unit is ""m-1"". Chlor_a_K_490 is integral chlorophyll, calculated using the Level 2 values chlorophyll a divided by K(490). The physical quantity unit is ""mg/m-2"".The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_SST_1day_NA ADEOS OCTS L3 GAC Binned Sea Surface Temperature (1-Day) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128855-JAXA.umm_json "ADEOS OCTS L3B GAC SST 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is daily L3B, Level 3 binned GAC (Global Area Coverage) sea surface temperature (SST) product (temporarily and spatially sampled) from level 2 data with performed Equidistant Cylindrical projection. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding sea surface temperature. The physical quantity unit is ""Kelvin"".The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_SST_1day_NA ADEOS OCTS L3 GAC Binned Sea Surface Temperature (1-Day) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128855-JAXA.umm_json "ADEOS OCTS L3B GAC SST 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is daily L3B, Level 3 binned GAC (Global Area Coverage) sea surface temperature (SST) product (temporarily and spatially sampled) from level 2 data with performed Equidistant Cylindrical projection. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding sea surface temperature. The physical quantity unit is ""Kelvin"".The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_SST_1month_NA ADEOS OCTS L3 GAC Binned Sea Surface Temperature (1-Month) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131229-JAXA.umm_json "ADEOS OCTS L3B GAC SST 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is monthly L3B, Level 3 binned GAC (Global Area Coverage) sea surface temperature (SST) product (temporarily and spatially sampled) from level 2 data with performed Equidistant Cylindrical projection. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding sea surface temperature. The physical quantity unit is ""Kelvin"".The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_SST_1month_NA ADEOS OCTS L3 GAC Binned Sea Surface Temperature (1-Month) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698131229-JAXA.umm_json "ADEOS OCTS L3B GAC SST 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena.This product is monthly L3B, Level 3 binned GAC (Global Area Coverage) sea surface temperature (SST) product (temporarily and spatially sampled) from level 2 data with performed Equidistant Cylindrical projection. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding sea surface temperature. The physical quantity unit is ""Kelvin"".The provided format is HDF4 format." proprietary
-ADEOS_OCTS_L3B_GAC_SST_1week_NA ADEOS OCTS L3 GAC Binned Sea Surface Temperature (1-Week) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128952-JAXA.umm_json "ADEOS OCTS L3B GAC SST 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weekly L3B, Level 3 binned GAC (Global Area Coverage) sea surface temperature (SST) product (temporarily and spatially sampled) from level 2 data with performed Equidistant Cylindrical projection. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding sea surface temperature. The physical quantity unit is ""Kelvin"".The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_SST_1week_NA ADEOS OCTS L3 GAC Binned Sea Surface Temperature (1-Week) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128952-JAXA.umm_json "ADEOS OCTS L3B GAC SST 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weekly L3B, Level 3 binned GAC (Global Area Coverage) sea surface temperature (SST) product (temporarily and spatially sampled) from level 2 data with performed Equidistant Cylindrical projection. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding sea surface temperature. The physical quantity unit is ""Kelvin"".The provided format is HDF4 format." proprietary
-ADEOS_OCTS_L3B_GAC_SST_1year_NA ADEOS OCTS L3 GAC Binned Sea Surface Temperature (1-Year) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130040-JAXA.umm_json "ADEOS OCTS L3B GAC SST 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan).Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3B, Level 3 binned GAC (Global Area Coverage) sea surface temperature (SST) product (temporarily and spatially sampled) from level 2 data with performed Equidistant Cylindrical projection. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding sea surface temperature. The physical quantity unit is ""Kelvin"".The provided format is HDF4 format." proprietary
+ADEOS_OCTS_L3B_GAC_SST_1week_NA ADEOS OCTS L3 GAC Binned Sea Surface Temperature (1-Week) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128952-JAXA.umm_json "ADEOS OCTS L3B GAC SST 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weekly L3B, Level 3 binned GAC (Global Area Coverage) sea surface temperature (SST) product (temporarily and spatially sampled) from level 2 data with performed Equidistant Cylindrical projection. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding sea surface temperature. The physical quantity unit is ""Kelvin"".The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_SST_1year_NA ADEOS OCTS L3 GAC Binned Sea Surface Temperature (1-Year) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130040-JAXA.umm_json "ADEOS OCTS L3B GAC SST 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan).Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3B, Level 3 binned GAC (Global Area Coverage) sea surface temperature (SST) product (temporarily and spatially sampled) from level 2 data with performed Equidistant Cylindrical projection. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding sea surface temperature. The physical quantity unit is ""Kelvin"".The provided format is HDF4 format." proprietary
+ADEOS_OCTS_L3B_GAC_SST_1year_NA ADEOS OCTS L3 GAC Binned Sea Surface Temperature (1-Year) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130040-JAXA.umm_json "ADEOS OCTS L3B GAC SST 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan).Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3B, Level 3 binned GAC (Global Area Coverage) sea surface temperature (SST) product (temporarily and spatially sampled) from level 2 data with performed Equidistant Cylindrical projection. GAG SST product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding sea surface temperature. The physical quantity unit is ""Kelvin"".The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_VI_1day_NA ADEOS OCTS L3 GAC Binned Vegetation indices (1-Day) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133288-JAXA.umm_json "ADEOS OCTS L3B GAC VI 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is daily L3B, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product (temporarily and spatially sampled) from level2 data. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding vegetation indices. The unit of geophysical quantity in this product is ""dimensionless"".The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_VI_1day_NA ADEOS OCTS L3 GAC Binned Vegetation indices (1-Day) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133288-JAXA.umm_json "ADEOS OCTS L3B GAC VI 1day dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is daily L3B, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product (temporarily and spatially sampled) from level2 data. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding vegetation indices. The unit of geophysical quantity in this product is ""dimensionless"".The provided format is HDF4 format." proprietary
-ADEOS_OCTS_L3B_GAC_VI_1month_NA ADEOS OCTS L3 GAC Binned Vegetation indices (1-Month) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130078-JAXA.umm_json "ADEOS OCTS L3B GAC VI 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is monthly L3B, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product (temporarily and spatially sampled) from level2 data. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding vegetation indices. The unit of geophysical quantity in this product is ""dimensionless"".The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_VI_1month_NA ADEOS OCTS L3 GAC Binned Vegetation indices (1-Month) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130078-JAXA.umm_json "ADEOS OCTS L3B GAC VI 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is monthly L3B, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product (temporarily and spatially sampled) from level2 data. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding vegetation indices. The unit of geophysical quantity in this product is ""dimensionless"".The provided format is HDF4 format." proprietary
+ADEOS_OCTS_L3B_GAC_VI_1month_NA ADEOS OCTS L3 GAC Binned Vegetation indices (1-Month) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130078-JAXA.umm_json "ADEOS OCTS L3B GAC VI 1month dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is monthly L3B, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product (temporarily and spatially sampled) from level2 data. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding vegetation indices. The unit of geophysical quantity in this product is ""dimensionless"".The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_VI_1week_NA ADEOS OCTS L3 GAC Binned Vegetation indices (1-Week) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133623-JAXA.umm_json "ADEOS OCTS L3B GAC VI 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weekly L3B, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product (temporarily and spatially sampled) from level2 data. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding vegetation indices. The unit of geophysical quantity in this product is ""dimensionless"".The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_VI_1week_NA ADEOS OCTS L3 GAC Binned Vegetation indices (1-Week) ALL STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133623-JAXA.umm_json "ADEOS OCTS L3B GAC VI 1week dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is weekly L3B, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product (temporarily and spatially sampled) from level2 data. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding vegetation indices. The unit of geophysical quantity in this product is ""dimensionless"".The provided format is HDF4 format." proprietary
ADEOS_OCTS_L3B_GAC_VI_1year_NA ADEOS OCTS L3 GAC Binned Vegetation indices (1-Year) JAXA STAC Catalog 1996-11-01 1997-07-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129233-JAXA.umm_json "ADEOS OCTS L3B GAC VI 1year dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is annually L3B, Level 3 Binned map GAC (Global Area Coverage) Vegetation Index (VI) product (temporarily and spatially sampled) from level2 data. GAG VI product is daily or weekly, monthly, annually integrated. These parameters are sum of binned pixel values for corresponding and squares of binned pixel values for corresponding vegetation indices. The unit of geophysical quantity in this product is ""dimensionless"".The provided format is HDF4 format." proprietary
@@ -1962,12 +1962,12 @@ ADEOS_OCTS_L3M_RTC_OCK_NA ADEOS OCTS L3 RTC Map Ocean Color (OCK) JAXA STAC Cata
ADEOS_OCTS_L3M_RTC_OCK_NA ADEOS OCTS L3 RTC Map Ocean Color (OCK) ALL STAC Catalog 1996-11-01 1997-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698129159-JAXA.umm_json "ADEOS OCTS L3M RTC OCK dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is L3M, Level 3 map RTC (Real Time Coverage) Ocean color (Diffuse attenuation coefficient at 490nm(K490)) product. Level 3 Map data are LAC or RTC data generated from Level 2 or Level 1B LAC or RTC products and RTC products are available only for pigment concentration, chlorophyll concentration, diffuse attenuation coefficient and sea surface temperature. These parameters are map data and palette data of diffuse attenuation coefficient at 490nm.The provided format if HDF4 format. The unit of geophysical quantity in this product is ""m-1""." proprietary
ADEOS_OCTS_L3M_RTC_OCP_NA ADEOS OCTS L3 RTC Map Ocean Color (OCP) ALL STAC Catalog 1996-11-01 1997-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130031-JAXA.umm_json "ADEOS OCTS L3M RTC OCK dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is L3M, Level 3 map RTC (Real Time Coverage) Ocean Color-CZCS like pigment concentration (OCP) product. Level 3 Map data are LAC or RTC data generated from Level 2 or Level 1B LAC or RTC products and RTC products are available only for pigment concentration, chlorophyll concentration, diffuse attenuation coefficient and sea surface temperature. These parameters are map data and palette data of CZCS-like Pigment Concentration.The provided format if HDF4 format. The unit of geophysical quantity in this product is ""mg m-3""." proprietary
ADEOS_OCTS_L3M_RTC_OCP_NA ADEOS OCTS L3 RTC Map Ocean Color (OCP) JAXA STAC Catalog 1996-11-01 1997-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130031-JAXA.umm_json "ADEOS OCTS L3M RTC OCK dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is L3M, Level 3 map RTC (Real Time Coverage) Ocean Color-CZCS like pigment concentration (OCP) product. Level 3 Map data are LAC or RTC data generated from Level 2 or Level 1B LAC or RTC products and RTC products are available only for pigment concentration, chlorophyll concentration, diffuse attenuation coefficient and sea surface temperature. These parameters are map data and palette data of CZCS-like Pigment Concentration.The provided format if HDF4 format. The unit of geophysical quantity in this product is ""mg m-3""." proprietary
-ADEOS_OCTS_L3M_RTC_SST_NA ADEOS OCTS L3 RTC Map Sea Surface Temperature JAXA STAC Catalog 1996-11-01 1997-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132977-JAXA.umm_json "ADEOS OCTS L3M RTC SST dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is L3M, Level 3 map RTC (Real Time Coverage) Sea Surface Temperature (SST) product. Level 3 Map data are LAC or RTC data generated from Level 2 or Level 1B LAC or RTC products and RTC products are available only for pigment concentration, chlorophyll concentration, diffuse attenuation coefficient and sea surface temperature. These parameters are map data and palette data of sea surface temperature.The provided format if HDF4 format. The unit of geophysical quantity in this product is ""Kelvin""." proprietary
ADEOS_OCTS_L3M_RTC_SST_NA ADEOS OCTS L3 RTC Map Sea Surface Temperature ALL STAC Catalog 1996-11-01 1997-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132977-JAXA.umm_json "ADEOS OCTS L3M RTC SST dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is L3M, Level 3 map RTC (Real Time Coverage) Sea Surface Temperature (SST) product. Level 3 Map data are LAC or RTC data generated from Level 2 or Level 1B LAC or RTC products and RTC products are available only for pigment concentration, chlorophyll concentration, diffuse attenuation coefficient and sea surface temperature. These parameters are map data and palette data of sea surface temperature.The provided format if HDF4 format. The unit of geophysical quantity in this product is ""Kelvin""." proprietary
-ADS_WRI Africa Data Sampler (ADS): Digital Data Sets for Africa Available from the World Resources Institute (WRI) SCIOPS STAC Catalog 1970-01-01 -16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214603260-SCIOPS.umm_json The following information was abstracted from a WRI Publications announcement: Africa Data Sampler (ADS) The ADS is an internationally comparable set of digital maps at a scale of 1:1 million for every country in Africa. The ADS is an integration of map data from several GIS databases. Roads, rivers, settlements, topography, and other essential base map features were extracted from the Arc/Info version of the Digital Chart of the World (ESRI, Redlands, CA). Data representing forests, wetlands, and protected areas from the Biodiversity Map Library (World Conservation Monitoring Center, Cambridge, UK), and sub-national boundaries and population estimates from the National Center for Geographic Information and Analysis (Santa Barbara, CA) were integrated with the DCW data sets. Over twenty layers of data are available for most countries. The ADS comprises a CD-ROM and User's Guide. The CD-ROM contains digital maps in PC ARC/INFO format for 53 countries in Robinson projection, five sample views in ArcView 1 format for each country, and ARC/INFO Export files for all countries in geographic projection. The 150-page User's Guide is available in both English and French and gives detailed information on the ADS data sources, data quality, and applications. The Africa Data Sampler is available on CD-ROM usable in UNIX, MS-DOS, or Macintosh environments. For more information on WRI publicatons on Africa, please see: http://www.wri.org/ proprietary
+ADEOS_OCTS_L3M_RTC_SST_NA ADEOS OCTS L3 RTC Map Sea Surface Temperature JAXA STAC Catalog 1996-11-01 1997-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698132977-JAXA.umm_json "ADEOS OCTS L3M RTC SST dataset is obtained from OCTS sensor onboard ADEOS and produced by NASDA (National Space Development Agency of Japan). Advanced Earth Observing Satellite (ADEOS) is sun-synchronous quasi-recurrent orbiter launched on August 17, 1996, and carries OCTS (Ocean Color and Temperature Scanner) and AVNIR (Advanced Visible and Near-Infrared Radiometer) sensor.The main objectives of ADEOS (MIDORI) is to contribute to elucidation of phenomena of the earth system through integrated observation of geophysical parameters using a number of sensors. ADEOS operation on orbit was given up on June 30, 1997, because the generated power was lost due to the accident of the blanket of the solar array paddle breaking. OCTS observes the amount of chlorophyll and various substances contained in the sea, sea surface temperature, cloud formation process, etc by receiving 12 bands of wavelengths from the visible light region to the thermal infrared region. The observation field of OCTS is about 1400km, and it is possible to scan in the north-south direction. Those sensors aim at collecting global data for mainly understanding the state of the ocean and its phenomena. This product is L3M, Level 3 map RTC (Real Time Coverage) Sea Surface Temperature (SST) product. Level 3 Map data are LAC or RTC data generated from Level 2 or Level 1B LAC or RTC products and RTC products are available only for pigment concentration, chlorophyll concentration, diffuse attenuation coefficient and sea surface temperature. These parameters are map data and palette data of sea surface temperature.The provided format if HDF4 format. The unit of geophysical quantity in this product is ""Kelvin""." proprietary
ADS_WRI Africa Data Sampler (ADS): Digital Data Sets for Africa Available from the World Resources Institute (WRI) ALL STAC Catalog 1970-01-01 -16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214603260-SCIOPS.umm_json The following information was abstracted from a WRI Publications announcement: Africa Data Sampler (ADS) The ADS is an internationally comparable set of digital maps at a scale of 1:1 million for every country in Africa. The ADS is an integration of map data from several GIS databases. Roads, rivers, settlements, topography, and other essential base map features were extracted from the Arc/Info version of the Digital Chart of the World (ESRI, Redlands, CA). Data representing forests, wetlands, and protected areas from the Biodiversity Map Library (World Conservation Monitoring Center, Cambridge, UK), and sub-national boundaries and population estimates from the National Center for Geographic Information and Analysis (Santa Barbara, CA) were integrated with the DCW data sets. Over twenty layers of data are available for most countries. The ADS comprises a CD-ROM and User's Guide. The CD-ROM contains digital maps in PC ARC/INFO format for 53 countries in Robinson projection, five sample views in ArcView 1 format for each country, and ARC/INFO Export files for all countries in geographic projection. The 150-page User's Guide is available in both English and French and gives detailed information on the ADS data sources, data quality, and applications. The Africa Data Sampler is available on CD-ROM usable in UNIX, MS-DOS, or Macintosh environments. For more information on WRI publicatons on Africa, please see: http://www.wri.org/ proprietary
-AERDB_D3_ABI_G16_1 ABI G16 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid ALL STAC Catalog 2019-05-01 2020-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3352252530-LAADS.umm_json The ABI G16 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid product, short-name AERDB_D3_ABI_G16, derived from the L2 (AERDB_L2_ABI_G16) input data, each D3 ABI/GOES-16 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element. Another statistic like standard deviation is also provided in some cases. The final retrievals used in the aggregation process are Quality Assurance (QA)-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-16 (GOES-16) Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_D3_ABI_G16 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_ABI_G16 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue proprietary
+ADS_WRI Africa Data Sampler (ADS): Digital Data Sets for Africa Available from the World Resources Institute (WRI) SCIOPS STAC Catalog 1970-01-01 -16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214603260-SCIOPS.umm_json The following information was abstracted from a WRI Publications announcement: Africa Data Sampler (ADS) The ADS is an internationally comparable set of digital maps at a scale of 1:1 million for every country in Africa. The ADS is an integration of map data from several GIS databases. Roads, rivers, settlements, topography, and other essential base map features were extracted from the Arc/Info version of the Digital Chart of the World (ESRI, Redlands, CA). Data representing forests, wetlands, and protected areas from the Biodiversity Map Library (World Conservation Monitoring Center, Cambridge, UK), and sub-national boundaries and population estimates from the National Center for Geographic Information and Analysis (Santa Barbara, CA) were integrated with the DCW data sets. Over twenty layers of data are available for most countries. The ADS comprises a CD-ROM and User's Guide. The CD-ROM contains digital maps in PC ARC/INFO format for 53 countries in Robinson projection, five sample views in ArcView 1 format for each country, and ARC/INFO Export files for all countries in geographic projection. The 150-page User's Guide is available in both English and French and gives detailed information on the ADS data sources, data quality, and applications. The Africa Data Sampler is available on CD-ROM usable in UNIX, MS-DOS, or Macintosh environments. For more information on WRI publicatons on Africa, please see: http://www.wri.org/ proprietary
AERDB_D3_ABI_G16_1 ABI G16 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid LAADS STAC Catalog 2019-05-01 2020-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3352252530-LAADS.umm_json The ABI G16 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid product, short-name AERDB_D3_ABI_G16, derived from the L2 (AERDB_L2_ABI_G16) input data, each D3 ABI/GOES-16 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element. Another statistic like standard deviation is also provided in some cases. The final retrievals used in the aggregation process are Quality Assurance (QA)-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-16 (GOES-16) Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_D3_ABI_G16 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_ABI_G16 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue proprietary
+AERDB_D3_ABI_G16_1 ABI G16 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid ALL STAC Catalog 2019-05-01 2020-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3352252530-LAADS.umm_json The ABI G16 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid product, short-name AERDB_D3_ABI_G16, derived from the L2 (AERDB_L2_ABI_G16) input data, each D3 ABI/GOES-16 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element. Another statistic like standard deviation is also provided in some cases. The final retrievals used in the aggregation process are Quality Assurance (QA)-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-16 (GOES-16) Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_D3_ABI_G16 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_ABI_G16 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue proprietary
AERDB_D3_ABI_G17_1 ABI G17 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid ALL STAC Catalog 2019-05-01 2020-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3352447655-LAADS.umm_json The ABI G17 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid product, short-name AERDB_D3_ABI_G17, derived from the L2 (AERDB_L2_ABI_G17) input data, each D3 ABI/GOES-17 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element. Another statistic like standard deviation is also provided in some cases. The final retrievals used in the aggregation process are Quality Assurance (QA)-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-17 (GOES-17) Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_D3_ABI_G17 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_ABI_G17 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue proprietary
AERDB_D3_ABI_G17_1 ABI G17 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid LAADS STAC Catalog 2019-05-01 2020-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3352447655-LAADS.umm_json The ABI G17 Deep Blue L3 Daily Aerosol Data, 1 x 1 degree grid product, short-name AERDB_D3_ABI_G17, derived from the L2 (AERDB_L2_ABI_G17) input data, each D3 ABI/GOES-17 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element. Another statistic like standard deviation is also provided in some cases. The final retrievals used in the aggregation process are Quality Assurance (QA)-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-17 (GOES-17) Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_D3_ABI_G17 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_ABI_G17 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue proprietary
AERDB_D3_AHI_H08_1 H08 Deep Blue Level 3 daily aerosol data, 1x1 degree grid LAADS STAC Catalog 2019-05-01 2020-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3352393947-LAADS.umm_json The H08 Deep Blue Level 3 daily aerosol data, 1x1 degree grid product, short-name AERDB_D3_AHI_H08, derived from the L2 (AERDB_L2_AHI_H08) input data, each D3 AHI/Himawari-8 product is produced daily at 1 x 1-degree horizontal resolution. In general, in this daily L3 (identified in the short-name as D3) aggregated product, each data field represents the arithmetic mean of all cells whose latitude and longitude places them within the bounds of each grid element. Another statistic like standard deviation is also provided in some cases. The final retrievals used in the aggregation process are Quality Assurance (QA)-filtered best-estimate values for cells that are measured on the day of interest. Further, at least three such retrievals are required to render the validity of a grid cell on any given day. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Himawari Imager (AHI) Himawari-8 Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_D3_AHI_H08 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_D3_AHI_H08 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue proprietary
@@ -1985,8 +1985,8 @@ AERDB_L2_VIIRS_SNPP_1.1 VIIRS/SNPP Deep Blue Aerosol L2 6-Min Swath 6km LAADS ST
AERDB_L2_VIIRS_SNPP_2 VIIRS/SNPP Deep Blue Aerosol L2 6 Min Swath 6km LAADS STAC Catalog 2012-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2600303218-LAADS.umm_json The VIIRS/SNPP Deep Blue Aerosol L2 6-Min Swath 6 km product from the Visible Infrared Imaging Radiometer Suite (VIIRS) determines atmospheric aerosol loading for daytime cloud-free snow-free scenes. This orbit-level product (Short-name: AERDB_L2_VIIRS_SNPP) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor’s scanning geometry and Earth’s curvature. Viewed differently, this product’s resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. The L2 Deep Blue AOT data products, at 550 nanometers reference wavelengths, are derived from particular VIIRS bands using two primary AOT retrieval algorithms: Deep Blue algorithm over land, and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over ocean. Although this product is called Deep Blue based on retrievals for the land algorithm, the data includes over-water retrievals as well. This L2 description pertains to the VIIRS Deep Blue Aerosol collection version 2.0 (C2) product. Significant changes have been made to the V2.0 Deep Blue/SOAR algorithms to further improve the data quality. For C2.0, the aerosol products are available for NOAA20 VIIRS in addition to SNPP. Some of changes in the retrieval algorithms and data products include, new SDS suite for prognostic uncertainties of 550 nm AOT over both land and ocean is added, surface pressure is better accounted for for both over-land and over-ocean retrievals by adding surface pressure nodes in the aerosol lookup table, and a number of other improvements which can be found from Product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_L2_VIIRS_SNPP For more information consult Deep Blue aerosol team Page at: https://deepblue.gsfc.nasa.gov proprietary
AERDB_L2_VIIRS_SNPP_NRT_1.1 VIIRS/SNPP Deep Blue Aerosol L2 6-Min Swath 6 km ASIPS STAC Catalog 2019-04-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1607549631-ASIPS.umm_json The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) deep blue aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, every 6 minutes, globally. The Deep Blue algorithm draws its heritage from previous applications to retrieve AOT from Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements over land. This orbit-level product (Short-name: AERDB_L2_VIIRS_SNPP) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor’s scanning geometry and Earth’s curvature. Viewed differently, this product’s resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. The L2 Deep Blue AOT data products, at 550 nanometers reference wavelengths, are derived from particular VIIRS bands using two primary AOT retrieval algorithms: Deep Blue algorithm over land, and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over ocean. Although this product is called Deep Blue based on retrievals for the land algorithm, the data includes over-water retrievals as well. proprietary
AERDB_L2_VIIRS_SNPP_NRT_2 VIIRS/SNPP Deep Blue Aerosol L2 6-Min Swath 6 km (v2.0) ASIPS STAC Catalog 2023-06-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2706359459-ASIPS.umm_json The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) deep blue aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, every 6 minutes, globally. The Deep Blue algorithm draws its heritage from previous applications to retrieve AOT from Sea‐viewing Wide Field‐of‐view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements over land. This orbit-level product (Short-name: AERDB_L2_VIIRS_SNPP) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor’s scanning geometry and Earth’s curvature. Viewed differently, this product’s resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. The L2 Deep Blue AOT data products, at 550 nanometers reference wavelengths, are derived from particular VIIRS bands using two primary AOT retrieval algorithms: Deep Blue algorithm over land, and the Satellite Ocean Aerosol Retrieval (SOAR) algorithm over ocean. Although this product is called Deep Blue based on retrievals for the land algorithm, the data includes over-water retrievals as well. proprietary
-AERDB_M3_ABI_G16_1 ABI G16 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid LAADS STAC Catalog 2019-05-01 2020-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3352280822-LAADS.umm_json The ABI G16 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid product, short-name AERDB_M3_ABI_G16, derived by aggregating the L3 daily (AERDB_D3_ABI_G16) input data, each M3 ABI/GOES-16 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the short-name as M3) product’s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product. As a mechanism to filter out poorly sampled grid elements, at least three valid days of data in the month are required to populate the monthly grid element. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-16 (GOES-16) Deep Blue Monthly Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO)) instruments. The AERDB_D3_ABI_G16 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_ABI_G16 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue proprietary
AERDB_M3_ABI_G16_1 ABI G16 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid ALL STAC Catalog 2019-05-01 2020-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3352280822-LAADS.umm_json The ABI G16 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid product, short-name AERDB_M3_ABI_G16, derived by aggregating the L3 daily (AERDB_D3_ABI_G16) input data, each M3 ABI/GOES-16 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the short-name as M3) product’s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product. As a mechanism to filter out poorly sampled grid elements, at least three valid days of data in the month are required to populate the monthly grid element. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-16 (GOES-16) Deep Blue Monthly Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO)) instruments. The AERDB_D3_ABI_G16 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_ABI_G16 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue proprietary
+AERDB_M3_ABI_G16_1 ABI G16 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid LAADS STAC Catalog 2019-05-01 2020-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3352280822-LAADS.umm_json The ABI G16 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid product, short-name AERDB_M3_ABI_G16, derived by aggregating the L3 daily (AERDB_D3_ABI_G16) input data, each M3 ABI/GOES-16 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the short-name as M3) product’s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product. As a mechanism to filter out poorly sampled grid elements, at least three valid days of data in the month are required to populate the monthly grid element. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-16 (GOES-16) Deep Blue Monthly Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO)) instruments. The AERDB_D3_ABI_G16 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_ABI_G16 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue proprietary
AERDB_M3_ABI_G17_1 ABI G17 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid ALL STAC Catalog 2019-05-01 2020-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3352241703-LAADS.umm_json The ABI G17 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid product, short-name AERDB_M3_ABI_G17, derived by aggregating the L3 daily (AERDB_D3_ABI_G17) input data, each M3 ABI/GOES-17 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the shortname as M3) product’s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product. As a mechanism to filter out poorly sampled grid elements, at least three valid days of data in the month are required to populate the monthly grid element. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-17 (GOES-17) Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_M3_ABI_G17 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_ABI_G17 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue proprietary
AERDB_M3_ABI_G17_1 ABI G17 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid LAADS STAC Catalog 2019-05-01 2020-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3352241703-LAADS.umm_json The ABI G17 Deep Blue L3 Monthly Aerosol Data, 1 x 1 degree grid product, short-name AERDB_M3_ABI_G17, derived by aggregating the L3 daily (AERDB_D3_ABI_G17) input data, each M3 ABI/GOES-17 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the shortname as M3) product’s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product. As a mechanism to filter out poorly sampled grid elements, at least three valid days of data in the month are required to populate the monthly grid element. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Baseline Imager (ABI) Geostationary Operational Environmental Satellite-17 (GOES-17) Deep Blue Daily Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_M3_ABI_G17 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_ABI_G17 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue proprietary
AERDB_M3_AHI_H08_1 H08 Deep Blue Level 3 monthly aerosol data, 1x1 degree grid LAADS STAC Catalog 2019-05-01 2020-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3352230787-LAADS.umm_json The H08 Deep Blue Level 3 Monthly aerosol data, 1x1 degree grid product, short-name AERDB_M3_AHI_H08, derived by aggregating the L3 daily (AERDB_D3_AHI_H08) input data, each M3 AHI/ Himawari-8 product is produced monthly at 1 x 1-degree horizontal resolution. This monthly L3 (identified in the shortname as M3) product’s statistics that include mean and standard deviation of the daily means are derived from the arithmetic mean values of the L3 daily product. As a mechanism to filter out poorly sampled grid elements, at least three valid days of data in the month are required to populate the monthly grid element. This first release of these products spans from May 2019 through April 2020 with a potential to generate additional temporal coverage in the future. The Level-3 (L3) Advanced Himawari Imager (AHI) Himawari-8 Deep Blue Aerosol Deep Blue Monthly Aerosol dataset is part of a 12-product suite produced by an Earth Science Research from Operational Geostationary Satellite Systems (ESROGSS)-funded project. The 12 products in this project include nine derived from three Geostationary Earth Observation (GEO) instruments and three from merged data from GEO and Low-Earth Orbit (LEO) instruments. The AERDB_M3_AHI_H08 product, in netCDF4 format, contains 48 Science Data Set (SDS) layers. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDB_M3_AHI_H08 Or, Deep Blue aerosol project webpage at: https://earth.gsfc.nasa.gov/climate/data/deep-blue proprietary
@@ -1999,10 +1999,10 @@ AERDT_L2_VIIRS_NOAA20_NRT_2 VIIRS/NOAA-20 Dark Target Aerosol L2 6-Min Swath (v2
AERDT_L2_VIIRS_SNPP_2 VIIRS/SNPP Dark Target Aerosol L2 6-Min Swath 6 km V2 LAADS STAC Catalog 2012-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2771506686-LAADS.umm_json The VIIRS/SNPP Dark Target Aerosol L2 6-Min Swath 6 km product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) incarnation of the dark target (DT) aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua mission’s MODIS instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible). This orbit-level product (Short-name: AERDT_L2_VIIRS_SNPP) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor's scanning geometry and Earth's curvature. Viewed differently, this product's resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. Hence, the Level-2 Dark Target Aerosol Optical Thickness data product incorporates 64 (750 m) pixels over a 6-minute acquisition. Version 2.0 constitutes the latest collection of the L2 Dark Target Aerosol product and contains improvements over its previous collection (v1.1). For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/AERDT_L2_VIIRS_SNPP Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary
AERDT_L2_VIIRS_SNPP_NRT_1.1 VIIRS/SNPP Dark Target Aerosol L2 6-Min Swath ASIPS STAC Catalog 2020-06-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1976333380-ASIPS.umm_json The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua mission’s MODIS instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible). proprietary
AERDT_L2_VIIRS_SNPP_NRT_2 VIIRS/SNPP Dark Target Aerosol L2 6-Min Swath (v2.0) ASIPS STAC Catalog 2023-11-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2812412751-ASIPS.umm_json The Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) NASA standard Level-2 (L2) dark target (DT) aerosol product provides satellite-derived measurements of Aerosol Optical Thickness (AOT) and their properties over land and ocean, and spectral AOT and their size parameters over oceans every 6 minutes, globally. The VIIRS incarnation of the DT aerosol product is based on the same DT algorithm that was developed and used to derive products from the Terra and Aqua mission’s MODIS instruments. Two separate and distinct DT algorithms exist. One helps retrieve aerosol information over ocean (dark in visible and longer wavelengths), while the second aids retrievals over vegetated/dark-soiled land (dark in the visible). This orbit-level product (Short-name: AERDT_L2_VIIRS_SNPP_NRT) has an at-nadir resolution of 6 km x 6 km, and progressively increases away from nadir given the sensor's scanning geometry and Earth's curvature. Viewed differently, this product's resolution accommodates 8 x 8 native VIIRS moderate-resolution (M-band) pixels that nominally have ~750 m horizontal pixel size. Hence, the Level-2 Dark Target Aerosol Optical Thickness data product incorporates 64 (750 m) pixels over a 6-minute acquisition. Version 2.0 constitutes the latest collection of the L2 Dark Target Aerosol product and contains improvements over its previous collection (v1.1). proprietary
-AERIALDIGI Aircraft Scanners ALL STAC Catalog 1987-10-06 -180, 24, -60, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.umm_json The National Aeronautics and Space Administration (NASA) Aircraft Scanners data set contains digital imagery acquired from several multispectral scanners, including Daedalus thematic mapper simulator scanners and the thermal infrared multispectral scanner. Data are collected from selected areas over the conterminous United States, Alaska, and Hawaii by NASA ER-2 and NASA C-130B aircraft, operating from the NASA Ames Research Center in Moffett Field, California, and by NASA Learjet aircraft, operating from Stennis Space Center in Bay St. Louis, Mississippi. Limited international acquisitions also are available. In cooperation with the Jet Propulsion Laboratory and Daedalus Enterprises,Inc., NASA developed several multispectral sensors. The data acquired from these sensors supports NASA's Airborne Science and Applications Program and have been identified as precursors to the instruments scheduled to fly on Earth Observing System platforms. THEMATIC MAPPER SIMULATOR The Thematic Mapper Simulator (TMS) sensor is a line scanning device designed for a variety of Earth science applications. Flown aboard NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field of View of 1.25 milliradians with a ground resolution of 81 feet (25 meters) at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per second with 716 pixels per scan line. Swath width is 8.3 nautical miles (15.4 kilometers) at 65,000 feet while the scanner's Field of View is 42.5 degrees. NS-001 MULTISPECTRAL SCANNER The NS-001multispectral scanner is a line scanning device designed to simulate Landsat thematic mapper (TM) sensor performance, including a near infrared/short-wave infrared band used in applications similar to those of the TM sensor (e.g., Earth resources mapping, vegetation/land cover mapping, geologic studies). Flown aboard NASA C-130B aircraft, the NS-001 sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a variable scan rate (10 to 100 scans per second) with 699 pixels per scan line, but the available motor drive supply restricts the maximum stable scan speed to approximately 85 revolutions per second. A scan rate of 100 revolutions per second is possible, but not probable, for short scan lines; therefore, a combination of factors, including aircraft flight requirements and maximum scan speed, prevent scanner operation below 1,500 feet. Swath width is 3.9 nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or field of regard for the sensor is 100 degrees, plus or minus 15 degrees for roll compensation. THERMAL INFRARED MULTISPECTRAL SCANNER The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line scanning device originally designed for geologic applications. Flown aboard NASA C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698 pixels per scan line. Swath width is 2.6 nautical miles (4.8 kilometers) at 10,000 feet while the scanner's Field of View is 76.56 degrees. proprietary
-AERIALDIGI Aircraft Scanners - AERIALDIGI CEOS_EXTRA STAC Catalog 1987-10-06 -180, 24, -60, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.umm_json The National Aeronautics and Space Administration (NASA) Aircraft Scanners data set contains digital imagery acquired from several multispectral scanners, including Daedalus thematic mapper simulator scanners and the thermal infrared multispectral scanner. Data are collected from selected areas over the conterminous United States, Alaska, and Hawaii by NASA ER-2 and NASA C-130B aircraft, operating from the NASA Ames Research Center in Moffett Field, California, and by NASA Learjet aircraft, operating from Stennis Space Center in Bay St. Louis, Mississippi. Limited international acquisitions also are available. In cooperation with the Jet Propulsion Laboratory and Daedalus Enterprises,Inc., NASA developed several multispectral sensors. The data acquired from these sensors supports NASA's Airborne Science and Applications Program and have been identified as precursors to the instruments scheduled to fly on Earth Observing System platforms. THEMATIC MAPPER SIMULATOR The Thematic Mapper Simulator (TMS) sensor is a line scanning device designed for a variety of Earth science applications. Flown aboard NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field of View of 1.25 milliradians with a ground resolution of 81 feet (25 meters) at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per second with 716 pixels per scan line. Swath width is 8.3 nautical miles (15.4 kilometers) at 65,000 feet while the scanner's Field of View is 42.5 degrees. NS-001 MULTISPECTRAL SCANNER The NS-001multispectral scanner is a line scanning device designed to simulate Landsat thematic mapper (TM) sensor performance, including a near infrared/short-wave infrared band used in applications similar to those of the TM sensor (e.g., Earth resources mapping, vegetation/land cover mapping, geologic studies). Flown aboard NASA C-130B aircraft, the NS-001 sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a variable scan rate (10 to 100 scans per second) with 699 pixels per scan line, but the available motor drive supply restricts the maximum stable scan speed to approximately 85 revolutions per second. A scan rate of 100 revolutions per second is possible, but not probable, for short scan lines; therefore, a combination of factors, including aircraft flight requirements and maximum scan speed, prevent scanner operation below 1,500 feet. Swath width is 3.9 nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or field of regard for the sensor is 100 degrees, plus or minus 15 degrees for roll compensation. THERMAL INFRARED MULTISPECTRAL SCANNER The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line scanning device originally designed for geologic applications. Flown aboard NASA C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698 pixels per scan line. Swath width is 2.6 nautical miles (4.8 kilometers) at 10,000 feet while the scanner's Field of View is 76.56 degrees. proprietary
AERIALDIGI Aircraft Scanners USGS_LTA STAC Catalog 1987-10-06 -180, 24, -60, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.umm_json The National Aeronautics and Space Administration (NASA) Aircraft Scanners data set contains digital imagery acquired from several multispectral scanners, including Daedalus thematic mapper simulator scanners and the thermal infrared multispectral scanner. Data are collected from selected areas over the conterminous United States, Alaska, and Hawaii by NASA ER-2 and NASA C-130B aircraft, operating from the NASA Ames Research Center in Moffett Field, California, and by NASA Learjet aircraft, operating from Stennis Space Center in Bay St. Louis, Mississippi. Limited international acquisitions also are available. In cooperation with the Jet Propulsion Laboratory and Daedalus Enterprises,Inc., NASA developed several multispectral sensors. The data acquired from these sensors supports NASA's Airborne Science and Applications Program and have been identified as precursors to the instruments scheduled to fly on Earth Observing System platforms. THEMATIC MAPPER SIMULATOR The Thematic Mapper Simulator (TMS) sensor is a line scanning device designed for a variety of Earth science applications. Flown aboard NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field of View of 1.25 milliradians with a ground resolution of 81 feet (25 meters) at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per second with 716 pixels per scan line. Swath width is 8.3 nautical miles (15.4 kilometers) at 65,000 feet while the scanner's Field of View is 42.5 degrees. NS-001 MULTISPECTRAL SCANNER The NS-001multispectral scanner is a line scanning device designed to simulate Landsat thematic mapper (TM) sensor performance, including a near infrared/short-wave infrared band used in applications similar to those of the TM sensor (e.g., Earth resources mapping, vegetation/land cover mapping, geologic studies). Flown aboard NASA C-130B aircraft, the NS-001 sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a variable scan rate (10 to 100 scans per second) with 699 pixels per scan line, but the available motor drive supply restricts the maximum stable scan speed to approximately 85 revolutions per second. A scan rate of 100 revolutions per second is possible, but not probable, for short scan lines; therefore, a combination of factors, including aircraft flight requirements and maximum scan speed, prevent scanner operation below 1,500 feet. Swath width is 3.9 nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or field of regard for the sensor is 100 degrees, plus or minus 15 degrees for roll compensation. THERMAL INFRARED MULTISPECTRAL SCANNER The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line scanning device originally designed for geologic applications. Flown aboard NASA C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698 pixels per scan line. Swath width is 2.6 nautical miles (4.8 kilometers) at 10,000 feet while the scanner's Field of View is 76.56 degrees. proprietary
+AERIALDIGI Aircraft Scanners ALL STAC Catalog 1987-10-06 -180, 24, -60, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1220566211-USGS_LTA.umm_json The National Aeronautics and Space Administration (NASA) Aircraft Scanners data set contains digital imagery acquired from several multispectral scanners, including Daedalus thematic mapper simulator scanners and the thermal infrared multispectral scanner. Data are collected from selected areas over the conterminous United States, Alaska, and Hawaii by NASA ER-2 and NASA C-130B aircraft, operating from the NASA Ames Research Center in Moffett Field, California, and by NASA Learjet aircraft, operating from Stennis Space Center in Bay St. Louis, Mississippi. Limited international acquisitions also are available. In cooperation with the Jet Propulsion Laboratory and Daedalus Enterprises,Inc., NASA developed several multispectral sensors. The data acquired from these sensors supports NASA's Airborne Science and Applications Program and have been identified as precursors to the instruments scheduled to fly on Earth Observing System platforms. THEMATIC MAPPER SIMULATOR The Thematic Mapper Simulator (TMS) sensor is a line scanning device designed for a variety of Earth science applications. Flown aboard NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field of View of 1.25 milliradians with a ground resolution of 81 feet (25 meters) at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per second with 716 pixels per scan line. Swath width is 8.3 nautical miles (15.4 kilometers) at 65,000 feet while the scanner's Field of View is 42.5 degrees. NS-001 MULTISPECTRAL SCANNER The NS-001multispectral scanner is a line scanning device designed to simulate Landsat thematic mapper (TM) sensor performance, including a near infrared/short-wave infrared band used in applications similar to those of the TM sensor (e.g., Earth resources mapping, vegetation/land cover mapping, geologic studies). Flown aboard NASA C-130B aircraft, the NS-001 sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a variable scan rate (10 to 100 scans per second) with 699 pixels per scan line, but the available motor drive supply restricts the maximum stable scan speed to approximately 85 revolutions per second. A scan rate of 100 revolutions per second is possible, but not probable, for short scan lines; therefore, a combination of factors, including aircraft flight requirements and maximum scan speed, prevent scanner operation below 1,500 feet. Swath width is 3.9 nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or field of regard for the sensor is 100 degrees, plus or minus 15 degrees for roll compensation. THERMAL INFRARED MULTISPECTRAL SCANNER The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line scanning device originally designed for geologic applications. Flown aboard NASA C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698 pixels per scan line. Swath width is 2.6 nautical miles (4.8 kilometers) at 10,000 feet while the scanner's Field of View is 76.56 degrees. proprietary
AERIALDIGI Aircraft Scanners - AERIALDIGI ALL STAC Catalog 1987-10-06 -180, 24, -60, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.umm_json The National Aeronautics and Space Administration (NASA) Aircraft Scanners data set contains digital imagery acquired from several multispectral scanners, including Daedalus thematic mapper simulator scanners and the thermal infrared multispectral scanner. Data are collected from selected areas over the conterminous United States, Alaska, and Hawaii by NASA ER-2 and NASA C-130B aircraft, operating from the NASA Ames Research Center in Moffett Field, California, and by NASA Learjet aircraft, operating from Stennis Space Center in Bay St. Louis, Mississippi. Limited international acquisitions also are available. In cooperation with the Jet Propulsion Laboratory and Daedalus Enterprises,Inc., NASA developed several multispectral sensors. The data acquired from these sensors supports NASA's Airborne Science and Applications Program and have been identified as precursors to the instruments scheduled to fly on Earth Observing System platforms. THEMATIC MAPPER SIMULATOR The Thematic Mapper Simulator (TMS) sensor is a line scanning device designed for a variety of Earth science applications. Flown aboard NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field of View of 1.25 milliradians with a ground resolution of 81 feet (25 meters) at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per second with 716 pixels per scan line. Swath width is 8.3 nautical miles (15.4 kilometers) at 65,000 feet while the scanner's Field of View is 42.5 degrees. NS-001 MULTISPECTRAL SCANNER The NS-001multispectral scanner is a line scanning device designed to simulate Landsat thematic mapper (TM) sensor performance, including a near infrared/short-wave infrared band used in applications similar to those of the TM sensor (e.g., Earth resources mapping, vegetation/land cover mapping, geologic studies). Flown aboard NASA C-130B aircraft, the NS-001 sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a variable scan rate (10 to 100 scans per second) with 699 pixels per scan line, but the available motor drive supply restricts the maximum stable scan speed to approximately 85 revolutions per second. A scan rate of 100 revolutions per second is possible, but not probable, for short scan lines; therefore, a combination of factors, including aircraft flight requirements and maximum scan speed, prevent scanner operation below 1,500 feet. Swath width is 3.9 nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or field of regard for the sensor is 100 degrees, plus or minus 15 degrees for roll compensation. THERMAL INFRARED MULTISPECTRAL SCANNER The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line scanning device originally designed for geologic applications. Flown aboard NASA C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698 pixels per scan line. Swath width is 2.6 nautical miles (4.8 kilometers) at 10,000 feet while the scanner's Field of View is 76.56 degrees. proprietary
+AERIALDIGI Aircraft Scanners - AERIALDIGI CEOS_EXTRA STAC Catalog 1987-10-06 -180, 24, -60, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231548706-CEOS_EXTRA.umm_json The National Aeronautics and Space Administration (NASA) Aircraft Scanners data set contains digital imagery acquired from several multispectral scanners, including Daedalus thematic mapper simulator scanners and the thermal infrared multispectral scanner. Data are collected from selected areas over the conterminous United States, Alaska, and Hawaii by NASA ER-2 and NASA C-130B aircraft, operating from the NASA Ames Research Center in Moffett Field, California, and by NASA Learjet aircraft, operating from Stennis Space Center in Bay St. Louis, Mississippi. Limited international acquisitions also are available. In cooperation with the Jet Propulsion Laboratory and Daedalus Enterprises,Inc., NASA developed several multispectral sensors. The data acquired from these sensors supports NASA's Airborne Science and Applications Program and have been identified as precursors to the instruments scheduled to fly on Earth Observing System platforms. THEMATIC MAPPER SIMULATOR The Thematic Mapper Simulator (TMS) sensor is a line scanning device designed for a variety of Earth science applications. Flown aboard NASA ER-2 aircraft, the TMS sensor has a nominal Instantaneous Field of View of 1.25 milliradians with a ground resolution of 81 feet (25 meters) at 65,000 feet. The TMS sensor scans at a rate of 12.5 scans per second with 716 pixels per scan line. Swath width is 8.3 nautical miles (15.4 kilometers) at 65,000 feet while the scanner's Field of View is 42.5 degrees. NS-001 MULTISPECTRAL SCANNER The NS-001multispectral scanner is a line scanning device designed to simulate Landsat thematic mapper (TM) sensor performance, including a near infrared/short-wave infrared band used in applications similar to those of the TM sensor (e.g., Earth resources mapping, vegetation/land cover mapping, geologic studies). Flown aboard NASA C-130B aircraft, the NS-001 sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a variable scan rate (10 to 100 scans per second) with 699 pixels per scan line, but the available motor drive supply restricts the maximum stable scan speed to approximately 85 revolutions per second. A scan rate of 100 revolutions per second is possible, but not probable, for short scan lines; therefore, a combination of factors, including aircraft flight requirements and maximum scan speed, prevent scanner operation below 1,500 feet. Swath width is 3.9 nautical miles (7.26 kilometers) at 10,000 feet, and the total scan angle or field of regard for the sensor is 100 degrees, plus or minus 15 degrees for roll compensation. THERMAL INFRARED MULTISPECTRAL SCANNER The Thermal Infrared Multispectral Scanner (TIMS) sensor is a line scanning device originally designed for geologic applications. Flown aboard NASA C-130B, NASA ER-2, and NASA Learjet aircraft, the TIMS sensor has a nominal Instantaneous Field of View of 2.5 milliradians with a ground resolution of 25 feet (7.6 meters) at 10,000 feet. The sensor has a selectable scan rate (7.3, 8.7, 12, or 25 scans per second) with 698 pixels per scan line. Swath width is 2.6 nautical miles (4.8 kilometers) at 10,000 feet while the scanner's Field of View is 76.56 degrees. proprietary
AERONET_aerosol_706_1 SAFARI 2000 AERONET Ground-based Aerosol Data, Dry Season 2000 ORNL_CLOUD STAC Catalog 1999-01-01 2001-12-31 28.03, -26.19, 28.03, -26.19 https://cmr.earthdata.nasa.gov/search/concepts/C2788355135-ORNL_CLOUD.umm_json AERONET (AErosol RObotic NETwork) is an optical ground-based aerosol monitoring network and data archive system. AERONET measurements of the column-integrated aerosol optical properties in the southern Africa region were made by sun-sky radiometers at several sites in August-September 2000 as a part of the SAFARI 2000 dry season aircraft campaign. AERONET is supported by NASA's Earth Observing System and expanded by federation with many non-NASA institutions. The network hardware consists of identical automatic sun-sky scanning spectral radiometers owned by national agencies and universities. Data from this collaboration provides globally-distributed near-real-time observations of aerosol spectral optical depths, aerosol size distributions, and precipitable water in diverse aerosol regimes. proprietary
AEROSE_0 Saharan Dust AERosols and Ocean Science Expeditions OB_DAAC STAC Catalog 2004-03-02 2017-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108358203-OB_DAAC.umm_json AEROSE is an internationally recognized series of trans-Atlantic field campaigns conducted onboard the NOAA Ship Ronald H. Brown designed to explore African air mass outflows and their impacts on climate, weather, and environmental health. proprietary
AE_5DSno_2 AMSR-E/Aqua 5-Day L3 Global Snow Water Equivalent EASE-Grids V002 NSIDC_ECS STAC Catalog 2002-06-20 2011-10-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179014698-NSIDC_ECS.umm_json These Level-3 Snow Water Equivalent (SWE) data sets contain SWE data and quality assurance flags mapped to Northern and Southern Hemisphere 25 km Equal-Area Scalable Earth Grids (EASE-Grids). proprietary
@@ -2027,8 +2027,8 @@ AFLVIS1B_1 AfriSAR LVIS L1B Geolocated Return Energy Waveforms V001 NSIDC_ECS ST
AFLVIS1B_1 AfriSAR LVIS L1B Geolocated Return Energy Waveforms V001 ALL STAC Catalog 2016-02-20 2016-03-08 8, -2, 12, 1 https://cmr.earthdata.nasa.gov/search/concepts/C1549378019-NSIDC_ECS.umm_json This data set contains return energy waveform data over Gabon, Africa. The measurements were taken by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of a NASA campaign, in collaboration with the European Space Agency (ESA) mission AfriSAR. proprietary
AFLVIS2_1 AfriSAR LVIS L2 Geolocated Surface Elevation Product V001 NSIDC_ECS STAC Catalog 2016-02-20 2016-03-08 8, -2, 12, 1 https://cmr.earthdata.nasa.gov/search/concepts/C1549378743-NSIDC_ECS.umm_json This data set contains surface elevation data over Gabon, Africa. The measurements were taken by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of a NASA campaign, in collaboration with the European Space Agency (ESA) mission AfriSAR. proprietary
AFLVIS2_1 AfriSAR LVIS L2 Geolocated Surface Elevation Product V001 ALL STAC Catalog 2016-02-20 2016-03-08 8, -2, 12, 1 https://cmr.earthdata.nasa.gov/search/concepts/C1549378743-NSIDC_ECS.umm_json This data set contains surface elevation data over Gabon, Africa. The measurements were taken by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of a NASA campaign, in collaboration with the European Space Agency (ESA) mission AfriSAR. proprietary
-AFOLVIS1A_1 AfriSAR LVIS L1A Geotagged Images V001 ALL STAC Catalog 2016-02-20 2016-03-08 8, -2, 12, 1 https://cmr.earthdata.nasa.gov/search/concepts/C1932134853-NSIDC_ECS.umm_json This data set contains geotagged images collected over Gabon, Africa. The images were taken by the NASA Digital Mapping Camera paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of a NASA campaign, in collaboration with the European Space Agency (ESA) mission AfriSAR. proprietary
AFOLVIS1A_1 AfriSAR LVIS L1A Geotagged Images V001 NSIDC_ECS STAC Catalog 2016-02-20 2016-03-08 8, -2, 12, 1 https://cmr.earthdata.nasa.gov/search/concepts/C1932134853-NSIDC_ECS.umm_json This data set contains geotagged images collected over Gabon, Africa. The images were taken by the NASA Digital Mapping Camera paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of a NASA campaign, in collaboration with the European Space Agency (ESA) mission AfriSAR. proprietary
+AFOLVIS1A_1 AfriSAR LVIS L1A Geotagged Images V001 ALL STAC Catalog 2016-02-20 2016-03-08 8, -2, 12, 1 https://cmr.earthdata.nasa.gov/search/concepts/C1932134853-NSIDC_ECS.umm_json This data set contains geotagged images collected over Gabon, Africa. The images were taken by the NASA Digital Mapping Camera paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of a NASA campaign, in collaboration with the European Space Agency (ESA) mission AfriSAR. proprietary
AG100_003 ASTER Global Emissivity Dataset, 100 meter, HDF5 V003 LPCLOUD STAC Catalog 2000-01-01 2008-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763266348-LPCLOUD.umm_json Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD07) (https://modis-atmos.gsfc.nasa.gov/MOD07_L2/index.html) atmospheric profiles and the MODerate spectral resolution TRANsmittance (MODTRAN 5.2 radiative transfer model). This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. AG100 data are available globally at spatial resolution of 100 meters. The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. proprietary
AG1km_003 ASTER Global Emissivity Dataset, 1 kilometer, HDF5 V003 LPCLOUD STAC Catalog 2000-01-01 2008-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763266350-LPCLOUD.umm_json Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD07) (https://modis-atmos.gsfc.nasa.gov/MOD07_L2/index.html) atmospheric profiles and the MODerate Spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model. This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. AG1KM data are available globally at spatial resolution of 1 kilometer. The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. proprietary
AG5KMMOH_041 ASTER Global Emissivity Dataset, Monthly, 0.05 deg, HDF5 V041 LPCLOUD STAC Catalog 2000-03-01 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763268461-LPCLOUD.umm_json Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) is a collection of monthly files (see known issues for gaps) for each year of global emissivity. The ASTER GED data products are generated for 2000 through 2015 using the ASTER Temperature Emissivity Separation (TES) algorithm atmospheric correction method. This algorithm method uses Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheric Profiles product (MOD07) (https://modis-atmos.gsfc.nasa.gov/MOD07_L2/index.html) and the MODerate spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model along with the snow cover data from the standard monthly MODIS/Terra snow cover monthly global 0.05 degree product (MOD10CM) (https://doi.org/10.5067/MODIS/MOD10CM.006), and vegetation information from the MODIS monthly gridded NDVI product (MOD13C2) (https://doi.org/10.5067/MODIS/MOD13C2.006). ASTER GED Monthly V041 data products are offered in Hierarchical Data Format 5 (HDF5). The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. proprietary
@@ -2054,12 +2054,12 @@ AIRABRAD_NRT_005 AIRS/Aqua L1B Near Real Time (NRT) AMSU (A1/A2) geolocated and
AIRABRAD_NRT_005 AIRS/Aqua L1B Near Real Time (NRT) AMSU (A1/A2) geolocated and calibrated brightness temperatures V005 (AIRABRAD_NRT) at GES DISC GES_DISC STAC Catalog 2015-12-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233769000-GES_DISC.umm_json "The AMSU-A Level 1B Near Real Time (NRT) product (AIRABRAD_NRT_005) differs from the routine product (AIRABRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AMSU-A instrument is co-aligned with AIRS so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. AMSU-A is primarily a temperature sounder that provides atmospheric information in the presence of clouds, which can be used to correct the AIRS infrared measurements for the effects of clouds. This is possible because non-precipitating clouds are for the most part transparent to microwave radiation, in contrast to visible and infrared radiation which are strongly scattered and absorbed by clouds. AMSU-A1 has 13 channels from 50 - 90 GHz and AMSU-A2 has 2 channels from 23 - 32 GHz. The AIRABRAD_NRT_005 products are stored in files (often referred to as ""granules"") that contain 6 minutes of data, 30 footprints across track by 45 lines along track." proprietary
AIRG2SSD_006 AIRS/Aqua L2G Precipitation Estimate V006 (AIRG2SSD) at GES DISC ALL STAC Catalog 2002-08-30 2016-09-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477375-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This precipitation estimate from AIRS is using TOVS-like algorithm, and is intended for merging into the precipitation product of the Global Precipitation Climatology Project (GPCP). The precipitation estimate from AIRS Level 2 Support product, which are 6-min swath granules (240 per day) are combined here into one daily ""Level 2G"" global grid with dimensions (24x1440x720). Thus every hour is a ""layer"", and the resulting grid cell size is 0.25 degree (~25 km). Thus the grid size is made to fit TRMM products. Since AIRS precipitation is retrieved at AMSU footprint resolution, which is about 45 km at nadir, many grid cells in this 0.25-deg grid are ""empty"". The data are stored such that the first line is the South Pole. The geolocation information for every hour-layer is also provided in the file." proprietary
AIRG2SSD_006 AIRS/Aqua L2G Precipitation Estimate V006 (AIRG2SSD) at GES DISC GES_DISC STAC Catalog 2002-08-30 2016-09-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477375-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This precipitation estimate from AIRS is using TOVS-like algorithm, and is intended for merging into the precipitation product of the Global Precipitation Climatology Project (GPCP). The precipitation estimate from AIRS Level 2 Support product, which are 6-min swath granules (240 per day) are combined here into one daily ""Level 2G"" global grid with dimensions (24x1440x720). Thus every hour is a ""layer"", and the resulting grid cell size is 0.25 degree (~25 km). Thus the grid size is made to fit TRMM products. Since AIRS precipitation is retrieved at AMSU footprint resolution, which is about 45 km at nadir, many grid cells in this 0.25-deg grid are ""empty"". The data are stored such that the first line is the South Pole. The geolocation information for every hour-layer is also provided in the file." proprietary
-AIRG2SSD_IRonly_006 AIRS/Aqua L2G Precipitation Estimate (AIRS-only) V006 (AIRG2SSD_IRonly) at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1618076955-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This precipitation estimate from AIRS IR only is using a TOVS-like algorithm, and is intended for merging into the precipitation product of the Global Precipitation Climatology Project (GPCP). The precipitation estimate from AIRS Level 2 Support product, which are 6-min swath granules (240 per day) are combined here into one daily ""Level 2G"" global grid with dimensions (24x1440x720). Thus every hour is a ""layer"", and the resulting grid cell size is 0.25 degree (~25 km). Thus the grid size is made to fit TRMM products. Since AIRS precipitation is retrieved at AMSU footprint resolution, which is about 45 km at nadir, many grid cells in this 0.25-deg grid are ""empty"". The data are stored such that the first line is the South Pole. The geolocation information for every hour-layer is also provided in the file." proprietary
AIRG2SSD_IRonly_006 AIRS/Aqua L2G Precipitation Estimate (AIRS-only) V006 (AIRG2SSD_IRonly) at GES DISC ALL STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1618076955-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This precipitation estimate from AIRS IR only is using a TOVS-like algorithm, and is intended for merging into the precipitation product of the Global Precipitation Climatology Project (GPCP). The precipitation estimate from AIRS Level 2 Support product, which are 6-min swath granules (240 per day) are combined here into one daily ""Level 2G"" global grid with dimensions (24x1440x720). Thus every hour is a ""layer"", and the resulting grid cell size is 0.25 degree (~25 km). Thus the grid size is made to fit TRMM products. Since AIRS precipitation is retrieved at AMSU footprint resolution, which is about 45 km at nadir, many grid cells in this 0.25-deg grid are ""empty"". The data are stored such that the first line is the South Pole. The geolocation information for every hour-layer is also provided in the file." proprietary
+AIRG2SSD_IRonly_006 AIRS/Aqua L2G Precipitation Estimate (AIRS-only) V006 (AIRG2SSD_IRonly) at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1618076955-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This precipitation estimate from AIRS IR only is using a TOVS-like algorithm, and is intended for merging into the precipitation product of the Global Precipitation Climatology Project (GPCP). The precipitation estimate from AIRS Level 2 Support product, which are 6-min swath granules (240 per day) are combined here into one daily ""Level 2G"" global grid with dimensions (24x1440x720). Thus every hour is a ""layer"", and the resulting grid cell size is 0.25 degree (~25 km). Thus the grid size is made to fit TRMM products. Since AIRS precipitation is retrieved at AMSU footprint resolution, which is about 45 km at nadir, many grid cells in this 0.25-deg grid are ""empty"". The data are stored such that the first line is the South Pole. The geolocation information for every hour-layer is also provided in the file." proprietary
AIRG2SSD_IRonly_7.0 AIRS/Aqua L2G Precipitation Estimate (AIRS-only) V7.0 at GES DISC ALL STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1702050366-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This precipitation estimate from AIRS IR only is using a TOVS-like algorithm, and is intended for merging into the precipitation product of the Global Precipitation Climatology Project (GPCP). The precipitation estimate from AIRS Level 2 Support product, which are 6-min swath granules (240 per day) are combined here into one daily ""Level 2G"" global grid with dimensions (24x1440x720). Thus every hour is a ""layer"", and the resulting grid cell size is 0.25 degree (~25 km). Thus the grid size is made to fit TRMM products. Since AIRS precipitation is retrieved at AMSU footprint resolution, which is about 45 km at nadir, many grid cells in this 0.25-deg grid are ""empty"". The data are stored such that the first line is the South Pole. The geolocation information for every hour-layer is also provided in the file." proprietary
AIRG2SSD_IRonly_7.0 AIRS/Aqua L2G Precipitation Estimate (AIRS-only) V7.0 at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1702050366-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This precipitation estimate from AIRS IR only is using a TOVS-like algorithm, and is intended for merging into the precipitation product of the Global Precipitation Climatology Project (GPCP). The precipitation estimate from AIRS Level 2 Support product, which are 6-min swath granules (240 per day) are combined here into one daily ""Level 2G"" global grid with dimensions (24x1440x720). Thus every hour is a ""layer"", and the resulting grid cell size is 0.25 degree (~25 km). Thus the grid size is made to fit TRMM products. Since AIRS precipitation is retrieved at AMSU footprint resolution, which is about 45 km at nadir, many grid cells in this 0.25-deg grid are ""empty"". The data are stored such that the first line is the South Pole. The geolocation information for every hour-layer is also provided in the file." proprietary
-AIRH2CCF_006 AIRS/Aqua L2 Cloud-Cleared Infrared Radiances (AIRS+AMSU+HSB) V006 (AIRH2CCF) at GES DISC ALL STAC Catalog 2002-08-30 2003-02-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477316-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRI2CCF. However, it contains science retrievals that use the HSB. Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRH2CCF_006 AIRS/Aqua L2 Cloud-Cleared Infrared Radiances (AIRS+AMSU+HSB) V006 (AIRH2CCF) at GES DISC GES_DISC STAC Catalog 2002-08-30 2003-02-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477316-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRI2CCF. However, it contains science retrievals that use the HSB. Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
+AIRH2CCF_006 AIRS/Aqua L2 Cloud-Cleared Infrared Radiances (AIRS+AMSU+HSB) V006 (AIRH2CCF) at GES DISC ALL STAC Catalog 2002-08-30 2003-02-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477316-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRI2CCF. However, it contains science retrievals that use the HSB. Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRH2CCF_7.0 Aqua/AIRS L2 Cloud-Cleared Infrared Radiances (AIRS+AMSU+HSB) V7.0 at GES DISC GES_DISC STAC Catalog 2002-08-30 2003-02-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805614-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRI2CCF. However, it contains science retrievals that use the HSB. Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRH2RET_006 AIRS/Aqua L2 Standard Physical Retrieval (AIRS+AMSU+HSB) V006 (AIRH2RET) at GES DISC GES_DISC STAC Catalog 2002-08-30 2003-02-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477376-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2RET. However, it contains science retrievals that use the HSB. Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRH2RET_006 AIRS/Aqua L2 Standard Physical Retrieval (AIRS+AMSU+HSB) V006 (AIRH2RET) at GES DISC ALL STAC Catalog 2002-08-30 2003-02-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477376-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2RET. However, it contains science retrievals that use the HSB. Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
@@ -2073,8 +2073,8 @@ AIRH3QPM_006 AIRS/Aqua L3 Monthly Quantization in Physical Units (AIRS+AMSU+HSB)
AIRH3QPM_006 AIRS/Aqua L3 Monthly Quantization in Physical Units (AIRS+AMSU+HSB) 5 degrees x 5 degrees V006 (AIRH3QPM) at GES DISC ALL STAC Catalog 2002-09-01 2003-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517242-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution). proprietary
AIRH3SP8_006 AIRS/Aqua L3 8-day Support Multiday Product (AIRS+AMSU+HSB) 1 degree x 1 degree V006 (AIRH3SP8) at GES DISC ALL STAC Catalog 2002-09-01 2003-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517226-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
AIRH3SP8_006 AIRS/Aqua L3 8-day Support Multiday Product (AIRS+AMSU+HSB) 1 degree x 1 degree V006 (AIRH3SP8) at GES DISC GES_DISC STAC Catalog 2002-09-01 2003-02-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517226-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
-AIRH3SPD_006 AIRS/Aqua L3 Daily Support Daily Product (AIRS+AMSU+HSB) 1 degree x 1 degree V006 (AIRH3SPD) at GES DISC ALL STAC Catalog 2002-08-31 2003-02-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517230-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
AIRH3SPD_006 AIRS/Aqua L3 Daily Support Daily Product (AIRS+AMSU+HSB) 1 degree x 1 degree V006 (AIRH3SPD) at GES DISC GES_DISC STAC Catalog 2002-08-31 2003-02-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517230-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
+AIRH3SPD_006 AIRS/Aqua L3 Daily Support Daily Product (AIRS+AMSU+HSB) 1 degree x 1 degree V006 (AIRH3SPD) at GES DISC ALL STAC Catalog 2002-08-31 2003-02-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517230-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
AIRH3SPD_7.0 Aqua/AIRS L3 Daily Support Daily Product (AIRS+AMSU+HSB) 1 degree x 1 degree V7.0 at GES DISC GES_DISC STAC Catalog 2002-08-31 2003-02-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805696-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. The value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the box. proprietary
AIRH3SPM_006 AIRS/Aqua L3 Monthly Support Product (AIRS+AMSU+HSB) 1 degree x 1 degree V006 (AIRH3SPM) at GES DISC GES_DISC STAC Catalog 2002-09-01 2003-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517247-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
AIRH3SPM_006 AIRS/Aqua L3 Monthly Support Product (AIRS+AMSU+HSB) 1 degree x 1 degree V006 (AIRH3SPM) at GES DISC ALL STAC Catalog 2002-09-01 2003-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517247-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
@@ -2087,10 +2087,10 @@ AIRH3STD_7.0 Aqua/AIRS L3 Daily Standard Physical Retrieval (AIRS+AMSU+HSB) 1 de
AIRH3STM_006 AIRS/Aqua L3 Monthly Standard Physical Retrieval (AIRS+AMSU+HSB) 1 degree x 1 degree V006 (AIRH3STM) at GES DISC ALL STAC Catalog 2002-09-01 2003-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517238-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX3STM. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. The AIRS Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. proprietary
AIRH3STM_006 AIRS/Aqua L3 Monthly Standard Physical Retrieval (AIRS+AMSU+HSB) 1 degree x 1 degree V006 (AIRH3STM) at GES DISC GES_DISC STAC Catalog 2002-09-01 2003-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517238-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX3STM. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. The AIRS Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. proprietary
AIRH3STM_7.0 Aqua/AIRS L3 Monthly Standard Physical Retrieval (AIRS+AMSU+HSB) 1 degree x 1 degree V7.0 at GES DISC GES_DISC STAC Catalog 2002-09-01 2003-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805701-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX3STM. However, it contains science retrievals that use the Humidity Sounder for Brazil (HSB). Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. The AIRS Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. proprietary
-AIRHBRAD_005 AIRS/Aqua L1B HSB geolocated and calibrated brightness temperatures V005 (AIRHBRAD) at GES DISC GES_DISC STAC Catalog 2002-05-24 2003-11-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477367-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The HSB level 1B data set contains HSB calibrated and geolocated brightness temperatures in degrees Kelvin. This data set is generated from HSB Level 1A digital numbers (DN), including 4 microwave channels in the 150 - 190 GHz region of the spectrum. A day's worth of data is divided into 240 scenes each of 6 minute duration. For the HSB measurements, an individual scene consists of 135 scanlines containing 90 cross-track footprints; thus there is a total of 135 x 90 = 12,150 footprints per HSB scene, which coincide very closely with the AIRS infrared footprints. HSB is primarily a humidity sounder that provides information on snow/ice cover and precipitation using the 150 GHz window channel, and the coarse distribution of moisture in the troposphere using the 183 GHz channels. Combined with simultaneous measurements from the AIRS and AMSU-A instruments, the calibrated HSB brightness temperatures will be used to initialize the atmospheric moisture profile required for the retrieval of the final AIRS geophysical products. An HSB level 1B daily summary browse product is also available to provide users with a global quick look capability when searching for data of interest. Summary Browse Products are high-level pictorial representations of AIRS Instrument (AIRS Infrared, AMSU-A and HSB) data designed as an aid to ordering data from the GSFC DISC or EDG. the HSB instrument failed in November of 2003. proprietary
AIRHBRAD_005 AIRS/Aqua L1B HSB geolocated and calibrated brightness temperatures V005 (AIRHBRAD) at GES DISC ALL STAC Catalog 2002-05-24 2003-11-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477367-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The HSB level 1B data set contains HSB calibrated and geolocated brightness temperatures in degrees Kelvin. This data set is generated from HSB Level 1A digital numbers (DN), including 4 microwave channels in the 150 - 190 GHz region of the spectrum. A day's worth of data is divided into 240 scenes each of 6 minute duration. For the HSB measurements, an individual scene consists of 135 scanlines containing 90 cross-track footprints; thus there is a total of 135 x 90 = 12,150 footprints per HSB scene, which coincide very closely with the AIRS infrared footprints. HSB is primarily a humidity sounder that provides information on snow/ice cover and precipitation using the 150 GHz window channel, and the coarse distribution of moisture in the troposphere using the 183 GHz channels. Combined with simultaneous measurements from the AIRS and AMSU-A instruments, the calibrated HSB brightness temperatures will be used to initialize the atmospheric moisture profile required for the retrieval of the final AIRS geophysical products. An HSB level 1B daily summary browse product is also available to provide users with a global quick look capability when searching for data of interest. Summary Browse Products are high-level pictorial representations of AIRS Instrument (AIRS Infrared, AMSU-A and HSB) data designed as an aid to ordering data from the GSFC DISC or EDG. the HSB instrument failed in November of 2003. proprietary
-AIRI2CCF_006 AIRS/Aqua L2 Cloud-Cleared Infrared Radiances (AIRS+AMSU) V006 (AIRI2CCF) at GES DISC ALL STAC Catalog 2002-08-30 2016-09-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477378-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
+AIRHBRAD_005 AIRS/Aqua L1B HSB geolocated and calibrated brightness temperatures V005 (AIRHBRAD) at GES DISC GES_DISC STAC Catalog 2002-05-24 2003-11-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477367-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The HSB level 1B data set contains HSB calibrated and geolocated brightness temperatures in degrees Kelvin. This data set is generated from HSB Level 1A digital numbers (DN), including 4 microwave channels in the 150 - 190 GHz region of the spectrum. A day's worth of data is divided into 240 scenes each of 6 minute duration. For the HSB measurements, an individual scene consists of 135 scanlines containing 90 cross-track footprints; thus there is a total of 135 x 90 = 12,150 footprints per HSB scene, which coincide very closely with the AIRS infrared footprints. HSB is primarily a humidity sounder that provides information on snow/ice cover and precipitation using the 150 GHz window channel, and the coarse distribution of moisture in the troposphere using the 183 GHz channels. Combined with simultaneous measurements from the AIRS and AMSU-A instruments, the calibrated HSB brightness temperatures will be used to initialize the atmospheric moisture profile required for the retrieval of the final AIRS geophysical products. An HSB level 1B daily summary browse product is also available to provide users with a global quick look capability when searching for data of interest. Summary Browse Products are high-level pictorial representations of AIRS Instrument (AIRS Infrared, AMSU-A and HSB) data designed as an aid to ordering data from the GSFC DISC or EDG. the HSB instrument failed in November of 2003. proprietary
AIRI2CCF_006 AIRS/Aqua L2 Cloud-Cleared Infrared Radiances (AIRS+AMSU) V006 (AIRI2CCF) at GES DISC GES_DISC STAC Catalog 2002-08-30 2016-09-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477378-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
+AIRI2CCF_006 AIRS/Aqua L2 Cloud-Cleared Infrared Radiances (AIRS+AMSU) V006 (AIRI2CCF) at GES DISC ALL STAC Catalog 2002-08-30 2016-09-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477378-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRI2CCF_7.0 Aqua/AIRS L2 Cloud-Cleared Infrared Radiances (AIRS+AMSU) V7.0 at GES DISC GES_DISC STAC Catalog 2002-08-30 2016-09-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805611-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRIBQAP_005 AIRS/Aqua L1B Infrared (IR) quality assurance subset V005 (AIRIBQAP) at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477368-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS IR Level 1B QA Subset contains Quality Assurance (QA) parameters that a user of may use to filter AIRS IR Level 1B radiance data to create a subset of analysis. QA parameters indicate quality of granule-per-channel, scan-per-channel, field of view, and channel and should be accessed before any data of analysis. It also contains ""glintlat"", ""glintlon"", and ""sun_glint_distant"" that users can use to check for possibility of solar glint contamination." proprietary
AIRIBQAP_005 AIRS/Aqua L1B Infrared (IR) quality assurance subset V005 (AIRIBQAP) at GES DISC ALL STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477368-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS IR Level 1B QA Subset contains Quality Assurance (QA) parameters that a user of may use to filter AIRS IR Level 1B radiance data to create a subset of analysis. QA parameters indicate quality of granule-per-channel, scan-per-channel, field of view, and channel and should be accessed before any data of analysis. It also contains ""glintlat"", ""glintlon"", and ""sun_glint_distant"" that users can use to check for possibility of solar glint contamination." proprietary
@@ -2100,84 +2100,84 @@ AIRIBRAD_005 AIRS/Aqua L1B Infrared (IR) geolocated and calibrated radiances V00
AIRIBRAD_005 AIRS/Aqua L1B Infrared (IR) geolocated and calibrated radiances V005 (AIRIBRAD) at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477369-GES_DISC.umm_json " WARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space. The thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the “spectral_freq” parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitude of the effect on brightness temperatures (BT) depends on the spectral gradient of each channel. Maximum BT shifts are approximately +- 0.5 K, although many channels experience far smaller BT shifts. Approximately 1803 channels have BT shifts of less than 0.1 K and 575 channels are now shifted in BT by more than 0.1 K, while 231 of these channels have BT shifts greater than 0.2 K. Users of the L1b v5 product who are concerned that these shifts may impact their science investigations and applications are encouraged to switch to the AIRS L1c v6.7.4 product, which, among many other improvements, converts the spectra to a fixed frequency grid. END OF WARNING. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1B data set contains AIRS calibrated and geolocated radiances in milliWatts/m^2/cm^-1/steradian for 2378 infrared channels in the 3.74 to 15.4 micron region of t he spectrum. The AIRS instrument is co-aligned with AMSU-A so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. The AIRIBRAD_005 products are stored in files (often referred to as ""granules"") that contain 6 minutes of data, 90 footprints across track by 135 lines along track." proprietary
AIRIBRAD_NRT_005 AIRS/Aqua L1B Near Real Time (NRT) Infrared (IR) geolocated and calibrated radiances V005 (AIRIBRAD_NRT) at GES DISC GES_DISC STAC Catalog 2015-12-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233768982-GES_DISC.umm_json " WARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space. The thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the “spectral_freq” parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitude of the effect on brightness temperatures (BT) depends on the spectral gradient of each channel. Maximum BT shifts are approximately +- 0.5 K, although many channels experience far smaller BT shifts. Approximately 1803 channels have BT shifts of less than 0.1 K and 575 channels are now shifted in BT by more than 0.1 K, while 231 of these channels have BT shifts greater than 0.2 K. Users of the L1b v5 product who are concerned that these shifts may impact their science investigations and applications are encouraged to switch to the AIRS L1c v6.7.4 product, which, among many other improvements, converts the spectra to a fixed frequency grid. END OF WARNING. The AIRS Level 1B Near Real Time (NRT) product (AIRIBRAD_NRT_005) differs from the routine product (AIRIBRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1B data set contains AIRS calibrated and geolocated radiances in milliWatts/m^2/cm^-1/steradian for 2378 infrared channels in the 3.74 to 15.4 micron region of t he spectrum. The AIRS instrument is co-aligned with AMSU-A so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. The AIRIBRAD_NRT_005 products are stored in files (often referred to as ""granules"") that contain 6 minutes of data, 90 footprints across track by 135 lines along track." proprietary
AIRIBRAD_NRT_005 AIRS/Aqua L1B Near Real Time (NRT) Infrared (IR) geolocated and calibrated radiances V005 (AIRIBRAD_NRT) at GES DISC ALL STAC Catalog 2015-12-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233768982-GES_DISC.umm_json " WARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space. The thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the “spectral_freq” parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitude of the effect on brightness temperatures (BT) depends on the spectral gradient of each channel. Maximum BT shifts are approximately +- 0.5 K, although many channels experience far smaller BT shifts. Approximately 1803 channels have BT shifts of less than 0.1 K and 575 channels are now shifted in BT by more than 0.1 K, while 231 of these channels have BT shifts greater than 0.2 K. Users of the L1b v5 product who are concerned that these shifts may impact their science investigations and applications are encouraged to switch to the AIRS L1c v6.7.4 product, which, among many other improvements, converts the spectra to a fixed frequency grid. END OF WARNING. The AIRS Level 1B Near Real Time (NRT) product (AIRIBRAD_NRT_005) differs from the routine product (AIRIBRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1B data set contains AIRS calibrated and geolocated radiances in milliWatts/m^2/cm^-1/steradian for 2378 infrared channels in the 3.74 to 15.4 micron region of t he spectrum. The AIRS instrument is co-aligned with AMSU-A so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. The AIRIBRAD_NRT_005 products are stored in files (often referred to as ""granules"") that contain 6 minutes of data, 90 footprints across track by 135 lines along track." proprietary
-AIRIBRAD_NRT_BUFR_005 AIRS/Aqua L1B Near Real Time (NRT) Infrared (IR) geolocated and calibrated radiances in BUFR format V005 (AIRIBRAD_NRT_BUFR) at GES DISC ALL STAC Catalog 2015-12-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233769001-GES_DISC.umm_json WARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space. The thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the “spectral_freq” parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitude of the effect on brightness temperatures (BT) depends on the spectral gradient of each channel. Maximum BT shifts are approximately +- 0.5 K, although many channels experience far smaller BT shifts. Approximately 1803 channels have BT shifts of less than 0.1 K and 575 channels are now shifted in BT by more than 0.1 K, while 231 of these channels have BT shifts greater than 0.2 K. Users of the L1b v5 product who are concerned that these shifts may impact their science investigations and applications are encouraged to switch to the AIRS L1c v6.7.4 product, which, among many other improvements, converts the spectra to a fixed frequency grid. END OF WARNING. This product is a 324-channel subset of the AIRIBRAD_NRT_005 product in which the AMSU footprints from AIRABRAD_NRT_005 product are also included and converted to binary Universal Form for the Representation of meteorological data (BUFR). The AIRS and AMSU Level 1B products differ from routine processing in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. proprietary
AIRIBRAD_NRT_BUFR_005 AIRS/Aqua L1B Near Real Time (NRT) Infrared (IR) geolocated and calibrated radiances in BUFR format V005 (AIRIBRAD_NRT_BUFR) at GES DISC GES_DISC STAC Catalog 2015-12-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233769001-GES_DISC.umm_json WARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space. The thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the “spectral_freq” parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitude of the effect on brightness temperatures (BT) depends on the spectral gradient of each channel. Maximum BT shifts are approximately +- 0.5 K, although many channels experience far smaller BT shifts. Approximately 1803 channels have BT shifts of less than 0.1 K and 575 channels are now shifted in BT by more than 0.1 K, while 231 of these channels have BT shifts greater than 0.2 K. Users of the L1b v5 product who are concerned that these shifts may impact their science investigations and applications are encouraged to switch to the AIRS L1c v6.7.4 product, which, among many other improvements, converts the spectra to a fixed frequency grid. END OF WARNING. This product is a 324-channel subset of the AIRIBRAD_NRT_005 product in which the AMSU footprints from AIRABRAD_NRT_005 product are also included and converted to binary Universal Form for the Representation of meteorological data (BUFR). The AIRS and AMSU Level 1B products differ from routine processing in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. proprietary
-AIRICRAD_6.7 AIRS/Aqua L1C Infrared (IR) resampled and corrected radiances V6.7 (AIRICRAD) at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1675477037-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1C data set contains AIRS infrared calibrated and geolocated radiances in W/m2/micron/ster. This data set is generated from AIRS level 1B data. The spectral coverage of L1C data is from 3.74 to 15.4 mm. The nominal spectral resolution lambda / delta lambda = 1200. The spectrum is sampled twice per spectral resolution element in a total of 2645 spectral channels. A day of AIRS data is divided into 240 granules (scenes) each of 6-minute duration. For the AIRS IR measurements, an individual granule contains 135 pixels across-track and 90 along-track pixels; there are total of 135 x 90 = 12,150 pixels per granule. AIRS employs a 49.5 degree crosstrack scanning with a 1.1 degree instantaneous field of view (IFOV) to provide twice daily coverage of essentially the entire globe in a 1:30 PM sun synchronous orbit with the 13.5 x 13.5 km2 spatial resolution at nadir. The L1C swath products are derived from the L1B swath products. The primary purpose of the level 1C is to generate the spectra of radiances without spectral gaps caused by the instrument design and bad spectral points. The AIRS L1C data can be used for comparative (with other IR measurements) studies and for weather-climate research. This is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page. For this collection the switchover occurred on June 1, 2020. proprietary
+AIRIBRAD_NRT_BUFR_005 AIRS/Aqua L1B Near Real Time (NRT) Infrared (IR) geolocated and calibrated radiances in BUFR format V005 (AIRIBRAD_NRT_BUFR) at GES DISC ALL STAC Catalog 2015-12-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233769001-GES_DISC.umm_json WARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space. The thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the “spectral_freq” parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitude of the effect on brightness temperatures (BT) depends on the spectral gradient of each channel. Maximum BT shifts are approximately +- 0.5 K, although many channels experience far smaller BT shifts. Approximately 1803 channels have BT shifts of less than 0.1 K and 575 channels are now shifted in BT by more than 0.1 K, while 231 of these channels have BT shifts greater than 0.2 K. Users of the L1b v5 product who are concerned that these shifts may impact their science investigations and applications are encouraged to switch to the AIRS L1c v6.7.4 product, which, among many other improvements, converts the spectra to a fixed frequency grid. END OF WARNING. This product is a 324-channel subset of the AIRIBRAD_NRT_005 product in which the AMSU footprints from AIRABRAD_NRT_005 product are also included and converted to binary Universal Form for the Representation of meteorological data (BUFR). The AIRS and AMSU Level 1B products differ from routine processing in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. proprietary
AIRICRAD_6.7 AIRS/Aqua L1C Infrared (IR) resampled and corrected radiances V6.7 (AIRICRAD) at GES DISC ALL STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1675477037-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1C data set contains AIRS infrared calibrated and geolocated radiances in W/m2/micron/ster. This data set is generated from AIRS level 1B data. The spectral coverage of L1C data is from 3.74 to 15.4 mm. The nominal spectral resolution lambda / delta lambda = 1200. The spectrum is sampled twice per spectral resolution element in a total of 2645 spectral channels. A day of AIRS data is divided into 240 granules (scenes) each of 6-minute duration. For the AIRS IR measurements, an individual granule contains 135 pixels across-track and 90 along-track pixels; there are total of 135 x 90 = 12,150 pixels per granule. AIRS employs a 49.5 degree crosstrack scanning with a 1.1 degree instantaneous field of view (IFOV) to provide twice daily coverage of essentially the entire globe in a 1:30 PM sun synchronous orbit with the 13.5 x 13.5 km2 spatial resolution at nadir. The L1C swath products are derived from the L1B swath products. The primary purpose of the level 1C is to generate the spectra of radiances without spectral gaps caused by the instrument design and bad spectral points. The AIRS L1C data can be used for comparative (with other IR measurements) studies and for weather-climate research. This is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page. For this collection the switchover occurred on June 1, 2020. proprietary
+AIRICRAD_6.7 AIRS/Aqua L1C Infrared (IR) resampled and corrected radiances V6.7 (AIRICRAD) at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1675477037-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1C data set contains AIRS infrared calibrated and geolocated radiances in W/m2/micron/ster. This data set is generated from AIRS level 1B data. The spectral coverage of L1C data is from 3.74 to 15.4 mm. The nominal spectral resolution lambda / delta lambda = 1200. The spectrum is sampled twice per spectral resolution element in a total of 2645 spectral channels. A day of AIRS data is divided into 240 granules (scenes) each of 6-minute duration. For the AIRS IR measurements, an individual granule contains 135 pixels across-track and 90 along-track pixels; there are total of 135 x 90 = 12,150 pixels per granule. AIRS employs a 49.5 degree crosstrack scanning with a 1.1 degree instantaneous field of view (IFOV) to provide twice daily coverage of essentially the entire globe in a 1:30 PM sun synchronous orbit with the 13.5 x 13.5 km2 spatial resolution at nadir. The L1C swath products are derived from the L1B swath products. The primary purpose of the level 1C is to generate the spectra of radiances without spectral gaps caused by the instrument design and bad spectral points. The AIRS L1C data can be used for comparative (with other IR measurements) studies and for weather-climate research. This is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page. For this collection the switchover occurred on June 1, 2020. proprietary
AIRICRAD_NRT_6.7 AIRS/Aqua L1C Near Real Time (NRT) Infrared (IR) resampled and corrected radiances V6.7 (AIRICRAD_NRT) at GES DISC ALL STAC Catalog 2002-09-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1712047294-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1C data set contains AIRS infrared calibrated and geolocated radiances in W/m2/micron/ster. This data set is generated from AIRS level 1B data. The spectral coverage of L1C data is from 3.74 to 15.4 mm. The nominal spectral resolution lambda / delta lambda = 1200. The spectrum is sampled twice per spectral resolution element in a total of 2645 spectral channels. A day of AIRS data is divided into 240 granules (scenes) each of 6-minute duration. For the AIRS IR measurements, an individual granule contains 135 pixels across-track and 90 along-track pixels; there are total of 135 x 90 = 12,150 pixels per granule. AIRS employs a 49.5 degree crosstrack scanning with a 1.1 degree instantaneous field of view (IFOV) to provide twice daily coverage of essentially the entire globe in a 1:30 PM sun synchronous orbit with the 13.5 x 13.5 km2 spatial resolution at nadir. The L1C swath products are derived from the L1B swath products. The primary purpose of the level 1C is to generate the spectra of radiances without spectral gaps caused by the instrument design and bad spectral points. The AIRS L1C data can be used for comparative (with other IR measurements) studies and for weather-climate research. As a Near Real Time (NRT) product this differs from AIRICRAD.6.7 AIRS differ from routine processing in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. proprietary
AIRICRAD_NRT_6.7 AIRS/Aqua L1C Near Real Time (NRT) Infrared (IR) resampled and corrected radiances V6.7 (AIRICRAD_NRT) at GES DISC GES_DISC STAC Catalog 2002-09-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1712047294-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1C data set contains AIRS infrared calibrated and geolocated radiances in W/m2/micron/ster. This data set is generated from AIRS level 1B data. The spectral coverage of L1C data is from 3.74 to 15.4 mm. The nominal spectral resolution lambda / delta lambda = 1200. The spectrum is sampled twice per spectral resolution element in a total of 2645 spectral channels. A day of AIRS data is divided into 240 granules (scenes) each of 6-minute duration. For the AIRS IR measurements, an individual granule contains 135 pixels across-track and 90 along-track pixels; there are total of 135 x 90 = 12,150 pixels per granule. AIRS employs a 49.5 degree crosstrack scanning with a 1.1 degree instantaneous field of view (IFOV) to provide twice daily coverage of essentially the entire globe in a 1:30 PM sun synchronous orbit with the 13.5 x 13.5 km2 spatial resolution at nadir. The L1C swath products are derived from the L1B swath products. The primary purpose of the level 1C is to generate the spectra of radiances without spectral gaps caused by the instrument design and bad spectral points. The AIRS L1C data can be used for comparative (with other IR measurements) studies and for weather-climate research. As a Near Real Time (NRT) product this differs from AIRICRAD.6.7 AIRS differ from routine processing in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. proprietary
AIRMISR_BARC_2001_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the BARC 2001 Campaign ALL STAC Catalog 2001-07-21 2001-07-21 -77.21, 38.73, -76.46, 39.31 https://cmr.earthdata.nasa.gov/search/concepts/C1000000701-LARC_ASDC.umm_json The AirMISR BARC 2001 data were acquired during a flight over the Beltsville Agricultural Research Center (BARC) on July 21, 2001. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRMISR_BARC_2001_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the BARC 2001 Campaign LARC_ASDC STAC Catalog 2001-07-21 2001-07-21 -77.21, 38.73, -76.46, 39.31 https://cmr.earthdata.nasa.gov/search/concepts/C1000000701-LARC_ASDC.umm_json The AirMISR BARC 2001 data were acquired during a flight over the Beltsville Agricultural Research Center (BARC) on July 21, 2001. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRMISR_BARTLETT_2003_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Bartlett 2003 Campaign ALL STAC Catalog 2003-08-24 2003-08-24 -71.6, 43.8, -70.92, 44.28 https://cmr.earthdata.nasa.gov/search/concepts/C1000000720-LARC_ASDC.umm_json The AIRMISR_BARTLETT_2003 data were acquired during a flight over the Bartlett Experimental Forest, New Hampshire, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 24, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There were a total of two runs during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction. proprietary
AIRMISR_BARTLETT_2003_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Bartlett 2003 Campaign LARC_ASDC STAC Catalog 2003-08-24 2003-08-24 -71.6, 43.8, -70.92, 44.28 https://cmr.earthdata.nasa.gov/search/concepts/C1000000720-LARC_ASDC.umm_json The AIRMISR_BARTLETT_2003 data were acquired during a flight over the Bartlett Experimental Forest, New Hampshire, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 24, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There were a total of two runs during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction. proprietary
-AIRMISR_CLAMS_2001_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the CLAMS 2001 Campaign ALL STAC Catalog 2001-07-12 2001-08-02 -78.82, 35.64, -74.01, 39.99 https://cmr.earthdata.nasa.gov/search/concepts/C1000000702-LARC_ASDC.umm_json The AIRMISR_CLAMS_2001 data were acquired during the CLAMS campaign on July 12, July 17, August 1, and August 2 of 2001. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) field campaign was held in the summer of 2001 at the CERES Ocean Validation Experiment (COVE) site in the Chesapeake Bay, 20 km east of Virginia Beach. CLAMS is a clear-sky, shortwave closure campaign in conjunction with MISR, CERES, MODIS-Atmospheres and the Global Aerosol Climatology Project (GACP). Its goals were to obtain more accurate broadband fluxes at sea surface and within the atmosphere, space-time variability of spectral BRDF of the sea surface, and aerosol retrievals. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRMISR_CLAMS_2001_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the CLAMS 2001 Campaign LARC_ASDC STAC Catalog 2001-07-12 2001-08-02 -78.82, 35.64, -74.01, 39.99 https://cmr.earthdata.nasa.gov/search/concepts/C1000000702-LARC_ASDC.umm_json The AIRMISR_CLAMS_2001 data were acquired during the CLAMS campaign on July 12, July 17, August 1, and August 2 of 2001. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) field campaign was held in the summer of 2001 at the CERES Ocean Validation Experiment (COVE) site in the Chesapeake Bay, 20 km east of Virginia Beach. CLAMS is a clear-sky, shortwave closure campaign in conjunction with MISR, CERES, MODIS-Atmospheres and the Global Aerosol Climatology Project (GACP). Its goals were to obtain more accurate broadband fluxes at sea surface and within the atmosphere, space-time variability of spectral BRDF of the sea surface, and aerosol retrievals. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
-AIRMISR_HARVARD_2003_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Harvard 2003 Campaign LARC_ASDC STAC Catalog 2003-08-24 2003-08-24 -72.45, 42.28, -71.81, 42.78 https://cmr.earthdata.nasa.gov/search/concepts/C1000000721-LARC_ASDC.umm_json The AIRMISR_HARVARD_2003 data set was acquired during a flight over the Harvard Forest, Massachusetts, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 24, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There were a total of two runs during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction. proprietary
+AIRMISR_CLAMS_2001_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the CLAMS 2001 Campaign ALL STAC Catalog 2001-07-12 2001-08-02 -78.82, 35.64, -74.01, 39.99 https://cmr.earthdata.nasa.gov/search/concepts/C1000000702-LARC_ASDC.umm_json The AIRMISR_CLAMS_2001 data were acquired during the CLAMS campaign on July 12, July 17, August 1, and August 2 of 2001. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) field campaign was held in the summer of 2001 at the CERES Ocean Validation Experiment (COVE) site in the Chesapeake Bay, 20 km east of Virginia Beach. CLAMS is a clear-sky, shortwave closure campaign in conjunction with MISR, CERES, MODIS-Atmospheres and the Global Aerosol Climatology Project (GACP). Its goals were to obtain more accurate broadband fluxes at sea surface and within the atmosphere, space-time variability of spectral BRDF of the sea surface, and aerosol retrievals. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRMISR_HARVARD_2003_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Harvard 2003 Campaign ALL STAC Catalog 2003-08-24 2003-08-24 -72.45, 42.28, -71.81, 42.78 https://cmr.earthdata.nasa.gov/search/concepts/C1000000721-LARC_ASDC.umm_json The AIRMISR_HARVARD_2003 data set was acquired during a flight over the Harvard Forest, Massachusetts, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 24, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There were a total of two runs during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction. proprietary
+AIRMISR_HARVARD_2003_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Harvard 2003 Campaign LARC_ASDC STAC Catalog 2003-08-24 2003-08-24 -72.45, 42.28, -71.81, 42.78 https://cmr.earthdata.nasa.gov/search/concepts/C1000000721-LARC_ASDC.umm_json The AIRMISR_HARVARD_2003 data set was acquired during a flight over the Harvard Forest, Massachusetts, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 24, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There were a total of two runs during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction. proprietary
AIRMISR_HOWLAND_2003_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Howland 2003 Campaign ALL STAC Catalog 2003-08-28 2003-08-28 -69.05, 44.95, -68.35, 45.45 https://cmr.earthdata.nasa.gov/search/concepts/C1000000703-LARC_ASDC.umm_json The AIRMISR_HOWLAND_2003 data were acquired during a field mission which overflew Howland Forest, Maine on August 28, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRMISR_HOWLAND_2003_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Howland 2003 Campaign LARC_ASDC STAC Catalog 2003-08-28 2003-08-28 -69.05, 44.95, -68.35, 45.45 https://cmr.earthdata.nasa.gov/search/concepts/C1000000703-LARC_ASDC.umm_json The AIRMISR_HOWLAND_2003 data were acquired during a field mission which overflew Howland Forest, Maine on August 28, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRMISR_KONVEX_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the KONza Validation EXperiment (KONVEX) ALL STAC Catalog 1999-07-13 1999-07-13 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000722-LARC_ASDC.umm_json The AIRMISR_KONVEX data were acquired during the KONza Validation EXperiment (KONVEX) which occurred 11 - 18 July 1999. The AIRMISR_KONVEX data were obtained on 13 July 1999, flight #36 only. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are:0 degrees or nadir26.1 degrees, fore and aft45.6 degrees, fore and aft60.0 degrees, fore and aft70.5 degrees, fore and aft For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are:443 nanometers, blue555 nanometers, green670 nanometers, red865 nanometers, near-infrared Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRMISR_KONVEX_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the KONza Validation EXperiment (KONVEX) LARC_ASDC STAC Catalog 1999-07-13 1999-07-13 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000722-LARC_ASDC.umm_json The AIRMISR_KONVEX data were acquired during the KONza Validation EXperiment (KONVEX) which occurred 11 - 18 July 1999. The AIRMISR_KONVEX data were obtained on 13 July 1999, flight #36 only. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are:0 degrees or nadir26.1 degrees, fore and aft45.6 degrees, fore and aft60.0 degrees, fore and aft70.5 degrees, fore and aft For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are:443 nanometers, blue555 nanometers, green670 nanometers, red865 nanometers, near-infrared Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
-AIRMISR_LUNAR_LAKE_2000_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Lunar Lake 2000 Campaign ALL STAC Catalog 2000-06-11 2000-06-11 -117.5, 36.86, -114.6, 39 https://cmr.earthdata.nasa.gov/search/concepts/C1000000723-LARC_ASDC.umm_json The AIRMISR_LUNAR_LAKE_2000 data were acquired during a flight over Lunar Lake, Nevada on June 11, 2000. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are:0 degrees or nadir26.1 degrees, fore and aft45.6 degrees, fore and aft60.0 degrees, fore and aft70.5 degrees, fore and aft For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are:443 nanometers, blue555 nanometers, green670 nanometers, red865 nanometers, near-infrared Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRMISR_LUNAR_LAKE_2000_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Lunar Lake 2000 Campaign LARC_ASDC STAC Catalog 2000-06-11 2000-06-11 -117.5, 36.86, -114.6, 39 https://cmr.earthdata.nasa.gov/search/concepts/C1000000723-LARC_ASDC.umm_json The AIRMISR_LUNAR_LAKE_2000 data were acquired during a flight over Lunar Lake, Nevada on June 11, 2000. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are:0 degrees or nadir26.1 degrees, fore and aft45.6 degrees, fore and aft60.0 degrees, fore and aft70.5 degrees, fore and aft For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are:443 nanometers, blue555 nanometers, green670 nanometers, red865 nanometers, near-infrared Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
-AIRMISR_LUNAR_LAKE_2001_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Lunar Lake 2001 Campaign LARC_ASDC STAC Catalog 2001-06-30 2001-06-30 -116.32, 38.13, -115.36, 38.73 https://cmr.earthdata.nasa.gov/search/concepts/C1000000704-LARC_ASDC.umm_json The AIRMISR_LUNAR_LAKE_2001 data were acquired during a flight over Lunar Lake, Nevada on June 30, 2001. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
+AIRMISR_LUNAR_LAKE_2000_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Lunar Lake 2000 Campaign ALL STAC Catalog 2000-06-11 2000-06-11 -117.5, 36.86, -114.6, 39 https://cmr.earthdata.nasa.gov/search/concepts/C1000000723-LARC_ASDC.umm_json The AIRMISR_LUNAR_LAKE_2000 data were acquired during a flight over Lunar Lake, Nevada on June 11, 2000. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are:0 degrees or nadir26.1 degrees, fore and aft45.6 degrees, fore and aft60.0 degrees, fore and aft70.5 degrees, fore and aft For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and ocean color studies. The center wavelengths of the 4 spectral bands are:443 nanometers, blue555 nanometers, green670 nanometers, red865 nanometers, near-infrared Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRMISR_LUNAR_LAKE_2001_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Lunar Lake 2001 Campaign ALL STAC Catalog 2001-06-30 2001-06-30 -116.32, 38.13, -115.36, 38.73 https://cmr.earthdata.nasa.gov/search/concepts/C1000000704-LARC_ASDC.umm_json The AIRMISR_LUNAR_LAKE_2001 data were acquired during a flight over Lunar Lake, Nevada on June 30, 2001. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
+AIRMISR_LUNAR_LAKE_2001_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Lunar Lake 2001 Campaign LARC_ASDC STAC Catalog 2001-06-30 2001-06-30 -116.32, 38.13, -115.36, 38.73 https://cmr.earthdata.nasa.gov/search/concepts/C1000000704-LARC_ASDC.umm_json The AIRMISR_LUNAR_LAKE_2001 data were acquired during a flight over Lunar Lake, Nevada on June 30, 2001. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRMISR_MONTEREY_1999_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Monterey 1999 Campaign LARC_ASDC STAC Catalog 1999-06-29 1999-07-13 -123.3, 35.1, -120.9, 37.4 https://cmr.earthdata.nasa.gov/search/concepts/C1000000724-LARC_ASDC.umm_json The AIRMISR_MONTEREY_1999 data were acquired on June 29, 1999 during a field mission which focused on Monterey, California. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRMISR_MONTEREY_1999_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Monterey 1999 Campaign ALL STAC Catalog 1999-06-29 1999-07-13 -123.3, 35.1, -120.9, 37.4 https://cmr.earthdata.nasa.gov/search/concepts/C1000000724-LARC_ASDC.umm_json The AIRMISR_MONTEREY_1999 data were acquired on June 29, 1999 during a field mission which focused on Monterey, California. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
-AIRMISR_MORGAN_MONROE_2003_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Morgan Monore 2003 Campaign LARC_ASDC STAC Catalog 2003-08-19 2003-08-19 -86.76, 39.05, -86.03, 39.6 https://cmr.earthdata.nasa.gov/search/concepts/C1000000725-LARC_ASDC.umm_json The AIRMISR_MORGAN_MONROE_2003 data were acquired during a flight over the Morgan Monroe State Forest, Indiana, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 19, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There were a total of two runs during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction. proprietary
AIRMISR_MORGAN_MONROE_2003_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Morgan Monore 2003 Campaign ALL STAC Catalog 2003-08-19 2003-08-19 -86.76, 39.05, -86.03, 39.6 https://cmr.earthdata.nasa.gov/search/concepts/C1000000725-LARC_ASDC.umm_json The AIRMISR_MORGAN_MONROE_2003 data were acquired during a flight over the Morgan Monroe State Forest, Indiana, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 19, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There were a total of two runs during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction. proprietary
-AIRMISR_ROGERS_LAKE_2001_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Roger's Lake 2001 Campaign ALL STAC Catalog 2001-06-06 2001-06-06 -118.06, 34.75, -117.51, 35.33 https://cmr.earthdata.nasa.gov/search/concepts/C1000000705-LARC_ASDC.umm_json The AIRMISR_ROGERS_LAKE_2001 data were acquired during a flight over Roger's Lake, California on June 6, 2001. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
+AIRMISR_MORGAN_MONROE_2003_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Morgan Monore 2003 Campaign LARC_ASDC STAC Catalog 2003-08-19 2003-08-19 -86.76, 39.05, -86.03, 39.6 https://cmr.earthdata.nasa.gov/search/concepts/C1000000725-LARC_ASDC.umm_json The AIRMISR_MORGAN_MONROE_2003 data were acquired during a flight over the Morgan Monroe State Forest, Indiana, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 19, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There were a total of two runs during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction. proprietary
AIRMISR_ROGERS_LAKE_2001_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Roger's Lake 2001 Campaign LARC_ASDC STAC Catalog 2001-06-06 2001-06-06 -118.06, 34.75, -117.51, 35.33 https://cmr.earthdata.nasa.gov/search/concepts/C1000000705-LARC_ASDC.umm_json The AIRMISR_ROGERS_LAKE_2001 data were acquired during a flight over Roger's Lake, California on June 6, 2001. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
-AIRMISR_SAFARI_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Southern African Fire Atmosphere Research Initiative 2000 Field Campaign LARC_ASDC STAC Catalog 2000-09-06 2000-09-14 9.08, -24.69, 31.49, -15.18 https://cmr.earthdata.nasa.gov/search/concepts/C1000000726-LARC_ASDC.umm_json The AIRMISR_SAFARI data were acquired on September 6, 7, 13 and 14, 2000 during the SAFARI 2000 campaign. The Southern African Fire Atmosphere Research Initiative (SAFARI) 2000 field campaign focused on the smoke and gases released into the environment of southern Africa by industrial, biological and man-made sources such as biomass burning. The area of study included Botswana, Lesotho, Malawi, Mozambique, Namibia, South Africa, Swaziland, Zambia, and Zimbabwe. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
+AIRMISR_ROGERS_LAKE_2001_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Roger's Lake 2001 Campaign ALL STAC Catalog 2001-06-06 2001-06-06 -118.06, 34.75, -117.51, 35.33 https://cmr.earthdata.nasa.gov/search/concepts/C1000000705-LARC_ASDC.umm_json The AIRMISR_ROGERS_LAKE_2001 data were acquired during a flight over Roger's Lake, California on June 6, 2001. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRMISR_SAFARI_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Southern African Fire Atmosphere Research Initiative 2000 Field Campaign ALL STAC Catalog 2000-09-06 2000-09-14 9.08, -24.69, 31.49, -15.18 https://cmr.earthdata.nasa.gov/search/concepts/C1000000726-LARC_ASDC.umm_json The AIRMISR_SAFARI data were acquired on September 6, 7, 13 and 14, 2000 during the SAFARI 2000 campaign. The Southern African Fire Atmosphere Research Initiative (SAFARI) 2000 field campaign focused on the smoke and gases released into the environment of southern Africa by industrial, biological and man-made sources such as biomass burning. The area of study included Botswana, Lesotho, Malawi, Mozambique, Namibia, South Africa, Swaziland, Zambia, and Zimbabwe. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
-AIRMISR_SERC_2003_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the SERC 2003 Campaign LARC_ASDC STAC Catalog 2003-08-20 2003-08-20 -76.85, 38.6, -76.28, 39.06 https://cmr.earthdata.nasa.gov/search/concepts/C1000000727-LARC_ASDC.umm_json The AIRMISR_SERC_2003 data were acquired during a flight over the Smithsonian Environmental Research Center, Maryland, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 20, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There was a total of one run during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction. proprietary
+AIRMISR_SAFARI_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Southern African Fire Atmosphere Research Initiative 2000 Field Campaign LARC_ASDC STAC Catalog 2000-09-06 2000-09-14 9.08, -24.69, 31.49, -15.18 https://cmr.earthdata.nasa.gov/search/concepts/C1000000726-LARC_ASDC.umm_json The AIRMISR_SAFARI data were acquired on September 6, 7, 13 and 14, 2000 during the SAFARI 2000 campaign. The Southern African Fire Atmosphere Research Initiative (SAFARI) 2000 field campaign focused on the smoke and gases released into the environment of southern Africa by industrial, biological and man-made sources such as biomass burning. The area of study included Botswana, Lesotho, Malawi, Mozambique, Namibia, South Africa, Swaziland, Zambia, and Zimbabwe. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRMISR_SERC_2003_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the SERC 2003 Campaign ALL STAC Catalog 2003-08-20 2003-08-20 -76.85, 38.6, -76.28, 39.06 https://cmr.earthdata.nasa.gov/search/concepts/C1000000727-LARC_ASDC.umm_json The AIRMISR_SERC_2003 data were acquired during a flight over the Smithsonian Environmental Research Center, Maryland, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 20, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There was a total of one run during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction. proprietary
+AIRMISR_SERC_2003_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the SERC 2003 Campaign LARC_ASDC STAC Catalog 2003-08-20 2003-08-20 -76.85, 38.6, -76.28, 39.06 https://cmr.earthdata.nasa.gov/search/concepts/C1000000727-LARC_ASDC.umm_json The AIRMISR_SERC_2003 data were acquired during a flight over the Smithsonian Environmental Research Center, Maryland, USA, target as part of the AirMISR deployments from the Wallops Flight Facility during the August 2003 campaign. This particular flight took place on August 20, 2003. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. There was a total of one run during this flight. A run comprises data collected from nine view angles acquired on a fixed flight azimuth angle. Each data file from one run contains either: a) Level 1B1 Radiometric product from one of the 9 camera angles or b) Level 1B2 Georectified radiance product from one of the 9 camera angles. Browse images in PNG format are available for the Level 1B1 product and browse images in JPEG format are available for the Level 1B2 product. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). The Level 1 Radiometric product contains data that are scaled to convert the digital output of the cameras to radiances and are conditioned to remove instrument-dependent effects. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the detector elements of each spectral band. These data have a 7-meter spatial resolution at nadir and around 30-meter at the most oblique 70.5 degree angles. The Level 1 Georectified radiance product contains the Level 1 radiometric product resampled to a 27.5 meter spatial resolution and mapped into a standard Universal Transverse Mercator (UTM) map projection. Initially the data are registered to each camera angle and to the ground. This processing is necessary because the nine views of each point on the ground are not acquired simultaneously. Once the map grid center points are located in the AirMISR imagery through the process of georectification, a radiance value obtained from the surrounding AirMISR pixels is assigned to that map grid center. Bilinear interpolation is used as the basis for computing the new radiance. A UTM grid point falling somewhere in the image data will have up to 4 surrounding points. The bilinear interpolated value is obtained using the fractional distance of the interpolation point in the cross-track direction and the fractional distance in the along-track direction. proprietary
AIRMISR_SNOW_ICE_2001_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Snow and Ice 2001 Campaign LARC_ASDC STAC Catalog 2001-03-08 2001-03-08 -114.4, 36.27, -106.38, 40.75 https://cmr.earthdata.nasa.gov/search/concepts/C1000000728-LARC_ASDC.umm_json The AIRMISR_SNOW_ICE_2001 data were acquired during the Colorado snow albedo field experiment in the Yampa Valley of Colorado during February and March, 2001. This experiment focused on snow albedo and atmospheric characterization as part of a validation effort for estimating snow albedo from the Multiangle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data. The validation site is located at 40.4N, 106.8W, just south of Steamboat Springs, Colorado. AirMISR and MODIS Airborne Simulator (MAS) data were collected on March 8, 2001. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRMISR_SNOW_ICE_2001_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Snow and Ice 2001 Campaign ALL STAC Catalog 2001-03-08 2001-03-08 -114.4, 36.27, -106.38, 40.75 https://cmr.earthdata.nasa.gov/search/concepts/C1000000728-LARC_ASDC.umm_json The AIRMISR_SNOW_ICE_2001 data were acquired during the Colorado snow albedo field experiment in the Yampa Valley of Colorado during February and March, 2001. This experiment focused on snow albedo and atmospheric characterization as part of a validation effort for estimating snow albedo from the Multiangle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data. The validation site is located at 40.4N, 106.8W, just south of Steamboat Springs, Colorado. AirMISR and MODIS Airborne Simulator (MAS) data were collected on March 8, 2001. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
-AIRMISR_WISCONSIN_2000_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Wisconsin 2000 Campaign ALL STAC Catalog 2000-03-03 2000-03-03 -98, 35.9, -90.2, 43.9 https://cmr.earthdata.nasa.gov/search/concepts/C1000000729-LARC_ASDC.umm_json The AIRMISR_WISCONSIN_2000 data were acquired during a field mission which overflew Wisconsin and the Atmospheric Radiation Measurement/Program Cloud And Radiation Testbed (ARM/CART) site in Oklahoma on March 3, 2000. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRMISR_WISCONSIN_2000_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Wisconsin 2000 Campaign LARC_ASDC STAC Catalog 2000-03-03 2000-03-03 -98, 35.9, -90.2, 43.9 https://cmr.earthdata.nasa.gov/search/concepts/C1000000729-LARC_ASDC.umm_json The AIRMISR_WISCONSIN_2000 data were acquired during a field mission which overflew Wisconsin and the Atmospheric Radiation Measurement/Program Cloud And Radiation Testbed (ARM/CART) site in Oklahoma on March 3, 2000. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
+AIRMISR_WISCONSIN_2000_1 Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) Data from the Wisconsin 2000 Campaign ALL STAC Catalog 2000-03-03 2000-03-03 -98, 35.9, -90.2, 43.9 https://cmr.earthdata.nasa.gov/search/concepts/C1000000729-LARC_ASDC.umm_json The AIRMISR_WISCONSIN_2000 data were acquired during a field mission which overflew Wisconsin and the Atmospheric Radiation Measurement/Program Cloud And Radiation Testbed (ARM/CART) site in Oklahoma on March 3, 2000. The Jet Propulsion Laboratory (JPL) in Pasadena, California provided the data. The Airborne Multi-angle Imaging SpectroRadiometer (AirMISR) is an airborne instrument for obtaining multi-angle imagery similar to that of the satellite-borne Multi-angle Imaging SpectroRadiometer (MISR) instrument, which is designed to contribute to studies of the Earth's ecology and climate. AirMISR flies on the NASA ER-2 aircraft. The Jet Propulsion Laboratory in Pasadena, California built the instrument for NASA. Unlike the satellite-borne MISR instrument, which has nine cameras oriented at various angles, AirMISR uses a single camera in a pivoting gimbal mount. A data run by the ER-2 aircraft is divided into nine segments, each with the camera positioned to a MISR look angle. The gimbal rotates between successive segments, such that each segment acquires data over the same area on the ground as the previous segment. This process is repeated until all nine angles of the target area are collected. The swath width, which varies from 11 km in the nadir to 32 km at the most oblique angle, is governed by the camera's instantaneous field-of-view of 7 meters cross-track x 6 meters along-track in the nadir view and 21 meters x 55 meters at the most oblique angle. The along-track image length at each angle is dictated by the timing required to obtain overlap imagery at all angles, and varies from about 9 km in the nadir to 26 km at the most oblique angle. Thus, the nadir image dictates the area of overlap that is obtained from all nine angles. A complete flight run takes approximately 13 minutes. The 9 camera viewing angles are: 0 degrees or nadir 26.1 degrees, fore and aft 45.6 degrees, fore and aft 60.0 degrees, fore and aft 70.5 degrees, fore and aft. For each of the camera angles, images are obtained at 4 spectral bands. The spectral bands can be used to identify vegetation and aerosols, estimate surface reflectance and for ocean color studies. The center wavelengths of the 4 spectral bands are: 443 nanometers, blue 555 nanometers, green 670 nanometers, red 865 nanometers, near-infrared. Two types of AirMISR data products are available - the Level 1 Radiometric product (L1B1) and the Level 1 Georectified radiance product (L1B2). proprietary
AIRS2CCF_006 AIRS/Aqua L2 Cloud-Cleared Infrared Radiances (AIRS-only) V006 (AIRS2CCF) at GES DISC ALL STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477380-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRI2CCF. It is a new product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products, and many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRS2CCF_006 AIRS/Aqua L2 Cloud-Cleared Infrared Radiances (AIRS-only) V006 (AIRS2CCF) at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477380-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRI2CCF. It is a new product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products, and many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRS2CCF_7.0 Aqua/AIRS L2 Cloud-Cleared Infrared Radiances (AIRS-only) V7.0 at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805601-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in several AMSU channels started to increase significantly (since June 2007). Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products, and many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
-AIRS2CCF_NRT_006 AIRS/Aqua L2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) V006 (AIRS2CCF_NRT) at GES DISC ALL STAC Catalog 2016-10-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1345119267-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) product (AIRS2CCF_NRT_006) differs from the routine product (AIRS2CCF_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file. The AIRS2CCF_NRT_006 products are stored in files (often referred to as ""granules"") that contain 6 minutes of data, 30 footprints across track by 45 lines along track." proprietary
AIRS2CCF_NRT_006 AIRS/Aqua L2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) V006 (AIRS2CCF_NRT) at GES DISC GES_DISC STAC Catalog 2016-10-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1345119267-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) product (AIRS2CCF_NRT_006) differs from the routine product (AIRS2CCF_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file. The AIRS2CCF_NRT_006 products are stored in files (often referred to as ""granules"") that contain 6 minutes of data, 30 footprints across track by 45 lines along track." proprietary
+AIRS2CCF_NRT_006 AIRS/Aqua L2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) V006 (AIRS2CCF_NRT) at GES DISC ALL STAC Catalog 2016-10-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1345119267-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) product (AIRS2CCF_NRT_006) differs from the routine product (AIRS2CCF_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file. The AIRS2CCF_NRT_006 products are stored in files (often referred to as ""granules"") that contain 6 minutes of data, 30 footprints across track by 45 lines along track." proprietary
AIRS2CCF_NRT_7.0 Aqua/AIRS L2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) V7.0 at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805606-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) product (AIRS2CCF_NRT_7.0) differs from the routine product (AIRS2CCF_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file. The AIRS2CCF_NRT_7.0 products are stored in files (often referred to as ""granules"") that contain 6 minutes of data, 30 footprints across track by 45 lines along track." proprietary
-AIRS2RET_006 AIRS/Aqua L2 Standard Physical Retrieval (AIRS-only) V006 (AIRS2RET) at GES DISC ALL STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477381-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2RET. It is a new product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRS2RET_006 AIRS/Aqua L2 Standard Physical Retrieval (AIRS-only) V006 (AIRS2RET) at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477381-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2RET. It is a new product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
+AIRS2RET_006 AIRS/Aqua L2 Standard Physical Retrieval (AIRS-only) V006 (AIRS2RET) at GES DISC ALL STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477381-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2RET. It is a new product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRS2RET_7.0 Aqua/AIRS L2 Standard Physical Retrieval (AIRS-only) V7.0 at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805619-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2RET. It produced using AIRS IR only because the radiometric noise in several AMSU channels began increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km for AMSU, and 13.5 for an IR footprint. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
-AIRS2RET_NRT_006 AIRS/Aqua L2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) V006 (AIRS2RET_NRT) at GES DISC GES_DISC STAC Catalog 2016-10-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1345119345-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) product (AIRS2RET_NRT_006) differs from the routine product (AIRS2RET_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRS2RET_NRT_006 AIRS/Aqua L2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) V006 (AIRS2RET_NRT) at GES DISC ALL STAC Catalog 2016-10-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1345119345-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) product (AIRS2RET_NRT_006) differs from the routine product (AIRS2RET_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
+AIRS2RET_NRT_006 AIRS/Aqua L2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) V006 (AIRS2RET_NRT) at GES DISC GES_DISC STAC Catalog 2016-10-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1345119345-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) product (AIRS2RET_NRT_006) differs from the routine product (AIRS2RET_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRS2RET_NRT_7.0 Aqua/AIRS L2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) V7.0 at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805625-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) product (AIRS2RET_NRT_7.0) differs from the routine product (AIRS2RET_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in several AMSU channels started to increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
-AIRS2SPC_005 AIRS/Aqua L2 CO2 support retrieval (AIRS-only) V005 (AIRS2SPC) at GES DISC GES_DISC STAC Catalog 2010-01-01 2017-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477370-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Support Products include higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb. An AIRS granule has been set as 6 minutes of data. This normally corresponds to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data. The Level 2 retrieval products and climatology CO2 are assumed as the initial state for the Vanishing Partial Derivative (VPD) retrieval algorithm that separately determines the tropospheric CO2 mixing ratio. The AIRS Level 2 tropospheric CO2 product is the average of the solutions for a 2 x 2 array of adjacent AIRS Level 2 retrieval spots, covering a 90 km x 90 km area at nadir. Retrievals for which the solutions for the 2 x 2 arrays satisfy a spatial coherence QA that requires agreement of the separate retrievals to be within 2 ppm in an RMS sense are included in the standard product. Retrievals that fail this QA test are included in the support product. proprietary
AIRS2SPC_005 AIRS/Aqua L2 CO2 support retrieval (AIRS-only) V005 (AIRS2SPC) at GES DISC ALL STAC Catalog 2010-01-01 2017-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477370-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Support Products include higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb. An AIRS granule has been set as 6 minutes of data. This normally corresponds to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data. The Level 2 retrieval products and climatology CO2 are assumed as the initial state for the Vanishing Partial Derivative (VPD) retrieval algorithm that separately determines the tropospheric CO2 mixing ratio. The AIRS Level 2 tropospheric CO2 product is the average of the solutions for a 2 x 2 array of adjacent AIRS Level 2 retrieval spots, covering a 90 km x 90 km area at nadir. Retrievals for which the solutions for the 2 x 2 arrays satisfy a spatial coherence QA that requires agreement of the separate retrievals to be within 2 ppm in an RMS sense are included in the standard product. Retrievals that fail this QA test are included in the support product. proprietary
-AIRS2STC_005 AIRS/Aqua L2 CO2 in the free troposphere (AIRS-only) V005 (AIRS2STC) at GES DISC ALL STAC Catalog 2010-01-01 2017-03-01 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477371-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Carbon Dioxide (CO2) Standard Retrieval Product consists of retrieved estimates of CO2, plus estimates of the errors associated with the retrieval. In contrast to AIRX2RET, the horizontal resolution of this standard product is about 110 km (1x1 degree). An AIRS granule has been set as 6 minutes of data, 15 footprints cross track by 22 lines along track. proprietary
+AIRS2SPC_005 AIRS/Aqua L2 CO2 support retrieval (AIRS-only) V005 (AIRS2SPC) at GES DISC GES_DISC STAC Catalog 2010-01-01 2017-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477370-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Support Products include higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb. An AIRS granule has been set as 6 minutes of data. This normally corresponds to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data. The Level 2 retrieval products and climatology CO2 are assumed as the initial state for the Vanishing Partial Derivative (VPD) retrieval algorithm that separately determines the tropospheric CO2 mixing ratio. The AIRS Level 2 tropospheric CO2 product is the average of the solutions for a 2 x 2 array of adjacent AIRS Level 2 retrieval spots, covering a 90 km x 90 km area at nadir. Retrievals for which the solutions for the 2 x 2 arrays satisfy a spatial coherence QA that requires agreement of the separate retrievals to be within 2 ppm in an RMS sense are included in the standard product. Retrievals that fail this QA test are included in the support product. proprietary
AIRS2STC_005 AIRS/Aqua L2 CO2 in the free troposphere (AIRS-only) V005 (AIRS2STC) at GES DISC GES_DISC STAC Catalog 2010-01-01 2017-03-01 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477371-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Carbon Dioxide (CO2) Standard Retrieval Product consists of retrieved estimates of CO2, plus estimates of the errors associated with the retrieval. In contrast to AIRX2RET, the horizontal resolution of this standard product is about 110 km (1x1 degree). An AIRS granule has been set as 6 minutes of data, 15 footprints cross track by 22 lines along track. proprietary
-AIRS2SUP_006 AIRS/Aqua L2 Support Retrieval (AIRS-only) V006 (AIRS2SUP) at GES DISC ALL STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477382-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2SUP. It is a new product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
+AIRS2STC_005 AIRS/Aqua L2 CO2 in the free troposphere (AIRS-only) V005 (AIRS2STC) at GES DISC ALL STAC Catalog 2010-01-01 2017-03-01 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477371-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Carbon Dioxide (CO2) Standard Retrieval Product consists of retrieved estimates of CO2, plus estimates of the errors associated with the retrieval. In contrast to AIRX2RET, the horizontal resolution of this standard product is about 110 km (1x1 degree). An AIRS granule has been set as 6 minutes of data, 15 footprints cross track by 22 lines along track. proprietary
AIRS2SUP_006 AIRS/Aqua L2 Support Retrieval (AIRS-only) V006 (AIRS2SUP) at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477382-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2SUP. It is a new product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
+AIRS2SUP_006 AIRS/Aqua L2 Support Retrieval (AIRS-only) V006 (AIRS2SUP) at GES DISC ALL STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477382-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2SUP. It is a new product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRS2SUP_7.0 Aqua/AIRS L2 Support Retrieval (AIRS-only) V7.0 at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805630-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRX2SUP. It is produced using AIRS IR only because the radiometric noise in several AMSU channels started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
-AIRS2SUP_NRT_006 AIRS/Aqua L2 Near Real Time (NRT) Support Retrieval (AIRS-only) V006 (AIRS2SUP_NRT) at GES DISC GES_DISC STAC Catalog 2016-10-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1345119372-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Support Retrieval (AIRS-only) product (AIRS2SUP_NRT_006) differs from the routine product (AIRS2SUP_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 scanlines. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRS2SUP_NRT_006 AIRS/Aqua L2 Near Real Time (NRT) Support Retrieval (AIRS-only) V006 (AIRS2SUP_NRT) at GES DISC ALL STAC Catalog 2016-10-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1345119372-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Support Retrieval (AIRS-only) product (AIRS2SUP_NRT_006) differs from the routine product (AIRS2SUP_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 scanlines. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
+AIRS2SUP_NRT_006 AIRS/Aqua L2 Near Real Time (NRT) Support Retrieval (AIRS-only) V006 (AIRS2SUP_NRT) at GES DISC GES_DISC STAC Catalog 2016-10-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1345119372-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Support Retrieval (AIRS-only) product (AIRS2SUP_NRT_006) differs from the routine product (AIRS2SUP_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 scanlines. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRS2SUP_NRT_7.0 Aqua/AIRS L2 Near Real Time (NRT) Support Retrieval (AIRS-only) V7.0 at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805636-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Support Retrieval (AIRS-only) product (AIRS2SUP_NRT_7.0) differs from the routine product (AIRS2SUP_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is product produced using AIRS IR only because the radiometric noise in several AMSU channels started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 scanlines. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRS3C28_005 AIRS/Aqua L3 8-day CO2 in the free troposphere (AIRS-only) 2.5 degrees x 2 degrees V005 (AIRS3C28) at GES DISC GES_DISC STAC Catalog 2009-12-25 2017-02-21 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517256-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 8-day Gridded Retrieval, from the AIRS instrument on board of Aqua satellite. It is 8-day gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3, 8-day, Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers an 8-day period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the 8-day period. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean. proprietary
AIRS3C28_005 AIRS/Aqua L3 8-day CO2 in the free troposphere (AIRS-only) 2.5 degrees x 2 degrees V005 (AIRS3C28) at GES DISC ALL STAC Catalog 2009-12-25 2017-02-21 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517256-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 8-day Gridded Retrieval, from the AIRS instrument on board of Aqua satellite. It is 8-day gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3, 8-day, Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers an 8-day period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the 8-day period. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean. proprietary
-AIRS3C2D_005 AIRS/Aqua L3 daily CO2 in the free troposphere (AIRS-only) 2.5 degrees x 2 degrees V005 (AIRS3C2D) at GES DISC ALL STAC Catalog 2010-01-01 2017-02-28 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517258-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Daily Gridded Retrieval, from the AIRS instrument on board of Aqua satellite. It is daily gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3 daily Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers a 24-hour period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the period. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean. proprietary
AIRS3C2D_005 AIRS/Aqua L3 daily CO2 in the free troposphere (AIRS-only) 2.5 degrees x 2 degrees V005 (AIRS3C2D) at GES DISC GES_DISC STAC Catalog 2010-01-01 2017-02-28 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517258-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Daily Gridded Retrieval, from the AIRS instrument on board of Aqua satellite. It is daily gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3 daily Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers a 24-hour period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the period. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean. proprietary
+AIRS3C2D_005 AIRS/Aqua L3 daily CO2 in the free troposphere (AIRS-only) 2.5 degrees x 2 degrees V005 (AIRS3C2D) at GES DISC ALL STAC Catalog 2010-01-01 2017-02-28 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517258-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Daily Gridded Retrieval, from the AIRS instrument on board of Aqua satellite. It is daily gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3 daily Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers a 24-hour period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the period. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean. proprietary
AIRS3C2M_005 AIRS/Aqua L3 Monthly CO2 in the free troposphere (AIRS-only) 2.5 degrees x 2 degrees V005 (AIRS3C2M) at GES DISC ALL STAC Catalog 2010-01-01 2017-02-28 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517264-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Monthly Gridded Retrieval, from the AIRS instrument on board of Aqua satellite. It is a monthly gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This quantity is not a total column quantity because the sensitivity function of the AIRS mid-tropospheric CO2 retrieval system peaks over the altitude range 6-10 km. The quantity is what results when the true atmospheric CO2 profile is weighted, level-by-level, by the AIRS sensitivity function. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers a calendar month. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the month. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 degree longitude x 2 degree latitude grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean. proprietary
AIRS3C2M_005 AIRS/Aqua L3 Monthly CO2 in the free troposphere (AIRS-only) 2.5 degrees x 2 degrees V005 (AIRS3C2M) at GES DISC GES_DISC STAC Catalog 2010-01-01 2017-02-28 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517264-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Monthly Gridded Retrieval, from the AIRS instrument on board of Aqua satellite. It is a monthly gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This quantity is not a total column quantity because the sensitivity function of the AIRS mid-tropospheric CO2 retrieval system peaks over the altitude range 6-10 km. The quantity is what results when the true atmospheric CO2 profile is weighted, level-by-level, by the AIRS sensitivity function. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers a calendar month. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the month. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 degree longitude x 2 degree latitude grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean. proprietary
-AIRS3QP5_006 AIRS/Aqua L3 5-day Quantization in Physical Units (AIRS-only) 5 degrees x 5 degrees V006 (AIRS3QP5) at GES DISC GES_DISC STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517265-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS/Aqua Level 3 pentad quantization product is in physical units (AIRS Only). The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of five days. Pentads always start on the 1st, 6th, 11th, 16th, 21st, and 26th days of the month and may contain as few as 3 days of data or as much as 6 days. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution). proprietary
AIRS3QP5_006 AIRS/Aqua L3 5-day Quantization in Physical Units (AIRS-only) 5 degrees x 5 degrees V006 (AIRS3QP5) at GES DISC ALL STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517265-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS/Aqua Level 3 pentad quantization product is in physical units (AIRS Only). The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of five days. Pentads always start on the 1st, 6th, 11th, 16th, 21st, and 26th days of the month and may contain as few as 3 days of data or as much as 6 days. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution). proprietary
-AIRS3QPM_006 AIRS/Aqua L3 Monthly Quantization in Physical Units (AIRS-only) 5 degrees x 5 degrees V006 (AIRS3QPM) at GES DISC ALL STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517283-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS/Aqua Level 3 monthly quantization product is in physical units (AIRS Only). The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution). proprietary
+AIRS3QP5_006 AIRS/Aqua L3 5-day Quantization in Physical Units (AIRS-only) 5 degrees x 5 degrees V006 (AIRS3QP5) at GES DISC GES_DISC STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517265-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS/Aqua Level 3 pentad quantization product is in physical units (AIRS Only). The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of five days. Pentads always start on the 1st, 6th, 11th, 16th, 21st, and 26th days of the month and may contain as few as 3 days of data or as much as 6 days. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution). proprietary
AIRS3QPM_006 AIRS/Aqua L3 Monthly Quantization in Physical Units (AIRS-only) 5 degrees x 5 degrees V006 (AIRS3QPM) at GES DISC GES_DISC STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517283-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS/Aqua Level 3 monthly quantization product is in physical units (AIRS Only). The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution). proprietary
-AIRS3SP8_006 AIRS/Aqua L3 8-day Support Product (AIRS-only) 1 degree X 1 degree V006 (AIRS3SP8) at GES DISC GES_DISC STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517268-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
+AIRS3QPM_006 AIRS/Aqua L3 Monthly Quantization in Physical Units (AIRS-only) 5 degrees x 5 degrees V006 (AIRS3QPM) at GES DISC ALL STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517283-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS/Aqua Level 3 monthly quantization product is in physical units (AIRS Only). The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution). proprietary
AIRS3SP8_006 AIRS/Aqua L3 8-day Support Product (AIRS-only) 1 degree X 1 degree V006 (AIRS3SP8) at GES DISC ALL STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517268-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
+AIRS3SP8_006 AIRS/Aqua L3 8-day Support Product (AIRS-only) 1 degree X 1 degree V006 (AIRS3SP8) at GES DISC GES_DISC STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517268-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
AIRS3SPD_006 AIRS/Aqua L3 Daily Support Product (AIRS-only) 1 degree x 1 degree V006 (AIRS3SPD) at GES DISC ALL STAC Catalog 2002-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517272-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
AIRS3SPD_006 AIRS/Aqua L3 Daily Support Product (AIRS-only) 1 degree x 1 degree V006 (AIRS3SPD) at GES DISC GES_DISC STAC Catalog 2002-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517272-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
AIRS3SPD_7.0 Aqua/AIRS L3 Daily Support Product (AIRS-only) 1 degree x 1 degree V7.0 at GES DISC GES_DISC STAC Catalog 2002-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805657-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. The value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the box. proprietary
-AIRS3SPM_006 AIRS/Aqua L3 Monthly Support Product (AIRS-only) 1 degree x 1 degree V006 (AIRS3SPM) at GES DISC GES_DISC STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517285-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
AIRS3SPM_006 AIRS/Aqua L3 Monthly Support Product (AIRS-only) 1 degree x 1 degree V006 (AIRS3SPM) at GES DISC ALL STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517285-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
+AIRS3SPM_006 AIRS/Aqua L3 Monthly Support Product (AIRS-only) 1 degree x 1 degree V006 (AIRS3SPM) at GES DISC GES_DISC STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517285-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
AIRS3SPM_7.0 Aqua/AIRS L3 Monthly Support Product (AIRS-only) 1 degree x 1 degree V7.0 at GES DISC GES_DISC STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805668-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. The value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the box. proprietary
-AIRS3ST8_006 AIRS/Aqua L3 8-day Standard Physical Retrieval (AIRS-only) 1 degree X 1 degree V006 (AIRS3ST8) at GES DISC GES_DISC STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517287-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Only Level 3 8-Day Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers an 8-day period, or one-half of the Aqua orbit repeat cycle. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. proprietary
AIRS3ST8_006 AIRS/Aqua L3 8-day Standard Physical Retrieval (AIRS-only) 1 degree X 1 degree V006 (AIRS3ST8) at GES DISC ALL STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517287-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Only Level 3 8-Day Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers an 8-day period, or one-half of the Aqua orbit repeat cycle. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. proprietary
+AIRS3ST8_006 AIRS/Aqua L3 8-day Standard Physical Retrieval (AIRS-only) 1 degree X 1 degree V006 (AIRS3ST8) at GES DISC GES_DISC STAC Catalog 2002-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517287-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Only Level 3 8-Day Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers an 8-day period, or one-half of the Aqua orbit repeat cycle. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. proprietary
AIRS3STD_006 AIRS/Aqua L3 Daily Standard Physical Retrieval (AIRS-only) 1 degree x 1 degree V006 (AIRS3STD) at GES DISC GES_DISC STAC Catalog 2002-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517289-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Only Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts. Each file covers a temporal period of 24 hours for either the descending (equatorial crossing North to South @1:30 AM local time) or ascending (equatorial crossing South to North @1:30 PM local time) orbit. The data starts at the international dateline and progresses westward (as do the subsequent orbits of the satellite) so that neighboring gridded cells of data are no more than a swath of time apart (about 90 minutes). The two parts of a scan line crossing the dateline are included in separate L3 files, according to the date, so that data points in a grid box are always coincident in time. The edge of the AIRS Level 3 gridded cells is at the date line (the 180E/W longitude boundary). When plotted, this produces a map with 0 degrees longitude in the center of the image unless the bins are reordered. This method is preferred because the left (West) side of the image and the right (East) side of the image contain data farthest apart in time. The gridding scheme used by AIRS is the same as used by TOVS Pathfinder to create Level 3 products. The daily Level 3 products have gores between satellite paths where there is no coverage for that day. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. proprietary
AIRS3STD_006 AIRS/Aqua L3 Daily Standard Physical Retrieval (AIRS-only) 1 degree x 1 degree V006 (AIRS3STD) at GES DISC ALL STAC Catalog 2002-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517289-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Only Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts. Each file covers a temporal period of 24 hours for either the descending (equatorial crossing North to South @1:30 AM local time) or ascending (equatorial crossing South to North @1:30 PM local time) orbit. The data starts at the international dateline and progresses westward (as do the subsequent orbits of the satellite) so that neighboring gridded cells of data are no more than a swath of time apart (about 90 minutes). The two parts of a scan line crossing the dateline are included in separate L3 files, according to the date, so that data points in a grid box are always coincident in time. The edge of the AIRS Level 3 gridded cells is at the date line (the 180E/W longitude boundary). When plotted, this produces a map with 0 degrees longitude in the center of the image unless the bins are reordered. This method is preferred because the left (West) side of the image and the right (East) side of the image contain data farthest apart in time. The gridding scheme used by AIRS is the same as used by TOVS Pathfinder to create Level 3 products. The daily Level 3 products have gores between satellite paths where there is no coverage for that day. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. proprietary
AIRS3STD_7.0 Aqua/AIRS L3 Daily Standard Physical Retrieval (AIRS-only) 1 degree x 1 degree V7.0 at GES DISC GES_DISC STAC Catalog 2002-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805652-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts. Each file covers a temporal period of 24 hours for either the descending (equatorial crossing North to South at 1:30 AM local time) or ascending (equatorial crossing South to North at 1:30 PM local time) orbit. The data starts at the international dateline and progresses westward (as do the subsequent orbits of the satellite) so that neighboring gridded cells of data are no more than a swath of time apart (about 90 minutes). The two parts of a scan line crossing the dateline are included in separate L3 files, according to the date, so that data points in a grid box are always coincident in time. The edge of the AIRS Level 3 gridded cells is at the date line (the 180E/W longitude boundary). When plotted, this produces a map with 0 degrees longitude in the center of the image unless the bins are reordered. This method is preferred because the left (West) side of the image and the right (East) side of the image contain data farthest apart in time. The gridding scheme used by AIRS is the same as used by TOVS Pathfinder to create Level 3 products. The daily Level 3 products have gores between satellite paths where there is no coverage for that day. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. The value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the box. proprietary
@@ -2189,20 +2189,20 @@ AIRSAQIRL1B_8.0 AIRS/Aqua L1B Infrared (IR) geolocated and calibrated radiances
AIRSAQIRL1B_8.0 AIRS/Aqua L1B Infrared (IR) geolocated and calibrated radiances V8.0 (AIRSAQIRL1B) at GES DISC at GES DISC ALL STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3173400482-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1B data set contains AIRS calibrated and geolocated radiances in milliWatts/m^2/cm^-1/steradian for 2378 infrared channels in the 3.74 to 15.4 micron region of the spectrum. The AIRS Level 1B product consists of calibrated radiances, geolocation coordinates, quality control parameters, and calibration engineering support information. This product converts the AIRS raw data in digital counts to radiances referenced to SI (Système international) traceable standards established at NIST for each of the 2378 AIRS channels, and contains ancillary information pertaining to the instrument calibration and performance. In general, the differences between the previous version, V5, and V8 are extremely small and not significant for most applications, however the improvements may have relevance for certain climate applications. The differences also make the product more versatile and contains more information about the calibration that will be useful for future modifications. More information can be found in the AIRS Version 8 Level 1B ATBD and Test Report. The AIRS instrument is co-aligned with AMSU-A so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. The AIRSAQIRL1B_8 products are stored in files (often referred to as ""granules"") that contain 6 minutes of data, 90 footprints across track by 135 lines along track. " proprietary
AIRSAR_INT_JPG_1 AIRSAR_ALONGTRACK_INTERFEROMETRY_JPG ASF STAC Catalog 1998-10-25 2004-03-05 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213921626-ASF.umm_json AIRSAR along-track interferometric browse product JPG proprietary
AIRSAR_INT_JPG_1 AIRSAR_ALONGTRACK_INTERFEROMETRY_JPG ALL STAC Catalog 1998-10-25 2004-03-05 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213921626-ASF.umm_json AIRSAR along-track interferometric browse product JPG proprietary
-AIRSAR_NASA_JPL AirSAR Data and Images Database at NASA/JPL ALL STAC Catalog 1993-01-01 -130, 20, -65, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1214608235-SCIOPS.umm_json AirSAR is an airborne Synthetic Aperature Radar imaging radar instrument. AirSAR has been flown on many flights and is involved in many experiments. The AirSAR data and image database at NASA JPL contains survey and precision data as well as complex radar data. SAR radar imagery is also available from the AirSAR web site for a number of locations and time periods. The Survey, precision, and complex data sets consists of data in TOPSAR and POLSAR data modes from C-, L-, and P-band polarizations. See: & http://southport.jpl.nasa.gov/desc/AIRSdesc.html & for information on AirSAR and access to data and images. proprietary
AIRSAR_NASA_JPL AirSAR Data and Images Database at NASA/JPL SCIOPS STAC Catalog 1993-01-01 -130, 20, -65, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1214608235-SCIOPS.umm_json AirSAR is an airborne Synthetic Aperature Radar imaging radar instrument. AirSAR has been flown on many flights and is involved in many experiments. The AirSAR data and image database at NASA JPL contains survey and precision data as well as complex radar data. SAR radar imagery is also available from the AirSAR web site for a number of locations and time periods. The Survey, precision, and complex data sets consists of data in TOPSAR and POLSAR data modes from C-, L-, and P-band polarizations. See: & http://southport.jpl.nasa.gov/desc/AIRSdesc.html & for information on AirSAR and access to data and images. proprietary
+AIRSAR_NASA_JPL AirSAR Data and Images Database at NASA/JPL ALL STAC Catalog 1993-01-01 -130, 20, -65, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1214608235-SCIOPS.umm_json AirSAR is an airborne Synthetic Aperature Radar imaging radar instrument. AirSAR has been flown on many flights and is involved in many experiments. The AirSAR data and image database at NASA JPL contains survey and precision data as well as complex radar data. SAR radar imagery is also available from the AirSAR web site for a number of locations and time periods. The Survey, precision, and complex data sets consists of data in TOPSAR and POLSAR data modes from C-, L-, and P-band polarizations. See: & http://southport.jpl.nasa.gov/desc/AIRSdesc.html & for information on AirSAR and access to data and images. proprietary
AIRSAR_POL_3FP_1 AIRSAR_POLSAR_3_FREQ_POLARIMETRY ALL STAC Catalog 1990-03-02 2004-03-21 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213921661-ASF.umm_json AIRSAR three-frequency polarimetric frame product proprietary
AIRSAR_POL_3FP_1 AIRSAR_POLSAR_3_FREQ_POLARIMETRY ASF STAC Catalog 1990-03-02 2004-03-21 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213921661-ASF.umm_json AIRSAR three-frequency polarimetric frame product proprietary
AIRSAR_POL_SYN_3FP_1 AIRSAR_POLSAR_SYNOPTIC_3_FREQ_POLARIMETRY ASF STAC Catalog 1990-03-29 1991-07-16 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213928843-ASF.umm_json AIRSAR three-frequency polarimetric synoptic product proprietary
AIRSAR_POL_SYN_3FP_1 AIRSAR_POLSAR_SYNOPTIC_3_FREQ_POLARIMETRY ALL STAC Catalog 1990-03-29 1991-07-16 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213928843-ASF.umm_json AIRSAR three-frequency polarimetric synoptic product proprietary
-AIRSAR_TOP_C-DEM_STOKES_1 AIRSAR_TOPSAR_C-BAND_DEM_AND_STOKES ASF STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213927035-ASF.umm_json AIRSAR topographic SAR digital elevation model C_Stokes product proprietary
AIRSAR_TOP_C-DEM_STOKES_1 AIRSAR_TOPSAR_C-BAND_DEM_AND_STOKES ALL STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213927035-ASF.umm_json AIRSAR topographic SAR digital elevation model C_Stokes product proprietary
-AIRSAR_TOP_DEM_1 AIRSAR_TOPSAR_DEM ASF STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C179001730-ASF.umm_json AIRSAR topographic SAR digital elevation model product proprietary
+AIRSAR_TOP_C-DEM_STOKES_1 AIRSAR_TOPSAR_C-BAND_DEM_AND_STOKES ASF STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213927035-ASF.umm_json AIRSAR topographic SAR digital elevation model C_Stokes product proprietary
AIRSAR_TOP_DEM_1 AIRSAR_TOPSAR_DEM ALL STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C179001730-ASF.umm_json AIRSAR topographic SAR digital elevation model product proprietary
-AIRSAR_TOP_DEM_C_1 AIRSAR_TOPSAR_DEM_C ASF STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213925022-ASF.umm_json AIRSAR topographic SAR digital elevation model CTIF product proprietary
+AIRSAR_TOP_DEM_1 AIRSAR_TOPSAR_DEM ASF STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C179001730-ASF.umm_json AIRSAR topographic SAR digital elevation model product proprietary
AIRSAR_TOP_DEM_C_1 AIRSAR_TOPSAR_DEM_C ALL STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213925022-ASF.umm_json AIRSAR topographic SAR digital elevation model CTIF product proprietary
-AIRSAR_TOP_DEM_L_1 AIRSAR_TOPSAR_DEM_L ASF STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213926419-ASF.umm_json AIRSAR topographic SAR digital elevation model LTIF product proprietary
+AIRSAR_TOP_DEM_C_1 AIRSAR_TOPSAR_DEM_C ASF STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213925022-ASF.umm_json AIRSAR topographic SAR digital elevation model CTIF product proprietary
AIRSAR_TOP_DEM_L_1 AIRSAR_TOPSAR_DEM_L ALL STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213926419-ASF.umm_json AIRSAR topographic SAR digital elevation model LTIF product proprietary
+AIRSAR_TOP_DEM_L_1 AIRSAR_TOPSAR_DEM_L ASF STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213926419-ASF.umm_json AIRSAR topographic SAR digital elevation model LTIF product proprietary
AIRSAR_TOP_DEM_P_1 AIRSAR_TOPSAR_DEM_P ALL STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213926777-ASF.umm_json AIRSAR topographic SAR digital elevation model PTIF product proprietary
AIRSAR_TOP_DEM_P_1 AIRSAR_TOPSAR_DEM_P ASF STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213926777-ASF.umm_json AIRSAR topographic SAR digital elevation model PTIF product proprietary
AIRSAR_TOP_L-STOKES_1 AIRSAR_TOPSAR_L-BAND_STOKES ALL STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213927939-ASF.umm_json AIRSAR topographic SAR digital elevation model L_Stokes product proprietary
@@ -2210,49 +2210,49 @@ AIRSAR_TOP_L-STOKES_1 AIRSAR_TOPSAR_L-BAND_STOKES ASF STAC Catalog 1993-06-08 20
AIRSAR_TOP_P-STOKES_1 AIRSAR_TOPSAR_P-BAND_STOKES ALL STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213928209-ASF.umm_json AIRSAR topographic SAR digital elevation model P_Stokes product proprietary
AIRSAR_TOP_P-STOKES_1 AIRSAR_TOPSAR_P-BAND_STOKES ASF STAC Catalog 1993-06-08 2004-12-04 -172.880269, -27.388834, -49.704356, 69.25925 https://cmr.earthdata.nasa.gov/search/concepts/C1213928209-ASF.umm_json AIRSAR topographic SAR digital elevation model P_Stokes product proprietary
AIRSIL3MSOLR_6.1 Aqua AIRS Level 3 Spectral Outgoing Longwave Radiation (OLR) Monthly GES_DISC STAC Catalog 2002-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1697372449-GES_DISC.umm_json This L3 Spectral Outgoing Longwave Radiation (OLR) is derived using the AIRS radiances to compute spectral fluxes based on an algorithm developed by Xianglei Huang at the University of Michigan. It uses data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft. The Aqua AIRS Huang Level-3 Spectral OLR product contains OLR parameters derived from the AIRS version 6 data: all-sky and clear-sky OLR both spectrally resolved at 10 cm-1 bandwidth and integrated to a single value per grid square. This is monthly product on a 2x2 degree latitude/longitude grid. proprietary
-AIRSM_CPR_MAT_3.2 AIRS-AMSU variables-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRSM_CPR_MAT) at GES DISC GES_DISC STAC Catalog 2006-06-15 2012-12-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236224182-GES_DISC.umm_json "This is AIRS-CloudSat collocated subset, in NetCDF 4 format. These data contain collocated: AIRS/AMSU retrievals at AMSU footprints, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project. The basic task is to bring together retrievals of water vapor and cloud properties from multiple ""A-train"" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each ""scene"" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time ""matchups"" between pairs of instruments, and process years of satellite data to produce the climate data records. The short name for this collection is AIRSM_CPR_MAT Parameters contained in the data files include the following: Variable Name|Description|Units CH4_total_column|Retrieved total column CH4| (molecules/cm2) CloudFraction|CloudSat/CALIPSO Cloud Fraction| (None) CloudLayers| Number of hydrometeor layers| (count) clrolr|Clear-sky Outgoing Longwave Radiation|(Watts/m**2) CO_total_column|Retrieved total column CO| (molecules/cm2) CPR_Cloud_mask| CPR Cloud Mask |(None) Data_quality| Data Quality |(None) H2OMMRSat|Water vapor saturation mass mixing ratio|(gm/kg) H2OMMRStd|Water Vapor Mass Mixing Ratio |(gm/kg dry air) MODIS_Cloud_Fraction| MODIS 250m Cloud Fraction| (None) MODIS_scene_var |MODIS scene variability| (None) nSurfStd|1-based index of the first valid level|(None) O3VMRStd|Ozone Volume Mixing Ratio|(vmr) olr|All-sky Outgoing Longwave Radiation|(Watts/m**2) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) Sigma-Zero| Sigma-Zero| (dB*100) TAirMWOnlyStd|Atmospheric Temperature retrieved using only MW|(K) TCldTopStd|Cloud top temperature|(K) totH2OStd|Total precipitable water vapor| (kg/m**2) totO3Std|Total ozone burden| (Dobson) TSurfAir|Atmospheric Temperature at Surface|(K) TSurfStd|Surface skin temperature|(K) End of parameter information" proprietary
AIRSM_CPR_MAT_3.2 AIRS-AMSU variables-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRSM_CPR_MAT) at GES DISC ALL STAC Catalog 2006-06-15 2012-12-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236224182-GES_DISC.umm_json "This is AIRS-CloudSat collocated subset, in NetCDF 4 format. These data contain collocated: AIRS/AMSU retrievals at AMSU footprints, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project. The basic task is to bring together retrievals of water vapor and cloud properties from multiple ""A-train"" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each ""scene"" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time ""matchups"" between pairs of instruments, and process years of satellite data to produce the climate data records. The short name for this collection is AIRSM_CPR_MAT Parameters contained in the data files include the following: Variable Name|Description|Units CH4_total_column|Retrieved total column CH4| (molecules/cm2) CloudFraction|CloudSat/CALIPSO Cloud Fraction| (None) CloudLayers| Number of hydrometeor layers| (count) clrolr|Clear-sky Outgoing Longwave Radiation|(Watts/m**2) CO_total_column|Retrieved total column CO| (molecules/cm2) CPR_Cloud_mask| CPR Cloud Mask |(None) Data_quality| Data Quality |(None) H2OMMRSat|Water vapor saturation mass mixing ratio|(gm/kg) H2OMMRStd|Water Vapor Mass Mixing Ratio |(gm/kg dry air) MODIS_Cloud_Fraction| MODIS 250m Cloud Fraction| (None) MODIS_scene_var |MODIS scene variability| (None) nSurfStd|1-based index of the first valid level|(None) O3VMRStd|Ozone Volume Mixing Ratio|(vmr) olr|All-sky Outgoing Longwave Radiation|(Watts/m**2) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) Sigma-Zero| Sigma-Zero| (dB*100) TAirMWOnlyStd|Atmospheric Temperature retrieved using only MW|(K) TCldTopStd|Cloud top temperature|(K) totH2OStd|Total precipitable water vapor| (kg/m**2) totO3Std|Total ozone burden| (Dobson) TSurfAir|Atmospheric Temperature at Surface|(K) TSurfStd|Surface skin temperature|(K) End of parameter information" proprietary
+AIRSM_CPR_MAT_3.2 AIRS-AMSU variables-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRSM_CPR_MAT) at GES DISC GES_DISC STAC Catalog 2006-06-15 2012-12-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236224182-GES_DISC.umm_json "This is AIRS-CloudSat collocated subset, in NetCDF 4 format. These data contain collocated: AIRS/AMSU retrievals at AMSU footprints, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project. The basic task is to bring together retrievals of water vapor and cloud properties from multiple ""A-train"" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each ""scene"" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time ""matchups"" between pairs of instruments, and process years of satellite data to produce the climate data records. The short name for this collection is AIRSM_CPR_MAT Parameters contained in the data files include the following: Variable Name|Description|Units CH4_total_column|Retrieved total column CH4| (molecules/cm2) CloudFraction|CloudSat/CALIPSO Cloud Fraction| (None) CloudLayers| Number of hydrometeor layers| (count) clrolr|Clear-sky Outgoing Longwave Radiation|(Watts/m**2) CO_total_column|Retrieved total column CO| (molecules/cm2) CPR_Cloud_mask| CPR Cloud Mask |(None) Data_quality| Data Quality |(None) H2OMMRSat|Water vapor saturation mass mixing ratio|(gm/kg) H2OMMRStd|Water Vapor Mass Mixing Ratio |(gm/kg dry air) MODIS_Cloud_Fraction| MODIS 250m Cloud Fraction| (None) MODIS_scene_var |MODIS scene variability| (None) nSurfStd|1-based index of the first valid level|(None) O3VMRStd|Ozone Volume Mixing Ratio|(vmr) olr|All-sky Outgoing Longwave Radiation|(Watts/m**2) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) Sigma-Zero| Sigma-Zero| (dB*100) TAirMWOnlyStd|Atmospheric Temperature retrieved using only MW|(K) TCldTopStd|Cloud top temperature|(K) totH2OStd|Total precipitable water vapor| (kg/m**2) totO3Std|Total ozone burden| (Dobson) TSurfAir|Atmospheric Temperature at Surface|(K) TSurfStd|Surface skin temperature|(K) End of parameter information" proprietary
AIRS_CPR_IND_4.0 AIRS-CloudSat cloud mask and radar reflectivities collocation indexes V4.0 (AIRS_CPR_IND) at GES_DISC ALL STAC Catalog 2006-06-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236224151-GES_DISC.umm_json "Version 4.1 is the current version of the data set. Previous versions are no longer available and have been superseded by Version 4.1. This is AIRS-AMSU-CloudSat collocation indexes, in netCDF-4 format. These data map CloudSat profile indexes to the collocated AMSU field of views, and AIRS IR footprints, per AIRS 6-min granule time. Hence it can be considered as Level 1. These data are created within the frames of the MEaSUREs project. The basic task is to bring together retrievals of water vapor and cloud properties from multiple ""A-train"" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, & CloudSat), classify each ""scene"" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time ""matchups"" between pairs of instruments, and process years of satellite data to produce the climate data records. The short name for this collection is AIRS_CPR_IND" proprietary
AIRS_CPR_IND_4.0 AIRS-CloudSat cloud mask and radar reflectivities collocation indexes V4.0 (AIRS_CPR_IND) at GES_DISC GES_DISC STAC Catalog 2006-06-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236224151-GES_DISC.umm_json "Version 4.1 is the current version of the data set. Previous versions are no longer available and have been superseded by Version 4.1. This is AIRS-AMSU-CloudSat collocation indexes, in netCDF-4 format. These data map CloudSat profile indexes to the collocated AMSU field of views, and AIRS IR footprints, per AIRS 6-min granule time. Hence it can be considered as Level 1. These data are created within the frames of the MEaSUREs project. The basic task is to bring together retrievals of water vapor and cloud properties from multiple ""A-train"" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, & CloudSat), classify each ""scene"" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time ""matchups"" between pairs of instruments, and process years of satellite data to produce the climate data records. The short name for this collection is AIRS_CPR_IND" proprietary
-AIRS_CPR_MAT_3.2 AIRS-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRS_CPR_MAT) at GES DISC GES_DISC STAC Catalog 2006-06-15 2012-12-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236224153-GES_DISC.umm_json "This is AIRS-CloudSat collocated subset, in NetCDF-4 format. These data contain collocated: AIRS Level 1b radiances spectra, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project. The basic task is to bring together retrievals of water vapor and cloud properties from multiple ""A-train"" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each ""scene"" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time ""matchups"" between pairs of instruments, and process years of satellite data to produce the climate data records. The short name for this collection is AIRS_CPR_MAT Parameters contained in the data files include the following: Variable Name|Description|Units CldFrcStdErr|Cloud Fraction|(None) CloudLayers| Number of hydrometeor layers| (count) CPR_Cloud_mask| CPR Cloud Mask| (None) DEM_elevation| Digital Elevation Map| (m) dust_flag|Dust Flag|(None) latAIRS|AIRS IR latitude|(deg) Latitude|CloudSat Latitude |(degrees) LayerBase| Height of Layer Base| (m) LayerTop| Height of layer top| (m) lonAIRS|AIRS IR longitude|(deg) Longitude|CloudSat Longitude| (degrees) MODIS_cloud_flag| MOD35_bit_2and3_cloud_flag| (None) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) radiances|Radiances|(milliWatts/m**2/cm**-1/steradian) Sigma-Zero| Sigma-Zero| (dB*100) spectral_clear_indicator|Spectral Clear Indicator|(None) Vertical_binsize|CloudSat vertical binsize| (m) End of parameter information" proprietary
AIRS_CPR_MAT_3.2 AIRS-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRS_CPR_MAT) at GES DISC ALL STAC Catalog 2006-06-15 2012-12-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236224153-GES_DISC.umm_json "This is AIRS-CloudSat collocated subset, in NetCDF-4 format. These data contain collocated: AIRS Level 1b radiances spectra, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project. The basic task is to bring together retrievals of water vapor and cloud properties from multiple ""A-train"" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each ""scene"" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time ""matchups"" between pairs of instruments, and process years of satellite data to produce the climate data records. The short name for this collection is AIRS_CPR_MAT Parameters contained in the data files include the following: Variable Name|Description|Units CldFrcStdErr|Cloud Fraction|(None) CloudLayers| Number of hydrometeor layers| (count) CPR_Cloud_mask| CPR Cloud Mask| (None) DEM_elevation| Digital Elevation Map| (m) dust_flag|Dust Flag|(None) latAIRS|AIRS IR latitude|(deg) Latitude|CloudSat Latitude |(degrees) LayerBase| Height of Layer Base| (m) LayerTop| Height of layer top| (m) lonAIRS|AIRS IR longitude|(deg) Longitude|CloudSat Longitude| (degrees) MODIS_cloud_flag| MOD35_bit_2and3_cloud_flag| (None) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) radiances|Radiances|(milliWatts/m**2/cm**-1/steradian) Sigma-Zero| Sigma-Zero| (dB*100) spectral_clear_indicator|Spectral Clear Indicator|(None) Vertical_binsize|CloudSat vertical binsize| (m) End of parameter information" proprietary
+AIRS_CPR_MAT_3.2 AIRS-CloudSat cloud mask, radar reflectivities, and cloud classification matchups V3.2 (AIRS_CPR_MAT) at GES DISC GES_DISC STAC Catalog 2006-06-15 2012-12-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236224153-GES_DISC.umm_json "This is AIRS-CloudSat collocated subset, in NetCDF-4 format. These data contain collocated: AIRS Level 1b radiances spectra, CloudSat radar reflectivities, and MODIS cloud mask. These data are created within the frames of the MEaSUREs project. The basic task is to bring together retrievals of water vapor and cloud properties from multiple ""A-train"" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, CloudSat), classify each ""scene"" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time ""matchups"" between pairs of instruments, and process years of satellite data to produce the climate data records. The short name for this collection is AIRS_CPR_MAT Parameters contained in the data files include the following: Variable Name|Description|Units CldFrcStdErr|Cloud Fraction|(None) CloudLayers| Number of hydrometeor layers| (count) CPR_Cloud_mask| CPR Cloud Mask| (None) DEM_elevation| Digital Elevation Map| (m) dust_flag|Dust Flag|(None) latAIRS|AIRS IR latitude|(deg) Latitude|CloudSat Latitude |(degrees) LayerBase| Height of Layer Base| (m) LayerTop| Height of layer top| (m) lonAIRS|AIRS IR longitude|(deg) Longitude|CloudSat Longitude| (degrees) MODIS_cloud_flag| MOD35_bit_2and3_cloud_flag| (None) Radar_Reflectivity| Radar Reflectivity Factor| (dBZe) radiances|Radiances|(milliWatts/m**2/cm**-1/steradian) Sigma-Zero| Sigma-Zero| (dB*100) spectral_clear_indicator|Spectral Clear Indicator|(None) Vertical_binsize|CloudSat vertical binsize| (m) End of parameter information" proprietary
AIRS_MDS_IND_1.0 Aqua AIRS-MODIS Matchup Indexes V1.0 (AIRS_MDS_IND) at GES_DISC GES_DISC STAC Catalog 2003-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1385303964-GES_DISC.umm_json "This is Aqua AIRS-MODIS collocation indexes, in netCDF-4 format. These data map AIRS profile indexes to those of MODIS. The basic task is to bring together retrievals of water vapor and cloud properties from multiple ""A-train"" instruments (AIRS, AMSR-E, MODIS, AMSU, MLS, & CloudSat), classify each ""scene"" (instrument look) using the cloud information, and develop a merged, multi-sensor climatology of atmospheric water vapor as a function of altitude, stratified by the cloud classes. This is a large science analysis project that will require the use of SciFlo technologies to discover and organize all of the datasets, move and cache datasets as required, find space/time ""matchups"" between pairs of instruments, and process years of satellite data to produce the climate data records. The short name for this collections is AIRS_MDS_IND " proprietary
AIRS_MLS_IND_1.0 Aqua AIRS-MLS Matchup Indexes V1.0 (AIRS_MLS_IND) at GES_DISC GES_DISC STAC Catalog 2004-08-08 -180, -82, 180, 82 https://cmr.earthdata.nasa.gov/search/concepts/C1451934338-GES_DISC.umm_json This dataset is part of MEaSUREs 2012 Program, and represent Aqua/AIRS-Aura/MLS collocation indexes, in netCDF-4 format. These data map AIRS profile indexes to those of MLS. The A-Train provides water vapor (H2O) retrievals from both the Atmospheric Infrared Sounder (AIRS) and Microwave Limb Sounder (MLS). While AIRS loses sensitivity to H2O at the elevated portions of the upper troposphere (UT), MLS cannot detect H2O below 316 hPa. Therefore, to obtain a full profile of H2O in the whole column of air, this dataset manages to join the two products together by utilizing their own averaging kernels (AK). In doing so, the dataset builds a solid H2O of the whole column of air, which will help understand the H2O budget and many processes governing the humidity around the upper troposphere and lower stratosphere (UTLS). The short name for this collections is AIRS_MLS_IND proprietary
-AIRVBQAP_005 AIRS/Aqua L1B Visible/Near Infrared (VIS/NIR) quality assurance subset V005 (AIRVBQAP) at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477372-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Visible/Near Infrared (VIS/NIR) Level 1B QA Subset contains Quality Assurance (QA) parameters that a may use of filter AIRS VIS/NIR Level 1B radiance data to create a subset of analysis. It includes ""state"" that user should check before using any VIS/NIR Level 1B data radiance and ""glintlat"", ""glintlon"", and ""sun_glint_distant"" that users can use to check for possibility of solar glint contamination. AIRS VIS/NIR Level 1B radiance data can be found in AIRVBRAD." proprietary
AIRVBQAP_005 AIRS/Aqua L1B Visible/Near Infrared (VIS/NIR) quality assurance subset V005 (AIRVBQAP) at GES DISC ALL STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477372-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Visible/Near Infrared (VIS/NIR) Level 1B QA Subset contains Quality Assurance (QA) parameters that a may use of filter AIRS VIS/NIR Level 1B radiance data to create a subset of analysis. It includes ""state"" that user should check before using any VIS/NIR Level 1B data radiance and ""glintlat"", ""glintlon"", and ""sun_glint_distant"" that users can use to check for possibility of solar glint contamination. AIRS VIS/NIR Level 1B radiance data can be found in AIRVBRAD." proprietary
+AIRVBQAP_005 AIRS/Aqua L1B Visible/Near Infrared (VIS/NIR) quality assurance subset V005 (AIRVBQAP) at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477372-GES_DISC.umm_json "The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Visible/Near Infrared (VIS/NIR) Level 1B QA Subset contains Quality Assurance (QA) parameters that a may use of filter AIRS VIS/NIR Level 1B radiance data to create a subset of analysis. It includes ""state"" that user should check before using any VIS/NIR Level 1B data radiance and ""glintlat"", ""glintlon"", and ""sun_glint_distant"" that users can use to check for possibility of solar glint contamination. AIRS VIS/NIR Level 1B radiance data can be found in AIRVBRAD." proprietary
AIRVBQAP_NRT_005 AIRS/Aqua L1B Near Real Time (NRT) Visible/Near Infrared (VIS/NIR) quality assurance subset V005 (AIRVBQAP_NRT) at GES DISC GES_DISC STAC Catalog 2015-12-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233768983-GES_DISC.umm_json "The AIRS Level 1B Near Real Time (NRT) product (AIRVBQAP_NRT_005) differs from the routine product (AIRVBQAP_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) Visible/Near Infrared (VIS/NIR) instrument in combination with the AIRS Infrared Spectrometer, the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB) constitute an innovative atmospheric sounding group aboard the Earth Observing System (EOS) Aqua platform in a near-polar Sun-synchronous orbit with a 1:30 AM/PM equator crossing time and an ~705 km altitude. The AIRS Visible/Near Infrared (VIS/NIR) Level 1B QA Subset contains Quality Assurance (QA) parameters that a may use of filter AIRS VIS/NIR Level 1B radiance data to create a subset of analysis. It includes ""state"" that user should check before using any VIS/NIR Level 1B data radiance and ""glintlat"", ""glintlon"", and ""sun_glint_distant"" that users can use to check for possibility of solar glint contamination. AIRS VIS/NIR Level 1B radiance data can be found in AIRVBRAD." proprietary
AIRVBQAP_NRT_005 AIRS/Aqua L1B Near Real Time (NRT) Visible/Near Infrared (VIS/NIR) quality assurance subset V005 (AIRVBQAP_NRT) at GES DISC ALL STAC Catalog 2015-12-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233768983-GES_DISC.umm_json "The AIRS Level 1B Near Real Time (NRT) product (AIRVBQAP_NRT_005) differs from the routine product (AIRVBQAP_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) Visible/Near Infrared (VIS/NIR) instrument in combination with the AIRS Infrared Spectrometer, the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB) constitute an innovative atmospheric sounding group aboard the Earth Observing System (EOS) Aqua platform in a near-polar Sun-synchronous orbit with a 1:30 AM/PM equator crossing time and an ~705 km altitude. The AIRS Visible/Near Infrared (VIS/NIR) Level 1B QA Subset contains Quality Assurance (QA) parameters that a may use of filter AIRS VIS/NIR Level 1B radiance data to create a subset of analysis. It includes ""state"" that user should check before using any VIS/NIR Level 1B data radiance and ""glintlat"", ""glintlon"", and ""sun_glint_distant"" that users can use to check for possibility of solar glint contamination. AIRS VIS/NIR Level 1B radiance data can be found in AIRVBRAD." proprietary
AIRVBRAD_005 AIRS/Aqua L1B Visible/Near Infrared (VIS/NIR) geolocated and calibrated radiances V005 (AIRVBRAD) at GES DISC GES_DISC STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477373-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The VIS/NIR level 1B data set contains visible and near-infrared calibrated and geolocated radiances in W/m^2/micron/steradian. This data set includes 4 channels in the 0.4 to 1.0 um region of the spectrum. Each day of AIRS data are divided into 240 granules each of 6 minute duration. However, the VIS/NIR granules are only produced in the daytime so there will always be fewer VIS/NIR granules. The primary purpose of the VIS/NIR channels is the detection and flagging of significant inhomogeneities in the infrared field-of-view,which may adversely impact the quality of the temperature and moisture soundings. Therefore the VIS/NIR radiance product has a higher spatial resolution than the Infrared radiance product. Each VIS/NIR scan has 9 alongtrack footprints and 720 across track footprints. For ease in comparing with the infrared product which has 135 along track footprints and 90 across track footprints, the VIS/NIR product has additional dimensions to account for the 9 additional alongtrack and 8 additional across track footprints. proprietary
AIRVBRAD_005 AIRS/Aqua L1B Visible/Near Infrared (VIS/NIR) geolocated and calibrated radiances V005 (AIRVBRAD) at GES DISC ALL STAC Catalog 2002-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477373-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The VIS/NIR level 1B data set contains visible and near-infrared calibrated and geolocated radiances in W/m^2/micron/steradian. This data set includes 4 channels in the 0.4 to 1.0 um region of the spectrum. Each day of AIRS data are divided into 240 granules each of 6 minute duration. However, the VIS/NIR granules are only produced in the daytime so there will always be fewer VIS/NIR granules. The primary purpose of the VIS/NIR channels is the detection and flagging of significant inhomogeneities in the infrared field-of-view,which may adversely impact the quality of the temperature and moisture soundings. Therefore the VIS/NIR radiance product has a higher spatial resolution than the Infrared radiance product. Each VIS/NIR scan has 9 alongtrack footprints and 720 across track footprints. For ease in comparing with the infrared product which has 135 along track footprints and 90 across track footprints, the VIS/NIR product has additional dimensions to account for the 9 additional alongtrack and 8 additional across track footprints. proprietary
-AIRVBRAD_NRT_005 AIRS/Aqua L1B Near Real Time (NRT) Visible/Near Infrared (VIS/NIR) geolocated and calibrated radiances V005 (AIRVBRAD_NRT) at GES DISC ALL STAC Catalog 2018-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233768984-GES_DISC.umm_json "The AIRS Visible/Near Infrared (VIS/NIR) Level 1B Near Real Time (NRT) product (AIRVBRAD_NRT_005) differs from the routine product (AIRVBRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The AIRS VIS/NIR level 1B data set contains visible and near-infrared calibrated and geolocated radiances in W/m^2/micron/steradian for 4 channels in the 0.4 to 1.0 um region of the spectrum. The spectral range of the VIS/NIR channels are as follows: Channel 1 0.41 um - 0.44 um, Channel 2 0.58 um - 0.68 um, Channel 3 0.71 um - 0.92 um, Channel 4 0.49 um - 0.94 um. The AIRVBRAD_NRT_005 products are stored in files (often referred to as ""granules"") that contain 6 minutes of data, 90 footprints across track by 135 lines along track. The VIS/NIR granules are only produced in the daytime so there will always be fewer VIS/NIR granules than Infrared or microwave granules." proprietary
AIRVBRAD_NRT_005 AIRS/Aqua L1B Near Real Time (NRT) Visible/Near Infrared (VIS/NIR) geolocated and calibrated radiances V005 (AIRVBRAD_NRT) at GES DISC GES_DISC STAC Catalog 2018-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233768984-GES_DISC.umm_json "The AIRS Visible/Near Infrared (VIS/NIR) Level 1B Near Real Time (NRT) product (AIRVBRAD_NRT_005) differs from the routine product (AIRVBRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The AIRS VIS/NIR level 1B data set contains visible and near-infrared calibrated and geolocated radiances in W/m^2/micron/steradian for 4 channels in the 0.4 to 1.0 um region of the spectrum. The spectral range of the VIS/NIR channels are as follows: Channel 1 0.41 um - 0.44 um, Channel 2 0.58 um - 0.68 um, Channel 3 0.71 um - 0.92 um, Channel 4 0.49 um - 0.94 um. The AIRVBRAD_NRT_005 products are stored in files (often referred to as ""granules"") that contain 6 minutes of data, 90 footprints across track by 135 lines along track. The VIS/NIR granules are only produced in the daytime so there will always be fewer VIS/NIR granules than Infrared or microwave granules." proprietary
+AIRVBRAD_NRT_005 AIRS/Aqua L1B Near Real Time (NRT) Visible/Near Infrared (VIS/NIR) geolocated and calibrated radiances V005 (AIRVBRAD_NRT) at GES DISC ALL STAC Catalog 2018-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233768984-GES_DISC.umm_json "The AIRS Visible/Near Infrared (VIS/NIR) Level 1B Near Real Time (NRT) product (AIRVBRAD_NRT_005) differs from the routine product (AIRVBRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The AIRS VIS/NIR level 1B data set contains visible and near-infrared calibrated and geolocated radiances in W/m^2/micron/steradian for 4 channels in the 0.4 to 1.0 um region of the spectrum. The spectral range of the VIS/NIR channels are as follows: Channel 1 0.41 um - 0.44 um, Channel 2 0.58 um - 0.68 um, Channel 3 0.71 um - 0.92 um, Channel 4 0.49 um - 0.94 um. The AIRVBRAD_NRT_005 products are stored in files (often referred to as ""granules"") that contain 6 minutes of data, 90 footprints across track by 135 lines along track. The VIS/NIR granules are only produced in the daytime so there will always be fewer VIS/NIR granules than Infrared or microwave granules." proprietary
AIRX2RET_006 AIRS/Aqua L2 Standard Physical Retrieval (AIRS+AMSU) V006 (AIRX2RET) at GES DISC GES_DISC STAC Catalog 2002-08-30 2016-09-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477383-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities are also be part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRX2RET_006 AIRS/Aqua L2 Standard Physical Retrieval (AIRS+AMSU) V006 (AIRX2RET) at GES DISC ALL STAC Catalog 2002-08-30 2016-09-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477383-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities are also be part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRX2RET_7.0 Aqua/AIRS L2 Standard Physical Retrieval (AIRS+AMSU) V7.0 at GES DISC GES_DISC STAC Catalog 2002-08-30 2016-09-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805641-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS combination with the Advanced Microwave Sounding Unit (AMSU) constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities are also be part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
-AIRX2SPC_005 AIRS/Aqua L2 CO2 support retrieval (AIRS+AMSU) V005 (AIRX2SPC) at GES DISC ALL STAC Catalog 2002-09-01 2012-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477374-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. In particular, this support product focuses on the tropospheric CO2 retrieval. In general, AIRS Support Products include higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than in the Standard Product profiles. The horizontal resolution is 50 km. The intended users of the Support Product are researchers interested in generating forward radiance, or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data. This normally corresponds to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data. proprietary
AIRX2SPC_005 AIRS/Aqua L2 CO2 support retrieval (AIRS+AMSU) V005 (AIRX2SPC) at GES DISC GES_DISC STAC Catalog 2002-09-01 2012-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477374-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. In particular, this support product focuses on the tropospheric CO2 retrieval. In general, AIRS Support Products include higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than in the Standard Product profiles. The horizontal resolution is 50 km. The intended users of the Support Product are researchers interested in generating forward radiance, or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data. This normally corresponds to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data. proprietary
-AIRX2STC_005 AIRS/Aqua L2 CO2 in the free troposphere (AIRS+AMSU) V005 (AIRX2STC) at GES DISC ALL STAC Catalog 2002-09-01 2012-03-01 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477314-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Carbon Dioxide (CO2) Standard Retrieval Product consists of retrieved estimates of CO2, plus estimates of the errors associated with the retrieval. In contrast to AIRX2RET, the horizontal resolution of this standard product is about 110 km (1x1 degree). An AIRS granule has been set as 6 minutes of data, 15 footprints cross track by 22 lines along track. proprietary
+AIRX2SPC_005 AIRS/Aqua L2 CO2 support retrieval (AIRS+AMSU) V005 (AIRX2SPC) at GES DISC ALL STAC Catalog 2002-09-01 2012-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477374-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. In particular, this support product focuses on the tropospheric CO2 retrieval. In general, AIRS Support Products include higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than in the Standard Product profiles. The horizontal resolution is 50 km. The intended users of the Support Product are researchers interested in generating forward radiance, or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data. This normally corresponds to approximately 1/15 of an orbit but exactly 45 scanlines of AMSU-A data or 135 scanlines of AIRS and HSB data. proprietary
AIRX2STC_005 AIRS/Aqua L2 CO2 in the free troposphere (AIRS+AMSU) V005 (AIRX2STC) at GES DISC GES_DISC STAC Catalog 2002-09-01 2012-03-01 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477314-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Carbon Dioxide (CO2) Standard Retrieval Product consists of retrieved estimates of CO2, plus estimates of the errors associated with the retrieval. In contrast to AIRX2RET, the horizontal resolution of this standard product is about 110 km (1x1 degree). An AIRS granule has been set as 6 minutes of data, 15 footprints cross track by 22 lines along track. proprietary
-AIRX2SUP_006 AIRS/Aqua L2 Support Retrieval (AIRS+AMSU) V006 (AIRX2SUP) at GES DISC GES_DISC STAC Catalog 2002-08-30 2016-09-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477317-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than in the Standard Product profiles. The horizontal resolution is 50 km. The intended users of the Support Product are researchers interested in generating forward radiance, or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
+AIRX2STC_005 AIRS/Aqua L2 CO2 in the free troposphere (AIRS+AMSU) V005 (AIRX2STC) at GES DISC ALL STAC Catalog 2002-09-01 2012-03-01 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477314-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Carbon Dioxide (CO2) Standard Retrieval Product consists of retrieved estimates of CO2, plus estimates of the errors associated with the retrieval. In contrast to AIRX2RET, the horizontal resolution of this standard product is about 110 km (1x1 degree). An AIRS granule has been set as 6 minutes of data, 15 footprints cross track by 22 lines along track. proprietary
AIRX2SUP_006 AIRS/Aqua L2 Support Retrieval (AIRS+AMSU) V006 (AIRX2SUP) at GES DISC ALL STAC Catalog 2002-08-30 2016-09-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477317-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than in the Standard Product profiles. The horizontal resolution is 50 km. The intended users of the Support Product are researchers interested in generating forward radiance, or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
+AIRX2SUP_006 AIRS/Aqua L2 Support Retrieval (AIRS+AMSU) V006 (AIRX2SUP) at GES DISC GES_DISC STAC Catalog 2002-08-30 2016-09-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477317-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than in the Standard Product profiles. The horizontal resolution is 50 km. The intended users of the Support Product are researchers interested in generating forward radiance, or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRX2SUP_7.0 Aqua/AIRS L2 Support Retrieval (AIRS+AMSU) V7.0 at GES DISC GES_DISC STAC Catalog 2002-08-30 2016-09-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701828243-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU), AIRS constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product plus intermediate output (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 pressure levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than in the Standard Product profiles. The horizontal resolution is 50 km. The intended users of the Support Product are researchers interested in generating forward radiance, or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. proprietary
AIRX3C28_005 AIRS/Aqua L3 8-day CO2 in the free troposphere (AIRS+AMSU) 2.5 degrees x 2 degrees V005 (AIRX3C28) at GES DISC GES_DISC STAC Catalog 2002-09-01 2012-02-25 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517303-GES_DISC.umm_json Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 8-day Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is 8-day gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3, 8-day, Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers an 8-day period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the 8-day period. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean. proprietary
AIRX3C28_005 AIRS/Aqua L3 8-day CO2 in the free troposphere (AIRS+AMSU) 2.5 degrees x 2 degrees V005 (AIRX3C28) at GES DISC ALL STAC Catalog 2002-09-01 2012-02-25 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517303-GES_DISC.umm_json Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 8-day Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is 8-day gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3, 8-day, Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers an 8-day period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the 8-day period. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean. proprietary
AIRX3C2D_005 AIRS/Aqua L3 daily CO2 in the free troposphere (AIRS+AMSU) 2.5 degrees x 2 degrees V005 (AIRX3C2D) at GES DISC ALL STAC Catalog 2002-09-01 2012-02-29 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517305-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Daily Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is daily gridded data at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3 daily Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers a 24-hour period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the period. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean. proprietary
AIRX3C2D_005 AIRS/Aqua L3 daily CO2 in the free troposphere (AIRS+AMSU) 2.5 degrees x 2 degrees V005 (AIRX3C2D) at GES DISC GES_DISC STAC Catalog 2002-09-01 2012-02-29 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517305-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Daily Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is daily gridded data at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3 daily Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers a 24-hour period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the period. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean. proprietary
-AIRX3C2M_005 AIRS/Aqua L3 Monthly CO2 in the free troposphere (AIRS+AMSU) 2.5 degrees x 2 degrees V005 (AIRX3C2M) at GES DISC GES_DISC STAC Catalog 2002-09-01 2012-02-29 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517293-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Monthly Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is monthly gridded data at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This quantity is not a total column quantity because the sensitivity function of the AIRS mid-tropospheric CO2 retrieval system peaks over the altitude range 6-10 km. The quantity is what results when the true atmospheric CO2 profile is weighted, level-by-level, by the AIRS sensitivity function. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers a calendar month. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the month. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean. proprietary
AIRX3C2M_005 AIRS/Aqua L3 Monthly CO2 in the free troposphere (AIRS+AMSU) 2.5 degrees x 2 degrees V005 (AIRX3C2M) at GES DISC ALL STAC Catalog 2002-09-01 2012-02-29 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517293-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Monthly Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is monthly gridded data at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This quantity is not a total column quantity because the sensitivity function of the AIRS mid-tropospheric CO2 retrieval system peaks over the altitude range 6-10 km. The quantity is what results when the true atmospheric CO2 profile is weighted, level-by-level, by the AIRS sensitivity function. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers a calendar month. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the month. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean. proprietary
-AIRX3QP5_006 AIRS/Aqua L3 5-day Quantization in Physical Units (AIRS+AMSU) 5 degrees x 5 degrees V006 (AIRX3QP5) at GES DISC ALL STAC Catalog 2002-09-01 2016-09-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517308-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of five days from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. proprietary
+AIRX3C2M_005 AIRS/Aqua L3 Monthly CO2 in the free troposphere (AIRS+AMSU) 2.5 degrees x 2 degrees V005 (AIRX3C2M) at GES DISC GES_DISC STAC Catalog 2002-09-01 2012-02-29 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517293-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Monthly Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is monthly gridded data at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This quantity is not a total column quantity because the sensitivity function of the AIRS mid-tropospheric CO2 retrieval system peaks over the altitude range 6-10 km. The quantity is what results when the true atmospheric CO2 profile is weighted, level-by-level, by the AIRS sensitivity function. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers a calendar month. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the month. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean. proprietary
AIRX3QP5_006 AIRS/Aqua L3 5-day Quantization in Physical Units (AIRS+AMSU) 5 degrees x 5 degrees V006 (AIRX3QP5) at GES DISC GES_DISC STAC Catalog 2002-09-01 2016-09-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517308-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of five days from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. proprietary
-AIRX3QPM_006 AIRS/Aqua L3 Monthly Quantization in Physical Units (AIRS+AMSU) 5 degrees x 5 degrees V006 (AIRX3QPM) at GES DISC ALL STAC Catalog 2002-09-01 2016-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517296-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution). proprietary
+AIRX3QP5_006 AIRS/Aqua L3 5-day Quantization in Physical Units (AIRS+AMSU) 5 degrees x 5 degrees V006 (AIRX3QP5) at GES DISC ALL STAC Catalog 2002-09-01 2016-09-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517308-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of five days from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. proprietary
AIRX3QPM_006 AIRS/Aqua L3 Monthly Quantization in Physical Units (AIRS+AMSU) 5 degrees x 5 degrees V006 (AIRX3QPM) at GES DISC GES_DISC STAC Catalog 2002-09-01 2016-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517296-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution). proprietary
-AIRX3SP8_006 AIRS/Aqua L3 8-day Support Multiday Product (AIRS+AMSU) 1 degree x 1 degree V006 (AIRX3SP8) at GES DISC GES_DISC STAC Catalog 2002-09-01 2016-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517314-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
+AIRX3QPM_006 AIRS/Aqua L3 Monthly Quantization in Physical Units (AIRS+AMSU) 5 degrees x 5 degrees V006 (AIRX3QPM) at GES DISC ALL STAC Catalog 2002-09-01 2016-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517296-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The quantization products (QP) are distributional summaries derived from the Level-2 standard retrieval products (of swath type) to provide a more comprehensive set of statistical summaries than the traditional means and standard deviation. The QP products combine the Level 2 standard data parameters over grid cells of 5 x 5 deg spatial extent for temporal periods of a month. They preserve the multivariate distributional features of the original data and so provide a compressed data set that more accurately describes the disparate atmospheric states that is in the original Level-2 swath data set. The geophysical parameters are: Air Temperature and Water Vapor profiles (11 levels/layers), Cloud fraction (vertical distribution). proprietary
AIRX3SP8_006 AIRS/Aqua L3 8-day Support Multiday Product (AIRS+AMSU) 1 degree x 1 degree V006 (AIRX3SP8) at GES DISC ALL STAC Catalog 2002-09-01 2016-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517314-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
+AIRX3SP8_006 AIRS/Aqua L3 8-day Support Multiday Product (AIRS+AMSU) 1 degree x 1 degree V006 (AIRX3SP8) at GES DISC GES_DISC STAC Catalog 2002-09-01 2016-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517314-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
AIRX3SPD_006 AIRS/Aqua L3 Daily Support Product (AIRS+AMSU) 1 degree x 1 degree V006 (AIRX3SPD) at GES DISC GES_DISC STAC Catalog 2002-08-31 2016-09-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517317-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
AIRX3SPD_006 AIRS/Aqua L3 Daily Support Product (AIRS+AMSU) 1 degree x 1 degree V006 (AIRX3SPD) at GES DISC ALL STAC Catalog 2002-08-31 2016-09-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517317-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
AIRX3SPD_7.0 Aqua/AIRS L3 Daily Support Product (AIRS+AMSU) 1 degree x 1 degree V7.0 at GES DISC GES_DISC STAC Catalog 2002-08-31 2016-09-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805677-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) AIRS constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. The value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the box. proprietary
-AIRX3SPM_006 AIRS/Aqua L3 Monthly Support Product (AIRS+AMSU) 1 degree x 1 degree V006 (AIRX3SPM) at GES DISC ALL STAC Catalog 2002-09-01 2016-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517340-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
AIRX3SPM_006 AIRS/Aqua L3 Monthly Support Product (AIRS+AMSU) 1 degree x 1 degree V006 (AIRX3SPM) at GES DISC GES_DISC STAC Catalog 2002-09-01 2016-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517340-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
+AIRX3SPM_006 AIRS/Aqua L3 Monthly Support Product (AIRS+AMSU) 1 degree x 1 degree V006 (AIRX3SPM) at GES DISC ALL STAC Catalog 2002-09-01 2016-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517340-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. proprietary
AIRX3SPM_7.0 Aqua/AIRS L3 Monthly Support Product (AIRS+AMSU) 1 degree x 1 degree V7.0 at GES DISC GES_DISC STAC Catalog 2002-09-01 2016-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805687-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) AIRS constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included. The value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the box. proprietary
AIRX3ST8_006 AIRS/Aqua L3 8-day Standard Physical Retrieval (AIRS+AMSU) 1 degree x 1 degree V006 (AIRX3ST8) at GES DISC ALL STAC Catalog 2002-09-01 2016-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517323-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Level 3 8-Day Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers an 8-day period, or one-half of the Aqua orbit repeat cycle. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. proprietary
AIRX3ST8_006 AIRS/Aqua L3 8-day Standard Physical Retrieval (AIRS+AMSU) 1 degree x 1 degree V006 (AIRX3ST8) at GES DISC GES_DISC STAC Catalog 2002-09-01 2016-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1238517323-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Level 3 8-Day Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers an 8-day period, or one-half of the Aqua orbit repeat cycle. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. proprietary
@@ -2264,8 +2264,8 @@ AIRX3STM_006 AIRS/Aqua L3 Monthly Standard Physical Retrieval (AIRS+AMSU) 1 degr
AIRX3STM_7.0 Aqua/AIRS L3 Monthly Standard Physical Retrieval (AIRS+AMSU) 1 degree x 1 degree V7.0 at GES DISC GES_DISC STAC Catalog 2002-09-01 2016-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1701805681-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) AIRS constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. proprietary
AIRXAMAP_005 AIRS/Aqua Granule map product V005 (AIRXAMAP) at GES DISC GES_DISC STAC Catalog 2002-05-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233769004-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. The AIRS Granule Map Product consists of images of granule coverage in PDF and JPG format. The images are daily ones but updated every 6 minutes to capture any new available granule. Granules are assembled by ascending, descending, in north and south hemisphere, and the maps are in global cylindrical projection and satellite projection for better view. proprietary
AIRXAMAP_005 AIRS/Aqua Granule map product V005 (AIRXAMAP) at GES DISC ALL STAC Catalog 2002-05-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233769004-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. The AIRS Granule Map Product consists of images of granule coverage in PDF and JPG format. The images are daily ones but updated every 6 minutes to capture any new available granule. Granules are assembled by ascending, descending, in north and south hemisphere, and the maps are in global cylindrical projection and satellite projection for better view. proprietary
-AIRXBCAL_005 AIRS/Aqua L1B Calibration subset V005 (AIRXBCAL) at GES DISC ALL STAC Catalog 2002-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477315-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS/Aqua Level-1B calibration subset including clear cases, special calibration sites, random nadir spots, and high clouds. The AIRS Visible/Near Infrared (VIS/NIR) level 1B data set contains AIRS visible and near-infrared calibrated and geolocated radiances in W/m^2/micron/steradian. This data set is generated from AIRS level 1A digital numbers (DN), including 4 channels in the 0.4 to 1.0 um region of the spectrum. proprietary
AIRXBCAL_005 AIRS/Aqua L1B Calibration subset V005 (AIRXBCAL) at GES DISC GES_DISC STAC Catalog 2002-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477315-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS/Aqua Level-1B calibration subset including clear cases, special calibration sites, random nadir spots, and high clouds. The AIRS Visible/Near Infrared (VIS/NIR) level 1B data set contains AIRS visible and near-infrared calibrated and geolocated radiances in W/m^2/micron/steradian. This data set is generated from AIRS level 1A digital numbers (DN), including 4 channels in the 0.4 to 1.0 um region of the spectrum. proprietary
+AIRXBCAL_005 AIRS/Aqua L1B Calibration subset V005 (AIRXBCAL) at GES DISC ALL STAC Catalog 2002-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243477315-GES_DISC.umm_json The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS/Aqua Level-1B calibration subset including clear cases, special calibration sites, random nadir spots, and high clouds. The AIRS Visible/Near Infrared (VIS/NIR) level 1B data set contains AIRS visible and near-infrared calibrated and geolocated radiances in W/m^2/micron/steradian. This data set is generated from AIRS level 1A digital numbers (DN), including 4 channels in the 0.4 to 1.0 um region of the spectrum. proprietary
AIS_1968_borehole_1 Amery Ice Shelf Borehole Measurements in 1968 AU_AADC STAC Catalog 1968-08-30 1969-01-31 65, -74, 74, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214305647-AU_AADC.umm_json In 1968 the Australian Antarctic Division had a group of four men spend over a year on the Amery Ice Shelf. As part of their program of work, they drilled a number of boreholes on the shelf and took temperature readings at various depths down the hole. Stratigraphy notes and density measurements were also made on the holes drilled. The records of the recorded temperatures, notes on the temperature probes used, stratigraphy and density measurements, and a few notes on the work carried out, have been archived at the Australian Antarctic Division. Logbook(s): Glaciology Borehole Measurements, Amery 1968 - Borehole tempurature readings Glaciology Borehole Logs, Amery 1968 - Stratigraphy and density measurements AMERY ICE SHELF PARTY - February 1968 to February 1969 all IN on V2(67-68) all OUT on V2(68-69) Officer-in-Charge: Maxwell John Corry Medical Officer: Julian R Sansom Engineer(Electronics): Alan H F Nickols Senior Diesel Mechanic: Neville Joseph Collins Conducted a glaciological program including ice drilling and surveying the Lambert Glacier proprietary
AIS_1968_met_obs_1 Amery Ice Shelf Weather Observations in 1968 AU_AADC STAC Catalog 1968-01-01 1968-12-31 65, -74, 74, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214305648-AU_AADC.umm_json In 1968 the Australian Antarctic Division had a group of four men spend over a year on the Amery Ice Shelf. As part of their program of work, they took regular (usually daily) records of the weather where they were located. Observations included wind speed, wind direction, temperature, air pressure, cloud cover, and general notes about the weather condition. All log books have been archived at the Australian Antarctic Division. Copies of the document details forms for the logbooks are available for download from the provided URL. AMERY ICE SHELF PARTY - February 1968 to February 1969 all IN on V2(67-68) all OUT on V2(68-69) Officer-in-Charge: Maxwell John Corry Medical Officer: Julian R Sansom Engineer(Electronics): Alan H F Nickols Senior Diesel Mechanic: Neville Joseph Collins Conducted a glaciological programme including ice drilling and surveying the Lambert Glacier proprietary
AIS_1968_traverse_1 Amery Ice Shelf 1968 Traverse Reports and Logs AU_AADC STAC Catalog 1968-01-27 1969-02-18 65, -74, 74, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214305701-AU_AADC.umm_json In 1968 the Australian Antarctic Division had a team of four people spend just over a year on the Amery Ice Shelf, undertaking a range of studies including ice velocity determination (horizontal and vertical), ice thickness and surface profile measurements, snow accumulation studies, investigation of temperature, density and crystal structure of the ice, continuous measurements of wind velocities, air and snow temperatures, air pressure, humidity, and radiation. Many journals were kept during the traverse, detailing general daily activities carried out, but also including assorted measurements and observations that weren't specifically recorded in stand-alone logs. All logbooks have been archived at the Australian Antarctic Division. Copies of the document details forms for the logbooks is available for download from the provided URL. AMERY ICE SHELF PARTY - February 1968 to February 1969 all IN on V2(67-68) all OUT on V2(68-69) Officer-in-Charge: Maxwell John Corry Medical Officer: Julian R Sansom Engineer(Electronics): Alan H F Nickols Senior Diesel Mechanic: Neville Joseph Collins Conducted a glaciological program including ice drilling and surveying the Lambert Glacier proprietary
@@ -2278,18 +2278,18 @@ AJAX_MMS_1 Alpha Jet Atmopsheric eXperiment Meteorological Measurement System (M
AJAX_O3_1 Alpha Jet Atmospheric eXperiment Ozone Data LARC_ASDC STAC Catalog 2011-02-01 -125, 34, -114, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2166631705-LARC_ASDC.umm_json The Alpha Jet Atmospheric eXperiment (AJAX) is a partnership between NASA's Ames Research Center and H211, L.L.C., facilitating routine in-situ measurements over California, Nevada, and the coastal Pacific in support of satellite validation. The standard payload complement includes rigorously-calibrated ozone (O3), formaldehyde (HCHO), carbon dioxide (CO2), and methane (CH4) mixing ratios, as well as meteorological data including 3-D winds. Multiple vertical profiles (to ~8.5 km) can be accomplished in each 2-hr flight. The AJAX project has been collecting trace gas data on a regular basis in all seasons for over a decade, helping to assess satellite sensors' health and calibration over significant portions of their lifetimes, and complementing surface and tower-based observations collected elsewhere in the region. AJAX supports NASA's Orbiting Carbon Observatory (OCO-2/3) and Japan's Greenhouse Gases Observing Satellite (GOSAT) and GOSAT-2, and collaborates with many other research organizations (e.g. California Air Resources Board (CARB), NOAA, United States Forest Service (USFS), Environmental Protection Agency (EPA)). AJAX celebrated its 200th science flight in 2016, and previous studies have investigated topics as varied as stratospheric-to-tropospheric transport, forest fire plumes, atmospheric river events, long-range transport of pollution from Asia to the western US, urban outflow, and emissions from gas leaks, oil fields, and dairies. proprietary
AKFED_V1_1282_1 CARVE: Alaskan Fire Emissions Database (AKFED), 2001-2013 ORNL_CLOUD STAC Catalog 2001-01-01 2013-12-31 -168.5, 58, -141, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C2236222661-ORNL_CLOUD.umm_json This data set provides estimates of annual carbon emissions (kg carbon per square meter) from boreal fires at 450-m resolution for the state of Alaska between 2001 and 2013. To produce these data, daily burned area for 2001 to 2013 was mapped using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) combined with perimeters from the Alaska Large Fire Database. Carbon consumption was calibrated using available field measurements from black spruce forests in Alaska. Above- and below-ground carbon consumption were modeled based on environmental variables including elevation, day of burning within the fire season, pre-fire tree cover and the differenced normalized burn ratio (dNBR). Modeled uncertainties in carbon consumption are included in the data set. The derived burn area and carbon emissions product, referred to as the Alaskan Fire Emissions Database (AKFED), provides a resource for study of the environmental controls on daily fire dynamics, boreal fire emissions in biogeochemical models, and potential feedbacks from changing fire regimes. proprietary
AKWANAVT_0 Measurements taken in the Aegean and Black seas onboard the R/V Akwanavt OB_DAAC STAC Catalog 1997-10-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360095-OB_DAAC.umm_json Measurements taken in the Aegean and Black seas during 1997 onboard the R/V Akwanavt. proprietary
-AK_AVHRR Alaska AVHRR Twice-Monthly Composites USGS_LTA STAC Catalog 1990-06-16 -179, 51, -116, 70 https://cmr.earthdata.nasa.gov/search/concepts/C1220565954-USGS_LTA.umm_json The goal of the Alaska Advanced Very High Resolution Radiometer (AVHRR) project is to compile a time series data set of calibrated, georegistered daily observations and twice-monthly maximum normalized difference vegetation index (NDVI) composites for Alaska's annual growing season (April-October). This data set has applications for environmental monitoring and for assessing impacts of global climate change. An Alaska AVHRR data set is comprised of twice-monthly maximum NDVI composites of daily satellite observations. The NDVI composites contain 10 bands of information, including AVHRR channels 1-5, maximum NDVI, satellite zenith, solar zenith, and relative azimuth. The daily observations, bands 1-9, have been calibrated to reflectance, scaled to byte data, and geometrically registered to the Albers Equal-Area Conic map projection. The 10th band is a pointer to identify the date and scene ID of the source daily observation (scene) for each pixel. The compositing process required each daily overpass to be registered to a common map projection to ensure that from day to day each 1-km pixel represented the exact same ground location. The Albers Equal-Area Conic map projection provides for equal area representation, which enables easy measurement of area throughout the data. Each daily observation for the growing season was registered to a base image using image-to-image correlation. The NDVI data are calculated from the calibrated, geometrically registered daily observations. The NDVI value is the difference between near-infrared (AVHRR Channel 2) and visible (AVHRR Channel 1) reflectance values divided by total measured reflectance. A maximum NDVI compositing process was used on the daily observations. The NDVI is examined pixel by pixel for each observation during the compositing period to determine and retain the maximum value. Often when displaying data covering large areas, such as AVHRR data, it is beneficial to include an overlay of either familiar linework for reflectance or polygon data sets to derive statistical summaries of regions. All of the linework images represent lines in raster format as 1-km cells and the strata are represented as polygons registered to the AVHRR data. The linework and polygon data sets include international boundaries, Alaskan roads with the Trans-Alaska Pipeline, and a raster polygon mask of the State. proprietary
AK_AVHRR Alaska AVHRR Twice-Monthly Composites ALL STAC Catalog 1990-06-16 -179, 51, -116, 70 https://cmr.earthdata.nasa.gov/search/concepts/C1220565954-USGS_LTA.umm_json The goal of the Alaska Advanced Very High Resolution Radiometer (AVHRR) project is to compile a time series data set of calibrated, georegistered daily observations and twice-monthly maximum normalized difference vegetation index (NDVI) composites for Alaska's annual growing season (April-October). This data set has applications for environmental monitoring and for assessing impacts of global climate change. An Alaska AVHRR data set is comprised of twice-monthly maximum NDVI composites of daily satellite observations. The NDVI composites contain 10 bands of information, including AVHRR channels 1-5, maximum NDVI, satellite zenith, solar zenith, and relative azimuth. The daily observations, bands 1-9, have been calibrated to reflectance, scaled to byte data, and geometrically registered to the Albers Equal-Area Conic map projection. The 10th band is a pointer to identify the date and scene ID of the source daily observation (scene) for each pixel. The compositing process required each daily overpass to be registered to a common map projection to ensure that from day to day each 1-km pixel represented the exact same ground location. The Albers Equal-Area Conic map projection provides for equal area representation, which enables easy measurement of area throughout the data. Each daily observation for the growing season was registered to a base image using image-to-image correlation. The NDVI data are calculated from the calibrated, geometrically registered daily observations. The NDVI value is the difference between near-infrared (AVHRR Channel 2) and visible (AVHRR Channel 1) reflectance values divided by total measured reflectance. A maximum NDVI compositing process was used on the daily observations. The NDVI is examined pixel by pixel for each observation during the compositing period to determine and retain the maximum value. Often when displaying data covering large areas, such as AVHRR data, it is beneficial to include an overlay of either familiar linework for reflectance or polygon data sets to derive statistical summaries of regions. All of the linework images represent lines in raster format as 1-km cells and the strata are represented as polygons registered to the AVHRR data. The linework and polygon data sets include international boundaries, Alaskan roads with the Trans-Alaska Pipeline, and a raster polygon mask of the State. proprietary
-AK_North_Slope_NEE_CH4_Flux_1562_1 ABoVE: CO2 and CH4 Fluxes and Meteorology at Flux Tower Sites, Alaska, 2015-2017 ALL STAC Catalog 2015-01-01 2017-03-09 -157.41, 68.49, -155.75, 71.28 https://cmr.earthdata.nasa.gov/search/concepts/C2162122391-ORNL_CLOUD.umm_json This dataset provides CO2 and CH4 fluxes and meteorological parameters from five eddy covariance (EC) tower sites located at Barrow (three sites), Atqasuk (ATQ) and Ivotuk (IVO), Alaska. These locations form a 300-km north-south transect across Alaska's North Slope. Flux measurements include CO2, CH4, and H2O fluxes plus sensible and latent heat fluxes. Meteorological data include air temperature, wind speed, rain, soil temperature, PAR, radiation, soil water content, RH, ground heat fluxes, and air pressure. All data are reported at half-hourly intervals and cover the period 2015-01-01 to 2017-03-09. proprietary
+AK_AVHRR Alaska AVHRR Twice-Monthly Composites USGS_LTA STAC Catalog 1990-06-16 -179, 51, -116, 70 https://cmr.earthdata.nasa.gov/search/concepts/C1220565954-USGS_LTA.umm_json The goal of the Alaska Advanced Very High Resolution Radiometer (AVHRR) project is to compile a time series data set of calibrated, georegistered daily observations and twice-monthly maximum normalized difference vegetation index (NDVI) composites for Alaska's annual growing season (April-October). This data set has applications for environmental monitoring and for assessing impacts of global climate change. An Alaska AVHRR data set is comprised of twice-monthly maximum NDVI composites of daily satellite observations. The NDVI composites contain 10 bands of information, including AVHRR channels 1-5, maximum NDVI, satellite zenith, solar zenith, and relative azimuth. The daily observations, bands 1-9, have been calibrated to reflectance, scaled to byte data, and geometrically registered to the Albers Equal-Area Conic map projection. The 10th band is a pointer to identify the date and scene ID of the source daily observation (scene) for each pixel. The compositing process required each daily overpass to be registered to a common map projection to ensure that from day to day each 1-km pixel represented the exact same ground location. The Albers Equal-Area Conic map projection provides for equal area representation, which enables easy measurement of area throughout the data. Each daily observation for the growing season was registered to a base image using image-to-image correlation. The NDVI data are calculated from the calibrated, geometrically registered daily observations. The NDVI value is the difference between near-infrared (AVHRR Channel 2) and visible (AVHRR Channel 1) reflectance values divided by total measured reflectance. A maximum NDVI compositing process was used on the daily observations. The NDVI is examined pixel by pixel for each observation during the compositing period to determine and retain the maximum value. Often when displaying data covering large areas, such as AVHRR data, it is beneficial to include an overlay of either familiar linework for reflectance or polygon data sets to derive statistical summaries of regions. All of the linework images represent lines in raster format as 1-km cells and the strata are represented as polygons registered to the AVHRR data. The linework and polygon data sets include international boundaries, Alaskan roads with the Trans-Alaska Pipeline, and a raster polygon mask of the State. proprietary
AK_North_Slope_NEE_CH4_Flux_1562_1 ABoVE: CO2 and CH4 Fluxes and Meteorology at Flux Tower Sites, Alaska, 2015-2017 ORNL_CLOUD STAC Catalog 2015-01-01 2017-03-09 -157.41, 68.49, -155.75, 71.28 https://cmr.earthdata.nasa.gov/search/concepts/C2162122391-ORNL_CLOUD.umm_json This dataset provides CO2 and CH4 fluxes and meteorological parameters from five eddy covariance (EC) tower sites located at Barrow (three sites), Atqasuk (ATQ) and Ivotuk (IVO), Alaska. These locations form a 300-km north-south transect across Alaska's North Slope. Flux measurements include CO2, CH4, and H2O fluxes plus sensible and latent heat fluxes. Meteorological data include air temperature, wind speed, rain, soil temperature, PAR, radiation, soil water content, RH, ground heat fluxes, and air pressure. All data are reported at half-hourly intervals and cover the period 2015-01-01 to 2017-03-09. proprietary
+AK_North_Slope_NEE_CH4_Flux_1562_1 ABoVE: CO2 and CH4 Fluxes and Meteorology at Flux Tower Sites, Alaska, 2015-2017 ALL STAC Catalog 2015-01-01 2017-03-09 -157.41, 68.49, -155.75, 71.28 https://cmr.earthdata.nasa.gov/search/concepts/C2162122391-ORNL_CLOUD.umm_json This dataset provides CO2 and CH4 fluxes and meteorological parameters from five eddy covariance (EC) tower sites located at Barrow (three sites), Atqasuk (ATQ) and Ivotuk (IVO), Alaska. These locations form a 300-km north-south transect across Alaska's North Slope. Flux measurements include CO2, CH4, and H2O fluxes plus sensible and latent heat fluxes. Meteorological data include air temperature, wind speed, rain, soil temperature, PAR, radiation, soil water content, RH, ground heat fluxes, and air pressure. All data are reported at half-hourly intervals and cover the period 2015-01-01 to 2017-03-09. proprietary
AK_Regional_CO2_Flux_1389_1 CARVE: Net Ecosystem CO2 Exchange and Regional Carbon Budgets for Alaska, 2012-2014 ORNL_CLOUD STAC Catalog 2012-01-01 2014-12-31 -169, 50, -120, 74.5 https://cmr.earthdata.nasa.gov/search/concepts/C2236316034-ORNL_CLOUD.umm_json This data set provides estimates of 3-hourly net ecosystem CO2 exchange (NEE) at 0.5-degree resolution over the state of Alaska for 2012-2014. The NEE estimates are the output are from Geostatistical Inverse Modeling of a subset of CARVE aircraft CO2 data, WRF-STILT footprints, and PVPRM-SIF data from flux towers (CRV: located in Fox, AK and BRW: located just outside Barrow, AK). Daily mean NEE is also provided as calculated for all of Alaska and for four sub-regions (0.5-degree resolution) that were defined across Alaska, based on general landcover type: North Slope Tundra, South and West Tundra, Boreal Forests, and Mixed (all other). Also provided are derived annual carbon budgets for (1) all of Alaska with defined contributions from biogenic, fossil fuel, and biomass burning sources and (2) annual biogenic carbon budgets for the four landcover-type regions of Alaska. Provided for completeness are the CARVE aircraft atmospheric measurement data used in estimating NEE. proprietary
AK_Tundra_PFT_FractionalCover_1830_1 ABoVE: Tundra Plant Functional Type Continuous-Cover, North Slope, Alaska, 2010-2015 ORNL_CLOUD STAC Catalog 2010-07-01 2015-08-31 -167.48, 65.59, -143.98, 73.8 https://cmr.earthdata.nasa.gov/search/concepts/C2143401689-ORNL_CLOUD.umm_json This dataset provides predicted continuous-field cover for tundra plant functional types (PFTs), across ~125,000 km2 of Alaska's North Slope at 30-m resolution. The data cover the period 2010-07-01 to 2015-08-31. The data were derived using a random forest data-mining algorithm, predictors derived from Landsat satellite observations (surface reflectance composites for ~15-day periods from May-August), and field vegetation cover and site characterization data spanning bioclimatic and geomorphic gradients. The field vegetation cover was stratified by nine PFTs, plus open water, bare ground and litter, and using the cover metrics total cover (areal cover including the understory) and top cover (uppermost canopy or ground cover), resulting in a total of 19 field cover types. The field data and predictor values at the field sites are also included. proprietary
AK_Tundra_PFT_FractionalCover_1830_1 ABoVE: Tundra Plant Functional Type Continuous-Cover, North Slope, Alaska, 2010-2015 ALL STAC Catalog 2010-07-01 2015-08-31 -167.48, 65.59, -143.98, 73.8 https://cmr.earthdata.nasa.gov/search/concepts/C2143401689-ORNL_CLOUD.umm_json This dataset provides predicted continuous-field cover for tundra plant functional types (PFTs), across ~125,000 km2 of Alaska's North Slope at 30-m resolution. The data cover the period 2010-07-01 to 2015-08-31. The data were derived using a random forest data-mining algorithm, predictors derived from Landsat satellite observations (surface reflectance composites for ~15-day periods from May-August), and field vegetation cover and site characterization data spanning bioclimatic and geomorphic gradients. The field vegetation cover was stratified by nine PFTs, plus open water, bare ground and litter, and using the cover metrics total cover (areal cover including the understory) and top cover (uppermost canopy or ground cover), resulting in a total of 19 field cover types. The field data and predictor values at the field sites are also included. proprietary
-AK_Yukon_PFT_TopCover_2032_1.1 ABoVE: Modeled Top Cover by Plant Functional Type over Alaska and Yukon, 1985-2020 ORNL_CLOUD STAC Catalog 1985-01-01 2020-12-31 -176.1, 51, -122.5, 75.91 https://cmr.earthdata.nasa.gov/search/concepts/C2262496056-ORNL_CLOUD.umm_json This dataset contains data files of modeled top cover estimates by plant functional type (PFT) for the Arctic and Boreal Alaska and Yukon regions. Estimates are presented for single years at 5-year intervals from 1985 to 2020. Also included are root mean square error (RMSE) and source year, which indicate the specific year from which pixels in the top cover maps were derived. Plant functional types include conifer trees, broadleaf trees, deciduous shrubs, evergreen shrubs, graminoids, forbs, and light macrolichens. Estimates were derived through the combination of two stochastic gradient-boosting models that used environmental and spectral covariates. Environmental covariates represented topographic, climatic, permafrost, hydrographic, and phenological gradients, and spectral covariates were based on Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) data collected between 1984-2020. These maps catalog widespread changes in the distribution of PFTs occurring in the Arctic and boreal forest ecosystems, such as tundra shrub expansion, due to the intensification of disturbances such as fire and climate-driven vegetation dynamics. proprietary
AK_Yukon_PFT_TopCover_2032_1.1 ABoVE: Modeled Top Cover by Plant Functional Type over Alaska and Yukon, 1985-2020 ALL STAC Catalog 1985-01-01 2020-12-31 -176.1, 51, -122.5, 75.91 https://cmr.earthdata.nasa.gov/search/concepts/C2262496056-ORNL_CLOUD.umm_json This dataset contains data files of modeled top cover estimates by plant functional type (PFT) for the Arctic and Boreal Alaska and Yukon regions. Estimates are presented for single years at 5-year intervals from 1985 to 2020. Also included are root mean square error (RMSE) and source year, which indicate the specific year from which pixels in the top cover maps were derived. Plant functional types include conifer trees, broadleaf trees, deciduous shrubs, evergreen shrubs, graminoids, forbs, and light macrolichens. Estimates were derived through the combination of two stochastic gradient-boosting models that used environmental and spectral covariates. Environmental covariates represented topographic, climatic, permafrost, hydrographic, and phenological gradients, and spectral covariates were based on Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) data collected between 1984-2020. These maps catalog widespread changes in the distribution of PFTs occurring in the Arctic and boreal forest ecosystems, such as tundra shrub expansion, due to the intensification of disturbances such as fire and climate-driven vegetation dynamics. proprietary
+AK_Yukon_PFT_TopCover_2032_1.1 ABoVE: Modeled Top Cover by Plant Functional Type over Alaska and Yukon, 1985-2020 ORNL_CLOUD STAC Catalog 1985-01-01 2020-12-31 -176.1, 51, -122.5, 75.91 https://cmr.earthdata.nasa.gov/search/concepts/C2262496056-ORNL_CLOUD.umm_json This dataset contains data files of modeled top cover estimates by plant functional type (PFT) for the Arctic and Boreal Alaska and Yukon regions. Estimates are presented for single years at 5-year intervals from 1985 to 2020. Also included are root mean square error (RMSE) and source year, which indicate the specific year from which pixels in the top cover maps were derived. Plant functional types include conifer trees, broadleaf trees, deciduous shrubs, evergreen shrubs, graminoids, forbs, and light macrolichens. Estimates were derived through the combination of two stochastic gradient-boosting models that used environmental and spectral covariates. Environmental covariates represented topographic, climatic, permafrost, hydrographic, and phenological gradients, and spectral covariates were based on Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) data collected between 1984-2020. These maps catalog widespread changes in the distribution of PFTs occurring in the Arctic and boreal forest ecosystems, such as tundra shrub expansion, due to the intensification of disturbances such as fire and climate-driven vegetation dynamics. proprietary
ALAN_VIIRS_CONUS_1 Annual Summary of Artificial Light At Night from VIIRS/S-NPP at CONUS County and Census Tract V1 (ALAN_VIIRS_CONUS) at GES DISC GES_DISC STAC Catalog 2012-01-01 2020-12-31 -129.9979, 20.00208, -60.00208, 49.99792 https://cmr.earthdata.nasa.gov/search/concepts/C2650219940-GES_DISC.umm_json This product provides detailed information about the satellite-based data on artificial light at night (ALAN). The Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) nighttime lights (NTL) product (VNP46A4, DOI: 10.5067/VIIRS/VNP46A4.001 ) in NASA’s Black Marble suite is used to derive annual summary of ALAN levels throughout the CONUS at both county and tract level for the period of 2012-2020. The PI Dr. Qian Xiao is a member of NASA Heath and Air Quality Applied Sciences Team (HAQAST). proprietary
-ALERA ALERA AFES-LETKF experimental ensemble reanalysis SCIOPS STAC Catalog 2005-06-01 2007-01-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214593988-SCIOPS.umm_json ALERA is an experimental atmospheric reanalysis dataset for about one and a half years from 1 May 2005 produced on the Earth Simulator. It provides not only the ensemble mean but also spread of the ensemble members. The spread could be used as a measure of the analysis error. This datatset is produced under the collaboration among the Japan Meteorological Agency (JMA), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), and Chiba Institute of Science (CIS). ALERA may be used for research purposes for free under the terms and conditions . AFES (AGCM for the Earth Simulator) is run at a resolution of T159/L48 (about 80-km in the horizontal and 48 layers in the vertical). The ensemble size is chosen to be 40. Observational data excluding satellite radiances are assimillated using the LETKF (local ensemble transform Kalman filter). proprietary
ALERA ALERA AFES-LETKF experimental ensemble reanalysis ALL STAC Catalog 2005-06-01 2007-01-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214593988-SCIOPS.umm_json ALERA is an experimental atmospheric reanalysis dataset for about one and a half years from 1 May 2005 produced on the Earth Simulator. It provides not only the ensemble mean but also spread of the ensemble members. The spread could be used as a measure of the analysis error. This datatset is produced under the collaboration among the Japan Meteorological Agency (JMA), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), and Chiba Institute of Science (CIS). ALERA may be used for research purposes for free under the terms and conditions . AFES (AGCM for the Earth Simulator) is run at a resolution of T159/L48 (about 80-km in the horizontal and 48 layers in the vertical). The ensemble size is chosen to be 40. Observational data excluding satellite radiances are assimillated using the LETKF (local ensemble transform Kalman filter). proprietary
+ALERA ALERA AFES-LETKF experimental ensemble reanalysis SCIOPS STAC Catalog 2005-06-01 2007-01-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214593988-SCIOPS.umm_json ALERA is an experimental atmospheric reanalysis dataset for about one and a half years from 1 May 2005 produced on the Earth Simulator. It provides not only the ensemble mean but also spread of the ensemble members. The spread could be used as a measure of the analysis error. This datatset is produced under the collaboration among the Japan Meteorological Agency (JMA), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), and Chiba Institute of Science (CIS). ALERA may be used for research purposes for free under the terms and conditions . AFES (AGCM for the Earth Simulator) is run at a resolution of T159/L48 (about 80-km in the horizontal and 48 layers in the vertical). The ensemble size is chosen to be 40. Observational data excluding satellite radiances are assimillated using the LETKF (local ensemble transform Kalman filter). proprietary
ALERA2 ALERA AFES-LETKF experimental ensemble reanalysis 2 ALL STAC Catalog 2008-01-01 2013-01-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214603763-SCIOPS.umm_json ALERA2 is an experimental atmospheric reanalysis dataset from 1 Jan 2008 to 5 Jan 2013 produced on the Earth Simulator. This dataset is the second generation of ALERA. In ALERA2, the ensemble size is increased from 40 to 63 and the data assimilation system is updated from the previous one (see Enomoto et al. 2013). This dataset is produced by Japan Agency for Marine-Earth Science and Technology (JAMSTEC). ALERA2 may be used for research purposes for free under the terms and conditions. AFES (AGCM for the Earth Simulator) is run at a resolution of T119L48 (about 100 km in the horizontal and 48 layers in the vertical). The PREPBUFR complied by the National Centers for Environmental Prediction (NCEP) and archived at the University Corporation for Atmospheric Research (UCAR) is used for the observational data and assimilated using the LETKF (local ensemble transform Kalman filter). proprietary
ALERA2 ALERA AFES-LETKF experimental ensemble reanalysis 2 SCIOPS STAC Catalog 2008-01-01 2013-01-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214603763-SCIOPS.umm_json ALERA2 is an experimental atmospheric reanalysis dataset from 1 Jan 2008 to 5 Jan 2013 produced on the Earth Simulator. This dataset is the second generation of ALERA. In ALERA2, the ensemble size is increased from 40 to 63 and the data assimilation system is updated from the previous one (see Enomoto et al. 2013). This dataset is produced by Japan Agency for Marine-Earth Science and Technology (JAMSTEC). ALERA2 may be used for research purposes for free under the terms and conditions. AFES (AGCM for the Earth Simulator) is run at a resolution of T119L48 (about 100 km in the horizontal and 48 layers in the vertical). The PREPBUFR complied by the National Centers for Environmental Prediction (NCEP) and archived at the University Corporation for Atmospheric Research (UCAR) is used for the observational data and assimilated using the LETKF (local ensemble transform Kalman filter). proprietary
ALOS-2_CIRC_L1_RAD_NA ALOS-2/CIRC L1 Radiance JAXA STAC Catalog 2014-07-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130483-JAXA.umm_json "ALOS-2/CIRC L1 Radiance is obtained by Compact Infrared Camera (CIRC) onboard ALOS-2 and produced by the Japan Aerospace Exploration Agency (JAXA). The Advanced Land Observing Satellite-2 (ALOS-2, ""DAICHI-2"") is Sun-synchronous sub-recurrent Orbit satellite launched on May 24, which is a follow-on mission from the ALOS ""Daichi"". CIRC is an infrared sensor primarily intended for detecting forest fires, which present a serious problem for the various countries of Southeast Asia, particularly considering the effects of global warming and climate change. The spatial resolution and field of view are 210 m and 128 km à 96 km from an altitude of 628 km in the case of ALOS-2. Main characteristic of the CIRC is also an athermal optics. The athermal optics compensates the defocus due to the temperature change by using Germanium and Chalcogenide glass which have different coefficient of thermal expansion and temperature dependence of refractive index.This dataset includes radiance data derived from Level 0 data and the radiometric correction applied. The physical quantity is W/um/sr/m^2.The provided format is GeoTIFF. The spatial resolution is about 210 m. The projection method is UTM. The current version is 11.0." proprietary
@@ -2308,8 +2308,8 @@ ALOS_PSR_RTC_HIGH_1 ALOS_PALSAR_RTC_HIGH_RES ASF STAC Catalog 2006-03-23 2011-04
ALOS_PSR_RTC_LOW_1 ALOS_PALSAR_RTC_LOW_RES ASF STAC Catalog 2006-03-23 2011-04-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1206487217-ASF.umm_json PALSAR_Radiometric_Terrain_Corrected_low_res proprietary
ALTIKA_SARAL_L2_OST_XOGDR_f SARAL Near-Real-Time Value-added Operational Geophysical Data Record Sea Surface Height Anomaly POCLOUD STAC Catalog 2020-03-18 -180, -82, 180, 82 https://cmr.earthdata.nasa.gov/search/concepts/C2251465126-POCLOUD.umm_json These data are near-real-time (NRT) (within 7-9 hours of measurement) sea surface height anomalies (SSHA) from the AltiKa altimeter onboard the Satellite with ARgos and ALtiKa (SARAL). SARAL is a French(CNES)/Indian(SARAL) collaborative mission to measure sea surface height using the Ka-band AltiKa altimeter and was launched February 25, 2013. The major difference between these data and the Operational Geophysical Data Record (OGDR) data produced by the project is that the orbit from SARAL has been adjusted using SSHA differences with those from the OSTM/Jason-2 GPS-OGDR-SSHA product at inter-satellite crossover locations. This produces a more accurate NRT orbit altitude for SARAL with accuracy of 1.5 cm (RMS), taking advantage of the 1 cm (radial RMS) accuracy of the GPS-based orbit used for the OSTM/Jason-2 GPS-OGDR-SSHA product. This dataset also contains all data from the project (reduced) OGDR, and improved altimeter wind speeds and sea state bias correction. More information on the SARAL mission can be found at: http://www.aviso.oceanobs.com/en/missions/current-missions/saral.html proprietary
ALT_GPR_Barrow_1355_1 Pre-ABoVE: Active Layer Thickness and Soil Water Content, Barrow, Alaska, 2013 ORNL_CLOUD STAC Catalog 2013-08-10 2013-08-15 -157.27, 71.03, -155.77, 71.39 https://cmr.earthdata.nasa.gov/search/concepts/C2170969517-ORNL_CLOUD.umm_json This data set provides estimates of Active Layer Thickness (ALT) determined with ground-based measurements, and calculated soil volumetric water content (VWC) at four selected sites around Barrow, Alaska in August 2013. ALT was determined using a ground-penetrating radar (GPR) system and traditional mechanical probing. Calculated uncertainties are also included. GPR measurements were taken along four transects of varying length (approx. 1 to 7 km). Mechanical probing included several high-density surveys (every 1 m within 100-m survey line) along each GPR transect. VWC of the active layer soil was calculated at 3-8 calibration points per site where the probe measurement was exactly co-located with a GPR trace. proprietary
-ALT_Maps_AK_CA_2332_1 ABoVE: Upscaled Active Layer Thickness in Northern Alaska, 2014-2017 ORNL_CLOUD STAC Catalog 2014-01-01 2017-12-31 -171.8, 59.35, -133.05, 74.72 https://cmr.earthdata.nasa.gov/search/concepts/C2953829614-ORNL_CLOUD.umm_json The dataset consists of maps of estimated Active Layer Thickness (ALT) at 30-m resolution throughout the northern half of Alaska for the years 2014, 2015, and 2017. The maps were generated by using a machine learning-based regression and a set of spatial data layers to upscale ALT from narrow swaths of ALT that were retrieved from airborne high-resolution P-band Polarimetric Synthetic Aperture Radar (PolSAR) imagery. The data are provided in cloud-optimized GeoTIFF format. proprietary
ALT_Maps_AK_CA_2332_1 ABoVE: Upscaled Active Layer Thickness in Northern Alaska, 2014-2017 ALL STAC Catalog 2014-01-01 2017-12-31 -171.8, 59.35, -133.05, 74.72 https://cmr.earthdata.nasa.gov/search/concepts/C2953829614-ORNL_CLOUD.umm_json The dataset consists of maps of estimated Active Layer Thickness (ALT) at 30-m resolution throughout the northern half of Alaska for the years 2014, 2015, and 2017. The maps were generated by using a machine learning-based regression and a set of spatial data layers to upscale ALT from narrow swaths of ALT that were retrieved from airborne high-resolution P-band Polarimetric Synthetic Aperture Radar (PolSAR) imagery. The data are provided in cloud-optimized GeoTIFF format. proprietary
+ALT_Maps_AK_CA_2332_1 ABoVE: Upscaled Active Layer Thickness in Northern Alaska, 2014-2017 ORNL_CLOUD STAC Catalog 2014-01-01 2017-12-31 -171.8, 59.35, -133.05, 74.72 https://cmr.earthdata.nasa.gov/search/concepts/C2953829614-ORNL_CLOUD.umm_json The dataset consists of maps of estimated Active Layer Thickness (ALT) at 30-m resolution throughout the northern half of Alaska for the years 2014, 2015, and 2017. The maps were generated by using a machine learning-based regression and a set of spatial data layers to upscale ALT from narrow swaths of ALT that were retrieved from airborne high-resolution P-band Polarimetric Synthetic Aperture Radar (PolSAR) imagery. The data are provided in cloud-optimized GeoTIFF format. proprietary
ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_1 Gridded Altimeter Fields with Enhanced Coastal Coverage POCLOUD STAC Catalog 1992-10-14 2012-04-18 -111.5, 35.25, -132.25, 48.5 https://cmr.earthdata.nasa.gov/search/concepts/C2205120784-POCLOUD.umm_json The Gridded Altimeter Fields with Enhanced Coastal Coverage data product contains Sea Surface Height Anomalies (SSHA or SLA) and zonal and meridional geostrophic velocities for the US west coast encompassing 35.25 deg-48.5 deg N latitude and 227.75 deg-248.5 deg E longitude. This annually updated data product extends from October 14, 1992 through November 4, 2009. SSHA and current velocities are derived from the AVISO quarter degree DT UPD MSLA version 3.0 grids, 0.75 deg and greater away from the coast. Values within 0.75 deg of the coast are derived from tide gauge observations and interpolated out to the altimeter filled region. Details on how these data are derived can be found in: Saraceno, M., P. T. Strub, and P. M. Kosro (2008), Estimates of sea surface height and near-surface alongshore coastal currents from combinations of altimeters and tide gauges, J. Geophys. Res., 113, C11013, doi:10.1029/2008JC004756. proprietary
ALT_TIDE_GAUGE_L4_OST_SLA_US_WEST_COAST_DAILY_1 Gridded Altimeter Fields with Enhanced Coastal Coverage Daily POCLOUD STAC Catalog 1992-10-14 2011-01-19 -133, 35, -111, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2036882016-POCLOUD.umm_json The Gridded Altimeter Fields with Enhanced Coastal Coverage data product contains Sea Surface Height Anomalies (SSHA or SLA) and zonal and meridional geostrophic velocities for the US west coast encompassing 35.25 deg-48.5 deg N latitude and 227.75 deg-248.5 deg E longitude. This annually updated data product extends from October 14, 1992 through January 19, 2011. SSHA and current velocities are derived from the AVISO quarter degree DT UPD MSLA version 3.0 grids, 0.75 deg and greater away from the coast. Values within 0.75 deg of the coast are derived from tide gauge observations and interpolated out to the altimeter filled region. Details on how these data are derived can be found in: Saraceno, M., P. T. Strub, and P. M. Kosro (2008), Estimates of sea surface height and near-surface alongshore coastal currents from combinations of altimeters and tide gauges, J. Geophys. Res., 113, C11013, doi:10.1029/2008JC004756. proprietary
AM1EPHNE_6.1NRT Files containing only extrapolated orbital metadata, to be read via SDP Toolkit, Binary Format LANCEMODIS STAC Catalog 2016-01-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1426293893-LANCEMODIS.umm_json AM1EPHNE is the Terra Near Real Time (NRT) 2-hour spacecraft Extrapolated ephemeris data file in native format. The file name format is the following: AM1EPHNE.Ayyyyddd.hhmm.vvv.yyyydddhhmmss where from left to right: E = Extrapolated; N = Native format; A = AM1 (Terra); yyyy = data year, ddd = Julian data day, hh = data hour, mm = data minute; vvv = Version ID; yyyy = production year, ddd = Julian production day, hh = production hour, mm = production minute, and ss = production second. Data set information: http://modis.gsfc.nasa.gov/sci_team/ proprietary
@@ -2340,8 +2340,8 @@ AMSRE_STDMO_005 AMSR-E/Aqua level 3 global monthly Surface Soil Moisture Standar
AMT_0 Atlantic Meridional Transect (AMT) cruises OB_DAAC STAC Catalog 1995-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360099-OB_DAAC.umm_json Measurements taken during Atlantic Meridional Transect (AMT) cruises. proprietary
AMZ1-WFI-L4-SR-1_NA AMAZONIA-1/WFI - Level-4-SR - Cloud Optimized GeoTIFF INPE STAC Catalog 2024-01-01 2024-06-09 -135.151782, -45.613218, 106.18473, 63.78312 https://cmr.earthdata.nasa.gov/search/concepts/C3108204639-INPE.umm_json AMAZONIA-1/WFI Surface Reflectance product over Brazil. L4 SR product provides orthorectified surface reflectance images. This dataset is provided as Cloud Optimized GeoTIFF (COG). proprietary
ANACONDAS_0 Amazon iNfluence on the Atlantic: CarbOn export from Nitrogen fixation by DiAtom Symbioses (ANACONDAS) OB_DAAC STAC Catalog 2010-05-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360100-OB_DAAC.umm_json This research project sutided the effects of the Amazon River plume on the carbon and nitrogen cycling of the western tropical North Atlantic Ocean. Phytoplankton blooms triggered by the river plume are thought to be responsible for significant cabon dioxide drawdown from the atmosphere. Our team came together to try to understand the factors affecting the phytoplankton bloom and also the fate of its production, including the amount of carbon dioxide taken up by the plume. Fieldwork in the western tropical North Atlantic onboard the RV Knorr took place along the salinity gradient of the river plume (16 ppt to 36 ppt) at a series of stations within and adjacent to the pluem. proprietary
-ANARE-26_1 A qualitative investigation into scavenging of airborne sea salt over Macquarie Island. AU_AADC STAC Catalog 1961-01-24 1963-03-31 158.8833, -54.6333, 158.8833, -54.6333 https://cmr.earthdata.nasa.gov/search/concepts/C1214311732-AU_AADC.umm_json A comparative study made on the amount of sea salt (dominantly NaCl) deposited on Macquarie Island due to atmospheric precipitation. It is found that the scavenging of solid salt particles alone cannot account for all the salt budget over certain areas of the Island. It is considered that sea spray droplets carried aloft by winds and scavenged by precipitation in the immediate vicinity of the shoreline is responsible for this deficit. The fields in this dataset are: Site details: Altitude, Distance from west coast and Mean annual precipitation. Chemical component Bubble size diameter Mass of salt particle Dry salt particle radius Number of equivalent days of continuous precipitation Site: Plateau, Wireless Hill, Isthmus Dry salt particles Sea spray droplets Total fallout proprietary
ANARE-26_1 A qualitative investigation into scavenging of airborne sea salt over Macquarie Island. ALL STAC Catalog 1961-01-24 1963-03-31 158.8833, -54.6333, 158.8833, -54.6333 https://cmr.earthdata.nasa.gov/search/concepts/C1214311732-AU_AADC.umm_json A comparative study made on the amount of sea salt (dominantly NaCl) deposited on Macquarie Island due to atmospheric precipitation. It is found that the scavenging of solid salt particles alone cannot account for all the salt budget over certain areas of the Island. It is considered that sea spray droplets carried aloft by winds and scavenged by precipitation in the immediate vicinity of the shoreline is responsible for this deficit. The fields in this dataset are: Site details: Altitude, Distance from west coast and Mean annual precipitation. Chemical component Bubble size diameter Mass of salt particle Dry salt particle radius Number of equivalent days of continuous precipitation Site: Plateau, Wireless Hill, Isthmus Dry salt particles Sea spray droplets Total fallout proprietary
+ANARE-26_1 A qualitative investigation into scavenging of airborne sea salt over Macquarie Island. AU_AADC STAC Catalog 1961-01-24 1963-03-31 158.8833, -54.6333, 158.8833, -54.6333 https://cmr.earthdata.nasa.gov/search/concepts/C1214311732-AU_AADC.umm_json A comparative study made on the amount of sea salt (dominantly NaCl) deposited on Macquarie Island due to atmospheric precipitation. It is found that the scavenging of solid salt particles alone cannot account for all the salt budget over certain areas of the Island. It is considered that sea spray droplets carried aloft by winds and scavenged by precipitation in the immediate vicinity of the shoreline is responsible for this deficit. The fields in this dataset are: Site details: Altitude, Distance from west coast and Mean annual precipitation. Chemical component Bubble size diameter Mass of salt particle Dry salt particle radius Number of equivalent days of continuous precipitation Site: Plateau, Wireless Hill, Isthmus Dry salt particles Sea spray droplets Total fallout proprietary
ANARE-71_1 Adelie Penguin Colonies - Mawson Area and Rookery Islands ALL STAC Catalog 1981-01-01 1988-12-31 62.27, -67.63, 62.98, -67.54 https://cmr.earthdata.nasa.gov/search/concepts/C1214305707-AU_AADC.umm_json This dataset includes Adelie penguin colonies and coastline digitised from Eric J. Woehler, G.W. Johnstone and Harry R. Burton, 'ANARE Research Notes 71, The distribution and abundance of Adelie penguins, Pygoscelis adeliae, in the Mawson area and at the Rookery Islands (Specially Protected Area 2), 1981 and 1988'. proprietary
ANARE-71_1 Adelie Penguin Colonies - Mawson Area and Rookery Islands AU_AADC STAC Catalog 1981-01-01 1988-12-31 62.27, -67.63, 62.98, -67.54 https://cmr.earthdata.nasa.gov/search/concepts/C1214305707-AU_AADC.umm_json This dataset includes Adelie penguin colonies and coastline digitised from Eric J. Woehler, G.W. Johnstone and Harry R. Burton, 'ANARE Research Notes 71, The distribution and abundance of Adelie penguins, Pygoscelis adeliae, in the Mawson area and at the Rookery Islands (Specially Protected Area 2), 1981 and 1988'. proprietary
ANARE-74_1 An atlas of the lakes of the Larsemann Hills, Princess Elizabeth Land, Antarctica - ANARE Research Notes 74 AU_AADC STAC Catalog 1987-01-01 1987-02-28 76.1, -69.7, 76.6, -69.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214305650-AU_AADC.umm_json From the abstract of the ANARE Research Note: The Larsemann Hills are a series of granite and gneiss peninsulas extending into Prydz Bay, between the Amery Ice Shelf and the Sorsdal Glacier. They are dissected by steep-sided valleys produced by at least two glacial stages in the Holocene. There are over 150 freshwater lakes in the hills, ranging from small ponds less than 1 m deep, to glacial lakes up to 10 ha and 38 m deep. The lakes are young, with the oldest basins being about 9000 years old. Variations in the characteristics of the lakes reflect deglaciation history, proximity to the continental ice margin and exposure to the ocean. The main source of the water is snow melt, augmented by sea spray into the more exposed lakes. The waters are well mixed by katabatic winds. Most lakes thaw for up to 2 months in summer, but some are permanently frozen. The waters have mainly low conductivity and exceptionally low turbidity, and have near-neutral pH values. The ionic order is Na+ greater than Mg2+ greater than Ca2+ greater than K+. This reflects a strong marine influence, with calcium dominating in a very few catchments. The Larsemann Hills were discovered in 1935 by Captain Klarius Mikkelsen in the Thorshavn. Australian, Chinese and Russian stations were established in the area in the mid-late 1980's. Law (Australia) was commenced in 1986 when an Apple Hut was unloaded from MV Nella Dan. A subsequent visit was made during the 1986 winter. The first Australian scientific expedition visited the area during the 1986-87 austral summer. Progress Station (Russia) was occupied at the time. Building of Zhong Shan commenced in January 1989. ******************* Several files are associated with this metadata record: 1) A PDF copy of the original ANARE Research Note 2) A CSV file containing the data presented in the ANARE Research Note 3) A shapefile of the lakes presented in the ANARE Research Note The fields in this dataset are: lake_id lake_name (text) location (text description) longitude (decimal degrees) latitude (decimal degrees) altitude (m) lake_area (ha) catchment_area (ha) maximum_depth (m) dimensions (m) distance_from_polar_plateau (m) description (text) geology (text) water_temperature (C) pH water_conductivity (micro mho/cm) Eh (reduction potential, mV) ca_concentration (Ca++, ppm) mg_concentration (Mg++, ppm) na_concentration (NA+, ppm) k_concentration (K+, ppm) ionic_ratios_na_ca_mg_k (ionic ratio of na:(ca+mg+mk)) ionic_ratios_ca_na_k_mg (ionic ratio of ca:(na+k+mg)) bottom_sediment_grab_sample (text description of results) proprietary
@@ -2356,8 +2356,8 @@ AOL_0 Measurements taken off the New England Coast in 1997 OB_DAAC STAC Catalog
AOPEX_0 Advanced Optical Properties Experiment (AOPEX) Program ALL STAC Catalog 2004-07-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360105-OB_DAAC.umm_json Measurements made near Spain and Portugal under the AOPEX program. proprietary
AOPEX_0 Advanced Optical Properties Experiment (AOPEX) Program OB_DAAC STAC Catalog 2004-07-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360105-OB_DAAC.umm_json Measurements made near Spain and Portugal under the AOPEX program. proprietary
AOSNII_0 Autonomous Ocean Sampling Networks (AOSN) second deployment OB_DAAC STAC Catalog 2003-08-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360106-OB_DAAC.umm_json Measurements made under the Autonomous Ocean Sampling Networks (AOSN) second deployment in the Monterey Bay area in 2003. proprietary
-APG_ATLAS_1.0 Alaska PaleoGlacier Atlas: A Geospatial Compilation of Pleistocene Glacier Extents ALL STAC Catalog 1970-01-01 172, 51, -130, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214613400-SCIOPS.umm_json "Three decades after the last Alaska-wide compilations of glacial geology (Karlstrom et al., 1964; Coulter et al., 1965), we have coordinated a broadly collaborative effort to create a digital map of reconstructed Pleistocene glaciers. The Alaska PaleoGlacier Atlas is a geospatial summary of Pleistocene glaciation across Alaska. The layers in the atlas depict: 1) the extent of glaciers during the late Wisconsin glaciation (i.e. Last Glacial Maximum, about 20,000 years ago), and 2) the maximum extent reached during the last ca. 3 million years by the northwestern Cordilleran Ice Sheet, ice caps, and valley glaciers. The atlas is targeted for a scale of 1 to 1,000,000 -- suitable for visualization and regional analyses. Former glacier extents are based on decades of field-based mapping, air-photo interpretation, and a variety of dating methods. In all, the first version combines glacial-geologic information from 26 publications and 42 source maps. Revisions will be made and released as time and resources allow. A companion paper (Kaufman and Manley, subm.; part of an INQUA effort for a global atlas with regional reviews) summarizes the glacial-geologic evidence and highlights recent revisions, remaining uncertainties, and implications for paleoclimate forcing. See: ""http://instaar.Colorado.EDU/QGISL/ak_paleoglacier_atlas/apg_overview.html""" proprietary
APG_ATLAS_1.0 Alaska PaleoGlacier Atlas: A Geospatial Compilation of Pleistocene Glacier Extents SCIOPS STAC Catalog 1970-01-01 172, 51, -130, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214613400-SCIOPS.umm_json "Three decades after the last Alaska-wide compilations of glacial geology (Karlstrom et al., 1964; Coulter et al., 1965), we have coordinated a broadly collaborative effort to create a digital map of reconstructed Pleistocene glaciers. The Alaska PaleoGlacier Atlas is a geospatial summary of Pleistocene glaciation across Alaska. The layers in the atlas depict: 1) the extent of glaciers during the late Wisconsin glaciation (i.e. Last Glacial Maximum, about 20,000 years ago), and 2) the maximum extent reached during the last ca. 3 million years by the northwestern Cordilleran Ice Sheet, ice caps, and valley glaciers. The atlas is targeted for a scale of 1 to 1,000,000 -- suitable for visualization and regional analyses. Former glacier extents are based on decades of field-based mapping, air-photo interpretation, and a variety of dating methods. In all, the first version combines glacial-geologic information from 26 publications and 42 source maps. Revisions will be made and released as time and resources allow. A companion paper (Kaufman and Manley, subm.; part of an INQUA effort for a global atlas with regional reviews) summarizes the glacial-geologic evidence and highlights recent revisions, remaining uncertainties, and implications for paleoclimate forcing. See: ""http://instaar.Colorado.EDU/QGISL/ak_paleoglacier_atlas/apg_overview.html""" proprietary
+APG_ATLAS_1.0 Alaska PaleoGlacier Atlas: A Geospatial Compilation of Pleistocene Glacier Extents ALL STAC Catalog 1970-01-01 172, 51, -130, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214613400-SCIOPS.umm_json "Three decades after the last Alaska-wide compilations of glacial geology (Karlstrom et al., 1964; Coulter et al., 1965), we have coordinated a broadly collaborative effort to create a digital map of reconstructed Pleistocene glaciers. The Alaska PaleoGlacier Atlas is a geospatial summary of Pleistocene glaciation across Alaska. The layers in the atlas depict: 1) the extent of glaciers during the late Wisconsin glaciation (i.e. Last Glacial Maximum, about 20,000 years ago), and 2) the maximum extent reached during the last ca. 3 million years by the northwestern Cordilleran Ice Sheet, ice caps, and valley glaciers. The atlas is targeted for a scale of 1 to 1,000,000 -- suitable for visualization and regional analyses. Former glacier extents are based on decades of field-based mapping, air-photo interpretation, and a variety of dating methods. In all, the first version combines glacial-geologic information from 26 publications and 42 source maps. Revisions will be made and released as time and resources allow. A companion paper (Kaufman and Manley, subm.; part of an INQUA effort for a global atlas with regional reviews) summarizes the glacial-geologic evidence and highlights recent revisions, remaining uncertainties, and implications for paleoclimate forcing. See: ""http://instaar.Colorado.EDU/QGISL/ak_paleoglacier_atlas/apg_overview.html""" proprietary
APIS_1 APIS - Antarctic Pack Ice Seals 1994-1999, plus historical data from the 1980's AU_AADC STAC Catalog 1984-11-11 2000-01-10 48.88, -69.2256, 150.43, -58.93 https://cmr.earthdata.nasa.gov/search/concepts/C1214311736-AU_AADC.umm_json APIS data were collected between 1994 and 1999. This dataset also includes some historical data collected between 1985 and 1987. Both aerial and ship-board surveys were conducted. Studies on the behaviour of Pack-ice or Crabeater Seal (Lobodon carcinophagus) in the Southern Ocean and in the Australian Sector of Antarctica were also conducted as part of this study. Satellite tracking was used to determine their movement, durations on land and at sea, dive depths and dive duration etc. The four species of Antarctic pack ice seals (crabeater, leopard, Weddell, and Ross seals) are thought to comprise up to 50% or more of the world's total biomass of seals. As long-lived, top level predators in Southern Ocean ecosystems, pack ice seals are scientifically interesting because they can assist in monitoring shifts in ecosystem structure and function, especially changes that occur in sensitive polar areas in response to global climate changes. The APIS Program focuses on the ecological importance of pack ice seals and their interactions with physical and biotic features of their environment. This program is a collaborative, multi-disciplinary research initiative whose planning and implementation has involved scientists from more than a dozen countries. It is being developed and coordinated by the Group of Specialists on Seals of the Scientific Committee on Antarctic Research (SCAR), and represents an important contribution to SCAR's Antarctic Global Change Program. Australian researchers have undertaken an ambitious science program studying the distribution and abundance of pack ice seals in support of the APIS Program. An excellent overview of this work is provided at the Australian Antarctic Division's web site. The following paragraphs provide a brief progress report of some of that work through 1998. ------------------------------------------------------------------------------- Four years of developmental work have now been completed in preparation for the Australian contribution to the circumpolar survey that will take place in December 1998. Until recently the main effort has been directed towards designing and building a system for automatic data logging of line transect data by double observers. Two systems identical in concept have been designed for aerial survey and shipboard survey. The systems consist of a number of sighting guns and keypads linked to a central computer. The sightings guns are used to measure the exact time and angle of declination from the horizon of seals passing abeam of the survey platform. Also logged regularly (10 second intervals) are GPS position and altitude (aerial survey only). The aerial survey system also has an audio backup. The aerial survey system has been trialled over three seasons and the shipboard system over one season. Preliminary analysis of aerial data indicates that the essential assumption of the line transect method is badly violated, reinforcing the need for double observers. Assumption violation is likely to be less in shipboard survey, but assessment of the assumption of perfect sightability on the line is still important. User manuals have been written for both the aerial and shipboard systems. An aerial survey system is being constructed for use by BAS in the coming season. A backup manual system for aerial and shipboard survey has also been developed in the event of the automatic system failing. The aerial backup system uses the perspex sighting frame developed by the US. A database has been designed for storage and analysis of aerial and shipboard data. Importing of data is fast and easy, allowing post-survey analysis and review immediately after each day's survey effort. Aides for training observers have been developed. A video on species identification has been produced. A Powerpoint slide show has been designed to simulate aerial survey conditions and use of the automatic data logging system. Currently effort has been directed toward developing an optimal survey design. While a general survey plan is necessary, it must be flexible to deal with unpredictable ice and weather conditions. It is planned to use both the ship and two Sikorsky 76 helicopters as survey platforms. The ship will be used to survey into and out from stations, and inwards from the ice edge for approximately 60 miles. The helicopters will be used to survey southwards from the ship for distances up to 140 miles in favourable weather. Helicopters will fly in tandem, with transects 10 miles apart. Studies of crabeater seal haul-out behaviour have been conducted over the past four seasons. Twenty SLTDRs have been deployed in the breeding season (September-October). The length of deployments varies from a few days to 3 months. No transmissions have been received after mid-January, probably due to loss of instruments during the moult. Most instruments have transmitted data through the survey period of November-December. Haul-out behaviour is consistent between animals and years. However, five more instruments will be deployed in the survey season to ensure there is haul-out data concurrent with the survey effort. Some observations of penguins and whales were also made. The accompanying dataset includes three Microsoft Access databases (stored in both Access 97 and Access 2002 formats), as well as two Microsoft Word documents, which provide additional information about these data. The fields in this dataset are: Date Time Time since previous sighting Side (of aircraft/ship) Seen by (observer) Latitude Longitude Number of adults Number of pups Species (LPD - Leopard Seal, WED - Weddell Seal, SES - Southern Elephant Seal, CBE - Crabeater Seal, UNS - Unknown Seal, ADE - Adelie Penguin, ROS - Ross Seal, EMP - Emperor Penguin, MKE - Minke Whale, ORC - Orca Whale, UNP - Unknown Penguin, UNW - Unknown Whale) SpCert - How certain the observer was of correct identification - a tick indicates certainty Distance from Observer (metres) Movement Categories - N: no data, S: stationary, MB: moved body, MBP: moved body and position, movement distance: -99 no data, negative values moved towards flight line, positive distance moved away from flight line Distance dart gun fired from animal (in metres) Approach method (S = ship, H = helicopter, Z = unknown) Approach distance (metres) Group (S = single, P = pair, F = family (male, female and pup)) Sex Guessed Weight (kg) Drugs used Maximum Sedation Level (CS = Colin Southwell, MT = Mark Tahmidjis) Time to maximum sedation level Time to return to normal Heart rate (maximum, minimum) Respiration rate (maximum, minimum, resting) Arousal Level (1 = calm, 2 = slight, 3 = strong) Arousal Level Cat1 (1 = calm, 2 = 2+3 from above) Apnoea (maximum length of apnoea in minutes) Comments Time at depth - reading taken every 10 seconds, and whichever depth incremented upwards by 1. Time period (NT - 21:00-03:00, MN - 03:00-09:00, MD - 09:00-15:00, AF - 15:00-21:00) Seal Age - (A = Adult, SA = sub-Adult) WCId - Wildlife Computers Identification Number for SLTDR Length, width, girth (body, head, flippers) (cm) Blood, blubber, skin, hair, tooth, scat, nasal swab - sample taken, yes or no. In general, Y = Yes, N = No, ND = No Data This work was also completed as part of ASAC projects 775 and 2263. proprietary
APPSS_0 Observations from the Autonomous Polar Productivity Sampling System. OB_DAAC STAC Catalog 2011-08-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360107-OB_DAAC.umm_json Observations from the Autonomous Polar Productivity Sampling System. proprietary
APSF Aerial Photo Single Frames USGS_LTA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220567654-USGS_LTA.umm_json The Aerial Photography Single Frame Records collection is a large and diverse group of imagery acquired by Federal organizations from 1937 to the present. Over 6.4 million frames of photographic images are available for download as medium and high resolution digital products. The high resolution data provide access to photogrammetric quality scans of aerial photographs with sufficient resolution to reveal landscape detail and to facilitate the interpretability of landscape features. Coverage is predominantly over the United States and includes portions of Central America and Puerto Rico. Individual photographs vary in scale, size, film type, quality, and coverage. proprietary
@@ -2562,8 +2562,8 @@ AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY-CLIMATOLOGY_V5_5.0 Aquarius Official Release
AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY_V5_5.0 Aquarius Official Release Level 3 Wind Speed Standard Mapped Image Monthly Data V5.0 POCLOUD STAC Catalog 2011-08-25 2015-06-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2491757177-POCLOUD.umm_json Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath. proprietary
AQUARIUS_L3_WIND_SPEED_SMI_SEASONAL-CLIMATOLOGY_V5_5.0 Aquarius Official Release Level 3 Wind Speed Standard Mapped Image Seasonal Climatology Data V5.0 POCLOUD STAC Catalog 2011-08-25 2015-06-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2491757179-POCLOUD.umm_json Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath. proprietary
AQUARIUS_L4_OISSS_IPRC_7DAY_V5_5.0 IPRC/SOEST Aquarius V5.0 Optimally Interpolated Sea Surface Salinity 7-Day global Dataset POCLOUD STAC Catalog 2011-08-27 2015-06-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2617176747-POCLOUD.umm_json The IPRC/SOEST Aquarius OI-SSS v5 product is a level 4, near-global, 0.5 degree spatial resolution, 7-day, optimally interpolated salinity dataset based on version 5.0 of the AQUARIUS/SAC-D level 2 mission data. This is a PI led dataset produced at the International Pacific Research Center (IPRC) at the University of Hawaii (Manoa) School of Ocean and Earth Science and Technology. The optimal interpolation (OI) mapping procedure used to create this product corrects for systematic spatial biases in Aquarius SSS data with respect to near-surface in situ salinity observations and takes into account available statistical information about the signal and noise, specific to the Aquarius instrument. Bias fields are constructed by differencing in situ from Aquarius derived SSS fields obtained separately using ascending and descending satellite observations for each of the three Aquarius beams, and by removal of small-scale noise and low-pass filtering along-track using a two-dimensional Hanning window procedures prior to application of the OI algorithm. Additional enhancements for this new version of the product include: 1) The V5.0 (end-of mission) version of Aquarius Level-2 (swath) SSS data are used as input data for the OI SSS analysis. 2) The source of the first guess fields has changed from the APDRC Argo-derived SSS product to the average of four different in-situ based SSS products. 3) The bias correction algorithm has changed to adjust SSS retrievals for large-scale systematic biases on a repeat-track basis. 4) New, less restrictive thresholds are implemented to filter observations for land and ice contamination, thus improving coverage in the coastal areas and semi-enclosed seas. 5) Level-2 RFI masks for descending and ascending satellite passes are used to discard observations in specific geographic zones where excessive ascending-descending differences are observed due to contamination from undetected RFI. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath. The Aquarius polar orbit is sun synchronous at 657 km with a 6 pm, ascending node, and has a 7-Day repeat cycle. proprietary
-ARB_48_IN_LIDAR_1 Aerosol Research Branch (ARB) 48 inch Lidar Data LARC_ASDC STAC Catalog 1982-06-14 2001-12-04 -76.378, 37.1, -76.3, 37.106 https://cmr.earthdata.nasa.gov/search/concepts/C1000000706-LARC_ASDC.umm_json The ARB_48_IN_LIDAR data set contains data collected from a 48-inch lidar system located at NASA Langley Research Center. Each granule consists of one year of data. The days of data are different in each granule. Each measurement consists of four parameters: stratospheric integrated backscatter over altitude, altitude levels, scattering ratio at each altitude level, and aerosol backscattering coefficient at each altitude level. An image was produced to represent the data collected for each granule.The Aerosol Research Branch (ARB) Light Detection and Ranging (LIDAR) project has been taking ground based LIDAR measurements from Langley Research Center in Hampton, Virginia since May 1974. These LIDAR measurements provide high resolution vertical profiles of the upper tropospheric and stratospheric aerosols. The LIDAR system has evolved over the years and provides a valuable long-term history of the middle-latitude stratospheric aerosol.The measurements for ARB were made using a LIDAR system. This system uses a ruby laser that emits one joule per pulse with a repeat rate of 0.15 hertz (Hz) at a wavelength of 0.6943 micrometers. This system also uses a 48-inch cassegrainian configured telescope mounted on a movable platform. The transmitter laser beam has a divergence of about 1.0 mrad, and the maximum receiver field of view is 4.0 mrad. The LIDAR has a signal bandwidth of 1 MHz, and this is equal to a 150 meter vertical resolution. Three photomultiplier tubes are used to enhance the dynamic range. These tubes are electronically switched on at specific times after the laser has been fired. The photomultiplier tube output signals are processed by 12-bit Computer Automated Measurement and Control (CAMAC) based digitizers and acquired by a personal computer. The data are archived on optical discs. proprietary
ARB_48_IN_LIDAR_1 Aerosol Research Branch (ARB) 48 inch Lidar Data ALL STAC Catalog 1982-06-14 2001-12-04 -76.378, 37.1, -76.3, 37.106 https://cmr.earthdata.nasa.gov/search/concepts/C1000000706-LARC_ASDC.umm_json The ARB_48_IN_LIDAR data set contains data collected from a 48-inch lidar system located at NASA Langley Research Center. Each granule consists of one year of data. The days of data are different in each granule. Each measurement consists of four parameters: stratospheric integrated backscatter over altitude, altitude levels, scattering ratio at each altitude level, and aerosol backscattering coefficient at each altitude level. An image was produced to represent the data collected for each granule.The Aerosol Research Branch (ARB) Light Detection and Ranging (LIDAR) project has been taking ground based LIDAR measurements from Langley Research Center in Hampton, Virginia since May 1974. These LIDAR measurements provide high resolution vertical profiles of the upper tropospheric and stratospheric aerosols. The LIDAR system has evolved over the years and provides a valuable long-term history of the middle-latitude stratospheric aerosol.The measurements for ARB were made using a LIDAR system. This system uses a ruby laser that emits one joule per pulse with a repeat rate of 0.15 hertz (Hz) at a wavelength of 0.6943 micrometers. This system also uses a 48-inch cassegrainian configured telescope mounted on a movable platform. The transmitter laser beam has a divergence of about 1.0 mrad, and the maximum receiver field of view is 4.0 mrad. The LIDAR has a signal bandwidth of 1 MHz, and this is equal to a 150 meter vertical resolution. Three photomultiplier tubes are used to enhance the dynamic range. These tubes are electronically switched on at specific times after the laser has been fired. The photomultiplier tube output signals are processed by 12-bit Computer Automated Measurement and Control (CAMAC) based digitizers and acquired by a personal computer. The data are archived on optical discs. proprietary
+ARB_48_IN_LIDAR_1 Aerosol Research Branch (ARB) 48 inch Lidar Data LARC_ASDC STAC Catalog 1982-06-14 2001-12-04 -76.378, 37.1, -76.3, 37.106 https://cmr.earthdata.nasa.gov/search/concepts/C1000000706-LARC_ASDC.umm_json The ARB_48_IN_LIDAR data set contains data collected from a 48-inch lidar system located at NASA Langley Research Center. Each granule consists of one year of data. The days of data are different in each granule. Each measurement consists of four parameters: stratospheric integrated backscatter over altitude, altitude levels, scattering ratio at each altitude level, and aerosol backscattering coefficient at each altitude level. An image was produced to represent the data collected for each granule.The Aerosol Research Branch (ARB) Light Detection and Ranging (LIDAR) project has been taking ground based LIDAR measurements from Langley Research Center in Hampton, Virginia since May 1974. These LIDAR measurements provide high resolution vertical profiles of the upper tropospheric and stratospheric aerosols. The LIDAR system has evolved over the years and provides a valuable long-term history of the middle-latitude stratospheric aerosol.The measurements for ARB were made using a LIDAR system. This system uses a ruby laser that emits one joule per pulse with a repeat rate of 0.15 hertz (Hz) at a wavelength of 0.6943 micrometers. This system also uses a 48-inch cassegrainian configured telescope mounted on a movable platform. The transmitter laser beam has a divergence of about 1.0 mrad, and the maximum receiver field of view is 4.0 mrad. The LIDAR has a signal bandwidth of 1 MHz, and this is equal to a 150 meter vertical resolution. Three photomultiplier tubes are used to enhance the dynamic range. These tubes are electronically switched on at specific times after the laser has been fired. The photomultiplier tube output signals are processed by 12-bit Computer Automated Measurement and Control (CAMAC) based digitizers and acquired by a personal computer. The data are archived on optical discs. proprietary
ARB_California_Air_Quality_Data Air Quality Data (1980-1999) from the California Air Resources Board ALL STAC Catalog 1970-01-01 -124.9, 32.02, -113.61, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C1214610880-SCIOPS.umm_json "The California Air Resources Board has available two CD-ROMs (CDs) with 20 years of air quality data. Both CDs contain essentially the same air quality data, but provide these data in different formats. The CDs contain 20 years of Criteria Pollutant air quality data (1980-1999), 10 years of Toxics air quality data (1990-1999), 12 years of dichotomous sampler (Dichot) data (1988-1999), and 6 years of non-methane organic compound (NMOC) data (1994-1999). These CDs are updates to the air quality data CDs released before 2001. One of the many new additions to the new CDs is a hyperlinked version of supporting documents. The first CD contains data that are displayed graphically using Voyager (a program contained on the CD, which displays data on maps and as time series graphs). This CD also includes annual data summaries in table format, which can be viewed using selection buttons and pull-down menus. Graphing templates are available for plotting the annual data trends. The CD runs under Windows 3.1 and higher. Request CD Number: PTSD-00-013-CD The second CD contains the same data content as the first CD, but stores the data in other forms (ASCII, DBF, etc.) used by analysts who process their own data. This CD also includes annual and daily summaries in table format, which are accessible through selection buttons and pull-down menus. Graphing templates are available for plotting the annual data trends. Request CD Number: PTSD-00-014-CD There was not enough space to carry complete hourly data for all the years. Consequently, the hourly data for the earliest years have been made available for downloading from the internet: Voyager hourly files 1980-1989 ASCII hourly files 1980-1989 ""http://www.arb.ca.gov/aqd/aqdcd/aqdcddld.htm""" proprietary
ARB_California_Air_Quality_Data Air Quality Data (1980-1999) from the California Air Resources Board SCIOPS STAC Catalog 1970-01-01 -124.9, 32.02, -113.61, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C1214610880-SCIOPS.umm_json "The California Air Resources Board has available two CD-ROMs (CDs) with 20 years of air quality data. Both CDs contain essentially the same air quality data, but provide these data in different formats. The CDs contain 20 years of Criteria Pollutant air quality data (1980-1999), 10 years of Toxics air quality data (1990-1999), 12 years of dichotomous sampler (Dichot) data (1988-1999), and 6 years of non-methane organic compound (NMOC) data (1994-1999). These CDs are updates to the air quality data CDs released before 2001. One of the many new additions to the new CDs is a hyperlinked version of supporting documents. The first CD contains data that are displayed graphically using Voyager (a program contained on the CD, which displays data on maps and as time series graphs). This CD also includes annual data summaries in table format, which can be viewed using selection buttons and pull-down menus. Graphing templates are available for plotting the annual data trends. The CD runs under Windows 3.1 and higher. Request CD Number: PTSD-00-013-CD The second CD contains the same data content as the first CD, but stores the data in other forms (ASCII, DBF, etc.) used by analysts who process their own data. This CD also includes annual and daily summaries in table format, which are accessible through selection buttons and pull-down menus. Graphing templates are available for plotting the annual data trends. Request CD Number: PTSD-00-014-CD There was not enough space to carry complete hourly data for all the years. Consequently, the hourly data for the earliest years have been made available for downloading from the internet: Voyager hourly files 1980-1989 ASCII hourly files 1980-1989 ""http://www.arb.ca.gov/aqd/aqdcd/aqdcddld.htm""" proprietary
ARC02_0 Measurements in the Arctic region north of Alaska in 2002 OB_DAAC STAC Catalog 2002-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360110-OB_DAAC.umm_json Measurements from the Chukchi and Beaufort sea in the Arctic region north of Alaska in 2002. proprietary
@@ -2616,8 +2616,8 @@ ARNd0079_103 Franz Josef Land basemap CEOS_EXTRA STAC Catalog 1970-01-01 25, 23
ARNd0082_103 Jan Mayen Island basemap CEOS_EXTRA STAC Catalog 1970-01-01 3.88, 56.69, 32.56, 81.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232849393-CEOS_EXTRA.umm_json Jan Mayen Island coastline. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo generate file of the coastline of Jan Mayen Island Feature_type: arcs Vector ArcInfo generate file representing the coastline of Jan Mayen Island derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Norwegian Polar Institute internal format file of the Jan Mayen coastline Vector Norwegian Polar institute file representing the coastline of Jan Mayen island as received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 3 Vector Name: Several ArcInfo coverages representing basemap information for Jan Mayen Island Source Map Name: DCW Source Map Scale: 1000000 Projection: geographic Projection_desc: lat/long Projection_meas: metres Feature_type: polyarcpt Vector Several ArcInfo coverages representing basemap information for Jan Mayen Island extract from the Digital Chart fo the World CD-ROM. Layers include cultural point features (clpoint), drainage network (dnnet), data quality layer (dqnet), supplemental drainage points (dspoint), hyposography supplemental lines (hsline), hypsography supplemental points (hspoint), hypsography network (hynet), hypsography points (hypoint), political and oceanic boundaries (ponet). Members informations: Attached Vector(s): MemberID: 4 Vector Name: Several ArcInfo coverages representing basemap info for Jan Mayen Island in UTM Source Map Name: DCW Source Map Scale: 1000000 Projection: UTM Projection_desc: zone 29 Projection_meas: metres Feature_type: polyarcpt Vector Several ArcInfo coverages representing basemap information for Jan Mayen Island extract from the Digital Chart fo the World CD-ROM. Layers include drainage network (dnnet), hyposography supplemental lines (hsline), hypsography network (hynet), political and oceanic boundaries (ponet). Associated projection file used is included (geo-utm). proprietary
ARNd0083_103 Iceland basemap CEOS_EXTRA STAC Catalog 1970-01-01 -24.55, 62.81, -12.79, 67.01 https://cmr.earthdata.nasa.gov/search/concepts/C2232849025-CEOS_EXTRA.umm_json Basemap of Iceland Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo generate file of Iceland's coastline Vector ArcInfo generate file representing the coastline of Iceland derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Norwegian Polar Institute internal fromat files of the Icelandic coastline Vector Norwegian Polar Institute internal format files of the Icelandic coastline as received from the Norwegian Polar institute. proprietary
ARNd0084_103 Greenland basemap CEOS_EXTRA STAC Catalog 1970-01-01 -75.34, 56.78, -9.36, 86.6 https://cmr.earthdata.nasa.gov/search/concepts/C2232847703-CEOS_EXTRA.umm_json Basemap information of Greenland Incorrect bounding box Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo generate files of Greenlands coastline Vector Thirteen ArcInfo generate files representing the coastline of Greenland derived from data received from the Norwegian Polar Insitute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Norwegian Polar Institute internal format files of the Greenland coastline Vector Norwegian Polar Institute internal format files representing the coastline of Greenland as received from the norwegian Polar Institute. proprietary
-ARNd0086_103 Alaska basemap CEOS_EXTRA STAC Catalog 1970-01-01 -170, 51, -130, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2232847525-CEOS_EXTRA.umm_json Basemap of Alaska. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo generate file of Alaskan coastline Vector ArcInfo generate file representing the coastline of Alaska derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Norwegian Polar Institute internal format file of Alaskan coastline Vector Norwegian Polar Institute internal format file representing the coastline of Alaska as received from the Norwegian Polar Institute. proprietary
ARNd0086_103 Alaska basemap ALL STAC Catalog 1970-01-01 -170, 51, -130, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2232847525-CEOS_EXTRA.umm_json Basemap of Alaska. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo generate file of Alaskan coastline Vector ArcInfo generate file representing the coastline of Alaska derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Norwegian Polar Institute internal format file of Alaskan coastline Vector Norwegian Polar Institute internal format file representing the coastline of Alaska as received from the Norwegian Polar Institute. proprietary
+ARNd0086_103 Alaska basemap CEOS_EXTRA STAC Catalog 1970-01-01 -170, 51, -130, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2232847525-CEOS_EXTRA.umm_json Basemap of Alaska. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo generate file of Alaskan coastline Vector ArcInfo generate file representing the coastline of Alaska derived from data received from the Norwegian Polar Institute. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Norwegian Polar Institute internal format file of Alaskan coastline Vector Norwegian Polar Institute internal format file representing the coastline of Alaska as received from the Norwegian Polar Institute. proprietary
ARNd0098_103 Basemap - Nordic countries CEOS_EXTRA STAC Catalog 1970-01-01 -15, 35, 45, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2232848873-CEOS_EXTRA.umm_json Nordisk Kartdatabas/Nordic Cartographic Database. Details of the internal boundaries of the Nordic countries co-ordinated by the National Land Survey, Sweden. Area not strictly Scandinavia but Nordic. Members informations: Attached Vector(s): MemberID: 1 Vector Name: Export file of the fylke/lan/district boundaries of the Nordic countries Projection: geographic Projection_desc: lat/long Projection_meas: decimal degrees Feature_type: arcs Vector Export file of the fylke/lan/district boundaries of the Nordic countries. Members informations: Attached Vector(s): MemberID: 2 Vector Name: Export file of the kommune/county boundaries of the Nordic countries Projection: geographic Projection_desc: lat/long Projection_meas: decimal degrees Feature_type: arcs Vector Export file of the kommune/county boundaries of the Nordic countries. Members informations: Attached Vector(s): MemberID: 3 Vector Name: ArcInfo coverage of the coastline/borders of Norway, Sweden and Finland Feature_type: polyarcpt Vector ArcInfo coverage of the coastline/borders of Norway, Sweden and Finland. Members informations: Attached Vector(s): MemberID: 4 Vector Name: ArcInfo coverage of the coastline of Norway and border with Sweden Feature_type: polyarcpt Vector ArcInfo coverage of the coastline of Norway and border with Sweden. Members informations: Attached Vector(s): MemberID: 5 Vector Name: Clipped ver of ArcInfo coverage of coastline/border of Norway, Sweden & Finland Feature_type: polyarcpt Vector Clipped version of ArcInfo coverage of coastline/border of Norway, Sweden and Finland. proprietary
ARNd0105_103 Climate in Norway CEOS_EXTRA STAC Catalog 1970-01-01 3.88, 56.69, 32.56, 81.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232848243-CEOS_EXTRA.umm_json Climatic zones in Norway at different times of the year. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo coverages of climatic zones of Norway at different times of the year Feature_type: polys/arcs Vector ArcInfo coverages of climatic zones of Norway at different times of the year. Coverages include: kl623, klima613, klima622, klima623, klima626, klima626b, klima632, klima632b, klima653, klima653b, klima656, klima656b. Members informations: Attached Vector(s): MemberID: 2 Vector Name: ArcInfo coverage of temperature isolines for January. Feature_type: polyarcpt Vector ArcInfo coverage of temperature isolines for January, including Svalbard. Members informations: Attached Vector(s): MemberID: 3 Vector Name: ArcInfo coverage of temperature isolines for July. Feature_type: polyarcpt Vector ArcInfo coverage of temperature isolines for July, including Svalbard. proprietary
ARNd0117_103 Economic regions of Europe CEOS_EXTRA STAC Catalog 1970-01-01 -15, 35, 45, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2232849218-CEOS_EXTRA.umm_json Economic boundaries within Europe. This shows which countries belong to the E?S, the agreement between countries belonging to EFTA (European Trade Agreement) and the EU. Members informations: Attached Vector(s): MemberID: 1 Vector Name: ArcInfo coverage showing countries belonging to the E?S Source Map Name: WDBII Projection: geographic Projection_meas: decimal degrees Feature_type: polyarcpt Vector ArcInfo coverage showing countries belonging to the E?S, the agreement between countries belonging to EFTA (European Trade Agreement) and the EU. This coverage was taken from the World Databank II data set. Members informations: Attached Vector(s): MemberID: 2 Vector Name: ArcInfo coverage showing countries belonging to the E?S in a polar projection Projection: polar Projection_desc: long 10 0 0/lat 60 0 0 Projection_meas: metres Feature_type: polyarcpt Vector ArcInfo coverage showing countries belonging to the E?S (in a polar projection), the agreement between countries belonging to EFTA (European Trade Agreement) and the EU. This coverage was taken from the World Databank II data set. proprietary
@@ -2628,8 +2628,8 @@ ASAC_1 Basin Analysis of the Permo-Triassic Amery Group, Northern Prince Charles
ASAC_1001_1 Foraging of royal penguins and its relationship to the Antarctic Polar Frontal Zone AU_AADC STAC Catalog 1994-10-22 2000-01-12 158.9, -60, 165, -54.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214305716-AU_AADC.umm_json The factors that control the number of animals in a population are often difficult to understand. However, this basic understanding is central to managing those populations and assessing how they might respond to human induced pressures. For animals living in the Antarctic, like penguins, the marine environment that they depend on for food can vary due to natural events such as El Nino, and potentially due to human induced changes such as global warming. This study uses modern computer technology to track Royal penguins at sea and to monitor their time on land. By relating where the birds go to feed, what they feed on, and how successfully they catch their food to the survival rates of their chicks, this study will describe how fluctuations in a major Antarctic oceanographic feature (the Antarctic Polar Front) can influence the size of the Royal penguin population at Macquarie Island. Information on breeding success, diet and foraging success were collected each year between 1997-2001. Diving behaviour and at-sea movements were also quantified between 1997 and 1999. These data will also be available in the ARGOS satellite tracking database. Attached to this metadata record are ARGOS tracking data collected by Cindy Hull between 1994 and 2000. The tracking data have been collected from 19 different royal penguins. The download file contains a csv file with tracking data. proprietary
ASAC_1002_1 Biodiversity and low temperature biology of Antarctic yeasts AU_AADC STAC Catalog 1996-09-30 1997-03-31 62, -70, 159, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214311737-AU_AADC.umm_json Metadata record for data from ASAC Project 1002 See the link below for public details on this project. Taken from the abstracts of the referenced papers: A morphological and physiological characterization of yeast strains CBS 8908, CBS 8915, CBS 8920, CBS 8925(T) and CBS 8926, isolated from Antarctic soils, was performed. Phylogenetic analyses of the sequences of the D1/D2 regions and the adjacent internal transcribed spacer (ITS) regions of the large-subunit rDNA of these strains placed them into the Tremellales clade of the Hymenomycetes. The sequence data identified strains CBS 8908, CBS 8915 and CBS 8920 as belonging to the species Cryptococcus victoriae. Strains CBS 8925(T) and CBS 8926 were found to represent an unique clade within the Hymenomycetes, with Dioszegia crocea CBS 6714(T) being their closest phylogenetic relative. Fatty acid composition and proteome fingerprint data for these novel strains were also obtained. No sexual state was observed. A novel basidiomycetous species, Cryptococcus statzelliae, is proposed for strains CBS 8925(T) and CBS 8926. ####### Soil, snow and organic material, collected in November 1997 from the Vestfold Hills, Davis Base, Antarctica, were screened for yeasts. Two isolates, which were shown to be indistinguishable by rDNA sequencing and protein analysis by SDS-PAGE, are described in this communication as a novel species, Cryptococcus watticus sp. nov. (type culture, CBS 9496T=NRRL Y-27556T). Sequence analyses of the 26S rDNA D1/D2 region placed C. watticus in the hymenomycetous yeasts in a cluster with Holtermannia corniformis and Cryptococcus nyarrowii. This species has been allocated to the genus Cryptococcus on the basis of physiological and morphological characteristics. ####### In December 1997, 196 soil and snow samples were collected from Vestfold Hills, Davis Base, Antarctica. Two isolates, CBS 8804T (pink colonies) and CBS 8805 (yellow colonies), were shown by proteome analysis and DNA sequencing to represent the same species. Results from the sequencing of the D1/D2 region of the large rDNA subunit placed this species in the hymenomycetous tree in a unique sister clade to the Trichosporonales and the Tremellales. The clade consists of Holtermannia corniformis CBS 6979 and CBS strains 8804T, 8805, 8016, 7712, 7713 and 7743. Morphological and physiological characteristics placed this species in the genus Cryptococcus, with characteristics including the assimilation of D-glucuronate and myo-inositol, no fermentation, positive Diazonium blue B and urease reactions, absence of sexual reproduction and production of starch-like compounds. Fatty acid analysis identified large proportions of polyunsaturated lipids, mainly linoleic (C18:2) and, to a lesser extent, linolenic (C18:3) acids. On the basis of the physiological and phylogenetic data, isolates CBS 8804T and CBS 8805 are described as Cryptococcus nyarrowii sp. nov. ####### Worldwide glaciers are annually retreating due to global overheating and this phenomenon determines the potential lost of microbial diversity represented by psychrophilic microbial population sharing these peculiar habitats. In this context, yeast strains, all unable to grow above 20 degrees C, consisting of 42 strains from Antarctic soil and 14 strains isolated from Alpine Glacier, were isolated and grouped together based on similar morphological and physiological characteristics. Sequences of the D1/D2 and ITS regions of the ribosomal DNA confirmed the previous analyses and demonstrated that the strains belong to unknown species. Three new species are proposed: Mrakia robertii sp. nov. (type strain CBS 8912), Mrakia blollopis sp. nov. (type strain CBS 8921) and a related anamorphic species Mrakiella niccombsii sp. nov. (type strain CBS 8917). Phylogenetic analysis of the ITS region revealed that the new proposed species were closely related to each other within the Mrakia clade in the order Cystofilobasidiales, class Tremellomycetes. The Mrakia clade now contains 8 sub-clades. Teliospores were observed in all strains except CBS 8918 and for the Mrakiella niccombsii strains. proprietary
ASAC_1003_2 Further investigations of the effects of the Nella Dan oil spill AU_AADC STAC Catalog 1994-12-01 1995-03-31 158.76, -54.79, 158.965, -54.48 https://cmr.earthdata.nasa.gov/search/concepts/C1214311757-AU_AADC.umm_json Metadata record for data expected from ASAC Project 1003 Further investigations of the effects of the Nella Dan oil spill on intertidal benthic communities at Macquarie Island: continued recovery of kelp holdfast communities. See the link below for public details on this project. The project investigated spatial variation in kelp holdfast macrofaunal communities 7 years after the initial oil spill. The project was expanded to cover more sites than were sampled in projects 250 (ASAC_250) and 672 (ASAC_672). Results indicated that an impact was still detectable at one of the 3 oiled sites. This dataset contains the 1988 and 1994 data. Holdfast data from the 1994/1995 season is also included (comparing east versus west). The numbers are total individuals of each species that were found in each holdfast sample. This is a basic, though standard, species-abundance matrix. The site codes used in this project are: SB = Sandy Bay SEC = Secluded Bay BB = Buckles Bay GC = Garden Cove GG = Green Gorge GB = Goat Bay HMB = Half Moon Bay BAUER = Bauer Bay Other codes as for oil spill data The first number given after the site code is the site number at that sampling location. The second number is the replicate at that site. Thus sb(1)3 is Sandy Bay site 1, replicate 3. The fields in this dataset are: Species Year Site proprietary
-ASAC_1004_1 Air sampling and analysis from Antarctic firn and ice ALL STAC Catalog 1976-06-30 1998-12-31 111, -66.8, 114, -65.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214305651-AU_AADC.umm_json Air from the ice and firn (compressed snow) of the Antarctic ice sheet will be extracted and measured for atmospheric composition in the past. Gases of interest are greenhouse gases (carbon dioxide, methane, nitrous oxide) and ozone depleting gases (CFCs, halons). The aim is to understand the budgets of these important atmospheric constituents. The ice cores drilled for the gas measurements will also be measured for isotopic ratios and chemical impurities, which provides information about past climate. A download of 'Halocarbon data from Law Dome firn air and from Cape Grim' is available at the url given below. The fields in this dataset are: CFC HCFC HFC Halon Carbon tetrachloride methyl chloroform Age Concentration Uncertainty Methane CH4 Air age C13 CO2 Depth Ice age Methyl bromide Methyl chloride Chloroform Dichloromethane proprietary
ASAC_1004_1 Air sampling and analysis from Antarctic firn and ice AU_AADC STAC Catalog 1976-06-30 1998-12-31 111, -66.8, 114, -65.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214305651-AU_AADC.umm_json Air from the ice and firn (compressed snow) of the Antarctic ice sheet will be extracted and measured for atmospheric composition in the past. Gases of interest are greenhouse gases (carbon dioxide, methane, nitrous oxide) and ozone depleting gases (CFCs, halons). The aim is to understand the budgets of these important atmospheric constituents. The ice cores drilled for the gas measurements will also be measured for isotopic ratios and chemical impurities, which provides information about past climate. A download of 'Halocarbon data from Law Dome firn air and from Cape Grim' is available at the url given below. The fields in this dataset are: CFC HCFC HFC Halon Carbon tetrachloride methyl chloroform Age Concentration Uncertainty Methane CH4 Air age C13 CO2 Depth Ice age Methyl bromide Methyl chloride Chloroform Dichloromethane proprietary
+ASAC_1004_1 Air sampling and analysis from Antarctic firn and ice ALL STAC Catalog 1976-06-30 1998-12-31 111, -66.8, 114, -65.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214305651-AU_AADC.umm_json Air from the ice and firn (compressed snow) of the Antarctic ice sheet will be extracted and measured for atmospheric composition in the past. Gases of interest are greenhouse gases (carbon dioxide, methane, nitrous oxide) and ozone depleting gases (CFCs, halons). The aim is to understand the budgets of these important atmospheric constituents. The ice cores drilled for the gas measurements will also be measured for isotopic ratios and chemical impurities, which provides information about past climate. A download of 'Halocarbon data from Law Dome firn air and from Cape Grim' is available at the url given below. The fields in this dataset are: CFC HCFC HFC Halon Carbon tetrachloride methyl chloroform Age Concentration Uncertainty Methane CH4 Air age C13 CO2 Depth Ice age Methyl bromide Methyl chloride Chloroform Dichloromethane proprietary
ASAC_1005_1 Metal and organic contaminants in marine invertebrates from Antarctica AU_AADC STAC Catalog 1996-09-30 2000-03-31 110, -66, 110, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311738-AU_AADC.umm_json Metadata record for data from ASAC Project 1005 Metal and organic contaminants in marine invertebrates from Antarctica, field study of their concentrations, laboratory study of their toxicities. See the link below for public details on this project. Data from this project are now unrecoverable. Several publications arising from the work are attached to this metadata record, and are available to AAD staff only. Taken from the referenced publications: Bioaccumulation of Cd, Pb, Cu and Zn in the Antarctic gammaridean amphipod Paramoera walkeri was investigated at Casey station. The main goals were to provide information on accumulation strategies of the organisms tested and to verify toxicokinetic models as a predictive tool. The organisms accumulated metals upon exposure and it was possible to estimate significant model parameters of two compartment and hyperbolic models. These models were successfully verified in a second toxicokinetic study. However, the application of hyperbolic models appears to be more promising as a predictive tool for metals in amphipods compared to compartment models, which have failed to adequately predict metal accumulation in experiments with increasing external exposures in previous studies. The following kinetic bioconcentration factors (BCFs) for the theoretical equilibrium were determined: 150-630 (Cd), 1600-7000 (Pb), 1700-3800 (Cu) and 670-2400 (Zn). We find decreasing BCFs with increasing external metal dosing but similar results for treatments with and without natural UV radiation and for the combined effect of different exposure regimes (single versus multiple metal exposure) and/or the amphipod collective involved (Beall versus Denison Island). A tentative estimation showed the following sequence if sensitivity of P. walkeri to an increase of soluble metal exposure: 0.2-3.0 micrograms Cd per litre, 0.12-0.25 micrograms Pb per litre, 0.9-3.0 micrograms Cu per litre and 9-26 micrograms Zn per litre. Thus, the amphipod investigated proved to be more sensitive as biomonitor compared to gammarids from German coastal waters (with the exception of Cd) and to copepods from the Weddell Sea inferred from literature data. ####### This study provides information on LC50 toxicity tests and bioaccumulation of heavy metals in the nearshore Antarctic gammarid, Paramoera walkeri. The 4 day LC50 values were 970 micrograms per litre for copper and 670 micrograms per litre for cadmium. Net uptake rates and bioconcentration factors of these elements were determined under laboratory conditions. After 12 days of exposure to 30 micrograms per litre, the net uptake rates were 5.2 and 0.78 micrograms per gram per day and the bioconcentration factors were 2080 and 311 for copper and cadmium respectively. The body concentrations of copper were significantly correlated with the concentrations of this element in the water. Accumulation of copper and cadmium continued for the entire exposure suggesting that heavy metals concentrations were not regulated to constant concentrations in the body. Using literature data about two compartments (water-animal) first-order kinetic models, a very good agreement was found between body concentrations observed after exposure and model predicted. Exposure of P. walkeri to mixtures of copper and cadmium showed that accumulation of these elements can be assessed by addition of results obtained from single exposure, with only a small degree of uncertainty. The study provides information on the sensitivity of one Antarctic species towards contaminants, and the results were compared with data of similar species from lower latitudes. An important finding is that sensitivity to toxic chemicals and toxicokinetic parameters in the species investigated are comparable with those of non-polar species. The characteristics of bioaccumulation demonstrate that P. walkeri is a circumpolar species with the potential to be a standard biological indicator for use in monitoring programmes of Antarctic nearshore ecosystems. the use of model prediction provide further support to utilise these organisms for biomonitoring. ####### Heavy-metal concentrations were determined in tissues of different species of benthic invertebrates collected in the Casey region where an old waste-disposal tip site is a source of contamination. the species studied included the bivalve Laternula elliptica, starfish Notasterias armata, heart urchins Abatus nimrodi and A. ingens and gammaridean amphipod Paramoera walkeri. The specimens were collected at both reference and contaminated locations where lead was the priority element and copper was the next most important in terms of increased concentrations. The strong association between a gradient of contamination and concentrations in all species tested indicated that they are reflecting well the environmental changes, and that they appear as appropriate biological indicators of heavy-metal contamination. Aspects of the biology of species with different functional roles in the marine ecosystem are discussed in relation to their suitability for wider use in Antarctic monitoring programmes. For example, in terms of heavy-metal bioaccumulation, the bivalve appears as the most sensitive species to detect contamination; the starfish provides information on the transfer of metals through the food web while the heart urchin and gammarid gave indications of the spatial and temporal patterns of the environmental contamination. The information gathered about processes of contaminant uptake and partitioning among different tissues and species could be used in later studies to investigate the behaviour and the source of contaminants. proprietary
ASAC_100_1 Energetics of Lactation and Foraging in Antarctic and Subantarctic Fur Seals at Macquarie Island AU_AADC STAC Catalog 1988-11-01 1995-03-31 158, -54, 159, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214305715-AU_AADC.umm_json Metadata record for data from ASAC Project 100 See the link below for public details on this project. From the abstract of one of the referenced papers: Between November 1988 and March 1989, scats were collected from three species of fur seals (Arctocephalus forsteri, A. gazella and A. tropicalis) at the northern end of Macquarie Island and from A. forsteri between January and March 1989 at the southern end. All fed mainly on fish. For A. gazella/A. tropicalis an average of 99.2% of scats in monthly collections contained fish remains, while for A. forsteri the figure for North Head was 100% and for Hurd Point was 94.9%. Arctocephalus forsteri at Hurd Point took less fish and more penguins than at North Head and there were significant differences in the composiiton of the fish diet in two of three months. At North Head, the fish diet of A. gazella/A. tropicalis differed significantly from that of A. forsteri in three of the five months studied. Food resources for fur seals around Macquarie Island are considered to be less available than they are around Heard Island. proprietary
ASAC_1012_1 Biodiversity and ecophysiology of Antarctic sea-ice bacteria AU_AADC STAC Catalog 1996-07-01 1999-06-30 70, -68, 80, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214311759-AU_AADC.umm_json The data set includes information relevant for the study and description of sea-ice bacteria contains the following dataset subgroups and is organised by REFERENCE number. 1) Isolation data: strain designations (e.g. culture collection names are indicated for type cultures); media used for isolation and routine cultivation; temperature used for incubation; any special conditions (e.g. enrichment conditions) used for isolation; isolation site and type (e.g. sea-ice); availability of the indicated strain from the chief investigator (J. Bowman) 2) Phenotypic data: Includes morphological, physiological and biochemical tests performed. Details on how these were performed are indicated in the relevant reference. 3) Growth/temperature data: data for temperature related growth curves are given where available. Methods are indicated in the associated reference. 4) Fatty acid/chemotaxonomy data: fatty acid and other related data are given where available. Methods are indicated in the associated reference. 5) Genotypic data: data for DNA-guanosine/cytosine-content and genomic DNA:DNA hybridization are shown where available. Methods are indicated in the associated reference. 6) Phylogenetic data: data for sequences are cross-referenced to the GenBank database. In some cases, aligned sequence datasets are available in FASTA format and can be viewed in the programs BIOEDIT (www.mbio.ncsu.edu/BioEdit/bioedit.html) or CLUSTAL W (www.ebi.ac.uk/clustalw). 7) Other related published references which are useful or relevant to the dataset e.g. related sequences published subsequent to the ASAC study proprietary
@@ -2746,8 +2746,8 @@ ASAC_1216_1 Biogenic sediment history from diatom remains in cores along Wilkes
ASAC_1219_AAT_APen_CD_02_1 Cape Denison bird survey, November - December 2002 AU_AADC STAC Catalog 2002-11-01 2002-12-31 142.6506, -67.0144, 142.6911, -67.0036 https://cmr.earthdata.nasa.gov/search/concepts/C1214312554-AU_AADC.umm_json Diana Patterson carried out a census of Adelie Penguins and flying birds at Cape Denison in November, December 2002 at the request of Dr Eric Woehler. Diana was at Cape Denison as part of a Mawson's Huts Foundation expedition. The data include: 1 - Sketches of colony boundaries and nest locations and annotations on a map by Diana. The map was provided to Diana by Eric and showed bird colonies resulting from an earlier survey by Jim and Yvonne Claypole: refer to the metadata record 'Cape Denison Adelie Penguin census, November - December 1999'. GIS data with the polygon (colony) and point (nest) data has been created. 2 - Documents with notes and counts. These data and the data from other bird surveys at Cape Denison are being analysed by Eric and he and Diana intend to publish a paper. The data will then be released. These data have been incorporated into ASAC project 1219 (ASAC_1219). proprietary
ASAC_1219_AAT_APen_CD_97_1 Cape Denison Adelie Penguin census, November - December 1997 AU_AADC STAC Catalog 1997-11-25 1997-12-02 142, -67, 142, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214305766-AU_AADC.umm_json Penguin counts conducted between 25 November and 2 December 1997. The census covered the following areas and rookeries in the Cape Dension area: rookeries north of Gadget Hut, outside Greenholm Hut and both sides of harbour, Penguin Knob, Azimuth Hill, Memorial Hill, Lands End Ridge, below Sorensen's Hut, east of Sorensen's Hut. A total of 24542 penguins were censused for the Cape Denison area, excluding McKellar Island Rookeries. The fields in this dataset are: Area Rookery locations Number proprietary
ASAC_1219_AAT_APen_CD_99_1 Cape Denison Adelie Penguin census, November - December 1999 AU_AADC STAC Catalog 1997-11-25 1997-12-02 142.65, -67.01, 142.69, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214305767-AU_AADC.umm_json Adelie penguin census November - December 1999 by Jim and Yvonne Claypole following their winter at Cape Denison. A shapefile with the colony boundaries is available but counts are not available. On 2 March 2016 David Smith of the Australian Antarctic Data Centre contacted Jim Claypole to see if he and Yvonne still had a copy of the counts as the Australian Antarctic Data Centre does not have a copy of the counts. Jim and Yvonne recall emailing the results of their survey to the Australian Antarctic Division soon after returning to Australia after wintering at Cape Denison in 1999. On 11 April 2016 Jim Claypole advised David that unfortunately they had not been able to find any record of their survey and they didn't have emails from that time. proprietary
-ASAC_1219_AAT_APen_D_73_1 Adelie Penguin Distributions in the Davis Area, Antarctica ALL STAC Catalog 1973-11-08 1973-11-14 77, -69, 79, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214305752-AU_AADC.umm_json This dataset contains data on the habitats, distribution and numbers of Adelie Penguins (Pygoscellis adeliae) along the Vestfold Hills coast (including colonies on the mainland and offshore islands) during November 1973. The data are obtained from counts at the colonies and black and white photographs. Some aerial photographs were taken at Davis in 1981-82 and 1987-88, and will be compared to the results of this survey. The results are listed in the documentation. A total of 174178 26127 breeding pairs were counted. An increase in Adelie penguin population was found at most locations in East Antarctica. Data from this record has been incorporated into a larger Adelie penguin dataset described by the metadata record - Annual population counts at selected Adelie Penguin colonies within the AAT (SOE_seabird_candidate_sp_AP). It also falls under ASAC project 1219 (ASAC_1219). proprietary
ASAC_1219_AAT_APen_D_73_1 Adelie Penguin Distributions in the Davis Area, Antarctica AU_AADC STAC Catalog 1973-11-08 1973-11-14 77, -69, 79, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214305752-AU_AADC.umm_json This dataset contains data on the habitats, distribution and numbers of Adelie Penguins (Pygoscellis adeliae) along the Vestfold Hills coast (including colonies on the mainland and offshore islands) during November 1973. The data are obtained from counts at the colonies and black and white photographs. Some aerial photographs were taken at Davis in 1981-82 and 1987-88, and will be compared to the results of this survey. The results are listed in the documentation. A total of 174178 26127 breeding pairs were counted. An increase in Adelie penguin population was found at most locations in East Antarctica. Data from this record has been incorporated into a larger Adelie penguin dataset described by the metadata record - Annual population counts at selected Adelie Penguin colonies within the AAT (SOE_seabird_candidate_sp_AP). It also falls under ASAC project 1219 (ASAC_1219). proprietary
+ASAC_1219_AAT_APen_D_73_1 Adelie Penguin Distributions in the Davis Area, Antarctica ALL STAC Catalog 1973-11-08 1973-11-14 77, -69, 79, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214305752-AU_AADC.umm_json This dataset contains data on the habitats, distribution and numbers of Adelie Penguins (Pygoscellis adeliae) along the Vestfold Hills coast (including colonies on the mainland and offshore islands) during November 1973. The data are obtained from counts at the colonies and black and white photographs. Some aerial photographs were taken at Davis in 1981-82 and 1987-88, and will be compared to the results of this survey. The results are listed in the documentation. A total of 174178 26127 breeding pairs were counted. An increase in Adelie penguin population was found at most locations in East Antarctica. Data from this record has been incorporated into a larger Adelie penguin dataset described by the metadata record - Annual population counts at selected Adelie Penguin colonies within the AAT (SOE_seabird_candidate_sp_AP). It also falls under ASAC project 1219 (ASAC_1219). proprietary
ASAC_1219_AAT_APen_M_1 Adelie Penguin Distributions in the Mawson Area Antarctica ALL STAC Catalog 1982-01-14 1988-12-20 62, -68, 63, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214305753-AU_AADC.umm_json This dataset contains data on the habitats, distribution and numbers of Adelie Penguins (Pygoscellis adeliae) in the Mawson area, Antarctica during 1981 and 1988. The data are obtained from aerial photographs obtained at various times, during the 1981-82 and 1988-89 seasons. The results are listed in the documentation. Comparisons are made with census data collected in the 1971-72 summer. Data from this record has been incorporated into a larger Adelie penguin dataset described by the metadata record - Annual population counts at selected Adelie Penguin colonies within the AAT (SOE_seabird_candidate_sp_AP). It also falls under ASAC project 1219 (ASAC_1219). proprietary
ASAC_1219_AAT_APen_M_1 Adelie Penguin Distributions in the Mawson Area Antarctica AU_AADC STAC Catalog 1982-01-14 1988-12-20 62, -68, 63, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214305753-AU_AADC.umm_json This dataset contains data on the habitats, distribution and numbers of Adelie Penguins (Pygoscellis adeliae) in the Mawson area, Antarctica during 1981 and 1988. The data are obtained from aerial photographs obtained at various times, during the 1981-82 and 1988-89 seasons. The results are listed in the documentation. Comparisons are made with census data collected in the 1971-72 summer. Data from this record has been incorporated into a larger Adelie penguin dataset described by the metadata record - Annual population counts at selected Adelie Penguin colonies within the AAT (SOE_seabird_candidate_sp_AP). It also falls under ASAC project 1219 (ASAC_1219). proprietary
ASAC_1219_AAT_APen_M_Area_1 Area - population relationships for Adelie Penguin colonies at Mawson. AU_AADC STAC Catalog 1972-11-17 1988-12-20 45, -70, 75, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214305768-AU_AADC.umm_json The relationship between colony area and population density of Adelie Penguins Pygoscelis adeliae was examined to determine whether colony area, measured from aerial or satellite imagery, could be used to estimate population density, and hence detect changes in populations over time. Using maps drawn from vertical aerial photographs of Adelie Penguin colonies in the Mawson region, pair density ranged between 0.1 and 3.1 pairs/m2, with a mean of 0.63 - 0.3 pairs/m2. Colony area explained 96.4% of the variance in colony populations (range 90.4 - 99.6%) for 979 colonies at Mawson. Mean densities were not significantly different among the 19 islands in the region, but significant differences in mean pair density were observed among colonies in Mawson, Whitney Point (Casey, East Antarctica) and Cape Crozier (Ross Sea) populations. This work was completed as part of ASAC project 1219 (ASAC_1219). The fields in this dataset are: Island Latitude Longitude Date Colony area Breeding Pairs Breeding Pairs per square metre Area per nest Number of nests Number of adults proprietary
@@ -2809,8 +2809,8 @@ ASAC_13_1 Human interaction with the Antarctic environment AU_AADC STAC Catalog
ASAC_140_1 Marine Mammal Report of the AAE and BANZARE AU_AADC STAC Catalog 1911-01-01 1931-01-01 18, -67, 178, -33 https://cmr.earthdata.nasa.gov/search/concepts/C1214305793-AU_AADC.umm_json Metadata record for data from ASAC Project 140 See the link below for public details on this project. A published document includes reports of marine mammals from the Sir Douglas Mawson's Australasian Antarctic Expedition of 1911-14 (AAE) and the British, Australian and New Zealand Antarctic Research Expedition of 1929-31 (BANZARE). ). Five typescript reports and three manuscripts from archives of The Mawson Institute for Antarctic Research, University of Adelaide are published. Five deal with marine mammals of the AAE and three are from the BANZARE. A copy of the published ANARE Report is available for download from the provided URL. Australasian Antarctic Expedition of 1911-14. British, Australian and New Zealand Antarctic Research Expedition of 1929-31. proprietary
ASAC_156_1 Natural Freeze-drying of Water-degraded Timber Structures: Feasibility Study AU_AADC STAC Catalog 1990-12-12 1993-02-02 72, -68, 72, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214305795-AU_AADC.umm_json The objectives of this project were: To gather data from a small scale experimental freeze-drying unit, using natural local conditions, with a view to extending the experiment to a larger scale installation sufficient to deal with timbers from archaeological shipwrecks and other timber constructions. The climate of the Vestfold Hills at Davis Base is exceptionally dry and, apart from a short summer period, the temperature is below freezing. The dryness and the low temperature of the area makes it a theoretically ideal location to naturally freeze-dry large water-logged wooden items. A small 2m3 container housing a selection of waterlogged wood has been installed below ground level. Water vapour from the frozen samples is extracted by a wind driven venturi system. Changes in temperature, sample weight, and air pressure are logged and the data are transferred regularly to Canberra. The fields in this dataset are: Date Time Temperature Atmospheric Pressure Ice Weight Wood Weight Windspeed proprietary
ASAC_15_1 Deep Ice Drilling on Law Dome AU_AADC STAC Catalog 1989-01-01 1992-12-31 112.833, -66.7333, 112.8333, -66.733 https://cmr.earthdata.nasa.gov/search/concepts/C1214305794-AU_AADC.umm_json This dataset includes records from ANARE Research Notes 76; The scientific plan for the deep ice drilling on Law Dome. Per the abstract of the ANARE 76 report: Information on the past climatic and environmental conditions which existed on the surface of the earth and in its oceans, atmosphere and cryosphere can be gained by analysis of the solids, gases, water and dissolved matter contained in the Antarctic ice sheet. The Australian Antarctic Division will undertake a deep drilling program in the summer seasons 1989-90, 1990-91 and 1991-92 near the summit of Law Dome, Antarctica, to extract a 1240m ice core using an electromechanical drill in a fluid-filled borehole. The drill site has been selected to give optimum conditions for a detailed study of climatic and other changes. The snow accumulation rate at the site is 530 kg m^-2 a^-1, the surface temperature is -22 degrees C; and there is no evidence of the occurrence of surface melting in the summer months. It will be possible to determine an accurate age-depth scale for the core by counting annual layers, which are expected to be detectable to a depth of about 800m, equivalent to an age of 10,000 years. The age of the basal ice is expected to be of the order of 50,000 years. The report outlines the types of records it is intended to obtain from analysis of the core and surveys of the borehole, their potential applications and scientific justification. The recommended ice core analysis plan suggests the type and frequency of sampling required for the different parameters and describes the types of measurements and observations that will be made; e.g. visible features, oxygen and hydrogen isotope ratios, solid DC-conductivity, density, total gas content, gas composition, trace chemical and particulate content, radio-isotopes, crystal structure, etc. The high accumulation rate and low surface temperature at the site give excellent conditions for gas composition studies with an age resolution to as good as 20 years for the contained gases. It should also be possible to study the changes that have occurred in many parameters since the last ice age, at any temporal resolution from long term trends down to seasonal variations. Surveys of the borehole will be made to determine the vertical temperature profile and deformation rates inside the ice cap. The interrelation and interdependence of the various measurements is discussed. Experience gained from previous drilling and core analysis programs has been drawn upon to design a core processing and analysis plan. The schedule of activities has been arranged to optimise core conservation and the efficiency of the scheme. Available analysis facilities are reviewed and opportunities for collaboration with institutes in Australia and from other nations are highlighted. An outline of the logistic support required for the efficient running of the field program is included. A full environmental impact evaluation has been carried out elsewhere. A summary of the points addressed in the evaluation is included for information. They are specified in accordance with the Australian Antarctic Division guidelines. The fields in this dataset are: Year drilled Location Drilling method Depth of drilling Age at hole bottom Total thickness Mean annual surface temperature Annual accumulation This project was rolled into ASAC project 757 (ASAC_757). proprietary
-ASAC_194_1 A Study of the Nitrogen-fixing Microbiota of Macquarie Island Plant Communities AU_AADC STAC Catalog 1990-12-01 1991-01-31 158, -54.5, 159, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214305779-AU_AADC.umm_json The nitrogen fixing biota of Macquarie Island are dominated by cyanobacteria growing epiphytically or symbiotically with plants or lichens. Highest rates of acetylene reduction (N-fixation) were found in the leafy lichen Peltigera sp. Colonising herbfields and short grasslands, and in the coastal angiosperm Colobanthus muscoides. Significant rates of N-fixation were also associated with the liverwort Jamesoniella colorata commonly occurring in coastal and plateau mires, in a moss-bed of Dicranella cardotii colonising a land-slip face on the grassland slopes at 100m altitude, and within polsters of the mosses Ditrichum strictum and Andreaea sp. found in exposed localities on the plateau at 200-300m altitude. It was concluded that the common feature of plants supporting active N-fixation in dry habitats was the dense packing of stems and leaves, enabling water translocation to the cyanobacterial zone by wick action. Epiphytic cyanobacterial fixation in wet habitats was widespread and not restricted to plant species. This work was published in Polar Biology, 11: 601-606. proprietary
ASAC_194_1 A Study of the Nitrogen-fixing Microbiota of Macquarie Island Plant Communities ALL STAC Catalog 1990-12-01 1991-01-31 158, -54.5, 159, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214305779-AU_AADC.umm_json The nitrogen fixing biota of Macquarie Island are dominated by cyanobacteria growing epiphytically or symbiotically with plants or lichens. Highest rates of acetylene reduction (N-fixation) were found in the leafy lichen Peltigera sp. Colonising herbfields and short grasslands, and in the coastal angiosperm Colobanthus muscoides. Significant rates of N-fixation were also associated with the liverwort Jamesoniella colorata commonly occurring in coastal and plateau mires, in a moss-bed of Dicranella cardotii colonising a land-slip face on the grassland slopes at 100m altitude, and within polsters of the mosses Ditrichum strictum and Andreaea sp. found in exposed localities on the plateau at 200-300m altitude. It was concluded that the common feature of plants supporting active N-fixation in dry habitats was the dense packing of stems and leaves, enabling water translocation to the cyanobacterial zone by wick action. Epiphytic cyanobacterial fixation in wet habitats was widespread and not restricted to plant species. This work was published in Polar Biology, 11: 601-606. proprietary
+ASAC_194_1 A Study of the Nitrogen-fixing Microbiota of Macquarie Island Plant Communities AU_AADC STAC Catalog 1990-12-01 1991-01-31 158, -54.5, 159, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214305779-AU_AADC.umm_json The nitrogen fixing biota of Macquarie Island are dominated by cyanobacteria growing epiphytically or symbiotically with plants or lichens. Highest rates of acetylene reduction (N-fixation) were found in the leafy lichen Peltigera sp. Colonising herbfields and short grasslands, and in the coastal angiosperm Colobanthus muscoides. Significant rates of N-fixation were also associated with the liverwort Jamesoniella colorata commonly occurring in coastal and plateau mires, in a moss-bed of Dicranella cardotii colonising a land-slip face on the grassland slopes at 100m altitude, and within polsters of the mosses Ditrichum strictum and Andreaea sp. found in exposed localities on the plateau at 200-300m altitude. It was concluded that the common feature of plants supporting active N-fixation in dry habitats was the dense packing of stems and leaves, enabling water translocation to the cyanobacterial zone by wick action. Epiphytic cyanobacterial fixation in wet habitats was widespread and not restricted to plant species. This work was published in Polar Biology, 11: 601-606. proprietary
ASAC_2050_1 Antifreeze molecules in Nototheniid fish around Davis Station, Antarctica AU_AADC STAC Catalog 2000-01-01 2001-12-31 77, -70, 79, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214305796-AU_AADC.umm_json A report completed as part of this project is available for download from the URL given below. Extracts of the report are presented in the metadata record. See the report for full details. Several species of Antarctic fish were collected from the shallow waters off Davis Station during the 2000-01 season as part of a study examining the properties of 'antifreeze' proteins contained within the blood of these animals. Fish were sampled at regular intervals from a range of depths and various sites near the station. The main objectives of the study were to collect serum and selected tissues from Nototheniid (cod) and Channichthyid (ice fish) species. Over 170 fish were collected throughout the calendar year. Samples were taken as required, processed and the fish preserved for further analysis on return to Australia. In Australia the serum will be tested for special antifreeze molecules that allow these animals to live in water that is colder than the usual freezing point of their body fluids. Such molecules, once identified, may be synthesised in a laboratory, and have numerous potential practical applications, from the preservation of frozen foods, to preservation of blood plasma and organs for human transplant. Analyses of this nature will be undertaken at the University of Sydney. proprietary
ASAC_2085_1 Ice thickness, mass balance and dynamics of the ice sheet east of Davis, and of the Lambert Glacier AU_AADC STAC Catalog 1997-09-30 2001-03-31 78, -70, 80, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214305799-AU_AADC.umm_json Metadata record for data from ASAC Project 2085 See the link below for public details on this project. ---- Public Summary from Project ---- Over the past 20 years, Australian glaciologists have measured the ice thickness, snow accumulation rate and ice surface movement rate around the Antarctic continent, approximately following the 2,000 m elevation contour. They have completed this survey for the entire Australian Antarctic sector, except for one section between Davis and the Russian station, Mirny. This project will carry out the measurements in this last section. It will also carry out detailed measurements of ice thickness and ice movement rate on the Lambert Glacier and some of its tributaries. This glacier is the largest in the world and it drains about one eighth of the Antarctic ice mass into the sea. From these measurements, calculations of the mass flux (i.e. the amount of ice flowing through the section) are made. Changes over time in the mass flux indicate whether the ice sheet is getting larger or smaller, and this in turn is related to climate and sea level change. This project aims to determine the ice thickness, surface ice velocity and mass discharge of the region between Mirny and the Larsemann Hills. This is the remaining gap in the otherwise comprehensive ANARE measurements of mass discharge across the 2000 m elevation contour between 40E and 130E. Observations were conducted over three summer field seasons from Jan. 1998 to Jan. 2000, with the use two Sikorsky S76 long range helicopters based at Davis. Ice thickness was obtained with the ANARE 100 MHz ice radar mounted in one of the helicopters. The transmitter and receiver configurations are essentially the same as that used on the Lambert Glacier tractor traverse (see Higham et al.,1995). To accommodate speeds of up to 180 km/hr in airborne operations the slower digital oscilloscope system has been replaced by a high speed digital signal processor and a high speed analogue to digital converter. The airborne antenna used by the helicopter is smaller than that used by tractor traverses and the signal processing power of the DSP has been improved to compensate for reduced antenna gain. Ice velocity and surface elevation were measured at selected locations with dual frequency GPS instruments. Accumulation and gravity observations were also made at these sites. An automatic weather station (AWS) was installed at one of the survey sites 50 km south of Mt Brown. In addition to filling a major gap in the synoptic network, the AWS will be used to assist in the interpretation of a shallow snow core. proprietary
ASAC_2122_2 Easily measured call attributes can detect vocal differences between Weddell seals from two areas (Casey and Davis). AU_AADC STAC Catalog 1992-11-13 2010-12-31 78.0833, -68.5666, 110.6666, -66.2 https://cmr.earthdata.nasa.gov/search/concepts/C1214305841-AU_AADC.umm_json "Underwater vocalisations of Weddell seals were recorded at Casey (1997) and Davis (1992 and 1997) Antarctica. The goal of the study was to determine if it would be possible to identify geographic variations between the Casey and Davis seals using easily measured, narrow bandwidth calls (and not broadband or very short duration calls). Two observers measured the starting and ending frequency (Hz), duration (msec) and number of elements (discrete sounds) of four categories of calls; long duration trills, shorter descending frequency whistles, ascending frequency whistles and constant frequency mews. The statistical analyses considered all calls per base, single and multiple element calls, and individual call types. Except for trills, discriminant function analysis indicated less variation between the call attributes from Davis in 1992 and 1997 than between either of the Davis data sets and Casey 1997. The data set contains measures from 2966 calls; approximately 1000 calls per base and year. Up to 100 consecutive calls were measured from each recording location per day of recording so the data set indicates the relative occurrence of each of the call types per base and year. There were very few ascending whistles at Casey. All of the trills and mews contained a single element. This data set was published in Bioacoustics 11: 211-222. The fields in this dataset are: Observer Station Location Time Call Number Call Type Frequency Duration Elements Overlap In 2011, another download file was added to this record, providing recording locations made during the project in 2010. Furthermore: In 1997 Daniela Simon made some opportunistic recordings for the project near Casey. The recording locations were: Berkley Island 110 38'E, 66 12' 40""S Herring Island 110 40'E, 66 25'S O'Brien Bay 110 31'E, 66 18' 30""S Eyres Bay 110 32'E, 66 29"" 20""S The Davis sites: IN 1990 THERE WAS ONLY ONE RECORDING SITE - 78 12.5' E, 68 31.6' S IN 1997 RECORDINGS WERE MADE AT THE FOLLOWING SITES EAST SIDE OF WEDDELL ARM - 78 07.55' E 68 32.17' S PARTIZAN ISLAND - 78 13.66' E 68 29.57' S LONG FJORD - 78 18.95' E 68 30.24' S TOPOGRAV ISLAND - 78 12.40' E 68 29.33'S OFFSHORE - 77 58.73'E 68 26.35'S TRYNE BAY - 78 26.25'E 68 24.87'S LUCAS ISLAND - 77 57.00'E 68 30.36'S WYATT EARP ISLANDS - 78 31.51'E 68 21.31'S ================================================================================ The attached document is ""a listing of the Weddell seal breeding locations near Mawson where Patrick Abgrall in 2000 and Phil Rouget in 2002 made underwater recordings"". The sound recording effort in 2000 was not as high as it was in 2002, hence fewer locations are listed. The Abgrall sites are referred to in the paper 'Variation of Weddell seal underwater vocalizations over mesogeographic ranges' that Abgrall, Terhune Burton co-authored, published in Aquatic mammals in 2003. This paper also refers to the Casey and Davis sites above. The Rouget sites relate to the metadata record 'Weddell Seal underwater calling rates during the winter and spring near Mawson Station, Antarctica' Entry ID: ASAC_1132-1 In general the seals can create breathing holes in areas where tide cracks form, namely close to grounded icebergs, the shoreline and islands. I doubt that they could/would create breathing holes through solid 2 m ice." proprietary
@@ -2821,8 +2821,8 @@ ASAC_2201_1 Natural variability and human induced change in Antarctic nearshore
ASAC_2201_Bacterial_Mat_Infauna_1 Infaunal marine invertebrate fauna inside and outside of bacterial mats, Casey 2006-07 AU_AADC STAC Catalog 2006-11-10 2006-12-07 110.53, -66.28, 110.6, -66.23 https://cmr.earthdata.nasa.gov/search/concepts/C1214305807-AU_AADC.umm_json "Infaunal marine invertebrates were collected from inside and outside of patches of white bacterial mats from several sites in the Windmill Islands, Antarctica, around Casey station during the 2006-07 summer. Samples were collected from McGrady Cove inner and outer, the tide gauge near the Casey wharf, Stevenson's Cove and Brown Bay inner. Sediment cores of 10cm depth and 5cm diameter were collected by divers using a PVC corer from inside (4 cores) and outside (4 cores) each bacterial patch. The size of each patch varied from site to site. Cores were sieved at 500 microns and the extracted fauna preserved in 4 percent neutral buffered formalin. All fauna were counted and identified to species where possible or assigned to morphospecies based on previous infaunal sampling around Casey. An excel spreadsheet is available for download at the URL given below. The spreadsheet does not represent the complete dataset, and is only the bacterial mat infauna data. Regarding the infauna dataset: - in - in the mat or patch of bacteria and out is in the ""normal"" sediment surrounding the patch without evidence of any bacterial mat presence. - Patch numbers were allocated to ensure there was no confusion between patches in the same area. - Fauna names are our identification codes for each species. Some we have confirmed identifications for, some not. Species names, where we have them and as we get them, are listed against these codes in the Casey marine soft-sediment fauna identification guide. This work was completed as part of ASAC 2201 (ASAC_2201)." proprietary
ASAC_2201_Casey_SRE1_1 A manipulative field experiment examining the effect of contaminated sediment on the recruitment and recolonisation of soft-sediment infauna. AU_AADC STAC Catalog 1997-03-05 1997-11-18 110.52252, -66.2941, 110.54701, -66.27913 https://cmr.earthdata.nasa.gov/search/concepts/C1214305812-AU_AADC.umm_json The effect of location, depth and sediment contamination on recruitment of soft-sediment assemblages were examined in a pilot experiment at Casey Station, East Antarctica. Two locations were used, a polluted bay adjacent to an old disused tip site (Brown Bay) and an undisturbed control (O'Brien Bay). At each location two types of defaunated sediment (polluted and control) were placed at 2 depths, 15 m and 25 m. Sediments were left in place over the Austral winter, from March - November. There were large differences in recruitment between the two locations and depths and some differences between the two sediment types. Brown Bay had greater recruitment than O'Brien Bay. Shallow sites had generally greater recruitment than deep, but deep sites had greater diversity (H'), richness (d) and evenness (J'). Control sediment recruited greater numbers of arthropod, gammarid and isopod taxa. There were not only differences in abundance of taxa and assemblage structure but also in spatial variability and variability of populations of certain taxa, with recruitment to the control and deep locations more variable, and recruitment in the control sediment more variable than the polluted sediment. Recruitment was influenced by a combination of location, depth and sediment type. There is some evidence of an environmental impact at the polluted site. The majority of fauna recruiting to the experiment were highly motile colonizing species with non-pelagic lecithotrophic larvae, usually brooded and released as dispersing juveniles, such as gammarids, tanaids, isopods and gastropods. A total of 56 recruitment samples were collected. Samples were sieved at 500 micro metres and sorted mainly to species. Metal concentrations and total organic carbon concentrations are also included. Also links to ASAC 1100. The fields in this dataset are: Species Location Site Treatment (tmt) Site and replicate Toxicity Arsenic Cadmium Copper Lead Silver Zinc proprietary
ASAC_2201_Casey_SRE1_1 A manipulative field experiment examining the effect of contaminated sediment on the recruitment and recolonisation of soft-sediment infauna. ALL STAC Catalog 1997-03-05 1997-11-18 110.52252, -66.2941, 110.54701, -66.27913 https://cmr.earthdata.nasa.gov/search/concepts/C1214305812-AU_AADC.umm_json The effect of location, depth and sediment contamination on recruitment of soft-sediment assemblages were examined in a pilot experiment at Casey Station, East Antarctica. Two locations were used, a polluted bay adjacent to an old disused tip site (Brown Bay) and an undisturbed control (O'Brien Bay). At each location two types of defaunated sediment (polluted and control) were placed at 2 depths, 15 m and 25 m. Sediments were left in place over the Austral winter, from March - November. There were large differences in recruitment between the two locations and depths and some differences between the two sediment types. Brown Bay had greater recruitment than O'Brien Bay. Shallow sites had generally greater recruitment than deep, but deep sites had greater diversity (H'), richness (d) and evenness (J'). Control sediment recruited greater numbers of arthropod, gammarid and isopod taxa. There were not only differences in abundance of taxa and assemblage structure but also in spatial variability and variability of populations of certain taxa, with recruitment to the control and deep locations more variable, and recruitment in the control sediment more variable than the polluted sediment. Recruitment was influenced by a combination of location, depth and sediment type. There is some evidence of an environmental impact at the polluted site. The majority of fauna recruiting to the experiment were highly motile colonizing species with non-pelagic lecithotrophic larvae, usually brooded and released as dispersing juveniles, such as gammarids, tanaids, isopods and gastropods. A total of 56 recruitment samples were collected. Samples were sieved at 500 micro metres and sorted mainly to species. Metal concentrations and total organic carbon concentrations are also included. Also links to ASAC 1100. The fields in this dataset are: Species Location Site Treatment (tmt) Site and replicate Toxicity Arsenic Cadmium Copper Lead Silver Zinc proprietary
-ASAC_2201_Casey_SRE2_1 A manipulative field experiment examining the effect of contaminated sediment on the recruitment of soft-sediment infauna (Mar 1998 - Feb 1999). AU_AADC STAC Catalog 1998-02-11 1999-02-11 110.52252, -66.2941, 110.54701, -66.27913 https://cmr.earthdata.nasa.gov/search/concepts/C1418399552-AU_AADC.umm_json The effect of location and sediment contamination on recruitment of soft-sediment assemblages were examined in field experiment at Casey Station, East Antarctica. Four locations were used, a polluted bay adjacent to an old disused tip site (Brown Bay), a bay adjacent to the Casey Station sewage outfall, and two undisturbed control locations in O'Brien Bay. At each location two types of defaunated sediment (polluted and control) were placed 12 - 18 m, in experimental trays. Half of the experimental sediments were left in place over the Austral winter, from March - November, and the remaining sediments were collected after a total of one year, in February 1999. There were large differences in recruitment between the two locations and significant differences between the polluted and control sediment. There were not only differences in abundance of taxa and assemblage structure but also in spatial variability and variability of populations of certain taxa, with recruitment to the control locations more variable than polluted locations, and recruitment in the control sediment more variable than the polluted sediment. The majority of fauna recruiting to the experiment were highly motile colonizing species with non-pelagic lecithotrophic larvae, usually brooded and released as dispersing juveniles, such as gammarids, tanaids, isopods and gastropods. A total of 64 recruitment samples were collected after 9 months and 52 samples after one year. Samples were sieved at 500 micro m and sorted mainly to species. Samples are rows in data sheet. Site codes include place name (e.g. BB2) and experimental treatment (e.g. C1 - control 1). See accompanying sheet for full details of codes, including species names. Sediment chemistry data are means (and standard errors) for each treatment (averaged over 2 trays). Also links to ASAC 1100. The fields in this dataset are: Species Site Sample Abundance Toxicity Arsenic Cadmium Copper Lead Silver Zinc proprietary
ASAC_2201_Casey_SRE2_1 A manipulative field experiment examining the effect of contaminated sediment on the recruitment of soft-sediment infauna (Mar 1998 - Feb 1999). ALL STAC Catalog 1998-02-11 1999-02-11 110.52252, -66.2941, 110.54701, -66.27913 https://cmr.earthdata.nasa.gov/search/concepts/C1418399552-AU_AADC.umm_json The effect of location and sediment contamination on recruitment of soft-sediment assemblages were examined in field experiment at Casey Station, East Antarctica. Four locations were used, a polluted bay adjacent to an old disused tip site (Brown Bay), a bay adjacent to the Casey Station sewage outfall, and two undisturbed control locations in O'Brien Bay. At each location two types of defaunated sediment (polluted and control) were placed 12 - 18 m, in experimental trays. Half of the experimental sediments were left in place over the Austral winter, from March - November, and the remaining sediments were collected after a total of one year, in February 1999. There were large differences in recruitment between the two locations and significant differences between the polluted and control sediment. There were not only differences in abundance of taxa and assemblage structure but also in spatial variability and variability of populations of certain taxa, with recruitment to the control locations more variable than polluted locations, and recruitment in the control sediment more variable than the polluted sediment. The majority of fauna recruiting to the experiment were highly motile colonizing species with non-pelagic lecithotrophic larvae, usually brooded and released as dispersing juveniles, such as gammarids, tanaids, isopods and gastropods. A total of 64 recruitment samples were collected after 9 months and 52 samples after one year. Samples were sieved at 500 micro m and sorted mainly to species. Samples are rows in data sheet. Site codes include place name (e.g. BB2) and experimental treatment (e.g. C1 - control 1). See accompanying sheet for full details of codes, including species names. Sediment chemistry data are means (and standard errors) for each treatment (averaged over 2 trays). Also links to ASAC 1100. The fields in this dataset are: Species Site Sample Abundance Toxicity Arsenic Cadmium Copper Lead Silver Zinc proprietary
+ASAC_2201_Casey_SRE2_1 A manipulative field experiment examining the effect of contaminated sediment on the recruitment of soft-sediment infauna (Mar 1998 - Feb 1999). AU_AADC STAC Catalog 1998-02-11 1999-02-11 110.52252, -66.2941, 110.54701, -66.27913 https://cmr.earthdata.nasa.gov/search/concepts/C1418399552-AU_AADC.umm_json The effect of location and sediment contamination on recruitment of soft-sediment assemblages were examined in field experiment at Casey Station, East Antarctica. Four locations were used, a polluted bay adjacent to an old disused tip site (Brown Bay), a bay adjacent to the Casey Station sewage outfall, and two undisturbed control locations in O'Brien Bay. At each location two types of defaunated sediment (polluted and control) were placed 12 - 18 m, in experimental trays. Half of the experimental sediments were left in place over the Austral winter, from March - November, and the remaining sediments were collected after a total of one year, in February 1999. There were large differences in recruitment between the two locations and significant differences between the polluted and control sediment. There were not only differences in abundance of taxa and assemblage structure but also in spatial variability and variability of populations of certain taxa, with recruitment to the control locations more variable than polluted locations, and recruitment in the control sediment more variable than the polluted sediment. The majority of fauna recruiting to the experiment were highly motile colonizing species with non-pelagic lecithotrophic larvae, usually brooded and released as dispersing juveniles, such as gammarids, tanaids, isopods and gastropods. A total of 64 recruitment samples were collected after 9 months and 52 samples after one year. Samples were sieved at 500 micro m and sorted mainly to species. Samples are rows in data sheet. Site codes include place name (e.g. BB2) and experimental treatment (e.g. C1 - control 1). See accompanying sheet for full details of codes, including species names. Sediment chemistry data are means (and standard errors) for each treatment (averaged over 2 trays). Also links to ASAC 1100. The fields in this dataset are: Species Site Sample Abundance Toxicity Arsenic Cadmium Copper Lead Silver Zinc proprietary
ASAC_2201_Casey_SRE3_1 A manipulative field experiment examining the effect of heavy metal and hydrocarbon contaminated sediment on the recruitment of soft-sediment infauna. ALL STAC Catalog 1998-12-01 1999-02-17 110.52252, -66.2941, 110.54701, -66.27913 https://cmr.earthdata.nasa.gov/search/concepts/C1214305823-AU_AADC.umm_json The effects of hyrdocarbon and heavy metal contamination of marine sediments on recruitment of soft-sediment assemblages were examined in a field experiment at Casey Station, East Antarctica. Three locations were used, a polluted bay adjacent to an old disused tip site (Brown Bay) and two control locations (O'Brien Bay and Sparkes Bay). At each location three types of defaunated sediment (hydrocarbon treated, heavy metal treated and control) were placed at approximately 15 m depth and left in place for 3 months, from December to February. Sediments were artificially contaminated with hydrocarbons and metals at concentrations which were representative of levels found in sediments at contaminated sites around Casey Station. There were large differences in recruitment between the three locations and significant differences between the control and contaminated sediment. Sediments in the experiment were also examined for evidence of degradation and attenuation of hydrocarbons and heavy metals. A total of 104 recruitment samples were collected. Samples were sieved at 500 micro m and sorted mainly to species. Other work to arise from this experiment includes examination of the effects on diatom communities and microbial communities. Data includes fauna, metals and hydrocarbon concentrations in experiment. Pre-deployment concentrations (before experiment was deployed in water) are indicated as 'pre-deployment'. Concentrations of contaminants in sediments surrounding the experiment (within several metres) are indicated as 'surrounding'. This project also links to ASAC 1100. The fields in this dataset are: Location Site Treatment (tmt) Site and replicate Species Toxicity Arsenic Cadmium Copper Lead Silver Zinc Special Antarctic Blend Fuel (SAB) Lube TPH proprietary
ASAC_2201_Casey_SRE3_1 A manipulative field experiment examining the effect of heavy metal and hydrocarbon contaminated sediment on the recruitment of soft-sediment infauna. AU_AADC STAC Catalog 1998-12-01 1999-02-17 110.52252, -66.2941, 110.54701, -66.27913 https://cmr.earthdata.nasa.gov/search/concepts/C1214305823-AU_AADC.umm_json The effects of hyrdocarbon and heavy metal contamination of marine sediments on recruitment of soft-sediment assemblages were examined in a field experiment at Casey Station, East Antarctica. Three locations were used, a polluted bay adjacent to an old disused tip site (Brown Bay) and two control locations (O'Brien Bay and Sparkes Bay). At each location three types of defaunated sediment (hydrocarbon treated, heavy metal treated and control) were placed at approximately 15 m depth and left in place for 3 months, from December to February. Sediments were artificially contaminated with hydrocarbons and metals at concentrations which were representative of levels found in sediments at contaminated sites around Casey Station. There were large differences in recruitment between the three locations and significant differences between the control and contaminated sediment. Sediments in the experiment were also examined for evidence of degradation and attenuation of hydrocarbons and heavy metals. A total of 104 recruitment samples were collected. Samples were sieved at 500 micro m and sorted mainly to species. Other work to arise from this experiment includes examination of the effects on diatom communities and microbial communities. Data includes fauna, metals and hydrocarbon concentrations in experiment. Pre-deployment concentrations (before experiment was deployed in water) are indicated as 'pre-deployment'. Concentrations of contaminants in sediments surrounding the experiment (within several metres) are indicated as 'surrounding'. This project also links to ASAC 1100. The fields in this dataset are: Location Site Treatment (tmt) Site and replicate Species Toxicity Arsenic Cadmium Copper Lead Silver Zinc Special Antarctic Blend Fuel (SAB) Lube TPH proprietary
ASAC_2201_Casey_tiles_1_mobile_1 A manipulative field experiment examining the recruitment of mobile epifauna to hard-substratum at potentially impacted and control locations. ALL STAC Catalog 1997-11-15 1999-02-23 110.52252, -66.2941, 110.54701, -66.27913 https://cmr.earthdata.nasa.gov/search/concepts/C1214305843-AU_AADC.umm_json The recruitment of mobile epifauna on hard-substratum was examined in a field experiment using tiles. A total of 160 tiles were deployed at five locations, with 32 tiles at each location, arranged in a spatially nested design. There were three potentially impacted locations locations (two in Brown Bay and one in Shannon Bay) and two control locations (in O'Brien Bay). This metadata record describes data from the first sampling time only. Eight tiles were collected from each location 15 months after the initial deployment. The experiment was setup so that the combined recruitment of mobile epifauna to the upper and lower sides of the tiles could be examined. The sessile epifauna on the tiles were also collected and are described in a separate metadata record. A total of 40 samples are included in this data. Also links to ASAC 1100. proprietary
@@ -2831,10 +2831,10 @@ ASAC_2201_Casey_tiles_1_sessile_1 A manipulative field experiment examining the
ASAC_2201_Casey_tiles_1_sessile_1 A manipulative field experiment examining the recruitment of sessile epifauna to hard-substratum at potentially impacted and control locations. AU_AADC STAC Catalog 1997-11-15 1999-02-23 110.52252, -66.2941, 110.54701, -66.27913 https://cmr.earthdata.nasa.gov/search/concepts/C1214305824-AU_AADC.umm_json The recruitment of epifauna (sessile and mobile) on hard-substratum was examined in a field experiment using tiles. A total of 160 tiles were deployed at five locations, with 32 tiles at each location, arranged in a spatially nested design. There were three potentially impacted locations locations (two in Brown Bay and one in Shannon Bay) and two control locations (in O'Brien Bay). This metadata record describes data from the first sampling time only. Eight tiles were collected from each location 15 months after the initial deployment. The experiment was setup so that recruitment of sessile epifauna to both the upper and lower sides of the tiles could be examined. The mobile epifauna on the tiles were also collected and are described in a separate metadata record. Heavy recruitment was observed on the underside of the tile and only light recruitment was observed on the upper surface. Also links to ASAC 1100. proprietary
ASAC_2201_Depth_Experiment_1 Depth related changes in the composition of marine soft sediment infaunal invertebrate communities - faunal composition data - Casey 2006/07. AU_AADC STAC Catalog 2006-11-09 2006-12-14 110.516, -66.312, 110.575, -66.275 https://cmr.earthdata.nasa.gov/search/concepts/C1214305825-AU_AADC.umm_json Depth related changes in the composition of infaunal invertebrate communities were investigated at two sites in the Windmill Islands around Casey station, East Antarctica, during the 2006/07 summer. Sediment cores (10cm deep x 10cm diameter) were collected from 4 depths (7m, 11m, 17, and 22m) from each of three transects at two sites (McGrady Cove and O'Brien Bay 1). Cores were sieved through a 500 micron mesh and extracted fauna were preserved in 8% formalin and were later counted and identified to species or to morphospecies established through previous infaunal research at Casey. This work was conducted as part of ASAC 2201 (ASAC_2201). proprietary
ASAC_2201_Depth_Sediment_1 Depth related changes in the composition of marine soft sediment infaunal invertebrate communities - sediment characteristics data - Casey 2006/07. AU_AADC STAC Catalog 2006-11-09 2006-12-14 110.516, -66.312, 110.575, -66.275 https://cmr.earthdata.nasa.gov/search/concepts/C1214305844-AU_AADC.umm_json Depth related changes in sediment characteristics and the composition of infaunal invertebrate communities were investigated at two sites in the Windmill Islands around Casey station, East Antarctica, during the 2006/07 summer. Sediment characteristics were investigated via sediment cores (5cm deep x 5cm diameter) collected from 4 depths (7m, 11m, 17, and 22m) from each of three transects at two sites (McGrady Cove and O'Brien Bay 1). Measured sediment characteristics included grain size distribution, total organic carbon and the concentration of a range of heavy metals. This work was conducted as part of ASAC 2201 (ASAC_2201). proprietary
-ASAC_2201_HCL_0.5_1 0.5 hour 1 M HCl extraction data for the Windmill Islands marine sediments ALL STAC Catalog 1997-10-01 1999-03-31 110, -66, 110, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214305813-AU_AADC.umm_json These results are for the 0.5 hour extraction of HCl. See also the metadata records for the 4 hour extraction of HCl, and the time trial data for 1 M HCl extractions. A regional survey of potential contaminants in marine or estuarine sediments is often one of the first steps in a post-disturbance environmental impact assessment. Of the many different chemical extraction or digestion procedures that have been proposed to quantify metal contamination, partial acid extractions are probably the best overall compromise between selectivity, sensitivity, precision, cost and expediency. The extent to which measured metal concentrations relate to the anthropogenic fraction that is bioavailable is contentious, but is one of the desired outcomes of an assessment or prediction of biological impact. As part of a regional survey of metal contamination associated with Australia's past waste management activities in Antarctica, we wanted to identify an acid type and extraction protocol that would allow a reasonable definition of the anthropogenic bioavailable fraction for a large number of samples. From a kinetic study of the 1 M HCl extraction of two certified Certified Reference Materials (MESS-2 and PACS-2) and two Antarctic marine sediments, we concluded that a 4 hour extraction time allows the equilibrium dissolution of relatively labile metal contaminants, but does not favour the extraction of natural geogenic metals. In a regional survey of 88 marine samples from the Casey Station area of East Antarctica, the 4 h extraction procedure correlated best with biological data, and most clearly identified those sediments thought to be contaminated by runoff from abandoned waste disposal sites. Most importantly the 4 hour extraction provided better definition of the low to moderately contaminated locations by picking up small differences in anthropogenic metal concentrations. For the purposes of inter-regional comparison, we recommend a 4 hour 1 M HCl acid extraction as a standard method for assessing metal contamination in Antarctica. The fields in this dataset are Location Site Replicate Antimony Arsenic Cadmium Chromium Copper Iron Lead Manganese Nickel Silver Tin Zinc proprietary
ASAC_2201_HCL_0.5_1 0.5 hour 1 M HCl extraction data for the Windmill Islands marine sediments AU_AADC STAC Catalog 1997-10-01 1999-03-31 110, -66, 110, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214305813-AU_AADC.umm_json These results are for the 0.5 hour extraction of HCl. See also the metadata records for the 4 hour extraction of HCl, and the time trial data for 1 M HCl extractions. A regional survey of potential contaminants in marine or estuarine sediments is often one of the first steps in a post-disturbance environmental impact assessment. Of the many different chemical extraction or digestion procedures that have been proposed to quantify metal contamination, partial acid extractions are probably the best overall compromise between selectivity, sensitivity, precision, cost and expediency. The extent to which measured metal concentrations relate to the anthropogenic fraction that is bioavailable is contentious, but is one of the desired outcomes of an assessment or prediction of biological impact. As part of a regional survey of metal contamination associated with Australia's past waste management activities in Antarctica, we wanted to identify an acid type and extraction protocol that would allow a reasonable definition of the anthropogenic bioavailable fraction for a large number of samples. From a kinetic study of the 1 M HCl extraction of two certified Certified Reference Materials (MESS-2 and PACS-2) and two Antarctic marine sediments, we concluded that a 4 hour extraction time allows the equilibrium dissolution of relatively labile metal contaminants, but does not favour the extraction of natural geogenic metals. In a regional survey of 88 marine samples from the Casey Station area of East Antarctica, the 4 h extraction procedure correlated best with biological data, and most clearly identified those sediments thought to be contaminated by runoff from abandoned waste disposal sites. Most importantly the 4 hour extraction provided better definition of the low to moderately contaminated locations by picking up small differences in anthropogenic metal concentrations. For the purposes of inter-regional comparison, we recommend a 4 hour 1 M HCl acid extraction as a standard method for assessing metal contamination in Antarctica. The fields in this dataset are Location Site Replicate Antimony Arsenic Cadmium Chromium Copper Iron Lead Manganese Nickel Silver Tin Zinc proprietary
-ASAC_2201_HCL_4_1 4 hour 1 M HCl extraction data for the Windmill Islands marine sediments ALL STAC Catalog 1997-10-01 1999-03-31 110, -66, 110, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214305845-AU_AADC.umm_json These results are for the 4 hour extraction of HCl. See also the metadata records for the 0.5 hour extraction of HCl, and the time trial data for 1 M HCl extractions. A regional survey of potential contaminants in marine or estuarine sediments is often one of the first steps in a post-disturbance environmental impact assessment. Of the many different chemical extraction or digestion procedures that have been proposed to quantify metal contamination, partial acid extractions are probably the best overall compromise between selectivity, sensitivity, precision, cost and expediency. The extent to which measured metal concentrations relate to the anthropogenic fraction that is bioavailable is contentious, but is one of the desired outcomes of an assessment or prediction of biological impact. As part of a regional survey of metal contamination associated with Australia's past waste management activities in Antarctica, we wanted to identify an acid type and extraction protocol that would allow a reasonable definition of the anthropogenic bioavailable fraction for a large number of samples. From a kinetic study of the 1 M HCl extraction of two certified Certified Reference Materials (MESS-2 and PACS-2) and two Antarctic marine sediments, we concluded that a 4 hour extraction time allows the equilibrium dissolution of relatively labile metal contaminants, but does not favour the extraction of natural geogenic metals. In a regional survey of 88 marine samples from the Casey Station area of East Antarctica, the 4 h extraction procedure correlated best with biological data, and most clearly identified those sediments thought to be contaminated by runoff from abandoned waste disposal sites. Most importantly the 4 hour extraction provided better definition of the low to moderately contaminated locations by picking up small differences in anthropogenic metal concentrations. For the purposes of inter-regional comparison, we recommend a 4 hour 1 M HCl acid extraction as a standard method for assessing metal contamination in Antarctica. The fields in this dataset are Location Site Replicate Antimony Arsenic Cadmium Chromium Copper Iron Lead Manganese Nickel Silver Tin Zinc proprietary
+ASAC_2201_HCL_0.5_1 0.5 hour 1 M HCl extraction data for the Windmill Islands marine sediments ALL STAC Catalog 1997-10-01 1999-03-31 110, -66, 110, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214305813-AU_AADC.umm_json These results are for the 0.5 hour extraction of HCl. See also the metadata records for the 4 hour extraction of HCl, and the time trial data for 1 M HCl extractions. A regional survey of potential contaminants in marine or estuarine sediments is often one of the first steps in a post-disturbance environmental impact assessment. Of the many different chemical extraction or digestion procedures that have been proposed to quantify metal contamination, partial acid extractions are probably the best overall compromise between selectivity, sensitivity, precision, cost and expediency. The extent to which measured metal concentrations relate to the anthropogenic fraction that is bioavailable is contentious, but is one of the desired outcomes of an assessment or prediction of biological impact. As part of a regional survey of metal contamination associated with Australia's past waste management activities in Antarctica, we wanted to identify an acid type and extraction protocol that would allow a reasonable definition of the anthropogenic bioavailable fraction for a large number of samples. From a kinetic study of the 1 M HCl extraction of two certified Certified Reference Materials (MESS-2 and PACS-2) and two Antarctic marine sediments, we concluded that a 4 hour extraction time allows the equilibrium dissolution of relatively labile metal contaminants, but does not favour the extraction of natural geogenic metals. In a regional survey of 88 marine samples from the Casey Station area of East Antarctica, the 4 h extraction procedure correlated best with biological data, and most clearly identified those sediments thought to be contaminated by runoff from abandoned waste disposal sites. Most importantly the 4 hour extraction provided better definition of the low to moderately contaminated locations by picking up small differences in anthropogenic metal concentrations. For the purposes of inter-regional comparison, we recommend a 4 hour 1 M HCl acid extraction as a standard method for assessing metal contamination in Antarctica. The fields in this dataset are Location Site Replicate Antimony Arsenic Cadmium Chromium Copper Iron Lead Manganese Nickel Silver Tin Zinc proprietary
ASAC_2201_HCL_4_1 4 hour 1 M HCl extraction data for the Windmill Islands marine sediments AU_AADC STAC Catalog 1997-10-01 1999-03-31 110, -66, 110, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214305845-AU_AADC.umm_json These results are for the 4 hour extraction of HCl. See also the metadata records for the 0.5 hour extraction of HCl, and the time trial data for 1 M HCl extractions. A regional survey of potential contaminants in marine or estuarine sediments is often one of the first steps in a post-disturbance environmental impact assessment. Of the many different chemical extraction or digestion procedures that have been proposed to quantify metal contamination, partial acid extractions are probably the best overall compromise between selectivity, sensitivity, precision, cost and expediency. The extent to which measured metal concentrations relate to the anthropogenic fraction that is bioavailable is contentious, but is one of the desired outcomes of an assessment or prediction of biological impact. As part of a regional survey of metal contamination associated with Australia's past waste management activities in Antarctica, we wanted to identify an acid type and extraction protocol that would allow a reasonable definition of the anthropogenic bioavailable fraction for a large number of samples. From a kinetic study of the 1 M HCl extraction of two certified Certified Reference Materials (MESS-2 and PACS-2) and two Antarctic marine sediments, we concluded that a 4 hour extraction time allows the equilibrium dissolution of relatively labile metal contaminants, but does not favour the extraction of natural geogenic metals. In a regional survey of 88 marine samples from the Casey Station area of East Antarctica, the 4 h extraction procedure correlated best with biological data, and most clearly identified those sediments thought to be contaminated by runoff from abandoned waste disposal sites. Most importantly the 4 hour extraction provided better definition of the low to moderately contaminated locations by picking up small differences in anthropogenic metal concentrations. For the purposes of inter-regional comparison, we recommend a 4 hour 1 M HCl acid extraction as a standard method for assessing metal contamination in Antarctica. The fields in this dataset are Location Site Replicate Antimony Arsenic Cadmium Chromium Copper Iron Lead Manganese Nickel Silver Tin Zinc proprietary
+ASAC_2201_HCL_4_1 4 hour 1 M HCl extraction data for the Windmill Islands marine sediments ALL STAC Catalog 1997-10-01 1999-03-31 110, -66, 110, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214305845-AU_AADC.umm_json These results are for the 4 hour extraction of HCl. See also the metadata records for the 0.5 hour extraction of HCl, and the time trial data for 1 M HCl extractions. A regional survey of potential contaminants in marine or estuarine sediments is often one of the first steps in a post-disturbance environmental impact assessment. Of the many different chemical extraction or digestion procedures that have been proposed to quantify metal contamination, partial acid extractions are probably the best overall compromise between selectivity, sensitivity, precision, cost and expediency. The extent to which measured metal concentrations relate to the anthropogenic fraction that is bioavailable is contentious, but is one of the desired outcomes of an assessment or prediction of biological impact. As part of a regional survey of metal contamination associated with Australia's past waste management activities in Antarctica, we wanted to identify an acid type and extraction protocol that would allow a reasonable definition of the anthropogenic bioavailable fraction for a large number of samples. From a kinetic study of the 1 M HCl extraction of two certified Certified Reference Materials (MESS-2 and PACS-2) and two Antarctic marine sediments, we concluded that a 4 hour extraction time allows the equilibrium dissolution of relatively labile metal contaminants, but does not favour the extraction of natural geogenic metals. In a regional survey of 88 marine samples from the Casey Station area of East Antarctica, the 4 h extraction procedure correlated best with biological data, and most clearly identified those sediments thought to be contaminated by runoff from abandoned waste disposal sites. Most importantly the 4 hour extraction provided better definition of the low to moderately contaminated locations by picking up small differences in anthropogenic metal concentrations. For the purposes of inter-regional comparison, we recommend a 4 hour 1 M HCl acid extraction as a standard method for assessing metal contamination in Antarctica. The fields in this dataset are Location Site Replicate Antimony Arsenic Cadmium Chromium Copper Iron Lead Manganese Nickel Silver Tin Zinc proprietary
ASAC_2201_Long-term_Sediment_Metals_1 Heavy metal concentrations in marine sediments around Casey station, East Antarctica - long-term monitoring AU_AADC STAC Catalog 1997-10-22 2006-12-07 110.516, -66.35, 110.6, -66.23 https://cmr.earthdata.nasa.gov/search/concepts/C1214305826-AU_AADC.umm_json Sediment cores (5cm diameter x 10cm deep), collected as part of the long-term monitoring of the Thala Valley waste disposal site clean-up (Casey station), were sectioned and a portion of each core analysed for a range of heavy metals. Metals were extracted from the sediment via a 4 hour 1M HCl acid extraction. Concentrations were gained from ICP-MS analysis of the resulting extracts (ICP-MS conducted at the School of Chemistry, University of Tasmania). Cores were collected from various control and potentially impacted sites in the Windmill Islands around Casey station. This work was conducted as part of ASAC 2201 (ASAC_2201). proprietary
ASAC_2201_Runcie_1 Macroalgal responses to heavy metals and varying light levels at Casey Station AU_AADC STAC Catalog 2001-09-28 2002-12-30 110, -66.5, 110.5, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214305815-AU_AADC.umm_json 1. In situ chlorophyll fluorescence measurements using pulse amplitude technique (PAM) of macroalga Desmarestia menziesii, assessing adaptation to high light exposure after sea ice breakout, and impact of Thala Valley tip wastes. 2. In situ chlorophyll fluorescence measurements using pulse amplitude technique (PAM) of sediment diatom material assessing adaptation to high light exposure after sea ice breakout, and impact of Thala Valley tip wastes. 3. In situ chlorophyll fluorescence measurements using pulse amplitude technique (PAM) of sponge Latrunculia decipiens assessing adaptation to high light exposure after sea ice breakout. 4. Ecotoxicological experiments where Desmarestia menziesii was exposed to copper in indoor aquaria, aim to determine EC50, NOEC, LOEC for copper. 5. Field collections of various macroalgae for stable isotope analysis: for determination of physiological mechanisms. 6. Field collections of sponge and diatom material for pigment analysis. proprietary
ASAC_2201_field_lab_books_1 Copies of field and lab books associated with ASAC project 2201 - Natural variability and human induced change in Antarctic nearshore marine benthic communities AU_AADC STAC Catalog 1997-10-01 2012-03-31 78, -68, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214312630-AU_AADC.umm_json These are the scanned electronic copies of field and lab books used at Casey Station and Davis Station between 1997 and 2012 as part of ASAC (AAS) project 2201 - Natural variability and human induced change in Antarctic nearshore marine benthic communities. proprietary
@@ -2869,8 +2869,8 @@ ASAC_2348_1 Exopolysaccharides from Antarctic bacteria AU_AADC STAC Catalog 2003
ASAC_2350_1 Boron in Antarctic granulite-facies rocks: under what conditions is boron retained in the middle crust? AU_AADC STAC Catalog 1997-01-27 76.1, -69.6, 76.4, -69.1 https://cmr.earthdata.nasa.gov/search/concepts/C1214312573-AU_AADC.umm_json This metadata record describes data collected as part of ASAC project 2350 - Boron in Antarctic granulite-facies rocks: under what conditions is boron retained in the middle crust? As a direct result of the field mapping during this project (and previous fieldwork by myself and others) 'we' have produced a 1:25000 map of the geology of the Larsemann Hills. This was collaboration between the AAD and Geoscience Australia (with considerable assistance by Phil O'Brien and Henk Brolsma) and published by GA earlier in 2007. The map is referenced below. Additionally, several papers are linked to this record, plus copies of the field report and two documents which details the photos taken, and the locations of the field sites. Extended abstract The Larsemann Hills region is dominated by two major lithological associations, a Palaeoproterozoic felsic/mafic orthogneiss complex (Sostrene Orthogneiss) which occurs as basement to a sequence of pelitic, psammitic and felsic paragneiss (supergroup = Brattstrand Paragneiss) and felsic intrusives. The depositional age of the Brattstrand Paragneiss sequences are controversial but isotopic data suggest derivation from the basement Sostrene Orthogneiss. Current geochronology indicates that the region experienced medium to low pressure granulite-facies metamorphism during the Early Palaeozoic (~500 Ma). Although the paragneiss sequences record no evidence of earlier metamorphism, relicts of a previous metamorphic event at ~1000 Ma are preserved in the Sostrene Orthogneiss. Within the Larsemann Hills region, the Early Palaeozoic event is characterised by peak metamorphism of ~7 kbar at ~800-850 degrees C, with the post-peak evolution characterised by decompression, with some cooling, to 4 kbars at 750 degrees C, then to 2-3 kbar at 600-650 degrees C during final stages of orogenesis, with exhumation largely driven by crustal extension. Tectonic models generally argue for a continental-continental collisional scenario, with thermal input derived from a thinned mantle lithosphere. Structural evolution The various high-grade structural frameworks proposed by different workers have been distilled by Fitzsimons (1997) into three major events Da, Db and Dc which broadly correlates D1, D2 and D3 proposed by Stuwe et al. (1989), Thost et al. (1994), Carson et al. (1995b) and D1, D2 and D3-D6 of Dirks and Hand (1995) and D3, D4 and D5 of Fitzsimons and Harley (1991). Within the Larsemann Hills, the dominant outcrop structures are attributed to Db (using the nomenclature of Fitzsimons, 1997). Db can be sub-divided into low and high strain zones, low strain zones preserve complex multiple fold generations that fold lithological layering (Da) and high-strain zones which transpose Da into a new planar gneissosity, Db. Similarly, Dc high-strain zones overprint and locally transposes Db structures, which are completely replaced by a new gneissic layering, Sc, and mineral lineation, Lc, in the northern and southern regions of the Larsemann Hills. Much of the Larsemann Hills is, therefore, a window of Dc low-strain in which Db structures are preserved, although these are reorientated by large, relatively open, upright Dc low-strain folds. Fold hinges and mineral extension lineations preserved on gneissic surfaces within both domains are co-linear and have a characteristic orientation; easterly to southerly plunging for Db and consistently south-west plunging for Dc. The major difference of the structural scheme of Carson et al. (1995) and Dirks and Hand (1995) from other schemes is they present kinematic indicators and argue that Db is characterised by crustal compression along an easterly transport vector (D2 in their scheme), and a extensional domain, Dc, developed along a southwesterly transport vector (D3). They also argue on the basis of the co-linear nature of structures in both low- and high-strain zones within each domain that both low-strain and high-strain zones evolved synchronously and represent components within one structural episode rather than indicating overprinting relationships (e.g. as both Sa and Sb have parallel linear structural elements, then Sa and Sb developed synchronously). The description of two structural domains, characterised by parallel linear elements and, particularly, kinematics is a structural interpretation that is critical to the structural model proposed by Carson et al. (1995) and Dirks and Hand (1995). Post high-grade deformation is confined to the development of up to 20 cm wide, amphibolite-grade mylonite zones that formed along and within planar north-south trending garnet-sillimanite-spinel bearing pegmatites (Dirks et al., 1993; Carson et al., 1995). Movement sense is typically dextral, east-down along a moderately south-pitching sillimanite lineation and offsets are less than 20 metres. The Map A draft geological map (scale 1:25 000) of the Larsemann Hills was generated by Rupert Summerson (National Resource Information Centre) and Dr Doug E Thost (AGSO, = Geoscience Australia, GA) for the Australian Antarctic Division (AAD) on the 27 Jan 1997. Geological information depicted on that map is derived from a number of sources, primarily from unpublished field data of Carson (1991/92, 1992/93 and 1993/94), Carson et al. (1995b) and Stuwe et al. (1989), with additional geological interpretation by Doug Thost. That map was not published. With a view to upgrading that draft map to publication, Dr Chris Carson added new unpublished field data from Stornes Peninsula (Carson and Grew 2003/04, ASAC 2350) and appended and corrected known errors that existed on the original draft map. The current map therefore combines elements of the original draft geological map and the new geological information acquired by Carson and Grew (2003/04). The map is primarily a lithological map, illustrating the distribution of primary rock types present in the Larsemann Hills region. This work was conducted at SKM Consulting, 214 Northbourne Ave Canberra, between 29 March and 30 April 2004. Carson was assisted by Bruce Donaldson (MapInfo) and Gordon Sue (ArcView). The current map is overlaid on topographic information provided by Henk Brolsma of the AAD (coastline, rock boundaries, lakes, snowfields etc) that have been previously digitised from aerial photography flown on Jan/Feb 1998, at an elevation of 3000m. The mapping conducted by Carson and Grew during ASAC 2350 used two air photos covering the bulk of northern Stornes Peninsula (ANTC1063, Run 3 frame 96) and the outcrops between the southern end of Thala Fjord and the eastern end of Wilcock Bay (ANTC1063 run 5, frame 16). These photos were projected onto the WGS 1984 using UTM (zone 43) geographical co-ordinates and were then ortho-rectified using contour information based on the 1998 aerial photography to accurately match the provided topographic data. The new geological map was drafted in MapInfo v_7, lithological contacts were digitised and are either self enclosed or terminate at snow, lake, ice or coastline arcs, or another lithological boundary. The MapInfo layers containing the new geology polylines or arcs and the coastline, rock_bdy and snow polylines (supplied by AAD as *.shx autoCAD files) were then transferred to ArcView. Rock boundaries Many of the lithological boundaries defined on this map are approximate. This is a function of the diffuse, subtle and gradational nature of many of the rock boundaries in this complex high-grade geological terrain. Many of the lithological boundaries on Broknes Peninsula are approximate for this reason. Furthermore, many workers have acquired the geological information contained in this map over some 20 years. Many of the original notes, primary information, air photo overlays and detailed site data have been misplaced (or otherwise unavailable) during intervening years, preventing detailed reference to the primary source of geological information and some lithological boundaries may be derived from geological maps from published manuscripts. Lithological boundaries on Stornes Peninsula are generally accurate, largely due to the rather distinctive nature of the rock types found there, but also the cleaner nature of the rock surface, i.e. the lack of a deeply weathered surface, and access to superior recent colour air photo set which allows a better determination of the lithological boundaries. Renamed rock units Rock units originally represented in this current map have been provisionally reassessed and renamed according to naming systematics according to GA requirements. Many of the names listed in Carson et al. (1995b) and Stuwe et al. (1989) and CHINARE publications have been superseded. Fitzsimons (1997) subdivided all rocks types in southern Prydz Bay into two broad divisions; the Sostrene Orthogneiss and the Brattstrand Paragneiss. All of the metasedimentary units described here are formations within the Brattstrand paragneiss. The Brattstrand paragneiss is tentatively listed as the supergroup in stratigraphic terms within which all the listed formations or rock units occur. All Grid References (GR easting, northing) listed below are taken from the 1:25 000 topographic map published by the AAD in March 1991. - Psammite1 and psammite2 (from Carson et al. 1995b) have been unified on the basis that they are essentially and practically indistinguishable in the field. Renamed Gentner psammite based on the name of the peak on western Broknes Peninsula where outcrops of Gentner psammite are present, although the unit is widespread through out Larsemann Hills. This unit is described as a quartzo-feldspathic psammite, with variable amounts of garnet and biotite. May contain small pods of sillimanite-spinel and/or magnetite and hosts lenses of hornblende-plagioclase (*biotite, *opx) metabasite. Contacts with other units are gradational and diffuse, and as such it is difficult to place lithological boundaries with any certainty. - White Hill leucogneiss, named after distinctive unit on White Hill, central Stornes Peninsula (* White gneiss of Stuwe et al. 1989, * felsic cordierite gneiss of Carson et al. 1995b). Light grey leucocratic gneiss, variable biotite, quartz and plagioclase, locally contains 1-5 cm dia. course grained cordierite+quartz symplectites, with tightly folded K-feldspar bearing veins or leucosomes. Unit may locally be rarely garnet bearing. Possibly of volcanic derivation. Forms topographic highs as ridges and domes. Unit is best-observed at the type locality at White Hill (GR 543005 2299450) though many examples exist on Central eastern Stornes Peninsula. - Stuwe pelite (Stuwe et al. 1989, blue gneiss; Carson et al. 1995b composite pelite2). Characteristic dark coloured, sillimanite dominated pelite, variable amounts of cordierite usually greater than 25%, minor magnetite and/or spinel, and contains isoclinally folded leucosomes dominated by orange microcline. May contain large pods of sillimanite. Good example of this unit is on Gneiss Peak, western Stornes Peninsula. - Lake Ferris pelite. Garnet magnetite and/or spinel pelite with variable amounts of accessory sillimanite and cordierite (* pelite3 of Carson et al. 1995b). Typical example on ridges immediately south of Lake Ferris (grid reference 542800 2296750), and is relatively common on Stornes Peninsula. - Stornes gneiss. Grey biotite plagioclase gneiss, with characteristic layers and pods of course grained prismatine (= B-kornerupine) typically with fresh cordierite and biotite. Prismatine+cordierite+biotite pods may contain accessory grandidierite as mm scale needle-like crystals. Unit contains narrow dismembered K-feldspar leucosomes. Possible volcanic protolith (contain abundant prismatine layers and apatite pods, the unit is thus highly enriched in boron and phosphorus). The Mg-phosphate, wagnerite (Ren et al., 2003), is also found in the Stornes gneiss (central to western Stornes Peninsula) along discrete conformable layers. Individual orange subhedral crystals may reach 3 cm in diameter! - Thala tourmaline meta-quartzites. A package of tightly folded black granular (sugary) tourmaline quartzites interlayered with yellow quartzo-feldspathic psammites. Thala metaquartzite typically contain abundant borosilicates e.g. grandidierite and prismatine and phosphate minerals (possibly apatite or wagnerite). Good examples at grid reference 543400 2295600 on outcrops to SE of ice dome. Named after nearby Thala Fjord (to the east). Thala tourmaline quartzites may also appear as discontinous lenses or pods within the prismatine-bearing Stornes gneiss. May be a genetic relationship between Thala meta-quartzites and the borosilicate-rich Stornes gneiss. These units are also described in Carson et al (1995b). - Broknes paragneiss. Yellow-pale coloured garnet- and biotite-bearing felsic paragneiss, with rare to minor sillimanite, spinel, and cordierite. Renamed unit - the semi-pelite of Carson et al. (1995) and the yellow gneiss of Stuwe et al (1989). Named after Broknes Peninsula where this unit is widespread. - Tumbledown Hill meta-quartzites. Generally thin (1-10m wide) rusty coloured package of biotite psammite and dark glassy quartzite (with rare garnet) layers. Commonly with malachite staining on crusty surface. Typically deeply weathered. Clearly sulphide bearing based on weathering colourations with rare pyrrhotite observed. Named after Tumbledown Hill (GR 542200 229805, spot height 114m) where they outcrop as continuous ENE trending bands withinin the Blundell granitic orthogneiss. - Wilcock Bay pelite. Very distinctive unit, though of very limited occurrence. Pale-yellowish leucocratic rock unit with abundant borosilicate mineralogy, principally grandidierite and prismatine with lesser amounts of tourmaline and rare dumortierite. Sillimanite is common and in association with grandidierite where both minerals defined the local mineral lineation. Closely associated with Thala tourmaline meta-quartzites. Type locality at GR 543400 2295600. Also present at GR 544200 2295600. - Tassie Tarn metaquartzites. Narrow bands (~25m) of dark grey-blue biotite * magnetite quartzites and biotite psammites, that are intermittently exposed along central E-W axis of Stornes Peninsula. Can contain layers with large euhedral crystals (1-3cm) of orthopyroxene. Good examples at near Tassie Tarn at GR 541900 2298800 and on eastern Stornes Peninsula, GR 544150 2299600. - Easther Island porphyroblastic gneiss. Distinctive grey biotite-plagioclase gneiss with large (1-3cm) porphyroblasts of garnet and/or cordierite, typically with biotite absent halo. First described in a general sense by Stuwe and Powell (1989) but Carson et al. (1995b) described the rock type as granular-porphyroblastic gneiss. Excellent examples at Easther Island and also on southern Stornes Peninsula around GR 544100 2295450. Named after the island after which Kurt Stuwe described the occurrence of this rock type in Stuwe and Powell (1989) on Upsoy Island, however this island was renamed by AAD to Easther Island on the 1:25 000 topographic map. - Wilcock Bay quartzite*. (not named on new map, attributed on new map as biotite-garnet quartzite*). Comprised of biotite and garnet bearing quartzites, interleaved and infolded with narrow bands of Easther Island porphyroblastic gneiss. Contains quartz veins (+/- K-feldspar) and large 100-200 mm diameter. Masses of unknown brown phosphate, probably apatite or wagnerite. Of minor aerial extent and limited to outcrops to the southeast of Allison ice dome. - Thala Fjord paragneiss**. (not named on new map, attributed on new map as biotite quartzo-feldspathic paragneiss**) located at southern Stornes Peninsula. Biotite quartzo-feldspathic gneiss, with minor garnet and rare cordierite present as coronas on garnet. Garnet may reach 3 cm in dia. Good example at spot height 141 at GR 544300 2295150. Minor aerial extent, no specific geographical name assigned for this minor unit. - Allison quartzo-feldspathic gneiss. Similar to above but variable, but minor, sillimanite and cordierite, sillimanite aligned. Also hosts large pods of brown phosphate (apatite or wagnerite?) in quartzose (+/- K-feldspar) veins. Named after Allison ice dome, which appears on map in Stuwe et al. (1989) and is unnamed on current 1:25 000 topographic map. Good examples at on southern Stornes Peninsula, at GR 543450 2295400. - Donovan prismatine leucocratic gneiss. (Not named on new map, attributed on new map as leucocratic prismatine tourmaline paragneiss***). Very minor aerial extent as narrow discontinuous lenses. Pale yellow quartzose +/- feldspathic unit, with aggregates of course prismatine and cordierite that also contains both fine grained sugary and coarse (1-2cm dia.) euhedral tourmaline and patches of coarsely granular rounded quartz with interstitial anhedral tourmaline. Sparsely biotite-bearing and contains small crystals of a metallic opaque mineral, possibly rutile. Two known exposures at GR 541750 2298800, and 544470 2299700. Second location associated with margin of a large body of White Hill leucogneiss, to which this unit may be genetically related on the basis of lithological similarity. Intrusives (or possible intrusives) - Composite orthogneiss (undifferentiated) was renamed by Fitzsimons (1995) to Sostrene orthogneiss and that name will be used here. Typically inferred to represent basement to the meta-sedimentary sequences of the Larsemann Hills and is present on the northern offshore islands, (e.g. McLeod and Manning Islands) and on outcrops to the south west of McCarthy Point. Felsic component typically quartz-plagioclase*K-feldspar, biotite and locally garnet. Dismembered mafic layers contain variable amounts of hornblende, plagioclase, orthopyroxene, rarely clinopyroxene. Described in many publications for example Carson et al. (1995b), Stuwe et al. (1989), Fitzsimons (1995) to name a few. Several workers have also suggested a tentative correlation between the Sostrene Orthogneiss, the Archaean orthogneiss complex of the Vestfold Hills and tectonically reworked Archaean orthogneiss in the Rauer Islands, however, isotopic evidence suggest that these crustal fragments are temporally unrelated - Nella mafic granulite. Named after Nella Fjord. Unit is best exposed immediately to the north of Zhong Shan station and described by several CHINARE papers, Stuwe et al. (1989) and Carson et al. (1995b) under several names. The unit is a mafic granulite dominated by variable hornblende, orthopyroxene, clinopyroxene, plagioclase, *biotite. - Blundell granitic orthogneiss. Much of Stornes Peninsula was thought to be psammite1 (Carson et al. 1995b) but remapping during 2003/04 and more detailed examination suggest the unit that makes up much of southern Stornes Peninsula is a composite orthogneiss complex. The two orthogneiss units described by Carson et al (1995) from Tonagh Promontory as augen k-spar orthogneiss1 and (variably) porphyroblastic k-spar orthogneiss2 and these units probably represent the bulk of the subtypes that make up the Blundell granitic orthogneiss. Blundell granitic orthogneiss on Stornes Penisula is composed of these two components, typically a cream to yellowish coloured garnet-bearing felsic orthogneiss, locally with large K-feldspar megacrysts with minor biotite and is locally intermingled with a greyish variety with large (1-2cm) recrystallised K-feldspar augens. Where clear relationships are observed, the augen variety intrudes (variably) porphyroblastic variety, e.g. Carson et al (1995b, figure 5 page 157). - Johnston granitic orthogneiss. Leucocratic light grey felsic garnet-cordierite-biotite orthogneiss, with generally homogenous appearance. Best observed at NW tip of Johnston Fjord at GR 542000 2300100. Minor unit and probably gradational with, or is part of, the Blundell granitic orthogneiss. - Tassie Tarn pegmatite. Microcline bearing medium to course grained, variably foliated pegmatite, typically assoc. with Tassie Tarn quartzite unit. Common pegmatite but rarely of mappable size. May contain rare tourmaline+quartz symplectites. Possibly related to Progress Granite and in which case is early Cambrian in age. Good examples at GR544150 2299600 and 541850 2298750 on Stornes Peninsula. Geological mapping confidence The mapping represented on this map is subject to differing degrees of geological confidence, based on extent and detail of mapping in various areas. On Stornes Peninsula the geological confidence level is high, This is based on geological mapping during 2003/04 as part of ASAC project 2350. The geological boundaries are accurate and rock types have been repeatedly subject to ground truthing via numerous foot traverses during a long duration visit to the area of up to several weeks. Recently acquired aerial photography (1998) greatly assisted the on-ground geological interpretation. Broknes Peninsula the geological confidence level is also high, based on numerous geological observations and publications by CHINARE geologists, and mapping by Stuwe et al. (1989) and Carson et al. (1995b). Minor peninsulas (Grovnes and Brattnevet, located between Stornes and Broknes, as named in Stuwe et al. 1989), Tonagh Promontory, Fisher Is., Manning Is. and Lovering Is. geological confidence is medium (geological information is based on limited ground truthing during shorter duration visits, typically one or two days). Many the small offshore islands to the N and NW of Stornes Peninsula and numerous small outcrops inland have low geological confidence. These regions have either had very limited ground truthing, (i.e. one visit for several hours) or no ground truthing and geological interpretation might be based on aerial photos. Many very small outcrops have never been visited. These are small inland rocky exposures and very small offshore islands. They are unmapped and the rock types are uncertain and difficult to assess from aerial photography. The fields in the photolist are: Carson Site Number Grew Site Number Latitude Longitude Photos (roll, frame number) and comments Geological structure proprietary
ASAC_2355_1 Molecular studies of the origins and dispersal patterns of invertebrates in the Antarctic and subantarctic AU_AADC STAC Catalog 2003-09-30 2008-03-13 62.7, -69.75, 158.94, -53.1 https://cmr.earthdata.nasa.gov/search/concepts/C1214312574-AU_AADC.umm_json "Metadata record for data from ASAC Project 2355 See the link below for public details on this project. ---- Public Summary from Project ---- We will measure biodiversity of ecologically-important invertebrates (Collembola) in Antarctica, the sub-antarctic islands, and in Australia and New Zealand. Using molecular and morphological techniques we will contribute to understanding of species distributions, and provide molecular data that will lead to automated species identifications. This work is also related to ASAC project 2397, ""Introduced invasive terrestrial invertebrates on Macquarie Island: studies on ecology, origins and control"". Field work was completed by David Gee on projects 2397 and 2355 on Macquarie Island from 4 to 13th March 2008. See the word document in the download file for further information on the field work completed and the data collected. Four excel spreadsheets are also available in the download file - spreadsheets for amphipods, collembola, flatworms and isopods. Some general comments about the spreadsheets: * FID and Id columns are those that are automatically generated when creating a layer in ArcGis. * Counts of all organisms in samples has not yet been completed. Penny Greenslade will be attending to this in the future. However, some count numbers are present on the isopod and amphipod sheets - this refers to isopods and amphipods observed during field work. * The tag column is a standard column for naming any points in the NSW government system. Taken from the 2009-2010 Progress Report: Public summary of the season progress: Progress on this project has been excellent in its first year. The timely appointment of a new PhD student in Oct 2009 (funded by The University of Adelaide) was fortunate and he went south with one assistant in Dec 2009. We collected all the soil samples that had been planned for the 2009/2010 season. Collections were focused on the Vestfold Hills, Larsemann Hills, Hop Island, Mather Peninsula, Sansom Island where the majority of time was spent, with opportunistic sampling at Casey (ASMA) and Mawson (Framnes) whilst in transit on the Aurora Australis. Once we obtain the samples in Adelaide (arrived on V4) which were returned to Hobart frozen then the processing will commence to retrieve the invertebrates. The 2009-2010 data are accompanied by a spreadsheet detailing all column headings." proprietary
ASAC_2355_phylogeographic_1 Extreme Glacial Legacies: A Synthesis of the Antarctic Springtail Phylogeographic Record AU_AADC STAC Catalog 2010-01-01 2010-12-23 -180, -90, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214305883-AU_AADC.umm_json Some, or all, of the raw data for this project have been sourced from the Australian Antarctic Division Biodiversity Database. Taken from the abstract of the referenced paper: We review current phylogeographic knowledge from across the Antarctic terrestrial landscape with a focus on springtail taxa. We describe consistent patterns of high genetic diversity and structure among populations which have persisted in glacial refugia across Antarctica over both short (less than 2 Mya) and long (greater than 10 Mya) timescales. Despite a general concordance of results among species, we explain why location is important in determining population genetic patterns within bioregions. We complete our review by drawing attention to the main limitations in the field of Antarctic phylogeography, namely that the scope of geographic focus is often lacking within studies, and that large gaps remain in our phylogeographic knowledge for most terrestrial groups. proprietary
-ASAC_2357_2 10 year trend of levels of organochlorine pollutants in Antarctic seabirds ALL STAC Catalog 2003-12-16 2004-01-18 77.59, -68.93, 77.99, -68.755 https://cmr.earthdata.nasa.gov/search/concepts/C1214305884-AU_AADC.umm_json Metadata record for data from ASAC Project 2357 See the link below for public details on this project. ---- Public Summary from Project ---- Contaminants like PCBs and DDE have hardly been used Antarctica. Hence, this is an excellent place to monitor global background levels of these organochlorines. In this project concentrations in penguins and petrels will be compared to 10 years ago, which will show time trends of global background contamination levels. Data set description From several birds from Hop Island, Rauer Islands near Davis, samples were collected from preenoil (oil that birds excrete to preen their feathers. This preenoil was then analysed for organochlorine pollutants like polychlorinated biphenyls, (PCBs), hexachlorobenzene (HCB), DDE and dieldrin. The species under investigation were the Adelie penguin (Pygoscelis adeliae) and the Southern Fulmar (Fulmarus glacialoides). The samples were collected from adult breeding birds, and stored in -20 degrees C as soon as possible. The analysis was done with relatively standard but very optimised methods, using a gas-chromatograph and mass-selective detection. Data sheets: The data are available in excel-sheets, located at Alterra, The Netherlands (the affiliation of the PI Nico van den Brink.). Data are available on PCB153 (polychlorinated biphenyl congener numbered 153), hexachlorobenzene (HCB), DDE (a metabolite of the pesticide DDT), and dieldrin (an insecticide). The metadata are in 4 sheets (in meta data 2357.xls): 1. 'Concentrations fulmars' 2. 'Morphometric data fulmars' 3. 'Concentrations Adelies' 4. 'Morphometric data Adelies' The column headings are: 1. 'Concentrations fulmars' - Fulmar: bird number, corresponds with sheet 'morphometric data fulmars'. - PCB153: concentration of PCB-congener 153 (ng/g lipids) - HCB: concentration of hexachlorobenzene (ng/g lipids) - DDE: concentration of DDE (ng/g lipids) - Dieldrin: concentration of dieldrin (ng/g lipids) - Sample size weight of collected amount of preenoil 2. Morphometric data fulmars - Fulmar: bird number, corresponds with sheet 'Concentrations fulmars'. - Bill Length (mm): length of bill (tip to base) - Head Length (mm): length of head (tip of bill to back of head) - Tarsus (mm): length of tarsus - Wing Length (cm): length of right wing - Weight (kg): weight of bird (without bag) 3. 'Concentrations Adelies' Adelie: bird number, corresponds with sheet 'morphometric data Adelies'. - PCB153: concentration of PCB-congener 153 (ng/g lipids) - HCB: concentration of hexachlorobenzene (ng/g lipids) - DDE: concentration of DDE (ng/g lipids) - Dieldrin: concentration of dieldrin (ng/g lipids) - Sample size weight of collected amount of preenoil 4. 'Morphometric data Adelies' - Adelie: bird number, corresponds with sheet 'Concentrations Adelies'. - Bill (mm): length of bill (tip to base) - Head Length (mm): length of head (tip of bill to back of head) - Tarsus (mm): length of tarsus - Flipper Length (cm): length of right flipper (wing) - Weight (kg): weight of bird (without bag) In sheets on concentrations: less than d.l.: concentrations below detection limits. proprietary
ASAC_2357_2 10 year trend of levels of organochlorine pollutants in Antarctic seabirds AU_AADC STAC Catalog 2003-12-16 2004-01-18 77.59, -68.93, 77.99, -68.755 https://cmr.earthdata.nasa.gov/search/concepts/C1214305884-AU_AADC.umm_json Metadata record for data from ASAC Project 2357 See the link below for public details on this project. ---- Public Summary from Project ---- Contaminants like PCBs and DDE have hardly been used Antarctica. Hence, this is an excellent place to monitor global background levels of these organochlorines. In this project concentrations in penguins and petrels will be compared to 10 years ago, which will show time trends of global background contamination levels. Data set description From several birds from Hop Island, Rauer Islands near Davis, samples were collected from preenoil (oil that birds excrete to preen their feathers. This preenoil was then analysed for organochlorine pollutants like polychlorinated biphenyls, (PCBs), hexachlorobenzene (HCB), DDE and dieldrin. The species under investigation were the Adelie penguin (Pygoscelis adeliae) and the Southern Fulmar (Fulmarus glacialoides). The samples were collected from adult breeding birds, and stored in -20 degrees C as soon as possible. The analysis was done with relatively standard but very optimised methods, using a gas-chromatograph and mass-selective detection. Data sheets: The data are available in excel-sheets, located at Alterra, The Netherlands (the affiliation of the PI Nico van den Brink.). Data are available on PCB153 (polychlorinated biphenyl congener numbered 153), hexachlorobenzene (HCB), DDE (a metabolite of the pesticide DDT), and dieldrin (an insecticide). The metadata are in 4 sheets (in meta data 2357.xls): 1. 'Concentrations fulmars' 2. 'Morphometric data fulmars' 3. 'Concentrations Adelies' 4. 'Morphometric data Adelies' The column headings are: 1. 'Concentrations fulmars' - Fulmar: bird number, corresponds with sheet 'morphometric data fulmars'. - PCB153: concentration of PCB-congener 153 (ng/g lipids) - HCB: concentration of hexachlorobenzene (ng/g lipids) - DDE: concentration of DDE (ng/g lipids) - Dieldrin: concentration of dieldrin (ng/g lipids) - Sample size weight of collected amount of preenoil 2. Morphometric data fulmars - Fulmar: bird number, corresponds with sheet 'Concentrations fulmars'. - Bill Length (mm): length of bill (tip to base) - Head Length (mm): length of head (tip of bill to back of head) - Tarsus (mm): length of tarsus - Wing Length (cm): length of right wing - Weight (kg): weight of bird (without bag) 3. 'Concentrations Adelies' Adelie: bird number, corresponds with sheet 'morphometric data Adelies'. - PCB153: concentration of PCB-congener 153 (ng/g lipids) - HCB: concentration of hexachlorobenzene (ng/g lipids) - DDE: concentration of DDE (ng/g lipids) - Dieldrin: concentration of dieldrin (ng/g lipids) - Sample size weight of collected amount of preenoil 4. 'Morphometric data Adelies' - Adelie: bird number, corresponds with sheet 'Concentrations Adelies'. - Bill (mm): length of bill (tip to base) - Head Length (mm): length of head (tip of bill to back of head) - Tarsus (mm): length of tarsus - Flipper Length (cm): length of right flipper (wing) - Weight (kg): weight of bird (without bag) In sheets on concentrations: less than d.l.: concentrations below detection limits. proprietary
+ASAC_2357_2 10 year trend of levels of organochlorine pollutants in Antarctic seabirds ALL STAC Catalog 2003-12-16 2004-01-18 77.59, -68.93, 77.99, -68.755 https://cmr.earthdata.nasa.gov/search/concepts/C1214305884-AU_AADC.umm_json Metadata record for data from ASAC Project 2357 See the link below for public details on this project. ---- Public Summary from Project ---- Contaminants like PCBs and DDE have hardly been used Antarctica. Hence, this is an excellent place to monitor global background levels of these organochlorines. In this project concentrations in penguins and petrels will be compared to 10 years ago, which will show time trends of global background contamination levels. Data set description From several birds from Hop Island, Rauer Islands near Davis, samples were collected from preenoil (oil that birds excrete to preen their feathers. This preenoil was then analysed for organochlorine pollutants like polychlorinated biphenyls, (PCBs), hexachlorobenzene (HCB), DDE and dieldrin. The species under investigation were the Adelie penguin (Pygoscelis adeliae) and the Southern Fulmar (Fulmarus glacialoides). The samples were collected from adult breeding birds, and stored in -20 degrees C as soon as possible. The analysis was done with relatively standard but very optimised methods, using a gas-chromatograph and mass-selective detection. Data sheets: The data are available in excel-sheets, located at Alterra, The Netherlands (the affiliation of the PI Nico van den Brink.). Data are available on PCB153 (polychlorinated biphenyl congener numbered 153), hexachlorobenzene (HCB), DDE (a metabolite of the pesticide DDT), and dieldrin (an insecticide). The metadata are in 4 sheets (in meta data 2357.xls): 1. 'Concentrations fulmars' 2. 'Morphometric data fulmars' 3. 'Concentrations Adelies' 4. 'Morphometric data Adelies' The column headings are: 1. 'Concentrations fulmars' - Fulmar: bird number, corresponds with sheet 'morphometric data fulmars'. - PCB153: concentration of PCB-congener 153 (ng/g lipids) - HCB: concentration of hexachlorobenzene (ng/g lipids) - DDE: concentration of DDE (ng/g lipids) - Dieldrin: concentration of dieldrin (ng/g lipids) - Sample size weight of collected amount of preenoil 2. Morphometric data fulmars - Fulmar: bird number, corresponds with sheet 'Concentrations fulmars'. - Bill Length (mm): length of bill (tip to base) - Head Length (mm): length of head (tip of bill to back of head) - Tarsus (mm): length of tarsus - Wing Length (cm): length of right wing - Weight (kg): weight of bird (without bag) 3. 'Concentrations Adelies' Adelie: bird number, corresponds with sheet 'morphometric data Adelies'. - PCB153: concentration of PCB-congener 153 (ng/g lipids) - HCB: concentration of hexachlorobenzene (ng/g lipids) - DDE: concentration of DDE (ng/g lipids) - Dieldrin: concentration of dieldrin (ng/g lipids) - Sample size weight of collected amount of preenoil 4. 'Morphometric data Adelies' - Adelie: bird number, corresponds with sheet 'Concentrations Adelies'. - Bill (mm): length of bill (tip to base) - Head Length (mm): length of head (tip of bill to back of head) - Tarsus (mm): length of tarsus - Flipper Length (cm): length of right flipper (wing) - Weight (kg): weight of bird (without bag) In sheets on concentrations: less than d.l.: concentrations below detection limits. proprietary
ASAC_2363_1 Heard Island glacier fluctuations and climatic change - 2003/04 Fieldwork AU_AADC STAC Catalog 2003-12-16 2004-02-26 73.45, -53.3, 73.6, -53.1 https://cmr.earthdata.nasa.gov/search/concepts/C1214305885-AU_AADC.umm_json This report describes the data collected for ASAC project 2363 (a continuation of ASAC 1158). A full report of the data collected and the work completed is available for download with the dataset. The download file contains data in the following areas: Ablation Chemistry DEM - Digital Elevation Model Lagoon Bathymetry Meltwater Photos Report RES - Radio Echo Sounder Surveys Weather Observations This CD contains data collected by the Heard Island glaciology team during the 2003-04 Australian Antarctic Division expedition, as well as some data from the previous expedition in November 2000. The data report associated with these files is provided as a PDF in the Report folder. Description of data files available on CD Ablation folder survey_canes_ablation.xls Excel file with the measured height of each survey wand above snow/ice surface for the field season. BG35_pinger.xls Excel file with sonic ranger data for BG35. BG50_pinger.xls Excel file with sonic ranger data for BG50. Chemistry folder Ion_Chemistry.xls Excel file with analyses of chloride, sulphate, nitrate, Mg, Ca, Na of samples collected from crevasses and cores. Oxygen_isotopes.xls Excel file with dO18 analyses of samples collected from crevasses and cores. DEM folder dem_2003.grd ASCII file with the 2003 DEM grid, generated using Golden Software Surfer v7.0. Header file format is: id The identification string DSAA that identifies the file as an ASCII grid file. nx nynx is the integer number of grid lines along the X axis (columns) ny is the integer number of grid lines along the Y axis (rows) xlo xhi xlo is the minimum X value of the grid xhi is the maximum X value of the grid ylo yhi ylo is the minimum Y value of the grid yhi is the maximum Y value of the grid zlo zhi zlo is the minimum Z value of the grid zhi is the maximum Z value of the grid grid row 1 grid row 2 grid row 3 -these are the rows of Z values of the grid, organized in row order. Each row has a constant Y coordinate. Grid row 1 corresponds to ylo and the last grid row corresponds to yhi. Within each row, the Z values are arranged from xlo to xhi. Blanked grid nodes are given a Z value of 1.070141E+038. Rows are 39.855 m apart, Columns are 40 m apart. dem_11.xls Excel file with all points used to calculate the dem_2003.xls grid (refer to A2). The folder also contains high resolution jpeg images of Fig. 16 and the data distribution figure (A2). Lagoon bathymetry folder Folder containing Excel files with Easting, Northing (acquired using Garmin GPS V; WGS84, UTM zone 43) and depth (acquired using Garmin 'Fishfinder' depth sounder) for each lagoon surveyed. Also high resolution jpeg images of bathymetric maps reproduced in appendix A3. Meltwater folder Contains an excel file with stream profiles and flux calculations, and repeat measurements of Brown Lagoon outflow stream. Also contains jpeg photos of three of the inflow streams, and an image showing their location using the Quickbird satellite image for reference. Photos folder Contains jpeg digital photos used in this report. Report folder HI_data_report_screen.pdf HI_data_report_print.pdf This data report is reproduced as both a low and high resolution Adobe Acrobat PDF file, for on-screen viewing and printing respectively. RES folder BG_35_2000.xls Excel file with RES data for the BG35 profile, 2000 field season. RES.xls Excel file with RES data for the BG25 and BG20 profiles, 2003-04 field season. Surveys folder all_survey_points.xls Excel file with the position of the survey markers and additional points. daily_position_BG50.xls Excel file with daily (occasionally more frequent) DGPS position near BG50 kinematic_2000.xls Excel file with all DGPS kinematic surveys conducted during the 2000 field season. kinematic_surveys.xls Excel file with all DGPS kinematic surveys conducted during the 2003-04 field season. surface_site_surveys.xls Excel file with the DGPS repeat survey positions of each survey site, for the 2000 and 2003-04 field seasons, and velocity calculations for each epoch. terminus_surveys.xls Excel file with the DGPS surveys of the position of the glacier terminus. Weather observations folder AANDERAA_data.xls Excel file with data recorded by the automatic weather station at 550 m a.s.l. all_data_comparison.xls Excel file with compilation and graphs of all data from each of the Brown Glacier AWS. MAWS1_data.xls Excel file with data recorded by the automatic weather station at 115 m a.s.l. MAWS2_data.xls Excel file with data recorded by the automatic weather station at 920 m a.s.l. precipitation_record.xls Excel file with rain gauge records from Jacka Valley, Brown Hut, Spit Bay and Capsize Beach. ttec_T_RH_data.xls Excel file containing temperature and relative humidity data from the three T-TEC loggers, deployed at Jacka Valley, Capsize Beach, and Doppler Hill. wx station photos folder Folder containing jpeg photos of each of the weather stations, as well as the field camp observing sites. Missing is Spit Bay. proprietary
ASAC_2377_1 Hydroids of the BANZARE Antarctic expeditions 1929 - 1931 AU_AADC STAC Catalog 1929-01-01 1931-12-31 49.6, -67.76, 142.6, -65.1 https://cmr.earthdata.nasa.gov/search/concepts/C1214305887-AU_AADC.umm_json Metadata record for data from ASAC Project 2377 See the link below for public details on this project. ---- Public Summary from Project ---- The scientifically and historically important collection of marine hydroids from Sir Douglas Mawson's Antarctic BANZARE Expeditions 1929-1931 has never been studied. In view of the increasing scientific and economic interest in Antarctic seas study of these organisms could provide a valuable tool in assessment and management of marine protected areas. Taken from the referenced publication: The BANZARE Expeditions (British, Australian, New Zealand, Antarctic Research Expeditions) 1929-1931 sampled the marine benthos in the Southern Ocean, at the Kerguelen Islands, Heard Island, Macquarie Island, and south-west of Tasmania and along the coast of the Australian Antarctic Territory. Forty six stations at depths of 2 - 640 m were occupied along the Australian Antarctic Territory coast. Eight species of Halecium including five new and Hydrodendron arboreum were found and recorded from eight stations. Forty six stations were occupied along the Australian Antarctic Territory coast and samples collected using various trawls to depths of 640 m; some coastal collections were also made in shallow water 2 m deep. The hydroid collection was originally deposited in the British Museum, Natural History (BMNH), London. There, preserved material was sorted during the 1960s and microslide mounts prepared. A small amount of material left over from the earlier AAE (Australian Antarctic Expedition) 1911-1914 was also incorporated into the BANZARE collection as Station No. 1785. The entire BANZARE hydroid collection was sent to the National Museum of Victoria in Melbourne for identification. proprietary
ASAC_2381_1 Electrical conductivity of sea ice: a comparison of in-situ and laboratory measurements AU_AADC STAC Catalog 2003-09-26 2003-10-20 109.455, -65.2683, 117.7416, -63.9366 https://cmr.earthdata.nasa.gov/search/concepts/C1214305874-AU_AADC.umm_json Metadata record for data from ASAC Project 2381 See the link below for public details on this project. ---- Public Summary from Project ---- This project will compare sea ice electrical conductivities measured in-situ over lateral scales of tens of metres with those determined by small-scale laboratory measurements on core samples. These measurements will be used to determine appropriate bulk sea ice conductivities for interpretation of sea ice thickness from electromagnetic sounding data. Data collection for this project took place on voyage 1 of the Aurora Australis in the 2003/2004 season. A word document thoroughly detailing the project and the data are included in the download file. The fields in this dataset are: Latitude Longitude Time Date Conductivity Temperature proprietary
@@ -2919,11 +2919,11 @@ ASAC_2688_1 Antarctic associations with Australian and South American cold outbr
ASAC_2690_1 Accessory mineral behaviour during partial melting in the crust - improving the geochronology of granulite terrains. ALL STAC Catalog 2006-12-24 2007-03-02 76.0046, -68.408, 78.398, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214312718-AU_AADC.umm_json Metadata record for data expected ASAC Project 2690 See the link below for public details on this project. Relating ages, determined using the decay of radioactive elements in minerals, to geological events is central to understanding mountain building and continental evolution. This research will use carefully sampled rocks from Antarctica to improve current estimates of the distribution of elements that occur at only trace (parts per million) concentrations between the key mineral used in dating, zircon, and the common mineral garnet. This information will then be used to link zircon ages to the major events and processes that occurred during the assembly of ancient pieces of crust to form what is now East Antarctica, and other continents. Accessory mineral behaviour during partial melting in the crust - improving the geochronology of granulite terrains (ASAC_2690) The aim of this project was to collect geological material appropriate for evaluating the chemical signatures of the minerals zircon, monazite and garnet formed during various stages of partial melting, melt accumulation and melt extraction in rocks undergoing deformation and metamorphism at high grades. A second objective was to collect gneisses in which the mineral record of early metamorphic events might be clarified in the complex Prydz Bay region. Geologists SL Harley, NM Kelly and T Hokada undertook field work over the period 21 December 2006 to 2 March 2007. Samples were collected by the usual techniques, using hammer and chisel, and documented in the field using sketches, photographs and GPS. Sampling was complemented by detailed outcrop- and larger-scale mapping centred on defining the precise relationships between the rocks and mineral sites sampled. Localites / areas visited, mapped and sampled included the Larsemann Hills (Broknes Peninsula, Stornes Peninsula, McLeod Island, Manning Island), Steinnes, the Brattstrand Bluffs coast, the Rauer Group (Torckler, Varyag, Tango, Pchelka, Lunnyy, Sapozhok Islands; Mather and Macey Peninsulas), and two subareas in the Vestfold Hills (Taynaya Bay, Pioneer Crossing). The data set consists of an excel workbook containing three spreadsheets. The three spreadsheets provide listings of the geological rock samples collected during the course of fieldwork by SL Harley, NM Kelly and T Hokada respectively. Each sheet provides sample numbers/codes, locations by name (and by code name if used in the collectors field notebook) and by latitude and longitude given in terms of degrees and decimalised minutes. Each sample is also described in terms of its mineralogy, some aspects of its structural or relational setting, and purpose for collection. Sample numbers are described in the following way: The letters are the initials of the collector (sh, TH, NK) and the sample number is usually of the form year/num e.g. 06/45. The abbreviations used on the worksheets in the Excel spreadsheet are: Harley Sheet: Crd, Li-B: Li and B analysis of cordierite U-Pb, chem: geochemistry and U-Pb dating Zrc-Mon: zircon and monazite phase and chemical relations P-T: pressure and temperature calculations P-T-Ky: pressure and temperature calculations and evaluation of relict kyanite csil P-T-fluid: pressure, temperature and fluid composition calculations on calcsilicate mineral assemblage Kelly sheet: Acc / Gx: accessory mineral behaviour and geochronology Acc / Grt comp: accessory mineral / garnet relations and REE distributions Acc / Min: mineralogy of complex accessory phases Grt REE comp: composition of garnet in terms of trace elements Gx: geochronology Acc / Pet: petrology, pressure-temperature calculations and accessory mineral stability Pet: petrology and pressure-temperature calculations Kelly Mineral Assemblage abbreviations Bt biotite Cc calcite Cpx clinopyroxene Crd cordierite Diop diopside Fsp felsspar Grs grossular garnet Grt garnet Hbl hornblende Ilm ilmenite Kfs K-feldspar Krn kornerupine / prismatine Ky kyanite Mnz monazite Opq opaque Opx orthopyroxene Pl plagioclase Qtz quartz Scap scapolite Sil sillimanite Spl spinel Spr sapphirine Woll wollastonite Zrc zircon Hokada sheet: U-Pb zrn: zircon U-Pb geochronology The fields in this dataset are: Sample Number Location Name Date Location Code Latitude Longitude Field Description Collected For Additional Notes proprietary
ASAC_2690_1 Accessory mineral behaviour during partial melting in the crust - improving the geochronology of granulite terrains. AU_AADC STAC Catalog 2006-12-24 2007-03-02 76.0046, -68.408, 78.398, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214312718-AU_AADC.umm_json Metadata record for data expected ASAC Project 2690 See the link below for public details on this project. Relating ages, determined using the decay of radioactive elements in minerals, to geological events is central to understanding mountain building and continental evolution. This research will use carefully sampled rocks from Antarctica to improve current estimates of the distribution of elements that occur at only trace (parts per million) concentrations between the key mineral used in dating, zircon, and the common mineral garnet. This information will then be used to link zircon ages to the major events and processes that occurred during the assembly of ancient pieces of crust to form what is now East Antarctica, and other continents. Accessory mineral behaviour during partial melting in the crust - improving the geochronology of granulite terrains (ASAC_2690) The aim of this project was to collect geological material appropriate for evaluating the chemical signatures of the minerals zircon, monazite and garnet formed during various stages of partial melting, melt accumulation and melt extraction in rocks undergoing deformation and metamorphism at high grades. A second objective was to collect gneisses in which the mineral record of early metamorphic events might be clarified in the complex Prydz Bay region. Geologists SL Harley, NM Kelly and T Hokada undertook field work over the period 21 December 2006 to 2 March 2007. Samples were collected by the usual techniques, using hammer and chisel, and documented in the field using sketches, photographs and GPS. Sampling was complemented by detailed outcrop- and larger-scale mapping centred on defining the precise relationships between the rocks and mineral sites sampled. Localites / areas visited, mapped and sampled included the Larsemann Hills (Broknes Peninsula, Stornes Peninsula, McLeod Island, Manning Island), Steinnes, the Brattstrand Bluffs coast, the Rauer Group (Torckler, Varyag, Tango, Pchelka, Lunnyy, Sapozhok Islands; Mather and Macey Peninsulas), and two subareas in the Vestfold Hills (Taynaya Bay, Pioneer Crossing). The data set consists of an excel workbook containing three spreadsheets. The three spreadsheets provide listings of the geological rock samples collected during the course of fieldwork by SL Harley, NM Kelly and T Hokada respectively. Each sheet provides sample numbers/codes, locations by name (and by code name if used in the collectors field notebook) and by latitude and longitude given in terms of degrees and decimalised minutes. Each sample is also described in terms of its mineralogy, some aspects of its structural or relational setting, and purpose for collection. Sample numbers are described in the following way: The letters are the initials of the collector (sh, TH, NK) and the sample number is usually of the form year/num e.g. 06/45. The abbreviations used on the worksheets in the Excel spreadsheet are: Harley Sheet: Crd, Li-B: Li and B analysis of cordierite U-Pb, chem: geochemistry and U-Pb dating Zrc-Mon: zircon and monazite phase and chemical relations P-T: pressure and temperature calculations P-T-Ky: pressure and temperature calculations and evaluation of relict kyanite csil P-T-fluid: pressure, temperature and fluid composition calculations on calcsilicate mineral assemblage Kelly sheet: Acc / Gx: accessory mineral behaviour and geochronology Acc / Grt comp: accessory mineral / garnet relations and REE distributions Acc / Min: mineralogy of complex accessory phases Grt REE comp: composition of garnet in terms of trace elements Gx: geochronology Acc / Pet: petrology, pressure-temperature calculations and accessory mineral stability Pet: petrology and pressure-temperature calculations Kelly Mineral Assemblage abbreviations Bt biotite Cc calcite Cpx clinopyroxene Crd cordierite Diop diopside Fsp felsspar Grs grossular garnet Grt garnet Hbl hornblende Ilm ilmenite Kfs K-feldspar Krn kornerupine / prismatine Ky kyanite Mnz monazite Opq opaque Opx orthopyroxene Pl plagioclase Qtz quartz Scap scapolite Sil sillimanite Spl spinel Spr sapphirine Woll wollastonite Zrc zircon Hokada sheet: U-Pb zrn: zircon U-Pb geochronology The fields in this dataset are: Sample Number Location Name Date Location Code Latitude Longitude Field Description Collected For Additional Notes proprietary
ASAC_2691_1 Assessing the impact of contaminated sediments on hard-substrate Antarctic marine communities AU_AADC STAC Catalog 2005-10-01 2007-03-31 78, -68, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214312719-AU_AADC.umm_json Metadata record for data from ASAC Project 2691 See the link below for public details on this project. Contaminants may persist in marine sediments and be re-suspended during storms or by the activity of animals. This project will assess the impact of contaminated sediments on plants and animals that live directly above the sediment. Rocky-reef organisms form a large component of Antarctica's biodiversity and include algae as well as filter feeding animals such as sponges, lace corals, and fanworms. Many of these plants and animals live on boulders embedded within sediments. Information on the response of individuals, populations and communities to contamination will be used to develop sediment quality guidelines appropriate for the protection of the Antarctic environment. The toxicity of aqueous metals and metal-contaminated resuspended sediment to the spirorbid polychaete Spirorbis nordenskjoldi Ehlers, 1900 was assessed in assays conducted during the 2005/6 and 2006/7 field seasons. A more detailed description of the design of experiments and the methods used can be found in Hill et al, 2009. Spirorbids were exposed to aqueous solutions of copper, lead and zinc singularly, and in mixtures. Spirorbids were also exposed to resuspended metal-spiked sediments. Spirorbids attached to the brown alga Desmarestia sp were collected from Beall Island, Windmill Islands, East Antarctica, a clean site located approximately 2 km from Casey Station. Algae and animals were kept in the aquarium facility on station, in seawater maintained at 1 C and a 12-h light:dark photoperiod. Seawater was constantly aerated and changed every 5 to 6 d. Spirorbids were used within two weeks of their collection and fed once per week with plankton. Spirorbids were removed from the surface of algal blades 24 h before the start of a test, and allowed to recover in a constant-temperature chamber (CTC) at 0.5 C. Immediately before the start of tests, spirorbids were examined, and only healthy individuals were selected for tests. Spirorbids were determined to be healthy if their tentacular crown (fan) was extended and retracted quickly in response to stimuli. The download file contains further information on the data. proprietary
-ASAC_2720_ADCP_1 ADCP data collected during the SAZ-SENSE voyage, January-February 2007 ALL STAC Catalog 2007-01-17 2007-02-20 140.3, -54.27, 153.81, -43.05 https://cmr.earthdata.nasa.gov/search/concepts/C1214312777-AU_AADC.umm_json "Metadata record for data from ASAC Project 2720 See the link below for public details on this project. The overall objective is to characterise Southern Ocean marine ecosystems, their influence on carbon dioxide exchange with the atmosphere and the deep ocean, and their sensitivity to past and future global change including climate warming, ocean stratification, and ocean ... acidification from anthropogenic CO2 emissions. In particular we plan to take advantage of naturally-occurring, persistent, zonal variations in Southern Ocean primary production and biomass in the Australian Sector to investigate the effects of iron addition from natural sources, and CO2 addition from anthropogenic sources, on Southern Ocean plankton communities of differing initial structure and composition. These samples were collected on the SAZ-SENSE scientific voyage of the Australian Antarctic Program (Voyage 3 of the Aurora Australis, 2006-2007 season). SAZ-SENSE VOYAGE AU0703 ADCP DATA * The complete ADCP data for cruise au0703 are in the files: au070301.cny (ascii format) a0703dop.mat (matlab format) * The ""on station"" ADCP data (specifically, the data for which the ship speed was less than or equal to 0.35 m/s) are in the files: au0703_slow35.cny (ascii format) a0703dop_slow35.mat (matlab format) * The file bindep.dat shows the water depths (in metres) that correspond to the centre of each vertical bin. * The data are 30 minute averages. Each 30 minute averaging period starts from the time indicated. (so, e.g., an ensemble with time 120000 is the average from 120000 to 123000). * ADCP currents are absolute - i.e. ship's motion has been subtracted out. * Note that the top few bins can have bad data from water dragged along by the ship. * Beware of data when the ship is underway - it's often suspect. * Important data quality information can be found in the data report referenced above. * The figure a0703difship30.eps shows the speed difference between vertical bin 2 and all other bins, where the data have been divided up into different speed classes for ship speed. The apparent vertical shear for bins ~1-10, and below bin ~40, is an error, possibly due to acoustic ringing from an air/water interface in the seachest. Data where ship speed is 0 to 1 m/s does not show this error." proprietary
ASAC_2720_ADCP_1 ADCP data collected during the SAZ-SENSE voyage, January-February 2007 AU_AADC STAC Catalog 2007-01-17 2007-02-20 140.3, -54.27, 153.81, -43.05 https://cmr.earthdata.nasa.gov/search/concepts/C1214312777-AU_AADC.umm_json "Metadata record for data from ASAC Project 2720 See the link below for public details on this project. The overall objective is to characterise Southern Ocean marine ecosystems, their influence on carbon dioxide exchange with the atmosphere and the deep ocean, and their sensitivity to past and future global change including climate warming, ocean stratification, and ocean ... acidification from anthropogenic CO2 emissions. In particular we plan to take advantage of naturally-occurring, persistent, zonal variations in Southern Ocean primary production and biomass in the Australian Sector to investigate the effects of iron addition from natural sources, and CO2 addition from anthropogenic sources, on Southern Ocean plankton communities of differing initial structure and composition. These samples were collected on the SAZ-SENSE scientific voyage of the Australian Antarctic Program (Voyage 3 of the Aurora Australis, 2006-2007 season). SAZ-SENSE VOYAGE AU0703 ADCP DATA * The complete ADCP data for cruise au0703 are in the files: au070301.cny (ascii format) a0703dop.mat (matlab format) * The ""on station"" ADCP data (specifically, the data for which the ship speed was less than or equal to 0.35 m/s) are in the files: au0703_slow35.cny (ascii format) a0703dop_slow35.mat (matlab format) * The file bindep.dat shows the water depths (in metres) that correspond to the centre of each vertical bin. * The data are 30 minute averages. Each 30 minute averaging period starts from the time indicated. (so, e.g., an ensemble with time 120000 is the average from 120000 to 123000). * ADCP currents are absolute - i.e. ship's motion has been subtracted out. * Note that the top few bins can have bad data from water dragged along by the ship. * Beware of data when the ship is underway - it's often suspect. * Important data quality information can be found in the data report referenced above. * The figure a0703difship30.eps shows the speed difference between vertical bin 2 and all other bins, where the data have been divided up into different speed classes for ship speed. The apparent vertical shear for bins ~1-10, and below bin ~40, is an error, possibly due to acoustic ringing from an air/water interface in the seachest. Data where ship speed is 0 to 1 m/s does not show this error." proprietary
+ASAC_2720_ADCP_1 ADCP data collected during the SAZ-SENSE voyage, January-February 2007 ALL STAC Catalog 2007-01-17 2007-02-20 140.3, -54.27, 153.81, -43.05 https://cmr.earthdata.nasa.gov/search/concepts/C1214312777-AU_AADC.umm_json "Metadata record for data from ASAC Project 2720 See the link below for public details on this project. The overall objective is to characterise Southern Ocean marine ecosystems, their influence on carbon dioxide exchange with the atmosphere and the deep ocean, and their sensitivity to past and future global change including climate warming, ocean stratification, and ocean ... acidification from anthropogenic CO2 emissions. In particular we plan to take advantage of naturally-occurring, persistent, zonal variations in Southern Ocean primary production and biomass in the Australian Sector to investigate the effects of iron addition from natural sources, and CO2 addition from anthropogenic sources, on Southern Ocean plankton communities of differing initial structure and composition. These samples were collected on the SAZ-SENSE scientific voyage of the Australian Antarctic Program (Voyage 3 of the Aurora Australis, 2006-2007 season). SAZ-SENSE VOYAGE AU0703 ADCP DATA * The complete ADCP data for cruise au0703 are in the files: au070301.cny (ascii format) a0703dop.mat (matlab format) * The ""on station"" ADCP data (specifically, the data for which the ship speed was less than or equal to 0.35 m/s) are in the files: au0703_slow35.cny (ascii format) a0703dop_slow35.mat (matlab format) * The file bindep.dat shows the water depths (in metres) that correspond to the centre of each vertical bin. * The data are 30 minute averages. Each 30 minute averaging period starts from the time indicated. (so, e.g., an ensemble with time 120000 is the average from 120000 to 123000). * ADCP currents are absolute - i.e. ship's motion has been subtracted out. * Note that the top few bins can have bad data from water dragged along by the ship. * Beware of data when the ship is underway - it's often suspect. * Important data quality information can be found in the data report referenced above. * The figure a0703difship30.eps shows the speed difference between vertical bin 2 and all other bins, where the data have been divided up into different speed classes for ship speed. The apparent vertical shear for bins ~1-10, and below bin ~40, is an error, possibly due to acoustic ringing from an air/water interface in the seachest. Data where ship speed is 0 to 1 m/s does not show this error." proprietary
ASAC_2720_CTD_1 CTD data collected during the SAZ-SENSE voyage, January-February 2007 AU_AADC STAC Catalog 2007-01-17 2007-02-20 140.3, -54.27, 153.81, -43.05 https://cmr.earthdata.nasa.gov/search/concepts/C1214312778-AU_AADC.umm_json "Metadata record for data from ASAC Project 2720 See the link below for public details on this project. The overall objective is to characterise Southern Ocean marine ecosystems, their influence on carbon dioxide exchange with the atmosphere and the deep ocean, and their sensitivity to past and future global change including climate warming, ocean stratification, and ocean ... acidification from anthropogenic CO2 emissions. In particular we plan to take advantage of naturally-occurring, persistent, zonal variations in Southern Ocean primary production and biomass in the Australian Sector to investigate the effects of iron addition from natural sources, and CO2 addition from anthropogenic sources, on Southern Ocean plankton communities of differing initial structure and composition. These samples were collected on the SAZ-SENSE scientific voyage of the Australian Antarctic Program (Voyage 3 of the Aurora Australis, 2006-2007 season). SAZ-SENSE VOYAGE AU0703 CTD DATA Oceanographic measurements were collected aboard Aurora Australis cruise au0703 (voyage 3 2006/2007, 17th January to 20th February 2007) as part of the ""SAZ-SENSE"" experiment south of Tasmania, between 43 degrees and 55 degrees south. A total of 109 CTD vertical profile stations were taken to various depths, focussing chiefly on the upper water column. Over 1300 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, silicate, ammonia and nitrite), dissolved inorganic carbon, alkalinity, particulate organic carbon/nitrogen/silicate, dissolved and particulate barium, thorium, dissolved organic carbon, ammonium, pigments, phytoplankton, bacteria, viruses, diatoms, amino acids, and other biological parameters (list incomplete), using a 24 bottle rosette sampler. Near surface current profile data were collected by a ship mounted ADCP. Data from the array of ship's underway sensors are included in the data set. This report describes the processing/calibration of the CTD and ADCP data, and details the data quality. An offset correction is derived for the underway sea surface temperature and salinity data, by comparison with near surface CTD data." proprietary
-ASAC_2722_Adelie_Rauer_Vestfold_Nov1993_1 Adelie penguin colony boundaries at the Rauer Group and the Vestfold Hills, November 1993 ALL STAC Catalog 1993-11-24 1993-11-24 77.6292, -68.8433, 78.5775, -68.3486 https://cmr.earthdata.nasa.gov/search/concepts/C1606336940-AU_AADC.umm_json This dataset consists of two shapefiles created by Darren Southwell of the Australian Antarctic Division (AAD) by digitising the boundaries of adelie penguin colonies at the Rauer Group and the Vestfold Hills. The digitising was done from images resulting from the scanning and georeferencing of aerial photographs taken on 24 November 1993. The aerial photographs were taken for the AAD with a Linhof camera. Records of the photographs are included in the Australian Antarctic Data Centre's Aerial Photograph Catalogue. proprietary
ASAC_2722_Adelie_Rauer_Vestfold_Nov1993_1 Adelie penguin colony boundaries at the Rauer Group and the Vestfold Hills, November 1993 AU_AADC STAC Catalog 1993-11-24 1993-11-24 77.6292, -68.8433, 78.5775, -68.3486 https://cmr.earthdata.nasa.gov/search/concepts/C1606336940-AU_AADC.umm_json This dataset consists of two shapefiles created by Darren Southwell of the Australian Antarctic Division (AAD) by digitising the boundaries of adelie penguin colonies at the Rauer Group and the Vestfold Hills. The digitising was done from images resulting from the scanning and georeferencing of aerial photographs taken on 24 November 1993. The aerial photographs were taken for the AAD with a Linhof camera. Records of the photographs are included in the Australian Antarctic Data Centre's Aerial Photograph Catalogue. proprietary
+ASAC_2722_Adelie_Rauer_Vestfold_Nov1993_1 Adelie penguin colony boundaries at the Rauer Group and the Vestfold Hills, November 1993 ALL STAC Catalog 1993-11-24 1993-11-24 77.6292, -68.8433, 78.5775, -68.3486 https://cmr.earthdata.nasa.gov/search/concepts/C1606336940-AU_AADC.umm_json This dataset consists of two shapefiles created by Darren Southwell of the Australian Antarctic Division (AAD) by digitising the boundaries of adelie penguin colonies at the Rauer Group and the Vestfold Hills. The digitising was done from images resulting from the scanning and georeferencing of aerial photographs taken on 24 November 1993. The aerial photographs were taken for the AAD with a Linhof camera. Records of the photographs are included in the Australian Antarctic Data Centre's Aerial Photograph Catalogue. proprietary
ASAC_2722_SP_GLS_1 GLS tag deployments on Snow petrels (Pagodroma nivea) in 2011 and 2012 from Bechervaise Island, Mawson Coast and Filla Island, Rauer Group AU_AADC STAC Catalog 2011-01-09 2013-01-14 60, -68, 78, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214306493-AU_AADC.umm_json GPS tag deployments on Snow petrels (Pagodroma nivea) in 2011 from Bechervaise Island, Mawson Coast and Filla Island, Rauer Group, as part of AAS project 2722. Identifying potential threats from a changing environment on snow petrel populations requires understanding key ecological processes and their driving factors. This project focuses on determining driving factors for the species' at-sea distribution and foraging habitat. The data will be linked to spatio-temporally coincident data of biological and physical characteristics of the ecosystem to develop explanatory models and, where possible, predictive models to explore the outcomes of plausible scenarios of future environmental change on snow petrel populations. Tags were deployed on Snow Petrels in the Mawson and Davis areas for tracking purposes. The types of tags used were BAS (British Antarctic Survey) geolocators (Mk18) The GLS data are in hexadecimal format, and will need appropriate software to interpret them. proprietary
ASAC_2750_1 Geochemical and biological linkages in glacier ecosystems AU_AADC STAC Catalog 2008-10-01 2009-03-31 77, -68, 79, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214312810-AU_AADC.umm_json Metadata record for data from ASAC Project 2750 See the link below for public details on this project. Glaciers are not frozen rivers, but another aquatic ecosystem in the cryosphere. Most life on glaciers occurs in numerous shallow holes called cryoconites as simple microbial communities. We will study the functioning of these communities and link it to the important processes of carbon and nitrogen cycling. Biological processes change the nature of the glacier surface and may increase melting, which in turn may contribute to more rapid glacier retreat. Accession Numbers for three samples held in the Genbank library are as follows: Vestfold bacteria: GU298843 - GU298966 Vestfold eukaryotes: GU298125 - GU298216 Vestfold archaea: GU298283 - GU298285 This will include the sequences of every clone that was used in the Vestfold analysis. Taken from the 2008-2009 Progress Report: Project objectives: BACKGROUND Contrary to what is generally supposed glaciers are not lifeless, frozen rivers. One of the key factors for sustaining life is a source of liquid water. During summer there are significant quantities of liquid water on a glacier surface. Much of this water is contained in abundant, small, straight-sided holes that develop throughout summer on the glacier surface. These are known as cryoconites. They may be up to half a metre deep and half a metre wide and usually contain a layer of inorganic and organic material on their bottom. Qualitative observations of the contents of cryoconites have revealed biological elements including cyanobacteria, various algae including diatoms, snow algae and desmids, rotifers and fungi (Steinbeck, 1935; Charlesworth, 1957; Gerdel and Druet, 1960; Wharton et al., 1981; Takeuchi et al., 2001a). We conducted a quantitative study of cryoconites on a Svalbard glacier (Midre Lovenbreen) in 2000 (Sawstrom et al. 2002) which revealed concentrations of bacteria between 2.8 to 7.0 x 104 cell mL-1 in the sediment and water column and heterotrophic and autotrophic flagellates up to 4 x 102 mL-1. Effectively the cryoconites resembled Antarctic lakes in their community structure (Laybourn-Parry, 1997). Photosynthesis in cryoconites was high, reaching rates of 156.9 plus or minus 4.0 C L-1 h-1 in the bottom sediment and 1.2 plus or minus 0.27 C L-1 h-1 in the water column (Sawstrom et al. 2002). These rates are higher than those recorded in Arctic lakes (O'Brien, 1992; Markager et al., 1999). Given the density of cryoconites on the glacier surface in summer, the levels of carbon fixation on the whole glacier are likely to be significant. During biological processes nitrogen and phosphorus was recycled, and it is this biogeochemical cycling which explains anomalies seen by glaciologists in glacier nutrient budgets. Investigations of the glacier snow pack of Midre Lovenbreen in the high Arctic by two of the applicants has shown that it contains significant concentrations of organic carbon which sustains a community of bacteria, flagellates and viruses. That snow supports actively metabolising bacteria has been demonstrated in snow at the South Pole, where low rates of DNA and protein synthesis were measurable in a bacterial community that reached concentrations of 5000 cells mL-1 (Carpenter et al., 2000). Within the ice there may be liquid veins that provide microhabitats for bacteria. Ice cores from a Greenland glacier have revealed bacterial concentrations of 6 x 107 cell mL-1, and molecular analysis of cultures of viable bacteria from ancient ice cores showed considerable phylogenetic diversity, including new species (Sheridan et al., 2003). Photosynthetic processes occur in snow, mediated by phytoflagellates known as snow algae. They accumulate in clear annual patterns that can be used as a tool in dating snow accumulations (Yoshimura et al., 2000). Although nutrient cycling in snow-covered catchments has received significant attention over the last decade (see Jones et al, 2002), there have been few studies of the ecology of catchments characterised by permanent glacier ice. As indicated in (i) above there is compelling evidence that glaciers are biologically active entities. Recent work by one of the applicants has shown that nutrient cycling in Arctic glaciers involves transformation, loss and acquisition of important inorganic nutrients (N and P) on a sufficiently large scale to support the hypothesis that glaciers are important ecosystems (Hodson et al., In Press). On the Midre Lovenbreen and neighbouring Austre Broggerbreen glaciers, a significant sink of ammonium (NH4) exists accounting for 50% to 70% of inputs via bulk deposition, which ranged between 10 - 37 kg km-2 yr-1. Moreover, run-off of nitrate (NO3) exceeded depositional inputs. These glaciers also receive significant deposition of dissolved organic and particulate nitrogen as well as organic carbon (Hodson et al, In Press; Unpublished Data). All of this material supports a food web. Inorganic nutrients are required for photosynthesis by snow algae and the photosynthetic elements of cryoconite communities along with water, CO2, trace elements and light energy. Heterotrophic bacteria require a source of organic carbon as a food substrate. This can be supplied through deposition of organic carbon from the atmosphere, or by the photosynthetic communities that exude some of the organic carbon they manufacture during photosynthesis and through decomposition of dead organic matter. Bacteria also require sources of P and N, which can be of inorganic or organic origin. The grazers of bacteria, the flagellated, ciliated and sarcodine protozoa recycle N and P through metabolism and excretion. In addition some of the cyanobacteria of cryoconites are likely to be fixers of atmospheric nitrogen, and within the bacteria community there are likely to be nitrifying bacteria and other functional groups that play a role in the nitrogen cycle. All of these biological processes can be used to explain why the nutrient budgets of glaciers do not balance. Clearly nutrient cycling in glacier basins is dynamic, and is not solely related to deposition, elution and transport of solutes from the winter snow pack during melt. Glaciers are not homogeneous environments and undergo very considerable changes when summer melting occurs. A very important, but as yet unquantified source of surface heterogeneity is due to the capacity for biological elements to reduce albedo, and through differential melt rates beneath darker organic matter, cause the surface roughness to increase. Thus the biota influence the two key terms of glacier surface energy balance by enhancing radiative warming and turbulent heat transfer. The former is particularly significant because it probably helps sustain the cryoconite hole environment, and secondly because incident radiation is responsible for circa 80% of summer ablation (Hodson et al, In Press). For example a reduction in surface albedo from values typical of clean bare glacier ice (circa 0.4) to those typical of cryoconite punctuated glacier (ca 0.1) would therefore cause a 30% more incident radiation to be available for melting, having clear implications for glacier mass balance. In more extreme Antarctic environments, the impact of the dark organic material on the bottom of cryoconite holes is more significant, because solar heating of organic matter (typically entombed by a clear ice lid) is responsible for the only melting that takes place on or near the glacier surface (Fountain et al., in press). One of the aims of this proposal is to produce a wider picture of cryoconite formation and distribution. There is debate as to how they are formed. In summer they are filled to their surface by water that is usually less than 0.2oC, while in winter they refreeze. A direct positive relationship between elevation and cryoconite depth has been found (Gribbon, 1979), suggesting that the decrease in sensible and latent heat inputs to the glacier surface with altitude may encourage the formation of deeper holes. However, the formation of cryoconites is related to other terms in the surface energy balance of glacier ice, because dark wind blown organic and inorganic material is first deposited on the surface, and warms in the sun to melt a small depression in the ice. Once formed the depression grows into a cryoconite through a series of physical and biological processes (Gribbon, 1979; Wharton et al., 1985; Gerdel and Drouet, 1960). There is debate as to the exact contribution of biological and physical processes. Our own observations on Midre Lovenbreen suggest that cryoconites may persist from year to year, freezing and re-opening, and that new holes may be formed by different processes. It is quite evident that many of the cryoconites develop through the coalescence of very small holes developed from mm sized debris. However, the evolution of smaller (ca. 0.001 m2) holes in to the 1 m2 holes observed in the Antarctic is poorly understood. For example, in more extreme Antarctic glaciers of the Dry Valleys, these larger cryoconites typically have ice covers and are effectively entombed. Lack of contact with the atmosphere has very significant impacts on the water within the hole giving pH values as high as 11 and log10 p (CO2) values as low as -7 (Tranter et al. 2004). Surprisingly microbial life has adapted to these difficult environments. In the Arctic the holes are open to the atmosphere for most of the summer, and despite low temperatures there is significant productivity. Our preliminary observations in the Vestfold Hills indicate that cryoconites are common and that in summer they are open and not entombed. We will develop a glacier-wide, temporal picture of cryoconite development using imagery from a small uninhabited aerial vehicle (UAV), which together with on the ground measurements of physical, chemical and biological parameters, will enable us to gain an understanding of their formation, distribution and overall contribution to productivity and nutrient cycling. OBJECTIVES We aim to develop a picture of the linkages between biological and geochemical processes on the Sorsdal Glacier. In addition we aim to understand how cryoconite holes develop on the glacier and the extent of their coverage and relationship to biological processes. This proposal forms part of an International Polar Year project MERGE (led by Takeshi naganuma), that also includes studies of cryoconites in the American Dry Valleys and in the Arctic (Svalbard). This current proposal involves Laybourn-Parry (Nottingham - from October Keele University), Prof Martyn Tranter (Bristol University) and Dr A.J. Hodson (University of Sheffield). SPECIFIC AIMS 1. To produce carbon and nitrogen budgets for the Sorsdal Glacier. 2. To study the formation and distribution of cryoconite holes on a glacier wide scale and produce a model of their role in nitrogen and carbon cycling. 3. To produce a detailed picture of biological processes in cryoconites and to link this to carbon and nitrogen budgets (geochemistry). Progress against objectives: Please describe the progress you have made against each objective in the last twelve (12) months. The data collection for the listed objectives has been undertaken. Material is being returned for analysis at Sheffield University, UK and the University of Tasmania. However, time constraints of a short fieldwork season (5 weeks) will limit the outputs. We anticipate producing two papers. proprietary
ASAC_2763_1 Molecular analysis of microbiota trapped in ancient antarctic glacial ice AU_AADC STAC Catalog 2006-10-01 2007-03-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214312811-AU_AADC.umm_json "Metadata record for data from ASAC Project 2763 See the link below for public details on this project. Ancient Antarctic glacial ice is a potential resource of trapped microorganisms dating back several hundreds of thousand years that give a snapshot of the past. Nucleic acid, such as DNA, has been identified in samples as old as these from Bacteria, Archaea and Viruses, and this will be the focus of this study. Outcomes of this research will determine the type of organisms that become trapped in these ancient samples, and whether they are able to survive such an extreme condition, and may even lead to novel species being discovered, or even new genes and products. Project objectives: Determine the type of microorganisms that become trapped in ancient Antarctic glacial ice and ascertain whether glacial ice in Antarctica harbours biota that are of evolutionary or biotechnological interest. Public summary of the season progress: Ancient glacial ice samples were collected from ice cliff areas located near Casey Station during the just recent Summer expedition. Ice samples were transported back to Australia and will be subject to 454 sequencing analysis in the next few months in collaboration with Prof. Alan Cooper, Centre for Ancient DNA, University of Adelaide. Other work being performed was completed included an initial molecular-based microbial survey of unusual alkaline, permanently ice-covered, continental lakes located in the Framnes Mountains, inland from Mawson The first download file contains: 10 files File 1. Chemical and oxygen isotope data for Law Dome ice cores that were used in microbiological studies (isolation and DNA analyses). File 2. Chemical and oxygen isotope data for an Amery Ice Shelf ice core that was used in microbiological studies (isolation and DNA analyses). File 3. 16S rRNA gene sequence data obtained from Law Dome ice core samples. All sequences are clones derived after direct PCR amplification of DNA extracts, no isolates were obtained. File 4. 16S rRNA gene sequence data obtained from Amery Ice Shelf ice core sample. All sequences are clones derived after direct PCR amplification of DNA extracts, no isolates were definitively obtained. File 5. Soil and similar samples obtained from either the Vestfold Hills, Eastern Antarctica or from Macquarie Harbour. These soils are the source of actinobacteria screened in the project for antimicrobial activity. File 6. Actinobacteria/actinomycete isolates information detailing isolation procedure, colonial/cellular characteristics, and tentative identification. Barcode system was used to track isolates. Represents the ""(University of Tasmania Antarctic Actinobacteria) UTAA"" collection. File 7. Preliminary antimicrobial screening trial data for 1267 Antarctic actinobacterial isolates against 5 strains of Listeria monocytogenes. A secondary screen was performed to identify those with reliable bioactivity. File 8. Broader analysis of antimicrobial activities of Antarctic isolates against a panel of bacterial pathogenic bacteria. All strains were identified to species level within the genus Streptomyces. File 9. Hydrocarbon profiles of selected Antarctic actinobacterial strains against a range of aliphatic and polycyclic hydrocarbons. File 10. Phenotypic, chemotaxonomic and identification (by 16S rRNA gene sequencing) data of Antarctic hydrocarbon degrading strains. The second download file contains: 3 files: File 1. Framnes Mountain epiglacial lake sample data. Indicates lakes sampled, location, chemical data (pH, temperature). File 2. Cloned 16S rRNA gene sequences obtained from Patterned Lake water DNA extracts. All sequnces are in FASTA format. File 3. Cloned 16S rRNA gene sequences obtained from Sonic Lake water DNA extracts. All sequences are in FASTA format." proprietary
@@ -2936,10 +2936,10 @@ ASAC_288_1 Comparative Study of the Composition and Transport of Particulate Bio
ASAC_2899_1 Metagenomics of Antarctic Lakes: a Model for Defining Microbial Biogeochemical Processes in the Cold AU_AADC STAC Catalog 2006-10-01 2009-03-31 73.51, -68, 79, -53.11 https://cmr.earthdata.nasa.gov/search/concepts/C1214306496-AU_AADC.umm_json Metadata record for data from ASAC Project 2899 See the link below for public details on this project. We conducted a genomic analysis of Archaea and Bacteria collected from lakes in the Vestfold Hills, Antarctica. This provided a new level of understanding about the life forms inhabiting these cold lakes. Linked to knowledge of meteorological, geological, chemical and physical data that has been collected over years of previous research, the new genomic data will generate a complete understanding of how the microorganisms have evolved and how they have transformed and presently interact with the Antarctic environment. Deriving an integrated understanding of microbial ecology is essential for determining ways of preserving the health of the World's ecosystems. The data are available for download as an excel spreadsheet and a word document from the URL given below. The GPS coordinates where samples were collected from are as follows: (Note these are UTM (Universal Transverse Mercator) coordinates, from zone 44D) Ace Lake: 44D 0384881 (easting), 2401821 (northing) Deep Lake: 44D 0385351, 2391772 Organic Lake: 44D 0384928, 2403550 The fields in this dataset are: Water temperature - degrees Celsius Specific conductivity - micro Seimens per centimetre Conductivity - micro Seimens per centimetre Salinity - parts per trillion Dissolved oxygen % - % Dissolved oxygen concentration - milligrams per litre Dissolved oxygen charge - This is an engineering value. The value is unit less, the recommended reading is 50 plus or minus 25. If you have a low reading it generally means you need to replace the membrane and if you have a high reading you need to recondition the probe. PressureA (This a depth reading of the Sonde) - (pounds-force per square inch absolute) Water depth - metres pH pHmV (This is the pH millivolt reading that the probe is outputting the Sonde) - millivolts Turbidity - (nephelometric turbidity unit) BP (Barometric Air Pressure) - psi (pounds per square inch) Taken from the 2008-2009 Progress Report: Progress against objectives: New lake and ocean samples, including additional opportunistic samples from Heard Island, were obtained Oct-Dec 2008. All samples from 2006 forward are being processed. This includes DNA (metagenomics) and protein (proteomics). A great deal of bioinformatic analyses have been performed on metagenome data. Metaproteomics has also proceeded well. Details of some of the progress are as follows: In the reporting period 1,064,488 Sanger sequencing reads were produced with 967,410 passing quality control, which at an average of 700bp provided 677Mb of sequence data. The reads were produced in batches for each sample. We generated assembly statistics and phylogenetic profiles after the completion of each batch. Sample diversity then guided the sequence allocation for each sample. A number of pragmatic software tools have been created to perform the analyses. As an example, for one sample the whole sample assembly was characterised by read depth, GC content, di-nucleotide frequency (Tetra) and tri-nucleotide frequency (Tetra) on a per scaffold basis. The intrinsic properties then formed vectors in a feature space on which a self-organising map clustering analysis was performed. The cluster which comprised the most abundant species was isolated and the genes annotated. This represented 9 contigs with a total of 1.7Mb and 1683 predicted genes. For this sample, proteins were extracted and metaproteomics performed resulting in a total of 3970 confident peptides matched providing identities for 504 proteins (at least 2 peptide matches per protein) representing about 30% coverage. In comparison, a total of 170 proteins were identified against the non-redundant database. In other metaproteomic analyses, samples from 4 lake depths provided a total of 7,925 peptides providing the identification of 1015 proteins against the NCBI non-redundant protein database (matches not yet performed to annotated metagenome data). For testing detection limits and accuracy of identifications using a metaprotomics approach, a simulated mixed community study was performed using S. alaskensis and E. coli. This has shown that cell numbers, protein abundance and cell volumes all impact the ability to detect proteins of individual microorganisms within a population. The type and size of the database the metaproteomic dataset is searched against (non-redundant versus S. alaskensis + E. coli protein database) also resulted in differences in protein detection. The work has been useful for optimising parameters used for metaproteomics of the Antarctic samples. An interesting eukaryotic virus that dominates the biomass of one of the samples is being analysed with the present work focusing on classifying and characterising. Transmission electron microscopy of the water sample revealed virus-like particles of approximately 150nm but it was unclear from morphology if they represented a single virus type or several. Two complementary metagenomic assembly approaches are being used to produce the most complete assembly possible of the large viral sequences. The first assembly strategy follows a conventional metagenomic workflow consisting of assembly of the whole metagenomic dataset followed by taxonomic binning of the constructs. An initial assembly has been constructed after determining the optimum acceptable degree of error. A high degree of assembly was evident with the largest scaffold spanning 108kb with 6 X coverage. A BLASTx search of the five largest contigs (greater than 10kb) produced two alignments to Major Capsid Protein (MCP) genes; one to the short MCP gene of Chyrsochromulina ericina virus (28% identity) and the other to the full MCP gene of Phaeocytis pouchetii virus (76% identity). Sequence flanking the full MCP gene corresponds to conserved hypothetical protein sequences from Ostreococcus virus 5 (45% identity) and Paramecium sp. Chlorella virus AR158 (39% identity). These large deeply assembling contigs will be used to 'tune' the parameters to improve assembly of the entire metagenome. A preliminary attempt to bin the scaffolds using tetra nucleotide frequencies from the initial assembly has not completely resolved into clear taxonomic clusters. A multi-dimensional binning approach including sequence coverage, GC content, nucleotide frequencies along with identification of marker genes is being developed and will be applied once an optimum whole metagenomic assembly has been completed. Although the presence of conserved genes is a promising sign of accurate assembly, validation of the scaffolds by comparison to sequenced virus genomes is uninformative as viruses are poorly represented in the public databases and extremely diverse. Instead, a second assembly strategy is underway that will conservatively extract and compile the viral sequence. The reads assigned in an initial MEGAN analysis to the large dsDNA viral clade were used in a preliminary round of assembly. This first assembly will be used as a reference to recruit more overlapping fragments and combined in another round assembly extending the construct from the high confidence 'seeds'. Cycles of recruitment and assembly will continue until the assembly reaches an end point. This is a new method of assembly that potentially can be used to extract and produce confident assemblies of other species with no sequenced representatives. Comparison between this virus specific assembly and the conventional metagenomic assembly will allow evaluation of the fidelity of both processes. proprietary
ASAC_2899_Ace_1 Metagenomics of Antarctic Lakes: a Model for Defining Microbial Biogeochemical Processes in the Cold - Ace Lake Data AU_AADC STAC Catalog 2006-10-01 2009-03-31 77, -68, 79, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214312788-AU_AADC.umm_json Metadata record for data from ASAC Project 2899 See the link below for public details on this project. We conducted a genomic analysis of Archaea and Bacteria collected from lakes in the Vestfold Hills, Antarctica. This provided a new level of understanding about the life forms inhabiting these cold lakes. Linked to knowledge of meteorological, geological, chemical and physical data that has been collected over years of previous research, the new genomic data will generate a complete understanding of how the microorganisms have evolved and how they have transformed and presently interact with the Antarctic environment. Deriving an integrated understanding of microbial ecology is essential for determining ways of preserving the health of the World's ecosystems. This metadata record covers a specific dataset collected at Ace Lake in the Vestfold Hills. Comprehensive documentation describing this dataset is not available. For further information on the project, see other metadata records related to project 2899. proprietary
ASAC_2901_RAASTI_1 Investigation of sea ice physical processes in East Antarctica during early Spring - Measuring snow thickness over Antarctic sea ice with a helicopter-borne 2-8 GHz FMCW radar AU_AADC STAC Catalog 2007-09-04 2007-10-17 110, -68, 130, -64 https://cmr.earthdata.nasa.gov/search/concepts/C1214312815-AU_AADC.umm_json Public Summary for project 2901 This research will contribute to a large multi-disciplinary study of the physics and biology of the Antarctic sea ice zone in early Spring 2007. The physical characteristics of the sea ice will be directly measured using satellite-tracked drifting buoys, ice core analysis and drilled measurements, with detailed measurements of snow cover thickness and properties. Aircraft-based instrumentation will be used to expand our survey area beyond the ship's track and for remote sampling. The data collected will provide valuable ground-truthing for existing and future satellite missions and improve our understanding of the role of sea ice in the climate system. Project objectives: (i) to quantify the spatial variability in sea ice and snow cover properties over scales of metres to hundreds of kilometres in the region of 110 - 130 degrees E, in order to improve the accuracy of sea ice thickness estimates from satellite altimetry and polarimetric synthetic aperture radar (SAR) data. (ii) To determine the drift characteristics, and internal stress, of sea ice in the region 110 - 130 degrees E. (iii) To investigate the relationships between the physical sea ice environment and the structure of Southern Ocean ecosystems (joint with AAS Proposal 2767). Taken from the abstract of the PhD thesis accompanying the dataset: Antarctic sea ice and its snow cover are integral components of the global climate system, yet many aspects of their vertical dimensions are poorly understood, making their representation in global climate models poor. Remote sensing is the key to monitoring the dynamic nature of sea ice and its snow cover. Reliable and accurate snow thickness data from an airborne platform is currently a highly sought after data product. Remotely sensed snow thickness measurements can provide an indication of precipitation levels. These are predicted to increase with effects of climate change, and are difficult to measure as snow fall is frequently lost to wind-blown redistribution, sublimation and snow-ice formation. Additionally, accurate regional scale snow thickness data will increase the accuracy of sea ice thickness retrieval from satellite altimeter freeboard estimates. Airborne snow-depth investigation techniques are one method for providing regional estimation of these parameters. The airborne datasets are better suited to validating satellite algorithms, and are themselves easier to validate with in-situ measurement. The development and practicality of measuring snow thickness over sea ice in Antarctica using a helicopter-borne radar forms the subject of this thesis. The radar design, a 2-8 GHz Frequency Modulated Continuous Wave Radar, is a product of collaboration and the expertise at the Centre for Remote Sensing of Ice Sheets, Kansas University. This thesis presents a review of the theoretical basis of the interactions of electromagnetic waves with the snow and sea ice. The dominant general physical parameters pertinent to electromagnetic sensing are presented, and the necessary conditions for unambiguous identification of the air/snow and snow/ice interfaces by the radar are derived. It is found that the roughness's of the snow and ice surfaces are dominant determinants in the effectiveness of layer identification in this radar. Motivated by these results, the minimum sensitivity requirements for the radar are presented. Experiments with the radar mounted on a sled confirm that the radar is capable of unambiguously detecting snow thickness. Helicopter-borne experiments conducted during two voyages into the East Antarctic sea-ice zone show however, that the airborne data are highly affected by sweep frequency non-linearities, making identification of snow thickness difficult. A model for the source of these non-linearities in the radar is developed and verified, motivating the derivation of an error correcting algorithm. Application of the algorithm to the airborne data demonstrates that the radar is indeed receiving reflections from the air/snow and snow/ice interfaces. Consequently, this thesis presents the first in-situ validated snow thickness estimates over sea ice in Antarctica derived from a Frequency Modulated Continuous Wave radar on a helicopter-borne platform. Additionally, the ability of the radar to independently identify the air/snow and snow/ice interfaces allows for a relative estimate of roughness of the sea ice to be derived. This parameter is a critical component necessary for assessing the integrity of satellite snow-depth retrieval algorithms such as those using the data product provided by the Advanced Microwave Scanning Radiometer - Earth Observing System sensor on board NASA's Aqua satellite. This thesis provides a description, solution or mitigation of the many difficulties of operating a radar from a helicopter-borne platform, as well as tackling the difficulties presented in the study of heterogeneous media such as sea ice and its snow cover. In the future the accuracy of the snow-depth retrieval results can be increased as technical difficulties are overcome, and at the same time the radar architecture simplified. However, further validation studies are suggested to better understand the effect of heterogeneous nature of sea ice and its snow cover on the radar signature. RAASTI = Radar For Antarctic Snow Thickness Investigation proprietary
-ASAC_2904_1 Aliens in Antarctica - project to study exotic species and visitors in the Antarctic AU_AADC STAC Catalog 2007-09-30 2011-03-31 -180, -90, 180, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1214306505-AU_AADC.umm_json Metadata record for data expected from ASAC Project 2904 See the link below for public details on this project. International Polar Year (IPY) Aliens in Antarctica will assess the threat of humans carrying non-native seeds and spores into Antarctica. We will identify routes of transport and attempt to calculate how many seeds and spores are transported each year. Our data will be used to develop techniques to mitigate this threat and hence protect Antarctica. The impact of non-native (alien) species on ecosystems is one of the big issues of the 21st Century. Antarctica is not immune to this problem with some alien species having established on the Antarctic continent and on most sub-Antarctic islands. The impacts of alien species can include substantial loss of biodiversity and damage to ecosystem processes. Such impacts will be exacerbated by the rapid climate change, now being experienced in parts of Antarctica. Surrounded by the vast Southern Ocean, Antarctica's protective isolation is being chipped away by the movement of people and cargo to the region by national programs and the now booming tourist industry. Over 40,000 people travel to the Antarctic each year. This international project will assess the pathways of propagule (seeds, eggs, spores etc) transfer, the extent to which people from many nations, unintentionally carry propagules of alien species into the Antarctic region and the size of the threat. It will lead to the creation of appropriate mitigation methods by the Antarctic Treaty to protect the fragile Antarctic ecosystem. Furthermore, the project will provide valuable insight into the movement of alien propagules worldwide. It has been estimated that by 2010, the number of tourists crossing international boarders globally each year, will be around 1 billion people. The travel histories of some 15,000 Antarctic tourists and researchers will be complied, assisted by the cooperation of four tourist operators, 15 supply vessels of national Antarctic programmes, and six air operators. One thousand items of cargo from 7 National Antarctic programmes will be inspected for propagules of alien species. The study has the full support from the Council of Managers of National Antarctic Programs, the International Association of Antarctic Tour Operators, and researchers from seven nations. Taken from the 2008-2009 Progress Report: Progress against objectives: Considerable progress has been made on all objectives. All samples of propagules (greater than 1000 samples from over 50 voyages and examination of cargo/ food/ building material from 5 nations) have been sorted and propagules extracted. The majority of these propagules have been photographed and where possible identified. Analysis of the data is currently underway. Taken from the 2009-2010 Progress Report: Progress against objectives: The International Polar Year project is examining the type and amount of 'propagules' (seed, spores and eggs) that are unintentionally imported into the region on clothes, shoes or hand luggage, as well as how many propagules are likely to be deposited and whether they will germinate and grow. Cargo, fresh food and travellers' gear destined for Antarctica were inspected during the first season of IPY and are now currently being analysed. Considerable progress on the quantifiaction of the threat of alien species to Antarctic ecosystems has been made. Results of our analysies will be presented at ATCM 33. proprietary
ASAC_2904_1 Aliens in Antarctica - project to study exotic species and visitors in the Antarctic ALL STAC Catalog 2007-09-30 2011-03-31 -180, -90, 180, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1214306505-AU_AADC.umm_json Metadata record for data expected from ASAC Project 2904 See the link below for public details on this project. International Polar Year (IPY) Aliens in Antarctica will assess the threat of humans carrying non-native seeds and spores into Antarctica. We will identify routes of transport and attempt to calculate how many seeds and spores are transported each year. Our data will be used to develop techniques to mitigate this threat and hence protect Antarctica. The impact of non-native (alien) species on ecosystems is one of the big issues of the 21st Century. Antarctica is not immune to this problem with some alien species having established on the Antarctic continent and on most sub-Antarctic islands. The impacts of alien species can include substantial loss of biodiversity and damage to ecosystem processes. Such impacts will be exacerbated by the rapid climate change, now being experienced in parts of Antarctica. Surrounded by the vast Southern Ocean, Antarctica's protective isolation is being chipped away by the movement of people and cargo to the region by national programs and the now booming tourist industry. Over 40,000 people travel to the Antarctic each year. This international project will assess the pathways of propagule (seeds, eggs, spores etc) transfer, the extent to which people from many nations, unintentionally carry propagules of alien species into the Antarctic region and the size of the threat. It will lead to the creation of appropriate mitigation methods by the Antarctic Treaty to protect the fragile Antarctic ecosystem. Furthermore, the project will provide valuable insight into the movement of alien propagules worldwide. It has been estimated that by 2010, the number of tourists crossing international boarders globally each year, will be around 1 billion people. The travel histories of some 15,000 Antarctic tourists and researchers will be complied, assisted by the cooperation of four tourist operators, 15 supply vessels of national Antarctic programmes, and six air operators. One thousand items of cargo from 7 National Antarctic programmes will be inspected for propagules of alien species. The study has the full support from the Council of Managers of National Antarctic Programs, the International Association of Antarctic Tour Operators, and researchers from seven nations. Taken from the 2008-2009 Progress Report: Progress against objectives: Considerable progress has been made on all objectives. All samples of propagules (greater than 1000 samples from over 50 voyages and examination of cargo/ food/ building material from 5 nations) have been sorted and propagules extracted. The majority of these propagules have been photographed and where possible identified. Analysis of the data is currently underway. Taken from the 2009-2010 Progress Report: Progress against objectives: The International Polar Year project is examining the type and amount of 'propagules' (seed, spores and eggs) that are unintentionally imported into the region on clothes, shoes or hand luggage, as well as how many propagules are likely to be deposited and whether they will germinate and grow. Cargo, fresh food and travellers' gear destined for Antarctica were inspected during the first season of IPY and are now currently being analysed. Considerable progress on the quantifiaction of the threat of alien species to Antarctic ecosystems has been made. Results of our analysies will be presented at ATCM 33. proprietary
-ASAC_2904_Food_1 Aliens in Antarctica Project - Inspection of fresh food for alien propagules ALL STAC Catalog 2007-10-19 2008-03-14 60, -67, 160, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214306540-AU_AADC.umm_json International Polar Year (IPY) Aliens in Antarctica project aims to identify human-mediated pathways for alien propagules into the Antarctic ecosystem (www.aliensinantarctica.aq). As part of this international project, AAD staff examined fresh food and cargo for evidence of propagules prior to shipping south by the Australian Antarctic Program. This report summarises the findings of our food inspections. A total of 2094 items of fresh fruit and/or vegetables were inspected over the season. Of these 89% (1865 items) were deemed 'clean' (ie no evidence of propagules or infections), 191 (9%0 had evidence of fungal infections, and 54 items (2%) had invertebrates, soil or other propagules such as seeds. Apples, cantaloupes, carrots, grapefruit, limes, oranges, potatoes and tomatoes were recorded as consistently having clean rates of 90% or greater over the 07/08 shipping season. With regard to the food items found with propagules, a number of significant observations were made. The most notable of these was that of the 56 pears examined at the beginning of the season (Voyage 2) only one was deemed 'clean': the remainder (99%) were rotting with blue moulds. Similarly only 11% of onions destined for Voyage 2 and 49% of bananas were 'clean'; the remainder were observed with fungal infections or other propagules. Other notable observations were that some cabbages and iceberg lettuces were contaminated with soil, and live thrips and white flies (Bemisia sp?) were found in two boxes. proprietary
+ASAC_2904_1 Aliens in Antarctica - project to study exotic species and visitors in the Antarctic AU_AADC STAC Catalog 2007-09-30 2011-03-31 -180, -90, 180, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1214306505-AU_AADC.umm_json Metadata record for data expected from ASAC Project 2904 See the link below for public details on this project. International Polar Year (IPY) Aliens in Antarctica will assess the threat of humans carrying non-native seeds and spores into Antarctica. We will identify routes of transport and attempt to calculate how many seeds and spores are transported each year. Our data will be used to develop techniques to mitigate this threat and hence protect Antarctica. The impact of non-native (alien) species on ecosystems is one of the big issues of the 21st Century. Antarctica is not immune to this problem with some alien species having established on the Antarctic continent and on most sub-Antarctic islands. The impacts of alien species can include substantial loss of biodiversity and damage to ecosystem processes. Such impacts will be exacerbated by the rapid climate change, now being experienced in parts of Antarctica. Surrounded by the vast Southern Ocean, Antarctica's protective isolation is being chipped away by the movement of people and cargo to the region by national programs and the now booming tourist industry. Over 40,000 people travel to the Antarctic each year. This international project will assess the pathways of propagule (seeds, eggs, spores etc) transfer, the extent to which people from many nations, unintentionally carry propagules of alien species into the Antarctic region and the size of the threat. It will lead to the creation of appropriate mitigation methods by the Antarctic Treaty to protect the fragile Antarctic ecosystem. Furthermore, the project will provide valuable insight into the movement of alien propagules worldwide. It has been estimated that by 2010, the number of tourists crossing international boarders globally each year, will be around 1 billion people. The travel histories of some 15,000 Antarctic tourists and researchers will be complied, assisted by the cooperation of four tourist operators, 15 supply vessels of national Antarctic programmes, and six air operators. One thousand items of cargo from 7 National Antarctic programmes will be inspected for propagules of alien species. The study has the full support from the Council of Managers of National Antarctic Programs, the International Association of Antarctic Tour Operators, and researchers from seven nations. Taken from the 2008-2009 Progress Report: Progress against objectives: Considerable progress has been made on all objectives. All samples of propagules (greater than 1000 samples from over 50 voyages and examination of cargo/ food/ building material from 5 nations) have been sorted and propagules extracted. The majority of these propagules have been photographed and where possible identified. Analysis of the data is currently underway. Taken from the 2009-2010 Progress Report: Progress against objectives: The International Polar Year project is examining the type and amount of 'propagules' (seed, spores and eggs) that are unintentionally imported into the region on clothes, shoes or hand luggage, as well as how many propagules are likely to be deposited and whether they will germinate and grow. Cargo, fresh food and travellers' gear destined for Antarctica were inspected during the first season of IPY and are now currently being analysed. Considerable progress on the quantifiaction of the threat of alien species to Antarctic ecosystems has been made. Results of our analysies will be presented at ATCM 33. proprietary
ASAC_2904_Food_1 Aliens in Antarctica Project - Inspection of fresh food for alien propagules AU_AADC STAC Catalog 2007-10-19 2008-03-14 60, -67, 160, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214306540-AU_AADC.umm_json International Polar Year (IPY) Aliens in Antarctica project aims to identify human-mediated pathways for alien propagules into the Antarctic ecosystem (www.aliensinantarctica.aq). As part of this international project, AAD staff examined fresh food and cargo for evidence of propagules prior to shipping south by the Australian Antarctic Program. This report summarises the findings of our food inspections. A total of 2094 items of fresh fruit and/or vegetables were inspected over the season. Of these 89% (1865 items) were deemed 'clean' (ie no evidence of propagules or infections), 191 (9%0 had evidence of fungal infections, and 54 items (2%) had invertebrates, soil or other propagules such as seeds. Apples, cantaloupes, carrots, grapefruit, limes, oranges, potatoes and tomatoes were recorded as consistently having clean rates of 90% or greater over the 07/08 shipping season. With regard to the food items found with propagules, a number of significant observations were made. The most notable of these was that of the 56 pears examined at the beginning of the season (Voyage 2) only one was deemed 'clean': the remainder (99%) were rotting with blue moulds. Similarly only 11% of onions destined for Voyage 2 and 49% of bananas were 'clean'; the remainder were observed with fungal infections or other propagules. Other notable observations were that some cabbages and iceberg lettuces were contaminated with soil, and live thrips and white flies (Bemisia sp?) were found in two boxes. proprietary
+ASAC_2904_Food_1 Aliens in Antarctica Project - Inspection of fresh food for alien propagules ALL STAC Catalog 2007-10-19 2008-03-14 60, -67, 160, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214306540-AU_AADC.umm_json International Polar Year (IPY) Aliens in Antarctica project aims to identify human-mediated pathways for alien propagules into the Antarctic ecosystem (www.aliensinantarctica.aq). As part of this international project, AAD staff examined fresh food and cargo for evidence of propagules prior to shipping south by the Australian Antarctic Program. This report summarises the findings of our food inspections. A total of 2094 items of fresh fruit and/or vegetables were inspected over the season. Of these 89% (1865 items) were deemed 'clean' (ie no evidence of propagules or infections), 191 (9%0 had evidence of fungal infections, and 54 items (2%) had invertebrates, soil or other propagules such as seeds. Apples, cantaloupes, carrots, grapefruit, limes, oranges, potatoes and tomatoes were recorded as consistently having clean rates of 90% or greater over the 07/08 shipping season. With regard to the food items found with propagules, a number of significant observations were made. The most notable of these was that of the 56 pears examined at the beginning of the season (Voyage 2) only one was deemed 'clean': the remainder (99%) were rotting with blue moulds. Similarly only 11% of onions destined for Voyage 2 and 49% of bananas were 'clean'; the remainder were observed with fungal infections or other propagules. Other notable observations were that some cabbages and iceberg lettuces were contaminated with soil, and live thrips and white flies (Bemisia sp?) were found in two boxes. proprietary
ASAC_2914_2 Kelp rafts in the Southern Ocean: intercontinental travel for sessile and semi-sessile organisms. AU_AADC STAC Catalog 2010-03-25 2010-03-25 -180, -60, 180, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1214306525-AU_AADC.umm_json Metadata record for data from ASAC Project 2914 See the link below for public details on this project. Can animals raft between countries on floating seaweed? We aim to answer that question using powerful genetic tools. We can tell whether gene flow is strong between populations of animals by comparing their mitochondrial DNA; this could show us whether animals from one species in New Zealand are isolated from individuals of the same species in Chile. If they are not isolated, how are they managing to maintain gene flow? We know there are many millions of clumps of floating seaweed in the Southern Ocean, and these might provide a means of intercontinental travel for a range of small invertebrates. Project objectives: The primary objective of the project is to determine the effectiveness of rafting as a dispersal mechanism for sessile and semi-sessile organisms around the Southern Ocean using genetic tools. The secondary objectives, by which the primary objective will be addressed, are: - to examine the biogeography of bull kelp (Durvillaea antarctica) and its holdfast fauna around the Southern Ocean - to undertake genetic analysis of a wide range of macroalgal (seaweed) species throughout the Southern Ocean to assess 1) whether sea ice indeed extended further north than previously believed, and 2) the ecological and evolutionary impacts of historic ice scour on Southern Ocean islands. - to determine which holdfast invertebrates are the most common and ubiquitous in holdfasts of Durvillaea antarctica around the Southern Ocean - to compare the genetic structure of populations of both the kelp itself, and select invertebrate taxa* from its holdfasts, on a number of spatial scales: --- genetic variation at HOLDFAST level: are members of a single species, e.g., the isopod Limnoria stephenseni, closely related within a single holdfast? --- genetic variation at SITE level: are members of a single species, e.g., Durvillaea antarctica itself, closely related at one site? In this case, a 'site' means a single intertidal rock platform. --- genetic variation at NATIONAL level: are there distinct biogeographic separations of species, or does a single species show distinct genetic disjunction, along the Chilean coastline and around the south island of New Zealand? --- genetic variation at OCEAN level: are species clearly connected (by gene flow) between Southern Ocean landmasses? The landmasses of interest are: Chile, New Zealand, and the subantarctic islands on which Durvillaea antarctica grows. * The proposed taxa that this project will focus on are: the isopod genus Limnoria; the amphipod Parawaldeckia kidderi; the chiton genus Onithochiton; the polychaete worm families Terebellidae and Syllidae; a topshell; a bivalve; barnacles. Progress against objectives: Considerable progress has been made against the primary objective since the start of the project in 2006. We have collected (/ been sent) and analysed samples of bull-kelp (Durvillaea antarctica) and its associated invertebrate holdfast fauna from numerous sites around the Southern Ocean (subantarctic islands including Macquarie, Gough, Marion, Kerguelen, Crozet, Auckland, Antipodes, Campbell, Falkland Islands; along the coasts of New Zealand and Chile). Our results thus far have allowed us to determine not only that rafting facilitates long-distance dispersal of these otherwise sedentary taxa, but also that sea ice during the last ice ice likely had significant impacts on subantarctic intertidal ecosystems. Our conclusions have been published in several papers in high-impact journals. The secondary objectives, by which the primary objective will be addressed, are: - to examine the biogeography of bull kelp (Durvillaea antarctica) and its holdfast fauna - these objectives have now largely been achieved, and results published. - to undertake genetic analysis of a wide range of macroalgal (seaweed) species throughout the Southern Ocean - this part of the project is ongoing, and will make use of samples collected over the austral summer from Macquarie Island (and other locations around the southern hemisphere). all samples have now been collected and are being processed in the laboratory. - to determine which holdfast invertebrates are the most common and ubiquitous - this objective has been partially achieved (see Nikula et al. 2010), but research is ongoing. - to compare the genetic structure of populations of both the kelp itself, and select invertebrate taxa from its holdfasts, on a number of spatial scales - this objective has been partially achieved (see Nikula et al. 2010 for results of Limnoria and Parawaldeckia genetic research) but additional research on these and other taxa continues. The download file contains an excel spreadsheet detailing collection locations and accession numbers for the samples collected on Macquarie Island. A text document providing accession numbers for non-Antarctic related samples used in this project is also part of the download file. proprietary
ASAC_2918_1 Have stream invertebrate communities of Macquarie Island changed over 15 years and are they likely to respond to climate changes or other environmental factors? AU_AADC STAC Catalog 2007-09-30 2008-03-31 158, -54.8, 159, -54.2 https://cmr.earthdata.nasa.gov/search/concepts/C1214306543-AU_AADC.umm_json "Metadata record for data from ASAC Project 2918 See the link below for public details on this project. This project will assess the extent of changes to the freshwater stream invertebrate communities of Macquarie Island since they were last sampled 15 years ago. It will also assess whether spatial variation in these stream communities is related to changes in water temperature, it will experimentally examine the temperature tolerance of these freshwater taxa and will provide a long-term dataset to assess future changes, including those resulting from climate change. The use of stream macroinvertebrates as biomonitoring tools to detect impacts from human activities on Macquarie Island and other sub-Antarctic Islands will be examined. The download file contains a pdf document with several side-by-side comparison images taken in 1992 by Richard Marchant during his studies for ASAC project 555 (ASAC_555), ""A Survey of the Freshwater Macroinvertebrates in Streams and Lakes of Macquarie Island"", and in 2010 by James Doube. Also see the metadata record ""Stream invertebrate communities of Macquarie Island"" (AAS_3261) for more information." proprietary
ASAC_2933_1 Developing water and sediment quality guidelines for Antarctica: Responses of Antarctic marine biota to contaminants. AU_AADC STAC Catalog 2007-09-30 2012-03-31 110.48, -66.32, 110.56, -66.24 https://cmr.earthdata.nasa.gov/search/concepts/C1214306545-AU_AADC.umm_json Metadata record for data from AAS (ASAC) Project 2933. See the child records for access to the datasets. Public While it is generally thought that Antarctic organisms are highly sensitive to pollution, there is little data to support or disprove this. Such data is essential if realistic environmental guidelines, which take into account unique physical, biological and chemical characteristics of the Antarctic environment, are to be developed. Factors that modify bioavailability, and the effects of common contaminants on a range of Antarctic organisms from micro-algae to macro-invertebrates will be examined. Risk assessment techniques developed will provide the scientific basis for prioritising contaminated site remediation activities in marine environments, and will contribute to the development of guidelines specific to Antarctica. Project objectives: 1. Develop and use toxicity tests to characterise the responses of a range of Antarctic marine invertebrates, micro- and macro-algae to common inorganic and organic contaminants. 2. To examine factors controlling bioavailability and the influence of physical, chemical and biological properties unique to the Antarctic environment on the bioavailability and toxicity of contaminants to biota. 3. To compare the response of Antarctic biota to analogous species in Arctic, temperate and tropical environments in order to determine the applicability of using toxicity data and environmental guidelines developed in other regions of the world for use in the Antarctic. 4. Develop a suite of standard bioassay techniques using Antarctic species to assess the toxicity of mixtures of contaminants (aqueous and sediment-bound) including tip leachates, sewage effluents and contaminated sediments. 5. To establish risk assessment models to predict the potential hazards associated with contaminated sites in Antarctica to marine biota, and to develop Water and Sediment Quality Guidelines for Antarctica to set as targets for the remediation of contaminated marine environments. Taken from the 2008-2009 Progress Report: Progress against objectives: Due to logistical constraints, only a short field season (5 weeks) was conducted at Casey in 2008/09 and no berths were allocated solely to this project. A team of 6 scientists worked together on an intensive marine sampling program under TRENZ (AAS project 2948, CI Stark) in support of 5 different AAS projects, including this one. The lack of adequate personnel dedicated to this project and the limited time that we were allocated on station hindered progress and meant that no experiments on Antarctic organisms were able to be conducted in situ. The airlink was however successfully used to transport marine invertebrates collected at Casey and held in seawater at 0degC back to Hobart on 3 separate flights. These invertebrates are currently being maintained in the cold water ecotoxicology aquarium facilities at Kingston. Once they are sorted and where possible established in cultures, they will be used in toxicity tests. Progress against specific objects are: 1) Much effort and time has been put towards the husbandry and culture of the collected Antarctic marine invertebrates. Some species are now successfully breeding in the laboratory providing new generations and sensitive juvenile stages of invertebrates to work with in toxicity tests. This culturing capability, if able to be developed, will hugely extend opportunities for carrying out research for this project, by giving us access to live material over the winter months and during summer when berths to or space on station in Antarctica is limited. Toxicity tests using some of the common amphipods and gastropods collected in the 0809 season at Casey will commence shortly at Kingston. 2) Toxicity tests to commence shortly using invertebrates collected in the 0809 season now being maintained in the Ecotoxicology aquarium will focus on interactions and potentially synergistic effects of contaminants along with other environmental stressors including increases in temperature and decreases in salinity associated with predicted environmental changes in response to climate change. 3) A phD candidate has recently started on this project and is currently reviewing all available literature on the response of Antarctic species to contaminants and environmental stressors in comparison to related species from lower latitudes. 4) Invertebrates collected in the 0809 season that are being maintained in the Ecotoxicology aquarium will be screened in toxicity tests to commence shortly. Methods will then be developed using the most suitable and sensitive species to form the basis of standard bioassay procedures that can be used to test mixtures such as sewage effluents and tip leachates in the upcoming season. 5) The establishment of risk assessment models and Environmental Quality Guidelines for Antarctica is a long term goal of this project when data from the first 4 objectives can be synthesised and hence has not yet been addressed. Taken from the 2009-2010 Progress Report: Progress against objectives: Objectives 1 and 2: Metal effects on the behaviour and survival of three marine invertebrate species were investigated during the field season. Two replicate toxicity tests were conducted on the larvae of sea urchin Sterechinus neumayeri where combined effects of metal (copper and cadmium) and temperature (-1, 1 and 3 degrees Celsius) were to be investigated on developmental success. However, due to lower than optimal fertilisation success, both tests were terminated before any meaningful results could be derived. Four tests were conducted on the adult amphipod, Paramorea walkeri. Two replicate tests investigated combined metal (copper and cadmium) and temperature (-1, 1 and 3 degrees Celsius) effects, and two tests investigated the effects of copper, cadmium, lead, zinc and nickel exposure at ambient sea water temperature of -1 degrees Celsius. One test was conducted with the micro-gastropod Skenella paludionoides being exposed to copper, cadmium, lead, zinc and nickel at ambient sea water temperature. The larvae of bivalve Laternula sp. were also investigated as a potential test organism for metal toxicity. Strip spawning was conducted a number of times, however, this technique did not provide adequate levels of fertilisation success and as such, the toxicity tests on larval development were not completed. Objective 3: A phD candidate working on this project is in the process of compiling a review of all available date on the response of Antarctic species to contaminants and environmental stressors in comparison to related species from lower latitudes. This literature review will form a major component of her thesis' first chapter Objective 4: Methods for Standard bioassay procedures were developed using the most suitable and sensitive species, the amphipod Paramoera walkeri and the microgastropod Skenella paludionoides. These standard tests were then used to assess the toxicity of sewage effluent at Davis Station (in conjunction with project 3217). Objective 5: Toxicity tests on sewage effluent were conducted as part of a risk assessment to determine hazards associated with the current discharge. The determined toxicity of the sewage effluent will provide a basis for guideline recommendations on the required level of treatment and on what constitutes an adequate or 'safe' dilution factor for dispersal of the effluent discharge to the near shore marine environment. proprietary
@@ -2994,8 +2994,8 @@ ASAC_520_1 Anaesthetics and Ecology of the Southern Elephant Sea at Macquarie Is
ASAC_537_1 Corrosivity Mapping of Antarctica utilising exposure of standard alloy coupons AU_AADC STAC Catalog 1991-09-30 1996-03-31 -180, -90, 180, -44 https://cmr.earthdata.nasa.gov/search/concepts/C1214313012-AU_AADC.umm_json Antarctica is the world's coldest, driest, highest and least polluted continent. Accepted wisdom is that atmospheric corrosion rates in Antarctica should be low because of the extreme dry cold. Russian research suggested that temperatures below 0 degrees C alone are insufficient to eliminate corrosion although temperatures consistently below -25 degrees C will markedly decrease corrosivity. The severe and unfamiliar Antarctic conditions challenge assumptions about the behaviour of materials. In the 1960's, snow and ice was removed from Captain Scott's hut at Cape Evens revealing buried artefacts in excellent condition. The excavation changed the microclimate radically and significant deterioration of several materials, especially metals, has since occurred. The need to objectively measure corrosivity arose from the unexpectedly severe corrosion problems at several historic sites and the need to develop treatment and preventative conservation strategies. Significant corrosion problems also affect old sealing and whaling stations and artefacts on subantarctic islands. International cooperation has been sought to enable the exposure of standard steel coupons and measurement of atmospheric corrosivity rates in different climate zones in Antarctica. Ten locations on the continent and various sites on four subantarctic islands have been monitored, chosen because of the potential to access the site and availability of meteorological data from research bases and automatic weather stations. Observations are that the method is sufficiently sensitive to measure low rates of corrosion. The results are consistent with the Russian hyopothesis that temperatures below 0 degrees C alone will not significantly reduce corrosion. Steel corrosion rates range by a factor of more than 500 in Antarctica from the coast to far inland. Temperatures at coastal sites rarely exceed freezing and never at inland sites. A highly significant factor is atmospheric salt deposition since rain is rare. This project has determined that the lowest corrosivity rate ever measured is at Vostok, the coldest place on earth, which is 1200 km from the sea. The Heard Island document available in pdf form at the provided URL is reproduced with the permission of the Papers and Proceedings of the Royal Society of Tasmania. The paper was published in the Heard Island volume by the Royal Society of Tasmania (GPO Box 1166M, Hobart 7001, Tasmania, Australia) from whom the entire volume is available for A$22; plus postage (A$2;.45) for orders from within Australia and A$20; plus postage (A$6; in Asia and the Pacific and A$9; elsewhere; payment in Australian currency) for orders from beyond Australia. The fields for this dataset are: distance from sea (km) days exposed corrosivity mass loss (g) Blank loss (g) % blank loss proprietary
ASAC_555 A Survey of the Freshwater Macroinvertebrates in Streams and Lakes of Macquarie Island ALL STAC Catalog 1992-11-13 1992-12-03 158.7925, -54.7651, 158.9351, -54.5143 https://cmr.earthdata.nasa.gov/search/concepts/C1369983962-SCIOPS.umm_json In all, 15 sites on 12 streams were kick-sampled for invertebrates. Eleven fully aquatic taxa were found: a species of Iais (Isopoda: Janiridae); six species of oligochaetes (three enchytraeids, one tubificid, one naidid, one phreodrilid); a harpacticoid copepod; two nematode taxa; and Minona amnica, a turbellarian. Composition of this depauperate community changed little between sites, although one site disturbed by penguins had clearly fewer taxa. Aquatic insects (and fish) were absent, apart from three species of semi-aquatic diptera that occurred very sparsely. In terms of biomass, the streams were dominated by the oligochaetes. Data are presence absence data. See the publication for further details. The fields in this dataset are: Site Name Latitude Longitude Altitude (m) Water Temperature (C) pH Water Conductivity (micro siemens/cm) Stream width (cm) Stream Depth (cm) Stream Velocity (cm/s) Species proprietary
ASAC_555 A Survey of the Freshwater Macroinvertebrates in Streams and Lakes of Macquarie Island SCIOPS STAC Catalog 1992-11-13 1992-12-03 158.7925, -54.7651, 158.9351, -54.5143 https://cmr.earthdata.nasa.gov/search/concepts/C1369983962-SCIOPS.umm_json In all, 15 sites on 12 streams were kick-sampled for invertebrates. Eleven fully aquatic taxa were found: a species of Iais (Isopoda: Janiridae); six species of oligochaetes (three enchytraeids, one tubificid, one naidid, one phreodrilid); a harpacticoid copepod; two nematode taxa; and Minona amnica, a turbellarian. Composition of this depauperate community changed little between sites, although one site disturbed by penguins had clearly fewer taxa. Aquatic insects (and fish) were absent, apart from three species of semi-aquatic diptera that occurred very sparsely. In terms of biomass, the streams were dominated by the oligochaetes. Data are presence absence data. See the publication for further details. The fields in this dataset are: Site Name Latitude Longitude Altitude (m) Water Temperature (C) pH Water Conductivity (micro siemens/cm) Stream width (cm) Stream Depth (cm) Stream Velocity (cm/s) Species proprietary
-ASAC_555_1 A Survey of the Freshwater Macroinvertebrates in Streams and Lakes of Macquarie Island AU_AADC STAC Catalog 1992-11-13 1992-12-03 158.7925, -54.7651, 158.9351, -54.5143 https://cmr.earthdata.nasa.gov/search/concepts/C1214313015-AU_AADC.umm_json In all, 15 sites on 12 streams were kick-sampled for invertebrates. Eleven fully aquatic taxa were found: a species of Iais (Isopoda: Janiridae); six species of oligochaetes (three enchytraeids, one tubificid, one naidid, one phreodrilid); a harpacticoid copepod; two nematode taxa; and Minona amnica, a turbellarian. Composition of this depauperate community changed little between sites, although one site disturbed by penguins had clearly fewer taxa. Aquatic insects (and fish) were absent, apart from three species of semi-aquatic diptera that occurred very sparsely. In terms of biomass, the streams were dominated by the oligochaetes. Data are presence absence data. See the publication for further details. The fields in this dataset are: Site Name Latitude Longitude Altitude (m) Water Temperature (C) pH Water Conductivity (micro siemens/cm) Stream width (cm) Stream Depth (cm) Stream Velocity (cm/s) Species proprietary
ASAC_555_1 A Survey of the Freshwater Macroinvertebrates in Streams and Lakes of Macquarie Island ALL STAC Catalog 1992-11-13 1992-12-03 158.7925, -54.7651, 158.9351, -54.5143 https://cmr.earthdata.nasa.gov/search/concepts/C1214313015-AU_AADC.umm_json In all, 15 sites on 12 streams were kick-sampled for invertebrates. Eleven fully aquatic taxa were found: a species of Iais (Isopoda: Janiridae); six species of oligochaetes (three enchytraeids, one tubificid, one naidid, one phreodrilid); a harpacticoid copepod; two nematode taxa; and Minona amnica, a turbellarian. Composition of this depauperate community changed little between sites, although one site disturbed by penguins had clearly fewer taxa. Aquatic insects (and fish) were absent, apart from three species of semi-aquatic diptera that occurred very sparsely. In terms of biomass, the streams were dominated by the oligochaetes. Data are presence absence data. See the publication for further details. The fields in this dataset are: Site Name Latitude Longitude Altitude (m) Water Temperature (C) pH Water Conductivity (micro siemens/cm) Stream width (cm) Stream Depth (cm) Stream Velocity (cm/s) Species proprietary
+ASAC_555_1 A Survey of the Freshwater Macroinvertebrates in Streams and Lakes of Macquarie Island AU_AADC STAC Catalog 1992-11-13 1992-12-03 158.7925, -54.7651, 158.9351, -54.5143 https://cmr.earthdata.nasa.gov/search/concepts/C1214313015-AU_AADC.umm_json In all, 15 sites on 12 streams were kick-sampled for invertebrates. Eleven fully aquatic taxa were found: a species of Iais (Isopoda: Janiridae); six species of oligochaetes (three enchytraeids, one tubificid, one naidid, one phreodrilid); a harpacticoid copepod; two nematode taxa; and Minona amnica, a turbellarian. Composition of this depauperate community changed little between sites, although one site disturbed by penguins had clearly fewer taxa. Aquatic insects (and fish) were absent, apart from three species of semi-aquatic diptera that occurred very sparsely. In terms of biomass, the streams were dominated by the oligochaetes. Data are presence absence data. See the publication for further details. The fields in this dataset are: Site Name Latitude Longitude Altitude (m) Water Temperature (C) pH Water Conductivity (micro siemens/cm) Stream width (cm) Stream Depth (cm) Stream Velocity (cm/s) Species proprietary
ASAC_556_1 Dialects and Usage Patterns of Weddell Seal 'Leptonychotes weddelli' Underwater Vocalisations AU_AADC STAC Catalog 1991-11-28 1992-12-14 76, -69, 78, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214306637-AU_AADC.umm_json Underwater recordings of vocalisations of Weddell seals were obtained at 8 locations within the Vestfold Hills (7) and Larsemann Hills (1). The recordings were made near groups of seals on the ice during the mid to late part of the breeding season. Recordings were obtained using a variety of hydrophones and both Sony Digital Audio Tape (130 during 1992 season) and standard analogue cassette (60 during 1991 season) formats. Over 11,000 vocalizations were analyzed. The calls were classified into 12 major call types (Pahl et al. 1997 Australian Journal of Zoology 45:171-187). The underwater repertoire is different than that of the seals at McMurdo Sound or the Palmer Penninsula (Thomas et al. 1988 Hydrobiologica 165:279-284). The Weddell seals at the Vestfold Hills do not exhibit the between-fjord vocal differences reported by Morrice et al. (1994 Polar Biology 14:441-446). The relative usage of each call type did not vary between the earlier and later recordings (Pahl et al. 1996 Australian Journal of Zoology 44:75-79). The recordings are currently being used to support other studies on Weddell seal vocalizations. Legend for ASAC_556.csv - csv text format. The following legend describes the 39 variables in this file. The codes for some of the variables are presented in the 1997 publication: Pahl, B.C., Terhune, J.M., and Burton, H.R. 1997. Repertoire and geographic variation in underwater vocalisations of Weddell seals (Leptonychotes weddellii, Pinnipedia: Phocidae) at the Vestfold Hills, Antarctica. Australian Journal of Zoology 45: 171-187. The fields in this dataset are: VariableSubject or code 1LOCATION; recording location; see AJZ article, Figure 1 2DATE; reference day, (date of day 1 has been lost) 3YEAR; 1 = 1991, 2 = 1992 4CASSETTE; cassette number, identifies individual recordings 5CALNO; call number, case numbers of each call, sequential 6CTYPE; call type, provisional call type, subjective initial classification (see below) 7NOELM; number of elements (discrete sounds) in the call 8EL_NO; element within that call relating to next 12 variables, for variable 8, only data from the first element is used 9WVFRM; waveform of element, see AJZ article for codes 10CLSHP; call shape, see AJZ article, Figure 2 for codes 11E_D; duration of the first element (seconds) 12IND1; duration of the interval between the end of the first element and the start of the second element (seconds) 13CALLD; total duration of the call (all elements; seconds) 14INCD; duration between sequential calls (seconds) 15O_LAP; overlap, is call overlapped by another call? 0 = no, 1 = yes 16S2STM; unknown measure 17SFREQ; frequency at start of first element (Hz) 18EFREQ; frequency at end of first element (Hz) 19HFREQ; highest frequency of first element (Hz) 20LFREQ; lowest frequency of first element (Hz) 21E_NO; element number, half way through the call. Data for the next 9 variables relate to this element, applies only to multiple element calls 22CLSHP; call shape of the middle element, same code as variable 10 23WVFRM: waveform of the middle element, same code as variable 9 24E_D; duration of the middle element (seconds) 25IND1; duration of the inter-element interval before the middle element 26IND2; duration of the inter-element interval after the middle element 27SFREQ; frequency at start of the middle element (Hz) 28EFREQ; frequency at end of middle element (Hz) 29HFREQ; highest frequency of middle element (Hz) 30LFREQ; lowest frequency of middle element (Hz) 31E_NO; element number of the last element of the call. Data for the next 8 variables relate to this element, applies only to multiple element calls 32CLSHP; call shape of the last element, same code as variable 10 33WVFRM: waveform of the last element, same code as variable 9 34E_D; duration of the last element (seconds) 35IND2; duration of the inter-element interval before the last element 36SFREQ; frequency at start of the last element (Hz) 37EFREQ; frequency at end of last element (Hz) 38HFREQ; highest frequency of last element (Hz) 39LFREQ; lowest frequency of last element (Hz) Codes for call types (variable 6). The provisional call types were amalgamated into 50 call types that were arbitrarily numbered from 201 to 250. These were subsequently classified into 13 broad categories (Pahl et al. 1997). The amalgamation of the provisional call types of variable 6 into the 50 call types presented in Pahl et al. (1997) is as follows: Call TypeProvisional Call Types (variable 6) 2011 7, 24, 36, 72, 31, 40, 73, 77, 107, 110, 31, 136 2023, 46, 54, 128, 33, 13, 140, 10, 25, 9, 139, 88, 46, 27, 126, 67, 91, 27, 126, 135 20359 204113 20514, 48, 69, 64, 49, 19, 92, 43, 75, 127, 99 206122, 124 2072, 41, 58, 93 20847, 138 20962, 132 210102 211115 21221, 23, 45, 35 21368, 80, 84 214114 2154 216118 21752, 78 2185, 6, 11 219104 22017, 22, 65, 97, 32, 26 22128 22283, 100, 101, 111, 105 22329, 30, 42, 51, 44, 94, 95 22487 22512 22682 2278 22818, 20, 57, 108 229109, 119 23034, 70, 130, 53, 121 23163 23298, 120 23389 23490 23556, 117 23671, 106 23785 238103 23974 24096 24176, 123, 133 24281, 86 24315 244112 24538 24679 24739, 127, 129, 55, 60 24816, 37, 50 249116 25066 For additional information or clarification, please contact Dr. J. Terhune, Dept. of Biology, University of New Brunswick, P.O. Box 5050, Saint John, NB, Canada E2L 4L5, terhune@unbsj.ca or +1 506 648 5633. See the link below for public details on this project. proprietary
ASAC_562_1 Morphology, Origin and Significance of Ice Gullies in the Vestfold Hills AU_AADC STAC Catalog 1992-09-30 1994-03-31 76, -69, 78, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214306639-AU_AADC.umm_json Metadata record for data from ASAC Project 562 See the link below for public details on this project. From the abstracts of the referenced papers: A regional chemical boundary termed the 'salt line',in the Vestfold Hills of East Antarctica, was investigated using X-ray diffraction and electron probe analyses of surficial salts, and conductivity of surficial sediments. West of the salt line, halite and thenardite are abundant. These salts are derived from dispersal of marine aerosols,saturation of sediment by seawater during postglacial marine transgression,and glacial dispersal of salt-saturated fjord bottom sediments. East of the salt line,subglacial calcium carbonates and salts formed by chemical weathering of their substrates may be found. The weathering products are formed from chemically and morphologically diverse minerals,which include two minerals not found previously in Antarctica, dypingite and hydromagnesite, and the first confirmed occurrence of brushite. ######################## Three ice dams in southeastern Vestfold Hills, East Antarctica, dam a system of five lakes periodically, impounding more than ~1.5 x 106 m3 of water. Dam #a impounds 1.1 x 106 m3 of water, while dams #b and #c prevent the free drainage of the lake below Dam #a, and impound the remaining 0.4 x 106 m3. The mode of failure of these dams and the rate of impoundment release were not known until January 1993, when dams #a and #b failed, allowing a flood to travel along a channel incised in sediment, and into Crooked Lake at greater than 8 m3s-1; four times the peak midsummer discharge of the largest stream in Vestfold Hills. The flowpath from Lake #10 is determined by which of two dams fails first; the northwestern dam (#b) allows the impoundment to travel into Crooked Lake via Grimmia Gorge (observed during January 1993), and the northern dam (#c) into Crooked Lake via Sickle Lake, Lake Verkhneye and Foot Lake (observed during 1979 and 1990). Formation and failure of these Vestfold Hi lls ice dams is similar to snow dams described from the Canadian Arctic. Floods released from the failure of the Vestfold dams provides an alternative explanation for a sudden increase in discharge at Ellis Rapids in January, 1976. This evidence of abundant meltwater is at odds with sublimation till previously described from Vestfold Hills. ############################ Vestfold Hills, East Antarctica exhibits marked contrasts in the weathering surface, glacial sediments and terrain between its eastern and western parts. The boundary between these zones coincides with a regional chemical boundary termed the salt line. The area west of the salt line is saturated with marine-derived halite and thenardite that are particularly aggressive agents of rock weathering. In contrast, the area east of the salt line exhibits significantly fewer deposits of these salts. Rock surfaces west of the salt line are characterised by well-developed weathering forms, while glacial polish and striae are largely absent. In contrast, rock surfaces to the east commonly retain glacial polish and striae. In places, differential weathering has caused thin basaltic dykes and felsic veins to stand above the surrounding gneiss. The rate of lowering of the gneiss and dykes to the west of the salt line has been estimated at 0.024 mm and 0.015 mm per year respectively (Spate et al. 1995). These measurements suggest that the weathering surface in parts of Vestfold Hills may record more than 70 ka of subaerial exposure. Glacial sediments are much more abundant, coarser and better sorted northwest of the salt line than to the southeast. The abundant grus produced by physical weathering is coarser grained and better sorted than that produced by subglacial erosion. Such sediment lying on the land surface would be transported and redeposited during glacial advances. The change in nature of the sediments to either side of the salt line, together with the weathering forms found on clasts in the moraines, indicates that the weathering surface prior to the last glacial advance was similar to that of today and must also have developed during long periods of subaerial exposure. ########################### proprietary
ASAC_565_1 Morphology, Taxonomy and Ecology of Terrestrial Antarctic Ciliates and Testaceans (Protozoa) AU_AADC STAC Catalog 1993-11-01 1994-02-28 108, -67, 110, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214306660-AU_AADC.umm_json Project 565: The database provides a list of species of ciliates and testate amoebae (Protozoa: Ciliophora; Testacea) recorded in various edaphic habitats, e.g., mineral soils (fellfield), ornithogenic soils, terrestrial mosses, from ice-free coastal areas and inshore islands in the area of Casey Station, Wilkes Land, coastal continental Antarctica. 26 ciliate (9 first records for continental Antarctica, 1 undescribed) and 5 testacean species (3 new records) were found. Sea ice study (Weddell Sea): The ciliate biodivesity was studied in several types of sea ice (mainly young pancake ice) from the Weddell Sea, Antarctica, in the austral autumn 1992 (March-May) during the cruise ANT X/3 of RV Polarstern. 49 ciliate species were predominantly found in sea ice and 6 spp. in the pelagial; 20 of these were new to science. A word document containing a list of species that were recorded as part of the project is available for download from the provided URL. These data have also been incorporated into the biodiversity database. proprietary
@@ -3097,34 +3097,34 @@ AST_L1B_003 ASTER L1B Registered Radiance at the Sensor V003 LPDAAC_ECS STAC Cat
AST_L1T_003 ASTER Level 1 precision terrain corrected registered at-sensor radiance V003 LPDAAC_ECS STAC Catalog 2000-03-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000320-LPDAAC_ECS.umm_json The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B (AST_L1B) (https://doi.org/10.5067/ASTER/AST_L1B.003), that has been geometrically corrected, and rotated to a north-up UTM projection. The AST_L1T is created from a single resampling of the corresponding ASTER L1A (AST_L1A) (https://doi.org/10.5067/ASTER/AST_L1A.003) product. The bands available in the AST_L1T depend on the bands in the AST_L1A and can include up to three Visible and Near Infrared (VNIR) bands, six Shortwave Infrared (SWIR) bands, and five Thermal Infrared (TIR) bands. The AST_L1T dataset does not include the aft-looking VNIR band 3. The precision terrain correction process incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover). For daytime images, if the VNIR or SWIR telescope collected data and precision correction was attempted, each precision terrain corrected image will have an accompanying independent quality assessment. It will include the geometric correction available for distribution in both as a text file and a single band browse images with the valid GCPs overlaid. This multi-file product also includes georeferenced full resolution browse images. The number of browse images and the band combinations of the images depends on the bands available in the corresponding (AST_L1A) (https://doi.org/10.5067/ASTER/AST_L1A.003) dataset. proprietary
AST_L1T_031 ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance V031 LPDAAC_ECS STAC Catalog 2000-03-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2052604735-LPDAAC_ECS.umm_json The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) Version 3.1 data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B AST_L1B (https://doi.org/10.5067/ASTER/AST_L1B.003), that has been geometrically corrected and rotated to a north-up UTM projection. The AST_L1T V3.1 is created from a single resampling of the corresponding ASTER L1A AST_L1A (https://doi.org/10.5067/ASTER/AST_L1A.003) product. Radiometric calibration coefficients Version 5 (RCC V5) are applied to this product to improve the degradation curve derived from vicarious and lunar calibrations. The bands available in the AST_L1T V3.1 depend on the bands in the AST_L1A and can include up to three Visible and Near Infrared (VNIR) bands, six Shortwave Infrared (SWIR) bands, and five Thermal Infrared (TIR) bands. The AST_L1T V3.1 dataset does not include the aft-looking VNIR band 3. The 3.1 version uses a precision terrain correction process that incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover). For daytime images, if the VNIR or SWIR telescope collected data and precision correction was attempted, each precision terrain corrected image will have an accompanying independent quality assessment. It will include the geometric correction available for distribution in both a text file and a single band browse image with the valid GCPs overlaid. This multi-file product also includes georeferenced full resolution browse images. The number of browse images and the band combinations of the images depend on the bands available in the corresponding AST_L1A dataset. The AST_L1T V3.1 data product is only available through NASA’s Earthdata Search. The ASTER L1T V3.1 Order Instructions provide step-by-step directions for ordering this product. proprietary
ATCS_0 The A-Train Cloud Segmentation Dataset OB_DAAC STAC Catalog 2007-11-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2172083412-OB_DAAC.umm_json ATCS is a dataset designed to train deep learning models to volumetrically segment clouds from multi-angle satellite imagery. The dataset consists of spatiotemporally aligned patches of multi-angle polarimetry from the POLDER sensor aboard the PARASOL mission and vertical cloud profiles from the 2B-CLDCLASS product using the cloud profiling radar (CPR) aboard CloudSat. proprietary
-ATL02_006 ATLAS/ICESat-2 L1B Converted Telemetry Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2541211133-NSIDC_ECS.umm_json This data set (ATL02) contains science-unit-converted time-ordered telemetry data, calibrated for instrument effects, downlinked from the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The data are used by the ATLAS/ICESat-2 Science Investigator-led Processing System (SIPS) for system-level, quality control analysis and as source data for ATLAS/ICESat-2 Level-2 products and Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations. proprietary
ATL02_006 ATLAS/ICESat-2 L1B Converted Telemetry Data V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2547589158-NSIDC_CPRD.umm_json This data set (ATL02) contains science-unit-converted time-ordered telemetry data, calibrated for instrument effects, downlinked from the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The data are used by the ATLAS/ICESat-2 Science Investigator-led Processing System (SIPS) for system-level, quality control analysis and as source data for ATLAS/ICESat-2 Level-2 products and Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations. proprietary
-ATL03_006 ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2596864127-NSIDC_CPRD.umm_json This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03. proprietary
+ATL02_006 ATLAS/ICESat-2 L1B Converted Telemetry Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2541211133-NSIDC_ECS.umm_json This data set (ATL02) contains science-unit-converted time-ordered telemetry data, calibrated for instrument effects, downlinked from the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The data are used by the ATLAS/ICESat-2 Science Investigator-led Processing System (SIPS) for system-level, quality control analysis and as source data for ATLAS/ICESat-2 Level-2 products and Precision Orbit Determination (POD) and Precision Pointing Determination (PPD) computations. proprietary
ATL03_006 ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2559919423-NSIDC_ECS.umm_json This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03. proprietary
+ATL03_006 ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2596864127-NSIDC_CPRD.umm_json This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03. proprietary
ATL03_ANC_MASKS_1 ATLAS/ICESat-2 ATL03 Ancillary Masks, Version 1 NSIDCV0 STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2278879612-NSIDCV0.umm_json This ancillary ICESat-2 data set contains four static surface masks (land ice, sea ice, land, and ocean) provided by ATL03 to reduce the volume of data that each surface-specific along-track data product is required to process. For example, the land ice surface mask directs the ATL06 land ice algorithm to consider data from only those areas of interest to the land ice community. Similarly, the sea ice, land, and ocean masks direct ATL07, ATL08, and ATL12 algorithms, respectively. A detailed description of all four masks can be found in section 4 of the Algorithm Theoretical Basis Document (ATBD) for ATL03 linked under technical references. proprietary
-ATL04_006 ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.umm_json ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL04_006 ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553327-NSIDC_CPRD.umm_json ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
+ATL04_006 ATLAS/ICESat-2 L2A Normalized Relative Backscatter Profiles V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2561045326-NSIDC_ECS.umm_json ATL04 contains along-track normalized relative backscatter profiles of the atmosphere. The product includes full 532 nm (14 km) uncalibrated attenuated backscatter profiles at 25 times per second for vertical bins of approximately 30 meters. Calibration coefficient values derived from data within the polar regions are also included. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL06_006 ATLAS/ICESat-2 L3A Land Ice Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2670138092-NSIDC_CPRD.umm_json This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL06_006 ATLAS/ICESat-2 L3A Land Ice Height V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2564427300-NSIDC_ECS.umm_json This data set (ATL06) provides geolocated, land-ice surface heights (above the WGS 84 ellipsoid, ITRF2014 reference frame), plus ancillary parameters that can be used to interpret and assess the quality of the height estimates. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL07QL_006 ATLAS/ICESat-2 L3A Sea Ice Height Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548344839-NSIDC_ECS.umm_json ATL07QL is the quick look version of ATL07. Once final ATL07 files are available, the corresponding ATL07QL files will be removed. ATL07 contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
-ATL07_006 ATLAS/ICESat-2 L3A Sea Ice Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2713030505-NSIDC_CPRD.umm_json The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL07_006 ATLAS/ICESat-2 L3A Sea Ice Height V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2564625052-NSIDC_ECS.umm_json The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
+ATL07_006 ATLAS/ICESat-2 L3A Sea Ice Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2713030505-NSIDC_CPRD.umm_json The data set (ATL07) contains along-track heights for sea ice and open water leads (at varying length scales) relative to the WGS84 ellipsoid (ITRF2014 reference frame) after adjustment for geoidal and tidal variations, and inverted barometer effects. Height statistics and apparent reflectance are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL08QL_006 ATLAS/ICESat-2 L3A Land and Vegetation Height Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548345108-NSIDC_ECS.umm_json ATL08QL is the quick look version of ATL08. Once final ATL08 files are available the corresponding ATL08QL files will be removed. ATL08 contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
-ATL08_006 ATLAS/ICESat-2 L3A Land and Vegetation Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553260-NSIDC_CPRD.umm_json This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL08_006 ATLAS/ICESat-2 L3A Land and Vegetation Height V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565090645-NSIDC_ECS.umm_json This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
+ATL08_006 ATLAS/ICESat-2 L3A Land and Vegetation Height V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553260-NSIDC_CPRD.umm_json This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL09QL_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2551528419-NSIDC_ECS.umm_json ATL09QL is the quick look version of ATL09. Once final ATL09 files are available the corresponding ATL09QL files will be removed. ATL09 contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
-ATL09_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.umm_json This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL09_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2607017115-NSIDC_ECS.umm_json This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
+ATL09_006 ATLAS/ICESat-2 L3A Calibrated Backscatter Profiles and Atmospheric Layer Characteristics V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2649212495-NSIDC_CPRD.umm_json This data set (ATL09) contains calibrated, attenuated backscatter profiles, layer integrated attenuated backscatter, and other parameters including cloud layer height and atmospheric characteristics obtained from the data. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL10QL_006 ATLAS/ICESat-2 L3A Sea Ice Freeboard Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2551529078-NSIDC_ECS.umm_json ATL10QL is the quick look version of ATL10. Once final ATL10 files are available the corresponding ATL10QL files will be removed. ATL10 contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL10_006 ATLAS/ICESat-2 L3A Sea Ice Freeboard V006 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553243-NSIDC_CPRD.umm_json This data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL10_006 ATLAS/ICESat-2 L3A Sea Ice Freeboard V006 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2567856357-NSIDC_ECS.umm_json This data set (ATL10) contains estimates of sea ice freeboard, calculated using three different approaches. Sea ice leads used to establish the reference sea surface and descriptive statistics used in the height estimates are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
-ATL11_006 ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2750966856-NSIDC_ECS.umm_json This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations. proprietary
ATL11_006 ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006 NSIDC_CPRD STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2752556504-NSIDC_CPRD.umm_json This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations. proprietary
-ATL12_006 ATLAS/ICESat-2 L3A Ocean Surface Height V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.umm_json This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
+ATL11_006 ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series V006 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2750966856-NSIDC_ECS.umm_json This data set provides time series of land-ice surface heights derived from the ICESat-2 ATL06 Land Ice Height product. It is intended primarily as an input for higher level gridded products but can also be used on its own as a spatially organized product that allows easy access to height-change information derived from ICESat-2 observations. proprietary
ATL12_006 ATLAS/ICESat-2 L3A Ocean Surface Height V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2613553216-NSIDC_CPRD.umm_json This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
+ATL12_006 ATLAS/ICESat-2 L3A Ocean Surface Height V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2560378689-NSIDC_ECS.umm_json This data set (ATL12) contains along-track sea surface height of the global open ocean, including the ice-free seasonal ice zone and near-coast regions. Estimates of height distributions, significant wave height, sea state bias, and 10 m heights are also provided. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. proprietary
ATL13QL_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data Quick Look V006 NSIDC_ECS STAC Catalog 2024-08-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2650092501-NSIDC_ECS.umm_json ATL13QL is the quick look version of ATL13. Once final ATL13 files are available the corresponding ATL13QL files will be removed. ATL13 contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7 km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary
-ATL13_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.umm_json This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary
ATL13_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2684928243-NSIDC_CPRD.umm_json This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary
+ATL13_006 ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data V006 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2650116584-NSIDC_ECS.umm_json This data set (ATL13) contains along-track surface water products for inland water bodies. Inland water bodies include lakes, reservoirs, rivers, bays, estuaries and a 7km near-shore buffer. Principal data products include the along-track water surface height and standard deviation, subsurface signal (532 nm) attenuation, significant wave height, wind speed, and coarse depth to bottom topography (where data permit). proprietary
ATL14_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V003 NSIDC_CPRD STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776895337-NSIDC_CPRD.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary
ATL14_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V003 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776464127-NSIDC_ECS.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary
ATL14_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height V004 NSIDC_CPRD STAC Catalog 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3162179692-NSIDC_CPRD.umm_json This data set contains a high-resolution (100 m) gridded digital elevation model (DEM) for the Antarctic ice sheet and regions around the Arctic. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary
@@ -3133,20 +3133,20 @@ ATL15_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change
ATL15_003 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V003 NSIDC_ECS STAC Catalog 2019-03-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776464171-NSIDC_ECS.umm_json ATL14 and ATL15 bring the time-varying height estimates provided in ATLAS/ICESat-2 L3B Annual Land Ice Height (ATL11) into a gridded format. ATL14 is a high-resolution (100 m) digital elevation model (DEM) that provides spatially continuous gridded data of ice sheet surface height. The data can be used to initialize ice sheet models, as boundary conditions for atmospheric models, or to help with the reduction of other satellite data such as optical imagery or synthetic aperture radar (SAR). ATL15 provides coarser resolution (1 km, 10 km, 20 km, and 40 km) height-change maps at 3-month intervals, allowing for visualization of height-change patterns and calculation of integrated regional volume change. proprietary
ATL15_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V004 NSIDC_ECS STAC Catalog 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3159684532-NSIDC_ECS.umm_json This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary
ATL15_004 ATLAS/ICESat-2 L3B Gridded Antarctic and Arctic Land Ice Height Change V004 NSIDC_CPRD STAC Catalog 2019-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3162334027-NSIDC_CPRD.umm_json This data set contains land ice height changes and change rates for the Antarctic ice sheet and regions around the Arctic gridded at four spatial resolutions (1 km, 10 km, 20 km, and 40 km). The data are derived from the ATLAS/ICESat-2 L3B Slope-Corrected Land Ice Height Time Series product (ATL11). proprietary
-ATL16_005 ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2769337070-NSIDC_CPRD.umm_json This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary
ATL16_005 ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2737997243-NSIDC_ECS.umm_json This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary
-ATL17_005 ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.umm_json This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary
+ATL16_005 ATLAS/ICESat-2 L3B Weekly Gridded Atmosphere V005 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2769337070-NSIDC_CPRD.umm_json This product reports weekly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary
ATL17_005 ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005 NSIDC_ECS STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2737997483-NSIDC_ECS.umm_json This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary
-ATL19_003 ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.umm_json This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography. proprietary
+ATL17_005 ATLAS/ICESat-2 L3B Monthly Gridded Atmosphere V005 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2769338020-NSIDC_CPRD.umm_json This data set contains a gridded summary of monthly global cloud fraction, total column optical depth over the oceans, polar cloud fraction, blowing snow frequency, apparent surface reflectivity, and ground detection frequency. proprietary
ATL19_003 ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003 NSIDC_ECS STAC Catalog 2018-10-13 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2746899536-NSIDC_ECS.umm_json This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography. proprietary
+ATL19_003 ATLAS/ICESat-2 L3B Monthly Gridded Dynamic Ocean Topography V003 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2754956786-NSIDC_CPRD.umm_json This data set contains monthly gridded dynamic ocean topography (DOT), derived from along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided in this data set. Both single beam and all-beam gridded averages are available in this data set. Single beam averages are useful to identify biases among the beams and the all-beam averages are advised to use for physical oceanography. proprietary
ATL20_004 ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2666857908-NSIDC_ECS.umm_json ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection. proprietary
ATL20_004 ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard V004 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2753295020-NSIDC_CPRD.umm_json ATL20 contains daily and monthly gridded estimates of sea ice freeboard, derived from along-track freeboard estimates in the ATLAS/ICESat-2 L3A Sea Ice Freeboard product (ATL10). Data are gridded at 25 km using the SSM/I Polar Stereographic Projection. proprietary
ATL21_003 ATLAS/ICESat-2 L3B Daily and Monthly Gridded Polar Sea Surface Height Anomaly V003 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2753316241-NSIDC_CPRD.umm_json ATL21 contains daily and monthly gridded polar sea surface height (SSH) anomalies, derived from the along-track ATLAS/ICESat-2 L3A Sea Ice Height product (ATL10, V6). The ATL10 product identifies leads in sea ice and establishes a reference sea surface used to estimate SSH in 10 km along-track segments. ATL21 aggregates the ATL10 along-track SSH estimates and computes daily and monthly gridded SSH anomaly in NSIDC Polar Stereographic Northern and Southern Hemisphere 25 km grids. proprietary
ATL21_003 ATLAS/ICESat-2 L3B Daily and Monthly Gridded Polar Sea Surface Height Anomaly V003 NSIDC_ECS STAC Catalog 2018-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2737912334-NSIDC_ECS.umm_json ATL21 contains daily and monthly gridded polar sea surface height (SSH) anomalies, derived from the along-track ATLAS/ICESat-2 L3A Sea Ice Height product (ATL10, V6). The ATL10 product identifies leads in sea ice and establishes a reference sea surface used to estimate SSH in 10 km along-track segments. ATL21 aggregates the ATL10 along-track SSH estimates and computes daily and monthly gridded SSH anomaly in NSIDC Polar Stereographic Northern and Southern Hemisphere 25 km grids. proprietary
-ATL22_003 ATLAS/ICESat-2 L3B Mean Inland Surface Water Data V003 NSIDC_ECS STAC Catalog 2018-10-14 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2738530540-NSIDC_ECS.umm_json ATL22 is a derivative of the continuous Level 3A ATL13 Along Track Inland Surface Water Data product. ATL13 contains the high-resolution, along-track inland water surface profiles derived from analysis of the geolocated photon clouds from the ATL03 product. Starting from ATL13, ATL22 computes the mean surface water quantities with no additional photon analysis. The two data products, ATL22 and ATL13, can be used in conjunction as they include the same orbit and water body nomenclature independent from version numbers. proprietary
ATL22_003 ATLAS/ICESat-2 L3B Mean Inland Surface Water Data V003 NSIDC_CPRD STAC Catalog 2018-10-14 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2761722214-NSIDC_CPRD.umm_json ATL22 is a derivative of the continuous Level 3A ATL13 Along Track Inland Surface Water Data product. ATL13 contains the high-resolution, along-track inland water surface profiles derived from analysis of the geolocated photon clouds from the ATL03 product. Starting from ATL13, ATL22 computes the mean surface water quantities with no additional photon analysis. The two data products, ATL22 and ATL13, can be used in conjunction as they include the same orbit and water body nomenclature independent from version numbers. proprietary
-ATL23_001 ATLAS/ICESat-2 L3B Monthly 3-Month Gridded Dynamic Ocean Topography V001 NSIDC_ECS STAC Catalog 2018-10-13 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2692731693-NSIDC_ECS.umm_json This data set contains 3-month gridded averages of dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided. Both single beam and all-beam gridded averages are available. Simple averages, degree-of-freedom averages, and averages interpolated to the center of grid cells are included, as well as uncertainty estimates. proprietary
+ATL22_003 ATLAS/ICESat-2 L3B Mean Inland Surface Water Data V003 NSIDC_ECS STAC Catalog 2018-10-14 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2738530540-NSIDC_ECS.umm_json ATL22 is a derivative of the continuous Level 3A ATL13 Along Track Inland Surface Water Data product. ATL13 contains the high-resolution, along-track inland water surface profiles derived from analysis of the geolocated photon clouds from the ATL03 product. Starting from ATL13, ATL22 computes the mean surface water quantities with no additional photon analysis. The two data products, ATL22 and ATL13, can be used in conjunction as they include the same orbit and water body nomenclature independent from version numbers. proprietary
ATL23_001 ATLAS/ICESat-2 L3B Monthly 3-Month Gridded Dynamic Ocean Topography V001 NSIDC_CPRD STAC Catalog 2018-10-13 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2765424272-NSIDC_CPRD.umm_json This data set contains 3-month gridded averages of dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided. Both single beam and all-beam gridded averages are available. Simple averages, degree-of-freedom averages, and averages interpolated to the center of grid cells are included, as well as uncertainty estimates. proprietary
+ATL23_001 ATLAS/ICESat-2 L3B Monthly 3-Month Gridded Dynamic Ocean Topography V001 NSIDC_ECS STAC Catalog 2018-10-13 -180, -88, 180, 88 https://cmr.earthdata.nasa.gov/search/concepts/C2692731693-NSIDC_ECS.umm_json This data set contains 3-month gridded averages of dynamic ocean topography (DOT) over midlatitude, north-polar, and south-polar grids derived from the along-track ATLAS/ICESat-2 L3A Ocean Surface Height product (ATL12). Monthly gridded sea surface height (SSH) can be calculated by adding the mean DOT and the weighted average geoid height also provided. Both single beam and all-beam gridded averages are available. Simple averages, degree-of-freedom averages, and averages interpolated to the center of grid cells are included, as well as uncertainty estimates. proprietary
ATLAS_DEALIASED_SASS_L2_1 SEASAT SCATTEROMETER DEALIASED OCEAN WIND VECTORS (Atlas) POCLOUD STAC Catalog 1978-07-07 1978-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2617197627-POCLOUD.umm_json Contains wind speeds and directions derived from the Seasat-A Scatterometer (SASS), presented chronologically by swath for the period between 7 July 1978 and 10 October 1978. Robert Atlas et al. (1987) produced this product using an objective ambiguity removal scheme to dealias the wind vector data binned at 100 km cells, which were calculated by Frank Wentz. proprietary
ATLAS_Veg_Plots_1541_1 Arctic Vegetation Plots ATLAS Project North Slope and Seward Peninsula, AK, 1998-2000 ORNL_CLOUD STAC Catalog 1998-07-01 2000-07-29 -165.07, 64.73, -153.74, 71.32 https://cmr.earthdata.nasa.gov/search/concepts/C2162120307-ORNL_CLOUD.umm_json This data set provides environmental, soil, and vegetation data collected from study sites on the North Slope and Seward Peninsula of Alaska during the Arctic Transition in Land-Atmosphere System (ATLAS) project. ATLAS-1 sites on the North Slope, located in Barrow, Atqasuk, Oumalik, and Ivotuk, were sampled in 1998-1999. ATLAS-2 sites located at Council and Quartz Creek on the Seward Peninsula were sampled in 2000. Specific attributes include dominant vegetation species and cover, biomass, soil chemistry and moisture, leaf area index (LAI), normalized difference vegetation index (NDVI), topography and elevation, and plant cover abundance. proprietary
ATMOSL1_3 ATMOS L1 Spectra and Runlogs V3 (ATMOSL1) at GES DISC GES_DISC STAC Catalog 1985-04-30 1994-11-12 -180, -73, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2234896943-GES_DISC.umm_json This is the version 3 Atmospheric Trace Molecule Spectroscopy (ATMOS) Level 1 product containing spectra and runlog (i.e. ) information in a netCDF format. ATMOS is an infrared spectrometer (a Fourier transform interferometer) designed to derive vertical concentrations of various trace gases in the atmosphere, particularly the ozone depleting chlorine and fluorine based molecules. The transmission spectra are ratioed from ATMOS high sun observations, on a scale of 0 to 1. Data files also include time, geolocation and other information. The data were collected during four space shuttle missions: STS-51B/Spacelab 3 (April 30 to May 1, 1985), STS-45/ATLAS-1 (March 25 to April 2, 1992), STS-55/ATLAS-2 (April 8 to 16, 1993), and STS-66/ATLAS-3 (November 3 to 12, 1994). Data are written to separate files grouped by mission (sl3, at1, at2 or at3), and occultation type (sunrise or sunset) and number. proprietary
@@ -3279,22 +3279,23 @@ AVIRIS-NG_L2_Reflectance_2110_1 AVIRIS-NG L2 Surface Reflectance, Facility Instr
AVIRIS_FlightLine_Locator_2140_1.0 AVIRIS Facility Instruments: Flight Line Geospatial and Contextual Data ORNL_CLOUD STAC Catalog 2006-04-11 2022-11-03 -171.85, 9.2, 118.95, 84.36 https://cmr.earthdata.nasa.gov/search/concepts/C2662360177-ORNL_CLOUD.umm_json This dataset provides attributed geospatial and tabular information for identifying and querying flight lines of interest for the Airborne Visible InfraRed Imaging Spectrometer-Classic (AVIRIS-C) and Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) Facility Instrument collections. It includes attributed shapefile and GeoJSON files containing polygon representation of individual flights lines for all years and separate KMZ files for each year. These files allow users to visualize and query flight line locations using Geographic Information System (GIS) software. Tables of AVIRIS-C and AVIRIS-NG flight lines with attributed information include dates, bounding coordinates, site names, investigators involved, flight attributes, associated campaigns, and corresponding file names for associated L1B (radiance) and L2 (reflectance) files in the AVIRIS-C and AVIRIS-NG Facility Instrument Collections. Tabular information is also provided in comma-separated values (CSV) format. proprietary
AVISO_ADT ADT - Absolute Dynamic Topography SCIOPS STAC Catalog 2004-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214586177-SCIOPS.umm_json "Contents: along-track sea surface heights above geoid; dynamic topography is the sum of sea level anomaly (SLA) and mean dynamic topography (MDT, Rio05 here) Use: study of the general circulation (ocean gyres ...) The data are global mono altimeter satellite products, homogeneous with other satellites, available in near-real time and in delayed time in NetCDF format. In delayed time, two types of products are available: - ""Ref"" (Reference) series: homogeneous datasets based on two satellites (Topex/Poseidon, Jason-1 + ERS, Envisat) with the same groundtrack. Sampling is stable in time. - ""Upd"" (Updated) series: up-to-date datasets with up to four satellites at a given time (adding GFO and/or Topex/Poseidon on its new orbit). Sampling and Long Wavelength Errors determination are improved, but quality of the series is not homogeneous. Regional products with an improved quality are available in local areas (""http://www.aviso.oceanobs.com/html/donnees/produits/hauteurs/regional/"")" proprietary
AVISO_ADT ADT - Absolute Dynamic Topography ALL STAC Catalog 2004-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214586177-SCIOPS.umm_json "Contents: along-track sea surface heights above geoid; dynamic topography is the sum of sea level anomaly (SLA) and mean dynamic topography (MDT, Rio05 here) Use: study of the general circulation (ocean gyres ...) The data are global mono altimeter satellite products, homogeneous with other satellites, available in near-real time and in delayed time in NetCDF format. In delayed time, two types of products are available: - ""Ref"" (Reference) series: homogeneous datasets based on two satellites (Topex/Poseidon, Jason-1 + ERS, Envisat) with the same groundtrack. Sampling is stable in time. - ""Upd"" (Updated) series: up-to-date datasets with up to four satellites at a given time (adding GFO and/or Topex/Poseidon on its new orbit). Sampling and Long Wavelength Errors determination are improved, but quality of the series is not homogeneous. Regional products with an improved quality are available in local areas (""http://www.aviso.oceanobs.com/html/donnees/produits/hauteurs/regional/"")" proprietary
-AWI-EDMED_542_8 Aeromagnetic surveys of the Southern Ross Sea and North Victoria Land (Antarctica), 1990/1991, (project GANOVEX VI) SCIOPS STAC Catalog 1990-12-01 1991-03-30 -180, -90, 180, -63 https://cmr.earthdata.nasa.gov/search/concepts/C1214585787-SCIOPS.umm_json The aim of the aeromagnetic surveys in the Ross Sea and North Victoria Land are: a) to develop a model on the break-up of this part of Gondwana b) to map the ocean-continent boundary c) to develop an idea about the evolution of the area since the break-up of Gondwana d) to map the structures of the Transatlantic Mountains. The data were sampled every 10 s, corresponding to 500 m distance. The following instrument was used: PPM Geometics G 811. The geographical coverage is as follows: about 17000 km of aeromagnetic data have been collected in the Ross Sea and North Victoria Land, Antarctica. Data are available on request, but with special arrangement. proprietary
AWI-EDMED_542_8 Aeromagnetic surveys of the Southern Ross Sea and North Victoria Land (Antarctica), 1990/1991, (project GANOVEX VI) ALL STAC Catalog 1990-12-01 1991-03-30 -180, -90, 180, -63 https://cmr.earthdata.nasa.gov/search/concepts/C1214585787-SCIOPS.umm_json The aim of the aeromagnetic surveys in the Ross Sea and North Victoria Land are: a) to develop a model on the break-up of this part of Gondwana b) to map the ocean-continent boundary c) to develop an idea about the evolution of the area since the break-up of Gondwana d) to map the structures of the Transatlantic Mountains. The data were sampled every 10 s, corresponding to 500 m distance. The following instrument was used: PPM Geometics G 811. The geographical coverage is as follows: about 17000 km of aeromagnetic data have been collected in the Ross Sea and North Victoria Land, Antarctica. Data are available on request, but with special arrangement. proprietary
-A_Biotic_Database_of_Indo-Pacific_Marine_Mollusks_1.0 A Biotic Database of Indo-Pacific Marine Mollusks SCIOPS STAC Catalog 1824-01-01 2002-12-31 -179, -62.98, 180, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214622147-SCIOPS.umm_json Biotic Database of Indo-Pacific Marine Mollusks provides access to nomenclatural, distribution, and ecological information on Indo-Pacific Mollusks. Georeferenced specimen records from ANSP and AMS related to these names are available for search through the OBIS global digital atlas. Nomenclatural, distribution, and ecological information assembled from the literature is available for search on the web. This database attempts to document all names that have ever been applied to marine molluscs in the tropical Indo-West Pacific. This database provides information on the estimated 30,000 named species of mollusks in the Indo-Pacific region, with summary data on their distribution and ecology. A future objective is to combine Indo-Pacific data with existing databases for Western Atlantic and Europe marine mollusk species and for higher taxa of mollusks to form the basis of a global database of Mollusca. This database will provide a uniform framework for linking specimen records from museum collections and data from fisheries to show spatial and temporal patterns of occurrence and abundance. This database was compiled by teams at the Academy of Natural Sciences, the Australian Museum, the Muséum National d' Histoire Naturelle, and the California Academy of Sciences, with support from the Alfred P. Sloan Foundation, the National Oceanographic Partnership Program, and the Australian Biological Resources Study. This database is part of the Ocean Biogeographic Information System. As of 2006 May 19 the Database contains 84,147 names of all ranks, 72,597 species-group names, and 28,357 species names in current use, and 179,368 specimen records. proprietary
+AWI-EDMED_542_8 Aeromagnetic surveys of the Southern Ross Sea and North Victoria Land (Antarctica), 1990/1991, (project GANOVEX VI) SCIOPS STAC Catalog 1990-12-01 1991-03-30 -180, -90, 180, -63 https://cmr.earthdata.nasa.gov/search/concepts/C1214585787-SCIOPS.umm_json The aim of the aeromagnetic surveys in the Ross Sea and North Victoria Land are: a) to develop a model on the break-up of this part of Gondwana b) to map the ocean-continent boundary c) to develop an idea about the evolution of the area since the break-up of Gondwana d) to map the structures of the Transatlantic Mountains. The data were sampled every 10 s, corresponding to 500 m distance. The following instrument was used: PPM Geometics G 811. The geographical coverage is as follows: about 17000 km of aeromagnetic data have been collected in the Ross Sea and North Victoria Land, Antarctica. Data are available on request, but with special arrangement. proprietary
A_Biotic_Database_of_Indo-Pacific_Marine_Mollusks_1.0 A Biotic Database of Indo-Pacific Marine Mollusks ALL STAC Catalog 1824-01-01 2002-12-31 -179, -62.98, 180, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214622147-SCIOPS.umm_json Biotic Database of Indo-Pacific Marine Mollusks provides access to nomenclatural, distribution, and ecological information on Indo-Pacific Mollusks. Georeferenced specimen records from ANSP and AMS related to these names are available for search through the OBIS global digital atlas. Nomenclatural, distribution, and ecological information assembled from the literature is available for search on the web. This database attempts to document all names that have ever been applied to marine molluscs in the tropical Indo-West Pacific. This database provides information on the estimated 30,000 named species of mollusks in the Indo-Pacific region, with summary data on their distribution and ecology. A future objective is to combine Indo-Pacific data with existing databases for Western Atlantic and Europe marine mollusk species and for higher taxa of mollusks to form the basis of a global database of Mollusca. This database will provide a uniform framework for linking specimen records from museum collections and data from fisheries to show spatial and temporal patterns of occurrence and abundance. This database was compiled by teams at the Academy of Natural Sciences, the Australian Museum, the Muséum National d' Histoire Naturelle, and the California Academy of Sciences, with support from the Alfred P. Sloan Foundation, the National Oceanographic Partnership Program, and the Australian Biological Resources Study. This database is part of the Ocean Biogeographic Information System. As of 2006 May 19 the Database contains 84,147 names of all ranks, 72,597 species-group names, and 28,357 species names in current use, and 179,368 specimen records. proprietary
+A_Biotic_Database_of_Indo-Pacific_Marine_Mollusks_1.0 A Biotic Database of Indo-Pacific Marine Mollusks SCIOPS STAC Catalog 1824-01-01 2002-12-31 -179, -62.98, 180, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214622147-SCIOPS.umm_json Biotic Database of Indo-Pacific Marine Mollusks provides access to nomenclatural, distribution, and ecological information on Indo-Pacific Mollusks. Georeferenced specimen records from ANSP and AMS related to these names are available for search through the OBIS global digital atlas. Nomenclatural, distribution, and ecological information assembled from the literature is available for search on the web. This database attempts to document all names that have ever been applied to marine molluscs in the tropical Indo-West Pacific. This database provides information on the estimated 30,000 named species of mollusks in the Indo-Pacific region, with summary data on their distribution and ecology. A future objective is to combine Indo-Pacific data with existing databases for Western Atlantic and Europe marine mollusk species and for higher taxa of mollusks to form the basis of a global database of Mollusca. This database will provide a uniform framework for linking specimen records from museum collections and data from fisheries to show spatial and temporal patterns of occurrence and abundance. This database was compiled by teams at the Academy of Natural Sciences, the Australian Museum, the Muséum National d' Histoire Naturelle, and the California Academy of Sciences, with support from the Alfred P. Sloan Foundation, the National Oceanographic Partnership Program, and the Australian Biological Resources Study. This database is part of the Ocean Biogeographic Information System. As of 2006 May 19 the Database contains 84,147 names of all ranks, 72,597 species-group names, and 28,357 species names in current use, and 179,368 specimen records. proprietary
Absolutes_1 Magnetic observations collected from Antarctica and Macquarie Island since 1952 AU_AADC STAC Catalog 1952-01-01 62.6, -67.7, 159.05, -54.45 https://cmr.earthdata.nasa.gov/search/concepts/C1214311742-AU_AADC.umm_json Final one minute average values of the absolute geomagnetic field in the north (X), east (Y) and vertical (Z) components in units of nanoTesla (nT). Magnetic variometer data have been collected at Macquarie Is. since 1952; Mawson since 1955; and Casey since 1988. Data were not digital from Macquarie Is. until late 1984; from Mawson until late 1986. They have been digital from Casey since 1988. Data that are currently available from the GA website are not complete, but improving. The status of data availability is available on the website. Particular magnetic elements can be chosen to be plotted or tabulated data are available. proprietary
Academ_Kurchatov_0 Measurements made by the Akademik Kurchatov Russian research vessel OB_DAAC STAC Catalog 1988-06-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360085-OB_DAAC.umm_json Measurements made by the Akademik Kurchatov Russian research vessel in the Atlantic Ocean and Black Sea in 1988. proprietary
AcousticTrends_BlueFinLibrary_1 An annotated library of underwater acoustic recordings for testing and training automated algorithms for detecting Antarctic blue and fin whale sounds AU_AADC STAC Catalog 2005-01-01 2017-12-30 -56, -75, 168, -61 https://cmr.earthdata.nasa.gov/search/concepts/C1709216523-AU_AADC.umm_json This annotated library contains both a data set and a data product. The data set contains a sub-sample of underwater recordings made around Antarctica from 2005-2017. These recordings were curated and sub-sampled from a variety of national and academic recording campaigns. Recordings were made using a variety of different instruments, and sub-samples span 11 different combinations of site and year. Spatial coverage of the recordings includes sites in the Western Antarctic Peninsula, Atlantic, Indian, and Pacific sectors. Temporal coverage of recordings covers a representative sample throughout each recording year for the years of 2005, 2013, 2014, 2015, and 2017. The focus is on low-frequency sounds of blue and fin whales, so curated recordings have been downsampled to sample rates of either 250, 500, 1000 or 2000 Hz. Recordings are all in 16-bit wav format. The file name of each wav file contains a timestamp with the date and time of the start of that file. Recordings are contained in the /wav/ subfolder for each site-year (e.g. Casey2014/wav). The data product is in the form of annotations that describe the times within each WAV file that contain detections of blue and fin whale sounds. Each annotations are stored as a row in a tab-separated text file (with descriptive column headers), and each text file describes a particular type of sound. These annotation text files are formatted as Selection Tables that can be directly imported into the software program Raven Pro 1.5 (Cornell Bioacoustics Laboratory). Full description of the details of the creation and use of this dataset are described in the draft manuscript contained in the documentation folder. proprietary
+Acoustic_Data_Cape_Floristic_2372_1 BioSCape: BioSoundSCape Acoustic Recordings, South Africa, 2023 ORNL_CLOUD STAC Catalog 2023-06-05 2023-12-16 18.01, -34.82, 23.92, -31.37 https://cmr.earthdata.nasa.gov/search/concepts/C3366080352-ORNL_CLOUD.umm_json This dataset holds in situ sound recordings from sites in Greater Cape Floristic Region (GCFR), South Africa from June to December 2023. The recordings were collected as part of the Biodiversity Survey of the Cape (BioSCape) project, a multi-agency, NASA-led research project that integrates airborne imaging spectroscopy and lidar with a suite of measurements of biodiversity. BioSoundSCape is a BioSCape subproject seeking to relate ground-based measures of bioacoustic diversity to remote imagery. AudioMoth recorders were deployed at sites for 4 to 10 days of data collection (median = 7), and programmed to record 1 min of every 10, thus providing temporal sampling through day and night. Each recording was saved in a waveform audio file format with 16-bit digitization depth and a 48 kHz sampling rate. The recordings contain a wide range of environmental sounds such as biophony (e.g., birds, frogs, insects), anthropophony (e.g,. automobiles, airplanes) and geophony (e.g,. wind, rain). Sampling locations were stratified with respect to elevation, broad land use/land cover types, and time since wildfire disturbance. Most sites were within protected fynbos and Afromontane forest ecosystems. There were 538 sites in the wet season and 543 sites in the dry season, with most sites co-located between seasons. All sites were located within AVIRIS-NG hyperspectral acquisitions and 61% of sites were in LVIS lidar acquisitions. The dataset includes site information in tabular form and photographs of field sites. proprietary
Acoustic_Data_SonomaCounty_CA_2341_1 Soundscapes to Landscapes Acoustic Recordings, Sonoma County, CA, 2017-2022 ORNL_CLOUD STAC Catalog 2017-04-01 2022-07-11 -123.53, 38.14, -122.46, 38.85 https://cmr.earthdata.nasa.gov/search/concepts/C3288229127-ORNL_CLOUD.umm_json This dataset holds in situ sound recordings from sites in Sonoma County, California, USA as part of the Soundscapes to Landscapes citizen science project. Recordings were collected from 2017 to 2022 during the bird breeding season (mid-March thru mid-July). Sites (n=1399) were selected across the county; locations were stratified with respect to topographic position and broad land use/land cover types, such as forest, shrubland, herbaceous, urban, agriculture, and riparian areas. Two types of automated recorders were used: Android-based smartphones with attached microphones and AudioMoths. Recorders were deployed at sites for at least 3 days, and programmed to record 1 min of every 10, thus providing temporal sampling through day and night. Each recording was saved in a waveform audio file format (.wav) with 16-bit digitization depth and 44.1 kHz or 48 kHz sampling rate for smartphone and AudioMoth recorders, respectively. The dataset also includes site information including site location when so permitted by landowners in tabular form and photographs of field sites. proprietary
Acoustic_seals_1 Acoustic surveying of pack-ice seals ALL STAC Catalog 1996-10-05 2001-01-16 77, -68, 78, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311713-AU_AADC.umm_json Acoustic surveying Data from four acoustic surveys from the Aurora Australis from 1996-10-05 to 1996-10-31; 1997-10-09 to 1997-10-29; 1997-12-08 to 1998-01-06; and 1999-12-04 to 2001-01-16. Sonobouys deployed off the back of the ship, half an hour recording duration samples made concurrently with Colin Southwells visual surveys. Numbers of leopard seal calls audible from recordings measured by acoustic analysis. The fields in this dataset are: Tape # = the tape number and date Recording # = Recording number Buoy # = Sonobuoy number Buoy Freq = Sonobuoy frequency Longitude S = Longitude Decimal Longitude S = Decimal Longitude Latitude E = Latitude Decimal Latitude E = Decimal Latitude GMT = Greenwich Mean Time Local time = Local Time Serial Time = dd:mm:yy hh:mm Ship Speed Kts ICE Cover (/10) = Ice Cover in tenths Ice % cover = Percentage of Ice Cover Thick Ice: Ice Thickness 0 = 0; 1less than 2 cm; 2 = 2cm to 0.25m; 3= 0.25m to 0.5m; 4 = 0.5m - 1m; 5 greater than 1.0 m Ice Type: 1 = no information; 2 = grease or pancake; 3 = brash; 4 = floes first year; 5 = multiyear floes; 6 = first year rafted floes; 7 = multiyear rafted floes; 8 = mixed brash and 1st year floes; 9 = mixed brash and multiyear floes; 10 = icebergs; 11 = icebergs and brash; 12 = icebergs and 1st year floes; 13 = icebergs and multiyear floes; 14 = compacted pack ice; 15 = iceshelf; 16 = other; 17 = fast ice. Floe Width: 1 = less than 3 m; 2 = 3 - 10 m; 3 = 10 - 50m; 4 = 50 -100 m; 5 greater than 100 m. Weather: 1 = blue sky (0-20% cloud); 2 = partly cloudy (21-80%); 3 = cloudy (81-99%); 4 = overcast (100%); 5 = rain; 6 = mist; 7 = fog; 8 = fog patches; 9 = drizzle; 10 =snow; 11 = snow fog; 12 = rain fog. Algae: 1 = clear; 2 = slight colour; 3 = medium colour; 4 = dark brown patches; 5 = all dark brown Water Depth m Wind Speed Kts Wind Direction Air temp degrees C Rec Time = Duration of the recording made Gain = Recording gain on the amplifier Mammal Sounds? = Other mammal sounds. CS = unknown origin chain-saw like sound; P5/P6 = unknown origin pulsed sounds; NSL = unknown origin appears to be a new leopard seal sound; Wd = Weddell; LS = Leopard; KW = Killer Whale; RS = Ross LS Calls Total = Total number of leopard seal calls D = Total Low Descending trills H = Total High Double trills L = Total Low Double trills M = Total Medium Single trills O = Total Hoots with Single trills Juv LS = Total Juvenile Leopard seal calls NLS = Total New Leopard Seal Calls CS = Total Chain Saw Calls RS = Total Ross Seal Calls Wd = Total Weddell seal Calls P2-P5 = Total Pulsed calls proprietary
Acoustic_seals_1 Acoustic surveying of pack-ice seals AU_AADC STAC Catalog 1996-10-05 2001-01-16 77, -68, 78, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311713-AU_AADC.umm_json Acoustic surveying Data from four acoustic surveys from the Aurora Australis from 1996-10-05 to 1996-10-31; 1997-10-09 to 1997-10-29; 1997-12-08 to 1998-01-06; and 1999-12-04 to 2001-01-16. Sonobouys deployed off the back of the ship, half an hour recording duration samples made concurrently with Colin Southwells visual surveys. Numbers of leopard seal calls audible from recordings measured by acoustic analysis. The fields in this dataset are: Tape # = the tape number and date Recording # = Recording number Buoy # = Sonobuoy number Buoy Freq = Sonobuoy frequency Longitude S = Longitude Decimal Longitude S = Decimal Longitude Latitude E = Latitude Decimal Latitude E = Decimal Latitude GMT = Greenwich Mean Time Local time = Local Time Serial Time = dd:mm:yy hh:mm Ship Speed Kts ICE Cover (/10) = Ice Cover in tenths Ice % cover = Percentage of Ice Cover Thick Ice: Ice Thickness 0 = 0; 1less than 2 cm; 2 = 2cm to 0.25m; 3= 0.25m to 0.5m; 4 = 0.5m - 1m; 5 greater than 1.0 m Ice Type: 1 = no information; 2 = grease or pancake; 3 = brash; 4 = floes first year; 5 = multiyear floes; 6 = first year rafted floes; 7 = multiyear rafted floes; 8 = mixed brash and 1st year floes; 9 = mixed brash and multiyear floes; 10 = icebergs; 11 = icebergs and brash; 12 = icebergs and 1st year floes; 13 = icebergs and multiyear floes; 14 = compacted pack ice; 15 = iceshelf; 16 = other; 17 = fast ice. Floe Width: 1 = less than 3 m; 2 = 3 - 10 m; 3 = 10 - 50m; 4 = 50 -100 m; 5 greater than 100 m. Weather: 1 = blue sky (0-20% cloud); 2 = partly cloudy (21-80%); 3 = cloudy (81-99%); 4 = overcast (100%); 5 = rain; 6 = mist; 7 = fog; 8 = fog patches; 9 = drizzle; 10 =snow; 11 = snow fog; 12 = rain fog. Algae: 1 = clear; 2 = slight colour; 3 = medium colour; 4 = dark brown patches; 5 = all dark brown Water Depth m Wind Speed Kts Wind Direction Air temp degrees C Rec Time = Duration of the recording made Gain = Recording gain on the amplifier Mammal Sounds? = Other mammal sounds. CS = unknown origin chain-saw like sound; P5/P6 = unknown origin pulsed sounds; NSL = unknown origin appears to be a new leopard seal sound; Wd = Weddell; LS = Leopard; KW = Killer Whale; RS = Ross LS Calls Total = Total number of leopard seal calls D = Total Low Descending trills H = Total High Double trills L = Total Low Double trills M = Total Medium Single trills O = Total Hoots with Single trills Juv LS = Total Juvenile Leopard seal calls NLS = Total New Leopard Seal Calls CS = Total Chain Saw Calls RS = Total Ross Seal Calls Wd = Total Weddell seal Calls P2-P5 = Total Pulsed calls proprietary
-Active_Fluorescence_2001_0 Active fluorescence measurements in the Gulf Stream in 2001 OB_DAAC STAC Catalog 2001-06-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360093-OB_DAAC.umm_json Measurements in the Gulf Stream off the East Coast of the US in 2001 proprietary
Active_Fluorescence_2001_0 Active fluorescence measurements in the Gulf Stream in 2001 ALL STAC Catalog 2001-06-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360093-OB_DAAC.umm_json Measurements in the Gulf Stream off the East Coast of the US in 2001 proprietary
-Active_Layer_Thaw_Depths_1701_1 ABoVE: Soil Active Layer Thaw Depths at CRREL sites near Fairbanks, Alaska, 2014-2018 ALL STAC Catalog 2014-10-15 2018-10-15 -147.74, 64.87, -147.61, 64.95 https://cmr.earthdata.nasa.gov/search/concepts/C2143403378-ORNL_CLOUD.umm_json This dataset provides soil active layer thaw depth measurements collected along transects at three sites near Fairbanks, Alaska, USA. Measurements were made during the late summers of 2014-2018. The sites were located at Creamer's Field, the Permafrost Tunnel, and Farmer's Loop (two transects). Vegetation ecotypes along the transects are also reported. The US Army Corps of Engineers, Cold Regions Research and Engineering Laboratory (CRREL) owns and operates facilities at the Permafrost Tunnel and Farmer's Loop. The sites are suitable for manipulation experiments, installing permanent equipment, and establishing long-term measurements. proprietary
+Active_Fluorescence_2001_0 Active fluorescence measurements in the Gulf Stream in 2001 OB_DAAC STAC Catalog 2001-06-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360093-OB_DAAC.umm_json Measurements in the Gulf Stream off the East Coast of the US in 2001 proprietary
Active_Layer_Thaw_Depths_1701_1 ABoVE: Soil Active Layer Thaw Depths at CRREL sites near Fairbanks, Alaska, 2014-2018 ORNL_CLOUD STAC Catalog 2014-10-15 2018-10-15 -147.74, 64.87, -147.61, 64.95 https://cmr.earthdata.nasa.gov/search/concepts/C2143403378-ORNL_CLOUD.umm_json This dataset provides soil active layer thaw depth measurements collected along transects at three sites near Fairbanks, Alaska, USA. Measurements were made during the late summers of 2014-2018. The sites were located at Creamer's Field, the Permafrost Tunnel, and Farmer's Loop (two transects). Vegetation ecotypes along the transects are also reported. The US Army Corps of Engineers, Cold Regions Research and Engineering Laboratory (CRREL) owns and operates facilities at the Permafrost Tunnel and Farmer's Loop. The sites are suitable for manipulation experiments, installing permanent equipment, and establishing long-term measurements. proprietary
-Adelie_Aerial_Photography_Casey20102011_1 Aerial photography from the Casey region taken during January 2011 used for Adelie penguin analysis ALL STAC Catalog 2011-01-02 2011-01-23 108, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311744-AU_AADC.umm_json Aerial photographs were taken at 3 islands in the Cronk group and 3 islands in the Frazier group of the Windmill islands where occupancy surveys in 2010-11 found breeding Adelie penguin populations. The photographs were taken to estimate the size of breeding Adelie penguin populations. Photographs of the Cronk Island group were taken on the 2 January 2011. One flight was made along a northeast-southwest direction across the three main islands, Hollin, Midgley and Beall (see below). The flight started at 02:10:23 UTC and finished at 03:40:45 UTC. The SkyTraders crew were the flight and camera operators. The daily weather observations from Casey Station for 2 January 2011 were 14.0 hour of sunlight, winds from the North at 6-13 knots and 2/8 cloud cover. Photographs of the Frazier Island group were taken on the 23 January 2011. Aerial photos were taken from a CASA C212 airplane (VHA) flying at ~140 knots and ~750m altitude using a Nikon D200 camera with a 55 mm real lens which is converted to a 75 mm lens (including the focal length magnification factor of 1.5 for non-35mm format). The Nikon D200 camera was set to normal which allows for varied speed and aperture and was set on autofocus. A 3-second shutter closure interval was programmed using an external intervalometer. All photographs were recorded on the cameras internal memory card and downloaded after the flight was over. proprietary
+Active_Layer_Thaw_Depths_1701_1 ABoVE: Soil Active Layer Thaw Depths at CRREL sites near Fairbanks, Alaska, 2014-2018 ALL STAC Catalog 2014-10-15 2018-10-15 -147.74, 64.87, -147.61, 64.95 https://cmr.earthdata.nasa.gov/search/concepts/C2143403378-ORNL_CLOUD.umm_json This dataset provides soil active layer thaw depth measurements collected along transects at three sites near Fairbanks, Alaska, USA. Measurements were made during the late summers of 2014-2018. The sites were located at Creamer's Field, the Permafrost Tunnel, and Farmer's Loop (two transects). Vegetation ecotypes along the transects are also reported. The US Army Corps of Engineers, Cold Regions Research and Engineering Laboratory (CRREL) owns and operates facilities at the Permafrost Tunnel and Farmer's Loop. The sites are suitable for manipulation experiments, installing permanent equipment, and establishing long-term measurements. proprietary
Adelie_Aerial_Photography_Casey20102011_1 Aerial photography from the Casey region taken during January 2011 used for Adelie penguin analysis AU_AADC STAC Catalog 2011-01-02 2011-01-23 108, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311744-AU_AADC.umm_json Aerial photographs were taken at 3 islands in the Cronk group and 3 islands in the Frazier group of the Windmill islands where occupancy surveys in 2010-11 found breeding Adelie penguin populations. The photographs were taken to estimate the size of breeding Adelie penguin populations. Photographs of the Cronk Island group were taken on the 2 January 2011. One flight was made along a northeast-southwest direction across the three main islands, Hollin, Midgley and Beall (see below). The flight started at 02:10:23 UTC and finished at 03:40:45 UTC. The SkyTraders crew were the flight and camera operators. The daily weather observations from Casey Station for 2 January 2011 were 14.0 hour of sunlight, winds from the North at 6-13 knots and 2/8 cloud cover. Photographs of the Frazier Island group were taken on the 23 January 2011. Aerial photos were taken from a CASA C212 airplane (VHA) flying at ~140 knots and ~750m altitude using a Nikon D200 camera with a 55 mm real lens which is converted to a 75 mm lens (including the focal length magnification factor of 1.5 for non-35mm format). The Nikon D200 camera was set to normal which allows for varied speed and aperture and was set on autofocus. A 3-second shutter closure interval was programmed using an external intervalometer. All photographs were recorded on the cameras internal memory card and downloaded after the flight was over. proprietary
+Adelie_Aerial_Photography_Casey20102011_1 Aerial photography from the Casey region taken during January 2011 used for Adelie penguin analysis ALL STAC Catalog 2011-01-02 2011-01-23 108, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311744-AU_AADC.umm_json Aerial photographs were taken at 3 islands in the Cronk group and 3 islands in the Frazier group of the Windmill islands where occupancy surveys in 2010-11 found breeding Adelie penguin populations. The photographs were taken to estimate the size of breeding Adelie penguin populations. Photographs of the Cronk Island group were taken on the 2 January 2011. One flight was made along a northeast-southwest direction across the three main islands, Hollin, Midgley and Beall (see below). The flight started at 02:10:23 UTC and finished at 03:40:45 UTC. The SkyTraders crew were the flight and camera operators. The daily weather observations from Casey Station for 2 January 2011 were 14.0 hour of sunlight, winds from the North at 6-13 knots and 2/8 cloud cover. Photographs of the Frazier Island group were taken on the 23 January 2011. Aerial photos were taken from a CASA C212 airplane (VHA) flying at ~140 knots and ~750m altitude using a Nikon D200 camera with a 55 mm real lens which is converted to a 75 mm lens (including the focal length magnification factor of 1.5 for non-35mm format). The Nikon D200 camera was set to normal which allows for varied speed and aperture and was set on autofocus. A 3-second shutter closure interval was programmed using an external intervalometer. All photographs were recorded on the cameras internal memory card and downloaded after the flight was over. proprietary
Adelie_Aerial_Photography_Davis20092010_1 Aerial photography from the Davis region taken during November 2009 used for Adelie penguin analysis ALL STAC Catalog 2009-11-18 2009-11-23 77.58, -68.58, 78.58, -68.33 https://cmr.earthdata.nasa.gov/search/concepts/C1214311724-AU_AADC.umm_json Aerial photographs were taken at 39 islands in the Vestfold and Rauer Islands regions where occupancy surveys in 2008-09 found breeding Adelie penguin populations. The photographs were taken to estimate the size of breeding Adelie penguin populations. A total of six flights between 18-23 November 2009 were required to cover the Vestfold and Rauer coastlines. The first flight from 0355-0622 UTC on 18th November 2009 covered the southern Vestfolds (see download file). The second flight from 0746-0930 UTC on the 18th November 2009 covered Long Peninsula (see download file). The third flight from 0945-1132 UTC on the 19th November 2009 mostly covered the northern Vestfolds (Bandits, Mikkelson, Tryne, Wyatt Earp, but also covered Gardner (see download file). The fourth flight from 0734-0946 UTC on the 21st November 2009 repeated the previous flight over the northern Vestfolds after preliminary stitching showed that the coverage was not as good as desired. Also, the flight lines for Tryne, Mikkelson and Wyatt Earp were moved to use the north-south flight lines. The opportunity was also taken to repeat a flight over Gardner and perform other tasks (visit 'Woop Woop', the plateau skiway and perform a low level LIDAR scan on the blue ice runway) (see download file). The fifth flight from 1306-1556 UTC on the 21st November 2009 covered Hop and Filla Islands in the Rauers (see download file). The sixth and final flight from 0828-0951 UTC on the 23rd November 2009 covered the remaining Rauer Islands including Forpost, Torckler, Varyarg, Lunnyy and Kryuchock Islands (see download file). Vertical photos were taken along each flight line from a Squirrel AS350BA helicopter (VH-SES) flying at 80 knots and 750m altitude using a Hasselblad H3DII-50 camera with a 150 mm lens and 1/800th second shutter speed. A 3-second shutter closure interval was achieved using an SDK and intervalometer. The camera auto-focussed effectively at infinity using the software Phocus. proprietary
Adelie_Aerial_Photography_Davis20092010_1 Aerial photography from the Davis region taken during November 2009 used for Adelie penguin analysis AU_AADC STAC Catalog 2009-11-18 2009-11-23 77.58, -68.58, 78.58, -68.33 https://cmr.earthdata.nasa.gov/search/concepts/C1214311724-AU_AADC.umm_json Aerial photographs were taken at 39 islands in the Vestfold and Rauer Islands regions where occupancy surveys in 2008-09 found breeding Adelie penguin populations. The photographs were taken to estimate the size of breeding Adelie penguin populations. A total of six flights between 18-23 November 2009 were required to cover the Vestfold and Rauer coastlines. The first flight from 0355-0622 UTC on 18th November 2009 covered the southern Vestfolds (see download file). The second flight from 0746-0930 UTC on the 18th November 2009 covered Long Peninsula (see download file). The third flight from 0945-1132 UTC on the 19th November 2009 mostly covered the northern Vestfolds (Bandits, Mikkelson, Tryne, Wyatt Earp, but also covered Gardner (see download file). The fourth flight from 0734-0946 UTC on the 21st November 2009 repeated the previous flight over the northern Vestfolds after preliminary stitching showed that the coverage was not as good as desired. Also, the flight lines for Tryne, Mikkelson and Wyatt Earp were moved to use the north-south flight lines. The opportunity was also taken to repeat a flight over Gardner and perform other tasks (visit 'Woop Woop', the plateau skiway and perform a low level LIDAR scan on the blue ice runway) (see download file). The fifth flight from 1306-1556 UTC on the 21st November 2009 covered Hop and Filla Islands in the Rauers (see download file). The sixth and final flight from 0828-0951 UTC on the 23rd November 2009 covered the remaining Rauer Islands including Forpost, Torckler, Varyarg, Lunnyy and Kryuchock Islands (see download file). Vertical photos were taken along each flight line from a Squirrel AS350BA helicopter (VH-SES) flying at 80 knots and 750m altitude using a Hasselblad H3DII-50 camera with a 150 mm lens and 1/800th second shutter speed. A 3-second shutter closure interval was achieved using an SDK and intervalometer. The camera auto-focussed effectively at infinity using the software Phocus. proprietary
Adelie_Aerial_Photography_Davis20102011_1 Aerial photography from the Davis region taken during November 2010 used for Adelie penguin analysis AU_AADC STAC Catalog 2010-11-20 2010-11-20 75.28, -69.44, 78.98, -68.31 https://cmr.earthdata.nasa.gov/search/concepts/C1214311745-AU_AADC.umm_json Aerial photographs were taken at 16 islands between the Rauer Islands and the Amery Ice Shelf where occupancy surveys in 2009-10 and 2010-11 found breeding Adelie penguin populations. The photographs were taken to estimate the size of breeding Adelie penguin populations. The survey was completed in a single mission from 09:53-13:44 UTC on the 20th November 2010. The flight was split into two parts and covered the Svenner and Steinnes islands first, with a stop in the Larsemann Hills for refueling at Progress I, then further surveying around Lichen Island. Weather conditions during the flight were sunny. This resulted in substantial areas being in shadow. Part 1 of the flight mission: Svenner, Svenner south-east, Svenner south and Steinnes islands Vertical photos were taken along the flight lines from a Squirrel AS350BA helicopter (VH-SES) flying at 80 knots and 750m altitude using a Hasselblad H3DII-50 camera with a 150 mm lens and 1/800th second shutter speed. A 3-second shutter closure interval was achieved using an SDK and intervalometer. The camera auto-focussed effectively at infinity using the software Phocus. proprietary
@@ -3302,14 +3303,14 @@ Adelie_Aerial_Photography_Davis20102011_1 Aerial photography from the Davis regi
Adelie_Colony_Maps_Prydz_81-82_1 Historical Adelie penguin breeding colony maps in Prydz Bay, East Antarctica, 1981/82 AU_AADC STAC Catalog 1981-11-01 1982-01-31 73, -70, 86, -67 https://cmr.earthdata.nasa.gov/search/concepts/C2102891841-AU_AADC.umm_json The dataset comprises scanned copies of the boundaries of Adelie penguin breeding colonies and sections of island coastlines made from aerial photographs taken between 9-15 December 1981. The original tracings by Michael Whitehead were scanned by Colin Southwell. proprietary
Adelie_diet_BI_1 Adelie Penguin Dietary Data From Bechervaise Island Antarctica ALL STAC Catalog 1991-01-01 63, -68, 64, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311760-AU_AADC.umm_json "This dataset contains the results from surveys on the feeding habits of Adelie Penguins (Pygoscelis adeliae) on Bechervaise Island, Mawson, Antarctica. Surveys have been conducted since 1991, and are ongoing to determine the diet composition and prey species of penguins. Data for this project were compiled by Megan Tierney, as part of her PhD Thesis, and are presented in two excel spreadsheets. Also provided in the Related URL section, is a link to a trophic database of ""A compilation of dietary and related data from the Southern Ocean"". This database contains a large amount of other publicly available diet related data collected as part of the Australian Antarctic program." proprietary
Adelie_diet_BI_1 Adelie Penguin Dietary Data From Bechervaise Island Antarctica AU_AADC STAC Catalog 1991-01-01 63, -68, 64, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311760-AU_AADC.umm_json "This dataset contains the results from surveys on the feeding habits of Adelie Penguins (Pygoscelis adeliae) on Bechervaise Island, Mawson, Antarctica. Surveys have been conducted since 1991, and are ongoing to determine the diet composition and prey species of penguins. Data for this project were compiled by Megan Tierney, as part of her PhD Thesis, and are presented in two excel spreadsheets. Also provided in the Related URL section, is a link to a trophic database of ""A compilation of dietary and related data from the Southern Ocean"". This database contains a large amount of other publicly available diet related data collected as part of the Australian Antarctic program." proprietary
-Aeolian_Processes_McMurdo Aeolian Processes in the Dry Valleys SCIOPS STAC Catalog 2002-01-01 2003-02-28 162.00787, -77.6042, 163.13045, -77.36601 https://cmr.earthdata.nasa.gov/search/concepts/C1214614479-SCIOPS.umm_json This dataset contains data collected during studies of boundary layer winds and surface characteristics. These field experiments were designed to: 1. Understand and quantify the partitioning of wind shear stress between surface and roughness elements on (a) rocky surfaces and (b) surfaces with scatted rocks and intervening sand surface. 2. Test the Raupach et al (1993) shear stress partitioning model to estimate the entrainment threshold on surfaces covered with isolated roughness elements 3. Quantify the spatial distribution of surface shear stress on surfaces with scatted rocks and an intervening sand surface. 4. Understand relations between shear stress partitioning and transport of sand. The dataset includes measurements of: - Boundary Layer winds and surface shear stress - Wind speed at 6 heights above the surface (6.00 m, 3.65 m, 2.22 m, 1.35 m, 0.82 m, 0.50 m wind direction at 6 m and 0.82 m, temperature at 3.65 m. - Surface shear stress using Irwin sensors (Wyatt and Nickling, 1997) - Sand mass transport rates at the Victoria Valley site with static (Nickling and McKenna Neuman, 1997) and automated sand traps. Saltation intensity with Sensit sensor at the Victoria Valley site (Gillette and Stockton, 1986) - Wind force on simulated roughness elements using the Guelph force balance (Gillies et al., 2000; Grant and Nickling, 1998; Wyatt and Nickling, 1997). Data were sampled every 1 second and averaged for 1, 5, and 10 minute intervals. Derived data include estimates of wind shear velocity (u*), aerodynamic roughness (zo) Surface characterization data: Information on rock cover and roughness element geometry, and sand grain size and sorting parameters for surface sand and sand in transport in the Victoria Valley is also available. Datasets available: Data were obtained for 2 sites located on the north side of Lake Fryxell and in the Victoria Valley. There is also Irwin sensor calibration data for 2 sites: Wright Valley and Victoria Lower Glacier, which includes wind profile and temperature measurements. Data cover the following periods: - Wright Valley: January 11-14, 2002 - Lake Fryxell: January 15 - February 1, 2002; January 15 - February 3, 2003 - Victoria Lower Glacier: January 11-13, 2003 - Victoria Valley: January 15 - 31, 2003. Site locations are: - Lake Fryxell: 77 degrees 36.252 minutes; 163 degrees 07.827 minutes - Wright Valley: 77 degrees 31.363 minutes; 162 degrees 00.472 minutes - Victoria Valley: 77.366009935 degrees S, 162.320035048 degrees E These studies were funded by NSF grant OPP-0088136 References cited Gillette, D.A. and Stockton, P.H., 1986. Mass momentum and kinetic energy fluxes of saltating particles. In: W.G. Nickling (Editor), Aeolian Geomorphology. Allen and Unwin, Boston, London, Sydney, pp. 35-56. Gillies, J.A., Lancaster, N., Nickling, W.G. and Crawley, D., 2000. Field determination of drag forces and shear stress partitioning effects for a desert shrub (Sarcobatus vermiculatus, Greasewood). Journal of Geophysical Research, Atmospheres, 105(D20): 24871-24880. Grant, P.F. and Nickling, W.G., 1998. Direct field measurement of wind drag on vegetation for application to windbreak design and monitoring. Land Degradation and Development, 9: 57-66. Nickling, W.G. and McKenna Neuman, C., 1997. Wind tunnel evaluation of a wedge-shaped aeolian sediment trap. Geomorphology, 18(3-4): 333-346. Wyatt, V.E. and Nickling, W.G., 1997. Drag and shear stress partioning in sparse desert creosote communities. Canadian Jornal of Earth Sciences, 34: 1486-1498. proprietary
Aeolian_Processes_McMurdo Aeolian Processes in the Dry Valleys ALL STAC Catalog 2002-01-01 2003-02-28 162.00787, -77.6042, 163.13045, -77.36601 https://cmr.earthdata.nasa.gov/search/concepts/C1214614479-SCIOPS.umm_json This dataset contains data collected during studies of boundary layer winds and surface characteristics. These field experiments were designed to: 1. Understand and quantify the partitioning of wind shear stress between surface and roughness elements on (a) rocky surfaces and (b) surfaces with scatted rocks and intervening sand surface. 2. Test the Raupach et al (1993) shear stress partitioning model to estimate the entrainment threshold on surfaces covered with isolated roughness elements 3. Quantify the spatial distribution of surface shear stress on surfaces with scatted rocks and an intervening sand surface. 4. Understand relations between shear stress partitioning and transport of sand. The dataset includes measurements of: - Boundary Layer winds and surface shear stress - Wind speed at 6 heights above the surface (6.00 m, 3.65 m, 2.22 m, 1.35 m, 0.82 m, 0.50 m wind direction at 6 m and 0.82 m, temperature at 3.65 m. - Surface shear stress using Irwin sensors (Wyatt and Nickling, 1997) - Sand mass transport rates at the Victoria Valley site with static (Nickling and McKenna Neuman, 1997) and automated sand traps. Saltation intensity with Sensit sensor at the Victoria Valley site (Gillette and Stockton, 1986) - Wind force on simulated roughness elements using the Guelph force balance (Gillies et al., 2000; Grant and Nickling, 1998; Wyatt and Nickling, 1997). Data were sampled every 1 second and averaged for 1, 5, and 10 minute intervals. Derived data include estimates of wind shear velocity (u*), aerodynamic roughness (zo) Surface characterization data: Information on rock cover and roughness element geometry, and sand grain size and sorting parameters for surface sand and sand in transport in the Victoria Valley is also available. Datasets available: Data were obtained for 2 sites located on the north side of Lake Fryxell and in the Victoria Valley. There is also Irwin sensor calibration data for 2 sites: Wright Valley and Victoria Lower Glacier, which includes wind profile and temperature measurements. Data cover the following periods: - Wright Valley: January 11-14, 2002 - Lake Fryxell: January 15 - February 1, 2002; January 15 - February 3, 2003 - Victoria Lower Glacier: January 11-13, 2003 - Victoria Valley: January 15 - 31, 2003. Site locations are: - Lake Fryxell: 77 degrees 36.252 minutes; 163 degrees 07.827 minutes - Wright Valley: 77 degrees 31.363 minutes; 162 degrees 00.472 minutes - Victoria Valley: 77.366009935 degrees S, 162.320035048 degrees E These studies were funded by NSF grant OPP-0088136 References cited Gillette, D.A. and Stockton, P.H., 1986. Mass momentum and kinetic energy fluxes of saltating particles. In: W.G. Nickling (Editor), Aeolian Geomorphology. Allen and Unwin, Boston, London, Sydney, pp. 35-56. Gillies, J.A., Lancaster, N., Nickling, W.G. and Crawley, D., 2000. Field determination of drag forces and shear stress partitioning effects for a desert shrub (Sarcobatus vermiculatus, Greasewood). Journal of Geophysical Research, Atmospheres, 105(D20): 24871-24880. Grant, P.F. and Nickling, W.G., 1998. Direct field measurement of wind drag on vegetation for application to windbreak design and monitoring. Land Degradation and Development, 9: 57-66. Nickling, W.G. and McKenna Neuman, C., 1997. Wind tunnel evaluation of a wedge-shaped aeolian sediment trap. Geomorphology, 18(3-4): 333-346. Wyatt, V.E. and Nickling, W.G., 1997. Drag and shear stress partioning in sparse desert creosote communities. Canadian Jornal of Earth Sciences, 34: 1486-1498. proprietary
+Aeolian_Processes_McMurdo Aeolian Processes in the Dry Valleys SCIOPS STAC Catalog 2002-01-01 2003-02-28 162.00787, -77.6042, 163.13045, -77.36601 https://cmr.earthdata.nasa.gov/search/concepts/C1214614479-SCIOPS.umm_json This dataset contains data collected during studies of boundary layer winds and surface characteristics. These field experiments were designed to: 1. Understand and quantify the partitioning of wind shear stress between surface and roughness elements on (a) rocky surfaces and (b) surfaces with scatted rocks and intervening sand surface. 2. Test the Raupach et al (1993) shear stress partitioning model to estimate the entrainment threshold on surfaces covered with isolated roughness elements 3. Quantify the spatial distribution of surface shear stress on surfaces with scatted rocks and an intervening sand surface. 4. Understand relations between shear stress partitioning and transport of sand. The dataset includes measurements of: - Boundary Layer winds and surface shear stress - Wind speed at 6 heights above the surface (6.00 m, 3.65 m, 2.22 m, 1.35 m, 0.82 m, 0.50 m wind direction at 6 m and 0.82 m, temperature at 3.65 m. - Surface shear stress using Irwin sensors (Wyatt and Nickling, 1997) - Sand mass transport rates at the Victoria Valley site with static (Nickling and McKenna Neuman, 1997) and automated sand traps. Saltation intensity with Sensit sensor at the Victoria Valley site (Gillette and Stockton, 1986) - Wind force on simulated roughness elements using the Guelph force balance (Gillies et al., 2000; Grant and Nickling, 1998; Wyatt and Nickling, 1997). Data were sampled every 1 second and averaged for 1, 5, and 10 minute intervals. Derived data include estimates of wind shear velocity (u*), aerodynamic roughness (zo) Surface characterization data: Information on rock cover and roughness element geometry, and sand grain size and sorting parameters for surface sand and sand in transport in the Victoria Valley is also available. Datasets available: Data were obtained for 2 sites located on the north side of Lake Fryxell and in the Victoria Valley. There is also Irwin sensor calibration data for 2 sites: Wright Valley and Victoria Lower Glacier, which includes wind profile and temperature measurements. Data cover the following periods: - Wright Valley: January 11-14, 2002 - Lake Fryxell: January 15 - February 1, 2002; January 15 - February 3, 2003 - Victoria Lower Glacier: January 11-13, 2003 - Victoria Valley: January 15 - 31, 2003. Site locations are: - Lake Fryxell: 77 degrees 36.252 minutes; 163 degrees 07.827 minutes - Wright Valley: 77 degrees 31.363 minutes; 162 degrees 00.472 minutes - Victoria Valley: 77.366009935 degrees S, 162.320035048 degrees E These studies were funded by NSF grant OPP-0088136 References cited Gillette, D.A. and Stockton, P.H., 1986. Mass momentum and kinetic energy fluxes of saltating particles. In: W.G. Nickling (Editor), Aeolian Geomorphology. Allen and Unwin, Boston, London, Sydney, pp. 35-56. Gillies, J.A., Lancaster, N., Nickling, W.G. and Crawley, D., 2000. Field determination of drag forces and shear stress partitioning effects for a desert shrub (Sarcobatus vermiculatus, Greasewood). Journal of Geophysical Research, Atmospheres, 105(D20): 24871-24880. Grant, P.F. and Nickling, W.G., 1998. Direct field measurement of wind drag on vegetation for application to windbreak design and monitoring. Land Degradation and Development, 9: 57-66. Nickling, W.G. and McKenna Neuman, C., 1997. Wind tunnel evaluation of a wedge-shaped aeolian sediment trap. Geomorphology, 18(3-4): 333-346. Wyatt, V.E. and Nickling, W.G., 1997. Drag and shear stress partioning in sparse desert creosote communities. Canadian Jornal of Earth Sciences, 34: 1486-1498. proprietary
Aeolus-CalVal-DAWN_DC8_1 Aeolus CalVal DAWN Wind Profiles LARC_ASDC STAC Catalog 2019-04-17 2019-04-30 -159, 5, -113, 52 https://cmr.earthdata.nasa.gov/search/concepts/C1918229328-LARC_ASDC.umm_json AEOLUS-CALVAL-DAWN_DC8_1 is the Aeolus CalVal DAWN (Doppler Aerosol WiNd) Lidar Wind Profiles data product. Data was collected using the DAWN instrument on the Douglas (DC-8) Aircraft. Data collection for this product is complete. NASA conducted an airborne campaign from 17 April to 30 April 2019 to: 1) demonstrate the performance of the Doppler Aerosol WiNd Lidar (DAWN) and High Altitude Lidar Observatory (HALO) instruments across a range of aerosol, cloud, and weather conditions; 2) compare these measurements with the European Space Agency Aeolus mission to gain an initial perspective of Aeolus performance in preparation for a future international Aeolus Cal/Val airborne campaign; and 3) demonstrate how weather processes can be resolved and better understood through simultaneous airborne wind, water vapor (WV), and aerosol profile observations, coupled with numerical model and other remote sensing observations. Five NASA DC-8 aircraft flights, comprising 46 flight hours, were conducted over the Eastern Pacific and Southwest U.S., based out of NASA Armstrong Flight Research Center in Palmdale, CA and Kona, HI. Yankee Environmental Systems, Inc High Definition Sounding System (HDSS) eXpendable Digitial Dropsondes (XDD) were used to validate the DAWN and Aeolus wind observations. The LaRC Diode Laser Hygrometer instrument, which was integrated on the DC-8 in preparation for another NASA airborne campaign, provided in-situ WV measurements used during one flight to validate HALO and dropsonde WV profile products. proprietary
Aeolus-CalVal-DAWN_DC8_1 Aeolus CalVal DAWN Wind Profiles ALL STAC Catalog 2019-04-17 2019-04-30 -159, 5, -113, 52 https://cmr.earthdata.nasa.gov/search/concepts/C1918229328-LARC_ASDC.umm_json AEOLUS-CALVAL-DAWN_DC8_1 is the Aeolus CalVal DAWN (Doppler Aerosol WiNd) Lidar Wind Profiles data product. Data was collected using the DAWN instrument on the Douglas (DC-8) Aircraft. Data collection for this product is complete. NASA conducted an airborne campaign from 17 April to 30 April 2019 to: 1) demonstrate the performance of the Doppler Aerosol WiNd Lidar (DAWN) and High Altitude Lidar Observatory (HALO) instruments across a range of aerosol, cloud, and weather conditions; 2) compare these measurements with the European Space Agency Aeolus mission to gain an initial perspective of Aeolus performance in preparation for a future international Aeolus Cal/Val airborne campaign; and 3) demonstrate how weather processes can be resolved and better understood through simultaneous airborne wind, water vapor (WV), and aerosol profile observations, coupled with numerical model and other remote sensing observations. Five NASA DC-8 aircraft flights, comprising 46 flight hours, were conducted over the Eastern Pacific and Southwest U.S., based out of NASA Armstrong Flight Research Center in Palmdale, CA and Kona, HI. Yankee Environmental Systems, Inc High Definition Sounding System (HDSS) eXpendable Digitial Dropsondes (XDD) were used to validate the DAWN and Aeolus wind observations. The LaRC Diode Laser Hygrometer instrument, which was integrated on the DC-8 in preparation for another NASA airborne campaign, provided in-situ WV measurements used during one flight to validate HALO and dropsonde WV profile products. proprietary
Aeolus-CalVal-Dropsondes_DC8_1 Aeolus CalVal Dropsonde Profiles ALL STAC Catalog 2019-04-18 2019-04-30 -159, 5, -113, 52 https://cmr.earthdata.nasa.gov/search/concepts/C1918229595-LARC_ASDC.umm_json Aeolus-CalVal-Dropsondes_DC8_1 is the Aeolus CalVal Dropsonde Profiles data product. Data was collected using Dropsondes from the Douglas (DC-8) Aircraft. Data collection for this product is complete. NASA conducted an airborne campaign from 17 April to 30 April 2019 to: 1) demonstrate the performance of the Doppler Aerosol WiNd Lidar (DAWN) and High Altitude Lidar Observatory (HALO) instruments across a range of aerosol, cloud, and weather conditions; 2) compare these measurements with the European Space Agency Aeolus mission to gain an initial perspective of Aeolus performance in preparation for a future international Aeolus Cal/Val airborne campaign; and 3) demonstrate how weather processes can be resolved and better understood through simultaneous airborne wind, water vapor (WV), and aerosol profile observations, coupled with numerical model and other remote sensing observations. Five NASA DC-8 aircraft flights, comprising 46 flight hours, were conducted over the Eastern Pacific and Southwest U.S., based out of NASA Armstrong Flight Research Center in Palmdale, CA and Kona, HI. Yankee Environmental Systems, Inc High Definition Sounding System (HDSS) eXpendable Digitial Dropsondes (XDD) were used to validate the DAWN and Aeolus wind observations. The LaRC Diode Laser Hygrometer instrument, which was integrated on the DC-8 in preparation for another NASA airborne campaign, provided in-situ WV measurements used during one flight to validate HALO and dropsonde WV profile products. proprietary
Aeolus-CalVal-Dropsondes_DC8_1 Aeolus CalVal Dropsonde Profiles LARC_ASDC STAC Catalog 2019-04-18 2019-04-30 -159, 5, -113, 52 https://cmr.earthdata.nasa.gov/search/concepts/C1918229595-LARC_ASDC.umm_json Aeolus-CalVal-Dropsondes_DC8_1 is the Aeolus CalVal Dropsonde Profiles data product. Data was collected using Dropsondes from the Douglas (DC-8) Aircraft. Data collection for this product is complete. NASA conducted an airborne campaign from 17 April to 30 April 2019 to: 1) demonstrate the performance of the Doppler Aerosol WiNd Lidar (DAWN) and High Altitude Lidar Observatory (HALO) instruments across a range of aerosol, cloud, and weather conditions; 2) compare these measurements with the European Space Agency Aeolus mission to gain an initial perspective of Aeolus performance in preparation for a future international Aeolus Cal/Val airborne campaign; and 3) demonstrate how weather processes can be resolved and better understood through simultaneous airborne wind, water vapor (WV), and aerosol profile observations, coupled with numerical model and other remote sensing observations. Five NASA DC-8 aircraft flights, comprising 46 flight hours, were conducted over the Eastern Pacific and Southwest U.S., based out of NASA Armstrong Flight Research Center in Palmdale, CA and Kona, HI. Yankee Environmental Systems, Inc High Definition Sounding System (HDSS) eXpendable Digitial Dropsondes (XDD) were used to validate the DAWN and Aeolus wind observations. The LaRC Diode Laser Hygrometer instrument, which was integrated on the DC-8 in preparation for another NASA airborne campaign, provided in-situ WV measurements used during one flight to validate HALO and dropsonde WV profile products. proprietary
-Aeolus-CalVal-HALO_DC8_1 Aeolus CalVal HALO Aerosol and Water Vapor Profiles and Images LARC_ASDC STAC Catalog 2019-04-17 2019-04-30 -159, 5, -113, 52 https://cmr.earthdata.nasa.gov/search/concepts/C1918229342-LARC_ASDC.umm_json Aeolus-CalVal-HALO_DC8_1 is the Aeolus CalVal HALO Aerosol and Water Vapor Profiles and Images data product. Data was collected using the High Altitude Lidar Observatory (HALO) instrument on the Douglas (DC-8) Aircraft. Data collection for this product is complete. NASA conducted an airborne campaign from 17 April to 30 April 2019 to: 1) demonstrate the performance of the Doppler Aerosol WiNd Lidar (DAWN) and High Altitude Lidar Observatory (HALO) instruments across a range of aerosol, cloud, and weather conditions; 2) compare these measurements with the European Space Agency Aeolus mission to gain an initial perspective of Aeolus performance in preparation for a future international Aeolus Cal/Val airborne campaign; and 3) demonstrate how weather processes can be resolved and better understood through simultaneous airborne wind, water vapor (WV), and aerosol profile observations, coupled with numerical model and other remote sensing observations. Five NASA DC-8 aircraft flights, comprising 46 flight hours, were conducted over the Eastern Pacific and Southwest U.S., based out of NASA Armstrong Flight Research Center in Palmdale, CA and Kona, HI. Yankee Environmental Systems, Inc High Definition Sounding System (HDSS) eXpendable Digitial Dropsondes (XDD) were used to validate the DAWN and Aeolus wind observations. The LaRC Diode Laser Hygrometer instrument, which was integrated on the DC-8 in preparation for another NASA airborne campaign, provided in-situ WV measurements used during one flight to validate HALO and dropsonde WV profile products. proprietary
Aeolus-CalVal-HALO_DC8_1 Aeolus CalVal HALO Aerosol and Water Vapor Profiles and Images ALL STAC Catalog 2019-04-17 2019-04-30 -159, 5, -113, 52 https://cmr.earthdata.nasa.gov/search/concepts/C1918229342-LARC_ASDC.umm_json Aeolus-CalVal-HALO_DC8_1 is the Aeolus CalVal HALO Aerosol and Water Vapor Profiles and Images data product. Data was collected using the High Altitude Lidar Observatory (HALO) instrument on the Douglas (DC-8) Aircraft. Data collection for this product is complete. NASA conducted an airborne campaign from 17 April to 30 April 2019 to: 1) demonstrate the performance of the Doppler Aerosol WiNd Lidar (DAWN) and High Altitude Lidar Observatory (HALO) instruments across a range of aerosol, cloud, and weather conditions; 2) compare these measurements with the European Space Agency Aeolus mission to gain an initial perspective of Aeolus performance in preparation for a future international Aeolus Cal/Val airborne campaign; and 3) demonstrate how weather processes can be resolved and better understood through simultaneous airborne wind, water vapor (WV), and aerosol profile observations, coupled with numerical model and other remote sensing observations. Five NASA DC-8 aircraft flights, comprising 46 flight hours, were conducted over the Eastern Pacific and Southwest U.S., based out of NASA Armstrong Flight Research Center in Palmdale, CA and Kona, HI. Yankee Environmental Systems, Inc High Definition Sounding System (HDSS) eXpendable Digitial Dropsondes (XDD) were used to validate the DAWN and Aeolus wind observations. The LaRC Diode Laser Hygrometer instrument, which was integrated on the DC-8 in preparation for another NASA airborne campaign, provided in-situ WV measurements used during one flight to validate HALO and dropsonde WV profile products. proprietary
+Aeolus-CalVal-HALO_DC8_1 Aeolus CalVal HALO Aerosol and Water Vapor Profiles and Images LARC_ASDC STAC Catalog 2019-04-17 2019-04-30 -159, 5, -113, 52 https://cmr.earthdata.nasa.gov/search/concepts/C1918229342-LARC_ASDC.umm_json Aeolus-CalVal-HALO_DC8_1 is the Aeolus CalVal HALO Aerosol and Water Vapor Profiles and Images data product. Data was collected using the High Altitude Lidar Observatory (HALO) instrument on the Douglas (DC-8) Aircraft. Data collection for this product is complete. NASA conducted an airborne campaign from 17 April to 30 April 2019 to: 1) demonstrate the performance of the Doppler Aerosol WiNd Lidar (DAWN) and High Altitude Lidar Observatory (HALO) instruments across a range of aerosol, cloud, and weather conditions; 2) compare these measurements with the European Space Agency Aeolus mission to gain an initial perspective of Aeolus performance in preparation for a future international Aeolus Cal/Val airborne campaign; and 3) demonstrate how weather processes can be resolved and better understood through simultaneous airborne wind, water vapor (WV), and aerosol profile observations, coupled with numerical model and other remote sensing observations. Five NASA DC-8 aircraft flights, comprising 46 flight hours, were conducted over the Eastern Pacific and Southwest U.S., based out of NASA Armstrong Flight Research Center in Palmdale, CA and Kona, HI. Yankee Environmental Systems, Inc High Definition Sounding System (HDSS) eXpendable Digitial Dropsondes (XDD) were used to validate the DAWN and Aeolus wind observations. The LaRC Diode Laser Hygrometer instrument, which was integrated on the DC-8 in preparation for another NASA airborne campaign, provided in-situ WV measurements used during one flight to validate HALO and dropsonde WV profile products. proprietary
Aeolus-CalVal-MetNav_DC8_1 Aeolus CalVal Meteorological and Navigational ALL STAC Catalog 2019-04-17 2019-04-30 -159, 5, -113, 52 https://cmr.earthdata.nasa.gov/search/concepts/C1918229851-LARC_ASDC.umm_json Aeolus-CalVal-MetNav_DC8_1 is the Aeolus CalVal Meteorological and Navigational data product. Data was collected using the Global Positioning System (GPS) instrument on the Douglas (DC-8) Aircraft. Data collection for this product is complete. NASA conducted an airborne campaign from 17 April to 30 April 2019 to: 1) demonstrate the performance of the Doppler Aerosol WiNd Lidar (DAWN) and High Altitude Lidar Observatory (HALO) instruments across a range of aerosol, cloud, and weather conditions; 2) compare these measurements with the European Space Agency Aeolus mission to gain an initial perspective of Aeolus performance in preparation for a future international Aeolus Cal/Val airborne campaign; and 3) demonstrate how weather processes can be resolved and better understood through simultaneous airborne wind, water vapor (WV), and aerosol profile observations, coupled with numerical model and other remote sensing observations. Five NASA DC-8 aircraft flights, comprising 46 flight hours, were conducted over the Eastern Pacific and Southwest U.S., based out of NASA Armstrong Flight Research Center in Palmdale, CA and Kona, HI. Yankee Environmental Systems, Inc High Definition Sounding System (HDSS) eXpendable Digitial Dropsondes (XDD) were used to validate the DAWN and Aeolus wind observations. The LaRC Diode Laser Hygrometer instrument, which was integrated on the DC-8 in preparation for another NASA airborne campaign, provided in-situ WV measurements used during one flight to validate HALO and dropsonde WV profile products. proprietary
Aeolus-CalVal-MetNav_DC8_1 Aeolus CalVal Meteorological and Navigational LARC_ASDC STAC Catalog 2019-04-17 2019-04-30 -159, 5, -113, 52 https://cmr.earthdata.nasa.gov/search/concepts/C1918229851-LARC_ASDC.umm_json Aeolus-CalVal-MetNav_DC8_1 is the Aeolus CalVal Meteorological and Navigational data product. Data was collected using the Global Positioning System (GPS) instrument on the Douglas (DC-8) Aircraft. Data collection for this product is complete. NASA conducted an airborne campaign from 17 April to 30 April 2019 to: 1) demonstrate the performance of the Doppler Aerosol WiNd Lidar (DAWN) and High Altitude Lidar Observatory (HALO) instruments across a range of aerosol, cloud, and weather conditions; 2) compare these measurements with the European Space Agency Aeolus mission to gain an initial perspective of Aeolus performance in preparation for a future international Aeolus Cal/Val airborne campaign; and 3) demonstrate how weather processes can be resolved and better understood through simultaneous airborne wind, water vapor (WV), and aerosol profile observations, coupled with numerical model and other remote sensing observations. Five NASA DC-8 aircraft flights, comprising 46 flight hours, were conducted over the Eastern Pacific and Southwest U.S., based out of NASA Armstrong Flight Research Center in Palmdale, CA and Kona, HI. Yankee Environmental Systems, Inc High Definition Sounding System (HDSS) eXpendable Digitial Dropsondes (XDD) were used to validate the DAWN and Aeolus wind observations. The LaRC Diode Laser Hygrometer instrument, which was integrated on the DC-8 in preparation for another NASA airborne campaign, provided in-situ WV measurements used during one flight to validate HALO and dropsonde WV profile products. proprietary
Aerosol_Sulfate_LowermostStrat_1868_1 ATom: Ultrafine Aerosol Characteristics and Formation, Lower Stratosphere, 2016-2018 ORNL_CLOUD STAC Catalog 2016-07-29 2018-05-21 -180, -80, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2677001224-ORNL_CLOUD.umm_json This dataset consists of (a) selected aerosol and gas-phase observations made on all four deployments of NASA Atmospheric Tomography Mission (ATom), (b) thermodynamic properties related to aerosol formation derived from these measurements, (c) 48-h back trajectories for ATom-4 observations, and (d) output from the Model of Aerosols and Ions in the Atmosphere (MAIA). ATom observations, thermodynamics, and back trajectories were inputs for MAIA model runs. MAIA runs focused on data from ATom-4 deployment, and output includes aerosol formation rates, and ultrafine particle size distributions and number concentrations in the lowermost stratosphere (LMS). ATom 1-4 deployments included all four seasons from 2016 to 2018. This investigation sought to understand how new particle formation (NPF) can occur in the LMS, factors influencing the amount of NPF, and other potential sources of ultrafine aerosols in this region of the atmosphere. The data are provided in comma-separated value (CSV) format. proprietary
@@ -3317,110 +3318,110 @@ Aerosol_Sz_Dist_South_Pole_1.0 Aerosol Size Distributions Measured at the South
Aerosol_Sz_Dist_South_Pole_1.0 Aerosol Size Distributions Measured at the South Pole during ISCAT SCIOPS STAC Catalog 1998-12-01 2000-12-29 -180, -90, 180, -63 https://cmr.earthdata.nasa.gov/search/concepts/C1214611768-SCIOPS.umm_json This data set contains the physical aerosol size distributions measured at the South Pole during December 1998 and December 2000. The size range covered by these measurements was 3 [nm] to 250 [nm] in 1998 and 3 [nm] to 2000 [nm] in 2000. For 1998 measurements, total particle concentration for Dp > ~ 3[nm] and concentrations for 3 [nm] < Dp < 10 [nm] is available from 12/01/1998 to 12/31/1998 except over 12/09/1998 ~ 12/13/1998. They measured by the prototype Ultrafine Condensation Particle Counter, equipped with Pulse Height Analysis (PHA-UCPC) Particle size distributions for 10 [nm] < Dp < 250 [nm] is available from 12/16/1998 to 12/31/1998. They were measured by a Scanning Mobility Particle Spectrometer. For 2000 measurements, total particle concentration for Dp > ~ 3[nm] and concentrations for 3 [nm] < Dp < 10 [nm] is available from 12/01/2000 to 12/29/2000 except over 12/22/2000 ~ 12/24/2000. They measured by the white-light 3025 Ultrafine Condensation Particle Counter, equipped with Pulse Height Analysis (PHA-UCPC) Particle size distributions for 10 [nm] < Dp < 250 [nm] is available from 12/01/2000 to 12/29/2000. They were measured by a Scanning Mobility Particle Spectrometer. A PMS LASAIR measured particle size distributions for 100 [nm] to 2000 [nm] from 12/01/2000 to 12/29/2000. Typical data collection frequencies are ~ 5 minutes in all instruments. All length(size) units are in [um]. Following are the meanings of the variables. concentration [#/cc]: number of particles in a cubic centimeter of air. surface area [um^2/cc]: surface area concentrations of particles, assuming all particles are sphere. volume [um^3/cc]: volume concentrations of particles, assuming all particles are sphere proprietary
Aerosol_char_and_snow_chem_TNB Aerosol characterization and snow chemistry at Terra Nova Bay ALL STAC Catalog 1988-01-01 1990-02-28 164.1138, -74.7119, 164.1138, -74.7119 https://cmr.earthdata.nasa.gov/search/concepts/C1214615639-SCIOPS.umm_json Antarctic aerosol was sampled at Terra Nova Bay using an inertial spectrometer at high flow rate. This instrument can sample aerosol and deposit particles on a membrane filter with size separation. The density of single particles and average density vs. aerodynamic diameter has been evaluated. Chemical composition of aerosol particles was determined by analyzing samples taken on millipore filters by scanning electron microscope and x-ray energy spectrometer. The results from this investigation are such that for particles with radius > 0.5 micron, frequency of sea-salt increases when aerodynamic diameter decreases. An opposite behavior is displayed by crustal elements. A chlorine loss in sea-salt particles has been observed. The suggested mechanism for this loss is: H2SO4 2NaCl = Na2SO4 2HCl. Condensation nuclei (CN) concentrations were measured at Terra Nova Bay with an alcohol-based particle counter. In January 1989 the mean value for CN was 490. The concentrations of eight major ions (Cl-, NO-3, SO42-, Na , K , Ca2 , Mg , H ) were determined from fresh snow samples. These showed that precipitation is acidic, a fact depending on H2SO4, HCl and HNO3. proprietary
Aerosol_char_and_snow_chem_TNB Aerosol characterization and snow chemistry at Terra Nova Bay SCIOPS STAC Catalog 1988-01-01 1990-02-28 164.1138, -74.7119, 164.1138, -74.7119 https://cmr.earthdata.nasa.gov/search/concepts/C1214615639-SCIOPS.umm_json Antarctic aerosol was sampled at Terra Nova Bay using an inertial spectrometer at high flow rate. This instrument can sample aerosol and deposit particles on a membrane filter with size separation. The density of single particles and average density vs. aerodynamic diameter has been evaluated. Chemical composition of aerosol particles was determined by analyzing samples taken on millipore filters by scanning electron microscope and x-ray energy spectrometer. The results from this investigation are such that for particles with radius > 0.5 micron, frequency of sea-salt increases when aerodynamic diameter decreases. An opposite behavior is displayed by crustal elements. A chlorine loss in sea-salt particles has been observed. The suggested mechanism for this loss is: H2SO4 2NaCl = Na2SO4 2HCl. Condensation nuclei (CN) concentrations were measured at Terra Nova Bay with an alcohol-based particle counter. In January 1989 the mean value for CN was 490. The concentrations of eight major ions (Cl-, NO-3, SO42-, Na , K , Ca2 , Mg , H ) were determined from fresh snow samples. These showed that precipitation is acidic, a fact depending on H2SO4, HCl and HNO3. proprietary
-Aerosol_opt_char_at_BTN_station Aerosol optical characteristics at BTN station SCIOPS STAC Catalog 2001-12-01 2002-02-28 164.1, -74.7, 164.1, -74.7 https://cmr.earthdata.nasa.gov/search/concepts/C1214615144-SCIOPS.umm_json Measurements performed at BTN (Icaro Camp) in the austral summer 2001 - 2002 with the PREDE POM 01L sun-photometer. It detects direct solar radiative flux as well as diffuse at selected scattering angles and at six wavelengths. Aerosol optical characteristics were derived making use of Nakajima inversion code SKYRAD. Aerosol optical depth was evaluated at 6 channels centered at 315, 400, 500, 870, 940, 1020 nm wavelength bands. The sampling time interval is about 15 minutes. The air mass is also given. Data were collected under cloudless-sky conditions. An in situ radiometer calibration is also performed by means of a modified Langley plot. proprietary
Aerosol_opt_char_at_BTN_station Aerosol optical characteristics at BTN station ALL STAC Catalog 2001-12-01 2002-02-28 164.1, -74.7, 164.1, -74.7 https://cmr.earthdata.nasa.gov/search/concepts/C1214615144-SCIOPS.umm_json Measurements performed at BTN (Icaro Camp) in the austral summer 2001 - 2002 with the PREDE POM 01L sun-photometer. It detects direct solar radiative flux as well as diffuse at selected scattering angles and at six wavelengths. Aerosol optical characteristics were derived making use of Nakajima inversion code SKYRAD. Aerosol optical depth was evaluated at 6 channels centered at 315, 400, 500, 870, 940, 1020 nm wavelength bands. The sampling time interval is about 15 minutes. The air mass is also given. Data were collected under cloudless-sky conditions. An in situ radiometer calibration is also performed by means of a modified Langley plot. proprietary
+Aerosol_opt_char_at_BTN_station Aerosol optical characteristics at BTN station SCIOPS STAC Catalog 2001-12-01 2002-02-28 164.1, -74.7, 164.1, -74.7 https://cmr.earthdata.nasa.gov/search/concepts/C1214615144-SCIOPS.umm_json Measurements performed at BTN (Icaro Camp) in the austral summer 2001 - 2002 with the PREDE POM 01L sun-photometer. It detects direct solar radiative flux as well as diffuse at selected scattering angles and at six wavelengths. Aerosol optical characteristics were derived making use of Nakajima inversion code SKYRAD. Aerosol optical depth was evaluated at 6 channels centered at 315, 400, 500, 870, 940, 1020 nm wavelength bands. The sampling time interval is about 15 minutes. The air mass is also given. Data were collected under cloudless-sky conditions. An in situ radiometer calibration is also performed by means of a modified Langley plot. proprietary
Aerosol_opt_depths_at_BTN Aerosol optical depths at BTN station SCIOPS STAC Catalog 1988-12-01 1994-02-28 164.1138, -74.7119, 164.1138, -74.7119 https://cmr.earthdata.nasa.gov/search/concepts/C1214615123-SCIOPS.umm_json Measurements performed at BTN (Icaro Camp) in the austral summers 1988 and 1993 with the UVISIR-2 sun-photometer built at the FISBAT-Institute (cfr. References below). Aerosol optical depth was evaluated taking into account molecular scattering and gaseous absorption as H2O, O3 and NO2 (cfr. references below). Aerosol optical depths were evaluated at 8 channels centered in the 400 - 1050 nm wavelength range. Because each scanning has the physical meaning of an instantaneous picture of the atmosphere (with the sun at elevation h), we use a single average time for each scanning . The scanning time interval is about 1.5 minutes. The relative optical air mass is also given. Data was taken under clear-sky conditions. Legal maximum value of optical depth depends on turbidity daily conditions and wavelength, ranging from 0.03 and 0.15.All values are given with 3 digit. Missing data are indicated with a 999.000 value. proprietary
Aerosol_opt_depths_at_BTN Aerosol optical depths at BTN station ALL STAC Catalog 1988-12-01 1994-02-28 164.1138, -74.7119, 164.1138, -74.7119 https://cmr.earthdata.nasa.gov/search/concepts/C1214615123-SCIOPS.umm_json Measurements performed at BTN (Icaro Camp) in the austral summers 1988 and 1993 with the UVISIR-2 sun-photometer built at the FISBAT-Institute (cfr. References below). Aerosol optical depth was evaluated taking into account molecular scattering and gaseous absorption as H2O, O3 and NO2 (cfr. references below). Aerosol optical depths were evaluated at 8 channels centered in the 400 - 1050 nm wavelength range. Because each scanning has the physical meaning of an instantaneous picture of the atmosphere (with the sun at elevation h), we use a single average time for each scanning . The scanning time interval is about 1.5 minutes. The relative optical air mass is also given. Data was taken under clear-sky conditions. Legal maximum value of optical depth depends on turbidity daily conditions and wavelength, ranging from 0.03 and 0.15.All values are given with 3 digit. Missing data are indicated with a 999.000 value. proprietary
-AfriSAR_AGB_Maps_1681_1 AfriSAR: Aboveground Biomass for Lope, Mabounie, Mondah, and Rabi Sites, Gabon ALL STAC Catalog 2016-02-01 2016-03-31 9.3, -1.95, 11.64, 0.61 https://cmr.earthdata.nasa.gov/search/concepts/C2734261660-ORNL_CLOUD.umm_json This dataset provides gridded estimates of aboveground biomass (AGB) for four sites in Gabon at 0.25 ha (50 m) resolution derived with field measurements and airborne LiDAR data collected from 2010 to 2016. The sites represent a mix of forested, savannah, and some agricultural and disturbed landcover types: Lope site, within Lope National Park; Mabounie, mostly forested site; Mondah Forest, protected area; and the Rabi forest site, part of the Smithsonian Institution of Global Earth Observatories world-wide network of forest plots. Plot-level biophysical measurements of tree diameter and tree height (or estimated by allometry) were performed at 1 ha and 0.25 ha scales on multiple plots at each site and used to derive AGB for each tree and then summed for each plot. Aerial LiDAR scans were used to construct digital elevation models (DEM) and digital surface models (DSM), and then the DEM and DSM were used to construct a canopy height model (CHM) at 1 m resolution. After checking site-plot locations against the CHM, mean canopy height (MCH) was computed over each 0.25 ha. A single regression model relating MCH and AGB estimates, incorporating local height based on the trunk DBH (HD) relationships, was produced for all sites and combined with the CHM layer to construct biomass maps at 0.25 ha resolution. proprietary
AfriSAR_AGB_Maps_1681_1 AfriSAR: Aboveground Biomass for Lope, Mabounie, Mondah, and Rabi Sites, Gabon ORNL_CLOUD STAC Catalog 2016-02-01 2016-03-31 9.3, -1.95, 11.64, 0.61 https://cmr.earthdata.nasa.gov/search/concepts/C2734261660-ORNL_CLOUD.umm_json This dataset provides gridded estimates of aboveground biomass (AGB) for four sites in Gabon at 0.25 ha (50 m) resolution derived with field measurements and airborne LiDAR data collected from 2010 to 2016. The sites represent a mix of forested, savannah, and some agricultural and disturbed landcover types: Lope site, within Lope National Park; Mabounie, mostly forested site; Mondah Forest, protected area; and the Rabi forest site, part of the Smithsonian Institution of Global Earth Observatories world-wide network of forest plots. Plot-level biophysical measurements of tree diameter and tree height (or estimated by allometry) were performed at 1 ha and 0.25 ha scales on multiple plots at each site and used to derive AGB for each tree and then summed for each plot. Aerial LiDAR scans were used to construct digital elevation models (DEM) and digital surface models (DSM), and then the DEM and DSM were used to construct a canopy height model (CHM) at 1 m resolution. After checking site-plot locations against the CHM, mean canopy height (MCH) was computed over each 0.25 ha. A single regression model relating MCH and AGB estimates, incorporating local height based on the trunk DBH (HD) relationships, was produced for all sites and combined with the CHM layer to construct biomass maps at 0.25 ha resolution. proprietary
+AfriSAR_AGB_Maps_1681_1 AfriSAR: Aboveground Biomass for Lope, Mabounie, Mondah, and Rabi Sites, Gabon ALL STAC Catalog 2016-02-01 2016-03-31 9.3, -1.95, 11.64, 0.61 https://cmr.earthdata.nasa.gov/search/concepts/C2734261660-ORNL_CLOUD.umm_json This dataset provides gridded estimates of aboveground biomass (AGB) for four sites in Gabon at 0.25 ha (50 m) resolution derived with field measurements and airborne LiDAR data collected from 2010 to 2016. The sites represent a mix of forested, savannah, and some agricultural and disturbed landcover types: Lope site, within Lope National Park; Mabounie, mostly forested site; Mondah Forest, protected area; and the Rabi forest site, part of the Smithsonian Institution of Global Earth Observatories world-wide network of forest plots. Plot-level biophysical measurements of tree diameter and tree height (or estimated by allometry) were performed at 1 ha and 0.25 ha scales on multiple plots at each site and used to derive AGB for each tree and then summed for each plot. Aerial LiDAR scans were used to construct digital elevation models (DEM) and digital surface models (DSM), and then the DEM and DSM were used to construct a canopy height model (CHM) at 1 m resolution. After checking site-plot locations against the CHM, mean canopy height (MCH) was computed over each 0.25 ha. A single regression model relating MCH and AGB estimates, incorporating local height based on the trunk DBH (HD) relationships, was produced for all sites and combined with the CHM layer to construct biomass maps at 0.25 ha resolution. proprietary
AfriSAR_LVIS_Footprint_Cover_1591_1 AfriSAR: Canopy Cover and Vertical Profile Metrics Derived from LVIS, Gabon, 2016 ORNL_CLOUD STAC Catalog 2016-02-20 2016-03-08 8.73, -2.29, 12.01, 0.7 https://cmr.earthdata.nasa.gov/search/concepts/C2734258863-ORNL_CLOUD.umm_json This dataset includes footprint-level canopy structure products derived from data collected using NASA's Land, Vegetation, and Ice Sensor (LVIS) during flights over five forested sites in Gabon during February and March 2016. Three types of canopy structure information are included for each flight: 1) vertical profiles of canopy cover fraction in 1-meter bins, 2) vertical profiles of plant area index (PAI) in 1-meter bins, and 3) footprint summary data of total recorded energy, leaf area index, canopy cover fraction, and vertical foliage profiles in 10-meter bins. Canopy structure metrics are provided for each waveform (20-m footprint) collected by the LVIS instrument. These data were collected by NASA as part of the AfriSAR project. AfriSAR is a NASA collaboration with the European Space Agency (ESA), German Aerospace Center (DLR), and the Gabonese Space Agency (AGEOS) that is collecting data useful for deriving forest canopy structure and will help prepare for and calibrate current and upcoming spaceborne missions that aim to gauge the role of forests in Earth's carbon cycle. proprietary
AfriSAR_LVIS_Footprint_Cover_1591_1 AfriSAR: Canopy Cover and Vertical Profile Metrics Derived from LVIS, Gabon, 2016 ALL STAC Catalog 2016-02-20 2016-03-08 8.73, -2.29, 12.01, 0.7 https://cmr.earthdata.nasa.gov/search/concepts/C2734258863-ORNL_CLOUD.umm_json This dataset includes footprint-level canopy structure products derived from data collected using NASA's Land, Vegetation, and Ice Sensor (LVIS) during flights over five forested sites in Gabon during February and March 2016. Three types of canopy structure information are included for each flight: 1) vertical profiles of canopy cover fraction in 1-meter bins, 2) vertical profiles of plant area index (PAI) in 1-meter bins, and 3) footprint summary data of total recorded energy, leaf area index, canopy cover fraction, and vertical foliage profiles in 10-meter bins. Canopy structure metrics are provided for each waveform (20-m footprint) collected by the LVIS instrument. These data were collected by NASA as part of the AfriSAR project. AfriSAR is a NASA collaboration with the European Space Agency (ESA), German Aerospace Center (DLR), and the Gabonese Space Agency (AGEOS) that is collecting data useful for deriving forest canopy structure and will help prepare for and calibrate current and upcoming spaceborne missions that aim to gauge the role of forests in Earth's carbon cycle. proprietary
AfriSAR_Mondah_Field_Data_1580_1 AfriSAR: Mondah Forest Tree Species, Biophysical, and Biomass Data, Gabon, 2016 ORNL_CLOUD STAC Catalog 2016-03-01 2016-03-23 9.32, 0.54, 9.42, 0.62 https://cmr.earthdata.nasa.gov/search/concepts/C2734258563-ORNL_CLOUD.umm_json This dataset provides plot-level estimates of basal area, aboveground biomass, number of trees, maximum tree height, and basal-area-weighted wood specific gravity that were derived from observations of nearly 6,700 individual trees including tree family, species, DBH, the height of each tree, and their x, y location within 25 x 25 m subplots. These field data were collected from 15 1-hectare plots located across the Mondah Forest of Gabon as part of the AfriSAR Campaign in 2016. These biophysical and biomass data were used for training models to derive the AfriSAR remote sensing-based aboveground biomass products. proprietary
AfriSAR_Mondah_Field_Data_1580_1 AfriSAR: Mondah Forest Tree Species, Biophysical, and Biomass Data, Gabon, 2016 ALL STAC Catalog 2016-03-01 2016-03-23 9.32, 0.54, 9.42, 0.62 https://cmr.earthdata.nasa.gov/search/concepts/C2734258563-ORNL_CLOUD.umm_json This dataset provides plot-level estimates of basal area, aboveground biomass, number of trees, maximum tree height, and basal-area-weighted wood specific gravity that were derived from observations of nearly 6,700 individual trees including tree family, species, DBH, the height of each tree, and their x, y location within 25 x 25 m subplots. These field data were collected from 15 1-hectare plots located across the Mondah Forest of Gabon as part of the AfriSAR Campaign in 2016. These biophysical and biomass data were used for training models to derive the AfriSAR remote sensing-based aboveground biomass products. proprietary
-African_Marine_Atlas African Marine Atlas CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232459316-CEOS_EXTRA.umm_json The African Marine Atlas developed by the Ocean Data and Information Network for Africa (ODINAFRICA) was officially launched on 23 February 2007 at the IOC Project Office for International Oceanographic Data and Information Exchange (IODE) in Ostend, Belgium. The African Marine Atlas provides substantial maps, images, data and information to coastalama_screen_400x310.shkl.jpg resource managers, planners and decision-makers from various administrative institutions and specialized agencies in Africa. The Atlas will be of immense benefit to national institutions and a variety of users such as environmentalists, local administrators, park managers, scientific community, fishing cooperatives, tourists, hotel keepers, teachers, NGOs, the general public, and any other interested persons. It has over 800 downloadable data products derived from the fields of marine geo-sphere, hydrosphere, atmosphere, biosphere, geopolitical and the human socio-economic dimensions. The Atlas indicates areas of intense use along the coastline requiring careful management and provides potential foresight on likely consequences of specific decisions. Further, the Atlas indicates gaps in knowledge and information base, where additional efforts may be directed. The Atlas will also act in other ways as a guide to recreational opportunities and tourist attractions. In developing the Atlas, the main objective was to collate available geospatial datasets and information on the marine environment and to summarize it into an African Marine Atlas suite. The website is one of a set of Marine Atlas products that will include web data services, web mapping and an Atlas publication when completed. The Atlas was realized through intensive work between May 2006 and February 2007 by a team of 16 marine scientists and GIS experts from NODC’s in Benin, Ghana, Kenya, Mauritania, Mauritius, Mozambique, Namibia, Senegal, Seychelles, South Africa, and Tanzania. International ocean data experts provided key inputs in data analysis. It is based on an extensive survey of coastal and marine data needs undertaken in early 2006 in all the countries participating in ODINAFRICA. Primary partners in this project were the United Nations Environment Programme (UNEP), and the African Coelecanth Ecosystem Programme (ACEP). UNEP will develop a clearinghouse and information system on coastal and marine resources of Eastern Africa from the regional atlas. The Atlas has brought great benefits to participating national institutions and Africa as a whole, by encouraging scientists to work together, learn new techniques, and build teams that will continue to regularly update the Atlas with national and local scale data sets. _____________________________________________________________________ proprietary
African_Marine_Atlas African Marine Atlas ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232459316-CEOS_EXTRA.umm_json The African Marine Atlas developed by the Ocean Data and Information Network for Africa (ODINAFRICA) was officially launched on 23 February 2007 at the IOC Project Office for International Oceanographic Data and Information Exchange (IODE) in Ostend, Belgium. The African Marine Atlas provides substantial maps, images, data and information to coastalama_screen_400x310.shkl.jpg resource managers, planners and decision-makers from various administrative institutions and specialized agencies in Africa. The Atlas will be of immense benefit to national institutions and a variety of users such as environmentalists, local administrators, park managers, scientific community, fishing cooperatives, tourists, hotel keepers, teachers, NGOs, the general public, and any other interested persons. It has over 800 downloadable data products derived from the fields of marine geo-sphere, hydrosphere, atmosphere, biosphere, geopolitical and the human socio-economic dimensions. The Atlas indicates areas of intense use along the coastline requiring careful management and provides potential foresight on likely consequences of specific decisions. Further, the Atlas indicates gaps in knowledge and information base, where additional efforts may be directed. The Atlas will also act in other ways as a guide to recreational opportunities and tourist attractions. In developing the Atlas, the main objective was to collate available geospatial datasets and information on the marine environment and to summarize it into an African Marine Atlas suite. The website is one of a set of Marine Atlas products that will include web data services, web mapping and an Atlas publication when completed. The Atlas was realized through intensive work between May 2006 and February 2007 by a team of 16 marine scientists and GIS experts from NODC’s in Benin, Ghana, Kenya, Mauritania, Mauritius, Mozambique, Namibia, Senegal, Seychelles, South Africa, and Tanzania. International ocean data experts provided key inputs in data analysis. It is based on an extensive survey of coastal and marine data needs undertaken in early 2006 in all the countries participating in ODINAFRICA. Primary partners in this project were the United Nations Environment Programme (UNEP), and the African Coelecanth Ecosystem Programme (ACEP). UNEP will develop a clearinghouse and information system on coastal and marine resources of Eastern Africa from the regional atlas. The Atlas has brought great benefits to participating national institutions and Africa as a whole, by encouraging scientists to work together, learn new techniques, and build teams that will continue to regularly update the Atlas with national and local scale data sets. _____________________________________________________________________ proprietary
+African_Marine_Atlas African Marine Atlas CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232459316-CEOS_EXTRA.umm_json The African Marine Atlas developed by the Ocean Data and Information Network for Africa (ODINAFRICA) was officially launched on 23 February 2007 at the IOC Project Office for International Oceanographic Data and Information Exchange (IODE) in Ostend, Belgium. The African Marine Atlas provides substantial maps, images, data and information to coastalama_screen_400x310.shkl.jpg resource managers, planners and decision-makers from various administrative institutions and specialized agencies in Africa. The Atlas will be of immense benefit to national institutions and a variety of users such as environmentalists, local administrators, park managers, scientific community, fishing cooperatives, tourists, hotel keepers, teachers, NGOs, the general public, and any other interested persons. It has over 800 downloadable data products derived from the fields of marine geo-sphere, hydrosphere, atmosphere, biosphere, geopolitical and the human socio-economic dimensions. The Atlas indicates areas of intense use along the coastline requiring careful management and provides potential foresight on likely consequences of specific decisions. Further, the Atlas indicates gaps in knowledge and information base, where additional efforts may be directed. The Atlas will also act in other ways as a guide to recreational opportunities and tourist attractions. In developing the Atlas, the main objective was to collate available geospatial datasets and information on the marine environment and to summarize it into an African Marine Atlas suite. The website is one of a set of Marine Atlas products that will include web data services, web mapping and an Atlas publication when completed. The Atlas was realized through intensive work between May 2006 and February 2007 by a team of 16 marine scientists and GIS experts from NODC’s in Benin, Ghana, Kenya, Mauritania, Mauritius, Mozambique, Namibia, Senegal, Seychelles, South Africa, and Tanzania. International ocean data experts provided key inputs in data analysis. It is based on an extensive survey of coastal and marine data needs undertaken in early 2006 in all the countries participating in ODINAFRICA. Primary partners in this project were the United Nations Environment Programme (UNEP), and the African Coelecanth Ecosystem Programme (ACEP). UNEP will develop a clearinghouse and information system on coastal and marine resources of Eastern Africa from the regional atlas. The Atlas has brought great benefits to participating national institutions and Africa as a whole, by encouraging scientists to work together, learn new techniques, and build teams that will continue to regularly update the Atlas with national and local scale data sets. _____________________________________________________________________ proprietary
African_Rainfall_Patterns_1263_1 Spatio-temporal Characteristics of Rainfall in Africa, 0.25 degrees, from 1998-2012 ORNL_CLOUD STAC Catalog 1998-01-01 2012-12-31 -20, -40, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2776874873-ORNL_CLOUD.umm_json This data set describes rainfall distribution statistics over the African continent, including Madagascar. The rainfall estimates are based on data from the NASA Tropical Rainfall Measuring Mission (TRMM) measured between 1998 and 2012. Rainfall patterns were quantified using a gamma-based function and two Markov chain parameters with the aim to summarize the rainfall pattern to a small number of parameters and processes. These summary statistics are suitable for temporal downscaling.These data provide gridded (0.25 x 0.25-degree) estimates of 14-year mean monthly rainfall total amount (mm), frequency (count), intensity (mm/hr), and duration (hrs) of rainfall, as well as Markov chain and gamma-distribution parameters for use in temporal downscaling. The data are presented as a series of 12 netCDF (*.nc) files. proprietary
Afrisar_LVIS_Biomass_VProfiles_1775_1 AfriSAR: Gridded Forest Biomass and Canopy Metrics Derived from LVIS, Gabon, 2016 ORNL_CLOUD STAC Catalog 2016-02-20 2016-03-08 9.18, -2.29, 12.02, 0.63 https://cmr.earthdata.nasa.gov/search/concepts/C2734261748-ORNL_CLOUD.umm_json This dataset contains gridded forest characterization products derived from full-waveform lidar data acquired by NASA's airborne Land, Vegetation, and Ice Sensor (LVIS) instrument for five forested sites in Gabon, Africa, during the 2016 NASA-ESA AfriSAR campaign. The LVIS lidar instrument was flown over study sites in Lope, Mondah/Akanda, Pongara, Rabi, and Mabouni from February to March 2016. Derived canopy cover, canopy heights, bare ground elevation, plant area index (PAI), and foliage height diversity (FHD), and respective uncertainties are provided at a 25 m resolution for each of the five study sites. Aboveground biomass density (AGBD) and uncertainty were modeled at 50 m and 100 m resolutions for the Lope, Mondah, and Mabounie sites using field inventory data and waveform height and cover metrics. Lidar grid cell data collection statistics (i.e., number of shots and flight lines) and a data mask are also included. This research leverages high-quality forest inventory datasets collected during the AfriSAR campaign for one of the least studied and most unique forest ecosystems in the world. proprietary
Afrisar_LVIS_Biomass_VProfiles_1775_1 AfriSAR: Gridded Forest Biomass and Canopy Metrics Derived from LVIS, Gabon, 2016 ALL STAC Catalog 2016-02-20 2016-03-08 9.18, -2.29, 12.02, 0.63 https://cmr.earthdata.nasa.gov/search/concepts/C2734261748-ORNL_CLOUD.umm_json This dataset contains gridded forest characterization products derived from full-waveform lidar data acquired by NASA's airborne Land, Vegetation, and Ice Sensor (LVIS) instrument for five forested sites in Gabon, Africa, during the 2016 NASA-ESA AfriSAR campaign. The LVIS lidar instrument was flown over study sites in Lope, Mondah/Akanda, Pongara, Rabi, and Mabouni from February to March 2016. Derived canopy cover, canopy heights, bare ground elevation, plant area index (PAI), and foliage height diversity (FHD), and respective uncertainties are provided at a 25 m resolution for each of the five study sites. Aboveground biomass density (AGBD) and uncertainty were modeled at 50 m and 100 m resolutions for the Lope, Mondah, and Mabounie sites using field inventory data and waveform height and cover metrics. Lidar grid cell data collection statistics (i.e., number of shots and flight lines) and a data mask are also included. This research leverages high-quality forest inventory datasets collected during the AfriSAR campaign for one of the least studied and most unique forest ecosystems in the world. proprietary
AgriFieldNet Competition Dataset_1 AgriFieldNet Competition Dataset ALL STAC Catalog 2020-01-01 2023-01-01 76.2448319, 18.9414403, 88.0460054, 28.3269976 https://cmr.earthdata.nasa.gov/search/concepts/C2781412563-MLHUB.umm_json This dataset contains crop types of agricultural fields in four states of Uttar Pradesh, Rajasthan, Odisha and Bihar in northern India. There are 13 different classes in the dataset including Fallow land and 12 crop types of Wheat, Mustard, Lentil, Green pea, Sugarcane, Garlic, Maize, Gram, Coriander, Potato, Bersem, and Rice. The dataset is split to train and test collections as part of the AgriFieldNet India Competition. Ground reference data for this dataset is collected by IDinsight’s [Data on Demand](https://www.idinsight.org/services/data-on-demand/) team. Radiant Earth Foundation carried out the training dataset curation and publication. This training dataset is generated through a grant from the Enabling Crop Analytics at Scale ([ECAAS](https://cropanalytics.net/)) Initiative funded by [The Bill & Melinda Gates Foundation](https://www.gatesfoundation.org/) and implemented by [Tetra Tech](https://www.tetratech.com/). proprietary
AgriFieldNet Competition Dataset_1 AgriFieldNet Competition Dataset MLHUB STAC Catalog 2020-01-01 2023-01-01 76.2448319, 18.9414403, 88.0460054, 28.3269976 https://cmr.earthdata.nasa.gov/search/concepts/C2781412563-MLHUB.umm_json This dataset contains crop types of agricultural fields in four states of Uttar Pradesh, Rajasthan, Odisha and Bihar in northern India. There are 13 different classes in the dataset including Fallow land and 12 crop types of Wheat, Mustard, Lentil, Green pea, Sugarcane, Garlic, Maize, Gram, Coriander, Potato, Bersem, and Rice. The dataset is split to train and test collections as part of the AgriFieldNet India Competition. Ground reference data for this dataset is collected by IDinsight’s [Data on Demand](https://www.idinsight.org/services/data-on-demand/) team. Radiant Earth Foundation carried out the training dataset curation and publication. This training dataset is generated through a grant from the Enabling Crop Analytics at Scale ([ECAAS](https://cropanalytics.net/)) Initiative funded by [The Bill & Melinda Gates Foundation](https://www.gatesfoundation.org/) and implemented by [Tetra Tech](https://www.tetratech.com/). proprietary
-AirMOSS_Field_Data_Harvard_1677_1 AirMOSS: In Situ Soil Moisture and Tree Measurements, Harvard Forest, 2012-2013 ORNL_CLOUD STAC Catalog 2012-10-15 2013-08-22 -72.18, 42.54, -71.18, 42.55 https://cmr.earthdata.nasa.gov/search/concepts/C2258527524-ORNL_CLOUD.umm_json This dataset provides in situ measurements of soil temperature, moisture, conductivity, measured diameter of tree at breast height (DBH) and total height collected at the Harvard Forest, Petersham, Massachusetts, USA, during October 2012 and July - August 2013. These measurements were collected in support of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project to validate root-zone soil measurements and carbon flux model estimates. proprietary
AirMOSS_Field_Data_Harvard_1677_1 AirMOSS: In Situ Soil Moisture and Tree Measurements, Harvard Forest, 2012-2013 ALL STAC Catalog 2012-10-15 2013-08-22 -72.18, 42.54, -71.18, 42.55 https://cmr.earthdata.nasa.gov/search/concepts/C2258527524-ORNL_CLOUD.umm_json This dataset provides in situ measurements of soil temperature, moisture, conductivity, measured diameter of tree at breast height (DBH) and total height collected at the Harvard Forest, Petersham, Massachusetts, USA, during October 2012 and July - August 2013. These measurements were collected in support of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project to validate root-zone soil measurements and carbon flux model estimates. proprietary
-AirMOSS_L1_Sigma0_BERMS_1406_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, BERMS, Canada, 2012-2015 ALL STAC Catalog 2012-10-04 2015-09-28 -106.68, 53.56, -104.14, 54.02 https://cmr.earthdata.nasa.gov/search/concepts/C2273976116-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the BERMS (Boreal Ecosystem Research and Monitoring Sites), in Saskatchewan, Canada. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
+AirMOSS_Field_Data_Harvard_1677_1 AirMOSS: In Situ Soil Moisture and Tree Measurements, Harvard Forest, 2012-2013 ORNL_CLOUD STAC Catalog 2012-10-15 2013-08-22 -72.18, 42.54, -71.18, 42.55 https://cmr.earthdata.nasa.gov/search/concepts/C2258527524-ORNL_CLOUD.umm_json This dataset provides in situ measurements of soil temperature, moisture, conductivity, measured diameter of tree at breast height (DBH) and total height collected at the Harvard Forest, Petersham, Massachusetts, USA, during October 2012 and July - August 2013. These measurements were collected in support of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project to validate root-zone soil measurements and carbon flux model estimates. proprietary
AirMOSS_L1_Sigma0_BERMS_1406_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, BERMS, Canada, 2012-2015 ORNL_CLOUD STAC Catalog 2012-10-04 2015-09-28 -106.68, 53.56, -104.14, 54.02 https://cmr.earthdata.nasa.gov/search/concepts/C2273976116-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the BERMS (Boreal Ecosystem Research and Monitoring Sites), in Saskatchewan, Canada. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
-AirMOSS_L1_Sigma0_Chamel_1407_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Chamela, Mexico, 2012-2015 ORNL_CLOUD STAC Catalog 2013-06-15 2015-04-21 -105.25, 19.29, -104.16, 20.3 https://cmr.earthdata.nasa.gov/search/concepts/C2274742460-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Chamela Biological Station, in Jalisco, Mexico. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
+AirMOSS_L1_Sigma0_BERMS_1406_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, BERMS, Canada, 2012-2015 ALL STAC Catalog 2012-10-04 2015-09-28 -106.68, 53.56, -104.14, 54.02 https://cmr.earthdata.nasa.gov/search/concepts/C2273976116-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the BERMS (Boreal Ecosystem Research and Monitoring Sites), in Saskatchewan, Canada. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
AirMOSS_L1_Sigma0_Chamel_1407_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Chamela, Mexico, 2012-2015 ALL STAC Catalog 2013-06-15 2015-04-21 -105.25, 19.29, -104.16, 20.3 https://cmr.earthdata.nasa.gov/search/concepts/C2274742460-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Chamela Biological Station, in Jalisco, Mexico. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
-AirMOSS_L1_Sigma0_DukeFr_1408_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Duke Forest, 2012-2015 ORNL_CLOUD STAC Catalog 2012-10-13 2015-09-10 -80.04, 35.39, -78.5, 36.43 https://cmr.earthdata.nasa.gov/search/concepts/C2274852550-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Duke Forest site in North Carolina. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
+AirMOSS_L1_Sigma0_Chamel_1407_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Chamela, Mexico, 2012-2015 ORNL_CLOUD STAC Catalog 2013-06-15 2015-04-21 -105.25, 19.29, -104.16, 20.3 https://cmr.earthdata.nasa.gov/search/concepts/C2274742460-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Chamela Biological Station, in Jalisco, Mexico. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
AirMOSS_L1_Sigma0_DukeFr_1408_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Duke Forest, 2012-2015 ALL STAC Catalog 2012-10-13 2015-09-10 -80.04, 35.39, -78.5, 36.43 https://cmr.earthdata.nasa.gov/search/concepts/C2274852550-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Duke Forest site in North Carolina. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
-AirMOSS_L1_Sigma0_Harvrd_1409_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Harvard Forest, 2012-2015 ALL STAC Catalog 2012-10-15 2015-09-09 -72.39, 42.18, -71.85, 43.56 https://cmr.earthdata.nasa.gov/search/concepts/C2274853114-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Harvard Forest site in Massachusetts. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
+AirMOSS_L1_Sigma0_DukeFr_1408_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Duke Forest, 2012-2015 ORNL_CLOUD STAC Catalog 2012-10-13 2015-09-10 -80.04, 35.39, -78.5, 36.43 https://cmr.earthdata.nasa.gov/search/concepts/C2274852550-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Duke Forest site in North Carolina. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
AirMOSS_L1_Sigma0_Harvrd_1409_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Harvard Forest, 2012-2015 ORNL_CLOUD STAC Catalog 2012-10-15 2015-09-09 -72.39, 42.18, -71.85, 43.56 https://cmr.earthdata.nasa.gov/search/concepts/C2274853114-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Harvard Forest site in Massachusetts. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
+AirMOSS_L1_Sigma0_Harvrd_1409_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Harvard Forest, 2012-2015 ALL STAC Catalog 2012-10-15 2015-09-09 -72.39, 42.18, -71.85, 43.56 https://cmr.earthdata.nasa.gov/search/concepts/C2274853114-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Harvard Forest site in Massachusetts. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
AirMOSS_L1_Sigma0_Howlnd_1410_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Howland Forest, 2012-2015 ALL STAC Catalog 2012-10-15 2015-09-09 -69.11, 44.5, -68.25, 46.02 https://cmr.earthdata.nasa.gov/search/concepts/C2274853415-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Howland Forest site in Maine. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
AirMOSS_L1_Sigma0_Howlnd_1410_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Howland Forest, 2012-2015 ORNL_CLOUD STAC Catalog 2012-10-15 2015-09-09 -69.11, 44.5, -68.25, 46.02 https://cmr.earthdata.nasa.gov/search/concepts/C2274853415-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Howland Forest site in Maine. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
-AirMOSS_L1_Sigma0_LaSelv_1411_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, La Selva, 2012-2015 ALL STAC Catalog 2013-02-20 2015-02-24 -85.14, 9.74, -83.27, 11.05 https://cmr.earthdata.nasa.gov/search/concepts/C2273946359-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the La Selva Biological Station in Costa Rica. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
AirMOSS_L1_Sigma0_LaSelv_1411_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, La Selva, 2012-2015 ORNL_CLOUD STAC Catalog 2013-02-20 2015-02-24 -85.14, 9.74, -83.27, 11.05 https://cmr.earthdata.nasa.gov/search/concepts/C2273946359-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the La Selva Biological Station in Costa Rica. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
+AirMOSS_L1_Sigma0_LaSelv_1411_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, La Selva, 2012-2015 ALL STAC Catalog 2013-02-20 2015-02-24 -85.14, 9.74, -83.27, 11.05 https://cmr.earthdata.nasa.gov/search/concepts/C2273946359-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the La Selva Biological Station in Costa Rica. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
AirMOSS_L1_Sigma0_Metoli_1412_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Metolius, 2012-2015 ALL STAC Catalog 2012-09-18 2015-09-29 -122.86, 43.99, -120.89, 44.69 https://cmr.earthdata.nasa.gov/search/concepts/C2274874175-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Metolius site in Oregon. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
AirMOSS_L1_Sigma0_Metoli_1412_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Metolius, 2012-2015 ORNL_CLOUD STAC Catalog 2012-09-18 2015-09-29 -122.86, 43.99, -120.89, 44.69 https://cmr.earthdata.nasa.gov/search/concepts/C2274874175-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Metolius site in Oregon. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
-AirMOSS_L1_Sigma0_Moisst_1413_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, MOISST, 2012-2015 ALL STAC Catalog 2012-10-24 2015-08-14 -99, 35.78, -96.82, 36.89 https://cmr.earthdata.nasa.gov/search/concepts/C2274886681-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the MOISST site in Oklahoma. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
AirMOSS_L1_Sigma0_Moisst_1413_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, MOISST, 2012-2015 ORNL_CLOUD STAC Catalog 2012-10-24 2015-08-14 -99, 35.78, -96.82, 36.89 https://cmr.earthdata.nasa.gov/search/concepts/C2274886681-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the MOISST site in Oklahoma. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
-AirMOSS_L1_Sigma0_TonziR_1414_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Tonzi Ranch, 2012-2015 ALL STAC Catalog 2013-02-05 2015-05-31 -121.2, 37.38, -119.93, 38.59 https://cmr.earthdata.nasa.gov/search/concepts/C2275408033-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Tonzi Ranch site in California. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
+AirMOSS_L1_Sigma0_Moisst_1413_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, MOISST, 2012-2015 ALL STAC Catalog 2012-10-24 2015-08-14 -99, 35.78, -96.82, 36.89 https://cmr.earthdata.nasa.gov/search/concepts/C2274886681-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the MOISST site in Oklahoma. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
AirMOSS_L1_Sigma0_TonziR_1414_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Tonzi Ranch, 2012-2015 ORNL_CLOUD STAC Catalog 2013-02-05 2015-05-31 -121.2, 37.38, -119.93, 38.59 https://cmr.earthdata.nasa.gov/search/concepts/C2275408033-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Tonzi Ranch site in California. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
-AirMOSS_L1_Sigma0_Walnut_1415_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Walnut Gulch, 2012-2015 ORNL_CLOUD STAC Catalog 2012-09-20 2015-09-01 -111.24, 31.58, -109.48, 32.08 https://cmr.earthdata.nasa.gov/search/concepts/C2275408187-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Walnut Gulch site in Arizona. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
+AirMOSS_L1_Sigma0_TonziR_1414_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Tonzi Ranch, 2012-2015 ALL STAC Catalog 2013-02-05 2015-05-31 -121.2, 37.38, -119.93, 38.59 https://cmr.earthdata.nasa.gov/search/concepts/C2275408033-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Tonzi Ranch site in California. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
AirMOSS_L1_Sigma0_Walnut_1415_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Walnut Gulch, 2012-2015 ALL STAC Catalog 2012-09-20 2015-09-01 -111.24, 31.58, -109.48, 32.08 https://cmr.earthdata.nasa.gov/search/concepts/C2275408187-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Walnut Gulch site in Arizona. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
-AirMOSS_L2_3_RZ_Soil_Moisture_1418_1.1 AirMOSS: L2/3 Volumetric Soil Moisture Profiles Derived From Radar, 2012-2015 ALL STAC Catalog 2012-09-18 2015-09-29 -123.28, 9.88, -68.32, 54.13 https://cmr.earthdata.nasa.gov/search/concepts/C2274733329-ORNL_CLOUD.umm_json This dataset provides level 2/3 root zone soil moisture (RZSM) estimates at multiple depths at 90-m spatial resolution from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over ten sites across North America. AirMOSS produces estimates of RZSM with data from a P-band synthetic aperture radar (SAR) flown on a NASA Gulfstream-III aircraft. The resulting soil moisture estimates capture the effects of gradients of soil, topography, and vegetation heterogeneity over an area of approximately 100km x 25km at each of the study sites. AirMOSS flight campaigns took place at least biannually from 2012 to 2015 at each site. proprietary
+AirMOSS_L1_Sigma0_Walnut_1415_1 AirMOSS: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Walnut Gulch, 2012-2015 ORNL_CLOUD STAC Catalog 2012-09-20 2015-09-01 -111.24, 31.58, -109.48, 32.08 https://cmr.earthdata.nasa.gov/search/concepts/C2275408187-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Walnut Gulch site in Arizona. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale. proprietary
AirMOSS_L2_3_RZ_Soil_Moisture_1418_1.1 AirMOSS: L2/3 Volumetric Soil Moisture Profiles Derived From Radar, 2012-2015 ORNL_CLOUD STAC Catalog 2012-09-18 2015-09-29 -123.28, 9.88, -68.32, 54.13 https://cmr.earthdata.nasa.gov/search/concepts/C2274733329-ORNL_CLOUD.umm_json This dataset provides level 2/3 root zone soil moisture (RZSM) estimates at multiple depths at 90-m spatial resolution from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over ten sites across North America. AirMOSS produces estimates of RZSM with data from a P-band synthetic aperture radar (SAR) flown on a NASA Gulfstream-III aircraft. The resulting soil moisture estimates capture the effects of gradients of soil, topography, and vegetation heterogeneity over an area of approximately 100km x 25km at each of the study sites. AirMOSS flight campaigns took place at least biannually from 2012 to 2015 at each site. proprietary
+AirMOSS_L2_3_RZ_Soil_Moisture_1418_1.1 AirMOSS: L2/3 Volumetric Soil Moisture Profiles Derived From Radar, 2012-2015 ALL STAC Catalog 2012-09-18 2015-09-29 -123.28, 9.88, -68.32, 54.13 https://cmr.earthdata.nasa.gov/search/concepts/C2274733329-ORNL_CLOUD.umm_json This dataset provides level 2/3 root zone soil moisture (RZSM) estimates at multiple depths at 90-m spatial resolution from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over ten sites across North America. AirMOSS produces estimates of RZSM with data from a P-band synthetic aperture radar (SAR) flown on a NASA Gulfstream-III aircraft. The resulting soil moisture estimates capture the effects of gradients of soil, topography, and vegetation heterogeneity over an area of approximately 100km x 25km at each of the study sites. AirMOSS flight campaigns took place at least biannually from 2012 to 2015 at each site. proprietary
AirMOSS_L2_Carbon_Flux_1420_1 AirMOSS: L2 Airborne Carbon Flux at Selected AirMOSS Sites, 2012-2014 ALL STAC Catalog 2012-07-07 2014-06-01 -79.5, 35.77, -68.43, 45.64 https://cmr.earthdata.nasa.gov/search/concepts/C2273359223-ORNL_CLOUD.umm_json This data set contains carbon flux measurements recorded by an aircraft at the Duke, Harvard, and Howland Forest sites during the summers of 2012-2014 as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Frequent measurements of CO2 and H2O were obtained using a cavity ring down spectrometer on board the Airborne Laboratory for Atmospheric Research, operated by Purdue University. Estimates of surface CO2 flux, sensible and latent heat fluxes, their corresponding uncertainties, and average wind speed and direction are provided for each of the 26 flights. proprietary
AirMOSS_L2_Carbon_Flux_1420_1 AirMOSS: L2 Airborne Carbon Flux at Selected AirMOSS Sites, 2012-2014 ORNL_CLOUD STAC Catalog 2012-07-07 2014-06-01 -79.5, 35.77, -68.43, 45.64 https://cmr.earthdata.nasa.gov/search/concepts/C2273359223-ORNL_CLOUD.umm_json This data set contains carbon flux measurements recorded by an aircraft at the Duke, Harvard, and Howland Forest sites during the summers of 2012-2014 as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Frequent measurements of CO2 and H2O were obtained using a cavity ring down spectrometer on board the Airborne Laboratory for Atmospheric Research, operated by Purdue University. Estimates of surface CO2 flux, sensible and latent heat fluxes, their corresponding uncertainties, and average wind speed and direction are provided for each of the 26 flights. proprietary
-AirMOSS_L2_Inground_Soil_Moist_1416_1 AirMOSS: L2 Hourly In-Ground Soil Moisture at AirMOSS Sites, 2011-2015 ALL STAC Catalog 2011-09-01 2015-12-31 -121.56, 19.51, -72.17, 53.92 https://cmr.earthdata.nasa.gov/search/concepts/C2279583354-ORNL_CLOUD.umm_json This data set provides level 2 (L2) hourly volumetric (cm3/cm3) soil moisture profiles from in-ground sensors at seven North American sites as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Three profiles were installed at each site, sampling at seven different depths per profile (2 cm to 80 cm). Initial sampling began at three sites in September 2011 and additional sites were added during 2012 and 2013. All sampling concluded in December 2015. The AirMOSS project used an airborne radar instrument to estimate root-zone soil moisture at 10 study sites across North America. These in-ground soil moisture data were collected to calibrate and validate the AirMOSS data. proprietary
AirMOSS_L2_Inground_Soil_Moist_1416_1 AirMOSS: L2 Hourly In-Ground Soil Moisture at AirMOSS Sites, 2011-2015 ORNL_CLOUD STAC Catalog 2011-09-01 2015-12-31 -121.56, 19.51, -72.17, 53.92 https://cmr.earthdata.nasa.gov/search/concepts/C2279583354-ORNL_CLOUD.umm_json This data set provides level 2 (L2) hourly volumetric (cm3/cm3) soil moisture profiles from in-ground sensors at seven North American sites as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Three profiles were installed at each site, sampling at seven different depths per profile (2 cm to 80 cm). Initial sampling began at three sites in September 2011 and additional sites were added during 2012 and 2013. All sampling concluded in December 2015. The AirMOSS project used an airborne radar instrument to estimate root-zone soil moisture at 10 study sites across North America. These in-ground soil moisture data were collected to calibrate and validate the AirMOSS data. proprietary
-AirMOSS_L2_Precipitation_1417_1 AirMOSS: L2 Hourly Precipitation at AirMOSS Sites, 2011-2015 ALL STAC Catalog 2011-09-01 2015-12-31 -121.56, 19.51, -72.17, 53.92 https://cmr.earthdata.nasa.gov/search/concepts/C2279583671-ORNL_CLOUD.umm_json This data set provides level 2 (L2) calibrated hourly precipitation (cm/hr) from rain gauges at seven North American sites as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Three gauges were installed at each site. Initial sampling began at three sites in September 2011 and additional sites were added during 2012 and 2013. All sampling concluded in December 2015. The AirMOSS project used an airborne radar instrument to estimate root-zone soil moisture at 10 study sites across North America. These precipitation data were collected in conjunction with in-ground soil moisture data in order to calibrate and validate the AirMOSS data. proprietary
+AirMOSS_L2_Inground_Soil_Moist_1416_1 AirMOSS: L2 Hourly In-Ground Soil Moisture at AirMOSS Sites, 2011-2015 ALL STAC Catalog 2011-09-01 2015-12-31 -121.56, 19.51, -72.17, 53.92 https://cmr.earthdata.nasa.gov/search/concepts/C2279583354-ORNL_CLOUD.umm_json This data set provides level 2 (L2) hourly volumetric (cm3/cm3) soil moisture profiles from in-ground sensors at seven North American sites as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Three profiles were installed at each site, sampling at seven different depths per profile (2 cm to 80 cm). Initial sampling began at three sites in September 2011 and additional sites were added during 2012 and 2013. All sampling concluded in December 2015. The AirMOSS project used an airborne radar instrument to estimate root-zone soil moisture at 10 study sites across North America. These in-ground soil moisture data were collected to calibrate and validate the AirMOSS data. proprietary
AirMOSS_L2_Precipitation_1417_1 AirMOSS: L2 Hourly Precipitation at AirMOSS Sites, 2011-2015 ORNL_CLOUD STAC Catalog 2011-09-01 2015-12-31 -121.56, 19.51, -72.17, 53.92 https://cmr.earthdata.nasa.gov/search/concepts/C2279583671-ORNL_CLOUD.umm_json This data set provides level 2 (L2) calibrated hourly precipitation (cm/hr) from rain gauges at seven North American sites as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Three gauges were installed at each site. Initial sampling began at three sites in September 2011 and additional sites were added during 2012 and 2013. All sampling concluded in December 2015. The AirMOSS project used an airborne radar instrument to estimate root-zone soil moisture at 10 study sites across North America. These precipitation data were collected in conjunction with in-ground soil moisture data in order to calibrate and validate the AirMOSS data. proprietary
+AirMOSS_L2_Precipitation_1417_1 AirMOSS: L2 Hourly Precipitation at AirMOSS Sites, 2011-2015 ALL STAC Catalog 2011-09-01 2015-12-31 -121.56, 19.51, -72.17, 53.92 https://cmr.earthdata.nasa.gov/search/concepts/C2279583671-ORNL_CLOUD.umm_json This data set provides level 2 (L2) calibrated hourly precipitation (cm/hr) from rain gauges at seven North American sites as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Three gauges were installed at each site. Initial sampling began at three sites in September 2011 and additional sites were added during 2012 and 2013. All sampling concluded in December 2015. The AirMOSS project used an airborne radar instrument to estimate root-zone soil moisture at 10 study sites across North America. These precipitation data were collected in conjunction with in-ground soil moisture data in order to calibrate and validate the AirMOSS data. proprietary
AirMOSS_L4_Daily_NEE_1422_1 AirMOSS: L4 Daily Modeled Net Ecosystem Exchange (NEE), AirMOSS sites, 2012-2014 ORNL_CLOUD STAC Catalog 2012-01-01 2014-10-30 -122.88, 31.49, -68.34, 45.79 https://cmr.earthdata.nasa.gov/search/concepts/C2262413649-ORNL_CLOUD.umm_json This data set provides Level 4 daily estimates of Net Ecosystem Exchange (NEE) of CO2 at a spatial resolution of 30 arc-seconds (~1 km) for seven of the sites covered by the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) flights, each site spanning ~2500 km2. The daily NEE estimates are generally available from October 2012 through October 2014, although the exact time ranges vary by site. The AirMOSS L4 daily NEE were produced by the Ecosystem Demography Biosphere Model (ED2) augmented by the AirMOSS-derived L2/3 root zone soil moisture data as an additional input. The AirMOSS soil moisture data were used to estimate the sensitivity of carbon fluxes to soil moisture and to diagnose and improve estimation and prediction of NEE by constraining the model's predictions of soil moisture and its impact on above- and below-ground fluxes. proprietary
AirMOSS_L4_Daily_NEE_1422_1 AirMOSS: L4 Daily Modeled Net Ecosystem Exchange (NEE), AirMOSS sites, 2012-2014 ALL STAC Catalog 2012-01-01 2014-10-30 -122.88, 31.49, -68.34, 45.79 https://cmr.earthdata.nasa.gov/search/concepts/C2262413649-ORNL_CLOUD.umm_json This data set provides Level 4 daily estimates of Net Ecosystem Exchange (NEE) of CO2 at a spatial resolution of 30 arc-seconds (~1 km) for seven of the sites covered by the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) flights, each site spanning ~2500 km2. The daily NEE estimates are generally available from October 2012 through October 2014, although the exact time ranges vary by site. The AirMOSS L4 daily NEE were produced by the Ecosystem Demography Biosphere Model (ED2) augmented by the AirMOSS-derived L2/3 root zone soil moisture data as an additional input. The AirMOSS soil moisture data were used to estimate the sensitivity of carbon fluxes to soil moisture and to diagnose and improve estimation and prediction of NEE by constraining the model's predictions of soil moisture and its impact on above- and below-ground fluxes. proprietary
-AirMOSS_L4_RZ_Soil_Moisture_1421_1 AirMOSS: L4 Modeled Volumetric Root Zone Soil Moisture, 2012-2015 ORNL_CLOUD STAC Catalog 2012-09-21 2015-09-28 -123.28, 19.12, -68.12, 54.13 https://cmr.earthdata.nasa.gov/search/concepts/C2258632707-ORNL_CLOUD.umm_json This data set provides hourly gridded soil moisture estimates derived from hydrologic modeling at nine AirMOSS sites across North America. The AirMOSS L4 RZSM product represents a temporal interpolation of intermittent AirMOSS L2/3 RZSM retrievals into a temporally-continuous, multi-layer, hourly soil moisture product. The L4 RZSM data have the same spatial resolution (3-arcsecs or ~100 m), and the same temporal coverage (generally Fall 2012 through Fall 2015), as the underlying L2/3 RZSM data. The L4 RZSM data were produced by the integration of the Level 2/3 product and other ancillary information into the Penn State Integrated Hydrologic Model (PIHM). Many key applications for AirMOSS data products, including the calculation of net ecosystem exchange (NEE), require temporally continuous RZSM estimates such as those provided here. proprietary
AirMOSS_L4_RZ_Soil_Moisture_1421_1 AirMOSS: L4 Modeled Volumetric Root Zone Soil Moisture, 2012-2015 ALL STAC Catalog 2012-09-21 2015-09-28 -123.28, 19.12, -68.12, 54.13 https://cmr.earthdata.nasa.gov/search/concepts/C2258632707-ORNL_CLOUD.umm_json This data set provides hourly gridded soil moisture estimates derived from hydrologic modeling at nine AirMOSS sites across North America. The AirMOSS L4 RZSM product represents a temporal interpolation of intermittent AirMOSS L2/3 RZSM retrievals into a temporally-continuous, multi-layer, hourly soil moisture product. The L4 RZSM data have the same spatial resolution (3-arcsecs or ~100 m), and the same temporal coverage (generally Fall 2012 through Fall 2015), as the underlying L2/3 RZSM data. The L4 RZSM data were produced by the integration of the Level 2/3 product and other ancillary information into the Penn State Integrated Hydrologic Model (PIHM). Many key applications for AirMOSS data products, including the calculation of net ecosystem exchange (NEE), require temporally continuous RZSM estimates such as those provided here. proprietary
+AirMOSS_L4_RZ_Soil_Moisture_1421_1 AirMOSS: L4 Modeled Volumetric Root Zone Soil Moisture, 2012-2015 ORNL_CLOUD STAC Catalog 2012-09-21 2015-09-28 -123.28, 19.12, -68.12, 54.13 https://cmr.earthdata.nasa.gov/search/concepts/C2258632707-ORNL_CLOUD.umm_json This data set provides hourly gridded soil moisture estimates derived from hydrologic modeling at nine AirMOSS sites across North America. The AirMOSS L4 RZSM product represents a temporal interpolation of intermittent AirMOSS L2/3 RZSM retrievals into a temporally-continuous, multi-layer, hourly soil moisture product. The L4 RZSM data have the same spatial resolution (3-arcsecs or ~100 m), and the same temporal coverage (generally Fall 2012 through Fall 2015), as the underlying L2/3 RZSM data. The L4 RZSM data were produced by the integration of the Level 2/3 product and other ancillary information into the Penn State Integrated Hydrologic Model (PIHM). Many key applications for AirMOSS data products, including the calculation of net ecosystem exchange (NEE), require temporally continuous RZSM estimates such as those provided here. proprietary
AirMOSS_L4_Regional_NEE_1423_1 AirMOSS: L4 Modeled Net Ecosystem Exchange (NEE), Continental USA, 2012-2014 ORNL_CLOUD STAC Catalog 2012-01-01 2014-10-31 -124.94, 25.06, -66.94, 53.06 https://cmr.earthdata.nasa.gov/search/concepts/C2274237497-ORNL_CLOUD.umm_json This data set provides Level 4 estimates of Net Ecosystem Exchange (NEE) of CO2 across the conterminous USA at a spatial resolution of 50 km. Modeled estimates are provided at hourly and monthly temporal resolutions, from January 2012 through October 2014. The AirMOSS L4 Regional NEE data were produced by the Ecosystem Demography Biosphere Model (ED2) augmented by the AirMOSS-derived L2/3 root zone soil moisture data as an additional input. The AirMOSS soil moisture data were used to estimate the sensitivity of carbon fluxes to soil moisture and to diagnose and improve estimation and prediction of NEE by constraining the model's predictions of soil moisture and its impact on above- and below-ground fluxes. proprietary
AirMOSS_L4_Regional_NEE_1423_1 AirMOSS: L4 Modeled Net Ecosystem Exchange (NEE), Continental USA, 2012-2014 ALL STAC Catalog 2012-01-01 2014-10-31 -124.94, 25.06, -66.94, 53.06 https://cmr.earthdata.nasa.gov/search/concepts/C2274237497-ORNL_CLOUD.umm_json This data set provides Level 4 estimates of Net Ecosystem Exchange (NEE) of CO2 across the conterminous USA at a spatial resolution of 50 km. Modeled estimates are provided at hourly and monthly temporal resolutions, from January 2012 through October 2014. The AirMOSS L4 Regional NEE data were produced by the Ecosystem Demography Biosphere Model (ED2) augmented by the AirMOSS-derived L2/3 root zone soil moisture data as an additional input. The AirMOSS soil moisture data were used to estimate the sensitivity of carbon fluxes to soil moisture and to diagnose and improve estimation and prediction of NEE by constraining the model's predictions of soil moisture and its impact on above- and below-ground fluxes. proprietary
AirMSPI_ACEPOL_Ellipsoid-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 ellipsoid-projected georegistered radiance product acquired during the NASA ACEPOL flight campaign Oct-Nov 2017 LARC_ASDC STAC Catalog 2017-10-19 2017-11-09 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1497270064-LARC_ASDC.umm_json AirMSPI_ACEPOL_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI ellipsoid-projected georegistered radiance products acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign. ACEPOL was based out of Armstrong Flight Research Center in Palmdale, CA, and focused on assessing the capabilities of proposed future instruments by the ACE pre-formulation study to answer fundamental science questions associated with aerosols, clouds, air quality and global ocean ecosystems. AirMSPI data were acquired from October 19 through November 9, 2017. proprietary
AirMSPI_ACEPOL_Ellipsoid-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 ellipsoid-projected georegistered radiance product acquired during the NASA ACEPOL flight campaign Oct-Nov 2017 ALL STAC Catalog 2017-10-19 2017-11-09 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1497270064-LARC_ASDC.umm_json AirMSPI_ACEPOL_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI ellipsoid-projected georegistered radiance products acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign. ACEPOL was based out of Armstrong Flight Research Center in Palmdale, CA, and focused on assessing the capabilities of proposed future instruments by the ACE pre-formulation study to answer fundamental science questions associated with aerosols, clouds, air quality and global ocean ecosystems. AirMSPI data were acquired from October 19 through November 9, 2017. proprietary
-AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the NASA ACEPOL flight campaign Oct-Nov 2017 ALL STAC Catalog 2017-10-19 2017-11-09 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1497274864-LARC_ASDC.umm_json AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance products acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign. ACEPOL was based out of Armstrong Flight Research Center in Palmdale, CA, and focused on assessing the capabilities of proposed future instruments by the ACE pre-formulation study to answer fundamental science questions associated with aerosols, clouds, air quality and global ocean ecosystems. AirMSPI data were acquired from October 19 through November 9, 2017. proprietary
AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the NASA ACEPOL flight campaign Oct-Nov 2017 LARC_ASDC STAC Catalog 2017-10-19 2017-11-09 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1497274864-LARC_ASDC.umm_json AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance products acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign. ACEPOL was based out of Armstrong Flight Research Center in Palmdale, CA, and focused on assessing the capabilities of proposed future instruments by the ACE pre-formulation study to answer fundamental science questions associated with aerosols, clouds, air quality and global ocean ecosystems. AirMSPI data were acquired from October 19 through November 9, 2017. proprietary
-AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data_6 AirMSPI version 6 ellipsoid-projected georegistered radiance product acquired during the CalWater-2 flight campaign Jan-Feb 2015 ALL STAC Catalog 2015-01-20 2015-02-24 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1525897121-LARC_ASDC.umm_json AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI Ellipsoid-projected georegistered radiance product acquired during the Precipitation, Aerosols, and Pacific Atmospheric Rivers Experiment (CalWater-2) flight campaign Jan-Feb 2015. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Precipitation, Aerosols, and Pacific Atmospheric Rivers Experiment (CalWater-2) flight campaign, which was conducted in partnership between NASA, NOAA, DOE, NSF, Scripps Institution of Oceanography and Colorado State University. The campaign focused on the study of atmospheric rivers and interaction with aerosols offshore of California in the North Pacific. NASA’s ER-2 high-altitude research aircraft, with AirMSPI, was based out of Palmdale, CA. AirMSPI data were acquired from January 20 through February 24, 2015. proprietary
+AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the NASA ACEPOL flight campaign Oct-Nov 2017 ALL STAC Catalog 2017-10-19 2017-11-09 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1497274864-LARC_ASDC.umm_json AirMSPI_ACEPOL_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance products acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) flight campaign. ACEPOL was based out of Armstrong Flight Research Center in Palmdale, CA, and focused on assessing the capabilities of proposed future instruments by the ACE pre-formulation study to answer fundamental science questions associated with aerosols, clouds, air quality and global ocean ecosystems. AirMSPI data were acquired from October 19 through November 9, 2017. proprietary
AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data_6 AirMSPI version 6 ellipsoid-projected georegistered radiance product acquired during the CalWater-2 flight campaign Jan-Feb 2015 LARC_ASDC STAC Catalog 2015-01-20 2015-02-24 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1525897121-LARC_ASDC.umm_json AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI Ellipsoid-projected georegistered radiance product acquired during the Precipitation, Aerosols, and Pacific Atmospheric Rivers Experiment (CalWater-2) flight campaign Jan-Feb 2015. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Precipitation, Aerosols, and Pacific Atmospheric Rivers Experiment (CalWater-2) flight campaign, which was conducted in partnership between NASA, NOAA, DOE, NSF, Scripps Institution of Oceanography and Colorado State University. The campaign focused on the study of atmospheric rivers and interaction with aerosols offshore of California in the North Pacific. NASA’s ER-2 high-altitude research aircraft, with AirMSPI, was based out of Palmdale, CA. AirMSPI data were acquired from January 20 through February 24, 2015. proprietary
+AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data_6 AirMSPI version 6 ellipsoid-projected georegistered radiance product acquired during the CalWater-2 flight campaign Jan-Feb 2015 ALL STAC Catalog 2015-01-20 2015-02-24 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1525897121-LARC_ASDC.umm_json AirMSPI_CalWater-2_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI Ellipsoid-projected georegistered radiance product acquired during the Precipitation, Aerosols, and Pacific Atmospheric Rivers Experiment (CalWater-2) flight campaign Jan-Feb 2015. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Precipitation, Aerosols, and Pacific Atmospheric Rivers Experiment (CalWater-2) flight campaign, which was conducted in partnership between NASA, NOAA, DOE, NSF, Scripps Institution of Oceanography and Colorado State University. The campaign focused on the study of atmospheric rivers and interaction with aerosols offshore of California in the North Pacific. NASA’s ER-2 high-altitude research aircraft, with AirMSPI, was based out of Palmdale, CA. AirMSPI data were acquired from January 20 through February 24, 2015. proprietary
AirMSPI_CalWater-2_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI version 6 terrain-projected georegistered radiance product acquired during the CalWater-2 flight campaign Jan-Feb 2015 LARC_ASDC STAC Catalog 2015-01-20 2015-02-24 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1525897219-LARC_ASDC.umm_json AirMSPI_CalWater-2_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance product acquired during the Precipitation, Aerosols, and Pacific Atmospheric Rivers Experiment (CalWater-2) flight campaign Jan-Feb 2015. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Precipitation, Aerosols, and Pacific Atmospheric Rivers Experiment (CalWater-2) flight campaign, which was conducted in partnership between NASA, NOAA, DOE, NSF, Scripps Institution of Oceanography and Colorado State University. The campaign focused on the study of atmospheric rivers and interaction with aerosols offshore of California in the North Pacific. NASA’s ER-2 high-altitude research aircraft, with AirMSPI, was based out of Palmdale, CA. AirMSPI data were acquired from January 20 through February 24, 2015. proprietary
AirMSPI_CalWater-2_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI version 6 terrain-projected georegistered radiance product acquired during the CalWater-2 flight campaign Jan-Feb 2015 ALL STAC Catalog 2015-01-20 2015-02-24 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1525897219-LARC_ASDC.umm_json AirMSPI_CalWater-2_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance product acquired during the Precipitation, Aerosols, and Pacific Atmospheric Rivers Experiment (CalWater-2) flight campaign Jan-Feb 2015. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Precipitation, Aerosols, and Pacific Atmospheric Rivers Experiment (CalWater-2) flight campaign, which was conducted in partnership between NASA, NOAA, DOE, NSF, Scripps Institution of Oceanography and Colorado State University. The campaign focused on the study of atmospheric rivers and interaction with aerosols offshore of California in the North Pacific. NASA’s ER-2 high-altitude research aircraft, with AirMSPI, was based out of Palmdale, CA. AirMSPI data were acquired from January 20 through February 24, 2015. proprietary
-AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI version 6 terrain-projected georegistered radiance product acquired during the FIREX-AQ flight campaign LARC_ASDC STAC Catalog 2019-08-01 2019-08-22 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1945170198-LARC_ASDC.umm_json AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance product acquired during the NASA/NOAA Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) flight campaign Aug 2019. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the NASA/NOAA Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) flight campaign. The NASA ER-2 with the AirMSPI instrument conducted flights from Aug 1 to Aug 21 and was based out of Armstrong Flight Research Center in Palmdale, California. proprietary
AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI version 6 terrain-projected georegistered radiance product acquired during the FIREX-AQ flight campaign ALL STAC Catalog 2019-08-01 2019-08-22 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1945170198-LARC_ASDC.umm_json AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance product acquired during the NASA/NOAA Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) flight campaign Aug 2019. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the NASA/NOAA Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) flight campaign. The NASA ER-2 with the AirMSPI instrument conducted flights from Aug 1 to Aug 21 and was based out of Armstrong Flight Research Center in Palmdale, California. proprietary
+AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI version 6 terrain-projected georegistered radiance product acquired during the FIREX-AQ flight campaign LARC_ASDC STAC Catalog 2019-08-01 2019-08-22 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1945170198-LARC_ASDC.umm_json AirMSPI_FIREX-AQ_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance product acquired during the NASA/NOAA Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) flight campaign Aug 2019. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the NASA/NOAA Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) flight campaign. The NASA ER-2 with the AirMSPI instrument conducted flights from Aug 1 to Aug 21 and was based out of Armstrong Flight Research Center in Palmdale, California. proprietary
AirMSPI_ImPACT-PM_Ellipsoid-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 ellipsoid-projected georegistered radiance product acquired during the ImPACT-PM flight campaign LARC_ASDC STAC Catalog 2016-07-05 2016-07-08 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289466-LARC_ASDC.umm_json AirMSPI_ImPACT-PM_Ellipsoid-projected_Georegistered_Radiance_Data is an AirMSPI ellipsoid-projected georegistered radiance product acquired during the JPL and Caltech Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT-PM) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT-PM) flight campaign, which involved the ER-2 based out of Armstrong Flight Research Center in Palmdale, CA and a Navy Twin Otter flying the Caltech CIRPAS suite of instruments based out of Monterey, CA. The campaign was conducted to test a strategy to use multi-angle, spectro-polarimetric remote sensing to retrieve information on the distributions of atmospheric particle types, with emphasis on carbon-containing compounds, as a precursor to NASA’s Multi-Angle Imager for Aerosols, an Earth Venture-Instrument currently in formulation. AirMSPI data were acquired from July 5 through July 8, 2016. proprietary
AirMSPI_ImPACT-PM_Ellipsoid-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 ellipsoid-projected georegistered radiance product acquired during the ImPACT-PM flight campaign ALL STAC Catalog 2016-07-05 2016-07-08 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289466-LARC_ASDC.umm_json AirMSPI_ImPACT-PM_Ellipsoid-projected_Georegistered_Radiance_Data is an AirMSPI ellipsoid-projected georegistered radiance product acquired during the JPL and Caltech Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT-PM) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT-PM) flight campaign, which involved the ER-2 based out of Armstrong Flight Research Center in Palmdale, CA and a Navy Twin Otter flying the Caltech CIRPAS suite of instruments based out of Monterey, CA. The campaign was conducted to test a strategy to use multi-angle, spectro-polarimetric remote sensing to retrieve information on the distributions of atmospheric particle types, with emphasis on carbon-containing compounds, as a precursor to NASA’s Multi-Angle Imager for Aerosols, an Earth Venture-Instrument currently in formulation. AirMSPI data were acquired from July 5 through July 8, 2016. proprietary
-AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the ImPACT-PM flight campaign LARC_ASDC STAC Catalog 2016-07-05 2016-07-08 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289469-LARC_ASDC.umm_json AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data is an AirMSPI terrain-projected georegistered radiance product acquired during the JPL and Caltech Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT-PM) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) and include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT-PM) flight campaign, which involved the ER-2 based out of Armstrong Flight Research Center in Palmdale, CA and a Navy Twin Otter flying the Caltech CIRPAS suite of instruments based out of Monterey, CA. The campaign was conducted to test a strategy to use multi-angle, spectro-polarimetric remote sensing to retrieve information on the distributions of atmospheric particle types, with emphasis on carbon-containing compounds, as a precursor to NASA’s Multi-Angle Imager for Aerosols, an Earth Venture-Instrument currently in formulation. AirMSPI data were acquired from July 5 through July 8, 2016. proprietary
AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the ImPACT-PM flight campaign ALL STAC Catalog 2016-07-05 2016-07-08 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289469-LARC_ASDC.umm_json AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data is an AirMSPI terrain-projected georegistered radiance product acquired during the JPL and Caltech Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT-PM) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) and include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT-PM) flight campaign, which involved the ER-2 based out of Armstrong Flight Research Center in Palmdale, CA and a Navy Twin Otter flying the Caltech CIRPAS suite of instruments based out of Monterey, CA. The campaign was conducted to test a strategy to use multi-angle, spectro-polarimetric remote sensing to retrieve information on the distributions of atmospheric particle types, with emphasis on carbon-containing compounds, as a precursor to NASA’s Multi-Angle Imager for Aerosols, an Earth Venture-Instrument currently in formulation. AirMSPI data were acquired from July 5 through July 8, 2016. proprietary
+AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the ImPACT-PM flight campaign LARC_ASDC STAC Catalog 2016-07-05 2016-07-08 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289469-LARC_ASDC.umm_json AirMSPI_ImPACT-PM_Terrain-projected_Georegistered_Radiance_Data is an AirMSPI terrain-projected georegistered radiance product acquired during the JPL and Caltech Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT-PM) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) and include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Imaging Polarimetric Assessment and Characterization of Tropospheric Particulate Matter (ImPACT-PM) flight campaign, which involved the ER-2 based out of Armstrong Flight Research Center in Palmdale, CA and a Navy Twin Otter flying the Caltech CIRPAS suite of instruments based out of Monterey, CA. The campaign was conducted to test a strategy to use multi-angle, spectro-polarimetric remote sensing to retrieve information on the distributions of atmospheric particle types, with emphasis on carbon-containing compounds, as a precursor to NASA’s Multi-Angle Imager for Aerosols, an Earth Venture-Instrument currently in formulation. AirMSPI data were acquired from July 5 through July 8, 2016. proprietary
AirMSPI_ORACLES_Ellipsoid-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 ellipsoid-projected georegistered radiance product acquired during the NASA ORACLES flight campaign Jul-Oct 2016 ALL STAC Catalog 2016-07-28 2016-10-06 -126, -24, 15, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1461093150-LARC_ASDC.umm_json AirMSPI_ORACLES_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI Ellipsoid-projected georegistered radiance product acquired during the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridional planes. Files are distributed in HDF-EOS-5 format. This release of AirMPSI data contains all targets acquired during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) flight campaign, including the check-out and transit flights. ORACLES was based out of Walvis Bay, Namibia and focused on the South Atlantic Ocean off the coast of Namibia and Angola. AirMSPI was acquired from July 28 to October 6, 2016. More details about the ORACLES campaign and AirMSPI participation can be found at https://espo.nasa.gov/oracles (link is external). proprietary
AirMSPI_ORACLES_Ellipsoid-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 ellipsoid-projected georegistered radiance product acquired during the NASA ORACLES flight campaign Jul-Oct 2016 LARC_ASDC STAC Catalog 2016-07-28 2016-10-06 -126, -24, 15, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1461093150-LARC_ASDC.umm_json AirMSPI_ORACLES_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI Ellipsoid-projected georegistered radiance product acquired during the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridional planes. Files are distributed in HDF-EOS-5 format. This release of AirMPSI data contains all targets acquired during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) flight campaign, including the check-out and transit flights. ORACLES was based out of Walvis Bay, Namibia and focused on the South Atlantic Ocean off the coast of Namibia and Angola. AirMSPI was acquired from July 28 to October 6, 2016. More details about the ORACLES campaign and AirMSPI participation can be found at https://espo.nasa.gov/oracles (link is external). proprietary
AirMSPI_ORACLES_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the NASA ORACLES flight campaign Jul-Oct 2016 ALL STAC Catalog 2016-07-28 2016-10-06 -126, -24, 15, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1459296627-LARC_ASDC.umm_json AirMSPI_ORACLES_Terrain-projected_Georegistered_Radiance_Data are AirMSPI Terrain-projected georegistered radiance product acquired during the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridional planes. Files are distributed in HDF-EOS-5 format. This release of AirMPSI data contains all targets acquired during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) flight campaign, including the check-out and transit flights. ORACLES was based out of Walvis Bay, Namibia and focused on the South Atlantic Ocean off the coast of Namibia and Angola. AirMSPI was acquired from July 28 to October 6, 2016. More details about the ORACLES campaign and AirMSPI participation can be found at https://espo.nasa.gov/oracles (link is external). proprietary
AirMSPI_ORACLES_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the NASA ORACLES flight campaign Jul-Oct 2016 LARC_ASDC STAC Catalog 2016-07-28 2016-10-06 -126, -24, 15, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1459296627-LARC_ASDC.umm_json AirMSPI_ORACLES_Terrain-projected_Georegistered_Radiance_Data are AirMSPI Terrain-projected georegistered radiance product acquired during the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridional planes. Files are distributed in HDF-EOS-5 format. This release of AirMPSI data contains all targets acquired during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) flight campaign, including the check-out and transit flights. ORACLES was based out of Walvis Bay, Namibia and focused on the South Atlantic Ocean off the coast of Namibia and Angola. AirMSPI was acquired from July 28 to October 6, 2016. More details about the ORACLES campaign and AirMSPI participation can be found at https://espo.nasa.gov/oracles (link is external). proprietary
-AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data_5 AirMSPI version 5 ellipsoid-projected georegistered radiance product acquired during the NASA PODEX flight campaign January-February 2013 ALL STAC Catalog 2013-01-14 2013-02-06 -130, 28, -114, 42.5 https://cmr.earthdata.nasa.gov/search/concepts/C1461089729-LARC_ASDC.umm_json AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI Ellipsoid-projected georegistered radiance product acquired during the NASA Polarimeter Definition Experiment (PODEX) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Polarimeter Definition Experiment (PODEX) flight campaign. PODEX was based out of NASA’s Armstrong (formerly Dryden) Flight Research Center in Palmdale, CA, and focused on clouds and aerosols in and around California. AirMSPI data were acquired from January 14 through February 6, 2013. proprietary
AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data_5 AirMSPI version 5 ellipsoid-projected georegistered radiance product acquired during the NASA PODEX flight campaign January-February 2013 LARC_ASDC STAC Catalog 2013-01-14 2013-02-06 -130, 28, -114, 42.5 https://cmr.earthdata.nasa.gov/search/concepts/C1461089729-LARC_ASDC.umm_json AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI Ellipsoid-projected georegistered radiance product acquired during the NASA Polarimeter Definition Experiment (PODEX) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Polarimeter Definition Experiment (PODEX) flight campaign. PODEX was based out of NASA’s Armstrong (formerly Dryden) Flight Research Center in Palmdale, CA, and focused on clouds and aerosols in and around California. AirMSPI data were acquired from January 14 through February 6, 2013. proprietary
-AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data_5 AirMSPI version 5 terrain-projected georegistered radiance product acquired during the NASA PODEX flight campaign Jan-Feb 2013 ALL STAC Catalog 2013-01-14 2013-02-06 -130, 28, -114, 42.5 https://cmr.earthdata.nasa.gov/search/concepts/C1461084366-LARC_ASDC.umm_json AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance product acquired during the NASA Polarimeter Definition Experiment (PODEX) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Polarimeter Definition Experiment (PODEX) flight campaign. PODEX was based out of NASA’s Armstrong (formerly Dryden) Flight Research Center in Palmdale, CA, and focused on clouds and aerosols in and around California. AirMSPI data were acquired from January 14 through February 6, 2013. proprietary
+AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data_5 AirMSPI version 5 ellipsoid-projected georegistered radiance product acquired during the NASA PODEX flight campaign January-February 2013 ALL STAC Catalog 2013-01-14 2013-02-06 -130, 28, -114, 42.5 https://cmr.earthdata.nasa.gov/search/concepts/C1461089729-LARC_ASDC.umm_json AirMSPI_PODEX_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI Ellipsoid-projected georegistered radiance product acquired during the NASA Polarimeter Definition Experiment (PODEX) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Polarimeter Definition Experiment (PODEX) flight campaign. PODEX was based out of NASA’s Armstrong (formerly Dryden) Flight Research Center in Palmdale, CA, and focused on clouds and aerosols in and around California. AirMSPI data were acquired from January 14 through February 6, 2013. proprietary
AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data_5 AirMSPI version 5 terrain-projected georegistered radiance product acquired during the NASA PODEX flight campaign Jan-Feb 2013 LARC_ASDC STAC Catalog 2013-01-14 2013-02-06 -130, 28, -114, 42.5 https://cmr.earthdata.nasa.gov/search/concepts/C1461084366-LARC_ASDC.umm_json AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance product acquired during the NASA Polarimeter Definition Experiment (PODEX) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Polarimeter Definition Experiment (PODEX) flight campaign. PODEX was based out of NASA’s Armstrong (formerly Dryden) Flight Research Center in Palmdale, CA, and focused on clouds and aerosols in and around California. AirMSPI data were acquired from January 14 through February 6, 2013. proprietary
-AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 ellipsoid-projected georegistered radiance product acquired during the RADEX flight campaign ALL STAC Catalog 2015-11-10 2015-12-13 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289474-LARC_ASDC.umm_json AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data is an AirMSPI ellipsoid-projected georegistered radiance product acquired during the Radar Definition Experiment (RADEX) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) and include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Radar Definition Experiment (RADEX) flight campaign, which was based out of Joint Base Lewis-McChord, Washington. The campaign focused on characterizing new radar instruments being tested for future NASA satellite missions with AirMSPI providing additional cloud characterization. AirMSPI data were acquired from November 10 through December 13, 2015. proprietary
+AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data_5 AirMSPI version 5 terrain-projected georegistered radiance product acquired during the NASA PODEX flight campaign Jan-Feb 2013 ALL STAC Catalog 2013-01-14 2013-02-06 -130, 28, -114, 42.5 https://cmr.earthdata.nasa.gov/search/concepts/C1461084366-LARC_ASDC.umm_json AirMSPI_PODEX_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance product acquired during the NASA Polarimeter Definition Experiment (PODEX) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Polarimeter Definition Experiment (PODEX) flight campaign. PODEX was based out of NASA’s Armstrong (formerly Dryden) Flight Research Center in Palmdale, CA, and focused on clouds and aerosols in and around California. AirMSPI data were acquired from January 14 through February 6, 2013. proprietary
AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 ellipsoid-projected georegistered radiance product acquired during the RADEX flight campaign LARC_ASDC STAC Catalog 2015-11-10 2015-12-13 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289474-LARC_ASDC.umm_json AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data is an AirMSPI ellipsoid-projected georegistered radiance product acquired during the Radar Definition Experiment (RADEX) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) and include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Radar Definition Experiment (RADEX) flight campaign, which was based out of Joint Base Lewis-McChord, Washington. The campaign focused on characterizing new radar instruments being tested for future NASA satellite missions with AirMSPI providing additional cloud characterization. AirMSPI data were acquired from November 10 through December 13, 2015. proprietary
-AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the RADEX flight campaign LARC_ASDC STAC Catalog 2015-11-10 2015-12-13 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289473-LARC_ASDC.umm_json AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data is an AirMSPI terrain-projected georegistered radiance product acquired during the Radar Definition Experiment (RADEX) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) and include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Radar Definition Experiment (RADEX) flight campaign, which was based out of Joint Base Lewis-McChord, Washington. The campaign focused on characterizing new radar instruments being tested for future NASA satellite missions with AirMSPI providing additional cloud characterization. AirMSPI data were acquired from November 10 through December 13, 2015. proprietary
+AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 ellipsoid-projected georegistered radiance product acquired during the RADEX flight campaign ALL STAC Catalog 2015-11-10 2015-12-13 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289474-LARC_ASDC.umm_json AirMSPI_RADEX_Ellipsoid-projected_Georegistered_Radiance_Data is an AirMSPI ellipsoid-projected georegistered radiance product acquired during the Radar Definition Experiment (RADEX) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) and include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Radar Definition Experiment (RADEX) flight campaign, which was based out of Joint Base Lewis-McChord, Washington. The campaign focused on characterizing new radar instruments being tested for future NASA satellite missions with AirMSPI providing additional cloud characterization. AirMSPI data were acquired from November 10 through December 13, 2015. proprietary
AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the RADEX flight campaign ALL STAC Catalog 2015-11-10 2015-12-13 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289473-LARC_ASDC.umm_json AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data is an AirMSPI terrain-projected georegistered radiance product acquired during the Radar Definition Experiment (RADEX) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) and include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Radar Definition Experiment (RADEX) flight campaign, which was based out of Joint Base Lewis-McChord, Washington. The campaign focused on characterizing new radar instruments being tested for future NASA satellite missions with AirMSPI providing additional cloud characterization. AirMSPI data were acquired from November 10 through December 13, 2015. proprietary
-AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data_5 AirMSPI ellipsoid-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign August-September 2013, V005 ALL STAC Catalog 2013-08-01 2013-09-23 -127, 14, -73, 53 https://cmr.earthdata.nasa.gov/search/concepts/C1459696652-LARC_ASDC.umm_json AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI ellipsoid-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) flight campaign. SEAC4RS was primarily based out of Ellington Field in Houston, Texas (initial flights were based out of Armstrong Flight Research Center in Palmdale, CA), and focused on clouds and aerosols in the United States. AirMSPI data were acquired from August 1 through September 23, 2013. proprietary
+AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the RADEX flight campaign LARC_ASDC STAC Catalog 2015-11-10 2015-12-13 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289473-LARC_ASDC.umm_json AirMSPI_RADEX_Terrain-projected_Georegistered_Radiance_Data is an AirMSPI terrain-projected georegistered radiance product acquired during the Radar Definition Experiment (RADEX) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) and include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Radar Definition Experiment (RADEX) flight campaign, which was based out of Joint Base Lewis-McChord, Washington. The campaign focused on characterizing new radar instruments being tested for future NASA satellite missions with AirMSPI providing additional cloud characterization. AirMSPI data were acquired from November 10 through December 13, 2015. proprietary
AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data_5 AirMSPI ellipsoid-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign August-September 2013, V005 LARC_ASDC STAC Catalog 2013-08-01 2013-09-23 -127, 14, -73, 53 https://cmr.earthdata.nasa.gov/search/concepts/C1459696652-LARC_ASDC.umm_json AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI ellipsoid-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) flight campaign. SEAC4RS was primarily based out of Ellington Field in Houston, Texas (initial flights were based out of Armstrong Flight Research Center in Palmdale, CA), and focused on clouds and aerosols in the United States. AirMSPI data were acquired from August 1 through September 23, 2013. proprietary
-AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data_5 AirMSPI terrain-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign August-September 2013, V005 ALL STAC Catalog 2013-08-01 2013-09-23 -126, 15, -74, 52 https://cmr.earthdata.nasa.gov/search/concepts/C1459696669-LARC_ASDC.umm_json AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) flight campaign. SEAC4RS was primarily based out of Ellington Field in Houston, Texas (initial flights were based out of Armstrong Flight Research Center in Palmdale, CA), and focused on clouds and aerosols in the United States. AirMSPI data were acquired from August 1 through September 23, 2013. proprietary
+AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data_5 AirMSPI ellipsoid-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign August-September 2013, V005 ALL STAC Catalog 2013-08-01 2013-09-23 -127, 14, -73, 53 https://cmr.earthdata.nasa.gov/search/concepts/C1459696652-LARC_ASDC.umm_json AirMSPI_SEAC4RS_Ellipsoid-projected_Georegistered_Radiance_Data are AirMSPI ellipsoid-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) flight campaign. SEAC4RS was primarily based out of Ellington Field in Houston, Texas (initial flights were based out of Armstrong Flight Research Center in Palmdale, CA), and focused on clouds and aerosols in the United States. AirMSPI data were acquired from August 1 through September 23, 2013. proprietary
AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data_5 AirMSPI terrain-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign August-September 2013, V005 LARC_ASDC STAC Catalog 2013-08-01 2013-09-23 -126, 15, -74, 52 https://cmr.earthdata.nasa.gov/search/concepts/C1459696669-LARC_ASDC.umm_json AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) flight campaign. SEAC4RS was primarily based out of Ellington Field in Houston, Texas (initial flights were based out of Armstrong Flight Research Center in Palmdale, CA), and focused on clouds and aerosols in the United States. AirMSPI data were acquired from August 1 through September 23, 2013. proprietary
+AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data_5 AirMSPI terrain-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign August-September 2013, V005 ALL STAC Catalog 2013-08-01 2013-09-23 -126, 15, -74, 52 https://cmr.earthdata.nasa.gov/search/concepts/C1459696669-LARC_ASDC.umm_json AirMSPI_SEAC4RS_Terrain-projected_Georegistered_Radiance_Data are AirMSPI terrain-projected georegistered radiance product acquired during the NASA SEAC4RS flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering- and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) flight campaign. SEAC4RS was primarily based out of Ellington Field in Houston, Texas (initial flights were based out of Armstrong Flight Research Center in Palmdale, CA), and focused on clouds and aerosols in the United States. AirMSPI data were acquired from August 1 through September 23, 2013. proprietary
AirMSPI_SPEX-PR_Ellipsoid-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 ellipsoid-projected georegistered radiance product acquired during the SPEX-PR flight campaign LARC_ASDC STAC Catalog 2016-02-02 2016-02-09 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289471-LARC_ASDC.umm_json AirMSPI_SPEX-PR_Ellipsoid-projected_Georegistered_Radiance_Data is an AirMSPI ellipsoid-projected georegistered radiance product acquired during the SPEX engineering flights + Porter Ranch gas leak overflights (SPEX-PR) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the SPEX engineering flights + Porter Ranch gas leak overflights (SPEX-PR) flight campaign, which was based out of Armstrong Flight Research Center in Palmdale, CA. The SPEX engineering flights conducted on February 2 through February 5, 2016 focused on the checkout of another polarimeter, SPEX airborne, built by SRON Netherlands Institute for Space Research, with AirMSPI providing validation. On February 9, the ER-2 overflew the Porter Ranch, California natural gas leak with AirMSPI and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) collecting data. proprietary
AirMSPI_SPEX-PR_Ellipsoid-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 ellipsoid-projected georegistered radiance product acquired during the SPEX-PR flight campaign ALL STAC Catalog 2016-02-02 2016-02-09 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289471-LARC_ASDC.umm_json AirMSPI_SPEX-PR_Ellipsoid-projected_Georegistered_Radiance_Data is an AirMSPI ellipsoid-projected georegistered radiance product acquired during the SPEX engineering flights + Porter Ranch gas leak overflights (SPEX-PR) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the SPEX engineering flights + Porter Ranch gas leak overflights (SPEX-PR) flight campaign, which was based out of Armstrong Flight Research Center in Palmdale, CA. The SPEX engineering flights conducted on February 2 through February 5, 2016 focused on the checkout of another polarimeter, SPEX airborne, built by SRON Netherlands Institute for Space Research, with AirMSPI providing validation. On February 9, the ER-2 overflew the Porter Ranch, California natural gas leak with AirMSPI and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) collecting data. proprietary
-AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the SPEX-PR flight campaign LARC_ASDC STAC Catalog 2016-02-02 2016-02-09 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289472-LARC_ASDC.umm_json AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data is an AirMSPI terrain-projected georegistered radiance product acquired during the SPEX engineering flights + Porter Ranch gas leak overflights (SPEX-PR) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the SPEX engineering flights + Porter Ranch gas leak overflights (SPEX-PR) flight campaign, which was based out of Armstrong Flight Research Center in Palmdale, CA. The SPEX engineering flights conducted on February 2 through February 5, 2016 focused on the checkout of another polarimeter, SPEX airborne, built by SRON Netherlands Institute for Space Research, with AirMSPI providing validation. On February 9, the ER-2 overflew the Porter Ranch, California natural gas leak with AirMSPI and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) collecting data. proprietary
AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the SPEX-PR flight campaign ALL STAC Catalog 2016-02-02 2016-02-09 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289472-LARC_ASDC.umm_json AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data is an AirMSPI terrain-projected georegistered radiance product acquired during the SPEX engineering flights + Porter Ranch gas leak overflights (SPEX-PR) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the SPEX engineering flights + Porter Ranch gas leak overflights (SPEX-PR) flight campaign, which was based out of Armstrong Flight Research Center in Palmdale, CA. The SPEX engineering flights conducted on February 2 through February 5, 2016 focused on the checkout of another polarimeter, SPEX airborne, built by SRON Netherlands Institute for Space Research, with AirMSPI providing validation. On February 9, the ER-2 overflew the Porter Ranch, California natural gas leak with AirMSPI and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) collecting data. proprietary
-AirSWOT_Orthomosaic_WaterMask_1655_1 ABoVE: AirSWOT Radar, Orthomosaic, and Water Masks, Yukon Flats Basin, Alaska, 2015 ALL STAC Catalog 2015-06-15 2015-06-15 -148, 65.93, -145, 66.9 https://cmr.earthdata.nasa.gov/search/concepts/C2162179805-ORNL_CLOUD.umm_json This dataset provides NASA AirSWOT Ka-band (35.75 GHz) radar interferometry data products for water surface elevation (WSE), a derived color-infrared (CIR) digital image orthomosaic, and derived lake/wetland and river channel water masks at 3.6 x 3.6 m resolution for a study area of ~3,300 km2 in the Yukon Flats Basin (YFB) in eastern interior Alaska. The data were collected during a flight over the region on June 15, 2015.These data were collected to validate AirSWOT WSE mappings and to improve the understanding of surface water flow through complex Arctic-Boreal wetland systems. proprietary
+AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data_6 AirMSPI verison 6 terrain-projected georegistered radiance product acquired during the SPEX-PR flight campaign LARC_ASDC STAC Catalog 2016-02-02 2016-02-09 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1517289472-LARC_ASDC.umm_json AirMSPI_SPEX-PR_Terrain-projected_Georegistered_Radiance_Data is an AirMSPI terrain-projected georegistered radiance product acquired during the SPEX engineering flights + Porter Ranch gas leak overflights (SPEX-PR) flight campaign. AirMSPI Level 1B2 products contain radiometric and polarimetric images of clouds, aerosols, and the surface of the Earth. In particular, products contain map-projected data at 8 wavelengths: 355, 380, 445, 470, 555, 660, 865, and 935 nm. The data products include radiance, time, solar zenith, solar azimuth, view zenith, and view azimuth for all spectral bands. Wavelengths for which polarization information is available (470, 660, and 865 nm) also include the Stokes parameters Q and U, as well as degree of linear polarization (DOLP) and angle of linear polarization (AOLP). Q, U, and AOLP are reported relative to both the scattering and view meridian planes. Files are distributed in HDF-EOS-5 format. This release of AirMSPI data contains all targets acquired during the SPEX engineering flights + Porter Ranch gas leak overflights (SPEX-PR) flight campaign, which was based out of Armstrong Flight Research Center in Palmdale, CA. The SPEX engineering flights conducted on February 2 through February 5, 2016 focused on the checkout of another polarimeter, SPEX airborne, built by SRON Netherlands Institute for Space Research, with AirMSPI providing validation. On February 9, the ER-2 overflew the Porter Ranch, California natural gas leak with AirMSPI and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) collecting data. proprietary
AirSWOT_Orthomosaic_WaterMask_1655_1 ABoVE: AirSWOT Radar, Orthomosaic, and Water Masks, Yukon Flats Basin, Alaska, 2015 ORNL_CLOUD STAC Catalog 2015-06-15 2015-06-15 -148, 65.93, -145, 66.9 https://cmr.earthdata.nasa.gov/search/concepts/C2162179805-ORNL_CLOUD.umm_json This dataset provides NASA AirSWOT Ka-band (35.75 GHz) radar interferometry data products for water surface elevation (WSE), a derived color-infrared (CIR) digital image orthomosaic, and derived lake/wetland and river channel water masks at 3.6 x 3.6 m resolution for a study area of ~3,300 km2 in the Yukon Flats Basin (YFB) in eastern interior Alaska. The data were collected during a flight over the region on June 15, 2015.These data were collected to validate AirSWOT WSE mappings and to improve the understanding of surface water flow through complex Arctic-Boreal wetland systems. proprietary
+AirSWOT_Orthomosaic_WaterMask_1655_1 ABoVE: AirSWOT Radar, Orthomosaic, and Water Masks, Yukon Flats Basin, Alaska, 2015 ALL STAC Catalog 2015-06-15 2015-06-15 -148, 65.93, -145, 66.9 https://cmr.earthdata.nasa.gov/search/concepts/C2162179805-ORNL_CLOUD.umm_json This dataset provides NASA AirSWOT Ka-band (35.75 GHz) radar interferometry data products for water surface elevation (WSE), a derived color-infrared (CIR) digital image orthomosaic, and derived lake/wetland and river channel water masks at 3.6 x 3.6 m resolution for a study area of ~3,300 km2 in the Yukon Flats Basin (YFB) in eastern interior Alaska. The data were collected during a flight over the region on June 15, 2015.These data were collected to validate AirSWOT WSE mappings and to improve the understanding of surface water flow through complex Arctic-Boreal wetland systems. proprietary
Airborne_Insitu_Measurements_1784_1 ATom: In-Situ Measurements of Airflow and Aerosols from Multiple Airborne Campaigns ORNL_CLOUD STAC Catalog 2013-06-10 2018-05-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2676984303-ORNL_CLOUD.umm_json This dataset provides results of selected in-situ measurements of airflow and aerosol particles collected during the following airborne campaigns: NASA Atmospheric Tomography (ATom), Saharan Aerosol Long-range Transport and Aerosol-Cloud-interaction Experiment (SALTRACE), and Absorbing aerosol layers in a changing climate: aging, lifetime and dynamics (A-LIFE). The airborne campaigns were conducted between 2013-06-10 and 2018-05-21. Depending upon the aircraft instrumentation per flight and campaign, the data include aircraft position, relative humidity, temperature, pressure, angle of attack (AOA), the probe location, true and probe air speeds, and aerosol particle diameters as extracted from Cloud Imaging Probe (CIP) images for the ATom and A-LIFE flights. Also provided are the results of combining the airborne data with numerical modeling to simulate particle sampling efficiency. Simulations investigated how airflow around wing-mounted instruments affected sampling efficiency and the induced errors for different realistic flight conditions. proprietary
-Airborne_radiotracers Airborne radiotracers ALL STAC Catalog 1995-12-01 2004-02-28 164.1, -74.72, 164.12, -74.65 https://cmr.earthdata.nasa.gov/search/concepts/C1214620828-SCIOPS.umm_json Natural radionuclides including 222Rn, 220Rn, 210Pb, 7Be have been used to examine a large variety of relevant atmospheric processes. Routine measurements of these naturally occurring radionuclides in Antarctica. Zucchelli Station and at Campo Icaro, help to understand the atmospheric composition and its variations. 222Rn, 220Rn are measured in situ with a dedicated low level alpha spectrometer working in continuous mode, with a time resolution of two hours. 210Pb and 7Be are measured on aerosol filters sampled with a high volume device every three days. Measurements are carried out in Bologna using HPGe spectrometers. proprietary
Airborne_radiotracers Airborne radiotracers SCIOPS STAC Catalog 1995-12-01 2004-02-28 164.1, -74.72, 164.12, -74.65 https://cmr.earthdata.nasa.gov/search/concepts/C1214620828-SCIOPS.umm_json Natural radionuclides including 222Rn, 220Rn, 210Pb, 7Be have been used to examine a large variety of relevant atmospheric processes. Routine measurements of these naturally occurring radionuclides in Antarctica. Zucchelli Station and at Campo Icaro, help to understand the atmospheric composition and its variations. 222Rn, 220Rn are measured in situ with a dedicated low level alpha spectrometer working in continuous mode, with a time resolution of two hours. 210Pb and 7Be are measured on aerosol filters sampled with a high volume device every three days. Measurements are carried out in Bologna using HPGe spectrometers. proprietary
+Airborne_radiotracers Airborne radiotracers ALL STAC Catalog 1995-12-01 2004-02-28 164.1, -74.72, 164.12, -74.65 https://cmr.earthdata.nasa.gov/search/concepts/C1214620828-SCIOPS.umm_json Natural radionuclides including 222Rn, 220Rn, 210Pb, 7Be have been used to examine a large variety of relevant atmospheric processes. Routine measurements of these naturally occurring radionuclides in Antarctica. Zucchelli Station and at Campo Icaro, help to understand the atmospheric composition and its variations. 222Rn, 220Rn are measured in situ with a dedicated low level alpha spectrometer working in continuous mode, with a time resolution of two hours. 210Pb and 7Be are measured on aerosol filters sampled with a high volume device every three days. Measurements are carried out in Bologna using HPGe spectrometers. proprietary
Akademik_Sergey_Vavilov_0 Measurements onboard the Russian R/V Akademik Sergey Vavilov OB_DAAC STAC Catalog 1998-08-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360094-OB_DAAC.umm_json Measurements from the Barents Sea north of Russia made during 1998 by the Russian research vessel, the Akademik Sergey Vavilov. proprietary
Alaska_Arctic_Tundra_Veg_Map_1353_1 Arctic Alaska Vegetation, Geobotanical, Physiographic Maps, 1993-2005 ORNL_CLOUD STAC Catalog 1993-06-01 2005-03-30 -173.05, 57.08, -138.54, 71.37 https://cmr.earthdata.nasa.gov/search/concepts/C2170968664-ORNL_CLOUD.umm_json This data set provides the spatial distributions of vegetation types, geobotanical characteristics, and physiographic features for the Arctic tundra region of Alaska for the period 1993-2005. Specific attributes include dominant vegetation, bioclimate subzones, floristic subprovinces, landscape types, lake coverage, and substrate chemistry. This data set generally includes areas North and West of the forest boundary and excludes areas that have a boreal flora such as the Aleutian Islands and alpine tundra regions south of treeline. proprietary
Alaska_L4_WRF_STILT_Footprints_1544_1 Pre-ABoVE: Gridded Footprints from WRF-STILT Model, Barrow, Alaska, 1982-2011 ORNL_CLOUD STAC Catalog 1982-08-10 2011-10-15 -180, 30, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2170970879-ORNL_CLOUD.umm_json "This dataset provides Stochastic Time-Inverted Lagrangian Transport model outputs for receptors located at the NOAA Barrow Alaska Observatory for 12 selected years (15 August to 15 October) across the 30-year, 1982 to 2011, study timeframe. Meteorological fields from version 3.5.1 of the Weather Research and Forecasting model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the ""receptor"" location), to create the adjoint of the transport model in the form of a ""footprint"" field. The footprint, with units of mixing ratio (ppm --- CO2; ppb --- CH4) per (umol m-2 s-1 --- CO2; nmol m-2 s-1 --- CH4), quantifies the influence of upwind surface fluxes on concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. The simulation results included in this dataset are crucial for understanding changes in Arctic carbon cycling and are part of a retrospective analysis to link changes in atmospheric composition at Arctic receptor sites with shifts in ecosystem structure and function. Each file provides the surface influence-function footprints on a lat/lon/time grid from WRF-STILT simulations for the receptor location." proprietary
Alaska_L4_WRF_STILT_Particle_1571_1 Pre-ABoVE: Particle Trajectories for WRF-STILT Model, Barrow, AK, 1982-2011 ORNL_CLOUD STAC Catalog 1982-08-10 2011-10-15 -180, 30, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2170971482-ORNL_CLOUD.umm_json "This dataset provides Stochastic Time-Inverted Lagrangian Transport model outputs for receptors located at the NOAA Barrow Alaska Observatory for 12 selected years (15 August to 15 October) across the 30-year, 1982 to 2011, study timeframe. Meteorological fields from version 3.5.1 of the Weather Research and Forecasting model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the ""receptor"" location), to create the adjoint of the transport model in the form of a ""footprint"" field. The footprint, with units of mixing ratio (ppm --- CO2; ppb --- CH4) per (umol m-2 s-1 --- CO2; nmol m-2 s-1 --- CH4), quantifies the influence of upwind surface fluxes on concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. The simulation results included in this dataset are crucial for understanding changes in Arctic carbon cycling and are part of a retrospective analysis to link changes in atmospheric composition at Arctic receptor sites with shifts in ecosystem structure and function." proprietary
-Alaska_Lake_Pond_Maps_2134_1 ABoVE: Lake and Pond Extents in Alaskan Boreal and Tundra Subregions, 2019-2021 ORNL_CLOUD STAC Catalog 2019-05-19 2021-09-28 -164.4, 60.76, -143.84, 67.21 https://cmr.earthdata.nasa.gov/search/concepts/C2612824429-ORNL_CLOUD.umm_json This dataset provides polygon spatial files of lake and pond extents for three sub-regions of Interior Alaska's boreal forest, and one tundra region located in Alaska's Yukon-Kuskokwim Delta. Files provide lake and pond extents of standing water without wetland vegetation or other obstructions with a minimum area of 0.01 ha. Water extents were derived from Planet Labs PlanetScope imagery with resolution of 3.125 m. A deep learning model (U-Net) was applied to PlanetScope orthotile imagery from Planet Labs' Dove-R and Super Dove satellites. The U-Net model used the red, green, blue, and near-infrared bands along with a slope raster derived from a 30-m digital elevation model (DEM) as inputs. The U-Net detected water bodies in all available cloud-free images from the snow-free period (May-September) of 2019-2021. Water body data are provided as 3-year composites (2019-2021) for all four regions and monthly climatological composites (May-September) over 2019-2021 for the three boreal forest regions. The composite water files indicate the presence of open, standing water in >40% of valid PlanetScope observations for a given composite time-slice. Files are provided in shapefile format. proprietary
Alaska_Lake_Pond_Maps_2134_1 ABoVE: Lake and Pond Extents in Alaskan Boreal and Tundra Subregions, 2019-2021 ALL STAC Catalog 2019-05-19 2021-09-28 -164.4, 60.76, -143.84, 67.21 https://cmr.earthdata.nasa.gov/search/concepts/C2612824429-ORNL_CLOUD.umm_json This dataset provides polygon spatial files of lake and pond extents for three sub-regions of Interior Alaska's boreal forest, and one tundra region located in Alaska's Yukon-Kuskokwim Delta. Files provide lake and pond extents of standing water without wetland vegetation or other obstructions with a minimum area of 0.01 ha. Water extents were derived from Planet Labs PlanetScope imagery with resolution of 3.125 m. A deep learning model (U-Net) was applied to PlanetScope orthotile imagery from Planet Labs' Dove-R and Super Dove satellites. The U-Net model used the red, green, blue, and near-infrared bands along with a slope raster derived from a 30-m digital elevation model (DEM) as inputs. The U-Net detected water bodies in all available cloud-free images from the snow-free period (May-September) of 2019-2021. Water body data are provided as 3-year composites (2019-2021) for all four regions and monthly climatological composites (May-September) over 2019-2021 for the three boreal forest regions. The composite water files indicate the presence of open, standing water in >40% of valid PlanetScope observations for a given composite time-slice. Files are provided in shapefile format. proprietary
+Alaska_Lake_Pond_Maps_2134_1 ABoVE: Lake and Pond Extents in Alaskan Boreal and Tundra Subregions, 2019-2021 ORNL_CLOUD STAC Catalog 2019-05-19 2021-09-28 -164.4, 60.76, -143.84, 67.21 https://cmr.earthdata.nasa.gov/search/concepts/C2612824429-ORNL_CLOUD.umm_json This dataset provides polygon spatial files of lake and pond extents for three sub-regions of Interior Alaska's boreal forest, and one tundra region located in Alaska's Yukon-Kuskokwim Delta. Files provide lake and pond extents of standing water without wetland vegetation or other obstructions with a minimum area of 0.01 ha. Water extents were derived from Planet Labs PlanetScope imagery with resolution of 3.125 m. A deep learning model (U-Net) was applied to PlanetScope orthotile imagery from Planet Labs' Dove-R and Super Dove satellites. The U-Net model used the red, green, blue, and near-infrared bands along with a slope raster derived from a 30-m digital elevation model (DEM) as inputs. The U-Net detected water bodies in all available cloud-free images from the snow-free period (May-September) of 2019-2021. Water body data are provided as 3-year composites (2019-2021) for all four regions and monthly climatological composites (May-September) over 2019-2021 for the three boreal forest regions. The composite water files indicate the presence of open, standing water in >40% of valid PlanetScope observations for a given composite time-slice. Files are provided in shapefile format. proprietary
Alaska_Yukon_NDVI_1614_1 ABoVE: MODIS-derived Maximum NDVI, Northern Alaska and Yukon Territory for 2002-2017 ALL STAC Catalog 2002-06-01 2017-08-30 -175.76, 52.17, -97.93, 68.97 https://cmr.earthdata.nasa.gov/search/concepts/C2162145492-ORNL_CLOUD.umm_json This dataset provides the maximum Normalized Difference Vegetation Index (NDVI) at 1-km resolution over northern Alaska, USA and the Yukon Territory, Canada for each year from 2002-2017, as well as a 16 year maximum NDVI product. MODIS products MOD13Q1 and MYD13Q1 from Collection 6 were acquired at 250-m pixel size from June 1-August 30 of each year. Within each growing season from 2002-2017, the maximum NDVI was determined for each pixel. These maximum NDVI values were then aggregated to 1-km by selecting the maximum NDVI from the sixteen 250-m pixels values nested within each 1-km pixel. A long-term 16-year maximum NDVI was then derived from the time series of annual maximum NDVI values. proprietary
Alaska_Yukon_NDVI_1614_1 ABoVE: MODIS-derived Maximum NDVI, Northern Alaska and Yukon Territory for 2002-2017 ORNL_CLOUD STAC Catalog 2002-06-01 2017-08-30 -175.76, 52.17, -97.93, 68.97 https://cmr.earthdata.nasa.gov/search/concepts/C2162145492-ORNL_CLOUD.umm_json This dataset provides the maximum Normalized Difference Vegetation Index (NDVI) at 1-km resolution over northern Alaska, USA and the Yukon Territory, Canada for each year from 2002-2017, as well as a 16 year maximum NDVI product. MODIS products MOD13Q1 and MYD13Q1 from Collection 6 were acquired at 250-m pixel size from June 1-August 30 of each year. Within each growing season from 2002-2017, the maximum NDVI was determined for each pixel. These maximum NDVI values were then aggregated to 1-km by selecting the maximum NDVI from the sixteen 250-m pixels values nested within each 1-km pixel. A long-term 16-year maximum NDVI was then derived from the time series of annual maximum NDVI values. proprietary
Alaskan_CH4_CO2_Fluxes_1316_1 CARVE: CH4, CO2, and CO Atmospheric Concentrations, CARVE Tower, Alaska, 2012-2014 ORNL_CLOUD STAC Catalog 2012-01-01 2014-12-31 -147.6, 64.99, -147.6, 64.99 https://cmr.earthdata.nasa.gov/search/concepts/C2236240052-ORNL_CLOUD.umm_json "This data set provides hourly atmospheric concentrations of methane (CH4), carbon dioxide (CO2), and carbon monoxide (CO) as mole fractions, from January 2012 to December 2014 measured at the CARVE flux tower in Fox, Alaska (17 km north of Fairbanks) as part of NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). High-resolution meteorological fields from the Polar Weather Research and Forecasting (WRF) model coupled with the Stochastic Time-Inverted Lagrangian Transport model (WRF- STILT), along with the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM) were used to determine the influence region of the tower site and investigate the inter-annual and seasonal variability of regional fluxes of CO2 and CH4 in boreal Alaska using the tower observations. Modeled estimates of CH4, CO2, and CO background concentrations are provided. The WRF-STILT model ""footprints"" for the CARVE tower are provided with this data set." proprietary
Alaskan_CO2_Flux_1325_1.1 CARVE: Monthly Atmospheric CO2 Concentrations (2009-2013) and Modeled Fluxes, Alaska ORNL_CLOUD STAC Catalog 1990-01-01 2200-01-01 -180, 50.9, -129.3, 71.4 https://cmr.earthdata.nasa.gov/search/concepts/C2236279313-ORNL_CLOUD.umm_json This data set reports monthly averages of atmospheric CO2 concentration from satellite and airborne observations between 2009 and 2013 and simulated present and future monthly concentrations and land-atmosphere CO2 flux for periods between 1990 and 2200. Atmospheric CO2 concentration measurements were obtained from Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) and NOAA Arctic Coast Guard (ACG) flights, the Greenhouse Gases Observing Satellite (GOSAT), and NOAA/ESRL vertical profile measurements at Poker Flat, Alaska (PFA). Present and future monthly CO2 concentrations and fluxes were simulated using the GEOS-Chem global tracer model and the Community Land Model, Version 4.5, for multiple regional flux and permafrost thaw scenarios. proprietary
-Albedo_Boreal_North_America_1605_1.1 ABoVE: MODIS-Derived Daily Mean Blue Sky Albedo for Northern North America, 2000-2017 ORNL_CLOUD STAC Catalog 2000-02-24 2017-04-22 -173.09, 41.68, -52.62, 79.08 https://cmr.earthdata.nasa.gov/search/concepts/C2113058037-ORNL_CLOUD.umm_json This dataset contains MODIS-derived daily mean shortwave blue sky albedo for northern North America (i.e., Canada and Alaska) and a set of quality control flags for each albedo value to aid in user interpretation. The data cover the period of February 24, 2000 through April 22, 2017. The blue sky albedo data were derived from the MODIS 500-m version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters MCD43A1 dataset (MCD43A1.006, https://doi.org/10.5067/MODIS/MCD43A1.006) (Schaaf & Wang, 2015a, please refer to the MCD43 documentation and user guides for more information). Blue sky refers to albedo calculated under real-world conditions with a combination of both diffuse and direct lighting based on atmospheric and view-geometry conditions. Daily mean albedo was calculated by averaging hourly instantaneous blue sky albedo values weighted by the solar insolation for each time interval. Potter et al. (2019, https://doi.org/10.1111/gcb.14888) is the associated paper for this dataset. Note the actual extent of the dataset in Figure 1 of the User Guide. Users are encouraged to refer to the User Guide for further important information about the use of this dataset. proprietary
Albedo_Boreal_North_America_1605_1.1 ABoVE: MODIS-Derived Daily Mean Blue Sky Albedo for Northern North America, 2000-2017 ALL STAC Catalog 2000-02-24 2017-04-22 -173.09, 41.68, -52.62, 79.08 https://cmr.earthdata.nasa.gov/search/concepts/C2113058037-ORNL_CLOUD.umm_json This dataset contains MODIS-derived daily mean shortwave blue sky albedo for northern North America (i.e., Canada and Alaska) and a set of quality control flags for each albedo value to aid in user interpretation. The data cover the period of February 24, 2000 through April 22, 2017. The blue sky albedo data were derived from the MODIS 500-m version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters MCD43A1 dataset (MCD43A1.006, https://doi.org/10.5067/MODIS/MCD43A1.006) (Schaaf & Wang, 2015a, please refer to the MCD43 documentation and user guides for more information). Blue sky refers to albedo calculated under real-world conditions with a combination of both diffuse and direct lighting based on atmospheric and view-geometry conditions. Daily mean albedo was calculated by averaging hourly instantaneous blue sky albedo values weighted by the solar insolation for each time interval. Potter et al. (2019, https://doi.org/10.1111/gcb.14888) is the associated paper for this dataset. Note the actual extent of the dataset in Figure 1 of the User Guide. Users are encouraged to refer to the User Guide for further important information about the use of this dataset. proprietary
+Albedo_Boreal_North_America_1605_1.1 ABoVE: MODIS-Derived Daily Mean Blue Sky Albedo for Northern North America, 2000-2017 ORNL_CLOUD STAC Catalog 2000-02-24 2017-04-22 -173.09, 41.68, -52.62, 79.08 https://cmr.earthdata.nasa.gov/search/concepts/C2113058037-ORNL_CLOUD.umm_json This dataset contains MODIS-derived daily mean shortwave blue sky albedo for northern North America (i.e., Canada and Alaska) and a set of quality control flags for each albedo value to aid in user interpretation. The data cover the period of February 24, 2000 through April 22, 2017. The blue sky albedo data were derived from the MODIS 500-m version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters MCD43A1 dataset (MCD43A1.006, https://doi.org/10.5067/MODIS/MCD43A1.006) (Schaaf & Wang, 2015a, please refer to the MCD43 documentation and user guides for more information). Blue sky refers to albedo calculated under real-world conditions with a combination of both diffuse and direct lighting based on atmospheric and view-geometry conditions. Daily mean albedo was calculated by averaging hourly instantaneous blue sky albedo values weighted by the solar insolation for each time interval. Potter et al. (2019, https://doi.org/10.1111/gcb.14888) is the associated paper for this dataset. Note the actual extent of the dataset in Figure 1 of the User Guide. Users are encouraged to refer to the User Guide for further important information about the use of this dataset. proprietary
Alder_Shrub_Soil_Alaska_V2_2300_2 ABoVE: Alder Shrub Cover and Soil Properties, Alaska, 2019, V2 ORNL_CLOUD STAC Catalog 2018-08-14 2019-08-28 -150.71, 66.34, -149.71, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2840822238-ORNL_CLOUD.umm_json This dataset holds measures of vegetative cover and soil characteristics for sites in interior Alaska, U.S., along the James W. Dalton Highway (Alaska Route 11). The field data were collected during August in 2018 and 2019 to study the expansion of shrub cover, particularly alders (Alnus spp.) in tundra ecosystems and the potential impact of shrubs on soil properties. Samples were measured along transects at 5- to 10-m intervals. Soil samples were collected and analyzed in the laboratory. Vegetation variables include percent cover of mosses, lichens, graminoid species, shrubs, alder, birch (Betula spp.), and willow (Salix spp.) along with the biomass, size, and age structure of alder. An allometric model to estimate alder biomass was developed. Soil metrics include moisture content, conductivity, bulk density, carbon and nitrogen content and isotope ratios. The data include the maximum annual Normalized Difference Vegetation Index (NDVI) for 2019 and the trend in maximum NDVI for 2000-2020. This is version 2 of this dataset.The data are provided in comma-separated values (CSV) format. proprietary
Alder_Shrub_Soil_Alaska_V2_2300_2 ABoVE: Alder Shrub Cover and Soil Properties, Alaska, 2019, V2 ALL STAC Catalog 2018-08-14 2019-08-28 -150.71, 66.34, -149.71, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2840822238-ORNL_CLOUD.umm_json This dataset holds measures of vegetative cover and soil characteristics for sites in interior Alaska, U.S., along the James W. Dalton Highway (Alaska Route 11). The field data were collected during August in 2018 and 2019 to study the expansion of shrub cover, particularly alders (Alnus spp.) in tundra ecosystems and the potential impact of shrubs on soil properties. Samples were measured along transects at 5- to 10-m intervals. Soil samples were collected and analyzed in the laboratory. Vegetation variables include percent cover of mosses, lichens, graminoid species, shrubs, alder, birch (Betula spp.), and willow (Salix spp.) along with the biomass, size, and age structure of alder. An allometric model to estimate alder biomass was developed. Soil metrics include moisture content, conductivity, bulk density, carbon and nitrogen content and isotope ratios. The data include the maximum annual Normalized Difference Vegetation Index (NDVI) for 2019 and the trend in maximum NDVI for 2000-2020. This is version 2 of this dataset.The data are provided in comma-separated values (CSV) format. proprietary
Algal_Toxicity_Project_1 Heavy metal toxicity to Antarctic macroalgae measured using a robotic PAM fluorometer AU_AADC STAC Catalog 2005-12-30 2006-03-21 110, -66.5, 110.5, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311764-AU_AADC.umm_json Experiments were carried out at Casey Station over the summer of 2005-2006 to investigate declines in chlorophyll fluorescence following from exposure to seawater spiked with heavy metals. Chlorophyll fluorescence was measured using a pulse amplitude modulated (PAM) fluorometer. The PAM device was mounted to a robotic arm, which could be programmed using a laptop computer to automatically position the device at a constant height above 18 separate test chambers. The test chambers each contained 2130mL of metal-spiked seawater which was fanned by an electric motor across an aluminium sample holder (approximately 2.5cm x 2.5cm) containing a macroalgal specimen. The test chambers were placed in a tank and maintained at a constant temperature by circulating coolant water. Rock-attached specimens of the species Desmerestia menziesii, Palmaria decipiens and Himantothallus grandifolius were collected either by divers or from the shallow nearshore from uncontaminated areas of the Casey region (~6-12m depth). Specimens of these species were exposed to single-toxicant test solutions containing copper, zinc or cadmium for durations ranging from 1.5-6.5d. A total of eighteen experiments were performed during the summer. Each experiment yielded a set of 2D image files that traced variations in fluorescence parameters over the duration. All studied species demonstrated a decline in several fluorescence parameters including minimal (Fo`) and maximal fluorescence yield (Fm`) and, to a lesser extent, effective quantum yield (delta F/Fm` or, alternatively, Y(II)) following from several days' exposure to dissolved copper. D. menziesii and H. grandifolius also demonstrated a decline in fluorescence after exposure to zinc, albeit slower than copper, but not after exposure to cadmium. In contrast to the logarithmic decline observed following from copper exposure, the decline due to zinc toxicity occurred only after a brief increase in fluorescence at around 50h. Data available: Image files taken hourly by the PAM device. These are sorted into folders for each experiment, with the folder title describing the experiment number, the species tested, the metal tested and the duration of the test. Each image file has the file extension *.pim and can be opened using the Imaging-WIN software package (also provided) here. Each image file is titled in the format : [Test Chamber number] - [date] - [24h time]. For example, 'T01-20060204-121539' corresponds to an image file taken from Test Chamber 1 on the 4th of February 2006 at 12h15m39s. In each folder, two other files are presented. The first is a *.pim file titled 'T01-darkadapted', and is an image file taken immediately before the beginning of the test and records the response of the Test Chamber 1 specimen to control water after being kept in total darkness for between 10-30min. Fluorescence parameters of dark-adapted specimens are often used as a measure of specimen health. The second file is a .txt file that describes the nominal concentrations of the test solutions in each chamber (at the time of posting this metadata, chemical analyses of water samples have not been completed). Excel spreadsheets. Also provided here are MS Excel spreadsheets for some (but not all) experiments (E1-E6). These spreadsheets were produced by arbitrarily designating specific 'zones' in the first image taken for each Test Chamber. The same zones were visually located in each subsequent image taken for that Test Chamber, and the Fm', Fo' and delta F/Fm` values for each zone were exported to the spreadsheet. These spreadsheets represent a first attempt at data analysis, although it is expected that the final approach will involve more complicated image editing software. PDF files. Finally, also provided are two *.pdf files which contain the scanned laboratory notebook compiled during the summer. This notebook contains the details of water sample labelling, as well as labelling of algal samples collected for associated projects during the summer. It also contains details of the dimensions of the PAM apparatus. Software. Installation package for ImagingWIN software, version 1.01k. JPEG files. Photographs showing the set-up of the PAM apparatus. This work has been completed as part of ASAC projects 2201, 2566 and 2697 (ASAC_2201, ASAC_2256, ASAC_2697). proprietary
@@ -3430,19 +3431,19 @@ Aliens_in_Antarctica_Invertebrates_2000_2013_1 Alien invertebrates collected tho
Aliens_in_Antarctica_Invertebrates_2000_2013_1 Alien invertebrates collected though the Australian Antarctic Program 2000-2013 AU_AADC STAC Catalog 2000-01-01 2013-12-31 62.87, -68.58, 158.86, -42.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214311765-AU_AADC.umm_json To quantify and identify alien invertebrate transfer to Antarctica our research utilised two methods. Firstly, we examined the Australian Antarctic Division's (AAD) alien invertebrate collection of samples from Australian Antarctic research stations, cargo handling facility, and supply ships. Secondly, we implemented a trapping regime at key locations and on supply ships during the 2012-13 shipping season. Furthermore, we utilised a trapping dataset from similar locations collected in 2002-2004. The Collection Since 2000, the AAD has encouraged Antarctic expeditioners and staff to collect and record alien invertebrate incursions from its four Antarctic research stations, supply ships, a transport aircraft, the cargo facility in Hobart in the wharf precinct of Hobart, and its cargo warehouse in semi-rural Kingston, Tasmania, Australia. Furthermore in 2004, an electronic database for logging environmental incident reports was created. These reports instigate a chain of management response. Incident reports can be generated regardless of whether a physical specimen is collected. Alien invertebrate collection kits - colloquially known as critter kits, were dispatched to ships and stations by the AAD's Environmental Officer. The kits contained sample jars, collecting equipment, data capturing notebooks with defined fields (date, collector, location, notes) to record collection details, barcodes to enable identification of individual collection events and instructions for providing guidance to those not usually engaged in collection of invertebrates. Any specimens collected were returned to Australia along with collection information. We identified these specimens to the most resolved taxonomic level possible. Any records not paired with a physical specimen (i.e. an incident report with no collection) could not be formally identified and were therefore omitted from taxonomic analysis. The only exception was where the specimen was identified by the collector as a 'spider', 'fly', 'snail' or 'moth' which were categorised as Araneae, Diptera, Gastropoda, and Lepidoptera respectively. In these cases, it was deemed that the distinct form and familiarity of these invertebrates even to non-experts generated correct evaluations of the specimens to a coarse taxonomic level. During the 2012-13 season expeditioners were repeatedly briefed to be especially vigilant to search for and collect any invertebrates. All specimens and incident reports were reviewed to determine vectors and location information. Vector categories were nominated as food, ship, aircraft, and various cargo types. Additional information associated with the specimen was used to determine the specific cargo type. Where invertebrates were 'hidden' in containers, 'trapped' or 'entangled' in cargo materials the vector was deemed 'container and packaging materials'. The supply ships and aircraft were considered vectors given they both travel south and attract invertebrates in their own right, via colours, lights and invertebrates windblown onto their surfaces. General location categories were: 'wharf/cargo facility', 'ships/aircraft', and the four research stations - Macquarie Island (54 degrees 30' S 158 degrees 57' E), Casey (66.28 degrees S, 110.52 degrees E), Davis (68.57 degrees S, 77.96 degrees E) and Mawson (67.60 degrees S, 62.86 degrees E). Samples with unknown vectors or undocumented locations were excluded from analyses. Trapping Two types of traps were deployed on supply ships and at the cargo facility in 2012-13. Battery operated 8 watt ultra-violet light traps (Australian Entomological Supplies, Sydney, NSW) were complemented with colour pan traps constructed of yellow and white plastic plates 18 cm in diameter, smeared with Tangle Trap (R) brush-on, petroleum-based insect trap coating. These colours were chosen because they are the most attractive to targeted flying insects such as flies, wasps, aphids and thrips. Trapping was undertaken on two ships, which collectively undertook five voyages to Antarctica from Hobart from October to February 2012-13. We attempted to deploy traps at several times during the journey - leaving port, at sea, and approaching the destination (land). However, variable sea conditions among voyages influenced the frequency of trap deployment. Light traps were automatically activated by dark conditions and were illuminated for up to 12 hours at a time. The traps were placed in areas which were dark at night, and colour traps were placed in areas with access to the outdoors and proximity to food. At the cargo facility in Hobart, Australia, light and colour traps were deployed for approximately three consecutive days while the ship was in port undergoing cargo loading prior to departure for the Antarctic. During the course of the season, we deployed 39 light trap night for a total of 418 hours. Fifty-eight yellow and 58 white traps were exposed for a total of 7440 hours each. Expeditioners and staff were briefed prior to departure to encourage increased vigilance for ad hoc invertebrate collection at the cargo facility and on the supply ships. Previous trapping data In 2002-2004 trapping was undertaken at the Kingston cargo warehouse and the cargo facility in the spring and summer. Blue and yellow colour sticky traps were deployed for several weeks at a time. The quantity and identity of taxa from the 2002-04 trapping exercise were compared with our comparable trapping from 2012-13. proprietary
Aliens_in_Antarctica_seed_identifications_1 Aliens in Antarctica - Seed Identifications data AU_AADC STAC Catalog 2007-09-01 2008-03-31 62, -67, 160, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214311727-AU_AADC.umm_json In the 2007/2008 southern summer season a stratified random selection of travellers to the Antarctic were sampled for propagules on their way to Antarctica or sub-Antarctic islands. This file lists the plant seeds that were found in the samples. Identification of the seeds was done mainly by comparing the seeds (or more often photographs of the seeds) with photographs of seeds in seed-atlases and in databases on the web (see the list below). Because often we had only a single specimen of a specific seed morphotype, we did not use any destructive methods (e.g. making cross-sections of the seed). All seeds have been stored, so they are available for further study. For each identification a confidence level was given on a 4-point scale (0 = no identification available; 1 = low confidence in identification: it may be the taxon listed, but it would not be surprising if it was not; 2 = moderate confidence: we think it is the taxon indicated, but we may be wrong; and 3 = high confidence = we are convinced it is the taxon indicated). Sometimes it was not possible to see if something was a seed or not. Whenever we had serious doubts about something being a seed, it was not counted as such. This way we may well have discarded (figuratively: all material has been kept) some seeds, but this will result at most in a somewhat conservative estimate of the propagule load of the samples. Equally we have discounted seeds that were seriously damaged, and thus not viable. Again in general we were fairly conservative in this matter. All seeds were grouped in groups that were morphologically different (morphotypes), and for which we suggest they are different species (or groups of closely related species) . All morphotypes were given a unique number. Most seeds were identified more or less independently by several people. Subsequently differences in identification were checked and discussed, until some consensus was reached. Where no consensus was reached, identification was given at the taxonomic level where we agreed, and lower levels were given as unknown. For quite a number of seeds we did not arrive at an identification even at the family level. Resources used for seed identification Botha C (2001) Common weeds of crops and gardens in South Africa. Ark grain crops institute. Potchefstroom Cappers R T J, Bekker R M, Jans J E A. (2006) Digital seed Atlas of the Netherlands. Barkhuis Publishing. Groningen. Corner, E. J. H. (1976). Seeds of Dicotyledons. Cambridge University Press, Cambridge. Kirkbride, J.H., Jr., C.R. Gunn, and M.J. Dallwitz. 2006. Family Guide for Fruits and Seeds, version 1.0. URL: https://nt.ars-grin.gov/SeedsFruits/keys/FrSdFam/Index.cfm. Accessed July-November 2009. Martin, A. C., Barkley, W. D. (1961). Seed Identification Manual. University of California Press. Millennium seed bank project (Kew) Seed identification database. URL: http://data.kew.org/sid/. Accessed July-November 2009. Seed ID Workshop. Department of Horticulture and Crop Science, The Ohio State University. URL: http://www.oardc.ohio-state.edu/seedid/ . Accessed July-November 2009. Seeds of Success Collections at the Bend Seed Extractory. URL: unknown - may be: https://www.blm.gov/programs/natural-resources/native-plant-communities/native-plant-and-seed-material-development/collection. Accessed July-November 2009. UBC Botanical Garden Seed Collection. URL: https://botanicalgarden.ubc.ca/research-collections/plant-collections/. Accessed July-November 2009. Webb C J, Simpson M J A (2001). Seeds of New Zealand gymnosperms and dicotyledons, Christchurch, N.Z. : Manuka Press. The seeds were identified by Dr N.J.M. Gremmen, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands Dr. D.M. Bergstrom, Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia. Chris Ware, , Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia. Dr. J. Lee, Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa proprietary
Aliens_in_Antarctica_seed_identifications_1 Aliens in Antarctica - Seed Identifications data ALL STAC Catalog 2007-09-01 2008-03-31 62, -67, 160, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214311727-AU_AADC.umm_json In the 2007/2008 southern summer season a stratified random selection of travellers to the Antarctic were sampled for propagules on their way to Antarctica or sub-Antarctic islands. This file lists the plant seeds that were found in the samples. Identification of the seeds was done mainly by comparing the seeds (or more often photographs of the seeds) with photographs of seeds in seed-atlases and in databases on the web (see the list below). Because often we had only a single specimen of a specific seed morphotype, we did not use any destructive methods (e.g. making cross-sections of the seed). All seeds have been stored, so they are available for further study. For each identification a confidence level was given on a 4-point scale (0 = no identification available; 1 = low confidence in identification: it may be the taxon listed, but it would not be surprising if it was not; 2 = moderate confidence: we think it is the taxon indicated, but we may be wrong; and 3 = high confidence = we are convinced it is the taxon indicated). Sometimes it was not possible to see if something was a seed or not. Whenever we had serious doubts about something being a seed, it was not counted as such. This way we may well have discarded (figuratively: all material has been kept) some seeds, but this will result at most in a somewhat conservative estimate of the propagule load of the samples. Equally we have discounted seeds that were seriously damaged, and thus not viable. Again in general we were fairly conservative in this matter. All seeds were grouped in groups that were morphologically different (morphotypes), and for which we suggest they are different species (or groups of closely related species) . All morphotypes were given a unique number. Most seeds were identified more or less independently by several people. Subsequently differences in identification were checked and discussed, until some consensus was reached. Where no consensus was reached, identification was given at the taxonomic level where we agreed, and lower levels were given as unknown. For quite a number of seeds we did not arrive at an identification even at the family level. Resources used for seed identification Botha C (2001) Common weeds of crops and gardens in South Africa. Ark grain crops institute. Potchefstroom Cappers R T J, Bekker R M, Jans J E A. (2006) Digital seed Atlas of the Netherlands. Barkhuis Publishing. Groningen. Corner, E. J. H. (1976). Seeds of Dicotyledons. Cambridge University Press, Cambridge. Kirkbride, J.H., Jr., C.R. Gunn, and M.J. Dallwitz. 2006. Family Guide for Fruits and Seeds, version 1.0. URL: https://nt.ars-grin.gov/SeedsFruits/keys/FrSdFam/Index.cfm. Accessed July-November 2009. Martin, A. C., Barkley, W. D. (1961). Seed Identification Manual. University of California Press. Millennium seed bank project (Kew) Seed identification database. URL: http://data.kew.org/sid/. Accessed July-November 2009. Seed ID Workshop. Department of Horticulture and Crop Science, The Ohio State University. URL: http://www.oardc.ohio-state.edu/seedid/ . Accessed July-November 2009. Seeds of Success Collections at the Bend Seed Extractory. URL: unknown - may be: https://www.blm.gov/programs/natural-resources/native-plant-communities/native-plant-and-seed-material-development/collection. Accessed July-November 2009. UBC Botanical Garden Seed Collection. URL: https://botanicalgarden.ubc.ca/research-collections/plant-collections/. Accessed July-November 2009. Webb C J, Simpson M J A (2001). Seeds of New Zealand gymnosperms and dicotyledons, Christchurch, N.Z. : Manuka Press. The seeds were identified by Dr N.J.M. Gremmen, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands Dr. D.M. Bergstrom, Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia. Chris Ware, , Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia. Dr. J. Lee, Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa proprietary
-Aliens_in_Antarctica_survey_data_1 Aliens in Antarctica - General Visitor Survey and Visitor Clothing Survey data ALL STAC Catalog 2007-09-01 2008-03-31 62, -67, 160, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214311749-AU_AADC.umm_json In principle all Antarctic visitors in the 2007/2008 southern summer season received a questionnaire called the General Visitor Survey (GVS) about previous use of their clothing and other equipment, and their travel pattern in the year before their Antarctic visit (pages 1 and 2 of the questionnaire Aliens_in_Antarctica_QUESTIONNAIRE_2.5.pdf). Passengers that were sampled for propagules also filled in the GVS questionnaire, but with a third page, with questions about the previous use of specific items of clothing and other gear. The data from this page is called the Visitor Clothing Survey (VCS). To collect the data from the questionnaire forms these were optically scanned by a specialized company, and the results were sent to the investigators in spreadsheets. Some forms arrived only after the scanning was completed. From these we entered the data by hand. On the packets with questionnaires and samples the name of the ship/airplane was written, as well as the date of collection of the data and/or samples. Questionnaires were available in various languages, so most people could fill in a questionnaire in their own language. A total of ca. 5024 GVS forms were received. In addition to these, some 845 VCS questionnaires were received (file = Aliens_in_Antarctica_VCS_questionnaire_data.xls). Of the VCS questionnaire the first 2 pages were identical to the GVS form, and the data from the first 2 pages of all VCS forms were added to the GVS data (none of the visitors filled in both forms), bringing the total up to 5869. Personnel The data were collected by a large number of volunteers on the various ships and airplanes travelling to the Antarctic in the 2007/2008 summer season. Responsible for the organisation of the data collecting were: Dr. A.H.L. Huiskes, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands Dr. D.M. Bergstrom, Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia. Dr. K. Hughes, British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road Cambridge CB3 0E T, UK Dr. M. Lebouvier, University of Rennes 1, Station Biologique, 35380 Paimpont, France Dr. J. Lee, Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa Dr. S. Imura, National Institute of Polar Research, Tokyo, Japan Dr N.J.M. Gremmen, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands, was responsible for the organisation of the data in digital form. proprietary
Aliens_in_Antarctica_survey_data_1 Aliens in Antarctica - General Visitor Survey and Visitor Clothing Survey data AU_AADC STAC Catalog 2007-09-01 2008-03-31 62, -67, 160, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214311749-AU_AADC.umm_json In principle all Antarctic visitors in the 2007/2008 southern summer season received a questionnaire called the General Visitor Survey (GVS) about previous use of their clothing and other equipment, and their travel pattern in the year before their Antarctic visit (pages 1 and 2 of the questionnaire Aliens_in_Antarctica_QUESTIONNAIRE_2.5.pdf). Passengers that were sampled for propagules also filled in the GVS questionnaire, but with a third page, with questions about the previous use of specific items of clothing and other gear. The data from this page is called the Visitor Clothing Survey (VCS). To collect the data from the questionnaire forms these were optically scanned by a specialized company, and the results were sent to the investigators in spreadsheets. Some forms arrived only after the scanning was completed. From these we entered the data by hand. On the packets with questionnaires and samples the name of the ship/airplane was written, as well as the date of collection of the data and/or samples. Questionnaires were available in various languages, so most people could fill in a questionnaire in their own language. A total of ca. 5024 GVS forms were received. In addition to these, some 845 VCS questionnaires were received (file = Aliens_in_Antarctica_VCS_questionnaire_data.xls). Of the VCS questionnaire the first 2 pages were identical to the GVS form, and the data from the first 2 pages of all VCS forms were added to the GVS data (none of the visitors filled in both forms), bringing the total up to 5869. Personnel The data were collected by a large number of volunteers on the various ships and airplanes travelling to the Antarctic in the 2007/2008 summer season. Responsible for the organisation of the data collecting were: Dr. A.H.L. Huiskes, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands Dr. D.M. Bergstrom, Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia. Dr. K. Hughes, British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road Cambridge CB3 0E T, UK Dr. M. Lebouvier, University of Rennes 1, Station Biologique, 35380 Paimpont, France Dr. J. Lee, Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa Dr. S. Imura, National Institute of Polar Research, Tokyo, Japan Dr N.J.M. Gremmen, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands, was responsible for the organisation of the data in digital form. proprietary
+Aliens_in_Antarctica_survey_data_1 Aliens in Antarctica - General Visitor Survey and Visitor Clothing Survey data ALL STAC Catalog 2007-09-01 2008-03-31 62, -67, 160, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214311749-AU_AADC.umm_json In principle all Antarctic visitors in the 2007/2008 southern summer season received a questionnaire called the General Visitor Survey (GVS) about previous use of their clothing and other equipment, and their travel pattern in the year before their Antarctic visit (pages 1 and 2 of the questionnaire Aliens_in_Antarctica_QUESTIONNAIRE_2.5.pdf). Passengers that were sampled for propagules also filled in the GVS questionnaire, but with a third page, with questions about the previous use of specific items of clothing and other gear. The data from this page is called the Visitor Clothing Survey (VCS). To collect the data from the questionnaire forms these were optically scanned by a specialized company, and the results were sent to the investigators in spreadsheets. Some forms arrived only after the scanning was completed. From these we entered the data by hand. On the packets with questionnaires and samples the name of the ship/airplane was written, as well as the date of collection of the data and/or samples. Questionnaires were available in various languages, so most people could fill in a questionnaire in their own language. A total of ca. 5024 GVS forms were received. In addition to these, some 845 VCS questionnaires were received (file = Aliens_in_Antarctica_VCS_questionnaire_data.xls). Of the VCS questionnaire the first 2 pages were identical to the GVS form, and the data from the first 2 pages of all VCS forms were added to the GVS data (none of the visitors filled in both forms), bringing the total up to 5869. Personnel The data were collected by a large number of volunteers on the various ships and airplanes travelling to the Antarctic in the 2007/2008 summer season. Responsible for the organisation of the data collecting were: Dr. A.H.L. Huiskes, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands Dr. D.M. Bergstrom, Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia. Dr. K. Hughes, British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road Cambridge CB3 0E T, UK Dr. M. Lebouvier, University of Rennes 1, Station Biologique, 35380 Paimpont, France Dr. J. Lee, Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa Dr. S. Imura, National Institute of Polar Research, Tokyo, Japan Dr N.J.M. Gremmen, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands, was responsible for the organisation of the data in digital form. proprietary
Aliens_in_Antarctica_visitor_data_1 Aliens in Antarctica - Clothing Item and Propagule data ALL STAC Catalog 2007-09-01 2008-03-31 62, -67, 160, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214311728-AU_AADC.umm_json In the 2007/2008 southern summer season a stratified random selection of travellers to the Antarctic were sampled for propagules on their way to Antarctica or sub-Antarctic islands. This dataset lists the number of seeds found on each visitor, as well as the number of different seed morphotypes (species) per visitor. In addition data on visitor characteristics are given, derived from the Visitor Clothing Survey (VCS) questionnaire data (see separate download link). Sampling was done by cleaning out the outer clothing (jackets, outer trousers, hats, gloves), insulation layer (jerseys, fleece), backpacks, camera bags, daypacks, boots and shoes, and walking poles and camera tripods, using Philips FC 9154 Performer Animal Care vacuum cleaners. All material was collected in nylon mesh bags, placed just behind the suction opening. For all people performing the sampling a detailed instruction DVD was provided. Each sample in its mesh bag was placed a plastic bag, and put in an envelope, together with the matching questionnaire. Similarly the dust bag used (a new dust bag was inserted in the vacuum cleaner for each person sampled) was put in a labelled plastic bag. Plastic bag, each page of the questionnaire, and the envelope were labelled with a barcode sticker, a different barcode for each sampled person. On the plastic bag with the mesh bag with the sample was indicated which item(s) was (were) sampled. At the end of the field season all questionnaires and samples were returned to the Netherlands (samples collected from people travelling through South America), South Africa (people leaving from Cape Town), Japan (people travelling with the Japanese national program vessel), or Australia (people travelling from Australia or New Zealand). Here the samples were weighed, and sorted into plant seeds, other plant propagules (large fragments of moss, hepatics or lichens), invertebrate animal remains, and other material. Whenever possible all different items of clothing etc. were sampled separately. In this way separate samples per item were collected from 350 people. The dataset lists the number of seeds found on separate items of clothing or other equipment per visitor, as well as the number of different seed morphotypes (species) per visitor. The number of seeds and number of species (morphotypes) is based on the results of the seed identifications (see metadata record Aliens_in_Antarctica_seed_identifications). Items that were sampled separately were: J Outer Jacket T Outer trousers I Insulating layer H Headwear G Gloves/mittens F Outdoor footwear B Various bags S Camera tripods/walking sticks In addition data on visitor characteristics are given, derived from the VCS questionnaire data (see metadata record Aliens_in_Antarctica_survey_data). Personnel The samples were collected by a large number of volunteers on the various ships and airplanes travelling to the Antarctic in the 2007/2008 summer season. Volunteers were shown an instruction video on how to collect the samples. Responsible for the organisation of the data collecting were: Dr. A.H.L. Huiskes, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands Dr. D.M. Bergstrom, Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia. Dr. K. Hughes, British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road Cambridge CB3 0E T, UK Dr. M. Lebouvier, University of Rennes 1, Station Biologique, 35380 Paimpont, France Dr. J. Lee, Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa Dr. S. Imura, National Institute of Polar Research, Tokyo, Japan The samples were sorted by Dr N.J.M. Gremmen, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands Dr. K. Kiefer, Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia. Dr. J. Lee, Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa M. Tsujimoto, National Institute of Polar Research, Tokyo, Japan Dr N.J.M. Gremmen, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands, was responsible for the organization of the data in digital form. proprietary
Aliens_in_Antarctica_visitor_data_1 Aliens in Antarctica - Clothing Item and Propagule data AU_AADC STAC Catalog 2007-09-01 2008-03-31 62, -67, 160, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214311728-AU_AADC.umm_json In the 2007/2008 southern summer season a stratified random selection of travellers to the Antarctic were sampled for propagules on their way to Antarctica or sub-Antarctic islands. This dataset lists the number of seeds found on each visitor, as well as the number of different seed morphotypes (species) per visitor. In addition data on visitor characteristics are given, derived from the Visitor Clothing Survey (VCS) questionnaire data (see separate download link). Sampling was done by cleaning out the outer clothing (jackets, outer trousers, hats, gloves), insulation layer (jerseys, fleece), backpacks, camera bags, daypacks, boots and shoes, and walking poles and camera tripods, using Philips FC 9154 Performer Animal Care vacuum cleaners. All material was collected in nylon mesh bags, placed just behind the suction opening. For all people performing the sampling a detailed instruction DVD was provided. Each sample in its mesh bag was placed a plastic bag, and put in an envelope, together with the matching questionnaire. Similarly the dust bag used (a new dust bag was inserted in the vacuum cleaner for each person sampled) was put in a labelled plastic bag. Plastic bag, each page of the questionnaire, and the envelope were labelled with a barcode sticker, a different barcode for each sampled person. On the plastic bag with the mesh bag with the sample was indicated which item(s) was (were) sampled. At the end of the field season all questionnaires and samples were returned to the Netherlands (samples collected from people travelling through South America), South Africa (people leaving from Cape Town), Japan (people travelling with the Japanese national program vessel), or Australia (people travelling from Australia or New Zealand). Here the samples were weighed, and sorted into plant seeds, other plant propagules (large fragments of moss, hepatics or lichens), invertebrate animal remains, and other material. Whenever possible all different items of clothing etc. were sampled separately. In this way separate samples per item were collected from 350 people. The dataset lists the number of seeds found on separate items of clothing or other equipment per visitor, as well as the number of different seed morphotypes (species) per visitor. The number of seeds and number of species (morphotypes) is based on the results of the seed identifications (see metadata record Aliens_in_Antarctica_seed_identifications). Items that were sampled separately were: J Outer Jacket T Outer trousers I Insulating layer H Headwear G Gloves/mittens F Outdoor footwear B Various bags S Camera tripods/walking sticks In addition data on visitor characteristics are given, derived from the VCS questionnaire data (see metadata record Aliens_in_Antarctica_survey_data). Personnel The samples were collected by a large number of volunteers on the various ships and airplanes travelling to the Antarctic in the 2007/2008 summer season. Volunteers were shown an instruction video on how to collect the samples. Responsible for the organisation of the data collecting were: Dr. A.H.L. Huiskes, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands Dr. D.M. Bergstrom, Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia. Dr. K. Hughes, British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road Cambridge CB3 0E T, UK Dr. M. Lebouvier, University of Rennes 1, Station Biologique, 35380 Paimpont, France Dr. J. Lee, Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa Dr. S. Imura, National Institute of Polar Research, Tokyo, Japan The samples were sorted by Dr N.J.M. Gremmen, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands Dr. K. Kiefer, Australian Antarctic Division, Department of the Environment, Water, Heritage and the Arts, 203 Channel Highway, Kingston 7050, Australia. Dr. J. Lee, Centre for Invasion Biology, Department of Botany and Zoology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa M. Tsujimoto, National Institute of Polar Research, Tokyo, Japan Dr N.J.M. Gremmen, Netherlands Institute of Ecology, P.O. Box 140, 4400 AC Yerseke, The Netherlands, was responsible for the organization of the data in digital form. proprietary
Amery_Ht_1968_1 Ice Shelf Surface Elevation data: Amery Ice Shelf 1968 AU_AADC STAC Catalog 1968-10-01 1969-02-28 69, -71, 73, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214305704-AU_AADC.umm_json Ice shelf surface elevation data from an oversnow ground-based traverse along the centre of the Amery Ice Shelf from A509 (69.06 S, 72.15 E) to T4 (71.22 S, 69.48 E), including two transverse arms; between G1 (69.49 S, 71.72 E) and A119 (69.81 S, 73.28 E); and between T3 (70.79 S, 68.89 E) and T2 (71.00 S, 70.75 E) during the 1968 spring-summer season. More information can be found at the BEDMAP website. The fields in this dataset are: Mission ID Latitude Longitude Ice Thickness Surface Elevation Water Column Thickness Bed Elevation proprietary
Amery_Ht_88-89_1 Ice Shelf Surface Elevation data: Amery Ice Shelf 1988-89 AU_AADC STAC Catalog 1988-12-01 1989-12-31 63, -74, 82, -68 https://cmr.earthdata.nasa.gov/search/concepts/C1214311729-AU_AADC.umm_json A Lambert Glacier - Amery Ice Shelf series of airborne (Squirrel helicopter and Twin Otter fixed wing) RES and surface elevation profiles were conducted over two summer seasons; 1988/89 and 1989/90. Altogether nearly 10,000 km of various flight paths were undertaken, operating out of Mawson (67.60 S, 62.88 E), Davis (68.58 S, 77.97 E), Dovers (70.22 S, 65.87 E) or Beaver Lake (70.80 S, 68.18 E). More information can be found at the BEDMAP website. The fields in this dataset are: mission_id (unique mission identifier) latitude (decimal degrees) longitude (decimal degrees) ice_thickness (m) surface_elevation (m) water_column_thickness (m) bed_elevation (m) proprietary
-Annual_30m_AGB_1808_1 ABoVE: Annual Aboveground Biomass for Boreal Forests of ABoVE Core Domain, 1984-2014 ORNL_CLOUD STAC Catalog 1984-01-01 2014-12-31 -165.41, 51.78, -101.74, 69.73 https://cmr.earthdata.nasa.gov/search/concepts/C2111720412-ORNL_CLOUD.umm_json This dataset provides estimated annual aboveground biomass (AGB) density for live woody (tree and shrub) species and corresponding standard errors at a 30 m spatial resolution for the boreal forest biome portion of the Core Study Domain of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) Project (Alaska and Canada) over the time period 1984-2014. The data were derived from a time series of Landsat-5 and Landsat-7 surface reflectance imagery and full-waveform lidar returns from the Geoscience Laser Altimeter System (GLAS) flown onboard IceSAT from 2004 to 2008. The Change Detection and Classification (CCDC) model-fitting algorithm was used to estimate the seasonal variability in surface reflectance, and AGB density data were produced by applying allometric equations to the GLAS lidar data. A Gradient Boosted Machines machine learning algorithm was used to predict annual AGB density across the study domain given the seasonal variability in surface reflectance and other predictors. The data received statistical smoothing to reduce noise and uncertainty was estimated at the pixel level. These data contribute to the characterization of how biomass stocks are responding to climate and disturbance in boreal forests. proprietary
Annual_30m_AGB_1808_1 ABoVE: Annual Aboveground Biomass for Boreal Forests of ABoVE Core Domain, 1984-2014 ALL STAC Catalog 1984-01-01 2014-12-31 -165.41, 51.78, -101.74, 69.73 https://cmr.earthdata.nasa.gov/search/concepts/C2111720412-ORNL_CLOUD.umm_json This dataset provides estimated annual aboveground biomass (AGB) density for live woody (tree and shrub) species and corresponding standard errors at a 30 m spatial resolution for the boreal forest biome portion of the Core Study Domain of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) Project (Alaska and Canada) over the time period 1984-2014. The data were derived from a time series of Landsat-5 and Landsat-7 surface reflectance imagery and full-waveform lidar returns from the Geoscience Laser Altimeter System (GLAS) flown onboard IceSAT from 2004 to 2008. The Change Detection and Classification (CCDC) model-fitting algorithm was used to estimate the seasonal variability in surface reflectance, and AGB density data were produced by applying allometric equations to the GLAS lidar data. A Gradient Boosted Machines machine learning algorithm was used to predict annual AGB density across the study domain given the seasonal variability in surface reflectance and other predictors. The data received statistical smoothing to reduce noise and uncertainty was estimated at the pixel level. These data contribute to the characterization of how biomass stocks are responding to climate and disturbance in boreal forests. proprietary
+Annual_30m_AGB_1808_1 ABoVE: Annual Aboveground Biomass for Boreal Forests of ABoVE Core Domain, 1984-2014 ORNL_CLOUD STAC Catalog 1984-01-01 2014-12-31 -165.41, 51.78, -101.74, 69.73 https://cmr.earthdata.nasa.gov/search/concepts/C2111720412-ORNL_CLOUD.umm_json This dataset provides estimated annual aboveground biomass (AGB) density for live woody (tree and shrub) species and corresponding standard errors at a 30 m spatial resolution for the boreal forest biome portion of the Core Study Domain of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) Project (Alaska and Canada) over the time period 1984-2014. The data were derived from a time series of Landsat-5 and Landsat-7 surface reflectance imagery and full-waveform lidar returns from the Geoscience Laser Altimeter System (GLAS) flown onboard IceSAT from 2004 to 2008. The Change Detection and Classification (CCDC) model-fitting algorithm was used to estimate the seasonal variability in surface reflectance, and AGB density data were produced by applying allometric equations to the GLAS lidar data. A Gradient Boosted Machines machine learning algorithm was used to predict annual AGB density across the study domain given the seasonal variability in surface reflectance and other predictors. The data received statistical smoothing to reduce noise and uncertainty was estimated at the pixel level. These data contribute to the characterization of how biomass stocks are responding to climate and disturbance in boreal forests. proprietary
Annual_Burned_Area_Maps_1708_1 Annual Burned Area from Landsat, Mawas, Central Kalimantan, Indonesia, 1997-2015 ORNL_CLOUD STAC Catalog 1997-01-01 2015-12-31 114.39, -2.5, 114.61, -2.21 https://cmr.earthdata.nasa.gov/search/concepts/C2389021866-ORNL_CLOUD.umm_json This dataset provides maps of annual burned area for the part of Mawas conservation program in Central Kalimantan, Indonesia from 1997 through 2015. Landsat imagery (TM, ETM+, OLI/TIR) at 30 m resolution was obtained for this 19-year period, including the variables surface reflectance, brightness temperature, and pixel quality assurance, plus the indices NDVI, NDMI, NBR, NBR2, SAVI, and MSAVI. The MODIS active fire product (MCD14) was used to define when fires occurred. Random Forest classifications were used to separate burned and unburned 30-m pixels with inputs of composites of Landsat indices and thermal bands, based on the pre- and post-fire values. proprietary
-Annual_Landcover_ABoVE_1691_1 ABoVE: Landsat-derived Annual Dominant Land Cover Across ABoVE Core Domain, 1984-2014 ALL STAC Catalog 1984-01-01 2014-12-31 -170.01, 50.26, -98.97, 76.23 https://cmr.earthdata.nasa.gov/search/concepts/C2143403402-ORNL_CLOUD.umm_json This dataset provides two 30-m resolution time series products of annual land cover classifications over the Arctic Boreal Vulnerability Experiment (ABoVE) core domain for each year of the period 1984-2014. The data are the annual dominant plant functional type in a given 30-m pixel derived from Landsat surface reflectance, landcover training data mapped across the ABoVE domain (using Random Forests modeling, with clustering and interpretation of field photography) and very high resolution imagery to assign land cover classifications. One product has a 15-class land cover classification that breaks out forest and shrub types into several additional classes; the other product provides a simplified, 10-class approach. Classification accuracy assessment results are provided per year. Assessments were based on a probability-based random sample of reference data that supported statistically robust estimation of areas and uncertainties in mapped areas. proprietary
Annual_Landcover_ABoVE_1691_1 ABoVE: Landsat-derived Annual Dominant Land Cover Across ABoVE Core Domain, 1984-2014 ORNL_CLOUD STAC Catalog 1984-01-01 2014-12-31 -170.01, 50.26, -98.97, 76.23 https://cmr.earthdata.nasa.gov/search/concepts/C2143403402-ORNL_CLOUD.umm_json This dataset provides two 30-m resolution time series products of annual land cover classifications over the Arctic Boreal Vulnerability Experiment (ABoVE) core domain for each year of the period 1984-2014. The data are the annual dominant plant functional type in a given 30-m pixel derived from Landsat surface reflectance, landcover training data mapped across the ABoVE domain (using Random Forests modeling, with clustering and interpretation of field photography) and very high resolution imagery to assign land cover classifications. One product has a 15-class land cover classification that breaks out forest and shrub types into several additional classes; the other product provides a simplified, 10-class approach. Classification accuracy assessment results are provided per year. Assessments were based on a probability-based random sample of reference data that supported statistically robust estimation of areas and uncertainties in mapped areas. proprietary
-Annual_Seasonality_Greenness_1698_1 ABoVE: Annual Phenology Derived from Landsat across the ABoVE Core Domain, 1984-2014 ALL STAC Catalog 1984-01-01 2014-12-31 -170.01, 50.26, -98.97, 75.01 https://cmr.earthdata.nasa.gov/search/concepts/C2111930592-ORNL_CLOUD.umm_json This dataset provides annual maps of the timing of spring onset with leaf emergence, of autumn onset with leaf senescence, and of peak greenness for each 30 m pixel derived from Landsat time series of Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) observations from 1984 to 2014. The ABoVE core domain includes 169 ABoVE grid tiles across Alaska, USA and Alberta, British Columbia, Northwest Territories, Nunavut, Saskatchewan, and Yukon, Canada. The data provided for deriving seasonality includes the total number of cloud-free observations, r-squared values between observed and spline-predicted Enhanced Vegetation Index (EVI), long-term average minimum EVI, long-term average maximum EVI, long-term average spring onset, long-term average autumn onset, annual spring onset, and annual autumn onset. The data provided for peak greenness includes annual peak Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), annual composite red reflectance, annual composite NIR reflectance, annual composite shortwave infrared reflectance (band 6, SWIR1), annual composite shortwave infrared reflectance (band 7, SWIR2), number of dates used to calculate composites, and day of year of associated maximum composite. proprietary
+Annual_Landcover_ABoVE_1691_1 ABoVE: Landsat-derived Annual Dominant Land Cover Across ABoVE Core Domain, 1984-2014 ALL STAC Catalog 1984-01-01 2014-12-31 -170.01, 50.26, -98.97, 76.23 https://cmr.earthdata.nasa.gov/search/concepts/C2143403402-ORNL_CLOUD.umm_json This dataset provides two 30-m resolution time series products of annual land cover classifications over the Arctic Boreal Vulnerability Experiment (ABoVE) core domain for each year of the period 1984-2014. The data are the annual dominant plant functional type in a given 30-m pixel derived from Landsat surface reflectance, landcover training data mapped across the ABoVE domain (using Random Forests modeling, with clustering and interpretation of field photography) and very high resolution imagery to assign land cover classifications. One product has a 15-class land cover classification that breaks out forest and shrub types into several additional classes; the other product provides a simplified, 10-class approach. Classification accuracy assessment results are provided per year. Assessments were based on a probability-based random sample of reference data that supported statistically robust estimation of areas and uncertainties in mapped areas. proprietary
Annual_Seasonality_Greenness_1698_1 ABoVE: Annual Phenology Derived from Landsat across the ABoVE Core Domain, 1984-2014 ORNL_CLOUD STAC Catalog 1984-01-01 2014-12-31 -170.01, 50.26, -98.97, 75.01 https://cmr.earthdata.nasa.gov/search/concepts/C2111930592-ORNL_CLOUD.umm_json This dataset provides annual maps of the timing of spring onset with leaf emergence, of autumn onset with leaf senescence, and of peak greenness for each 30 m pixel derived from Landsat time series of Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) observations from 1984 to 2014. The ABoVE core domain includes 169 ABoVE grid tiles across Alaska, USA and Alberta, British Columbia, Northwest Territories, Nunavut, Saskatchewan, and Yukon, Canada. The data provided for deriving seasonality includes the total number of cloud-free observations, r-squared values between observed and spline-predicted Enhanced Vegetation Index (EVI), long-term average minimum EVI, long-term average maximum EVI, long-term average spring onset, long-term average autumn onset, annual spring onset, and annual autumn onset. The data provided for peak greenness includes annual peak Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), annual composite red reflectance, annual composite NIR reflectance, annual composite shortwave infrared reflectance (band 6, SWIR1), annual composite shortwave infrared reflectance (band 7, SWIR2), number of dates used to calculate composites, and day of year of associated maximum composite. proprietary
+Annual_Seasonality_Greenness_1698_1 ABoVE: Annual Phenology Derived from Landsat across the ABoVE Core Domain, 1984-2014 ALL STAC Catalog 1984-01-01 2014-12-31 -170.01, 50.26, -98.97, 75.01 https://cmr.earthdata.nasa.gov/search/concepts/C2111930592-ORNL_CLOUD.umm_json This dataset provides annual maps of the timing of spring onset with leaf emergence, of autumn onset with leaf senescence, and of peak greenness for each 30 m pixel derived from Landsat time series of Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) observations from 1984 to 2014. The ABoVE core domain includes 169 ABoVE grid tiles across Alaska, USA and Alberta, British Columbia, Northwest Territories, Nunavut, Saskatchewan, and Yukon, Canada. The data provided for deriving seasonality includes the total number of cloud-free observations, r-squared values between observed and spline-predicted Enhanced Vegetation Index (EVI), long-term average minimum EVI, long-term average maximum EVI, long-term average spring onset, long-term average autumn onset, annual spring onset, and annual autumn onset. The data provided for peak greenness includes annual peak Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), annual composite red reflectance, annual composite NIR reflectance, annual composite shortwave infrared reflectance (band 6, SWIR1), annual composite shortwave infrared reflectance (band 7, SWIR2), number of dates used to calculate composites, and day of year of associated maximum composite. proprietary
Annual_Thaw_Slump_1724_1 ABoVE: Annual Thaw Slump Expansion on East Fork Chandalar River, Alaska, 2008-2017 ALL STAC Catalog 2008-08-23 2017-09-17 -146.07, 67.63, -146.06, 67.64 https://cmr.earthdata.nasa.gov/search/concepts/C2143402706-ORNL_CLOUD.umm_json This dataset provides a time series of spatial data showing the expansion of a thaw slump on the East Fork Chandalar River near the community of Venetie, Alaska, from 2008 through 2017. The erosion of vegetated areas along the river was documented by manually digitizing imagery from ESRI basemaps and Landsat 5 (TM), 7 (ETM+), and 8 (OLI), using the band combination of shortwave infrared 2, shortwave infrared 1, and red. proprietary
Annual_Thaw_Slump_1724_1 ABoVE: Annual Thaw Slump Expansion on East Fork Chandalar River, Alaska, 2008-2017 ORNL_CLOUD STAC Catalog 2008-08-23 2017-09-17 -146.07, 67.63, -146.06, 67.64 https://cmr.earthdata.nasa.gov/search/concepts/C2143402706-ORNL_CLOUD.umm_json This dataset provides a time series of spatial data showing the expansion of a thaw slump on the East Fork Chandalar River near the community of Venetie, Alaska, from 2008 through 2017. The erosion of vegetated areas along the river was documented by manually digitizing imagery from ESRI basemaps and Landsat 5 (TM), 7 (ETM+), and 8 (OLI), using the band combination of shortwave infrared 2, shortwave infrared 1, and red. proprietary
Antarctic_Meteorology_1 Antarctic Climate Data Collected by Australian Agencies AU_AADC STAC Catalog 1948-01-01 60, -68, 159, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1214305711-AU_AADC.umm_json "This record provides a listing of meteorological data collected in the Australian Antarctic Territory by members of the Australian Antarctic program (and it's predecessors) and the Bureau of Meteorology. The data have been obtained by manual observations and by automatic weather stations. All data are available from the Bureau of Meteorology, and are considered to be the authoritative source of weather data in the Australian Antarctic Territory (as they have been quality checked). Raw data directly from the automatic weather stations at the stations is available at https://data.aad.gov.au/aws. The data available here includes: - Automatic Weather Station data from 7 sites - Casey, Davis, Macquarie Island, Mawson, Wilkins, Davis Whoop Whoop, and Casey Skiway South. Data resolution varies, but is approximately every 30 minutes. - Daily weather data from 48 sites. Note - not all of these sites are still operational. - Synoptic weather data from 53 sites. Note - not all of these sites are still operational. - Terrestrial soil data from 4 sites. Note - not all of these sites are still operational. - Upper air data from 5 sites. Note - not all of these sites are still operational. - High resolution, 1 minute automatic weather station data from 7 sites - Casey, Davis, Macquarie Island, Mawson, Wilkins, Davis Whoop Whoop, and Casey Skiway South. - Daily and Synoptic data from a number of decommissioned sites. Site details of 24 sites. For full site listings, seeing the file for station details within each dataset (""HM01X_StnDet""). Meteorology data from Wilkes Station, Antarctica 1960 - 1968 - data collected include: temperature (maximum and minimum; dry bulb; wet bulb; dew point), air pressure, wind (direction,speed and maximum gust; run (greater than 3 m)), phenomena, sunshine, cloud. Meteorology data from Casey Station (current) (300017), Antarctica 1989 ongoing, surface measurements - location 66.2792 S, 110.5356 E, with a barometric height of 42.3m. Data collected include the following: temperature (maximum and minimum; dry bulb), air pressure, wind (direction;speed), humidity, rainfall, sunshine, cloud, visibility. An AWS is now in operation at Casey station. Meteorology data from Davis Station (300000), Antarctica 1957 ongoing, surface measurements - location 68.5772 S, 77.9725 E, with a station height of 16.0m and a barometric height of 22.3m. - location 66.2792 S, 110.5356 E, with a barometric height of 42.3m. Data collected include the following: temperature (maximum and minimum; dry bulb; terrestrial minimum, soil temperature), air pressure, wind (direction, speed; run), rainfall, sunshine, cloud, humidity, visibility. An AWS is now in operation at Davis station. Meteorology data from Mawson Station (300001), Antarctica 1954 ongoing, surface measurements - location 67.6014 S, 62.8731 E, with a station height of 9.9m and a barometric height of 16.0m. Data collected include the following: temperature (maximum and minimum; dry bulb), air pressure, wind (direction,speed), humidity, cloud, rainfall, sunshine. An AWS is now in operation at Mawson station. Meteorology data from Macquarie Island Station (300004), 1948 ongoing, surface measurements - location 54.4997 S, 158.9522 E, with a station height of 6.0m, a barometric height of 8.3m and an aerodrome height of 6.0m. Data collected include the following: temperature (maximum and minimum; dry bulb; wet bulb; terrestrial minimum; soil 10cm,20cm,50cm,100cm), air pressure, wind (direction; speed; run), rainfall, sunshine, cloud, visibility, humidity, sea state, radiation. An AWS is now in operation at Macquarie Island station. Meteorology data from Heard Island (Atlas Cove) Station (300005), first installed 1948 - location 53.02 S, 73.39 E, with a station height of 3.0m, and a barometric height of 3.5m. Data collected include the following: temperature, air pressure, rainfall. Meteorology data from Heard Island (The Spit) Station (300028), installed 1992 - location 53.1069 S, 73.7211 E, with a station height of 12.0m and a barometric height of 12.5m. Data collected include the following: temperature (air and minimum terrestrial), air pressure, humidity, wind direction, sunshine, cloud. Meteorology data from Casey Station (current) (300017), Antarctica 1989 ongoing, upper atmosphere measurements - location 66.2792 S, 110.5356 E, with a barometric height of 42.3m. Data collected include the following: upper atmospheric temperature (via a radiosonde), upper atmospheric wind (using a wind find radar). Meteorology data from Davis Station (300000), Antarctica 1957 ongoing, upper atmosphere measurements - location 68.5772 S, 77.9725 E, with a station height of 16.0m and a barometric height of 22.3m. Data collected include the following: upper atmospheric temperature (using radiosonde), upper atmosphere wind (using wind find radar). Meteorology data from Mawson Station (300001), Antarctica 1954 ongoing, upper atmosphere measurements - location 67.6014 S, 62.8731 E, with a station height of 9.9m and a barometric height of 16.0m. Data collected include the following: upper atmosphere temperature and wind (using sounding processor and GPS). Meteorology data from Macquarie Island Station (300004), 1948 ongoing, upper atmosphere measurements - location 54.4997 S, 158.9522 E, with a station height of 6.0m, a barometric height of 8.3m and an aerodrome height of 6.0m. Data collected include the following: upper atmosphere temperature and wind (collected using wind find radar and radiosondes). Meteorology data from Knuckey Peaks Station (300009), 1975 - 1984 - location 67.8 S, 53.5 E. Meteorology data from Heard Island (Atlas Cove) Station (300005), first installed 1948, upper atmosphere measurements - location 53.02 S, 73.39 E, with a station height of 3.0m, and a barometric height of 3.5m. Data recorded include: upper atmosphere temperature, upper atmosphere wind. Meteorology data from Mount King Satellite of Mawson Station (300010), Antarctica, 1975 - 1984 - location 67.1 S, 52.5 E, with a station height of 112.5m. Data recorded include: temperature (dry bulb), air pressure, humidity, visibility, and some upper atmosphere measurements. Meteorology data from Lanyon Junction Station (300011), Antarctica 1983 to 1987 - location 66.3 S, 110.8667 E, with a station height of 470.0m. Observational records include: humidity charts, thermograph charts, pilot balloon flights, and surface observations. Meteorology data from Haupt Nunatak (Casey) Automatic Weather Station (site 300012), installed 1994 - located at 66.5819 S, 110.6939 E near Casey station, with a station height of 81.4m and a barometer height of 83.4m. Data recorded include: barometric pressure, wind direction, speed and gust, and air temperature. Meteorology data from Depot Peak site (300013), Antarctica, installed 1990 - location 69.05 S, 164.6 E, and has a station height of 1600 m. Instruments at the site include: barometer, cup anemometer and humicap (temperature and humidity). Meteorology data from Edgeworth David (Bunger Hills) Station (300014), Antarctica, 1986 to 1989 - location 66.25 S, 100.6036 E, with a station height of 6.0m and a barometric height of 7.0m. Meteorology data from Law Base Station (300015),Antarctica, 1989 - 1992 - location 69.4167 S, 76.5 E, with a station height of 77.0m. Meteorology data from Dovers Station (300016), Antarctica, 1988 to 1992 - located at 70.2333 S, 65.85 E, with a station height of 1058.0m and a barometric height of 1059.0m. Data recorded include: Air pressure, air temperature, humidity, wind speed and direction, cloud, visibility and upper atmosphere data. Meteorology data from Balaena Island Automatic Weather Station (300032), installed 1994 - location 66.017 S, 111.0833 E, 22.21 Nm NE of Casey, with a station height of 8.0m and a barometric height of 10m. Data collected from this AWS include: Wind speed and direction, wind gust, air temperature and barometric pressure. Meteorology data from Snyder Rocks Automatic Weather Station (300033), Antarctica, installed 1994 - located at 66.55 S, 107.75 E, with a station height of 40m and a barometric height of 42m. Data collected include: air temperature, barometric pressure, wind speed, direction and gust. Meteorology data from Law Dome Summit South Automatic Weather Station (300034), Antarctica, installed 1995 - location 66.717 S, 112.9333 E, with a station height of 1375.0 m. Data collected include: air pressure, air temperature, wind speed and direction. Meteorology data from Casey(old) Station, Antarctica 1969 - 1989. Data collected include: temperature (maximum and minimum; dry bulb; wet bulb; dew point), air pressure, wind (direction,speed and maximum gust; run (greater than 3 m)), phenomena, sunshine, cloud, radiation (global,diffuse)." proprietary
@@ -3566,8 +3567,8 @@ Aqua_AMSR-E_L3_WV_1day_0.25deg_NA Aqua/AMSR-E L3 Water Vapor (1-Day, 0.25 deg) J
Aqua_AMSR-E_L3_WV_1month_0.25deg_NA Aqua/AMSR-E L3 Water Vapor (1-Month, 0.25 deg) JAXA STAC Catalog 2002-06-01 2011-10-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128781-JAXA.umm_json "Aqua/AMSR-E L3 Water Vapor (1-Month, 0.25 deg) dataset is obtained from the AMSR-E sensor onboard Aqua and produced by the Japan Aerospace Exploration Agency (JAXA). Aqua of NASA was launched on May 4th, 2002 in Sun-synchronous sub-recurrent Orbit. Aqua observes various kinds of physical phenomena related to water and energy circulation from space. Aqua data promoted the research activities for interactions between the atmosphere, oceans and lands, and their effects on climate changes. AMSR-E scans the Earth's surface by mechanically rotating the antenna and acquires radiance data of the Earth's surface. Each frequency band is monitored by vertical and horizontal polarized wave. It conically scans and keeps an angle of incidence on the earth surface (a nominal of 55 degrees) and accomplishes a swath width of about 1450 km. The AMSR-E reached its limit to maintain the antenna rotation speed necessary for regular observations, and the AMSR-E restarted its observation in slow rotation mode (2 rotations per minute) on December 4, 2012. However, the AMSR-E reached its limit to maintain the antenna rotation speed necessary for slow rotation mode and it automatically halted its observation and rotation on December 4, 2015. Level 3 data inputs level 1B and level 2 data and projected to the map in accordance with the specified projection technique (Equi-Rectangular (EQR) or Polar Stereographic (PS)), and then the arithmetic average of 1 day is computed on each grid. Moreover, level 3 data for 1 day of each geophysical parameter is inputted for 1 month, arithmetic average of 1 month is computed on each grid, as the same way as 1 day average calculation. This product includes monthly mean Water Vapor (WV). PWI (water vapor index) is converted to total water vapor content (PWA, kg/m^2) using a look-up table, which is designed as the provability of PWA with AMSR retrievals is equivalent to that of PWA with radio sonde. The physical quantity unit is kg/m^2. The provided format is HDF4. The statistical period is 1 month. The spatial resolution is 0.25 deg. The current version of the product is ""Version 7"". The projection method is EQR. The generation unit is global." proprietary
ArabianSea_2011_0 Measurements made in the monsoonal Arabian Sea in 2011 OB_DAAC STAC Catalog 2011-03-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360108-OB_DAAC.umm_json Measurements made in the monsoonal Arabian Sea in 2011. proprietary
Arc00_0 Measurements in the Arctic north of Alaska during 2000 OB_DAAC STAC Catalog 2000-08-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360109-OB_DAAC.umm_json Measurements from the Beaufort and Chukchi seas in the Arctic north of Alaska during 2000. proprietary
-ArcOD_2006B1 Abundance and diversity of the Amphipoda (Crustacea) from the Greenlandic shelf ALL STAC Catalog 2001-10-23 2004-10-29 -52.58, 60, -37.2, 64.42 https://cmr.earthdata.nasa.gov/search/concepts/C1214594916-SCIOPS.umm_json The species composition of Amphipoda (Crustacea: Malacostraca: Peracarida) of the Greenland shelf south of 65°N was investigated by means of 18 epibenthic samples over a sampling period of three years (2001, 2002, 2004). The samples were taken using a Rauschert sledge in depths between 106 and 251 m. In total, 62,205 specimens were identified belonging to 154 species. The amphipods from the South Greenland shelf represent in general a homogeneously distributed community with respect to evenness (J’), diversity (H’) and Hurlbert’s rarefaction E (S500). Multivariate analyses of the species abundances divided the amphipods into a southeastern and southwestern fauna. Among the species most contributing to the separation between East and West, Hardametopa nasuta, Photis reinhardi and Phoxocephalus holboelli were identified. With respect to evenness and diversity, the amphipod community was stable over the three years. We used the WORMS database to present species in this metadata. proprietary
ArcOD_2006B1 Abundance and diversity of the Amphipoda (Crustacea) from the Greenlandic shelf SCIOPS STAC Catalog 2001-10-23 2004-10-29 -52.58, 60, -37.2, 64.42 https://cmr.earthdata.nasa.gov/search/concepts/C1214594916-SCIOPS.umm_json The species composition of Amphipoda (Crustacea: Malacostraca: Peracarida) of the Greenland shelf south of 65°N was investigated by means of 18 epibenthic samples over a sampling period of three years (2001, 2002, 2004). The samples were taken using a Rauschert sledge in depths between 106 and 251 m. In total, 62,205 specimens were identified belonging to 154 species. The amphipods from the South Greenland shelf represent in general a homogeneously distributed community with respect to evenness (J’), diversity (H’) and Hurlbert’s rarefaction E (S500). Multivariate analyses of the species abundances divided the amphipods into a southeastern and southwestern fauna. Among the species most contributing to the separation between East and West, Hardametopa nasuta, Photis reinhardi and Phoxocephalus holboelli were identified. With respect to evenness and diversity, the amphipod community was stable over the three years. We used the WORMS database to present species in this metadata. proprietary
+ArcOD_2006B1 Abundance and diversity of the Amphipoda (Crustacea) from the Greenlandic shelf ALL STAC Catalog 2001-10-23 2004-10-29 -52.58, 60, -37.2, 64.42 https://cmr.earthdata.nasa.gov/search/concepts/C1214594916-SCIOPS.umm_json The species composition of Amphipoda (Crustacea: Malacostraca: Peracarida) of the Greenland shelf south of 65°N was investigated by means of 18 epibenthic samples over a sampling period of three years (2001, 2002, 2004). The samples were taken using a Rauschert sledge in depths between 106 and 251 m. In total, 62,205 specimens were identified belonging to 154 species. The amphipods from the South Greenland shelf represent in general a homogeneously distributed community with respect to evenness (J’), diversity (H’) and Hurlbert’s rarefaction E (S500). Multivariate analyses of the species abundances divided the amphipods into a southeastern and southwestern fauna. Among the species most contributing to the separation between East and West, Hardametopa nasuta, Photis reinhardi and Phoxocephalus holboelli were identified. With respect to evenness and diversity, the amphipod community was stable over the three years. We used the WORMS database to present species in this metadata. proprietary
ArcticNET_0 Artic Network of Centres of Excellence of Canada OB_DAAC STAC Catalog 2005-08-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2018199934-OB_DAAC.umm_json Measurements taken in Hudson Bay on board the icebreaker C.C.G.S. Amundsen to gain knowledge on marine coastal ecosystems as part of the ArcticNet program in 2005 and 2010. ArcticNet is a Network of Centres of Excellence of Canada to study the impacts of climate change in the Canadian North. proprietary
ArcticTreeLine_Dendrometry_Env_2185_1 Dendrometer, Soil, and Weather Observations, Arctic Tree Line, AK and NWT, 2016-2019 ORNL_CLOUD STAC Catalog 2016-06-07 2019-09-13 -149.76, 67.97, -133.53, 68.73 https://cmr.earthdata.nasa.gov/search/concepts/C2756301678-ORNL_CLOUD.umm_json This dataset provides in situ measurements of radial tree growth of selected white spruce (Picea glauca) and black spruce (Picea mariana) trees, as well as simultaneous in situ measurements of environmental variables (air temperature, air pressure, relative humidity, soil temperature, volumetric water content, and solar irradiance) at two Arctic treeline sites: one in the Brooks Range of Alaska (AK), USA, and the other near Inuvik, Northwest Territories (NWT), Canada. In AK, 36 trees were monitored from June 7, 2016 to September 13, 2019, and in NWT, 24 trees were monitored from July 5, 2017 to July 25, 2019 with a sampling interval of 5- or 20-minutes for radial tree growth and 5-minutes for all environmental variables. The dendrometer data included in this dataset are only those gathered from 2016-2017. Dendrometer data from 2018-2019 are available from a related dataset. The data were collected to better understand the influence of environmental variables on radial tree growth dynamics. The data are provided in comma-separated values (CSV) format. proprietary
ArcticTreeLine_Spruce_CO2_WV_1948_1 Spruce Leaf, Tree Traits, and Respiration at Range Extremes, AK and NY, USA, 2018 ORNL_CLOUD STAC Catalog 2018-06-06 2018-06-23 -149.96, 41.4, -74.02, 67.89 https://cmr.earthdata.nasa.gov/search/concepts/C2515313617-ORNL_CLOUD.umm_json This dataset provides in situ measurements of needle-level gas-exchange and leaf traits from Picea glauca (white spruce) from a field site located in the northern latitudinal forest-tundra ecotone (FTE) near the Dalton Highway in northern Alaska, and from one study site located in Black Rock Forest, New York, USA. Measurements were collected with an open flow portable photosynthesis system (Li6400XT) and custom-built temperature-controlled cuvette. Respiration as a function of leaf temperature was measured continuously as the needle temperature was ramped from approximately 5 to 65 degrees C, at a constant rate of 1 degree C per minute. Additional data include tree diameter at breast height (dbh), leaf area, photosynthetic rate, intercellular C02, conductance to H20, tree height, and data from raw temperature curves. Results are reported on both a leaf area and leaf mass basis. The data are for the period 2018-06-06 to 2018-06-23 and are provided in comma-separated (CSV) format. proprietary
@@ -3588,16 +3589,16 @@ B01_0 Measurements off the Virginia coast in 2005 OB_DAAC STAC Catalog 2005-03-3
B02_0 Mid-Atlantic coastal region measurements in 2005 OB_DAAC STAC Catalog 2005-03-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360126-OB_DAAC.umm_json Measurements made near the mid-Atlantic coastal region of the continental shelf in 2005. proprietary
B031_Band_1.0 Adelie penguin banding data 1994-2014 from the California Avian Data Center hosted by Point Blue Conservation Science ALL STAC Catalog 1994-12-15 2017-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214593927-SCIOPS.umm_json Bands put on Adélie penguin chicks and adults, Ross Island, Antarctica, starting in 1996. Bands were attached at Cape Royds, Cape Bird, Cape Crozier, and Beaufort Island. proprietary
B031_Band_1.0 Adelie penguin banding data 1994-2014 from the California Avian Data Center hosted by Point Blue Conservation Science SCIOPS STAC Catalog 1994-12-15 2017-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214593927-SCIOPS.umm_json Bands put on Adélie penguin chicks and adults, Ross Island, Antarctica, starting in 1996. Bands were attached at Cape Royds, Cape Bird, Cape Crozier, and Beaufort Island. proprietary
-B031_ChickCon_1.0 Adelie penguin chick measurements from the California Avian Data Center hosted by Point Reyes Blue Conservation Science SCIOPS STAC Catalog 1996-12-25 2017-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214593929-SCIOPS.umm_json Measurements of chick flippers and mass taken at weekly intervals beginning 12/1996 (ongoing). proprietary
B031_ChickCon_1.0 Adelie penguin chick measurements from the California Avian Data Center hosted by Point Reyes Blue Conservation Science ALL STAC Catalog 1996-12-25 2017-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214593929-SCIOPS.umm_json Measurements of chick flippers and mass taken at weekly intervals beginning 12/1996 (ongoing). proprietary
-B031_chickcount_1.0 Adelie penguin chick counts 1997-2014 from the California Avian Data Center hosted by Point Blue Conservation Science ALL STAC Catalog 1997-01-15 2017-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214593928-SCIOPS.umm_json Annual counts of Adelie penguin chicks at Capes Royds and Crozier, beginning in 1996 (ongoing). proprietary
+B031_ChickCon_1.0 Adelie penguin chick measurements from the California Avian Data Center hosted by Point Reyes Blue Conservation Science SCIOPS STAC Catalog 1996-12-25 2017-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214593929-SCIOPS.umm_json Measurements of chick flippers and mass taken at weekly intervals beginning 12/1996 (ongoing). proprietary
B031_chickcount_1.0 Adelie penguin chick counts 1997-2014 from the California Avian Data Center hosted by Point Blue Conservation Science SCIOPS STAC Catalog 1997-01-15 2017-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214593928-SCIOPS.umm_json Annual counts of Adelie penguin chicks at Capes Royds and Crozier, beginning in 1996 (ongoing). proprietary
+B031_chickcount_1.0 Adelie penguin chick counts 1997-2014 from the California Avian Data Center hosted by Point Blue Conservation Science ALL STAC Catalog 1997-01-15 2017-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214593928-SCIOPS.umm_json Annual counts of Adelie penguin chicks at Capes Royds and Crozier, beginning in 1996 (ongoing). proprietary
B031_diet_1.0 Adelie penguin diet data from the California Avian Data Center hosted by Point Blue Conservation Science ALL STAC Catalog 1996-12-15 2017-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214593919-SCIOPS.umm_json Diet of Adelie Penguins at Capes Crozier and Royds, Ross Island, beginning in 1996 (ongoing). proprietary
B031_diet_1.0 Adelie penguin diet data from the California Avian Data Center hosted by Point Blue Conservation Science SCIOPS STAC Catalog 1996-12-15 2017-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214593919-SCIOPS.umm_json Diet of Adelie Penguins at Capes Crozier and Royds, Ross Island, beginning in 1996 (ongoing). proprietary
B031_gls_1.0 Adelie penguin Geolocation Sensor data 2003-2007 from the California Avian Data Center hosted by Point Blue Conservation Science ALL STAC Catalog 2003-01-01 2007-01-31 165, -77.6, -155, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214593926-SCIOPS.umm_json Geolocation data from Adelie Penguins, 2003-2006. proprietary
B031_gls_1.0 Adelie penguin Geolocation Sensor data 2003-2007 from the California Avian Data Center hosted by Point Blue Conservation Science SCIOPS STAC Catalog 2003-01-01 2007-01-31 165, -77.6, -155, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214593926-SCIOPS.umm_json Geolocation data from Adelie Penguins, 2003-2006. proprietary
-B031_resight_1.0 Adelie penguin resighting data from the California Avian Data Center hosted by Point Blue Conservation Science SCIOPS STAC Catalog 1997-12-15 2017-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214593877-SCIOPS.umm_json Data on resighting of banded Adelie penguins, Capes Crozier and Royds, Ross Island, Antarctica. proprietary
B031_resight_1.0 Adelie penguin resighting data from the California Avian Data Center hosted by Point Blue Conservation Science ALL STAC Catalog 1997-12-15 2017-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214593877-SCIOPS.umm_json Data on resighting of banded Adelie penguins, Capes Crozier and Royds, Ross Island, Antarctica. proprietary
+B031_resight_1.0 Adelie penguin resighting data from the California Avian Data Center hosted by Point Blue Conservation Science SCIOPS STAC Catalog 1997-12-15 2017-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214593877-SCIOPS.umm_json Data on resighting of banded Adelie penguins, Capes Crozier and Royds, Ross Island, Antarctica. proprietary
B031_sat_1.0 Adelie penguin satellite position data from the California Avian Data Center hosted by Point Blue Conservation Science SCIOPS STAC Catalog 2000-12-15 2013-01-31 165, -77.6, -150, -70 https://cmr.earthdata.nasa.gov/search/concepts/C1214593930-SCIOPS.umm_json Satellite positions from Adelie penguins, Ross Island, Antarctica. proprietary
B031_sat_1.0 Adelie penguin satellite position data from the California Avian Data Center hosted by Point Blue Conservation Science ALL STAC Catalog 2000-12-15 2013-01-31 165, -77.6, -150, -70 https://cmr.earthdata.nasa.gov/search/concepts/C1214593930-SCIOPS.umm_json Satellite positions from Adelie penguins, Ross Island, Antarctica. proprietary
B031_tdr_1.0 Adelie penguin dive data 1999-2014 from the California Avian Data Center hosted by Point Blue Conservation Science SCIOPS STAC Catalog 1999-12-15 2014-01-31 165, -77.6, -150, -70 https://cmr.earthdata.nasa.gov/search/concepts/C1214593878-SCIOPS.umm_json Diving data from Adelie penguins. proprietary
@@ -3743,8 +3744,8 @@ BANd0209_113 Elevation map of Philippines from Global Elevation data ETOPO5 CEOS
BANd0210_113 Hydrology (Rivers and Lakes) map of Philippines from WBDII CEOS_EXTRA STAC Catalog 1970-01-01 116.68, 4.85, 127.23, 19.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232846656-CEOS_EXTRA.umm_json Digital map of Hydrology consisting Rivers and Lakes of Philippines compiled from the World Boundary Database II (WBDII) proprietary
BANd0213_113 Nature Reserves in the Coastal Zone of China CEOS_EXTRA STAC Catalog 1970-01-01 70.83, 15.06, 137.97, 56.58 https://cmr.earthdata.nasa.gov/search/concepts/C2232847866-CEOS_EXTRA.umm_json Nature Reserves in the Coastal Zone of China with general sites information proprietary
BANd0214_113 Coral Reef of China CEOS_EXTRA STAC Catalog 1970-01-01 70.83, 15.06, 137.97, 56.58 https://cmr.earthdata.nasa.gov/search/concepts/C2232848933-CEOS_EXTRA.umm_json Coral Reef of China with names proprietary
-BANd0216_113 Administrative map of Vietnam ALL STAC Catalog 1970-01-01 101.43, 7.75, 110.25, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2232848581-CEOS_EXTRA.umm_json District boundaries of Vietnam proprietary
BANd0216_113 Administrative map of Vietnam CEOS_EXTRA STAC Catalog 1970-01-01 101.43, 7.75, 110.25, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2232848581-CEOS_EXTRA.umm_json District boundaries of Vietnam proprietary
+BANd0216_113 Administrative map of Vietnam ALL STAC Catalog 1970-01-01 101.43, 7.75, 110.25, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2232848581-CEOS_EXTRA.umm_json District boundaries of Vietnam proprietary
BANd0217_113 Geological map of Vietnam CEOS_EXTRA STAC Catalog 1970-01-01 101.43, 7.75, 110.25, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2232848441-CEOS_EXTRA.umm_json Geological complex of Vietnam proprietary
BANd0218_113 Main rivers of Vietnam CEOS_EXTRA STAC Catalog 1970-01-01 101.43, 7.75, 110.25, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2232847445-CEOS_EXTRA.umm_json Main rivers of Vietnam proprietary
BANd0219_113 Main roads of Vietnam CEOS_EXTRA STAC Catalog 1970-01-01 101.43, 7.75, 110.25, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2232847235-CEOS_EXTRA.umm_json Main roads of Vietnam proprietary
@@ -3760,8 +3761,8 @@ BENEFIT_0 Measurements made off the Namibian and South African coasts between 20
BEST_0 Bering Ecosystem STudy (BEST) project OB_DAAC STAC Catalog 2008-07-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360145-OB_DAAC.umm_json The HLY0803 cruise of the USCG cutter Healy was an NSF funded cruise for the Bering Ecosystem Study (BEST) project that was focused on the impact of sea ice on the marine ecology of the region. In particular it focused on pathways of nutrients and organic matter that lead to the abundant upper trophic levels and valuable fisheries on the Bering Sea continental shelf. The cruise covered most of the eastern Bering Sea shelf from the Aleutian Islands to St. Lawrence Island with 177 unique stations that included CTD casts, bio-optics casts, MOCNESS tows, CALVet tows, bongo tows, multicore drops and sediment trap deployments. proprietary
BESTsed24 Accumulation of Dioxins and Furans in Sediment and Biota ALL STAC Catalog 1991-09-01 1991-09-01 -123, 45, -122, 46 https://cmr.earthdata.nasa.gov/search/concepts/C1214610437-SCIOPS.umm_json Monitoring of sediment and crayfish (Pacifastacus leniusculus) was conducted in order to satisfy monitoring requirements set forth in the City of St. Helens National Discharge Elimination System (NPDES) Permit (Tetra Tech 1992). Samples were collected from five sites to evaluate the accumulation of dioxins and furans in sediment and crayfish. Sediment and crayfish sampling primarily focused on locations downriver from the location of the outfall pipe. Sediment samples were collected and analyzed for seventeen dioxin/furan congeners, particle size distribution, total solids, and total organic carbon. All sediment data are presented on dry weight basis and TOC-normalized values are also provided in the report. Sampling station latitude and longitude were recorded from geographic coordinates provided by a Trimble Navigation Global Positioning System receiver. The area of study was the Lower Columbia River-St. Helens. Each sediment sample consisted of a composite of at least four grab samples. Surface sediments (top 2 cm) were transferred to a stainless steel bowl and homogenized with a stainless steel spatula. The samples were placed in jars and stored on ice except for the samples designated for TOC analysis. These samples were stored on dry ice. Target analytes were seventeen dioxin and furan congeners. Conventional analyses included particle size, total solids, and total organic carbon (TOC). Analytical techniques included dioxins and furans (EPA Method 1613A), TOC (modified EPA Method 415.1), total solids (EPA Method 160.3.), particle size (Puget Sound Estuary Program Protocols). All results are reported on a dry weight basis. The information for this metadata was taken from the Columbia River Basin: Sediment Database Abstracts. proprietary
BESTsed24 Accumulation of Dioxins and Furans in Sediment and Biota SCIOPS STAC Catalog 1991-09-01 1991-09-01 -123, 45, -122, 46 https://cmr.earthdata.nasa.gov/search/concepts/C1214610437-SCIOPS.umm_json Monitoring of sediment and crayfish (Pacifastacus leniusculus) was conducted in order to satisfy monitoring requirements set forth in the City of St. Helens National Discharge Elimination System (NPDES) Permit (Tetra Tech 1992). Samples were collected from five sites to evaluate the accumulation of dioxins and furans in sediment and crayfish. Sediment and crayfish sampling primarily focused on locations downriver from the location of the outfall pipe. Sediment samples were collected and analyzed for seventeen dioxin/furan congeners, particle size distribution, total solids, and total organic carbon. All sediment data are presented on dry weight basis and TOC-normalized values are also provided in the report. Sampling station latitude and longitude were recorded from geographic coordinates provided by a Trimble Navigation Global Positioning System receiver. The area of study was the Lower Columbia River-St. Helens. Each sediment sample consisted of a composite of at least four grab samples. Surface sediments (top 2 cm) were transferred to a stainless steel bowl and homogenized with a stainless steel spatula. The samples were placed in jars and stored on ice except for the samples designated for TOC analysis. These samples were stored on dry ice. Target analytes were seventeen dioxin and furan congeners. Conventional analyses included particle size, total solids, and total organic carbon (TOC). Analytical techniques included dioxins and furans (EPA Method 1613A), TOC (modified EPA Method 415.1), total solids (EPA Method 160.3.), particle size (Puget Sound Estuary Program Protocols). All results are reported on a dry weight basis. The information for this metadata was taken from the Columbia River Basin: Sediment Database Abstracts. proprietary
-BESTsed25 Accumulation of Dioxins and Furans in Sediment and Biota in the Lower Columbia Wauna River Area SCIOPS STAC Catalog 1991-09-01 1991-09-01 -123, 47, -122, 48 https://cmr.earthdata.nasa.gov/search/concepts/C1214610438-SCIOPS.umm_json Monitoring of sediment and crayfish (Pacifastacus leniusculus) was conducted in order to satisfy monitoring requirements set forth in the James River Wauna Mill's National Discharge Elimination System (NPDES) Permit (Tetra Tech 1992). Samples were collected from five sites to evaluate the accumulation of dioxins and furans in sediment and crayfish. Sediment and crayfish sampling primarily focused on locations downriver from the location of the outfall pipe. Sediment samples were collected and analyzed for seventeen dioxin/furan congeners, particle size distribution, total solids, and total organic carbon. Data are presented on a dry weight basis and TOC-normalized values are also provided in the report. Sampling station latitude and longitude were recorded from geographic coordinates provided by a Trimble Navigation Global Positioning System receiver. The area of study was the Lower Columbia River-Wauna. Each sediment sample consisted of a composite of at least four grab samples. Surface sediments (top 2 cm) were transferred to a stainless steel bowl and homogenized with a stainless steel spatula. The samples were placed in jars and stored on ice except for the samples designated for TOC analysis. These samples were stored on dry ice. Target analytes were Seventeen dioxin and furan congeners. Conventional analyses included particle size, total solids, and total organic carbon (TOC). Analytical techniques included Dioxins and furans (EPA Method 1613A), TOC (modified EPA Method 415.1), total solids (EPA Method 160.3.), particle size (Puget Sound Estuary Program Protocols). All results are reported on a dry weight basis. The information for this metadata was taken from the Columbia River Basin: Sediment Database Abstracts. proprietary
BESTsed25 Accumulation of Dioxins and Furans in Sediment and Biota in the Lower Columbia Wauna River Area ALL STAC Catalog 1991-09-01 1991-09-01 -123, 47, -122, 48 https://cmr.earthdata.nasa.gov/search/concepts/C1214610438-SCIOPS.umm_json Monitoring of sediment and crayfish (Pacifastacus leniusculus) was conducted in order to satisfy monitoring requirements set forth in the James River Wauna Mill's National Discharge Elimination System (NPDES) Permit (Tetra Tech 1992). Samples were collected from five sites to evaluate the accumulation of dioxins and furans in sediment and crayfish. Sediment and crayfish sampling primarily focused on locations downriver from the location of the outfall pipe. Sediment samples were collected and analyzed for seventeen dioxin/furan congeners, particle size distribution, total solids, and total organic carbon. Data are presented on a dry weight basis and TOC-normalized values are also provided in the report. Sampling station latitude and longitude were recorded from geographic coordinates provided by a Trimble Navigation Global Positioning System receiver. The area of study was the Lower Columbia River-Wauna. Each sediment sample consisted of a composite of at least four grab samples. Surface sediments (top 2 cm) were transferred to a stainless steel bowl and homogenized with a stainless steel spatula. The samples were placed in jars and stored on ice except for the samples designated for TOC analysis. These samples were stored on dry ice. Target analytes were Seventeen dioxin and furan congeners. Conventional analyses included particle size, total solids, and total organic carbon (TOC). Analytical techniques included Dioxins and furans (EPA Method 1613A), TOC (modified EPA Method 415.1), total solids (EPA Method 160.3.), particle size (Puget Sound Estuary Program Protocols). All results are reported on a dry weight basis. The information for this metadata was taken from the Columbia River Basin: Sediment Database Abstracts. proprietary
+BESTsed25 Accumulation of Dioxins and Furans in Sediment and Biota in the Lower Columbia Wauna River Area SCIOPS STAC Catalog 1991-09-01 1991-09-01 -123, 47, -122, 48 https://cmr.earthdata.nasa.gov/search/concepts/C1214610438-SCIOPS.umm_json Monitoring of sediment and crayfish (Pacifastacus leniusculus) was conducted in order to satisfy monitoring requirements set forth in the James River Wauna Mill's National Discharge Elimination System (NPDES) Permit (Tetra Tech 1992). Samples were collected from five sites to evaluate the accumulation of dioxins and furans in sediment and crayfish. Sediment and crayfish sampling primarily focused on locations downriver from the location of the outfall pipe. Sediment samples were collected and analyzed for seventeen dioxin/furan congeners, particle size distribution, total solids, and total organic carbon. Data are presented on a dry weight basis and TOC-normalized values are also provided in the report. Sampling station latitude and longitude were recorded from geographic coordinates provided by a Trimble Navigation Global Positioning System receiver. The area of study was the Lower Columbia River-Wauna. Each sediment sample consisted of a composite of at least four grab samples. Surface sediments (top 2 cm) were transferred to a stainless steel bowl and homogenized with a stainless steel spatula. The samples were placed in jars and stored on ice except for the samples designated for TOC analysis. These samples were stored on dry ice. Target analytes were Seventeen dioxin and furan congeners. Conventional analyses included particle size, total solids, and total organic carbon (TOC). Analytical techniques included Dioxins and furans (EPA Method 1613A), TOC (modified EPA Method 415.1), total solids (EPA Method 160.3.), particle size (Puget Sound Estuary Program Protocols). All results are reported on a dry weight basis. The information for this metadata was taken from the Columbia River Basin: Sediment Database Abstracts. proprietary
BFO_dsp01_ccrs_avhrr_landcover_589_1 BOREAS Follow-On DSP-01 NBIOME Level-4 AVHRR Land Cover, Canada, Ver. 1.1, 1995 ORNL_CLOUD STAC Catalog 1995-04-11 1995-11-01 -178, 34, -9, 67 https://cmr.earthdata.nasa.gov/search/concepts/C2761735337-ORNL_CLOUD.umm_json This land cover product was produced by NBIOME to generate an up-to-date, spatially and temporally consistent land cover map of the landmass of Canada for use by scientists and other users interested in environmental information at national and regional scales. This data set is gridded and was produced from 10-day composite data of surface parameters. proprietary
BFO_dsp01_ccrs_tm_landcover_588_1 BOREAS Follow-On DSP-01 Landsat TM Land Cover Mosaic of the BOREAS Transect ORNL_CLOUD STAC Catalog 1991-08-09 1998-08-28 -107, 52, -96, 57 https://cmr.earthdata.nasa.gov/search/concepts/C2956486000-ORNL_CLOUD.umm_json The objective of this land cover mosaic is to provide a data product that characterizes the detailed land cover of a significant portion of the BOREAS Region. Seven Landsat-5 TM images have been assembled to completely cover the BOREAS Transect. proprietary
BFO_dsp04_ers_freeze-thaw_maps_590_1 BOREAS Follow-On DSP-04 1994 ERS-1 Level-4 Landscape Freeze/Thaw Maps, Ver. 1.0 ORNL_CLOUD STAC Catalog 1994-02-14 1994-12-14 -111, 48, -90, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2956500512-ORNL_CLOUD.umm_json The BOREAS DSP-4 team acquired and analyzed imaging radar data from the ESA's ERS-1 over a complete annual cycle at the BOREAS sites in Canada in 1994 to detect shifts in radar backscatter related to varying environmental conditions. Two independent transitions correlating with snow melt and soil thaw onset, and possible canopy thaw were revealed by the data. proprietary
@@ -3818,8 +3819,8 @@ BRD_LSC003_MAHA MAHA Stream Order Fish Community Study CEOS_EXTRA STAC Catalog 1
BRD_LSC_AMERSHAD001 American Shad Riverine Habitat Requirements CEOS_EXTRA STAC Catalog 1990-01-01 1992-01-01 -76, 41, -75, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231548710-CEOS_EXTRA.umm_json Field evaluations of existing habitat suitability index (HSI) models for spawning adults, eggs, and larvae of American shad (Alosa sapidissima) were conducted in 1990-1992; initial models for juveniles in nursery habitats were developed. Fish abundance in various habitats of the upper Delaware River was quantified by (1) observation of adult spawning activity, (2) collection of eggs and larvae with metered plankton and drift nets, and (3) enumeration of juveniles by underwater observation and seining techniques. Regression analysis, principal component analysis, and range analysis were used to relate abundance to an array of physical habitat variables potentially influencing fish distributions. No HSI model was previously developed for juvenile American shad in riverine habitats. Four physical habitat variables were correlated with juvenile abundance: water temperature, dissolved oxygen (covariates), river depth, and turbidity. Regression analysis, principal component analysis, and range analysis were used to relate abundance to an array of physical habitat variables potentially influencing fish distributions. The Research and Development Laboratory-Wellsboro (RDL-W) is located on 55 acres near Wellsboro, Pennsylvania (Tioga County). Laboratory facilities include 3 modern buildings, 8x200-foot concrete raceways, 3 production wells, and support equipment. Core Capabilities The RDL-W conducts research for restoration of depleted fisheries and other aquatic biological resources. A diversified research program in ecology, conservation technology, genetics, and physiology emphases the integration of laboratory and field studies to develop scientifically sound approaches to the management of aquatic ecosystems. Research is directed primarily towards development of information and technology to increase understanding of aquatic ecosystems in the northeastern United States and to assist client agencies to better manage these ecosystems and their biota. Technical assistance is provided to clients throughout the nation. proprietary
BRMCR2_1 Pre-IceBridge MCoRDS L2 Ice Thickness V001 NSIDC_ECS STAC Catalog 1993-06-23 2007-09-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1726730204-NSIDC_ECS.umm_json This data set contains depth sounder measurements of ice elevation, ice surface, ice bottom, and ice thickness over Greenland and Antarctica, acquired by the Multichannel Coherent Radar Depth Sounder (MCoRDS). proprietary
BROKE-WEST_12kHZ_Bathy_Data_1 Bathymetry Data from the 12KHZ sounder on the BROKE-West voyage of the Aurora Australis, 2006 AU_AADC STAC Catalog 2006-01-02 2006-03-12 30, -70, 80, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214313366-AU_AADC.umm_json Readme - Bathymetry Files Data for BROKE-WEST 2006 1) Zipped folder contains .csv files created from each acoustics ev file for Transects 1 to 11. 2) These files contain subsections of each transect of variable length (usually between 50 and 100 km). 3) No data exists for files; Transect01_01 and 01_02 as the sea floor was greater than 5000m deep in these areas and was below the range set for the sounder. 4) Each file contains 11 columns of data; Ping_date, Ping_time, Ping_milliseconds, Latitude, Longitude, Position_status, Depth, Line_Status, Ping_status, Altitude, GPS_UTC time. 5) For practical purposes, the columns of interest will be Ping_date, Ping_time, Latitude, Longitude and Depth. Other columns are ancillary acoustics information and can be ignored. Line status should be 1 (meaning good) as sea floor was only picked when it could be easily defined. If the sea floor could not be visually defined or was deemed to uncertain, it was not picked in the echogram. Hence sea floor may not be totally contiguous. 6) Depth of the sea floor was only defined for those areas deemed to be 'on transect', i.e. straight transects for acoustics survey purposes. Deviations from the transect, i.e. to pick up moorings, conduct target or routine trawls or visit nice looking bergs were deemed 'off transect' and were excluded from the analysis. 7) Sea floor depth was primarly defined for the purposes of the acoustics analysis, i.e. exclusion from the echograms. Hence the values in the files are for the 'sea floor exclusion line' that is set above the true sea floor in order to exclude noise from the analysis. This means the sea floor depths in these files are likely to be an underestimate of the true depth. The uncertainty is likely to be of the order of 2 to 10m. 8) Another source of error is that depth was calculated with values of absorption coefficient and sound speed set to default values derived from pre-cruise hydrographic data. One value for each parameter was applied to the whole data set. These values were; 0.028 dB/m (120 KhZ), 0.010 dB/m (38kHz), 0.041 dB/m (200 kHz), 0.0017 dB/m (12kHz - bathy sounder) for absorption coefficient and 1456 m/s for sound speed. 9) These values will be recalculated from the oceanographic data derived during the voyage and applied to the data set during post-processing (forthcoming analyses for May-June 2006). Revision of these parameters may cause a slight shift in the calculated depths, although this is likely to be small. 10) Reprocessing of the data may also result in more accurate bottom detection. This data should be available post June 2006 and will be sent to interested parties as soon as it is completed. 11) Dataset was created by Esmee van Wijk. proprietary
-BROKE-West_ACS_1 ACS data collected on the BROKE-West voyage of the Aurora Australis, 2006 ALL STAC Catalog 2006-01-17 2006-02-28 30, -69.1, 80, -59.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214308312-AU_AADC.umm_json Profiles of visible light absorption and attenuation coefficients were measured in the upper 100m of the water column. Data Acquisition: The Wetlabs ACS spectral absorption and attenuation meter was mounted on a deployment frame together with a Seabird pump, a Wetlabs DH-4 data logger and two battery packs. This set-up was as recommended in the Wetlabs manual. The logger was set to control the ACS once the on/off magnet had been inserted. The data acquisition program comprised 2 minutes delay time to allow the instrument to be deployed over the stern; 30 seconds warm-up time; 30 seconds flush time during which the pump was activated, and finally 12 minutes of data acquisition. Physically, the instrument was attached to the winch, the magnet was inserted as soon as permission to deploy had been obtained from the bridge, the instrument was lowered directly to 20m, until 1.5 minutes since insertion of the magnet. The instrument was then brought to just below the surface and lowered at 0.5m per second to a depth of 100m, then retrieved at the same speed. Once the instrument was back on deck the magnet was removed to prevent dry operation of the pump. The data logger received an instrument-specific binary format data file for each deployment, with automatic sequential file numbering. These files were uploaded after each deployment. Data Processing: The Wetlabs software program WAP was used to extract ascii data from the binary files. This procedure included corrections for internal instrument temperature and the latest manufacturer's calibration for wavelength. Note that although daily calibrations were performed during the cruise, the manufacturer advised against using these calibrations as conditions were suboptimal (milli-Q water not fresh, environment not totally dry or well temperature-controlled). A matlab script, acs.m, written by the principal investigator, continues the data processing. Data recorded in air are discarded, remaining data are binned to 2m depth intervals, occasional spurious data with a discontinuity in absorption or attenuation spectra are removed, and a correction is applied to account for differences in ocean water temperature and salinity compared to the calibration conditions. This final step uses first-cut CTD data courtesy of the oceanography team (Bindoff et al). Not yet complete (as of 2006-03-10): Remaining spurious data need to be weeded out by hand. These include non-systematic quirks such as occurrence of bubbles or larger particles in the optical path. The depth needs to be corrected for an offset of some 4m plus the difference between the pressure sensor location and the ACS-inlet location. Dataset Format: For each 100m profile, a single ascii file is available, comprising instrument calibration data and a time sequence of attenuation and absorption spectra. By placing each of the profile files from one cruise transect in a single directory, the acs.m routine can be applied to one leg at a time, yielding matlab fields of [station, depth (0:2m:100m), wavelength (87 wavelengths)]. The acs.m script includes details of which CTD station number refers to which ACS file number. This information is also supplied in the station log file jill_brokew_stations.xls. Acronyms Used: ACS - Absorption (a) Attenuation (c) Spectral meter, produced by Wetlabs CTD - Conductivity, Temperature, Pressure. This work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679). proprietary
BROKE-West_ACS_1 ACS data collected on the BROKE-West voyage of the Aurora Australis, 2006 AU_AADC STAC Catalog 2006-01-17 2006-02-28 30, -69.1, 80, -59.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214308312-AU_AADC.umm_json Profiles of visible light absorption and attenuation coefficients were measured in the upper 100m of the water column. Data Acquisition: The Wetlabs ACS spectral absorption and attenuation meter was mounted on a deployment frame together with a Seabird pump, a Wetlabs DH-4 data logger and two battery packs. This set-up was as recommended in the Wetlabs manual. The logger was set to control the ACS once the on/off magnet had been inserted. The data acquisition program comprised 2 minutes delay time to allow the instrument to be deployed over the stern; 30 seconds warm-up time; 30 seconds flush time during which the pump was activated, and finally 12 minutes of data acquisition. Physically, the instrument was attached to the winch, the magnet was inserted as soon as permission to deploy had been obtained from the bridge, the instrument was lowered directly to 20m, until 1.5 minutes since insertion of the magnet. The instrument was then brought to just below the surface and lowered at 0.5m per second to a depth of 100m, then retrieved at the same speed. Once the instrument was back on deck the magnet was removed to prevent dry operation of the pump. The data logger received an instrument-specific binary format data file for each deployment, with automatic sequential file numbering. These files were uploaded after each deployment. Data Processing: The Wetlabs software program WAP was used to extract ascii data from the binary files. This procedure included corrections for internal instrument temperature and the latest manufacturer's calibration for wavelength. Note that although daily calibrations were performed during the cruise, the manufacturer advised against using these calibrations as conditions were suboptimal (milli-Q water not fresh, environment not totally dry or well temperature-controlled). A matlab script, acs.m, written by the principal investigator, continues the data processing. Data recorded in air are discarded, remaining data are binned to 2m depth intervals, occasional spurious data with a discontinuity in absorption or attenuation spectra are removed, and a correction is applied to account for differences in ocean water temperature and salinity compared to the calibration conditions. This final step uses first-cut CTD data courtesy of the oceanography team (Bindoff et al). Not yet complete (as of 2006-03-10): Remaining spurious data need to be weeded out by hand. These include non-systematic quirks such as occurrence of bubbles or larger particles in the optical path. The depth needs to be corrected for an offset of some 4m plus the difference between the pressure sensor location and the ACS-inlet location. Dataset Format: For each 100m profile, a single ascii file is available, comprising instrument calibration data and a time sequence of attenuation and absorption spectra. By placing each of the profile files from one cruise transect in a single directory, the acs.m routine can be applied to one leg at a time, yielding matlab fields of [station, depth (0:2m:100m), wavelength (87 wavelengths)]. The acs.m script includes details of which CTD station number refers to which ACS file number. This information is also supplied in the station log file jill_brokew_stations.xls. Acronyms Used: ACS - Absorption (a) Attenuation (c) Spectral meter, produced by Wetlabs CTD - Conductivity, Temperature, Pressure. This work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679). proprietary
+BROKE-West_ACS_1 ACS data collected on the BROKE-West voyage of the Aurora Australis, 2006 ALL STAC Catalog 2006-01-17 2006-02-28 30, -69.1, 80, -59.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214308312-AU_AADC.umm_json Profiles of visible light absorption and attenuation coefficients were measured in the upper 100m of the water column. Data Acquisition: The Wetlabs ACS spectral absorption and attenuation meter was mounted on a deployment frame together with a Seabird pump, a Wetlabs DH-4 data logger and two battery packs. This set-up was as recommended in the Wetlabs manual. The logger was set to control the ACS once the on/off magnet had been inserted. The data acquisition program comprised 2 minutes delay time to allow the instrument to be deployed over the stern; 30 seconds warm-up time; 30 seconds flush time during which the pump was activated, and finally 12 minutes of data acquisition. Physically, the instrument was attached to the winch, the magnet was inserted as soon as permission to deploy had been obtained from the bridge, the instrument was lowered directly to 20m, until 1.5 minutes since insertion of the magnet. The instrument was then brought to just below the surface and lowered at 0.5m per second to a depth of 100m, then retrieved at the same speed. Once the instrument was back on deck the magnet was removed to prevent dry operation of the pump. The data logger received an instrument-specific binary format data file for each deployment, with automatic sequential file numbering. These files were uploaded after each deployment. Data Processing: The Wetlabs software program WAP was used to extract ascii data from the binary files. This procedure included corrections for internal instrument temperature and the latest manufacturer's calibration for wavelength. Note that although daily calibrations were performed during the cruise, the manufacturer advised against using these calibrations as conditions were suboptimal (milli-Q water not fresh, environment not totally dry or well temperature-controlled). A matlab script, acs.m, written by the principal investigator, continues the data processing. Data recorded in air are discarded, remaining data are binned to 2m depth intervals, occasional spurious data with a discontinuity in absorption or attenuation spectra are removed, and a correction is applied to account for differences in ocean water temperature and salinity compared to the calibration conditions. This final step uses first-cut CTD data courtesy of the oceanography team (Bindoff et al). Not yet complete (as of 2006-03-10): Remaining spurious data need to be weeded out by hand. These include non-systematic quirks such as occurrence of bubbles or larger particles in the optical path. The depth needs to be corrected for an offset of some 4m plus the difference between the pressure sensor location and the ACS-inlet location. Dataset Format: For each 100m profile, a single ascii file is available, comprising instrument calibration data and a time sequence of attenuation and absorption spectra. By placing each of the profile files from one cruise transect in a single directory, the acs.m routine can be applied to one leg at a time, yielding matlab fields of [station, depth (0:2m:100m), wavelength (87 wavelengths)]. The acs.m script includes details of which CTD station number refers to which ACS file number. This information is also supplied in the station log file jill_brokew_stations.xls. Acronyms Used: ACS - Absorption (a) Attenuation (c) Spectral meter, produced by Wetlabs CTD - Conductivity, Temperature, Pressure. This work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679). proprietary
BROKE-West_ADCP_1 ADCP current velocity data for CTD stations of the BROKE-West voyage of the Aurora Australis, 2006 ALL STAC Catalog 2005-12-31 2006-03-03 29.898, -69.216, 115.746, -31.964 https://cmr.earthdata.nasa.gov/search/concepts/C1214313367-AU_AADC.umm_json The Acoustic Doppler Current Profiler (ADCP) data were acquired constantly over the duration of the Australian 2006 V3 BROKE-West survey. Data presented here are the results of 1/2 hour integrations of the cruise data from the start of the voyage in Fremantle, Australia, to the start of the return leg just north of Australia's Davis Station in Antarctica (-66.56S, 77.98E). North and eastward components of the current velocity are given for depths up to 300m below the surface along the ship track. Data Acquisition: The shipboard ADCP is a continuous broadband recording device that operates over the duration of the voyage, ensonifying the water column once a second. As the instrument is fixed to the ship, it has a range of approximately 250m deep. Data from the shipboard Ashtek 3 dimensional GPS system is used along with bottom tracking data (when the water is shallow enough i.e. less than 250m) and automatically integrated into ADCP ping data to provide absolute current velocities. Data Processing: The ship ADCP constantly and automatically collects and stores raw .rawdp binary files in ensembles of three minutes worth of pings. This is regularly automatically collated into larger .adp files containing data for several hours (200+ ensembles). This data are processed for use in analysis using specialist software provided by Mark Rosenberg (mark.rosenberg AT utas.edu.au) that integrates together data from the ADCP .adp files for periods (30 minutes in this case) over a give time (from cruise start to the 3-Mar-2006). This produces .any ASCII files. These ASCII files are read into the Matlab processing package using scripts provided by Sergeui Sokolov (sergeui.sokolov AT csiro.au) which then produces the .mat matlab data files covered by this metadata. ADCP data requires proper calibration with respect to ship motion, which were not carried out for this data set, and could cause significant change when processed properly after the voyage. Dataset format: The processed ADCP file is given in matlab .mat format. All 1/2 hour integrations of ADCP data for BROKE-West from 3 days (31-dec-2005) before departure from Fremantle, to the 3-Mar-2006 are included, each column in each matrix or array representing an individual 1/2 hour integration in chronological order. There are numerous gaps in the data that occurred when the ADCP crashed and was not immediately reset or when bad data prevented processing. The location can be identified by plotting a scatter plot of longitude vs latitude, and the times by plotting the julian date. The matlab variables contained in the BROKE_West_ADCP.mat file are contained inside the adcp structure: lon: Longitude (decimal degrees) lat: Latitude (decimal degrees) time: Each column gives the year month day and hour of collection of the corresponding columns in the other variables. depth: Depth of each corresponding velocity value for each 1/2 profile. 60 fixed bin depths are given for each profile. (meters) press: As for depth but given in db. (db) u: Absolute current eastward component in ms-1 for each depth and profile. v: Absolute current northward component in ms-1 for each depth and profile. unav: Ship absolute eastward component in ms-1 for each profile vnav: Ship absolute northward component in ms-1 for each profile jtime: Julian date for each profile (julian days) badvals: Indexes of anomolous latitude and longitude values Acronyms used: ADCP: Accoustic Doppler Current Profiler IASOS: Institute of Antarctic and Southern Ocean Studies CSIRO: Commonwealth Scientific and Industrial Research Organisation This work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679). proprietary
BROKE-West_ADCP_1 ADCP current velocity data for CTD stations of the BROKE-West voyage of the Aurora Australis, 2006 AU_AADC STAC Catalog 2005-12-31 2006-03-03 29.898, -69.216, 115.746, -31.964 https://cmr.earthdata.nasa.gov/search/concepts/C1214313367-AU_AADC.umm_json The Acoustic Doppler Current Profiler (ADCP) data were acquired constantly over the duration of the Australian 2006 V3 BROKE-West survey. Data presented here are the results of 1/2 hour integrations of the cruise data from the start of the voyage in Fremantle, Australia, to the start of the return leg just north of Australia's Davis Station in Antarctica (-66.56S, 77.98E). North and eastward components of the current velocity are given for depths up to 300m below the surface along the ship track. Data Acquisition: The shipboard ADCP is a continuous broadband recording device that operates over the duration of the voyage, ensonifying the water column once a second. As the instrument is fixed to the ship, it has a range of approximately 250m deep. Data from the shipboard Ashtek 3 dimensional GPS system is used along with bottom tracking data (when the water is shallow enough i.e. less than 250m) and automatically integrated into ADCP ping data to provide absolute current velocities. Data Processing: The ship ADCP constantly and automatically collects and stores raw .rawdp binary files in ensembles of three minutes worth of pings. This is regularly automatically collated into larger .adp files containing data for several hours (200+ ensembles). This data are processed for use in analysis using specialist software provided by Mark Rosenberg (mark.rosenberg AT utas.edu.au) that integrates together data from the ADCP .adp files for periods (30 minutes in this case) over a give time (from cruise start to the 3-Mar-2006). This produces .any ASCII files. These ASCII files are read into the Matlab processing package using scripts provided by Sergeui Sokolov (sergeui.sokolov AT csiro.au) which then produces the .mat matlab data files covered by this metadata. ADCP data requires proper calibration with respect to ship motion, which were not carried out for this data set, and could cause significant change when processed properly after the voyage. Dataset format: The processed ADCP file is given in matlab .mat format. All 1/2 hour integrations of ADCP data for BROKE-West from 3 days (31-dec-2005) before departure from Fremantle, to the 3-Mar-2006 are included, each column in each matrix or array representing an individual 1/2 hour integration in chronological order. There are numerous gaps in the data that occurred when the ADCP crashed and was not immediately reset or when bad data prevented processing. The location can be identified by plotting a scatter plot of longitude vs latitude, and the times by plotting the julian date. The matlab variables contained in the BROKE_West_ADCP.mat file are contained inside the adcp structure: lon: Longitude (decimal degrees) lat: Latitude (decimal degrees) time: Each column gives the year month day and hour of collection of the corresponding columns in the other variables. depth: Depth of each corresponding velocity value for each 1/2 profile. 60 fixed bin depths are given for each profile. (meters) press: As for depth but given in db. (db) u: Absolute current eastward component in ms-1 for each depth and profile. v: Absolute current northward component in ms-1 for each depth and profile. unav: Ship absolute eastward component in ms-1 for each profile vnav: Ship absolute northward component in ms-1 for each profile jtime: Julian date for each profile (julian days) badvals: Indexes of anomolous latitude and longitude values Acronyms used: ADCP: Accoustic Doppler Current Profiler IASOS: Institute of Antarctic and Southern Ocean Studies CSIRO: Commonwealth Scientific and Industrial Research Organisation This work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679). proprietary
BROKE-West_CTD_Niskin_1 CTD Niskin data collected from the BROKE-West voyage of the Aurora Australis, 2006 AU_AADC STAC Catalog 2006-01-01 2006-03-20 29.92, -69.21, 80.04, -61.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214313347-AU_AADC.umm_json 3 litres of seawater were collected every 2nd CTD (conductivity, temperature and depth) cast on every CTD transect of the BROKE-West voyage. 7 CTD transects were completed on the BROKE-West voyage, all on southwards legs. Samples were collected at 6 depths in the top 200 m of the water column using niskin bottles. 2 litres were filtered through polycarbonate filters and 1 litre was filtered through a fibreglass filter. Chemical digestion of the polycarbonate filter enabled us to determine the particulate silicon concentration for each sample (using the nutrient autoanalyser onboard the Aurora Australis, see hydrochemistry section), fibreglass filters have been dried and stored for CHN analysis back on shore. This work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679). proprietary
@@ -3839,8 +3840,8 @@ BROKE-West_krill_larvae_1 Larval krill data collected during the BROKE-West voya
BROKE-West_mm_acoustics_2 Marine mammal acoustic survey data from sonobuoy deployments on the BROKE-WEST Survey AU_AADC STAC Catalog 2006-01-04 2006-02-28 29.98, -69.12, 103.71, -36.58 https://cmr.earthdata.nasa.gov/search/concepts/C1214313374-AU_AADC.umm_json Data Acquisition: DIFAR (DIrectional Fixing And Ranging) 53D sonobuoys were deployed every 30 minutes of longitude during each of the north-south sampling transects as part of the acoustic survey for marine mammals. Sonobuoys were also deployed opportunistically when large numbers of whales (in particular minke whales) were sighted. Additionally, on the initial E-W transect (#12) sonobouys were deployed prior to the majority of CTD stations. The VHF receiving system for the sonobuoys aboard the ship began with a 6 element YAGI antenna mounted atop the ship's mast. The sonobuoy's VHF signal output from the YAGI was amplified through an Advanced Receiver Research VHF amplifier and received on ICOM PCR-1000 VHF receivers modified to improve low frequency audio output. The audio signal passed through a low pass anti-alias filter (National Instruments analogue bessel SCXI module) and was recorded onto a laptop through a National Instruments E-series (model 6062E) sound card at a sampling rate of 48kHz. Difar sonobuoys have an effective audio response up to 2.5kHz before the low-pass filter roll-off starts. DIFAR bearing information is carried on 7.5 and 15kHz carrier frequencies. Once sonobuoys were deployed, recordings were made for at least 70 minutes unless the sonobuoy failed or the signal was lost. During recordings at CTD stations, recordings were typically made for the length of time it took to complete the CTD (4 or more hours). Data Processing: Signals were monitored in real-time during acquisition using Ishmael software (Dave Mellinger, http://www.bioacoustics.us/ishmael.html). A scrolling spectrogram (FFT size: 16384 samples, overlap: 50%, frequency range displayed: 0-1000 Hz, time scaling: 5 sec/cm) was monitored in real-time. Sounds of interest were clipped and the time and description were logged in the sonobuoy deployment data logs. Bearings to sounds were attained with a modified version of DiFarV (Mark McDonald, http://www.whaleacoustics.com ). Note that bearings to the ship noise given by DifarV are ~180 degrees off for an as yet undetermined reason (potentially deep cold water propagation effects), but the bearings to whale sounds and other sounds of interest are thought to be correct. This appears to be the case with a series of light bulb calibration tests I did, suggesting that bearings to other sounds are in fact, correct. After acquisition, recordings were also post-processed in Ishmael with two further passes, one examining 0-2.5kHz, and another monitoring 0-1kHz again, to ensure as many marine mammal sounds as possible were identified. Clips were also re-examined when necessary to ensure species were correctly identified. In instances when apparently multiple whales were calling, calculated bearings were used to determine whether the sounds came from different bearings, and hence, different whales. Dataset Format: The dataset description is in an excel workbook, with a summary sheet at the front. The summary sheet has a single line summarising each sonobuoy deployment. The sonobuoy deployment data log sheets are separated by days when the deployment began. Each is marked by date - eg 01.10 is the 10th of January. Each deployment has an initial entry and the following rows are a running log of the sonobuoy recording session. The data sheets and the summary sheet are in the following format with column headers from left to right: Observer(real time/post-processing)Summary of the sounds that occurred within the sample (70 minutes) Total recording length (in minutes) Date UTC time of deployment Initial latitude (decimal degrees) Initial Longitude (decimal degrees) Depth setting of sonobuoy hydrophone (90, 120, or 300m) National Instruments sound card gain (0, 5, or 10 times) Ship heading (true degrees) Ship speed (knots) Distance of deployment from CTD location (if applicable) UTC time of events (applies mainly to log of events in sonobuoy deployment data log) Species or sound description (applies mainly to sonobuoy deployment data log) Comments Sonobuoy type Raw data files are stored on a series of external hard drives. This work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679). proprietary
BROKE-West_particulates_1 "Filter Pad absorption measurements of suspended particulate matter -
data from the BROKE-West voyage of the Aurora Australis, 2006" AU_AADC STAC Catalog 2006-01-09 2006-02-28 30, -69.1, 80, -59.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214308460-AU_AADC.umm_json Particulates in the water were concentrated onto 25mm glass fibre filters. Light transmission and reflection through the filters was measured using a spectrophotometer to yield spectral absorption coefficients. Data Acquisition: Water samples were taken from Niskin bottles mounted on the CTD rosette. Two or three depths were selected at each station, using the CTD fluorometer profile to identify the depth of maximum fluorescence and below the fluorescence maximum. One sample was always taken at 10m, provided water was available, as a reference depth for comparisons with satellite data (remote sensing international standard). Water sampling was carried out after other groups, leading to a considerable time delay of between half an hour and 3 hours, during which particulates are likely to have sedimented within the Niskin bottle, and algae photoadapted to the dark. In order to minimise problems of sedimentation, as large a sample as practical was taken. Often so little water remained in the Niskin bottle that the entire remnant was taken. Where less than one litre remained, leftover sample water was taken from the HPLC group. Water samples were filtered through 25mm diameter GF/F filters under a low vacuum (less than 5mmHg), in the dark. Filters were stored in tissue capsules in liquid nitrogen and transported to the lab for analysis after the cruise. Three water samples were filtered through GF/F filters under gravity, with 2 30ml pre-rinses to remove organic substances from the filter, and brought to the laboratory for further filtration through 0.2micron membrane filters. Filters were analysed in batches of 3 to 7, with all depths at each station being analysed within the same batch to ensure comparability. Filters were removed one batch at a time and place on ice in the dark. Once defrosted, the filters were placed upon a drop of filtered seawater in a clean petri dish and returned to cold, dark conditions. One by one, the filters were placed on a clean glass plate and scanned from 200 to 900nm in a spectrophotometer equipped with an integrating sphere. A fresh baseline was taken with each new batch using 2 blank filters from the same batch as the sample filters, soaked in filtered seawater. After scanning, the filters were placed on a filtration manifold, soaked in methanol for between 1 and 2 hours to extract pigments, and rinsed with filtered seawater. They were then scanned again against blanks soaked in methanol and rinsed in filtered seawater. Data Processing: The initial scan of total particulate matter, ap, and the second scan of non-pigmented particles, anp, were corrected for baseline wandering by setting the near-infrared absorption to zero. This technique requires correction for enhanced scattering within the filter, which has been reported to vary with species. One dilution series was carried out at station 118 to allow calculation of the correction (beta-factor). Since it is debatable whether this factor will be applicable to all samples, no correction has been applied to the dataset. Potential users should contact JSchwarz for advice on this matter when using the data quantitatively. Not yet complete: Comparison of the beta-factor calculated for station 118 with the literature values. Comparison of phytoplankton populations from station 118 with those found at other stations to evaluate the applicability of the beta-factor. Dataset Format: Two files: phyto_absorp_brokew.txt and phyto_absorp_brokew_2.txt: covering stations 4 to 90 and 91 to 118, respectively. Note that not every station was sampled. File format: Matlab-readable ascii text with 3 'header' lines: Row 1: col.1=-999, col.2 to end = ctd number Row 2: col.1=-999, col.2 to end = sample depth in metres Row 3: col.1=-999, col.2 to end = 1 for total absorption by particulates, 2 for absorption by non-pigmented particles Row 4 to end: col.1=wavelength in nanometres, col.2 to end = absorption coefficient corresponding to station, depth and type given in rows 1 to 3 of the same column. This work was completed as part of ASAC projects 2655 and 2679 (ASAC_2655, ASAC_2679). proprietary
-BROKE_Documentation_Logs_1 A collection of logs and documentation associated with the BROKE voyage of the Aurora Australis in the 1995/1996 season ALL STAC Catalog 1996-01-19 1996-03-31 70, -67, 165, -44 https://cmr.earthdata.nasa.gov/search/concepts/C1214313364-AU_AADC.umm_json A collection of scanned logs and documentation from the BROKE cruise of the Aurora Australis in the 1995/1996 season. Available logs include: BROKE V4 1995/1996 Catch Composition - 2 Logs BROKE V4 1995/1996 Krill Larvae Log BROKE V4 1995/1996 Krill Morphometrics - 3 logs BROKE V4 1995/1996 Trawl Log BROKE V4 1995/1996 Wet Lab Log See the logs for further details. proprietary
BROKE_Documentation_Logs_1 A collection of logs and documentation associated with the BROKE voyage of the Aurora Australis in the 1995/1996 season AU_AADC STAC Catalog 1996-01-19 1996-03-31 70, -67, 165, -44 https://cmr.earthdata.nasa.gov/search/concepts/C1214313364-AU_AADC.umm_json A collection of scanned logs and documentation from the BROKE cruise of the Aurora Australis in the 1995/1996 season. Available logs include: BROKE V4 1995/1996 Catch Composition - 2 Logs BROKE V4 1995/1996 Krill Larvae Log BROKE V4 1995/1996 Krill Morphometrics - 3 logs BROKE V4 1995/1996 Trawl Log BROKE V4 1995/1996 Wet Lab Log See the logs for further details. proprietary
+BROKE_Documentation_Logs_1 A collection of logs and documentation associated with the BROKE voyage of the Aurora Australis in the 1995/1996 season ALL STAC Catalog 1996-01-19 1996-03-31 70, -67, 165, -44 https://cmr.earthdata.nasa.gov/search/concepts/C1214313364-AU_AADC.umm_json A collection of scanned logs and documentation from the BROKE cruise of the Aurora Australis in the 1995/1996 season. Available logs include: BROKE V4 1995/1996 Catch Composition - 2 Logs BROKE V4 1995/1996 Krill Larvae Log BROKE V4 1995/1996 Krill Morphometrics - 3 logs BROKE V4 1995/1996 Trawl Log BROKE V4 1995/1996 Wet Lab Log See the logs for further details. proprietary
BROKE_Fish_Zooplankton_RM8_1 Fish and zooplankton from RMT-8 net hauls on the BROKE voyage AU_AADC STAC Catalog 1996-01-19 1996-03-31 80, -67, 150, -55 https://cmr.earthdata.nasa.gov/search/concepts/C1299545242-AU_AADC.umm_json Taken from the abstracts of the referenced papers: Distribution patterns of pelagic fish, larvae and juveniles collected by RMT trawls during BROKE survey to CCAMLR Division 58.4.1 were investigated. Nearly 2000 individuals, weighing 1210 g, were collected from approximately 1.5 million cubic metres of the upper 200 m of ocean, supporting the theory that Antarctic ichthyoplankton has low biomass. The collection consisted mainly of P. antarcticum larvae and juveniles and E. antarctica sub-adults, with a range of other notothenioid fish and myctophids. Three distinct biogeographic zones, with characteristic ichthyo- and zooplankton assemblages, were identified. The Oceanic Zone was dominated by myctophids and, in the western reaches, the paralepidid N. coasti. The shelf break zone comprised of myctophids, and the juveniles of notothenioid fish. The shelf zone consisted of notothenioid juveniles and sub-adults. Characteristic water masses and associated zooplankton assemblages were found throughout these three zones. Analysis of fish stomach contents indicated feeding on locally abundant zooplankton taxa. There was niche-partitioning of prey taxa and size classes, between both sympatric species and between different ontogenetic stages. Fish distributions corresponded to known patterns, and extended the geographic range of several species. ##### Zooplankton data from routine 0-200 m oblique trawls were analysed using cluster analysis and non-metric multidimensional scaling to define the communities in Eastern Antarctica (80-150 E), their distribution patterns, indicator species, and species affinities. Three communities were defined based on routine trawls. The Main Oceanic Community comprising herbivorous copepods, chaetognaths, and the euphausiid Thysanoessa macrura dominated the area west of 120 E. The area east of 120 E was dominated by Salpa thompsoni. The third community located in the neritic zone was dominated by Euphausia crystallorophias. Antarctic krill Euphausia superba did not form a distinct community in its own right, unlike previous observations in Prydz Bay. Krill were distributed throughout most of the survey area but generally in higher abundances towards the shelf break. Overall, krill abundance was low compared with previous net surveys in Prydz Bay. Three main types of assemblages were identified based on target trawls. The first group was dominated by krill (mean 1149 individuals per 1000 cubic metres) which represented greater than 99% of Group 1 catches in terms of numbers and biomass. Group 2 comprised the bulk of target trawls and comprised a wide diversity of species typical of the main oceanic community, with a mean abundance approximately half of that observed in the routine trawls. The third group comprised trawls in the neritic zone dominated by E. crystallorophias. No salp-dominated aggregation was found. While E. superba did not dominate a distinct community geographically as seen in previous Prydz Bay surveys, it did dominate discrete layers or aggregations, showing that both horizontal and vertical separation of communities exist. ##### The download file contains the following documents: 199596040Composition.csv 199596040Density.csv 199596040Biomass.csv proprietary
BROKE_Krill_Scans_1 BROKE transects and krill aggregations - scanned maps of voyage 4 of the Aurora Australis, 1995-1996 AU_AADC STAC Catalog 1996-01-19 1996-03-31 80, -66.5, 150, -63 https://cmr.earthdata.nasa.gov/search/concepts/C1214313312-AU_AADC.umm_json The download file contains files (Broke 1, Broke 2 and Broke 3) in three formats resulting from the scanning of three plots of BROKE transects with annotations about krill aggregation. The tiff is the primary file from the scanning. The jpeg and pdf were created from the tiff for quick viewing. The numbered points on the plots are trawl locations. The annotations include information about krill aggregation from the echosounder and also information from the trawls. The data contributed to the two papers listed in the references section. BROKE was a marine science cruise conducted by the Aurora Australis during the 1995-1996 season (voyage 4). proprietary
BROKE_at_sea_obs_1 At sea observations made on the BROKE voyage of the Aurora Australis, 1995-1996 AU_AADC STAC Catalog 1996-01-19 1996-03-31 70, -67, 165, -44 https://cmr.earthdata.nasa.gov/search/concepts/C1667367994-AU_AADC.umm_json A collection of at sea observations made of icebergs, seabirds and whales on the BROKE voyage of the Aurora Australis during the 1995-1996 summer season. The data are mostly text or csv files and document observations of icebergs, seabirds and whales, giving times and/or locations. Further supporting information may be included in the data download, or in other metadata records relating to the BROKE voyage (as opposed to the later BROKE-West voyage). proprietary
@@ -3869,8 +3870,8 @@ Biogenic_CO2flux_SIF_SMUrF_1899_1 Urban Biogenic CO2 fluxes: GPP, Reco and NEE E
Biology_Bunger_Hills_1977_1 A biological reconnaissance of the Bunger Hills, March 1977 - R.J. Barker ALL STAC Catalog 1977-03-02 1977-03-02 100, -66.35, 101.5, -65.85 https://cmr.earthdata.nasa.gov/search/concepts/C1291623089-AU_AADC.umm_json Scanned copy of the title document. Taken from the abstract of the report: The Bunger Hills, situated between latitudes 65 degrees, 51 minutes and 66 degrees 20 minutes South and longitudes 100 degrees and 101 degrees 30 minutes East, were visited by members of the Australian National Antarctic Research Expedition (ANARE) on the 2nd of March, 1977. Biological and geological samples were collected. This report presents a summary of the information obtained and reviews the earlier history and scientific work in the Bunger Hills by other nations. proprietary
Biology_Bunger_Hills_1977_1 A biological reconnaissance of the Bunger Hills, March 1977 - R.J. Barker AU_AADC STAC Catalog 1977-03-02 1977-03-02 100, -66.35, 101.5, -65.85 https://cmr.earthdata.nasa.gov/search/concepts/C1291623089-AU_AADC.umm_json Scanned copy of the title document. Taken from the abstract of the report: The Bunger Hills, situated between latitudes 65 degrees, 51 minutes and 66 degrees 20 minutes South and longitudes 100 degrees and 101 degrees 30 minutes East, were visited by members of the Australian National Antarctic Research Expedition (ANARE) on the 2nd of March, 1977. Biological and geological samples were collected. This report presents a summary of the information obtained and reviews the earlier history and scientific work in the Bunger Hills by other nations. proprietary
Biology_Log_Adelie_Penguins_Vestfold_Hills_1973_1 Hand drawn maps of Adelie Penguin Colonies/Rookeries in the Vestfold Hills during 1973 AU_AADC STAC Catalog 1973-11-08 1973-11-14 77, -68, 78, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214313282-AU_AADC.umm_json This log contains notes and hand drawn maps of Adelie Penguin Colonies/Rookeries in the Vestfold Hills, collected during November, 1973. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
-Biology_Log_Adelie_Rookery_1957_Gardner_1 Adelie Penguin rookery observations made at Gardner Island in 1957 ALL STAC Catalog 1957-12-02 1957-12-27 77.867, -68.583, 77.867, -68.583 https://cmr.earthdata.nasa.gov/search/concepts/C1214313283-AU_AADC.umm_json This log contains observations made at an Adelie Penguin rookery at Gardner Island in 1957. At the time, Gardner Island was known as Breidneskollen. The observations were made in December of 1957. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Adelie_Rookery_1957_Gardner_1 Adelie Penguin rookery observations made at Gardner Island in 1957 AU_AADC STAC Catalog 1957-12-02 1957-12-27 77.867, -68.583, 77.867, -68.583 https://cmr.earthdata.nasa.gov/search/concepts/C1214313283-AU_AADC.umm_json This log contains observations made at an Adelie Penguin rookery at Gardner Island in 1957. At the time, Gardner Island was known as Breidneskollen. The observations were made in December of 1957. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
+Biology_Log_Adelie_Rookery_1957_Gardner_1 Adelie Penguin rookery observations made at Gardner Island in 1957 ALL STAC Catalog 1957-12-02 1957-12-27 77.867, -68.583, 77.867, -68.583 https://cmr.earthdata.nasa.gov/search/concepts/C1214313283-AU_AADC.umm_json This log contains observations made at an Adelie Penguin rookery at Gardner Island in 1957. At the time, Gardner Island was known as Breidneskollen. The observations were made in December of 1957. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Antarctic_Petrel_Photos_1961_1962_1 Antarctic Petrel photographs taken at Ardery Island and Lewis Island in 1961-1962 AU_AADC STAC Catalog 1961-01-01 1962-12-31 110.45, -66.371, 134.384, -66.104 https://cmr.earthdata.nasa.gov/search/concepts/C1214313284-AU_AADC.umm_json This log contains photographs of Antarctic Petrels taken in 1961-1962 at Ardery Island and Lewis Island. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Casey_1968_1969_1 Biology log from Casey station during 1968 and 1969. AU_AADC STAC Catalog 1968-01-01 1969-01-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313285-AU_AADC.umm_json This file contains biological observations collected in the Casey region during the 1968-1969 season. Observations were made on a dog trip, of giant petrels, skuas, penguins, snow petrels, seals, wilson's storm petrels, and whales. A number of photographs are also included in the file. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Casey_1972_1 Biology log from Casey station during 1972. AU_AADC STAC Catalog 1972-01-01 1972-12-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313286-AU_AADC.umm_json This file contains biological observations collected in the Casey region during 1972. Observations were made of giant petrels, skuas, penguins, snow petrels, leopard seals, elephant seals, wilson's storm petrels, Antarctic petrels, and whales. A number of photographs are also included in the file. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
@@ -3891,27 +3892,27 @@ Biology_Log_Davis_Mawson_1954_1960_1 Log of observations of seals, penguins, sku
Biology_Log_Davis_Report_1962_1 Log and report of observations of seals, penguins, skuas, petrels, and whales at Davis, 1962 AU_AADC STAC Catalog 1962-02-01 1963-02-16 77, -68, 78, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214306695-AU_AADC.umm_json This file contains a report and a log of biological observations made in the Davis region during 1962. It includes information on Elephant Seals, Leopard Seals, Crabeater Seals, Adelie Penguins, Emperor Penguins, Skuas, Silver-Grey Petrels, Antarctic Petrels, Cape Pigeons, Snow Petrels, Wilson's Storm Petrels, Giant Petrels and Whales The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Heard_Birds_1953_1 Log and report of observations of birds at Heard Island, 1953 AU_AADC STAC Catalog 1953-01-01 1953-12-31 73.5, -53.1, 73.5, -53.1 https://cmr.earthdata.nasa.gov/search/concepts/C1214306696-AU_AADC.umm_json This file contains a report and a log of biological observations of birds made at Heard Island during 1953. It includes information on King Penguins, Macaroni Penguins, Adelie Penguins, Rockhopper Penguins, Gentoo Penguins, Light-Mantled Sooty Albatross, Storm Petrels, Dominican Gulls, Cape Pigeons, Skuas, etc. It also includes information from earlier in the 1960s, including information on bird banding, and bird ordinance. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_1950s_1 Log and report of observations of birds and seals at Mawson and Davis Stations, 1954-1959 AU_AADC STAC Catalog 1954-02-01 1959-12-31 62, -68, 78, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214306697-AU_AADC.umm_json This file contains a report and a log of biological observations made at Mawson station during the 1950s, after the station was established in 1954. It contains observations of emperor Penguins, Adelie Penguins, Chinstrap Penguins, Giant Petrels, Cape Pigeons, Antarctic Petrels, Silver Grey Petrels, Snow Petrels, Wilson's Storm Petrels, McCormick Skuas, Dominican Gulls, Terns, Elephant Seals, Weddell Seals, Crabeater Seals and Leopard Seals. Some data are also provided for Davis Station. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
-Biology_Log_Mawson_1958_1962_1 A log of biological observations made at Mawson, Davis and Wilkes stations between 1958 and 1962 AU_AADC STAC Catalog 1958-01-01 1962-12-31 62, -68, 110, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214306698-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson, Davis and Wilkes stations between 1958 and 1962. The observations are primarily on flying birds (petrels, skuas, gulls), penguins and seals. The observed animals include: Snow Petrels, McCormick Skuas, Silver-Grey Petrels, Antarctic Petrels, Giant Petrels, Wilson's Storm Petrels, Cape Pigeons, Dominican Gulls, Crabeater Seals, Elephant Seals, Leopard Seals, Ross Seals, Weddell Seals, Emperor Penguins, Adelie Penguins, Chinstrap Penguins and Terns. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_1958_1962_1 A log of biological observations made at Mawson, Davis and Wilkes stations between 1958 and 1962 ALL STAC Catalog 1958-01-01 1962-12-31 62, -68, 110, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214306698-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson, Davis and Wilkes stations between 1958 and 1962. The observations are primarily on flying birds (petrels, skuas, gulls), penguins and seals. The observed animals include: Snow Petrels, McCormick Skuas, Silver-Grey Petrels, Antarctic Petrels, Giant Petrels, Wilson's Storm Petrels, Cape Pigeons, Dominican Gulls, Crabeater Seals, Elephant Seals, Leopard Seals, Ross Seals, Weddell Seals, Emperor Penguins, Adelie Penguins, Chinstrap Penguins and Terns. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
-Biology_Log_Mawson_1971_1974_1 A log of biological and sea ice observations made at Mawson station between 1971 and 1974 AU_AADC STAC Catalog 1971-01-01 1974-12-31 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214306699-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson station between 1971 and 1974. The observed animals include: Wilson's Storm Petrels, Petrels, Giant Petrels, Skuas, Emperor Penguins, Snow Petrels, Silver Grey Petrels, Antarctic Petrel, Weddell Seals, Crabeater Seals, Leopard Seals, Elephant Seals, Ross Seals and Whales. The log also includes a number of sea ice observations made at Mawson Station. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
+Biology_Log_Mawson_1958_1962_1 A log of biological observations made at Mawson, Davis and Wilkes stations between 1958 and 1962 AU_AADC STAC Catalog 1958-01-01 1962-12-31 62, -68, 110, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214306698-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson, Davis and Wilkes stations between 1958 and 1962. The observations are primarily on flying birds (petrels, skuas, gulls), penguins and seals. The observed animals include: Snow Petrels, McCormick Skuas, Silver-Grey Petrels, Antarctic Petrels, Giant Petrels, Wilson's Storm Petrels, Cape Pigeons, Dominican Gulls, Crabeater Seals, Elephant Seals, Leopard Seals, Ross Seals, Weddell Seals, Emperor Penguins, Adelie Penguins, Chinstrap Penguins and Terns. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_1971_1974_1 A log of biological and sea ice observations made at Mawson station between 1971 and 1974 ALL STAC Catalog 1971-01-01 1974-12-31 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214306699-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson station between 1971 and 1974. The observed animals include: Wilson's Storm Petrels, Petrels, Giant Petrels, Skuas, Emperor Penguins, Snow Petrels, Silver Grey Petrels, Antarctic Petrel, Weddell Seals, Crabeater Seals, Leopard Seals, Elephant Seals, Ross Seals and Whales. The log also includes a number of sea ice observations made at Mawson Station. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
-Biology_Log_Mawson_1977_1978_1 A log of biological and sea ice observations made at Mawson station between 1977 and 1978 AU_AADC STAC Catalog 1977-01-01 1978-01-31 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214306702-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson station between 1977 and 1978. The observed animals include: Weddell Seals, Skuas, Snow Petrels, Wilson's Storm Petrels, Pintado Petrels, Giant Petrels, Crabeater Seals, Elephant Seals, Leopard Seals and Adelie Penguins. The log also includes a number of sea ice observations made at Mawson Station. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
+Biology_Log_Mawson_1971_1974_1 A log of biological and sea ice observations made at Mawson station between 1971 and 1974 AU_AADC STAC Catalog 1971-01-01 1974-12-31 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214306699-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson station between 1971 and 1974. The observed animals include: Wilson's Storm Petrels, Petrels, Giant Petrels, Skuas, Emperor Penguins, Snow Petrels, Silver Grey Petrels, Antarctic Petrel, Weddell Seals, Crabeater Seals, Leopard Seals, Elephant Seals, Ross Seals and Whales. The log also includes a number of sea ice observations made at Mawson Station. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_1977_1978_1 A log of biological and sea ice observations made at Mawson station between 1977 and 1978 ALL STAC Catalog 1977-01-01 1978-01-31 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214306702-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson station between 1977 and 1978. The observed animals include: Weddell Seals, Skuas, Snow Petrels, Wilson's Storm Petrels, Pintado Petrels, Giant Petrels, Crabeater Seals, Elephant Seals, Leopard Seals and Adelie Penguins. The log also includes a number of sea ice observations made at Mawson Station. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
-Biology_Log_Mawson_1980_1981_1 A log of biological observations at Mawson station during 1980 and 1981 AU_AADC STAC Catalog 1980-04-18 1981-12-26 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313293-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson station in 1980 and 1981. The logs include observations of adelie penguins, snow petrels, leopard seals, pintado petrels, skuas, antarctic petrels, wilson's storm petrels, southern giant petrels, dominican gulls, silver grey petrels, fulmars, killer whales, minke whales, elephant seals, sea spiders, crabeater seals and Antarctic terns. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
+Biology_Log_Mawson_1977_1978_1 A log of biological and sea ice observations made at Mawson station between 1977 and 1978 AU_AADC STAC Catalog 1977-01-01 1978-01-31 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214306702-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson station between 1977 and 1978. The observed animals include: Weddell Seals, Skuas, Snow Petrels, Wilson's Storm Petrels, Pintado Petrels, Giant Petrels, Crabeater Seals, Elephant Seals, Leopard Seals and Adelie Penguins. The log also includes a number of sea ice observations made at Mawson Station. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_1980_1981_1 A log of biological observations at Mawson station during 1980 and 1981 ALL STAC Catalog 1980-04-18 1981-12-26 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313293-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson station in 1980 and 1981. The logs include observations of adelie penguins, snow petrels, leopard seals, pintado petrels, skuas, antarctic petrels, wilson's storm petrels, southern giant petrels, dominican gulls, silver grey petrels, fulmars, killer whales, minke whales, elephant seals, sea spiders, crabeater seals and Antarctic terns. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
-Biology_Log_Mawson_1982_1 A log of biological observations at Mawson station in 1982 ALL STAC Catalog 1982-01-01 1983-01-09 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313294-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson station in 1982. The logs include observations of pintadoo petrels, emperor penguins, killer whales, elephant seals, leopard seals, minke whales, crabeater seals, adelie penguins, silver-grey petrels, wilson's storm petrels, antarctic petrels and skuas The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
+Biology_Log_Mawson_1980_1981_1 A log of biological observations at Mawson station during 1980 and 1981 AU_AADC STAC Catalog 1980-04-18 1981-12-26 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313293-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson station in 1980 and 1981. The logs include observations of adelie penguins, snow petrels, leopard seals, pintado petrels, skuas, antarctic petrels, wilson's storm petrels, southern giant petrels, dominican gulls, silver grey petrels, fulmars, killer whales, minke whales, elephant seals, sea spiders, crabeater seals and Antarctic terns. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_1982_1 A log of biological observations at Mawson station in 1982 AU_AADC STAC Catalog 1982-01-01 1983-01-09 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313294-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson station in 1982. The logs include observations of pintadoo petrels, emperor penguins, killer whales, elephant seals, leopard seals, minke whales, crabeater seals, adelie penguins, silver-grey petrels, wilson's storm petrels, antarctic petrels and skuas The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
+Biology_Log_Mawson_1982_1 A log of biological observations at Mawson station in 1982 ALL STAC Catalog 1982-01-01 1983-01-09 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313294-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson station in 1982. The logs include observations of pintadoo petrels, emperor penguins, killer whales, elephant seals, leopard seals, minke whales, crabeater seals, adelie penguins, silver-grey petrels, wilson's storm petrels, antarctic petrels and skuas The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_Antarctic_Petrels_1972_1990_1 A log of biological observations of Antarctic Petrels made at Mawson station between 1972 and 1990 AU_AADC STAC Catalog 1972-04-21 1990-10-09 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214308305-AU_AADC.umm_json This file contains a log of biological observations of Antarctic Petrels taken at Mawson Station between 1972 and 1990. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_Antarctic_Petrels_1972_1990_1 A log of biological observations of Antarctic Petrels made at Mawson station between 1972 and 1990 ALL STAC Catalog 1972-04-21 1990-10-09 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214308305-AU_AADC.umm_json This file contains a log of biological observations of Antarctic Petrels taken at Mawson Station between 1972 and 1990. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_Fishing_1978_1985_1 A log of fishing activities at Mawson station during 1979 and 1985 AU_AADC STAC Catalog 1979-02-08 1985-09-20 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313295-AU_AADC.umm_json This file contains a log of fishing activities undertaken at Mawson station in 1979 and 1985. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_Fishing_1978_1985_1 A log of fishing activities at Mawson station during 1979 and 1985 ALL STAC Catalog 1979-02-08 1985-09-20 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313295-AU_AADC.umm_json This file contains a log of fishing activities undertaken at Mawson station in 1979 and 1985. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_Macquarie_Bird_Banding_1959_1965_1 Log and report of bird banding at Macquarie Island and Mawson Station, 1959-1965 AU_AADC STAC Catalog 1959-11-29 1966-02-04 158.86, -54.62, 158.87, -54.61 https://cmr.earthdata.nasa.gov/search/concepts/C1214308306-AU_AADC.umm_json This file contains a report of bird banding undertaken on penguin and flying bird species at Macquarie Island and Mawson station from 1959-1965. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
-Biology_Log_Mawson_Pintardo_Petrels_1972_1988_1 A log of biological observations of Pintardo Petrels made at Mawson station between 1972 and 1988 ALL STAC Catalog 1971-02-10 1988-11-03 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214308307-AU_AADC.umm_json This file contains a log of biological observations of Pintardo Petrels taken at Mawson Station between 1972 and 1988. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_Pintardo_Petrels_1972_1988_1 A log of biological observations of Pintardo Petrels made at Mawson station between 1972 and 1988 AU_AADC STAC Catalog 1971-02-10 1988-11-03 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214308307-AU_AADC.umm_json This file contains a log of biological observations of Pintardo Petrels taken at Mawson Station between 1972 and 1988. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
+Biology_Log_Mawson_Pintardo_Petrels_1972_1988_1 A log of biological observations of Pintardo Petrels made at Mawson station between 1972 and 1988 ALL STAC Catalog 1971-02-10 1988-11-03 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214308307-AU_AADC.umm_json This file contains a log of biological observations of Pintardo Petrels taken at Mawson Station between 1972 and 1988. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_Seals_1974_1979_1 A log of biological observations of Weddell Seals and Leopard Seals made at Mawson station between 1974 and 1979 AU_AADC STAC Catalog 1974-01-15 1979-10-19 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214308308-AU_AADC.umm_json This file contains a log of biological observations of Weddell Seals and Leopard Seals taken at Mawson Station between 1974 and 1979. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_Seals_1974_1979_1 A log of biological observations of Weddell Seals and Leopard Seals made at Mawson station between 1974 and 1979 ALL STAC Catalog 1974-01-15 1979-10-19 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214308308-AU_AADC.umm_json This file contains a log of biological observations of Weddell Seals and Leopard Seals taken at Mawson Station between 1974 and 1979. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
-Biology_Log_Mawson_Skuas_1982_1990_1 A log of biological observations at Mawson station of skuas from 1982 to 1990 ALL STAC Catalog 1982-03-10 1990-10-22 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313303-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson station between 1982 and 1990. The logs comprise observations of skuas. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_Skuas_1982_1990_1 A log of biological observations at Mawson station of skuas from 1982 to 1990 AU_AADC STAC Catalog 1982-03-10 1990-10-22 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313303-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson station between 1982 and 1990. The logs comprise observations of skuas. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
+Biology_Log_Mawson_Skuas_1982_1990_1 A log of biological observations at Mawson station of skuas from 1982 to 1990 ALL STAC Catalog 1982-03-10 1990-10-22 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313303-AU_AADC.umm_json This file contains a log of biological observations undertaken at Mawson station between 1982 and 1990. The logs comprise observations of skuas. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_Snow_Petrels_1971_1990_1 A log of biological observations of Snow Petrels made at Mawson station between 1971 and 1990 ALL STAC Catalog 1971-12-29 1990-10-08 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214308309-AU_AADC.umm_json This file contains a log of biological observations of Snow Petrels taken at Mawson Station between 1971 and 1990. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Mawson_Snow_Petrels_1971_1990_1 A log of biological observations of Snow Petrels made at Mawson station between 1971 and 1990 AU_AADC STAC Catalog 1971-12-29 1990-10-08 62, -67, 63, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214308309-AU_AADC.umm_json This file contains a log of biological observations of Snow Petrels taken at Mawson Station between 1971 and 1990. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Wilkes_1961_1 Biology report from Wilkes Station, 1961 AU_AADC STAC Catalog 1961-01-01 1961-12-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313321-AU_AADC.umm_json This file contains a biology report produced at Wilkes Station in 1961. Contributors to the report were R. Penney, D.F. Soucek, L. Jones and N. Orton. The report comprises data pertaining to: Adelie penguins Emperor penguins Silver-grey petrels Antarctic petrels Cape pigeons Giant petrels Skuas Snow petrels Wilson's storm petrels Weddell seals Leopard seals Elephant seals Ross seals Killer whales The hard copy of the map has been archived by the Australian Antarctic Division library. proprietary
@@ -3924,13 +3925,13 @@ Biology_Log_Wilkes_1968_1969_1 Biology log from Wilkes station during 1968 and 1
Biology_Log_Wilkes_Ardery_1963_1 Biology report for Ardery Island, Wilkes Station, January 1963 AU_AADC STAC Catalog 1963-01-01 1963-01-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313326-AU_AADC.umm_json This file contains a biology report from Wilkes station in 1963. The report pertains specifically to a visit to Ardery Island in January, 1963 by F. Soucek. The report contains biological observations, as well as an extract from a publication and some hand-drawn maps. The observations were made of: South polar skuas Giant petrels Cape pigeons Silver-grey petrels Antarctic petrels Snow petrels Wilson's storm petrels The hard copy of the map has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Wilkes_Banding_1966_1 Banding report for Wilkes Station, 1966 AU_AADC STAC Catalog 1966-01-01 1966-12-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313340-AU_AADC.umm_json This file contains a banding report Wilkes station in 1966. The observations were made of: Adelie penguins Silver-grey petrels The hard copy of the file has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Wilkes_Banding_1968_1969_1 Banding information for Wilkes Station, 1968-1969 AU_AADC STAC Catalog 1968-01-01 1969-12-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313327-AU_AADC.umm_json This file contains a report from Wilkes station in 1968-1969 detailing the banding program undertaken in the Windmill Islands. The document primarily relates to South Polar Skuas, but also mentions Wilson's Storm Petrels, and Snow Petrels. The hard copy of the file has been archived by the Australian Antarctic Division library. proprietary
-Biology_Log_Wilkes_Bird_Banding_1962_1963_1 A log of bird banding and zoological observations made at Wilkes Station and the Windmill Islands, 1962-1963 ALL STAC Catalog 1962-01-01 1963-12-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313305-AU_AADC.umm_json This file contains a record of bird banding activities undertaken at Wilkes Station in 1962 and 1963. It also contains a log of observations made on animals in the area, as well as some hand-drawn maps. This document was compiled by F. Soucek. Animals observed include: Adelie penguins McCormick skuas Wilson's storm petrels Giant petrels Silver grey petrels Snow petrels Antarctic petrels Snow petrels Antarctic terns Cape pigeons Emperor penguins Weddell seals Elephant seals Leopard seals Crabeater seals Whales The hard copy of the map has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Wilkes_Bird_Banding_1962_1963_1 A log of bird banding and zoological observations made at Wilkes Station and the Windmill Islands, 1962-1963 AU_AADC STAC Catalog 1962-01-01 1963-12-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313305-AU_AADC.umm_json This file contains a record of bird banding activities undertaken at Wilkes Station in 1962 and 1963. It also contains a log of observations made on animals in the area, as well as some hand-drawn maps. This document was compiled by F. Soucek. Animals observed include: Adelie penguins McCormick skuas Wilson's storm petrels Giant petrels Silver grey petrels Snow petrels Antarctic petrels Snow petrels Antarctic terns Cape pigeons Emperor penguins Weddell seals Elephant seals Leopard seals Crabeater seals Whales The hard copy of the map has been archived by the Australian Antarctic Division library. proprietary
+Biology_Log_Wilkes_Bird_Banding_1962_1963_1 A log of bird banding and zoological observations made at Wilkes Station and the Windmill Islands, 1962-1963 ALL STAC Catalog 1962-01-01 1963-12-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313305-AU_AADC.umm_json This file contains a record of bird banding activities undertaken at Wilkes Station in 1962 and 1963. It also contains a log of observations made on animals in the area, as well as some hand-drawn maps. This document was compiled by F. Soucek. Animals observed include: Adelie penguins McCormick skuas Wilson's storm petrels Giant petrels Silver grey petrels Snow petrels Antarctic petrels Snow petrels Antarctic terns Cape pigeons Emperor penguins Weddell seals Elephant seals Leopard seals Crabeater seals Whales The hard copy of the map has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Wilkes_Skuas_1957_1958_1 A map of banding stations for a study on the distribution of south polar skuas in 1957-1958 ALL STAC Catalog 1957-01-01 1958-12-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214313306-AU_AADC.umm_json This file contains a map of banding stations for a distribution study of south polar skuas. The map is of the entire Antarctic continent and shows stations from the International Geophysical Year, 1957-1958 and from the US Navy Operation, Deep Freeze II, 1956-1957. The hard copy of the map has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Wilkes_Skuas_1957_1958_1 A map of banding stations for a study on the distribution of south polar skuas in 1957-1958 AU_AADC STAC Catalog 1957-01-01 1958-12-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214313306-AU_AADC.umm_json This file contains a map of banding stations for a distribution study of south polar skuas. The map is of the entire Antarctic continent and shows stations from the International Geophysical Year, 1957-1958 and from the US Navy Operation, Deep Freeze II, 1956-1957. The hard copy of the map has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Wilkes_Wildlife_Sightings_1963_1 Log of wildlife sightings at Wilkes Station, 1963 AU_AADC STAC Catalog 1963-01-01 1963-01-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313308-AU_AADC.umm_json This file contains a log of wildlife sightings made at Wilkes Station in 1963. Each sheet of the log is for a single month. The listed species include: Skuas Adelie penguins Emperor penguins Giant petrels Wilson's storm petrels Silver grey petrels Antarctic petrels Snow petrels Pintado Weddell seals Elephant seals Leopard seals The hard copy of the file has been archived by the Australian Antarctic Division library. proprietary
-Biology_Log_Wilkes_Zoology_1959_1961_1 A log of zoological observations made at Wilkes Station and the Windmill Islands, 1959-1961 AU_AADC STAC Catalog 1959-01-01 1961-12-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313334-AU_AADC.umm_json This file contains a log of zoological observations made by Richard Penney at Wilkes station from 1959 to 1961. The observations were made in the Windmill Islands, at locations such as Clarke Island, Frazier Islands (Islets), Ardery Island (Islet), Odbert Island and Petersen Island. The observations were made of, adelie penguins, emperor penguins, south polar skuas, giant petrels, cape pigeons, silver-grey petrels, antarctic petrels, snow petrels, wilson's storm petrels, terns, ross seals, crabeater seals, elephant seals, weddell seals, leopard seals and killer whales. Bird banding is also covered in the report. The download file contains the official copy of the report, as well as Richard Penney's personal copy, which includes some handwritten notes. The hard copy of the map has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Wilkes_Zoology_1959_1961_1 A log of zoological observations made at Wilkes Station and the Windmill Islands, 1959-1961 ALL STAC Catalog 1959-01-01 1961-12-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313334-AU_AADC.umm_json This file contains a log of zoological observations made by Richard Penney at Wilkes station from 1959 to 1961. The observations were made in the Windmill Islands, at locations such as Clarke Island, Frazier Islands (Islets), Ardery Island (Islet), Odbert Island and Petersen Island. The observations were made of, adelie penguins, emperor penguins, south polar skuas, giant petrels, cape pigeons, silver-grey petrels, antarctic petrels, snow petrels, wilson's storm petrels, terns, ross seals, crabeater seals, elephant seals, weddell seals, leopard seals and killer whales. Bird banding is also covered in the report. The download file contains the official copy of the report, as well as Richard Penney's personal copy, which includes some handwritten notes. The hard copy of the map has been archived by the Australian Antarctic Division library. proprietary
+Biology_Log_Wilkes_Zoology_1959_1961_1 A log of zoological observations made at Wilkes Station and the Windmill Islands, 1959-1961 AU_AADC STAC Catalog 1959-01-01 1961-12-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313334-AU_AADC.umm_json This file contains a log of zoological observations made by Richard Penney at Wilkes station from 1959 to 1961. The observations were made in the Windmill Islands, at locations such as Clarke Island, Frazier Islands (Islets), Ardery Island (Islet), Odbert Island and Petersen Island. The observations were made of, adelie penguins, emperor penguins, south polar skuas, giant petrels, cape pigeons, silver-grey petrels, antarctic petrels, snow petrels, wilson's storm petrels, terns, ross seals, crabeater seals, elephant seals, weddell seals, leopard seals and killer whales. Bird banding is also covered in the report. The download file contains the official copy of the report, as well as Richard Penney's personal copy, which includes some handwritten notes. The hard copy of the map has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Windmill_Islands_Fauna_1961_1962_1 Fauna survey in the Windmill Islands, 1961-1962 AU_AADC STAC Catalog 1961-01-01 1962-12-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313341-AU_AADC.umm_json This file contains a report on the fauna of the Windmill Islands, 1961-1962. The file contains information on photographic records, banding data, and nest marking. Species included in the report are: Adelie penguins Silver grey petrels Giant petrels Pintado petrels Snow petrels Antarctic petrels The hard copy of the file has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Windmill_Islands_Fleas_1961_1 Bird Fleas in the Windmill Islands, 1961 AU_AADC STAC Catalog 1961-11-01 1961-12-31 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313335-AU_AADC.umm_json This file contains a report on bird fleas in the Windmill Islands in 1961. The report contains data on fleas collected, as well as general information on the bird fleas and how they affect silver grey petrels. The hard copy of the file has been archived by the Australian Antarctic Division library. proprietary
Biology_Log_Windmill_Islands_Photographs_1961_1962_1 General photographs of the Windmill Islands region taken in December 1961 and January 1962 AU_AADC STAC Catalog 1961-12-10 1962-01-15 109.5, -67, 111.5, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313309-AU_AADC.umm_json This log contains general photographs of the Windmill Islands region, including photos of the Peterson Glacier, Cameron Island and Berkley Island. The photos were taken between December 1961 and January 1962. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
@@ -3949,8 +3950,8 @@ Boreal_CanopyCover_StandAge_2012_1 ABoVE: Tree Canopy Cover and Stand Age from L
Boreal_Fire_Severity_Metrics_1520_1 Fire Intensity and Burn Severity Metrics for Circumpolar Boreal Forests, 2001-2013 ORNL_CLOUD STAC Catalog 2001-01-01 2013-12-31 -180, 40, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2767484391-ORNL_CLOUD.umm_json This data set provides products characterizing immediate and longer-term ecosystem changes from fires in the circumpolar boreal forests of Northern Eurasia and North America. The data include fire intensity (fire radiative power; FRP), increase in spring albedo, decrease in tree cover, normalized burn ratio, normalized difference vegetation index, and land surface temperature, as well as three derived fire metrics: crown scorch, vegetation destruction, and fire-induced tree mortality. Longer-term changes are indicated by mean albedo determined 5-12 years after fires, mean percent decrease in tree cover 5-7 years after fires, and mean annual burned percentage. The data cover the period 2001-2013 and are provided at quarter, half, and one degree resolutions for boreal forests within the 40 to 80 degree North circumpolar region. The data were derived from a variety of sources including MODIS products, climate reanalysis data, and forest inventories. A data file with identified boreal forest area (pixels), as defined by climate and vegetation type, and a file with the defined North American and Eurasian boreal forest study regions are included. proprietary
Bot_Bibliography_1 Compiled bibliography of Antarctic/subantarctic related botanical references AU_AADC STAC Catalog 1823-01-01 -180, -90, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214313361-AU_AADC.umm_json Antarctic Botanical Bibliography compiled by Dr Ron Lewis Smith of the British Antarctic Survey. There are 3,076 records in this bibliography. The fields in this dataset are: year author title journal proprietary
BowdoinBuoy_0 Bowdoin buoy measurements OB_DAAC STAC Catalog 2007-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360166-OB_DAAC.umm_json Measurements made from the Bowdoin buoy network and ECOHAB project since 2007 near Portland, Maine. proprietary
-BurnedArea_Emissions_AK_YT_NWT_1812_2 ABoVE: Ignitions, Burned Area, and Emissions of Fires in AK, YT, and NWT, 2001-2018 ORNL_CLOUD STAC Catalog 2001-01-01 2018-12-31 -167, 51.63, -99.98, 79.26 https://cmr.earthdata.nasa.gov/search/concepts/C2111719486-ORNL_CLOUD.umm_json This dataset provides estimates of daily burned area, carbon emissions, and uncertainty, and daily fire ignition locations for boreal fires in Alaska, U.S., and in the Yukon and Northwest Territories, Canada. The data are at 500 m resolution for the 18-year period from 2001-2018. Burned area was retrieved from combining fire perimeter data from the Alaskan and Canadian Large Fire Databases with surface reflectance and active fire data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6. Per-pixel carbon consumption was estimated based on a statistical relationship between field estimates of pyrogenic consumption and several environmental variables. To derive the carbon consumption estimates, the approach from Alaskan Fire Emissions Database (AKFED) was updated and extended for the period 2001-2018. Fire weather variables, temperature, and the drought code complemented remotely sensed tree cover and burn severity as model predictors. Fire ignition location and timing were extracted from the daily burned area maps. proprietary
BurnedArea_Emissions_AK_YT_NWT_1812_2 ABoVE: Ignitions, Burned Area, and Emissions of Fires in AK, YT, and NWT, 2001-2018 ALL STAC Catalog 2001-01-01 2018-12-31 -167, 51.63, -99.98, 79.26 https://cmr.earthdata.nasa.gov/search/concepts/C2111719486-ORNL_CLOUD.umm_json This dataset provides estimates of daily burned area, carbon emissions, and uncertainty, and daily fire ignition locations for boreal fires in Alaska, U.S., and in the Yukon and Northwest Territories, Canada. The data are at 500 m resolution for the 18-year period from 2001-2018. Burned area was retrieved from combining fire perimeter data from the Alaskan and Canadian Large Fire Databases with surface reflectance and active fire data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6. Per-pixel carbon consumption was estimated based on a statistical relationship between field estimates of pyrogenic consumption and several environmental variables. To derive the carbon consumption estimates, the approach from Alaskan Fire Emissions Database (AKFED) was updated and extended for the period 2001-2018. Fire weather variables, temperature, and the drought code complemented remotely sensed tree cover and burn severity as model predictors. Fire ignition location and timing were extracted from the daily burned area maps. proprietary
+BurnedArea_Emissions_AK_YT_NWT_1812_2 ABoVE: Ignitions, Burned Area, and Emissions of Fires in AK, YT, and NWT, 2001-2018 ORNL_CLOUD STAC Catalog 2001-01-01 2018-12-31 -167, 51.63, -99.98, 79.26 https://cmr.earthdata.nasa.gov/search/concepts/C2111719486-ORNL_CLOUD.umm_json This dataset provides estimates of daily burned area, carbon emissions, and uncertainty, and daily fire ignition locations for boreal fires in Alaska, U.S., and in the Yukon and Northwest Territories, Canada. The data are at 500 m resolution for the 18-year period from 2001-2018. Burned area was retrieved from combining fire perimeter data from the Alaskan and Canadian Large Fire Databases with surface reflectance and active fire data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6. Per-pixel carbon consumption was estimated based on a statistical relationship between field estimates of pyrogenic consumption and several environmental variables. To derive the carbon consumption estimates, the approach from Alaskan Fire Emissions Database (AKFED) was updated and extended for the period 2001-2018. Fire weather variables, temperature, and the drought code complemented remotely sensed tree cover and burn severity as model predictors. Fire ignition location and timing were extracted from the daily burned area maps. proprietary
Burned_Area_Depth_AK_CA_2063_1 ABoVE: Burned Area, Depth, and Combustion for Alaska and Canada, 2001-2019 ORNL_CLOUD STAC Catalog 2001-01-01 2019-12-31 -167.96, 42.88, -48.78, 72.95 https://cmr.earthdata.nasa.gov/search/concepts/C2308233596-ORNL_CLOUD.umm_json This dataset provides annual gridded estimates of fire locations and associated burn fraction per pixel for Alaska and Canada at approximately 500 m spatial resolution for the period 2001-2019. Gridded predictions of carbon combustion and burn depth for the same period within the ABoVE extended domain using the burn area maps and field data are also available. Fire locations and date of burn (DOB) were detected by MODIS-derived active fire products. Burned area was primarily estimated from finer-scale Landsat imagery using a differenced Normalized Burn Ratio (dNBR) algorithm and upscaled to an approximate 500 m MODIS resolution. Aboveground combustion, belowground combustion, and burn depth were statistically modeled at the pixel level for every mapped burned pixel in the ABoVE extended domain based on field observations across Alaska and western Canada. Predictor variables included remotely sensed indicators of fire severity, topography, soils, climate, and fire weather. Quality flags for burned area and combustion are available. Fire is the dominant disturbance agent in Alaskan and Canadian boreal ecosystems and releases large amounts of carbon into the atmosphere. These data are useful for studies of disturbance, fire ecology, and carbon cycling in boreal ecosystems. proprietary
Burned_Area_Depth_AK_CA_2063_1 ABoVE: Burned Area, Depth, and Combustion for Alaska and Canada, 2001-2019 ALL STAC Catalog 2001-01-01 2019-12-31 -167.96, 42.88, -48.78, 72.95 https://cmr.earthdata.nasa.gov/search/concepts/C2308233596-ORNL_CLOUD.umm_json This dataset provides annual gridded estimates of fire locations and associated burn fraction per pixel for Alaska and Canada at approximately 500 m spatial resolution for the period 2001-2019. Gridded predictions of carbon combustion and burn depth for the same period within the ABoVE extended domain using the burn area maps and field data are also available. Fire locations and date of burn (DOB) were detected by MODIS-derived active fire products. Burned area was primarily estimated from finer-scale Landsat imagery using a differenced Normalized Burn Ratio (dNBR) algorithm and upscaled to an approximate 500 m MODIS resolution. Aboveground combustion, belowground combustion, and burn depth were statistically modeled at the pixel level for every mapped burned pixel in the ABoVE extended domain based on field observations across Alaska and western Canada. Predictor variables included remotely sensed indicators of fire severity, topography, soils, climate, and fire weather. Quality flags for burned area and combustion are available. Fire is the dominant disturbance agent in Alaskan and Canadian boreal ecosystems and releases large amounts of carbon into the atmosphere. These data are useful for studies of disturbance, fire ecology, and carbon cycling in boreal ecosystems. proprietary
Byrd-SipleDome-CO2-GICC05_1 Byrd and Siple Dome CO2 data on the GICC05 timescale (9--21 ka) AU_AADC STAC Catalog 2012-07-22 2012-07-22 119.5167, -81.6667, 148.8167, -80.0167 https://cmr.earthdata.nasa.gov/search/concepts/C1214308481-AU_AADC.umm_json Antarctic ice cores provide clear evidence of a close coupling between variations in Antarctic temperature and the atmospheric concentration of CO2 during the glacial/interglacial cycles of at least the past 800-thousand years. Precise information on the relative timing of the temperature and CO2 changes can assist in refining our understanding of the physical processes involved in this coupling. Here, we focus on the last deglaciation, 19 000 to 11 000 yr before present, during which CO2 concentrations increased by ~80 parts per million by volume and Antarctic temperature increased by ~10 degrees C. Utilising a recently developed proxy for regional Antarctic temperature, derived from five near-coastal ice cores and two ice core CO2 records with high dating precision, we show that the increase in CO2 likely lagged the increase in regional Antarctic temperature by less than 400 yr and that even a short lead of CO2 over temperature cannot be excluded. This result, consistent for both CO2 records, implies a faster coupling between temperature and CO2 than previous estimates, which had permitted up to millennial-scale lags. This work was done as part of project AAS 757. DESCRIPTION The regional Antarctic temperature proxy data series is avaliable elsewhere: ftp://ftp.ncdc.noaa.gov/pub/data/paleo/icecore/antarctica/antarctica2011iso.txt The locations and original references for the CO2 data and transfers to the GICC05 timescale are as follows: Byrd Location: 80 degrees 01'S 119 degrees 31'W Elevation: 1530 m asl Reference for transfer to GICC05 timescale: Pedro et al., Clim. Past. 8, 2012. Reference for CO2 data: (1) Neftel, A., Oeschger, H., Staffelbach, T., and Nature, 331, 609-11, doi:10.1038/331609a0, 1988. (2) Staffelbach, T., Stauffer, B., Sigg, A., and Oeschger, H.: CO2 measurements from polar ice cores - More data from different sites, Tellus B, 43, 91-6, doi:10.1034/j.1600-0889.1991.t01-1- 00003.x, 1991. Siple Dome Location: 81 degrees 40'S 148 degrees 49'W Elevation: 621 m asl Reference for transfer to GICC05 timescale: Pedro et al., Clim. Past. 8, 2012. Reference for CO2 data: Ahn, J., Wahlen, M., Deck, B. L., Brook, E. J., Mayewski, P. A., Taylor, K. C., and White, J. W. C.: A record of atmospheric CO2 during the last 40,000 years from the Siple Dome, Antarctica ice core, J. Geophys. Res., 109, D13305, doi:10.1029/2003JD004415, 2004. proprietary
@@ -4435,61 +4436,61 @@ CDDIS_VLBI_products_troposphere_1 CDDIS VLBI products troposphere CDDIS STAC Cat
CDDIS_VLBI_session_eops_1 CDDIS VLBI Session Earth Orientation Parameter Series (EOP-S) Product from NASA CDDIS CDDIS STAC Catalog 2002-02-13 2023-12-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3242579905-CDDIS.umm_json The Session Series EOP product is a series of EOP results, one for each geodetic session. Data are irregularly spaced and there are multiple results for days on which there were simultaneous sessions. Each series includes a minimum of one year of results. The operational EOP-S product is available on the IVS Data Centers 24 hours after availability of each new session data base. proprietary
CDIAC_AEROSOL_TRENDS93 Aerosol Optical Depth Measurements from Four NOAA/CMDL Monitoring Sites, in CDIAC, Trends '93 ALL STAC Catalog 1977-04-01 1992-07-31 -170, -90, -24, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214585030-SCIOPS.umm_json "Measurements of direct solar irradiance have been carried out since 1977 at each of four baseline atmospheric monitoring stations operated by NOAA/CMDL. The four stations are at: Barrow, Alaska (1977-1992) Mauna Loa, Hawaii (1977-1992) Samoa, Cape Matatula (1977-1992) South Pole, Antarctica (1977-1992) Monitoring is done by means of a wideband pyrheliometer. Measured values are compared with results of solar irradiance calculations to derive aerosol optical depth (AOD), defined as the aerosol component of the exponent in the exponential decrease in solar beam intensity as the beam passes through the atmosphere. The data are presented as monthly anomalies in relation to a baseline comprised of all AOD values from the nonvolcanic years at a given site, with mean seasonal variation removed. Please use the following dataset citation: Dutton, E.G. 1994. ""Aerosol optical depth measurements from four NOAA/CMDL monitoring sites"", pp. 484-492. In T.A. Boden, D.P. Kaiser, R.J. Sepanski, and F.W. Stoss (eds.), Trends '93: A Compendium of Data on Global Change. ORNL/CDIAC-65. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, TN, USA. CDIAC has provided an anonymous FTP area to all data files, retrieval codes, and descriptive files for all data available in TRENDS. The FTP address is CDIAC.ESD.ORNL.GOV and 128.219.24.36 and input your email address as the password. The data bases are arranged as subdirectories in /pub/trends93/trace that correspond to major chapter headings in TRENDS. The data files are arranged as xxxx.yyy where xxxx is the name of the station, country, site, region, or principle investigator and yyy is the page number in TRENDS '93 (example: maunaloa.19 refers to the Mauna Loa CO2 dataset tabulated on page 19 of TRENDS '93). ""ftp://cdiac.esd.ornl.gov/pub/trends93/""" proprietary
CDIAC_AEROSOL_TRENDS93 Aerosol Optical Depth Measurements from Four NOAA/CMDL Monitoring Sites, in CDIAC, Trends '93 SCIOPS STAC Catalog 1977-04-01 1992-07-31 -170, -90, -24, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214585030-SCIOPS.umm_json "Measurements of direct solar irradiance have been carried out since 1977 at each of four baseline atmospheric monitoring stations operated by NOAA/CMDL. The four stations are at: Barrow, Alaska (1977-1992) Mauna Loa, Hawaii (1977-1992) Samoa, Cape Matatula (1977-1992) South Pole, Antarctica (1977-1992) Monitoring is done by means of a wideband pyrheliometer. Measured values are compared with results of solar irradiance calculations to derive aerosol optical depth (AOD), defined as the aerosol component of the exponent in the exponential decrease in solar beam intensity as the beam passes through the atmosphere. The data are presented as monthly anomalies in relation to a baseline comprised of all AOD values from the nonvolcanic years at a given site, with mean seasonal variation removed. Please use the following dataset citation: Dutton, E.G. 1994. ""Aerosol optical depth measurements from four NOAA/CMDL monitoring sites"", pp. 484-492. In T.A. Boden, D.P. Kaiser, R.J. Sepanski, and F.W. Stoss (eds.), Trends '93: A Compendium of Data on Global Change. ORNL/CDIAC-65. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, TN, USA. CDIAC has provided an anonymous FTP area to all data files, retrieval codes, and descriptive files for all data available in TRENDS. The FTP address is CDIAC.ESD.ORNL.GOV and 128.219.24.36 and input your email address as the password. The data bases are arranged as subdirectories in /pub/trends93/trace that correspond to major chapter headings in TRENDS. The data files are arranged as xxxx.yyy where xxxx is the name of the station, country, site, region, or principle investigator and yyy is the page number in TRENDS '93 (example: maunaloa.19 refers to the Mauna Loa CO2 dataset tabulated on page 19 of TRENDS '93). ""ftp://cdiac.esd.ornl.gov/pub/trends93/""" proprietary
-CDIAC_DB1004 Alaskan Historical Climatology Network (HCN) Serial Temperature and Precipitation Data/CDIAC, DB1004 SCIOPS STAC Catalog 1828-01-01 1990-12-31 -180, 50, -130, 75 https://cmr.earthdata.nasa.gov/search/concepts/C1214584689-SCIOPS.umm_json The Alaskan Historical Climatology Network (HCN) database is a companion to the Historical Climatology Network (HCN) database for the contiguous United States (CDIAC NDP-019/R3). The Alaskan HCN contains monthly temperature (minimum, maximum, and mean) and total monthly precipitation data for 47 Alaskan stations. The data were derived from a variety of sources including the National Climatic Data Center (NOAA/NCDC) archives, the state climatologist for Alaska, and published literature. The period of record varies by station. The longest record is for the Sitka Magnetic Observatory (beginning in 1828), and most records extend through 1990. Unlike the HCN database (NDP-019/R3) for the continuous United States, adjustments have not been made to these climate records for time-of-observation differences, instrument changes, or station moves. Users of these data are urged to review information given in the station history file in order to identify stations with suitable records for their applications. The data are contained in three files: a data file containing all four climate variables: monthly minimum, maximum, and mean temperatures, and total monthly precipitation; a station history file; and, a station inventory file. ak_hcn.dat - Alaskan HCN Data File (1.64 Mb) ak_hcn.his - Alaskan HCN Station History File (148 kb) ak_hcn.sta - Alaskan HCN Station Inventory File (4.0 kb) The Alaskan HCN Data File consists of station and date information, temperature and precipitation data, monthly data flags (quality, location), and annual data values. The Alaskan HCN Station History File consists of station information (number, name, location), station flags (quality, instrument), and times of observations. The Alaskan HCN database was contributed to CDIAC by: T.R. Karl, R.G. Baldwin, M.G. Burgin, D.R. Easterling, R.W. Knight, and P.Y. Hughes of the NOAA/National Climatic Data Center (NCDC) in Asheville, NC. proprietary
CDIAC_DB1004 Alaskan Historical Climatology Network (HCN) Serial Temperature and Precipitation Data/CDIAC, DB1004 ALL STAC Catalog 1828-01-01 1990-12-31 -180, 50, -130, 75 https://cmr.earthdata.nasa.gov/search/concepts/C1214584689-SCIOPS.umm_json The Alaskan Historical Climatology Network (HCN) database is a companion to the Historical Climatology Network (HCN) database for the contiguous United States (CDIAC NDP-019/R3). The Alaskan HCN contains monthly temperature (minimum, maximum, and mean) and total monthly precipitation data for 47 Alaskan stations. The data were derived from a variety of sources including the National Climatic Data Center (NOAA/NCDC) archives, the state climatologist for Alaska, and published literature. The period of record varies by station. The longest record is for the Sitka Magnetic Observatory (beginning in 1828), and most records extend through 1990. Unlike the HCN database (NDP-019/R3) for the continuous United States, adjustments have not been made to these climate records for time-of-observation differences, instrument changes, or station moves. Users of these data are urged to review information given in the station history file in order to identify stations with suitable records for their applications. The data are contained in three files: a data file containing all four climate variables: monthly minimum, maximum, and mean temperatures, and total monthly precipitation; a station history file; and, a station inventory file. ak_hcn.dat - Alaskan HCN Data File (1.64 Mb) ak_hcn.his - Alaskan HCN Station History File (148 kb) ak_hcn.sta - Alaskan HCN Station Inventory File (4.0 kb) The Alaskan HCN Data File consists of station and date information, temperature and precipitation data, monthly data flags (quality, location), and annual data values. The Alaskan HCN Station History File consists of station information (number, name, location), station flags (quality, instrument), and times of observations. The Alaskan HCN database was contributed to CDIAC by: T.R. Karl, R.G. Baldwin, M.G. Burgin, D.R. Easterling, R.W. Knight, and P.Y. Hughes of the NOAA/National Climatic Data Center (NCDC) in Asheville, NC. proprietary
+CDIAC_DB1004 Alaskan Historical Climatology Network (HCN) Serial Temperature and Precipitation Data/CDIAC, DB1004 SCIOPS STAC Catalog 1828-01-01 1990-12-31 -180, 50, -130, 75 https://cmr.earthdata.nasa.gov/search/concepts/C1214584689-SCIOPS.umm_json The Alaskan Historical Climatology Network (HCN) database is a companion to the Historical Climatology Network (HCN) database for the contiguous United States (CDIAC NDP-019/R3). The Alaskan HCN contains monthly temperature (minimum, maximum, and mean) and total monthly precipitation data for 47 Alaskan stations. The data were derived from a variety of sources including the National Climatic Data Center (NOAA/NCDC) archives, the state climatologist for Alaska, and published literature. The period of record varies by station. The longest record is for the Sitka Magnetic Observatory (beginning in 1828), and most records extend through 1990. Unlike the HCN database (NDP-019/R3) for the continuous United States, adjustments have not been made to these climate records for time-of-observation differences, instrument changes, or station moves. Users of these data are urged to review information given in the station history file in order to identify stations with suitable records for their applications. The data are contained in three files: a data file containing all four climate variables: monthly minimum, maximum, and mean temperatures, and total monthly precipitation; a station history file; and, a station inventory file. ak_hcn.dat - Alaskan HCN Data File (1.64 Mb) ak_hcn.his - Alaskan HCN Station History File (148 kb) ak_hcn.sta - Alaskan HCN Station Inventory File (4.0 kb) The Alaskan HCN Data File consists of station and date information, temperature and precipitation data, monthly data flags (quality, location), and annual data values. The Alaskan HCN Station History File consists of station information (number, name, location), station flags (quality, instrument), and times of observations. The Alaskan HCN database was contributed to CDIAC by: T.R. Karl, R.G. Baldwin, M.G. Burgin, D.R. Easterling, R.W. Knight, and P.Y. Hughes of the NOAA/National Climatic Data Center (NCDC) in Asheville, NC. proprietary
CDIAC_DB1012 A Global 1x1 Degree Distribution of Atmospheric-Soil CO2 Consumption by Continental Weathering and Riverine HCO3 Yield, CDIAC/DB1012 SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214586079-SCIOPS.umm_json "This database (DB-1012) contains estimates of the net flux of atmospheric-soil carbon dioxide (CO2) produced by the Amiotte Sucjet and Probst model(1993) and the associated bicarbonate river flux (HCO3-). The data are referenced to a 1 degree by 1 degree global grid. The work was done at the Centre National de la Recherche Scientifique (CNRS) of Strasborg Cedex, France with the support of the Environment Programme of the European Communities to model the spatial distribution of atmospheric-soil CO2 consumption by chemical weathering of continental rocks. The result of the study is the database of CO2 consumption and transport of bicarbonate from rivers to the ocean in moles per kilometer squared per year (mol km2/yr). Amiotte Suchet and Probst developed a model that calculates the flux of atmospheric-soil CO2 consumed by chemical erosion of continental rock (i.e., rock weathering) and the bicarbonate river transfer to the ocean. The model is based on a set of empirical relationships between FCO2 and the drainage (runoff) on the major rock types outcropping on the continents. The model assumes that the consumption of atmospheric CO2 by continential weathering is primarily influenced by drainage, and the different types of rocks outcropping the continents. The estimates of flux in the model are the result of four processes: the identification of the empirical relationships between FCO2 and drainage for major rock types;, the development of a model (GEM-CO2) to estimate FCO2 and FHCO3-; the validation of GEM-CVO2 using three case studies; and the global application of GEM-CO2. In Phase I, rock types used to identify empirical relationships include: plutonic & metamorphic; sands & snadstones; acid volcanic; evaporitic; basalts; shales; and carbonate. In Phase III, the GEM-CO2 model results were validated using three large river basins: the Amazon and Cingo basins in tropical equatorial climates, and the Garonne (France) in temperate climate. In Phase IV, the model results were applied to a global grid. For each grid cell, a mean lithology was determined using lithological and soil maps published by the FAO-UNESCO (1971, 1975, 1976, 1978, and 1981) for each continent. The drainage intensity was calculated after Wilmott (1985) using mean monthly precipitation data supplied by NCAR. The DB-1012 consists of 4 files: a README file; estimates of CO2 and HCO3 flux in a global grid (64,800 cells), an exported ARC/INFO (TM Version 7) map, and a FORTRAN 77 program to read the data. CDIAC has provided an anonymous FTP area to all data files, retrieval codes, and descriptive files for the DB-1012 dataset. The FTP address is ""ftp://cdiac.esd.ornl.gov"" and input your email address as the password. The DB-1012 data are located in 'ftp://cdiac.esd.ornl.gov/pub/db1012'." proprietary
CDIAC_DB1012 A Global 1x1 Degree Distribution of Atmospheric-Soil CO2 Consumption by Continental Weathering and Riverine HCO3 Yield, CDIAC/DB1012 ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214586079-SCIOPS.umm_json "This database (DB-1012) contains estimates of the net flux of atmospheric-soil carbon dioxide (CO2) produced by the Amiotte Sucjet and Probst model(1993) and the associated bicarbonate river flux (HCO3-). The data are referenced to a 1 degree by 1 degree global grid. The work was done at the Centre National de la Recherche Scientifique (CNRS) of Strasborg Cedex, France with the support of the Environment Programme of the European Communities to model the spatial distribution of atmospheric-soil CO2 consumption by chemical weathering of continental rocks. The result of the study is the database of CO2 consumption and transport of bicarbonate from rivers to the ocean in moles per kilometer squared per year (mol km2/yr). Amiotte Suchet and Probst developed a model that calculates the flux of atmospheric-soil CO2 consumed by chemical erosion of continental rock (i.e., rock weathering) and the bicarbonate river transfer to the ocean. The model is based on a set of empirical relationships between FCO2 and the drainage (runoff) on the major rock types outcropping on the continents. The model assumes that the consumption of atmospheric CO2 by continential weathering is primarily influenced by drainage, and the different types of rocks outcropping the continents. The estimates of flux in the model are the result of four processes: the identification of the empirical relationships between FCO2 and drainage for major rock types;, the development of a model (GEM-CO2) to estimate FCO2 and FHCO3-; the validation of GEM-CVO2 using three case studies; and the global application of GEM-CO2. In Phase I, rock types used to identify empirical relationships include: plutonic & metamorphic; sands & snadstones; acid volcanic; evaporitic; basalts; shales; and carbonate. In Phase III, the GEM-CO2 model results were validated using three large river basins: the Amazon and Cingo basins in tropical equatorial climates, and the Garonne (France) in temperate climate. In Phase IV, the model results were applied to a global grid. For each grid cell, a mean lithology was determined using lithological and soil maps published by the FAO-UNESCO (1971, 1975, 1976, 1978, and 1981) for each continent. The drainage intensity was calculated after Wilmott (1985) using mean monthly precipitation data supplied by NCAR. The DB-1012 consists of 4 files: a README file; estimates of CO2 and HCO3 flux in a global grid (64,800 cells), an exported ARC/INFO (TM Version 7) map, and a FORTRAN 77 program to read the data. CDIAC has provided an anonymous FTP area to all data files, retrieval codes, and descriptive files for the DB-1012 dataset. The FTP address is ""ftp://cdiac.esd.ornl.gov"" and input your email address as the password. The DB-1012 data are located in 'ftp://cdiac.esd.ornl.gov/pub/db1012'." proprietary
CDIAC_NDP043C A Coastal Hazards Data Base for the U.S. West Coast, CDIAC/NDP043C SCIOPS STAC Catalog 1970-01-01 -130, 30, -116, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1214607746-SCIOPS.umm_json "[Adapted from the online documentation] The Numeric Data Package (NDP-043C) consists of a digital data base that may be used to identify coastlines along the U.S. West Coast that are at risk to sea-level rise. This data base integrates point, line, and polygon data for the U.S. West Coast into 0.25 degree latitude by 0.25 degree longitude grid cells and into 1:2,000,000 digitized line segments that can be used by raster or vector geographic information systems (GIS) as well as by non-GIS data bases. Each coastal grid cell and line segment contains data variables from the following seven data sets: elevation, geology, geomorphology, sea-level trends, shoreline displacement (erosion/accretion), tidal ranges, and wave heights. These variables may be used to calculate a Coastal Vulnerability Index (CVI). Two other Coastal Hazards Databases are available from CDIAC: Coastal Hazards Database for the U.S. East Coast ""http://cdiac.esd.ornl.gov/ndps/ndp043a.html"" Coastal Hazards Database for the U.S. Gulf Cost ""http://cdiac.esd.ornl.gov/ndps/ndp043b.html"" The data set is available free of charge as a numeric data package (NDP) from the Carbon Dioxide Information Analysis Center. The NDP consists of 21 data files including ASCII, ARC/INFO export files, FORTRAN, SAS, and documentation files. CDIAC has provided an anonymous FTP area to all data files, retrieval codes, and descriptive files for the NDP's that are presently available. The FTP address for the ndp043c database is: ""ftp://cdiac.esd.ornl.gov/pub/ndp043c"" or via anonymous ftp to: ftp cdiac.esd.ornl.gov login as ""anonymous"", enter email as password cd pub/ndp043c NDP043C can also be obtained via the WWW: ""http://cdiac.esd.ornl.gov/ndps/ndp043c.html"" Full documentation is available online at: ""http://cdiac.esd.ornl.gov/epubs/ndp/ndp043c/43c.htm""" proprietary
CDIAC_NDP043C A Coastal Hazards Data Base for the U.S. West Coast, CDIAC/NDP043C ALL STAC Catalog 1970-01-01 -130, 30, -116, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1214607746-SCIOPS.umm_json "[Adapted from the online documentation] The Numeric Data Package (NDP-043C) consists of a digital data base that may be used to identify coastlines along the U.S. West Coast that are at risk to sea-level rise. This data base integrates point, line, and polygon data for the U.S. West Coast into 0.25 degree latitude by 0.25 degree longitude grid cells and into 1:2,000,000 digitized line segments that can be used by raster or vector geographic information systems (GIS) as well as by non-GIS data bases. Each coastal grid cell and line segment contains data variables from the following seven data sets: elevation, geology, geomorphology, sea-level trends, shoreline displacement (erosion/accretion), tidal ranges, and wave heights. These variables may be used to calculate a Coastal Vulnerability Index (CVI). Two other Coastal Hazards Databases are available from CDIAC: Coastal Hazards Database for the U.S. East Coast ""http://cdiac.esd.ornl.gov/ndps/ndp043a.html"" Coastal Hazards Database for the U.S. Gulf Cost ""http://cdiac.esd.ornl.gov/ndps/ndp043b.html"" The data set is available free of charge as a numeric data package (NDP) from the Carbon Dioxide Information Analysis Center. The NDP consists of 21 data files including ASCII, ARC/INFO export files, FORTRAN, SAS, and documentation files. CDIAC has provided an anonymous FTP area to all data files, retrieval codes, and descriptive files for the NDP's that are presently available. The FTP address for the ndp043c database is: ""ftp://cdiac.esd.ornl.gov/pub/ndp043c"" or via anonymous ftp to: ftp cdiac.esd.ornl.gov login as ""anonymous"", enter email as password cd pub/ndp043c NDP043C can also be obtained via the WWW: ""http://cdiac.esd.ornl.gov/ndps/ndp043c.html"" Full documentation is available online at: ""http://cdiac.esd.ornl.gov/epubs/ndp/ndp043c/43c.htm""" proprietary
-CDIAC_NDP072_ORNL/CDIAC-120 A Database of Woody Vegetation Responses to Elevated Atmospheric CO2, CDIAC/NDP-072 SCIOPS STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C1214608277-SCIOPS.umm_json "Numeric Data Package NDP-072 replaces the database DB-1018 previously available from CDIAC. This data base contains enhancements, additional quality control and corrections to the data in DB-1018. NDP-072 is a multi-parameter database generated to aid in a statistically rigorous synthesis of research results on the response by woody plants to increased atmospheric CO2 levels. Eighty-four independent CO2-enrichment studies, covering 65 species and 35 response parameters, met the necessary criteria for inclusion in the database, reporting mean response, sample size and variance of the response (either as standard deviation or standard error). The data were retrieved from published literature and, in a few instances, from unpublished reports. The effects of environmental factors (e.g., drought, heat, ozone, ultraviolet-B radiation), and the effects of experimental conditions (e.g., duration of CO2 exposure, pot size, type of CO2 exposure facility) on plant responses to elevated CO2 levels can be explored with this database. The database consists of a 26-field data file of CO2-exposure experiment responses by woody plants, a paper-reference file, a paper-comment file and SAS (and FORTRAN-77 codes to read the data file. The database and full documentation is available from: ""http://cdiac.esd.ornl.gov/epubs/ndp/ndp072/ndp072.html""" proprietary
CDIAC_NDP072_ORNL/CDIAC-120 A Database of Woody Vegetation Responses to Elevated Atmospheric CO2, CDIAC/NDP-072 ALL STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C1214608277-SCIOPS.umm_json "Numeric Data Package NDP-072 replaces the database DB-1018 previously available from CDIAC. This data base contains enhancements, additional quality control and corrections to the data in DB-1018. NDP-072 is a multi-parameter database generated to aid in a statistically rigorous synthesis of research results on the response by woody plants to increased atmospheric CO2 levels. Eighty-four independent CO2-enrichment studies, covering 65 species and 35 response parameters, met the necessary criteria for inclusion in the database, reporting mean response, sample size and variance of the response (either as standard deviation or standard error). The data were retrieved from published literature and, in a few instances, from unpublished reports. The effects of environmental factors (e.g., drought, heat, ozone, ultraviolet-B radiation), and the effects of experimental conditions (e.g., duration of CO2 exposure, pot size, type of CO2 exposure facility) on plant responses to elevated CO2 levels can be explored with this database. The database consists of a 26-field data file of CO2-exposure experiment responses by woody plants, a paper-reference file, a paper-comment file and SAS (and FORTRAN-77 codes to read the data file. The database and full documentation is available from: ""http://cdiac.esd.ornl.gov/epubs/ndp/ndp072/ndp072.html""" proprietary
-CDIAC_NDP073 A Database of Herbaceous Vegetation Responses to Elevated Atmospheric CO2, CDAIC/NDP-073 ALL STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C1214608367-SCIOPS.umm_json "The Numeric Data Package NDP-073 is a multiparameter database of responses by herbaceous vegetation to increased atmospheric CO2 levels compiled from the literature. Seventy-eight independent CO2-enrichment studies, covering 53 species and 26 response parameters, reported mean response, sample size, and variance of the response (either as standard deviation or standard error). An additional 43 studies, covering 25 species and 6 response parameters, did not report variances. This numeric data package accompanies the Carbon Dioxide Information Analysis Center's (CDIAC's) NDP- 072 (""http://cdiac.esd.ornl.gov/epubs/ndp/ndp072/ndp072.html""), which provides similar information for woody vegetation. For more information, see: ""http://cdiac.esd.ornl.gov/epubs/ndp/ndp073/ndp073.html""" proprietary
+CDIAC_NDP072_ORNL/CDIAC-120 A Database of Woody Vegetation Responses to Elevated Atmospheric CO2, CDIAC/NDP-072 SCIOPS STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C1214608277-SCIOPS.umm_json "Numeric Data Package NDP-072 replaces the database DB-1018 previously available from CDIAC. This data base contains enhancements, additional quality control and corrections to the data in DB-1018. NDP-072 is a multi-parameter database generated to aid in a statistically rigorous synthesis of research results on the response by woody plants to increased atmospheric CO2 levels. Eighty-four independent CO2-enrichment studies, covering 65 species and 35 response parameters, met the necessary criteria for inclusion in the database, reporting mean response, sample size and variance of the response (either as standard deviation or standard error). The data were retrieved from published literature and, in a few instances, from unpublished reports. The effects of environmental factors (e.g., drought, heat, ozone, ultraviolet-B radiation), and the effects of experimental conditions (e.g., duration of CO2 exposure, pot size, type of CO2 exposure facility) on plant responses to elevated CO2 levels can be explored with this database. The database consists of a 26-field data file of CO2-exposure experiment responses by woody plants, a paper-reference file, a paper-comment file and SAS (and FORTRAN-77 codes to read the data file. The database and full documentation is available from: ""http://cdiac.esd.ornl.gov/epubs/ndp/ndp072/ndp072.html""" proprietary
CDIAC_NDP073 A Database of Herbaceous Vegetation Responses to Elevated Atmospheric CO2, CDAIC/NDP-073 SCIOPS STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C1214608367-SCIOPS.umm_json "The Numeric Data Package NDP-073 is a multiparameter database of responses by herbaceous vegetation to increased atmospheric CO2 levels compiled from the literature. Seventy-eight independent CO2-enrichment studies, covering 53 species and 26 response parameters, reported mean response, sample size, and variance of the response (either as standard deviation or standard error). An additional 43 studies, covering 25 species and 6 response parameters, did not report variances. This numeric data package accompanies the Carbon Dioxide Information Analysis Center's (CDIAC's) NDP- 072 (""http://cdiac.esd.ornl.gov/epubs/ndp/ndp072/ndp072.html""), which provides similar information for woody vegetation. For more information, see: ""http://cdiac.esd.ornl.gov/epubs/ndp/ndp073/ndp073.html""" proprietary
+CDIAC_NDP073 A Database of Herbaceous Vegetation Responses to Elevated Atmospheric CO2, CDAIC/NDP-073 ALL STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C1214608367-SCIOPS.umm_json "The Numeric Data Package NDP-073 is a multiparameter database of responses by herbaceous vegetation to increased atmospheric CO2 levels compiled from the literature. Seventy-eight independent CO2-enrichment studies, covering 53 species and 26 response parameters, reported mean response, sample size, and variance of the response (either as standard deviation or standard error). An additional 43 studies, covering 25 species and 6 response parameters, did not report variances. This numeric data package accompanies the Carbon Dioxide Information Analysis Center's (CDIAC's) NDP- 072 (""http://cdiac.esd.ornl.gov/epubs/ndp/ndp072/ndp072.html""), which provides similar information for woody vegetation. For more information, see: ""http://cdiac.esd.ornl.gov/epubs/ndp/ndp073/ndp073.html""" proprietary
CDIAC_NDP41_220_2 Global Historical Climatology Network, 1753-1990 ORNL_CLOUD STAC Catalog 1753-01-01 1990-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2759030200-ORNL_CLOUD.umm_json This data set contains monthly temperature, precipitation, sea-level pressure, and station-pressure data for thousands of meteorological stations worldwide. The database was compiled from pre-existing national, regional, and global collections of data as part of the Global Historical Climatology Network (GHCN) project, the goal of which is to produce, maintain, and make available a comprehensive global surface baseline climate data set for monitoring climate and detecting climate change. It contains data from roughly 6000 temperature stations, 7500 precipitation stations, 1800 sea level pressure stations, and 1800 station pressure stations. Each station has at least 10 years of data, 40% have more than 50 years of data. Spatial coverage is good over most of the globe, particularly for the United States and Europe. Data gaps are evident over the Amazon rainforest, the Sahara Desert, Greenland, and Antarctica. proprietary
-CDIAC_NDP43A A Coastal Hazards Data Base for the U.S. East Coast, CDIAC NDP-043A SCIOPS STAC Catalog 1970-01-01 -80, 25, -65, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214584799-SCIOPS.umm_json This NDP presents data on coastal geology, geomorphology, elevation, erosion, wave heights, tide ranges, and sea levels for the U.S. east coast. These data may be used either by nongeographic database management systems or by raster or vector geographic information systems (GISs). The database integrates several data sets (originally obtained as point, line, and polygon data) for the east coast into 0.25°-latitude by 0.25°-longitude grid cells. Each coastal grid cell contains 28 data variables. This NDP may be used to predict the response of coastal zones on the U.S. east coast to changes in local or global sea levels. Information on the geologic, geomorphic, and erosional states of the coast provides the basic data needed to predict the behavior of the coastal zone into the far future. Thus, these data may be seen as providing a baseline for the calculation of the relative vulnerability of the east coast to projected sea-level rises. This data will also be useful to research, educational, governmental, and private organizations interested in the present and future vulnerability of coastal areas to erosion and inundation. The data are in 13 files, the largest of which is 1.42 MB; the entire data base takes up 3.29 MB, excluding the ARC/INFOTM files. proprietary
CDIAC_NDP43A A Coastal Hazards Data Base for the U.S. East Coast, CDIAC NDP-043A ALL STAC Catalog 1970-01-01 -80, 25, -65, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214584799-SCIOPS.umm_json This NDP presents data on coastal geology, geomorphology, elevation, erosion, wave heights, tide ranges, and sea levels for the U.S. east coast. These data may be used either by nongeographic database management systems or by raster or vector geographic information systems (GISs). The database integrates several data sets (originally obtained as point, line, and polygon data) for the east coast into 0.25°-latitude by 0.25°-longitude grid cells. Each coastal grid cell contains 28 data variables. This NDP may be used to predict the response of coastal zones on the U.S. east coast to changes in local or global sea levels. Information on the geologic, geomorphic, and erosional states of the coast provides the basic data needed to predict the behavior of the coastal zone into the far future. Thus, these data may be seen as providing a baseline for the calculation of the relative vulnerability of the east coast to projected sea-level rises. This data will also be useful to research, educational, governmental, and private organizations interested in the present and future vulnerability of coastal areas to erosion and inundation. The data are in 13 files, the largest of which is 1.42 MB; the entire data base takes up 3.29 MB, excluding the ARC/INFOTM files. proprietary
+CDIAC_NDP43A A Coastal Hazards Data Base for the U.S. East Coast, CDIAC NDP-043A SCIOPS STAC Catalog 1970-01-01 -80, 25, -65, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214584799-SCIOPS.umm_json This NDP presents data on coastal geology, geomorphology, elevation, erosion, wave heights, tide ranges, and sea levels for the U.S. east coast. These data may be used either by nongeographic database management systems or by raster or vector geographic information systems (GISs). The database integrates several data sets (originally obtained as point, line, and polygon data) for the east coast into 0.25°-latitude by 0.25°-longitude grid cells. Each coastal grid cell contains 28 data variables. This NDP may be used to predict the response of coastal zones on the U.S. east coast to changes in local or global sea levels. Information on the geologic, geomorphic, and erosional states of the coast provides the basic data needed to predict the behavior of the coastal zone into the far future. Thus, these data may be seen as providing a baseline for the calculation of the relative vulnerability of the east coast to projected sea-level rises. This data will also be useful to research, educational, governmental, and private organizations interested in the present and future vulnerability of coastal areas to erosion and inundation. The data are in 13 files, the largest of which is 1.42 MB; the entire data base takes up 3.29 MB, excluding the ARC/INFOTM files. proprietary
CDIAC_NDP43B A Coastal Hazards Data Base for the U.S. Gulf Coast, CDIAC NDP-043B SCIOPS STAC Catalog 1993-01-01 -100, 25, -80, 33 https://cmr.earthdata.nasa.gov/search/concepts/C1214584741-SCIOPS.umm_json "This Numeric Data Package (NDP) contains a digital database that describes the U.S. Gulf Coast. The database integrates point, line, and polygon data for the U.S. Gulf Coast into 0.25 latitude by 0.25 longitude grid cells and into 1:2,000,000 digitized line segments that can be used by raster or vector geographic information systems (GIS) as well as non-GIS database systems. Each coastal grid cell and/or line segment contains data on elevation, geology, geomorphology, sea-level trends, shoreline displacement (erosion/accretion), tidal range, and wave heights. The database identifies seven of 22 variables as relative-risk variables to assess coastal vulnerability. The data can be used to create a coastal vulnerability index for each grid cell and/or line segment. The database and corresponding coastal vulnerability indices may be used to identify coastal zones that are at risk from coastal erosion or possible changes in sea level. The data are contained in five groups, available as ARC/INFO export files and as flat ASCII files for a total of 10 files, each less than 2 MB. This NDP is related to NDP-043A ""Coastal Hazards Data Base for the U.S. East Coast"" submitted by the same investigators as NDP-043B. All CDIAC numerical data packages include copies of pertinent literature discussing the data, summaries discussing the background, source and scope of the data, as well as applications, limitations and restrictions of the data." proprietary
CDIAC_NDP43B A Coastal Hazards Data Base for the U.S. Gulf Coast, CDIAC NDP-043B ALL STAC Catalog 1993-01-01 -100, 25, -80, 33 https://cmr.earthdata.nasa.gov/search/concepts/C1214584741-SCIOPS.umm_json "This Numeric Data Package (NDP) contains a digital database that describes the U.S. Gulf Coast. The database integrates point, line, and polygon data for the U.S. Gulf Coast into 0.25 latitude by 0.25 longitude grid cells and into 1:2,000,000 digitized line segments that can be used by raster or vector geographic information systems (GIS) as well as non-GIS database systems. Each coastal grid cell and/or line segment contains data on elevation, geology, geomorphology, sea-level trends, shoreline displacement (erosion/accretion), tidal range, and wave heights. The database identifies seven of 22 variables as relative-risk variables to assess coastal vulnerability. The data can be used to create a coastal vulnerability index for each grid cell and/or line segment. The database and corresponding coastal vulnerability indices may be used to identify coastal zones that are at risk from coastal erosion or possible changes in sea level. The data are contained in five groups, available as ARC/INFO export files and as flat ASCII files for a total of 10 files, each less than 2 MB. This NDP is related to NDP-043A ""Coastal Hazards Data Base for the U.S. East Coast"" submitted by the same investigators as NDP-043B. All CDIAC numerical data packages include copies of pertinent literature discussing the data, summaries discussing the background, source and scope of the data, as well as applications, limitations and restrictions of the data." proprietary
-CDIAC_TR051 A Comprehensive Precipitation Data Set for Global Land Areas, CDIAC/TR051 SCIOPS STAC Catalog 1851-01-01 1989-12-31 -180, -60, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1214610804-SCIOPS.umm_json "The Eischeid Surface Rain Gauge Observations data set consists of an inventory of the stations used for the climatology, total monthly precipitation data for 5,328 stations and gridded seasonal precipitation anomalies (in mm) for the period 1851-1989. The data were interpolated to a 4 deg latitude by 5 deg longitude grid extending from 60 S to 80 N. The total volume of the data set is 9.6 Mbytes and is available by ftp. The full documentation for this database and all data files are available via CDIAC's world wide web site at ""http://cdiac.esd.ornl.gov/ndps/tr051.html"" The data files are also available via anonymous FTP. FTP to 'cdiac.esd.ornl.gov' or 128.219.24.36, enter 'anonymous' as your user id and input your email address as the password. Then change directories to pub/tr051. ""ftp://cdiac.esd.ornl.gov/pub/tr051""" proprietary
CDIAC_TR051 A Comprehensive Precipitation Data Set for Global Land Areas, CDIAC/TR051 ALL STAC Catalog 1851-01-01 1989-12-31 -180, -60, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1214610804-SCIOPS.umm_json "The Eischeid Surface Rain Gauge Observations data set consists of an inventory of the stations used for the climatology, total monthly precipitation data for 5,328 stations and gridded seasonal precipitation anomalies (in mm) for the period 1851-1989. The data were interpolated to a 4 deg latitude by 5 deg longitude grid extending from 60 S to 80 N. The total volume of the data set is 9.6 Mbytes and is available by ftp. The full documentation for this database and all data files are available via CDIAC's world wide web site at ""http://cdiac.esd.ornl.gov/ndps/tr051.html"" The data files are also available via anonymous FTP. FTP to 'cdiac.esd.ornl.gov' or 128.219.24.36, enter 'anonymous' as your user id and input your email address as the password. Then change directories to pub/tr051. ""ftp://cdiac.esd.ornl.gov/pub/tr051""" proprietary
-CDMO_acemet01-12.02m ACE Basin National Estuarine Research Reserve Meteorological Metadata January - December 2002 Latest Update: February 11, 2005 ALL STAC Catalog 2002-01-01 2002-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590656-SCIOPS.umm_json Meteorological monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from at least one location within or adjacent to the reserve. Data are collected every 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60 minutes) and daily (1440 minutes) intervals. The parameters collected within these intervals are: averages, maximums and minimums of air temperature, relative humidity, barometric pressure, wind speed, wind direction, precipitation and photosynthetically active solar radiation. proprietary
+CDIAC_TR051 A Comprehensive Precipitation Data Set for Global Land Areas, CDIAC/TR051 SCIOPS STAC Catalog 1851-01-01 1989-12-31 -180, -60, 180, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1214610804-SCIOPS.umm_json "The Eischeid Surface Rain Gauge Observations data set consists of an inventory of the stations used for the climatology, total monthly precipitation data for 5,328 stations and gridded seasonal precipitation anomalies (in mm) for the period 1851-1989. The data were interpolated to a 4 deg latitude by 5 deg longitude grid extending from 60 S to 80 N. The total volume of the data set is 9.6 Mbytes and is available by ftp. The full documentation for this database and all data files are available via CDIAC's world wide web site at ""http://cdiac.esd.ornl.gov/ndps/tr051.html"" The data files are also available via anonymous FTP. FTP to 'cdiac.esd.ornl.gov' or 128.219.24.36, enter 'anonymous' as your user id and input your email address as the password. Then change directories to pub/tr051. ""ftp://cdiac.esd.ornl.gov/pub/tr051""" proprietary
CDMO_acemet01-12.02m ACE Basin National Estuarine Research Reserve Meteorological Metadata January - December 2002 Latest Update: February 11, 2005 SCIOPS STAC Catalog 2002-01-01 2002-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590656-SCIOPS.umm_json Meteorological monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from at least one location within or adjacent to the reserve. Data are collected every 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60 minutes) and daily (1440 minutes) intervals. The parameters collected within these intervals are: averages, maximums and minimums of air temperature, relative humidity, barometric pressure, wind speed, wind direction, precipitation and photosynthetically active solar radiation. proprietary
+CDMO_acemet01-12.02m ACE Basin National Estuarine Research Reserve Meteorological Metadata January - December 2002 Latest Update: February 11, 2005 ALL STAC Catalog 2002-01-01 2002-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590656-SCIOPS.umm_json Meteorological monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from at least one location within or adjacent to the reserve. Data are collected every 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60 minutes) and daily (1440 minutes) intervals. The parameters collected within these intervals are: averages, maximums and minimums of air temperature, relative humidity, barometric pressure, wind speed, wind direction, precipitation and photosynthetically active solar radiation. proprietary
CDMO_acemet01-12.03m ACE Basin National Estuarine Research Reserve Meteorological Metadata Report January - December 2003 SCIOPS STAC Catalog 2003-01-01 2003-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590676-SCIOPS.umm_json Meteorological monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from at least one location within or adjacent to the reserve. Data are collected every 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60 minutes) and daily (1440 minutes) intervals. The parameters collected within these intervals are: averages, maximums and minimums of air temperature, relative humidity, barometric pressure, wind speed, wind direction, precipitation and photosynthetically active solar radiation proprietary
CDMO_acemet01-12.03m ACE Basin National Estuarine Research Reserve Meteorological Metadata Report January - December 2003 ALL STAC Catalog 2003-01-01 2003-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590676-SCIOPS.umm_json Meteorological monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from at least one location within or adjacent to the reserve. Data are collected every 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60 minutes) and daily (1440 minutes) intervals. The parameters collected within these intervals are: averages, maximums and minimums of air temperature, relative humidity, barometric pressure, wind speed, wind direction, precipitation and photosynthetically active solar radiation proprietary
CDMO_acemet01-12.04m ACE Basin National Estuarine Research Reserve Meteorological Metadata Report January - December 2004 ALL STAC Catalog 2004-01-01 2004-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590685-SCIOPS.umm_json Meteorological monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from at least one location within or adjacent to the reserve. Data are collected every 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60 minutes) and daily (1440 minutes) intervals. The parameters collected within these intervals are: averages, maximums and minimums of air temperature, relative humidity, barometric pressure, wind speed, wind direction, precipitation and photosynthetically active solar radiation proprietary
CDMO_acemet01-12.04m ACE Basin National Estuarine Research Reserve Meteorological Metadata Report January - December 2004 SCIOPS STAC Catalog 2004-01-01 2004-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590685-SCIOPS.umm_json Meteorological monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from at least one location within or adjacent to the reserve. Data are collected every 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60 minutes) and daily (1440 minutes) intervals. The parameters collected within these intervals are: averages, maximums and minimums of air temperature, relative humidity, barometric pressure, wind speed, wind direction, precipitation and photosynthetically active solar radiation proprietary
CDMO_acemet03-12.01m ACE Basin (ACE) National Estuarine Research Reserve Meteorological Metadata Report March - December 2001 SCIOPS STAC Catalog 2001-03-01 2001-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590677-SCIOPS.umm_json Meteorological monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from at least one location within or adjacent to the reserve. Data are collected every 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60 minutes) and daily (1440 minutes) intervals. The parameters collected within these intervals are: averages, maximums and minimums of air temperature, relative humidity, barometric pressure, wind speed, wind direction, precipitation and photosynthetically active solar radiation proprietary
CDMO_acemet03-12.01m ACE Basin (ACE) National Estuarine Research Reserve Meteorological Metadata Report March - December 2001 ALL STAC Catalog 2001-03-01 2001-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590677-SCIOPS.umm_json Meteorological monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from at least one location within or adjacent to the reserve. Data are collected every 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60 minutes) and daily (1440 minutes) intervals. The parameters collected within these intervals are: averages, maximums and minimums of air temperature, relative humidity, barometric pressure, wind speed, wind direction, precipitation and photosynthetically active solar radiation proprietary
-CDMO_acenut01-12.02m ACE Basin NERR Nutrient Metadata January-December 2002 Latest Update: December 15, 2004 ALL STAC Catalog 2002-01-01 2002-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590686-SCIOPS.umm_json Meteorological monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from at least one location within or adjacent to the reserve. Data are collected every 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60 minutes) and daily (1440 minutes) intervals. The parameters collected within these intervals are: averages, maximums and minimums of air temperature, relative humidity, barometric pressure, wind speed, wind direction, precipitation and photosynthetically active solar radiation. proprietary
CDMO_acenut01-12.02m ACE Basin NERR Nutrient Metadata January-December 2002 Latest Update: December 15, 2004 SCIOPS STAC Catalog 2002-01-01 2002-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590686-SCIOPS.umm_json Meteorological monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from at least one location within or adjacent to the reserve. Data are collected every 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60 minutes) and daily (1440 minutes) intervals. The parameters collected within these intervals are: averages, maximums and minimums of air temperature, relative humidity, barometric pressure, wind speed, wind direction, precipitation and photosynthetically active solar radiation. proprietary
+CDMO_acenut01-12.02m ACE Basin NERR Nutrient Metadata January-December 2002 Latest Update: December 15, 2004 ALL STAC Catalog 2002-01-01 2002-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590686-SCIOPS.umm_json Meteorological monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from at least one location within or adjacent to the reserve. Data are collected every 5 seconds and averages are produced from this data at quarterly (15 minutes), hourly (60 minutes) and daily (1440 minutes) intervals. The parameters collected within these intervals are: averages, maximums and minimums of air temperature, relative humidity, barometric pressure, wind speed, wind direction, precipitation and photosynthetically active solar radiation. proprietary
CDMO_acenut01-12.03m ACE Basin NERR Nutrient Metadata January-December 2003 Latest Update: December 6, 2004 ALL STAC Catalog 2003-01-01 2003-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590678-SCIOPS.umm_json Nutrient monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from four locations within or adjacent to the reserve on a monthly basis of the following parameters: orthophosphate, ammonium, nitrite, nitrate, and chlorophyll a. Note: Reserves may collect additional parameters which are available by searching the Yearly Files directory. proprietary
CDMO_acenut01-12.03m ACE Basin NERR Nutrient Metadata January-December 2003 Latest Update: December 6, 2004 SCIOPS STAC Catalog 2003-01-01 2003-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590678-SCIOPS.umm_json Nutrient monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from four locations within or adjacent to the reserve on a monthly basis of the following parameters: orthophosphate, ammonium, nitrite, nitrate, and chlorophyll a. Note: Reserves may collect additional parameters which are available by searching the Yearly Files directory. proprietary
CDMO_acenut01-12.04m ACE Basin (ACE) NERR Nutrient Metadata January-December 2004 Latest Update: July 21, 2005 SCIOPS STAC Catalog 2004-01-01 2004-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590657-SCIOPS.umm_json Nutrient monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from four locations within or adjacent to the reserve on a monthly basis of the following parameters: orthophosphate, ammonium, nitrite, nitrate, and chlorophyll a. Note: Reserves may collect additional parameters which are available by searching the Yearly Files directory. proprietary
CDMO_acenut01-12.04m ACE Basin (ACE) NERR Nutrient Metadata January-December 2004 Latest Update: July 21, 2005 ALL STAC Catalog 2004-01-01 2004-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590657-SCIOPS.umm_json Nutrient monitoring is conducted at 26 National Estuarine Research Reserves (NERR) from four locations within or adjacent to the reserve on a monthly basis of the following parameters: orthophosphate, ammonium, nitrite, nitrate, and chlorophyll a. Note: Reserves may collect additional parameters which are available by searching the Yearly Files directory. proprietary
CDMO_acewq01-12.00m ACE Basin National Estuarine Research Reserve Water Quality Metadata Report January-December 2000 Latest Update: May 22, 2001 ALL STAC Catalog 2000-01-01 2000-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590679-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
CDMO_acewq01-12.00m ACE Basin National Estuarine Research Reserve Water Quality Metadata Report January-December 2000 Latest Update: May 22, 2001 SCIOPS STAC Catalog 2000-01-01 2000-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590679-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
-CDMO_acewq01-12.01m ACE Basin NERR Water Quality Metadata January-December 2001 Latest update: August 20, 2002 ALL STAC Catalog 2001-01-01 2001-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590687-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR)at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
CDMO_acewq01-12.01m ACE Basin NERR Water Quality Metadata January-December 2001 Latest update: August 20, 2002 SCIOPS STAC Catalog 2001-01-01 2001-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590687-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR)at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
+CDMO_acewq01-12.01m ACE Basin NERR Water Quality Metadata January-December 2001 Latest update: August 20, 2002 ALL STAC Catalog 2001-01-01 2001-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590687-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR)at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
CDMO_acewq01-12.02m ACE Basin (ACE) National Estuarine Research Reserve Water Quality Metadata January-December 2002 Latest update: May 12, 2003 SCIOPS STAC Catalog 2002-01-01 2002-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590700-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
CDMO_acewq01-12.02m ACE Basin (ACE) National Estuarine Research Reserve Water Quality Metadata January-December 2002 Latest update: May 12, 2003 ALL STAC Catalog 2002-01-01 2002-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590700-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
-CDMO_acewq01-12.04m ACE Basin (ACE) National Estuarine Research Reserve Water Quality Metadata January-December 2004 Report Latest edit: May 6, 2005 ALL STAC Catalog 2001-01-01 2004-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590701-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
CDMO_acewq01-12.04m ACE Basin (ACE) National Estuarine Research Reserve Water Quality Metadata January-December 2004 Report Latest edit: May 6, 2005 SCIOPS STAC Catalog 2001-01-01 2004-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590701-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
+CDMO_acewq01-12.04m ACE Basin (ACE) National Estuarine Research Reserve Water Quality Metadata January-December 2004 Report Latest edit: May 6, 2005 ALL STAC Catalog 2001-01-01 2004-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590701-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
CDMO_acewq01-12.96m ACE Basin National Estuarine Research Reserve January-December 1996 Metadata Report Lastest Update: September 26, 2001 SCIOPS STAC Catalog 1996-01-01 1996-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590688-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
CDMO_acewq01-12.96m ACE Basin National Estuarine Research Reserve January-December 1996 Metadata Report Lastest Update: September 26, 2001 ALL STAC Catalog 1996-01-01 1996-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590688-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
-CDMO_acewq01-12.97m ACE Basin National Estuarine Research Reserve January-December 1997 Water Quality Metadata Report Latest Update: September 26, 2001 ALL STAC Catalog 1997-01-01 1997-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590659-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
CDMO_acewq01-12.97m ACE Basin National Estuarine Research Reserve January-December 1997 Water Quality Metadata Report Latest Update: September 26, 2001 SCIOPS STAC Catalog 1997-01-01 1997-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590659-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
-CDMO_acewq01-12.98m ACE Basin National Estuarine Research Reserve January-December 1998 Water Quality Metadata Report Latest Update: September 26, 2001 SCIOPS STAC Catalog 1998-01-01 1998-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590693-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
+CDMO_acewq01-12.97m ACE Basin National Estuarine Research Reserve January-December 1997 Water Quality Metadata Report Latest Update: September 26, 2001 ALL STAC Catalog 1997-01-01 1997-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590659-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
CDMO_acewq01-12.98m ACE Basin National Estuarine Research Reserve January-December 1998 Water Quality Metadata Report Latest Update: September 26, 2001 ALL STAC Catalog 1998-01-01 1998-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590693-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
+CDMO_acewq01-12.98m ACE Basin National Estuarine Research Reserve January-December 1998 Water Quality Metadata Report Latest Update: September 26, 2001 SCIOPS STAC Catalog 1998-01-01 1998-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590693-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
CDMO_acewq01-12.99m ACE Basin (ACE) NERR Water Quality Metadata January-December 1999 Metadata Report Latest update: September 19, 2001 SCIOPS STAC Catalog 1999-01-01 1999-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590704-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
CDMO_acewq01-12.99m ACE Basin (ACE) NERR Water Quality Metadata January-December 1999 Metadata Report Latest update: September 19, 2001 ALL STAC Catalog 1999-01-01 1999-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590704-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
-CDMO_acewq03-12.95m ACE Basin National Estuarine Research Reserve March - December 1995 Metadata Report edited: 9/19/97 ALL STAC Catalog 1995-03-01 1995-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590694-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
CDMO_acewq03-12.95m ACE Basin National Estuarine Research Reserve March - December 1995 Metadata Report edited: 9/19/97 SCIOPS STAC Catalog 1995-03-01 1995-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590694-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
-CE1d0023_173 Administrative boundaries of Mohtamadeyas in Tunisia; 1989 ALL STAC Catalog 1974-01-01 1989-01-01 7, 30, 12, 35 https://cmr.earthdata.nasa.gov/search/concepts/C1214155160-SCIOPS.umm_json This coverage represents polygon features that describe the administrative boundaries down to Mohtamadeyas. Original Map name: Administrative boundaries of Mohtamadeyas Date of production: not mentioned Date collection: 1989 proprietary
+CDMO_acewq03-12.95m ACE Basin National Estuarine Research Reserve March - December 1995 Metadata Report edited: 9/19/97 ALL STAC Catalog 1995-03-01 1995-12-31 -80.67007, 32.32975, -80.27775, 32.669712 https://cmr.earthdata.nasa.gov/search/concepts/C1214590694-SCIOPS.umm_json Water quality monitoring is conducted at 26 National Estuarine Research Reserves (NERR) at four locations within or adjacent to the reserve. The following parameters are collected at least every 30 minutes: water temperature, specific conductivity, salinity, percent saturation, dissolved oxygen concentration, water depth, pH and turbidity. All water quality data loggers will be deployed from a known depth from the bottom at each site. proprietary
CE1d0023_173 Administrative boundaries of Mohtamadeyas in Tunisia; 1989 SCIOPS STAC Catalog 1974-01-01 1989-01-01 7, 30, 12, 35 https://cmr.earthdata.nasa.gov/search/concepts/C1214155160-SCIOPS.umm_json This coverage represents polygon features that describe the administrative boundaries down to Mohtamadeyas. Original Map name: Administrative boundaries of Mohtamadeyas Date of production: not mentioned Date collection: 1989 proprietary
-CE1d0029_173 Agroclimatological Zones, Jordan; 1977 SCIOPS STAC Catalog 1977-01-01 1980-01-01 34, 29, 39, 33 https://cmr.earthdata.nasa.gov/search/concepts/C1214155165-SCIOPS.umm_json This coverage represents polygons that describe the agroclimatological zones. Originating center: Natural Resource Authority in Amman proprietary
+CE1d0023_173 Administrative boundaries of Mohtamadeyas in Tunisia; 1989 ALL STAC Catalog 1974-01-01 1989-01-01 7, 30, 12, 35 https://cmr.earthdata.nasa.gov/search/concepts/C1214155160-SCIOPS.umm_json This coverage represents polygon features that describe the administrative boundaries down to Mohtamadeyas. Original Map name: Administrative boundaries of Mohtamadeyas Date of production: not mentioned Date collection: 1989 proprietary
CE1d0029_173 Agroclimatological Zones, Jordan; 1977 ALL STAC Catalog 1977-01-01 1980-01-01 34, 29, 39, 33 https://cmr.earthdata.nasa.gov/search/concepts/C1214155165-SCIOPS.umm_json This coverage represents polygons that describe the agroclimatological zones. Originating center: Natural Resource Authority in Amman proprietary
-CE1d0038_173 Administrative Units Boundaries of Jordan; 1977 SCIOPS STAC Catalog 1974-01-01 1977-01-01 34, 29, 39, 33 https://cmr.earthdata.nasa.gov/search/concepts/C1214155172-SCIOPS.umm_json This coverage represents polygon features that describe the administrative boundaries. Originating center: Natural Resource Authority in Amman proprietary
+CE1d0029_173 Agroclimatological Zones, Jordan; 1977 SCIOPS STAC Catalog 1977-01-01 1980-01-01 34, 29, 39, 33 https://cmr.earthdata.nasa.gov/search/concepts/C1214155165-SCIOPS.umm_json This coverage represents polygons that describe the agroclimatological zones. Originating center: Natural Resource Authority in Amman proprietary
CE1d0038_173 Administrative Units Boundaries of Jordan; 1977 ALL STAC Catalog 1974-01-01 1977-01-01 34, 29, 39, 33 https://cmr.earthdata.nasa.gov/search/concepts/C1214155172-SCIOPS.umm_json This coverage represents polygon features that describe the administrative boundaries. Originating center: Natural Resource Authority in Amman proprietary
+CE1d0038_173 Administrative Units Boundaries of Jordan; 1977 SCIOPS STAC Catalog 1974-01-01 1977-01-01 34, 29, 39, 33 https://cmr.earthdata.nasa.gov/search/concepts/C1214155172-SCIOPS.umm_json This coverage represents polygon features that describe the administrative boundaries. Originating center: Natural Resource Authority in Amman proprietary
CE1d0043_173 Administrative Map of Morocco; 1993 ALL STAC Catalog 1980-01-01 1993-01-01 -17, 21, -1, 36 https://cmr.earthdata.nasa.gov/search/concepts/C1214155148-SCIOPS.umm_json This coverage represents polygon features that describe the administrative boundaries. Original Map name: Carte Administrative. Originating center: Division de la Cartographie - Direction de la conservation Fonciere et des Traveaux Topographiques proprietary
CE1d0043_173 Administrative Map of Morocco; 1993 SCIOPS STAC Catalog 1980-01-01 1993-01-01 -17, 21, -1, 36 https://cmr.earthdata.nasa.gov/search/concepts/C1214155148-SCIOPS.umm_json This coverage represents polygon features that describe the administrative boundaries. Original Map name: Carte Administrative. Originating center: Division de la Cartographie - Direction de la conservation Fonciere et des Traveaux Topographiques proprietary
CEAMARC-200708_V3_IYGPT_2 Australia's Census of Antarctic Marine Life project - IYGPT Data collected on the CEAMARC cruise AU_AADC STAC Catalog 2007-12-17 2008-01-26 139.3, -67.0547, 145.5347, -61.9748 https://cmr.earthdata.nasa.gov/search/concepts/C1214308506-AU_AADC.umm_json "Australia's Census of Antarctic Marine Life project. This project is a part of the international ""Census of Antarctic Marine Life"" (CAML) which was conducted during the International Polar Year. It was a collaborative contribution by Australia and France to understand the biodiversity of the oceans surrounding Antarctica, with particular emphasis on the fishes of the eastern part of the Australian Antarctic Territory. The biodiversity data, when added to that obtained by all other nations participating in the CAML, serves as a robust reference for future examinations of the health of the Southern Ocean, and assists in the conservation and management of the region. Field sampling for this project was undertaken in the 2007/08 season, commencing in December and finishing in February 2008. Three ships surveyed the area with a range of traditional and modern sampling gear, including IYGPT (International Young Gadoid Pelagic Trawl (marine science equipment)) gear." proprietary
@@ -4502,28 +4503,28 @@ CEAMARC_CASO_200708030_EVENT_BATHYMETRY_PLOTS_1 2007-08 V3 CEAMARC-CASO Bathymet
CEAMARC_CASO_200708030_EVENT_BATHYMETRY_PLOTS_1 2007-08 V3 CEAMARC-CASO Bathymetry Plots Over Time During Events ALL STAC Catalog 2007-12-17 2008-01-26 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214308504-AU_AADC.umm_json A routine was developed in R ('bathy_plots.R') to plot bathymetry data over time during individual CEAMARC events. This is so we can analyse benthic data in relation to habitat, ie. did we trawl over a slope or was the sea floor relatively flat. Note that the depth range in the plots is autoscaled to the data, so a small range in depths appears as a scatetring of points. As long as you look at the depth scale though interpretation will be ok. The R files need a file of bathymetry data in '200708V3_one_minute.csv' which is a file containing a data export from the underway PostgreSQL ship database and 'events.csv' which is a stripped down version of the events export from the ship board events database export. If you wish to run the code again you may need to change the pathnames in the R script to relevant locations. If you have opened the csv files in excel at any stage and the R script gets an error you may need to format the date/time columns as yyyy-mm-dd hh;mm:ss, save and close the file as csv without opening it again and then run the R script. However, all output files are here for every CEAMARC event. Filenames contain a reference to CEAMARC event id. Files are in eps format and can be viewed using Ghostview which is available as a free download on the internet. proprietary
CEAMARC_CASO_200708_V3_BENTHIC_TRAWL_SAMPLES_1 "CEAMARC-CASO Benthic Trawl Samples - voyage 3 of the Aurora Australis,
2007-2008" AU_AADC STAC Catalog 2007-12-22 2008-01-18 139.01488, -67.07104, 150.06479, -62.757324 https://cmr.earthdata.nasa.gov/search/concepts/C1214308492-AU_AADC.umm_json Sampling strategy: Samples from trawls or sledges are sieved on the trawl deck then sorted in the wet lab per taxonomic group. Sorting may vary from high taxonomic levels (order, family) to specific ones according to expertise on board. For some taxa, sampling includes: up to 10 voucher specimens with a unique batch number; photos; tissue samples in 80% ethanol for DNA analysis (Barcoding and Phylogeny); 30 samples minimum for population genetics (for abundant species); sampling for isotopic measures; fish chromosomes preparations; primary fish cell lines and cryopreservation of fish tissues for permanent cell lines The database was intended to contain information about stations, events, gear, all material collected and associated samples listed above. currently only contains information on material collected and samples. Data was recorded on log sheets then transcribed into an Oracle database called cabo. Tailor made user interace for entering data. No export functionality. SQL database dump has been provided but there was no-one on the voyage to elaborate on the structure, this was promised post voyage along with some simple data exports to match the log sheets, so we have access to the data without the unfriendly database. proprietary
-CEAMARC_CASO_200708_V3_Biogeochemistry_EIMS_1 AAV30708 Biogeochemistry - EIMS Data Collected on the CEAMARC Cruise of the Aurora Australis 2007-2008 ALL STAC Catalog 2007-12-16 2008-01-27 141.76285, -67.04925, 147.85347, -43.12521 https://cmr.earthdata.nasa.gov/search/concepts/C1214308493-AU_AADC.umm_json Continuous underway measurements of sea surface (7 metres depth)dissolved gasses (co2, o2, argon, nitrogen)by quadrupole mass spectrometry (Electron Impact Mass Spectrometry - EIMS). ASCII encoded. 1 file per 24 hours. Naming convention: YYMMDD. Excel readable format. Column data (0/0 refers to ion mass, 7 ION masses detected in total): Cycle Date Time RelTime[s] '0/0' '0/1' '0/2' '0/3' '0/4' '0/5' '0/6' '0/7' '1/0' '2/0' '2/1' '2/2' '2/3' '2/4' '2/5' '2/6' '2/7' Measurements were made on the CEAMARC voyage of the Aurora Australis - voyage 3 of the 2008-2008 summer season. proprietary
CEAMARC_CASO_200708_V3_Biogeochemistry_EIMS_1 AAV30708 Biogeochemistry - EIMS Data Collected on the CEAMARC Cruise of the Aurora Australis 2007-2008 AU_AADC STAC Catalog 2007-12-16 2008-01-27 141.76285, -67.04925, 147.85347, -43.12521 https://cmr.earthdata.nasa.gov/search/concepts/C1214308493-AU_AADC.umm_json Continuous underway measurements of sea surface (7 metres depth)dissolved gasses (co2, o2, argon, nitrogen)by quadrupole mass spectrometry (Electron Impact Mass Spectrometry - EIMS). ASCII encoded. 1 file per 24 hours. Naming convention: YYMMDD. Excel readable format. Column data (0/0 refers to ion mass, 7 ION masses detected in total): Cycle Date Time RelTime[s] '0/0' '0/1' '0/2' '0/3' '0/4' '0/5' '0/6' '0/7' '1/0' '2/0' '2/1' '2/2' '2/3' '2/4' '2/5' '2/6' '2/7' Measurements were made on the CEAMARC voyage of the Aurora Australis - voyage 3 of the 2008-2008 summer season. proprietary
-CEAMARC_CASO_200708_V3_Biogeochemistry_PCO2_1 AAV30708 Biogeochemistry PCO2 Data Collected on the CEAMARC Cruise of the Aurora Australis 2007-2008 ALL STAC Catalog 2007-12-16 2008-01-27 141.76285, -67.04925, 147.85347, -43.12521 https://cmr.earthdata.nasa.gov/search/concepts/C1214308494-AU_AADC.umm_json Continuous underway measurements of sea surface (7 metres depth)and atmospheric carbon dioxide. Data format .txt extension comma delimited files. 1 file per 24 hours. Naming similar to AA03607_001-0000 (voyage_julian day_HH:MM). Excel readable format. 58 columns of data. Measurements were made on the CEAMARC voyage of the Aurora Australis - voyage 3 of the 2008-2008 summer season. proprietary
+CEAMARC_CASO_200708_V3_Biogeochemistry_EIMS_1 AAV30708 Biogeochemistry - EIMS Data Collected on the CEAMARC Cruise of the Aurora Australis 2007-2008 ALL STAC Catalog 2007-12-16 2008-01-27 141.76285, -67.04925, 147.85347, -43.12521 https://cmr.earthdata.nasa.gov/search/concepts/C1214308493-AU_AADC.umm_json Continuous underway measurements of sea surface (7 metres depth)dissolved gasses (co2, o2, argon, nitrogen)by quadrupole mass spectrometry (Electron Impact Mass Spectrometry - EIMS). ASCII encoded. 1 file per 24 hours. Naming convention: YYMMDD. Excel readable format. Column data (0/0 refers to ion mass, 7 ION masses detected in total): Cycle Date Time RelTime[s] '0/0' '0/1' '0/2' '0/3' '0/4' '0/5' '0/6' '0/7' '1/0' '2/0' '2/1' '2/2' '2/3' '2/4' '2/5' '2/6' '2/7' Measurements were made on the CEAMARC voyage of the Aurora Australis - voyage 3 of the 2008-2008 summer season. proprietary
CEAMARC_CASO_200708_V3_Biogeochemistry_PCO2_1 AAV30708 Biogeochemistry PCO2 Data Collected on the CEAMARC Cruise of the Aurora Australis 2007-2008 AU_AADC STAC Catalog 2007-12-16 2008-01-27 141.76285, -67.04925, 147.85347, -43.12521 https://cmr.earthdata.nasa.gov/search/concepts/C1214308494-AU_AADC.umm_json Continuous underway measurements of sea surface (7 metres depth)and atmospheric carbon dioxide. Data format .txt extension comma delimited files. 1 file per 24 hours. Naming similar to AA03607_001-0000 (voyage_julian day_HH:MM). Excel readable format. 58 columns of data. Measurements were made on the CEAMARC voyage of the Aurora Australis - voyage 3 of the 2008-2008 summer season. proprietary
+CEAMARC_CASO_200708_V3_Biogeochemistry_PCO2_1 AAV30708 Biogeochemistry PCO2 Data Collected on the CEAMARC Cruise of the Aurora Australis 2007-2008 ALL STAC Catalog 2007-12-16 2008-01-27 141.76285, -67.04925, 147.85347, -43.12521 https://cmr.earthdata.nasa.gov/search/concepts/C1214308494-AU_AADC.umm_json Continuous underway measurements of sea surface (7 metres depth)and atmospheric carbon dioxide. Data format .txt extension comma delimited files. 1 file per 24 hours. Naming similar to AA03607_001-0000 (voyage_julian day_HH:MM). Excel readable format. 58 columns of data. Measurements were made on the CEAMARC voyage of the Aurora Australis - voyage 3 of the 2008-2008 summer season. proprietary
CEAMARC_CASO_200708_V3_EVENTS_1 "CEAMARC-CASO Event List of voyage 3 of the Aurora Australis,
2007-2008" AU_AADC STAC Catalog 2007-12-16 2008-01-27 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214308426-AU_AADC.umm_json Two components. The first component is an even log for all station and instrument deployements. The second component is a log where start and end bottom times need to be recorded for instruments for example the benthic trawl. There is one file for each of the logs. Both logs need to be ideally merged into one to have one data source of event information. The start and end bottom times need to ideally go into the event logging system on the ship. 1) Event log for stations and all instrument deployments Stations and instrument deployments were recorded (including failures) over the progress of the voyage to provide a summary of all work carried out over voyage and and assigned an Event ID number for referencing data associated with these events. Data_Format Data was initially recorded in the ship board PostgreSQL database. Data was exported as a comma delimited file 'events.csv' at the end of the voyage. Column 1 - Setcode (voyage identifier of the form 200708030 meaning year 2007-08, voyage 3) Column 2 - Voyage Code (text voyage identifier) Column 3 - Transect ID (transect identifier, no transects were identified this voyage) Column 4 - Station ID (Station identifier, blank for events not associated with a station, CEAMARC project stations are pre-pended with 'CEAMARC', CASo stations are pre-pended with 'CASO', sampling near icebergs for trace metals pre-pended with 'ICEBERG', woCE SR3 transect sampling pre-pended with 'SR3'). Column 5 - Event ID (unique ID across voyage for individual events) Column 6 - Event Type (usually the instrument deployed, self explanatory. One event type 'Plankton Water Sample' refers to mass water sampling undertaken for genomics work). Column 7 - User Reference (id used by individual scientists to reference their data for this event. If left blank they are using the auto assigned event id from this table). Column 8 - Start Timestamp (start timestamp of the event in UTC). Column 9 - Start Latitude (start latitude of the event from the ship gps) Column 10 - Start Longitude (start longitude of the event from the ship gps) Column 11 - Start Bottom Depth (bottom depth at the start time of the event in metres from EK60 sounder bathymetry export) Column 12 - End Timestamp (end timestamp of the event in UTC) Column 13 - End Latitude (end latitude of the event from the ship gps) Column 14 - End Longitude (end longitude of the event from the ship gps) Column 15 - Duration (duration of the event in hours) Column 16 - End Bottom Depth (bottom depth at the end time of the event in metres from EK60 sounder bathymetry export) Column 17 - Min bottom Depth (minimum bottom depth encountered over event period from EK60 sounder bathymetry export) Column 17 - Avg Bottom Depth (average bottom depth encountered over event period from EK60 sounder bathymetry export) Column 18 - Max Bottom Depth (maximum bottom depth encountered over event period from EK60 sounder bathymetry export) Column 19 - Author (person who entered event details into logging system) Column 20 - Notes (notes peculiar to the event, may be blank) 2) Log of instrument bottom times. Excel spreadsheet 'Trawl_log_18_Jan_08_final.xls' Column A - Station number, these are all CEAMARC station numbers, matching stations in the event log pre-pended by 'CEAMARC'. Column B - Event ID (matching event log, sometimes blank as this log an contain entries on intended events that did not get carried out for some reason or another) Column C - Trawl Name (labelled trawl name, actually event type as the log started off with just trawl start/end bottom times, but was expanded to encompass other event types like grabs etc.) Column D - Date of the event. Column E - Ship Speed (in knots from displays of gps speed). Column F - Time instrument hit the water in utc Column G - Time instrument reached the bottom in utc. Column H - Time instrument left the bottom (i.e. hauling started) in utc. Column I - Time instrument on the deck (ie out of the water) Column J - Depth in meters read of EK60 sounder display (could be any time during event). Column K - Comments pertaining to the event. proprietary
-CEAMARC_CASO_200708_V3_IMAGES_1 2007-08 Voyage 3 of the Aurora Australis, CEAMARC-CASO Image Data - Stills and Video ALL STAC Catalog 2007-12-16 2008-01-27 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214313316-AU_AADC.umm_json Image data (both stills and video) collected from the CEAMARC-CASO voyage of the Aurora Australis during the 2007-2008 summer season. The data consist of a large number of images, plus documents detailing analysis methods, file descriptions and an AMSA (Australian Maritime Safety Authority) report. proprietary
CEAMARC_CASO_200708_V3_IMAGES_1 2007-08 Voyage 3 of the Aurora Australis, CEAMARC-CASO Image Data - Stills and Video AU_AADC STAC Catalog 2007-12-16 2008-01-27 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214313316-AU_AADC.umm_json Image data (both stills and video) collected from the CEAMARC-CASO voyage of the Aurora Australis during the 2007-2008 summer season. The data consist of a large number of images, plus documents detailing analysis methods, file descriptions and an AMSA (Australian Maritime Safety Authority) report. proprietary
+CEAMARC_CASO_200708_V3_IMAGES_1 2007-08 Voyage 3 of the Aurora Australis, CEAMARC-CASO Image Data - Stills and Video ALL STAC Catalog 2007-12-16 2008-01-27 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214313316-AU_AADC.umm_json Image data (both stills and video) collected from the CEAMARC-CASO voyage of the Aurora Australis during the 2007-2008 summer season. The data consist of a large number of images, plus documents detailing analysis methods, file descriptions and an AMSA (Australian Maritime Safety Authority) report. proprietary
CEAMARC_CASO_200708_V3_KRILL_2 2007-08 Voyage 3 of the Aurora Australis, CEAMARC-CASO Krill Data ALL STAC Catalog 2007-12-16 2008-01-27 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214313317-AU_AADC.umm_json Krill data collected from the CEAMARC-CASO voyage of the Aurora Australis during the 2007-2008 summer season. The data consist of a large number of images, plus documents detailing analysis methods and file descriptions. proprietary
CEAMARC_CASO_200708_V3_KRILL_2 2007-08 Voyage 3 of the Aurora Australis, CEAMARC-CASO Krill Data AU_AADC STAC Catalog 2007-12-16 2008-01-27 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214313317-AU_AADC.umm_json Krill data collected from the CEAMARC-CASO voyage of the Aurora Australis during the 2007-2008 summer season. The data consist of a large number of images, plus documents detailing analysis methods and file descriptions. proprietary
-CEAMARC_CASO_200708_V3_MINERALOGY_1 2007-08 Voyage 3 of the Aurora Australis, CEAMARC-CASO Mineralogy Biota Data ALL STAC Catalog 2007-12-16 2008-01-27 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214313408-AU_AADC.umm_json "Mineralogy data collected from the CEAMARC-CASO voyage of the Aurora Australis during the 2007-2008 summer season. The data consist of a large number of images, plus documents detailing analysis methods and file descriptions. Taken from the ""Methods"" document in the download file: CEAMARC MINERALOGY METHODS Margaret Lindsay August 2009 Mineralogy sampling method: (numbers in brackets refer to image below) Individual bags containing the samples taken during the CEAMARC 2007/08 voyage (1) were emptied in to a sorting tray and slightly defrosted to enable the biota to be separated and sorted in to like biota (2). Taxonomic samples were selected to represent different species. The taxonomy sample was moved onto the bench and allocated a STD barcode, a photo was taken (3) and the image number, barcode and 'identification' of the biota was recorded. From the taxonomy sample a small (larger than 0.05g) sample of the individual was dissected, weighed (4) and bagged separately, this sub-sample became the 'mineralogy sample' that were sent to Damien Gore at Macquarie University on 21/05/2009 for mineralogy analysis by Damien Gore and Peter Johnston. Samples were tracked using the Sample Tracking Database (located \\aad.gov.au\files\HIRP\new-shared-hirp\30 Samples tracking + LIMS (Lab Inf Management Sys)\Sample Tracking Database\HIRP STD Working). The key barcodes are: The nally bin's containing the CEAMARC samples are located in reefer 1 (-20 C) (barcode 11919). The original CEAMARC samples (parent container) are in nally bins 14762 and 14759. The taxonomy samples are in a nally barcoded as 70469 (contains 10 bags). The mineralogy samples are in a nally bin barcoded 70472 (contains three bags) and are currently at Macquarie University for mineralogy analysis. Data was entered during the lab process into the spreadsheet file - Sub sampling taxonomy and mineralogy.xls the details of the spreadsheets contents; The list below describes each column in the 'Taxonomy and Mineralogy', 'bamboo coral' and 'other analyses' sheets from the excel file - Sub sampling taxonomy and mineralogy.xls (location described in G:\CEAMARC\CEAMARC MINERALOGY FILE DESCRIPTIONS.doc) Date sampled Date that the taxonomic samples were dissected to obtain the mineralogy samples Parent barcode STD barcode for the nally bin that the samples are located in Site barcode STD barcode for the CEAMARC site and deployment CEAMARC site number CEAMARC voyage sample site number CEAMARC event number The CEAMARC voyage event number is the sampling devices deployment number, related to CEAMARC site number Taxonomy bag barcode STD barcode for the bag that contains the taxonomy samples Image number The image number of the taxonomy sample in it's entirety before dissected to obtain the mineralogy sample. Image contains the label from the initial sample and the sub sample barcode (for taxonomy) Sub sample barcode (for taxonomy) The STD barcode allocated to the taxonomy sample Analyses label for mineralogy The number (identical to sub sample barcode number) that identifies the mineralogy sample and links it back to the taxonomic sample. Analysis sample weight The weight in grams of the dissected part that is the mineralogy sample. Mineralogy bag barcode STD barcode for the bag that contains the mineralogy samples Identification Biota sample identification eg. Gorgonian, bryozoan, ophiuroids Mineralogy sample size Relative size of sample sent off for mineralogy analysis; small sample, medium sample or large sample. Taxonomy sample size Relative size of sample small sample; medium sample or large sample (suitable for further analysis). The 'KRILL' sheet in the above excel file has the following columns; Date sub sampled Date that the taxonomic samples were dissected to obtain the mineralogy samples Sample details Sample code used to label the krill sample Taxonomy bag barcode STD barcode for the bag that contains the taxonomy samples Image number The image number of the taxonomy sample in it's entirety before dissected to obtain the mineralogy sample. Image contains the label from the initial sample and the sub sample barcode (for taxonomy) Sub sample barcode (for taxonomy) The STD barcode allocated to the taxonomy sample Analyses label for mineralogy The number (identical to sub sample barcode number) that identifies the mineralogy sample and links it back to the taxonomic sample. Analysis sample weight The weight in grams of the dissected part that is the mineralogy sample. Mineralogy bag barcode STD barcode for the bag that contains the mineralogy samples Identification Biota sample identification eg. Gorgonian, bryozoan, ophiuroids Mineralogy sample size Relative size of sample sent off for mineralogy analysis; small sample, medium sample or large sample. Taxonomy sample size Relative size of sample small sample; medium sample or large sample (suitable for further analysis). Voyage The ANARE Voyage number and year is expressed as V4 02/03 Station Station number that the samples were obtained from Date Date that the samples were taken during the voyage Time Time that the samples were taken during the voyage Location Location that the samples were taken from during the voyage Net The RMT 8 and 1 were used to collect the krill Depth The depth that the samples were obtained from (25 meters) Total mineralogy samples 1033 mineralogy samples + 15 bamboo coral samples (+ 12 krill samples) = 1060 samples " proprietary
CEAMARC_CASO_200708_V3_MINERALOGY_1 2007-08 Voyage 3 of the Aurora Australis, CEAMARC-CASO Mineralogy Biota Data AU_AADC STAC Catalog 2007-12-16 2008-01-27 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214313408-AU_AADC.umm_json "Mineralogy data collected from the CEAMARC-CASO voyage of the Aurora Australis during the 2007-2008 summer season. The data consist of a large number of images, plus documents detailing analysis methods and file descriptions. Taken from the ""Methods"" document in the download file: CEAMARC MINERALOGY METHODS Margaret Lindsay August 2009 Mineralogy sampling method: (numbers in brackets refer to image below) Individual bags containing the samples taken during the CEAMARC 2007/08 voyage (1) were emptied in to a sorting tray and slightly defrosted to enable the biota to be separated and sorted in to like biota (2). Taxonomic samples were selected to represent different species. The taxonomy sample was moved onto the bench and allocated a STD barcode, a photo was taken (3) and the image number, barcode and 'identification' of the biota was recorded. From the taxonomy sample a small (larger than 0.05g) sample of the individual was dissected, weighed (4) and bagged separately, this sub-sample became the 'mineralogy sample' that were sent to Damien Gore at Macquarie University on 21/05/2009 for mineralogy analysis by Damien Gore and Peter Johnston. Samples were tracked using the Sample Tracking Database (located \\aad.gov.au\files\HIRP\new-shared-hirp\30 Samples tracking + LIMS (Lab Inf Management Sys)\Sample Tracking Database\HIRP STD Working). The key barcodes are: The nally bin's containing the CEAMARC samples are located in reefer 1 (-20 C) (barcode 11919). The original CEAMARC samples (parent container) are in nally bins 14762 and 14759. The taxonomy samples are in a nally barcoded as 70469 (contains 10 bags). The mineralogy samples are in a nally bin barcoded 70472 (contains three bags) and are currently at Macquarie University for mineralogy analysis. Data was entered during the lab process into the spreadsheet file - Sub sampling taxonomy and mineralogy.xls the details of the spreadsheets contents; The list below describes each column in the 'Taxonomy and Mineralogy', 'bamboo coral' and 'other analyses' sheets from the excel file - Sub sampling taxonomy and mineralogy.xls (location described in G:\CEAMARC\CEAMARC MINERALOGY FILE DESCRIPTIONS.doc) Date sampled Date that the taxonomic samples were dissected to obtain the mineralogy samples Parent barcode STD barcode for the nally bin that the samples are located in Site barcode STD barcode for the CEAMARC site and deployment CEAMARC site number CEAMARC voyage sample site number CEAMARC event number The CEAMARC voyage event number is the sampling devices deployment number, related to CEAMARC site number Taxonomy bag barcode STD barcode for the bag that contains the taxonomy samples Image number The image number of the taxonomy sample in it's entirety before dissected to obtain the mineralogy sample. Image contains the label from the initial sample and the sub sample barcode (for taxonomy) Sub sample barcode (for taxonomy) The STD barcode allocated to the taxonomy sample Analyses label for mineralogy The number (identical to sub sample barcode number) that identifies the mineralogy sample and links it back to the taxonomic sample. Analysis sample weight The weight in grams of the dissected part that is the mineralogy sample. Mineralogy bag barcode STD barcode for the bag that contains the mineralogy samples Identification Biota sample identification eg. Gorgonian, bryozoan, ophiuroids Mineralogy sample size Relative size of sample sent off for mineralogy analysis; small sample, medium sample or large sample. Taxonomy sample size Relative size of sample small sample; medium sample or large sample (suitable for further analysis). The 'KRILL' sheet in the above excel file has the following columns; Date sub sampled Date that the taxonomic samples were dissected to obtain the mineralogy samples Sample details Sample code used to label the krill sample Taxonomy bag barcode STD barcode for the bag that contains the taxonomy samples Image number The image number of the taxonomy sample in it's entirety before dissected to obtain the mineralogy sample. Image contains the label from the initial sample and the sub sample barcode (for taxonomy) Sub sample barcode (for taxonomy) The STD barcode allocated to the taxonomy sample Analyses label for mineralogy The number (identical to sub sample barcode number) that identifies the mineralogy sample and links it back to the taxonomic sample. Analysis sample weight The weight in grams of the dissected part that is the mineralogy sample. Mineralogy bag barcode STD barcode for the bag that contains the mineralogy samples Identification Biota sample identification eg. Gorgonian, bryozoan, ophiuroids Mineralogy sample size Relative size of sample sent off for mineralogy analysis; small sample, medium sample or large sample. Taxonomy sample size Relative size of sample small sample; medium sample or large sample (suitable for further analysis). Voyage The ANARE Voyage number and year is expressed as V4 02/03 Station Station number that the samples were obtained from Date Date that the samples were taken during the voyage Time Time that the samples were taken during the voyage Location Location that the samples were taken from during the voyage Net The RMT 8 and 1 were used to collect the krill Depth The depth that the samples were obtained from (25 meters) Total mineralogy samples 1033 mineralogy samples + 15 bamboo coral samples (+ 12 krill samples) = 1060 samples " proprietary
+CEAMARC_CASO_200708_V3_MINERALOGY_1 2007-08 Voyage 3 of the Aurora Australis, CEAMARC-CASO Mineralogy Biota Data ALL STAC Catalog 2007-12-16 2008-01-27 139.01488, -67.07104, 150.06479, -42.88246 https://cmr.earthdata.nasa.gov/search/concepts/C1214313408-AU_AADC.umm_json "Mineralogy data collected from the CEAMARC-CASO voyage of the Aurora Australis during the 2007-2008 summer season. The data consist of a large number of images, plus documents detailing analysis methods and file descriptions. Taken from the ""Methods"" document in the download file: CEAMARC MINERALOGY METHODS Margaret Lindsay August 2009 Mineralogy sampling method: (numbers in brackets refer to image below) Individual bags containing the samples taken during the CEAMARC 2007/08 voyage (1) were emptied in to a sorting tray and slightly defrosted to enable the biota to be separated and sorted in to like biota (2). Taxonomic samples were selected to represent different species. The taxonomy sample was moved onto the bench and allocated a STD barcode, a photo was taken (3) and the image number, barcode and 'identification' of the biota was recorded. From the taxonomy sample a small (larger than 0.05g) sample of the individual was dissected, weighed (4) and bagged separately, this sub-sample became the 'mineralogy sample' that were sent to Damien Gore at Macquarie University on 21/05/2009 for mineralogy analysis by Damien Gore and Peter Johnston. Samples were tracked using the Sample Tracking Database (located \\aad.gov.au\files\HIRP\new-shared-hirp\30 Samples tracking + LIMS (Lab Inf Management Sys)\Sample Tracking Database\HIRP STD Working). The key barcodes are: The nally bin's containing the CEAMARC samples are located in reefer 1 (-20 C) (barcode 11919). The original CEAMARC samples (parent container) are in nally bins 14762 and 14759. The taxonomy samples are in a nally barcoded as 70469 (contains 10 bags). The mineralogy samples are in a nally bin barcoded 70472 (contains three bags) and are currently at Macquarie University for mineralogy analysis. Data was entered during the lab process into the spreadsheet file - Sub sampling taxonomy and mineralogy.xls the details of the spreadsheets contents; The list below describes each column in the 'Taxonomy and Mineralogy', 'bamboo coral' and 'other analyses' sheets from the excel file - Sub sampling taxonomy and mineralogy.xls (location described in G:\CEAMARC\CEAMARC MINERALOGY FILE DESCRIPTIONS.doc) Date sampled Date that the taxonomic samples were dissected to obtain the mineralogy samples Parent barcode STD barcode for the nally bin that the samples are located in Site barcode STD barcode for the CEAMARC site and deployment CEAMARC site number CEAMARC voyage sample site number CEAMARC event number The CEAMARC voyage event number is the sampling devices deployment number, related to CEAMARC site number Taxonomy bag barcode STD barcode for the bag that contains the taxonomy samples Image number The image number of the taxonomy sample in it's entirety before dissected to obtain the mineralogy sample. Image contains the label from the initial sample and the sub sample barcode (for taxonomy) Sub sample barcode (for taxonomy) The STD barcode allocated to the taxonomy sample Analyses label for mineralogy The number (identical to sub sample barcode number) that identifies the mineralogy sample and links it back to the taxonomic sample. Analysis sample weight The weight in grams of the dissected part that is the mineralogy sample. Mineralogy bag barcode STD barcode for the bag that contains the mineralogy samples Identification Biota sample identification eg. Gorgonian, bryozoan, ophiuroids Mineralogy sample size Relative size of sample sent off for mineralogy analysis; small sample, medium sample or large sample. Taxonomy sample size Relative size of sample small sample; medium sample or large sample (suitable for further analysis). The 'KRILL' sheet in the above excel file has the following columns; Date sub sampled Date that the taxonomic samples were dissected to obtain the mineralogy samples Sample details Sample code used to label the krill sample Taxonomy bag barcode STD barcode for the bag that contains the taxonomy samples Image number The image number of the taxonomy sample in it's entirety before dissected to obtain the mineralogy sample. Image contains the label from the initial sample and the sub sample barcode (for taxonomy) Sub sample barcode (for taxonomy) The STD barcode allocated to the taxonomy sample Analyses label for mineralogy The number (identical to sub sample barcode number) that identifies the mineralogy sample and links it back to the taxonomic sample. Analysis sample weight The weight in grams of the dissected part that is the mineralogy sample. Mineralogy bag barcode STD barcode for the bag that contains the mineralogy samples Identification Biota sample identification eg. Gorgonian, bryozoan, ophiuroids Mineralogy sample size Relative size of sample sent off for mineralogy analysis; small sample, medium sample or large sample. Taxonomy sample size Relative size of sample small sample; medium sample or large sample (suitable for further analysis). Voyage The ANARE Voyage number and year is expressed as V4 02/03 Station Station number that the samples were obtained from Date Date that the samples were taken during the voyage Time Time that the samples were taken during the voyage Location Location that the samples were taken from during the voyage Net The RMT 8 and 1 were used to collect the krill Depth The depth that the samples were obtained from (25 meters) Total mineralogy samples 1033 mineralogy samples + 15 bamboo coral samples (+ 12 krill samples) = 1060 samples " proprietary
CEAMARC_CASO_200708_V3_Surface_Hydrochemistry_1 AAV30708 Biogeochemistry - Surface Hydrochemistry data taken from the CEAMARC Cruise of the Aurora Australis in the 2007-2008 Summer Season ALL STAC Catalog 2007-12-16 2008-01-26 141.76285, -67.04925, 147.85347, -43.12521 https://cmr.earthdata.nasa.gov/search/concepts/C1214308500-AU_AADC.umm_json Hydrochemistry of surface water. Parameters measured=salinity, oxygen, co2, oxygen isotope species, nutrients. All data have been stored in a single excel file. Measurements were made on the CEAMARC voyage of the Aurora Australis - voyage 3 of the 2008-2008 summer season. See other CEAMARC metadata records for more information. proprietary
CEAMARC_CASO_200708_V3_Surface_Hydrochemistry_1 AAV30708 Biogeochemistry - Surface Hydrochemistry data taken from the CEAMARC Cruise of the Aurora Australis in the 2007-2008 Summer Season AU_AADC STAC Catalog 2007-12-16 2008-01-26 141.76285, -67.04925, 147.85347, -43.12521 https://cmr.earthdata.nasa.gov/search/concepts/C1214308500-AU_AADC.umm_json Hydrochemistry of surface water. Parameters measured=salinity, oxygen, co2, oxygen isotope species, nutrients. All data have been stored in a single excel file. Measurements were made on the CEAMARC voyage of the Aurora Australis - voyage 3 of the 2008-2008 summer season. See other CEAMARC metadata records for more information. proprietary
CEAMARC_CASO_AAV30708_Biogeochemistry_1 AAV30708 Biogeochemistry - CO2 and Alkalinity bottle data collected on the CEAMARC Cruise of the Aurora Australis ALL STAC Catalog 2007-12-17 2008-01-21 141.76285, -67.04925, 147.85347, -43.12521 https://cmr.earthdata.nasa.gov/search/concepts/C1214308505-AU_AADC.umm_json Total carbon dioxide and total alkalinity analysis of niskin bottle samples collected on CTD casts. All data have been stored in a single excel file. Measurements were made on the CEAMARC voyage of the Aurora Australis - voyage 3 of the 2008-2008 summer season. See other CEAMARC metadata records for more information. proprietary
CEAMARC_CASO_AAV30708_Biogeochemistry_1 AAV30708 Biogeochemistry - CO2 and Alkalinity bottle data collected on the CEAMARC Cruise of the Aurora Australis AU_AADC STAC Catalog 2007-12-17 2008-01-21 141.76285, -67.04925, 147.85347, -43.12521 https://cmr.earthdata.nasa.gov/search/concepts/C1214308505-AU_AADC.umm_json Total carbon dioxide and total alkalinity analysis of niskin bottle samples collected on CTD casts. All data have been stored in a single excel file. Measurements were made on the CEAMARC voyage of the Aurora Australis - voyage 3 of the 2008-2008 summer season. See other CEAMARC metadata records for more information. proprietary
-CEAMARC_Diatom_Absolute_Abundance_1 Absolute abundance of diatoms from CEAMARC cores AU_AADC STAC Catalog 2012-06-01 2012-07-31 139, -67.5, 146, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1376848068-AU_AADC.umm_json This data provides the absolute abundance of diatom valves from cores recovered from the George V coast as part of the CEAMARC (Collaborative East Antarctic Marine Census) mission of 2007-2008. Data are presented as valves/gram dry weight of sediment. All samples analyzed were core top samples, however no age constraints have been established. Chaetoceros resting spores were included in the absolute abundance calculations. Slides were prepared following Rathburn et al 1997. proprietary
CEAMARC_Diatom_Absolute_Abundance_1 Absolute abundance of diatoms from CEAMARC cores ALL STAC Catalog 2012-06-01 2012-07-31 139, -67.5, 146, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1376848068-AU_AADC.umm_json This data provides the absolute abundance of diatom valves from cores recovered from the George V coast as part of the CEAMARC (Collaborative East Antarctic Marine Census) mission of 2007-2008. Data are presented as valves/gram dry weight of sediment. All samples analyzed were core top samples, however no age constraints have been established. Chaetoceros resting spores were included in the absolute abundance calculations. Slides were prepared following Rathburn et al 1997. proprietary
+CEAMARC_Diatom_Absolute_Abundance_1 Absolute abundance of diatoms from CEAMARC cores AU_AADC STAC Catalog 2012-06-01 2012-07-31 139, -67.5, 146, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1376848068-AU_AADC.umm_json This data provides the absolute abundance of diatom valves from cores recovered from the George V coast as part of the CEAMARC (Collaborative East Antarctic Marine Census) mission of 2007-2008. Data are presented as valves/gram dry weight of sediment. All samples analyzed were core top samples, however no age constraints have been established. Chaetoceros resting spores were included in the absolute abundance calculations. Slides were prepared following Rathburn et al 1997. proprietary
CEAMARC_Diatom_Abundance_1 Diatom abundance from CEAMARC coretop samples AU_AADC STAC Catalog 2012-06-01 2012-07-31 139.302983, -67.049233, 145.531316, -65.466733 https://cmr.earthdata.nasa.gov/search/concepts/C1338628670-AU_AADC.umm_json This dataset contains the abundance of diatom species found in the surface sediments from cores collected as part of the CEAMARC (Collaborative East Antarctic Marine Census) mission. The cores were collected from the George V basin along the Antarctic coast. Latitude, longitude and water depth data are included for each site. Sediments were prepared following standard diatom preparation techniques (Rathburn et al 1997). proprietary
CEAMARC_all_events_1 Event logs and station lists from all CEAMARC voyages by the Aurora Australis, Astrolabe and Umitaka Maru AU_AADC STAC Catalog 2004-01-19 2010-01-21 121.08, -67.099, 150.005, -42.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214313420-AU_AADC.umm_json Copies of the event logs/station lists taken from the Aurora Australis, Astrolabe and Umitaka Maru during their CEAMARC cruises (collaborative East Antarctic Marine Census). proprietary
-CEDAR_Imager Airglow/Aurora Video Imaging Data and All-Sky Camera Data from the CEDAR Data Base at NCAR/HAO SCIOPS STAC Catalog 1987-07-29 1990-03-30 -155, 20, 16, 79 https://cmr.earthdata.nasa.gov/search/concepts/C1214584192-SCIOPS.umm_json The Coupling, Energetics, and Dynamics of Atmospheric Regions (CEDAR) Data Base at NCAR/HAO holds data collected from airglow imagers and all-sky cameras. None of the imager data are in digital form in the CEDAR Data Base and must be obtained from the contact person. Video tapes from the imager at Millstone Hill are in the CEDAR Data Base. Other data are as follows: 1. Utah State University CCD imager data from October 6-23, 1993 which measured nightglow emissions over Hawaii (20N, 155W). Data are available from Michael Taylor. The Utah State University CCD Imager is operated by the Utah State University with support from the NSF. 2. Boston University Mobile Ionospheric Observatory (MIO) imaging system which operated from July 1987 to June 1989, and the CEDAR imager which started in September 1989. Both imagers are located at Millstone Hill (42.6N, 71.5W). Video tapes from 1987-1994 are available in the CEDAR Data Base. The contact person is Michael Mendillo. The CEDAR imager is operated at Millstone Hill by Boston University with support from the NSF. 3. All-sky camera data at Qaanaaq, Greenland (77.5N, 69.2W), at Longyearbyen, Sweden (78.2N,15.4E), at Ny Alesund, Svalbard (78.9N, 12.0E), and at Nord, Greenland (91.6N, 16.6W). These all-sky cameras are operated by the Air Force Research Laboratory (AFRL) at Hanscom AFB. All of the film data are available from Katsura Fukui at AFRL. The Qaanaaq and Nord all-sky cameras are operated by the Danish Meteorological Institute and owned by the U.S. Air Force Research Laboratory at Hanscom, AFB, MA. The Ny Alesund all-sky camera is operated by the University of Oslo and owned by the US AFRL. The CEDAR Data Base is accessible through the WWW and ftp, but users must have a valid access form, available from the WWW or ftp (see Access and Use constraints) or contact Barbara Emery (emery@ucar.edu). See the WWW site for additional information on accessing the data and Rules of the Road procedures. http://cedarweb.hao.ucar.edu/wiki/index.php/Data_Services:Rules_of_the_Road proprietary
CEDAR_Imager Airglow/Aurora Video Imaging Data and All-Sky Camera Data from the CEDAR Data Base at NCAR/HAO ALL STAC Catalog 1987-07-29 1990-03-30 -155, 20, 16, 79 https://cmr.earthdata.nasa.gov/search/concepts/C1214584192-SCIOPS.umm_json The Coupling, Energetics, and Dynamics of Atmospheric Regions (CEDAR) Data Base at NCAR/HAO holds data collected from airglow imagers and all-sky cameras. None of the imager data are in digital form in the CEDAR Data Base and must be obtained from the contact person. Video tapes from the imager at Millstone Hill are in the CEDAR Data Base. Other data are as follows: 1. Utah State University CCD imager data from October 6-23, 1993 which measured nightglow emissions over Hawaii (20N, 155W). Data are available from Michael Taylor. The Utah State University CCD Imager is operated by the Utah State University with support from the NSF. 2. Boston University Mobile Ionospheric Observatory (MIO) imaging system which operated from July 1987 to June 1989, and the CEDAR imager which started in September 1989. Both imagers are located at Millstone Hill (42.6N, 71.5W). Video tapes from 1987-1994 are available in the CEDAR Data Base. The contact person is Michael Mendillo. The CEDAR imager is operated at Millstone Hill by Boston University with support from the NSF. 3. All-sky camera data at Qaanaaq, Greenland (77.5N, 69.2W), at Longyearbyen, Sweden (78.2N,15.4E), at Ny Alesund, Svalbard (78.9N, 12.0E), and at Nord, Greenland (91.6N, 16.6W). These all-sky cameras are operated by the Air Force Research Laboratory (AFRL) at Hanscom AFB. All of the film data are available from Katsura Fukui at AFRL. The Qaanaaq and Nord all-sky cameras are operated by the Danish Meteorological Institute and owned by the U.S. Air Force Research Laboratory at Hanscom, AFB, MA. The Ny Alesund all-sky camera is operated by the University of Oslo and owned by the US AFRL. The CEDAR Data Base is accessible through the WWW and ftp, but users must have a valid access form, available from the WWW or ftp (see Access and Use constraints) or contact Barbara Emery (emery@ucar.edu). See the WWW site for additional information on accessing the data and Rules of the Road procedures. http://cedarweb.hao.ucar.edu/wiki/index.php/Data_Services:Rules_of_the_Road proprietary
+CEDAR_Imager Airglow/Aurora Video Imaging Data and All-Sky Camera Data from the CEDAR Data Base at NCAR/HAO SCIOPS STAC Catalog 1987-07-29 1990-03-30 -155, 20, 16, 79 https://cmr.earthdata.nasa.gov/search/concepts/C1214584192-SCIOPS.umm_json The Coupling, Energetics, and Dynamics of Atmospheric Regions (CEDAR) Data Base at NCAR/HAO holds data collected from airglow imagers and all-sky cameras. None of the imager data are in digital form in the CEDAR Data Base and must be obtained from the contact person. Video tapes from the imager at Millstone Hill are in the CEDAR Data Base. Other data are as follows: 1. Utah State University CCD imager data from October 6-23, 1993 which measured nightglow emissions over Hawaii (20N, 155W). Data are available from Michael Taylor. The Utah State University CCD Imager is operated by the Utah State University with support from the NSF. 2. Boston University Mobile Ionospheric Observatory (MIO) imaging system which operated from July 1987 to June 1989, and the CEDAR imager which started in September 1989. Both imagers are located at Millstone Hill (42.6N, 71.5W). Video tapes from 1987-1994 are available in the CEDAR Data Base. The contact person is Michael Mendillo. The CEDAR imager is operated at Millstone Hill by Boston University with support from the NSF. 3. All-sky camera data at Qaanaaq, Greenland (77.5N, 69.2W), at Longyearbyen, Sweden (78.2N,15.4E), at Ny Alesund, Svalbard (78.9N, 12.0E), and at Nord, Greenland (91.6N, 16.6W). These all-sky cameras are operated by the Air Force Research Laboratory (AFRL) at Hanscom AFB. All of the film data are available from Katsura Fukui at AFRL. The Qaanaaq and Nord all-sky cameras are operated by the Danish Meteorological Institute and owned by the U.S. Air Force Research Laboratory at Hanscom, AFB, MA. The Ny Alesund all-sky camera is operated by the University of Oslo and owned by the US AFRL. The CEDAR Data Base is accessible through the WWW and ftp, but users must have a valid access form, available from the WWW or ftp (see Access and Use constraints) or contact Barbara Emery (emery@ucar.edu). See the WWW site for additional information on accessing the data and Rules of the Road procedures. http://cedarweb.hao.ucar.edu/wiki/index.php/Data_Services:Rules_of_the_Road proprietary
CEOS_CalVal_Test_Site-Dome_C-Antarctica CEOS Cal Val Test Site - Dome C, Antarctica - Instrumented Site USGS_LTA STAC Catalog 1972-12-06 123, -76.6, 131.18, -74.5 https://cmr.earthdata.nasa.gov/search/concepts/C1220566821-USGS_LTA.umm_json On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes: Background: Reference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities. Requirement: Initiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner. Instrumented Sites: Dome C, Antarctica is one of eight instrumented sites that are CEOS Reference Test Sites. The CEOS instrumented sites are provisionally being called LANDNET. These instrumented sites are primarily used for field campaigns to obtain radiometric gain, and these sites can serve as a focus for international efforts, facilitating traceability and inter-comparison to evaluate biases of in-flight and future instruments in a harmonized manner. In the longer-term it is anticipated that these sites will all be fully automated and provide surface and atmospheric measurements to the WWW in an autonomous manner reducing some of the cost of a manned campaign, at present three can operate in this manner. proprietary
CEOS_CalVal_Test_Site-Dunhuang-China CEOS Cal Val Test Site - Dunhuang, China - Instrumented Site USGS_LTA STAC Catalog 1975-04-15 91.98, 39, 96.52, 41.45 https://cmr.earthdata.nasa.gov/search/concepts/C1220566840-USGS_LTA.umm_json On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes: Background: Reference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities. Requirement: Initiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner. Instrumented Sites: Dunhuang, China, is one of eight instrumented sites that are CEOS Reference Test Sites. The CEOS instrumented sites are provisionally being called LANDNET. These instrumented sites are primarily used for field campaigns to obtain radiometric gain, and these sites can serve as a focus for international efforts, facilitating traceability and inter-comparison to evaluate biases of in-flight and future instruments in a harmonized manner. In the longer-term it is anticipated that these sites will all be fully automated and provide surface and atmospheric measurements to the WWW in an autonomous manner reducing some of the cost of a manned campaign, at present three can operate in this manner. proprietary
CEOS_CalVal_Test_Site-Frenchman_Flat-USA CEOS Cal Val Test Site - Frenchman Flat, USA - Instrumented Site USGS_LTA STAC Catalog 1972-08-09 -115.9, 36.7, -115.8, 36.9 https://cmr.earthdata.nasa.gov/search/concepts/C1220566808-USGS_LTA.umm_json On the background of these requirements for sensor calibration, intercalibration and product validation, the subgroup on Calibration and Validation of the Committee on Earth Observing System (CEOS) formulated the following recommendation during the plenary session held in China at the end of 2004, with the goal of setting-up and operating an internet based system to provide sensor data, protocols and guidelines for these purposes: Background: Reference Datasets are required to support the understanding of climate change and quality assure operational services by Earth Observing satellites. The data from different sensors and the resulting synergistic data products require a high level of accuracy that can only be obtained through continuous traceable calibration and validation activities. Requirement: Initiate an activity to document a reference methodology to predict Top of Atmosphere (TOA) radiance for which currently flying and planned wide swath sensors can be intercompared, i.e. define a standard for traceability. Also create and maintain a fully accessible web page containing, on an instrument basis, links to all instrument characteristics needed for intercomparisons as specified above, ideally in a common format. In addition, create and maintain a database (e.g. SADE) of instrument data for specific vicarious calibration sites, including site characteristics, in a common format. Each agency is responsible for providing data for their instruments in this common format. Recommendation : The required activities described above should be supported for an implementation period of two years and a maintenance period over two subsequent years. The CEOS should encourage a member agency to accept the lead role in supporting this activity. CEOS should request all member agencies to support this activity by providing appropriate information and data in a timely manner. Instrumented Sites: Frenchman Flat, USA is one of eight instrumented sites that are CEOS Reference Test Sites. The CEOS instrumented sites are provisionally being called LANDNET. These instrumented sites are primarily used for field campaigns to obtain radiometric gain, and these sites can serve as a focus for international efforts, facilitating traceability and inter-comparison to evaluate biases of in-flight and future instruments in a harmonized manner. In the longer-term it is anticipated that these sites will all be fully automated and provide surface and atmospheric measurements to the WWW in an autonomous manner reducing some of the cost of a manned campaign, at present three can operate in this manner. proprietary
@@ -4705,34 +4706,34 @@ CER_SYN1deg-Month_Terra-MODIS_Edition4A CERES and GEO-Enhanced TOA, Within-Atmos
CER_SYN1deg-Month_Terra-NOAA20_Edition4A CERES and GEO-Enhanced TOA, Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly Terra-NOAA20 Edition4A LARC_ASDC STAC Catalog 2022-04-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2631920924-LARC_ASDC.umm_json CER_SYN1deg-Month_Terra-NOAA20_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere, and Surface Fluxes, Clouds and Aerosols Monthly Terra-NOAA20 Edition4A data product. Data was collected using the following instruments and platforms: Imaging Radiometers on the Geostationary Satellites platform, CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and MODIS on Terra; and CERES FM6 and VIIRS on NOAA-20. Data collection for this product is ongoing. CERES Synoptic (SYN) 1-degree products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, VIIRS, and geostationary satellite cloud properties along with atmospheric profiles provided by the NASA Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a three-hourly temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to accurately model variability between CERES observations. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a critical Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017. proprietary
CER_SYN1deg-Month_Terra-NPP_Edition1A CERES and GEO-Enhanced TOA, Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly Terra-NPP Edition1A LARC_ASDC STAC Catalog 2012-02-01 2017-11-30 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1424862293-LARC_ASDC.umm_json CER_SYN1deg-Month_Terra-NPP_Edition1A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA) Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly Terra-Suomi National Polar-orbiting Partnership (NPP) Edition1A data product. Data was collected using the CERES Imaging Radiometers on Geostationary Satellites; CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra; and FM5, CERES Scanner, and Visible-Infrared Imager-Radiometer Suite (VIIRS) on NPP. Data collection for this product is complete. The CERES SYN1deg products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a monthly temporal resolution on 1°-regional, zonal, and global spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions. The CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation. CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi NPP satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017. proprietary
CFL_0 Circumpolar Flaw Lead System Study OB_DAAC STAC Catalog 2008-03-24 2008-08-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2131352566-OB_DAAC.umm_json Measurements taken within the Cape Bathurst flaw lead on board the icebreaker C.C.G.S. Amundsen to examine how physical changes affect biological processes in the flaw lead through an entire annual cycle (October 2007 - August 2008). The circumpolar flaw lead occurs each year when the central pack ice moves away from the coastal fast ice creating an area of open water called a flaw lead. proprietary
-CH-OG-1-GPS-30S_0.0 30 sec GPS ground tracking data ALL STAC Catalog 2001-05-28 -63.51, -45.69, 170.42, 78.87 https://cmr.earthdata.nasa.gov/search/concepts/C1214586615-SCIOPS.umm_json This data set comprises GPS ground data of a sample rate of 30 sec, generated by decoding and sampling GPS high rate ground data. This raw data passed no quality control. The data are given in the Rinex 2.1 format. proprietary
CH-OG-1-GPS-30S_0.0 30 sec GPS ground tracking data SCIOPS STAC Catalog 2001-05-28 -63.51, -45.69, 170.42, 78.87 https://cmr.earthdata.nasa.gov/search/concepts/C1214586615-SCIOPS.umm_json This data set comprises GPS ground data of a sample rate of 30 sec, generated by decoding and sampling GPS high rate ground data. This raw data passed no quality control. The data are given in the Rinex 2.1 format. proprietary
+CH-OG-1-GPS-30S_0.0 30 sec GPS ground tracking data ALL STAC Catalog 2001-05-28 -63.51, -45.69, 170.42, 78.87 https://cmr.earthdata.nasa.gov/search/concepts/C1214586615-SCIOPS.umm_json This data set comprises GPS ground data of a sample rate of 30 sec, generated by decoding and sampling GPS high rate ground data. This raw data passed no quality control. The data are given in the Rinex 2.1 format. proprietary
CH4_Aircraft_STILT_footprints_1300_1 CARVE-ARCSS: Methane Loss From Arctic- Fluxes From the Alaskan North Slope, 2012-2014 ORNL_CLOUD STAC Catalog 2012-05-23 2014-12-31 -158, 68.3, -155, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C2236223020-ORNL_CLOUD.umm_json "This data set provides the results of (1) year-round measurements of methane (CH4) flux along with soil and air temperatures at five eddy covariance towers at sites located in the Alaskan Arctic tundra from June 2013 to December 2014 and (2) airborne CH4 and ozone (O3) measurements collected during Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) flight campaigns for years 2012 through 2014. The included site-level flux data at half-hourly intervals were calculated following standard eddy covariance data processing procedures. Also reported are daily mean methane flux, soil temperature with depth, and air temperature for each tower site. Also identified for each flux tower site were the ""zero curtain"" periods of extended cold when soil temperatures were poised near 0 degrees C. The reported CARVE airborne CH4 and O3 data were aggregated horizontally at 5 km intervals. Measurement heights are reported. These aircraft positions were treated as receptors in a Stochastic Time-Inverted Lagrangian Transport (STILT) model coupled with meteorology fields from the polar variant of the Weather and Research Forecasting model (WRF), in order to model the land surface influence on the aircraft-observed methane concentrations. The summed land surface influence on the aircraft data at each position is reported. For each airborne measurement, 2D surface influence fields (i.e. footprints) at two different spatial resolutions were derived using the WRF-STILT simulations. These gridded footprints are provided as netCDF formatted files. Regional C-CH4 fluxes were calculated from the CARVE CH4 data and footprints for the period 2012-2014 and are also included with this data set. Acknowledgements: Data collection efforts were funded by NSF ARCSS project ""Methane Loss From Arctic"" (ARCSS #1204263; http://www.nsf.gov/awardsearch/showAward?AWD_ID=1204263) and by NASA's Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE)." proprietary
CH4_CO2_WaterBodies_YK_Delta_2178_1 CO2 and CH4 Fluxes from Waterbodies, Yukon-Kuskokwim Delta, Alaska, 2016-2019 ORNL_CLOUD STAC Catalog 2016-07-07 2019-07-07 -163.82, 60.9, -162.07, 61.68 https://cmr.earthdata.nasa.gov/search/concepts/C2992461082-ORNL_CLOUD.umm_json "This dataset provides estimates of carbon dioxide (CO2) and methane (CH4) diffusive fluxes from waterbodies, and watershed landcover data for the central-interior of the Yukon-Kuskokwim Delta (YK delta), Alaska. Dissolved concentrations of methane and carbon dioxide were predicted using an integrated terrestrial-aquatic approach to scale observations based on landscape and waterbody remote sensing drivers. The observations include ~300 samples of surface water dissolved gases collected in July 2016-2019 from the central region of the YK Delta, Alaska. A machine learning model was used to generate estimated fluxes. Model inputs include Sentinel-2 MSI with derived normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), an Arctic digital elevation model (DEM) with derived slope and flow accumulation, Sentinel-1 C-band July and December VV and VH composites, and a landcover map. Waterbody size, shape, and reflectance were determined using object-based image analysis in Google Earth Engine. Landscape-level input data were averaged in non-nested sub-basins calculated using the System for Automated Geoscientific Analyses (SAGA) ""channel network"" algorithm at three threshold sizes. Cross validation was used to tune and select variables for gradient boosting models. The trained gradient boosting models were then used to predict dissolved methane and carbon dioxide in all waterbodies (~17,000) in the region. These aquatic concentrations were converted to fluxes using an average gas transfer velocity from observations (0.33 m/d). The data are provided in GeoTIFF and shapefile formats." proprietary
-CH4_Flux_BigTrail_Goldstream_1778_1 ABoVE: Methane Flux across Two Thermokarst Lake Ecosystems, Interior Alaska, 2018 ALL STAC Catalog 2018-07-17 2018-07-29 -147.85, 64.92, -147.82, 64.92 https://cmr.earthdata.nasa.gov/search/concepts/C2143402530-ORNL_CLOUD.umm_json This dataset provides diffusive methane (CH4) fluxes collected from two thermokarst lakes in the Goldstream Valley, north of Fairbanks in interior Alaska. Fluxes were collected from the littoral zones, adjacent shoreline, and upland vegetation. The data were collected during July 2018. Measurements were made using a mobile, closed chamber technique where chamber air was recirculated through a Los Gatos Research (LGR) Ultraportable Cavity Ring-down Spectrometer. The chamber was large enough to enclose emergent and upland vegetation up to 1.5 m in height, allowing plant-facilitated fluxes to be measured. These in situ measurements were used to verify spatial patterns in methane flux (i.e., exponential decay with distance from water) detected by NASA's Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG). proprietary
CH4_Flux_BigTrail_Goldstream_1778_1 ABoVE: Methane Flux across Two Thermokarst Lake Ecosystems, Interior Alaska, 2018 ORNL_CLOUD STAC Catalog 2018-07-17 2018-07-29 -147.85, 64.92, -147.82, 64.92 https://cmr.earthdata.nasa.gov/search/concepts/C2143402530-ORNL_CLOUD.umm_json This dataset provides diffusive methane (CH4) fluxes collected from two thermokarst lakes in the Goldstream Valley, north of Fairbanks in interior Alaska. Fluxes were collected from the littoral zones, adjacent shoreline, and upland vegetation. The data were collected during July 2018. Measurements were made using a mobile, closed chamber technique where chamber air was recirculated through a Los Gatos Research (LGR) Ultraportable Cavity Ring-down Spectrometer. The chamber was large enough to enclose emergent and upland vegetation up to 1.5 m in height, allowing plant-facilitated fluxes to be measured. These in situ measurements were used to verify spatial patterns in methane flux (i.e., exponential decay with distance from water) detected by NASA's Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG). proprietary
+CH4_Flux_BigTrail_Goldstream_1778_1 ABoVE: Methane Flux across Two Thermokarst Lake Ecosystems, Interior Alaska, 2018 ALL STAC Catalog 2018-07-17 2018-07-29 -147.85, 64.92, -147.82, 64.92 https://cmr.earthdata.nasa.gov/search/concepts/C2143402530-ORNL_CLOUD.umm_json This dataset provides diffusive methane (CH4) fluxes collected from two thermokarst lakes in the Goldstream Valley, north of Fairbanks in interior Alaska. Fluxes were collected from the littoral zones, adjacent shoreline, and upland vegetation. The data were collected during July 2018. Measurements were made using a mobile, closed chamber technique where chamber air was recirculated through a Los Gatos Research (LGR) Ultraportable Cavity Ring-down Spectrometer. The chamber was large enough to enclose emergent and upland vegetation up to 1.5 m in height, allowing plant-facilitated fluxes to be measured. These in situ measurements were used to verify spatial patterns in methane flux (i.e., exponential decay with distance from water) detected by NASA's Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG). proprietary
CH4_Fluxes_ThermokarstLakes_AK_1870_1 Methane Fluxes from Shorelines and Differing Surfaces, Big Trail Lake, Alaska, 2019 ORNL_CLOUD STAC Catalog 2019-07-04 2019-12-14 -147.82, 64.92, -147.82, 64.92 https://cmr.earthdata.nasa.gov/search/concepts/C2192619099-ORNL_CLOUD.umm_json This dataset provides methane fluxes from hot-spot and non-hot spot differing surfaces at Big Trail Lake (BTL) in the Goldstream Valley near Fairbanks, AK, USA. Measurements were taken at a remotely-sensed methane hotspot on the shoreline of a pond, adjacent to BTL with a Los Gatos Ultra-Portable Greenhouse Gas Analyzer (UGGA), and from various non-hotspot surfaces representative of the broader thermokarst lake ecosystem with bucket chambers. All data were collected between 2019-07-04 and 2019-12-04 during the daytime hours of 09:35-17:32 local time. A ground-based CH4 enhancement survey was performed on 2019-07-06 between 13:25-17:15 Alaska Daylight Time (AKDT), approximately two hours following an AVIRIS-NG overflight and hotspot detection at the Eastside Pond. Methane flux is reported in units of both mmol CH4 m-2 hr-1 and mg CH4 m-2 d-1. Flux errors are quantified for each proprietary
CH4_Plume_AVIRIS-NG_1727_1 Methane Plumes Derived from AVIRIS-NG over Point Sources across California, 2016-2017 ORNL_CLOUD STAC Catalog 2016-09-10 2017-11-13 -125.77, 32.35, -113.73, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C2389764676-ORNL_CLOUD.umm_json This dataset provides maps of methane (CH4) plumes along flight lines over identified methane point-source emitting infrastructure across the State of California, USA collected during 2016 and 2017. Methane plume locations were derived from Next-Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) overflights during the California Methane Survey. The survey was designed to cover at least 60% of the methane point source infrastructure in California guided by the Vista-CA dataset of identified locations of potential methane emitting facilities and infrastructure in three primary sectors (energy, agriculture, and waste). The purpose of the survey was to detect, quantify, and attribute point source emissions to specific infrastructure elements to improve the scientific understanding of regional methane budgets and to inform policy and planning activities that reduce methane emissions. proprietary
CHELTON_SEASAT_SASS_L3_1 SEASAT SCATTEROMETER DERIVED GLOBAL GRIDDED MONTHLY OCEAN WIND STRESS (Chelton) POCLOUD STAC Catalog 1978-07-07 1978-10-10 -180, -70, 180, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2617197622-POCLOUD.umm_json Contains monthly averaged ocean surface wind stress derived from Seasat-A Scatterometer (SASS) wind retrievals, from July 1978 until October 1978, gridded on a 2.5-degree by 2.5 degree global grid. The vector average wind stress is stored in units of dynes per centimeter squared (dyn/cm^2). Data is provided in formatted ASCII text. The primary data set used to construct these wind stress fields consists of 96 days of SASS vector winds supplied by Robert Atlas at GSFC. The directional ambiguities in the raw SASS data had been objectively removed using the GSFC Laboratory for Atmospheric Sciences atmospheric general circulation model. proprietary
CHEMTAX_1 Chemtax version 1.95 for calculating the taxonomic composition of phytoplankton populations AU_AADC STAC Catalog 2008-03-13 2008-03-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214308429-AU_AADC.umm_json CHEMTAX V1.95 This program was written by Chris Boucher, assisted by Harry Higgins and Simon Wright, for the Australian Antarctic Division. It is a stand-alone program that takes input from a Microsoft Excel worksheet. It calculates the taxonomic composition of phytoplankton populations based on pigment data and a table of the expected taxonomic composition and pigment:chl a ratios entered by the operator. It is based on CHEMTAX V1, which was a MATLAB script written by Mark Mackey (CSIRO) and published in Mackey et al (1996). The zip folder contains Chemtax.exe, Chemtax2.dll, Testrun195.xls, PicoDataWorkup.xls (example), CHEMTAXHelper for V195.xlm. Also included are two Word files (Chemtax 195 Instructions.doc, and Chaxmanw.rtf, which is the manual for Version 1).The latter manual contains details on the algorithms used in Chemtax, which are unchanged, but the operating instructions in that manual are superseded by those in Chemtax 195 Instructions.doc. Please note: CHEMTAX must not be used as a black box. It will not deduce what taxa are in the water. The user must input the expected taxa and their expected pigment composition, then CHEMTAX will calculate the contributions of each taxon to the total in each sample. It is imperative that the user understands the function of CHEMTAX, and the taxonomic distribution of pigments (including the potential ambiguities) if useful data are to be obtained. A detailed strategy for applying CHEMTAX (and interpreting pigment data in general) is given in Higgins et al (2011). An example of combining CHEMTAX with other data is given in Wright et al (2010). Higgins H.W., Wright S. W., Schluter L. (2011). Quantitative Interpretation of Chemotaxonomic Pigment Data, Chapter 6, Phytoplankton Pigments: Characterization, Chemotaxonomy and Applications in Oceanography, Suzanne Roy, Einar Skarstad Egeland, Geir Johnsen and Carole Anne Llewellyn (eds.) Cambridge University Press. Wright, SW, van den Enden, RL, Pearce, I, Davidson, AT, Scott FJ, Westwood, KJ (2010). Phytoplankton community structure and stocks in the Southern Ocean (30 - 80 degrees E) determined by CHEMTAX analysis of HPLC pigment signatures. Deep-Sea Research II 57, 758-778 A CHEMTAX User Forum has been set up at http://groups.google.com/forum/#!forum/chemtax_users. Registration: After downloading the files, please email the enclosed registration form to Simon.Wright@aad.gov.au with CHEMTAX in the title. Please note that Simon is semi-retired and may not respond immediately. proprietary
-CIESIN0122 Africa Real Time Environmental Monitoring Information System (ARTEMIS) ALL STAC Catalog 1982-01-01 -20, -35, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232282935-CEOS_EXTRA.umm_json "The ""Africa Real Time Environmental Monitoring Information System (ARTEMIS)"" is part of the FAO's use of satellite remote sensing techniques to improve the surveillance and forecasting capabilities of its Global Information and Early Warning System (GIEWS). ARTEMIS was developed as a result of close technical cooperation between the FAO and the NASA Goddard Space Flight Center, the University of Reading in the United Kingdom, and the National Aerospace Laboratory of the Netherlands. Since August 1988, the ARTEMIS system has been delivering the following products on a routine basis: ten-day and monthly cold cloud duration maps for the continent of Africa and the Near East (resolution 7.6 km); ten-day and monthly estimated rainfall maps for the Southern Sahara, the Sahel, Sudan, and the tropical countries of West Africa (resolution 7.6 km); ten-day and monthly composite vegetation index maps for Africa, and the Near East. In addition to these databases, ARTEMIS contains a ten-year vegetation index archive on a ten-day and monthly basis, developed jointly by NASA GSFC and the FAO Remote Sensing Centre. This archive allows for early assessment of current crop growing conditions by comparison with known situations in the past. LANGUAGE: English ACCESS/AVAILABILITY: ARTEMIS data products are available in photographic and digital formats. Analyzed infomation is communicated in bulletins and publications of Global Information and Early Warning System (GIEWS) and Emergency Centre for Locust Operations (ECLO) of FAO. For making ARTEMIS data available in a timely manner to users, more and more use is currently being made of electronic mail." proprietary
CIESIN0122 Africa Real Time Environmental Monitoring Information System (ARTEMIS) CEOS_EXTRA STAC Catalog 1982-01-01 -20, -35, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232282935-CEOS_EXTRA.umm_json "The ""Africa Real Time Environmental Monitoring Information System (ARTEMIS)"" is part of the FAO's use of satellite remote sensing techniques to improve the surveillance and forecasting capabilities of its Global Information and Early Warning System (GIEWS). ARTEMIS was developed as a result of close technical cooperation between the FAO and the NASA Goddard Space Flight Center, the University of Reading in the United Kingdom, and the National Aerospace Laboratory of the Netherlands. Since August 1988, the ARTEMIS system has been delivering the following products on a routine basis: ten-day and monthly cold cloud duration maps for the continent of Africa and the Near East (resolution 7.6 km); ten-day and monthly estimated rainfall maps for the Southern Sahara, the Sahel, Sudan, and the tropical countries of West Africa (resolution 7.6 km); ten-day and monthly composite vegetation index maps for Africa, and the Near East. In addition to these databases, ARTEMIS contains a ten-year vegetation index archive on a ten-day and monthly basis, developed jointly by NASA GSFC and the FAO Remote Sensing Centre. This archive allows for early assessment of current crop growing conditions by comparison with known situations in the past. LANGUAGE: English ACCESS/AVAILABILITY: ARTEMIS data products are available in photographic and digital formats. Analyzed infomation is communicated in bulletins and publications of Global Information and Early Warning System (GIEWS) and Emergency Centre for Locust Operations (ECLO) of FAO. For making ARTEMIS data available in a timely manner to users, more and more use is currently being made of electronic mail." proprietary
+CIESIN0122 Africa Real Time Environmental Monitoring Information System (ARTEMIS) ALL STAC Catalog 1982-01-01 -20, -35, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232282935-CEOS_EXTRA.umm_json "The ""Africa Real Time Environmental Monitoring Information System (ARTEMIS)"" is part of the FAO's use of satellite remote sensing techniques to improve the surveillance and forecasting capabilities of its Global Information and Early Warning System (GIEWS). ARTEMIS was developed as a result of close technical cooperation between the FAO and the NASA Goddard Space Flight Center, the University of Reading in the United Kingdom, and the National Aerospace Laboratory of the Netherlands. Since August 1988, the ARTEMIS system has been delivering the following products on a routine basis: ten-day and monthly cold cloud duration maps for the continent of Africa and the Near East (resolution 7.6 km); ten-day and monthly estimated rainfall maps for the Southern Sahara, the Sahel, Sudan, and the tropical countries of West Africa (resolution 7.6 km); ten-day and monthly composite vegetation index maps for Africa, and the Near East. In addition to these databases, ARTEMIS contains a ten-year vegetation index archive on a ten-day and monthly basis, developed jointly by NASA GSFC and the FAO Remote Sensing Centre. This archive allows for early assessment of current crop growing conditions by comparison with known situations in the past. LANGUAGE: English ACCESS/AVAILABILITY: ARTEMIS data products are available in photographic and digital formats. Analyzed infomation is communicated in bulletins and publications of Global Information and Early Warning System (GIEWS) and Emergency Centre for Locust Operations (ECLO) of FAO. For making ARTEMIS data available in a timely manner to users, more and more use is currently being made of electronic mail." proprietary
CIESIN_AfSIS_CLIMATE_ECV2014_2014.00 AfSIS Climate Collection: Essential Climate Variable (ECV) Soil Moisture, 2014 Release SCIOPS STAC Catalog 1978-11-01 2010-12-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604742-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Climate Collection's Essential Climate Variable (ECV) Soil Moisture data set contains rasters with the following calculations: time series average, time series monthly averages, and annual averages. These Africa continent-wide rasters were created using the soil moisture data for the period 1978-2010 provided by the European Space Agency (ESA) Soil Moisture Climate Change Initiative (CCI) project. The rasters have a daily temporal resolution, a spatial resolution of 30 kilometers, and are updated by AfSIS when observations are available and provided by ESA at http://www.esa-soilmoisture-cci.org. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
CIESIN_AfSIS_CLIMATE_ECV2014_2014.00 AfSIS Climate Collection: Essential Climate Variable (ECV) Soil Moisture, 2014 Release ALL STAC Catalog 1978-11-01 2010-12-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604742-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Climate Collection's Essential Climate Variable (ECV) Soil Moisture data set contains rasters with the following calculations: time series average, time series monthly averages, and annual averages. These Africa continent-wide rasters were created using the soil moisture data for the period 1978-2010 provided by the European Space Agency (ESA) Soil Moisture Climate Change Initiative (CCI) project. The rasters have a daily temporal resolution, a spatial resolution of 30 kilometers, and are updated by AfSIS when observations are available and provided by ESA at http://www.esa-soilmoisture-cci.org. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
CIESIN_AfSIS_CLIMATE_TRMM201401_2014.01 AfSIS Climate Collection: Tropical Rainfall Measuring Mission (TRMM), January 2014 Release SCIOPS STAC Catalog 1998-01-01 2013-12-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604720-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Climate Collection's Tropical Rainfall Measurement Mission (TRMM) data set contains rasters with the following calculations: time series average, time series Modified Fournier index (MFI), time series average number of rainy days, annual averages, annual MFI, and annual average number of rainy days, for precipitation. These Africa continent-wide calculations use the TRMM observations obtained by the National Aeronautics and Space Administration (NASA). The rasters have a daily temporal resolution, a spatial resolution of 30 kilometers, and are updated quarterly by AfSIS using data provided by the Columbia University International Research Institute for Climate and Society (IRI) at http://iridl.ldeo.columbia.edu. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
CIESIN_AfSIS_CLIMATE_TRMM201401_2014.01 AfSIS Climate Collection: Tropical Rainfall Measuring Mission (TRMM), January 2014 Release ALL STAC Catalog 1998-01-01 2013-12-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604720-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Climate Collection's Tropical Rainfall Measurement Mission (TRMM) data set contains rasters with the following calculations: time series average, time series Modified Fournier index (MFI), time series average number of rainy days, annual averages, annual MFI, and annual average number of rainy days, for precipitation. These Africa continent-wide calculations use the TRMM observations obtained by the National Aeronautics and Space Administration (NASA). The rasters have a daily temporal resolution, a spatial resolution of 30 kilometers, and are updated quarterly by AfSIS using data provided by the Columbia University International Research Institute for Climate and Society (IRI) at http://iridl.ldeo.columbia.edu. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
CIESIN_AfSIS_CLIMATE_WC2013_2013.00 AfSIS Climate Collection: WorldClim, 2013 Release SCIOPS STAC Catalog 1950-01-01 2000-12-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604711-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Climate Collection's WorldClim data set contains rasters with the following calculations: time series average for BIO1 temperature as well as time series average and time series Modified Fournier Index (MFI) for BIO12 precipitation. These Africa continent-wide calculations use the temperature and precipitation data for the period 1950-2000 created by WorldClim. The rasters contain interpolated weather station data with a spatial resolution of 1 kilometer, and are updated by AfSIS using data provided by WorldClim at http://www.worldclim.org. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
CIESIN_AfSIS_CLIMATE_WC2013_2013.00 AfSIS Climate Collection: WorldClim, 2013 Release ALL STAC Catalog 1950-01-01 2000-12-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604711-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Climate Collection's WorldClim data set contains rasters with the following calculations: time series average for BIO1 temperature as well as time series average and time series Modified Fournier Index (MFI) for BIO12 precipitation. These Africa continent-wide calculations use the temperature and precipitation data for the period 1950-2000 created by WorldClim. The rasters contain interpolated weather station data with a spatial resolution of 1 kilometer, and are updated by AfSIS using data provided by WorldClim at http://www.worldclim.org. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
-CIESIN_AfSIS_MODIS_ALB2012_2012.00 AfSIS MODIS Collection: Albedo, 2012 Release SCIOPS STAC Catalog 2000-02-01 2012-06-30 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604712-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Albedo data set contains rasters with the following calculations: time series average, time series standard deviation, and time series variance for white sky and black sky albedo. These Africa continent-wide calculations use surface reflectance data obtained by the National Aeronautics and Space Administration (NASA) MODIS MCD43A3 product. The rasters have a 16-day temporal resolution, a spatial resolution of 500 meters, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
CIESIN_AfSIS_MODIS_ALB2012_2012.00 AfSIS MODIS Collection: Albedo, 2012 Release ALL STAC Catalog 2000-02-01 2012-06-30 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604712-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Albedo data set contains rasters with the following calculations: time series average, time series standard deviation, and time series variance for white sky and black sky albedo. These Africa continent-wide calculations use surface reflectance data obtained by the National Aeronautics and Space Administration (NASA) MODIS MCD43A3 product. The rasters have a 16-day temporal resolution, a spatial resolution of 500 meters, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
-CIESIN_AfSIS_MODIS_LAIFPAR2012_2012.00 AfSIS MODIS Collection: Leaf Area Index - FPAR, 2012 Release ALL STAC Catalog 2000-02-01 2012-06-30 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604716-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) data sets contain rasters with the following calculations: time series average, time series standard deviation, and time series variance for LAI and FPAR. These Africa continent-wide calculations for surface photosynthesis use observations from the National Aeronautics and Space Administration (NASA) MODIS MCD43A3 product. The rasters have a 8-day temporal resolution, a spatial resolution of 1 kilometer, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
+CIESIN_AfSIS_MODIS_ALB2012_2012.00 AfSIS MODIS Collection: Albedo, 2012 Release SCIOPS STAC Catalog 2000-02-01 2012-06-30 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604712-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Albedo data set contains rasters with the following calculations: time series average, time series standard deviation, and time series variance for white sky and black sky albedo. These Africa continent-wide calculations use surface reflectance data obtained by the National Aeronautics and Space Administration (NASA) MODIS MCD43A3 product. The rasters have a 16-day temporal resolution, a spatial resolution of 500 meters, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
CIESIN_AfSIS_MODIS_LAIFPAR2012_2012.00 AfSIS MODIS Collection: Leaf Area Index - FPAR, 2012 Release SCIOPS STAC Catalog 2000-02-01 2012-06-30 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604716-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) data sets contain rasters with the following calculations: time series average, time series standard deviation, and time series variance for LAI and FPAR. These Africa continent-wide calculations for surface photosynthesis use observations from the National Aeronautics and Space Administration (NASA) MODIS MCD43A3 product. The rasters have a 8-day temporal resolution, a spatial resolution of 1 kilometer, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
-CIESIN_AfSIS_MODIS_LCT2012_2012.00 AfSIS MODIS Collection: Land Cover Type, 2012 Release ALL STAC Catalog 2001-01-01 2009-12-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604713-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Land Cover Type 2 data set is constructed for the continent of Africa using observations from the National Aeronautics and Space Administration (NASA) MODIS MCD12Q1 product. The grids have an annual temporal resolution, a spatial resolution of 500 meters, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
+CIESIN_AfSIS_MODIS_LAIFPAR2012_2012.00 AfSIS MODIS Collection: Leaf Area Index - FPAR, 2012 Release ALL STAC Catalog 2000-02-01 2012-06-30 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604716-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) data sets contain rasters with the following calculations: time series average, time series standard deviation, and time series variance for LAI and FPAR. These Africa continent-wide calculations for surface photosynthesis use observations from the National Aeronautics and Space Administration (NASA) MODIS MCD43A3 product. The rasters have a 8-day temporal resolution, a spatial resolution of 1 kilometer, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
CIESIN_AfSIS_MODIS_LCT2012_2012.00 AfSIS MODIS Collection: Land Cover Type, 2012 Release SCIOPS STAC Catalog 2001-01-01 2009-12-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604713-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Land Cover Type 2 data set is constructed for the continent of Africa using observations from the National Aeronautics and Space Administration (NASA) MODIS MCD12Q1 product. The grids have an annual temporal resolution, a spatial resolution of 500 meters, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
+CIESIN_AfSIS_MODIS_LCT2012_2012.00 AfSIS MODIS Collection: Land Cover Type, 2012 Release ALL STAC Catalog 2001-01-01 2009-12-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604713-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Land Cover Type 2 data set is constructed for the continent of Africa using observations from the National Aeronautics and Space Administration (NASA) MODIS MCD12Q1 product. The grids have an annual temporal resolution, a spatial resolution of 500 meters, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
CIESIN_AfSIS_MODIS_LST201404_2014.04 AfSIS MODIS Collection: Land Surface Temperature, April 2014 Release ALL STAC Catalog 2002-07-01 2014-03-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604721-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Land Surface Temperature data set contains rasters with the following calculations: time series average and time series monthly averages for day and night. These Africa continent-wide calculations use observations from the National Aeronautics and Space Administration (NASA) MODIS MYD11A2 product. The rasters have an 8-day temporal resolution, a spatial resolution of 1 kilometer, and are updated quarterly by AfSIS using data provided by the Columbia University International Research Institute for Climate and Society (IRI) at http://iridl.ldeo.columbia.edu. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
CIESIN_AfSIS_MODIS_LST201404_2014.04 AfSIS MODIS Collection: Land Surface Temperature, April 2014 Release SCIOPS STAC Catalog 2002-07-01 2014-03-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604721-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Land Surface Temperature data set contains rasters with the following calculations: time series average and time series monthly averages for day and night. These Africa continent-wide calculations use observations from the National Aeronautics and Space Administration (NASA) MODIS MYD11A2 product. The rasters have an 8-day temporal resolution, a spatial resolution of 1 kilometer, and are updated quarterly by AfSIS using data provided by the Columbia University International Research Institute for Climate and Society (IRI) at http://iridl.ldeo.columbia.edu. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
-CIESIN_AfSIS_MODIS_PP2012_2014.00 AfSIS MODIS Collection: Primary Productivity, 2012 Release SCIOPS STAC Catalog 2000-01-01 2010-12-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604723-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Primary Productivity data set contains rasters with the following calculations: time series average, time series variance, and annual averages for Net Primary Productivity (NPP) and Gross Primary Productivity (GPP). These Africa continent-wide calculations for vegetation productivity use observations from the National Aeronautics and Space Administration (NASA) MODIS MOD17A3 product. The rasters have a annual temporal resolution, a spatial resolution of 1 kilometer, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
CIESIN_AfSIS_MODIS_PP2012_2014.00 AfSIS MODIS Collection: Primary Productivity, 2012 Release ALL STAC Catalog 2000-01-01 2010-12-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604723-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Primary Productivity data set contains rasters with the following calculations: time series average, time series variance, and annual averages for Net Primary Productivity (NPP) and Gross Primary Productivity (GPP). These Africa continent-wide calculations for vegetation productivity use observations from the National Aeronautics and Space Administration (NASA) MODIS MOD17A3 product. The rasters have a annual temporal resolution, a spatial resolution of 1 kilometer, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
+CIESIN_AfSIS_MODIS_PP2012_2014.00 AfSIS MODIS Collection: Primary Productivity, 2012 Release SCIOPS STAC Catalog 2000-01-01 2010-12-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604723-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Primary Productivity data set contains rasters with the following calculations: time series average, time series variance, and annual averages for Net Primary Productivity (NPP) and Gross Primary Productivity (GPP). These Africa continent-wide calculations for vegetation productivity use observations from the National Aeronautics and Space Administration (NASA) MODIS MOD17A3 product. The rasters have a annual temporal resolution, a spatial resolution of 1 kilometer, and are updated annually by AfSIS using data provided by the U.S. Geological Survey (USGS) Land Processes Distributed Active Archive Center (LPDAAC) Data Pool at https://lpdaac.usgs.gov. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
CIESIN_AfSIS_MODIS_VEGIN201404_2014.04 AfSIS MODIS Collection: Vegetation Indices, April 2014 Release SCIOPS STAC Catalog 2000-02-01 2014-03-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604724-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Vegetation Indices data set contains rasters with the following calculations: time series average and time series monthly average for the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Red Reflectance Band 1, Near-Infrared Reflectance Band 2, Blue Reflectance Band 3, and Mid-Infrared Reflectance Band 7. These Africa continent-wide calculations for vegetation indices and surface reflectances use data from the National Aeronautics and Space Administration (NASA) MODIS MOD13Q1 product. The rasters have a 16-day temporal resolution, a spatial resolution of 250 meters, and are updated quarterly by AfSIS using data provided by the Columbia University International Research Institute for Climate and Society (IRI) at http://iridl.ldeo.columbia.edu. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
CIESIN_AfSIS_MODIS_VEGIN201404_2014.04 AfSIS MODIS Collection: Vegetation Indices, April 2014 Release ALL STAC Catalog 2000-02-01 2014-03-31 -20, -40, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C1214604724-SCIOPS.umm_json The Africa Soil Information Service (AfSIS) Moderate Resolution Imaging Spectroradiometer (MODIS) Collection's Vegetation Indices data set contains rasters with the following calculations: time series average and time series monthly average for the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Red Reflectance Band 1, Near-Infrared Reflectance Band 2, Blue Reflectance Band 3, and Mid-Infrared Reflectance Band 7. These Africa continent-wide calculations for vegetation indices and surface reflectances use data from the National Aeronautics and Space Administration (NASA) MODIS MOD13Q1 product. The rasters have a 16-day temporal resolution, a spatial resolution of 250 meters, and are updated quarterly by AfSIS using data provided by the Columbia University International Research Institute for Climate and Society (IRI) at http://iridl.ldeo.columbia.edu. The data are available in Geographic Tagged Image File Format (GeoTIFF) from the Africa Soil Information Service (AfSIS). proprietary
CIESIN_CHRR_NDH_CYCLONE_HFD_1.00 Global Cyclone Hazard Frequency and Distribution SEDAC STAC Catalog 1980-01-01 2000-12-31 -180, -58, 180, 85 https://cmr.earthdata.nasa.gov/search/concepts/C179001766-SEDAC.umm_json The Global Cyclone Hazard Frequency and Distribution is a 2.5 minute grid based on more than 1,600 storm tracks for the period 1 January 1980 through 31 December 2000 for the Atlantic, Pacific, and Indian Oceans that were assembled and modeled at UNEP/GRID-Geneva PreView. Windspeeds around storm tracks were modeled using Holland's model (1997) to assess the grid cells likely to have been exposed to high wind levels. Post-modeling, the cells were divided into deciles, 10 classes consisting of approximately equal number of grid cells. The higher the value of the grid cell, the higher the decile ranking and the greater the frequency of the hazard relative to other cells. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, United Nations Environment Programme Global Resource Information Database Geneva (UNEP/GRID-Geneva), and Columbia University Center for International Earth Science Information Network (CIESIN). proprietary
@@ -4820,28 +4821,28 @@ CIESIN_SEDAC_DEDC_ACE_V2_2.00 Altimeter Corrected Elevations, Version 2 (ACE2) S
CIESIN_SEDAC_ENERGY_NPPCLA_1.00 Population Exposure Estimates in Proximity to Nuclear Power Plants, Country-Level Aggregates SEDAC STAC Catalog 1990-01-01 2010-01-01 -180, -55.77, 180, 83.63 https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-SEDAC.umm_json The Population Exposure Estimates in Proximity to Nuclear Power Plants, Country-Level Aggregates data set consists of country-level estimates of total, urban, and rural populations and land area, country-wide, that are in proximity to a nuclear power plant. This data set was created using a global data set of point locations of nuclear power plants, with buffer zones at 30km, 75km, 150km, 300km, 600km, and 1200km, and the Global Population Count Grid Time Series Estimates, Version 1 to estimate the population within each buffer zone for the years 1990, 2000, and 2010. Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) Land and Geographic Unit Area Grids were used to estimate land area within each buffer zone. The GRUMPv1 Urban Extents Grid was used to further delineate population and land area estimates within urban and rural areas. All grids used for population, land area, and urban mask were of 1 km (30 arc-second) resolution. proprietary
CIESIN_SEDAC_ENERGY_NPPL_1.00 Population Exposure Estimates in Proximity to Nuclear Power Plants, Locations SEDAC STAC Catalog 1956-01-01 2012-12-31 -124.21, -34, 166.45, 68.05 https://cmr.earthdata.nasa.gov/search/concepts/C1000000480-SEDAC.umm_json The Population Exposure Estimates in Proximity to Nuclear Power Plants, Locations data set combines information from a global data set developed by Declan Butler of Nature News and the Power Reactor Information System (PRIS), an up-to-date database of nuclear reactors maintained by the International Atomic Energy Agency (IAEA). The locations of nuclear reactors around the world are represented as point features associated with reactor specification and performance history attributes as of March 2012. proprietary
CIESIN_SEDAC_EPI_2006_2006.00 Pilot 2006 Environmental Performance Index (EPI) SEDAC STAC Catalog 1994-01-01 2006-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179001815-SEDAC.umm_json The Pilot 2006 Environmental Performance Index (EPI) centers on two broad environmental protection objectives: (1) reducing environmental stresses on human health, and (2) promoting ecosystem vitality and sound natural resource management. Derived from a careful review of the environmental literature, these twin goals mirror the priorities expressed by policymakers. Environmental health and ecosystem vitality are gauged using sixteen indicators tracked in six well-established policy categories: Environmental Health, Air Quality, Water Resources, Productive Natural Resources, Biodiversity and Habitat, and Sustainable Energy. The Pilot 2006 EPI utilizes a proximity-to-target methodology focused on a core set of environmental outcomes linked to policy goals for which every government should be held accountable. By identifying specific targets and measuring how close each country comes to them, the EPI provides a factual foundation for policy analysis and a context for evaluating performance. Issue-by-issue and aggregate rankings facilitate cross-country comparisons both globally and within relevant peer groups. The Pilot 2006 EPI is the result of collaboration among the Yale Center for Environmental Law and Policy (YCELP), Columbia University Center for International Earth Science Information Network (CIESIN), World Economic Forum (WEF), and the Joint Research Centre (JRC), European Commission. proprietary
-CIESIN_SEDAC_EPI_2008_2008.00 2008 Environmental Performance Index (EPI) SEDAC STAC Catalog 1994-01-01 2007-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179001707-SEDAC.umm_json The 2008 Environmental Performance Index (EPI) centers on two broad environmental protection objectives: (1) reducing environmental stresses on human health, and (2) promoting ecosystem vitality and sound natural resource management. Derived from a careful review of the environmental literature, these twin goals mirror the priorities expressed by policymakers. Environmental health and ecosystem vitality are gauged using 25 indicators tracked in six well-established policy categories: Environmental Health (Environmental Burden of Disease, Water, and Air Pollution), Air Pollution (effects on ecosystems), Water (effects on ecosystems), Biodiversity and Habitat, Productive Natural Resources (Forestry, Fisheries, and Agriculture), and Climate Change. The 2008 EPI utilizes a proximity-to-target methodology in which performance on each indicator is rated on a 0 to 100 scale (100 represents �at target�). By identifying specific targets and measuring how close each country comes to them, the EPI provides a foundation for policy analysis and a context for evaluating performance. Issue-by-issue and aggregate rankings facilitate cross-country comparisons both globally and within relevant peer groups. The 2008 EPI is the result of collaboration among the Yale Center for Environmental Law and Policy (YCELP), Columbia University Center for International Earth Science Information Network (CIESIN), World Economic Forum (WEF), and the Joint Research Centre (JRC), European Commission. proprietary
CIESIN_SEDAC_EPI_2008_2008.00 2008 Environmental Performance Index (EPI) ALL STAC Catalog 1994-01-01 2007-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179001707-SEDAC.umm_json The 2008 Environmental Performance Index (EPI) centers on two broad environmental protection objectives: (1) reducing environmental stresses on human health, and (2) promoting ecosystem vitality and sound natural resource management. Derived from a careful review of the environmental literature, these twin goals mirror the priorities expressed by policymakers. Environmental health and ecosystem vitality are gauged using 25 indicators tracked in six well-established policy categories: Environmental Health (Environmental Burden of Disease, Water, and Air Pollution), Air Pollution (effects on ecosystems), Water (effects on ecosystems), Biodiversity and Habitat, Productive Natural Resources (Forestry, Fisheries, and Agriculture), and Climate Change. The 2008 EPI utilizes a proximity-to-target methodology in which performance on each indicator is rated on a 0 to 100 scale (100 represents �at target�). By identifying specific targets and measuring how close each country comes to them, the EPI provides a foundation for policy analysis and a context for evaluating performance. Issue-by-issue and aggregate rankings facilitate cross-country comparisons both globally and within relevant peer groups. The 2008 EPI is the result of collaboration among the Yale Center for Environmental Law and Policy (YCELP), Columbia University Center for International Earth Science Information Network (CIESIN), World Economic Forum (WEF), and the Joint Research Centre (JRC), European Commission. proprietary
-CIESIN_SEDAC_EPI_2010_2010.00 2010 Environmental Performance Index (EPI) ALL STAC Catalog 1994-01-01 2009-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179002147-SEDAC.umm_json The 2010 Environmental Performance Index (EPI) ranks 163 countries on environmental performance based on twenty-five indicators grouped within ten core policy categories addressing environmental health, air quality, water resource management, biodiversity and habitat, forestry, fisheries, agriculture, and climate change in the context of two broad objectives: environmental health and ecosystem vitality. The EPI�s proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. It was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 28, 2010. The 2010 EPI is the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). proprietary
+CIESIN_SEDAC_EPI_2008_2008.00 2008 Environmental Performance Index (EPI) SEDAC STAC Catalog 1994-01-01 2007-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179001707-SEDAC.umm_json The 2008 Environmental Performance Index (EPI) centers on two broad environmental protection objectives: (1) reducing environmental stresses on human health, and (2) promoting ecosystem vitality and sound natural resource management. Derived from a careful review of the environmental literature, these twin goals mirror the priorities expressed by policymakers. Environmental health and ecosystem vitality are gauged using 25 indicators tracked in six well-established policy categories: Environmental Health (Environmental Burden of Disease, Water, and Air Pollution), Air Pollution (effects on ecosystems), Water (effects on ecosystems), Biodiversity and Habitat, Productive Natural Resources (Forestry, Fisheries, and Agriculture), and Climate Change. The 2008 EPI utilizes a proximity-to-target methodology in which performance on each indicator is rated on a 0 to 100 scale (100 represents �at target�). By identifying specific targets and measuring how close each country comes to them, the EPI provides a foundation for policy analysis and a context for evaluating performance. Issue-by-issue and aggregate rankings facilitate cross-country comparisons both globally and within relevant peer groups. The 2008 EPI is the result of collaboration among the Yale Center for Environmental Law and Policy (YCELP), Columbia University Center for International Earth Science Information Network (CIESIN), World Economic Forum (WEF), and the Joint Research Centre (JRC), European Commission. proprietary
CIESIN_SEDAC_EPI_2010_2010.00 2010 Environmental Performance Index (EPI) SEDAC STAC Catalog 1994-01-01 2009-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179002147-SEDAC.umm_json The 2010 Environmental Performance Index (EPI) ranks 163 countries on environmental performance based on twenty-five indicators grouped within ten core policy categories addressing environmental health, air quality, water resource management, biodiversity and habitat, forestry, fisheries, agriculture, and climate change in the context of two broad objectives: environmental health and ecosystem vitality. The EPI�s proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. It was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 28, 2010. The 2010 EPI is the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). proprietary
-CIESIN_SEDAC_EPI_2012_2012.00 2012 Environmental Performance Index and Pilot Trend Environmental Performance Index ALL STAC Catalog 2000-01-01 2010-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000000-SEDAC.umm_json The 2012 Environmental Performance Index (EPI) ranks 132 countries on 22 performance indicators in the following 10 policy categories: environmental burden of disease, water (effects on human health), air pollution (effects on human health), air pollution (ecosystem effects), water resources (ecosystem effects), biodiversity and habitat, forestry, fisheries, agriculture and climate change. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. Each indicator has an associated environmental public health or ecosystem sustainability target. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The Pilot Trend Environmental Performance Index (Trend EPI) ranks countries on the change in their environmental performance over the last decade. As a complement to the EPI, the Trend EPI shows who is improving and who is declining over time. The 2012 EPI and Pilot Trend EPI were formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 27, 2012. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2012 EPI is at http://epi.yale.edu/. proprietary
+CIESIN_SEDAC_EPI_2010_2010.00 2010 Environmental Performance Index (EPI) ALL STAC Catalog 1994-01-01 2009-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179002147-SEDAC.umm_json The 2010 Environmental Performance Index (EPI) ranks 163 countries on environmental performance based on twenty-five indicators grouped within ten core policy categories addressing environmental health, air quality, water resource management, biodiversity and habitat, forestry, fisheries, agriculture, and climate change in the context of two broad objectives: environmental health and ecosystem vitality. The EPI�s proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. It was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 28, 2010. The 2010 EPI is the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). proprietary
CIESIN_SEDAC_EPI_2012_2012.00 2012 Environmental Performance Index and Pilot Trend Environmental Performance Index SEDAC STAC Catalog 2000-01-01 2010-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000000-SEDAC.umm_json The 2012 Environmental Performance Index (EPI) ranks 132 countries on 22 performance indicators in the following 10 policy categories: environmental burden of disease, water (effects on human health), air pollution (effects on human health), air pollution (ecosystem effects), water resources (ecosystem effects), biodiversity and habitat, forestry, fisheries, agriculture and climate change. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. Each indicator has an associated environmental public health or ecosystem sustainability target. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The Pilot Trend Environmental Performance Index (Trend EPI) ranks countries on the change in their environmental performance over the last decade. As a complement to the EPI, the Trend EPI shows who is improving and who is declining over time. The 2012 EPI and Pilot Trend EPI were formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 27, 2012. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2012 EPI is at http://epi.yale.edu/. proprietary
+CIESIN_SEDAC_EPI_2012_2012.00 2012 Environmental Performance Index and Pilot Trend Environmental Performance Index ALL STAC Catalog 2000-01-01 2010-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000000-SEDAC.umm_json The 2012 Environmental Performance Index (EPI) ranks 132 countries on 22 performance indicators in the following 10 policy categories: environmental burden of disease, water (effects on human health), air pollution (effects on human health), air pollution (ecosystem effects), water resources (ecosystem effects), biodiversity and habitat, forestry, fisheries, agriculture and climate change. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. Each indicator has an associated environmental public health or ecosystem sustainability target. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The Pilot Trend Environmental Performance Index (Trend EPI) ranks countries on the change in their environmental performance over the last decade. As a complement to the EPI, the Trend EPI shows who is improving and who is declining over time. The 2012 EPI and Pilot Trend EPI were formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 27, 2012. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2012 EPI is at http://epi.yale.edu/. proprietary
CIESIN_SEDAC_EPI_2014_2014.00 2014 Environmental Performance Index (EPI) ALL STAC Catalog 2002-01-01 2014-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000541-SEDAC.umm_json The 2014 Environmental Performance Index (EPI) ranks 178 countries on 20 performance indicators in the following 9 policy categories: health impacts, air quality, water and sanitation, water resources, agriculture, forests, fisheries, biodiversity and habitat, and climate and energy. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2014 EPI and component scores, backcast EPI scores for 2002-2012, and time-series source data. The 2014 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 25, 2014. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2014 EPI is at http://epi.yale.edu/. proprietary
CIESIN_SEDAC_EPI_2014_2014.00 2014 Environmental Performance Index (EPI) SEDAC STAC Catalog 2002-01-01 2014-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000541-SEDAC.umm_json The 2014 Environmental Performance Index (EPI) ranks 178 countries on 20 performance indicators in the following 9 policy categories: health impacts, air quality, water and sanitation, water resources, agriculture, forests, fisheries, biodiversity and habitat, and climate and energy. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2014 EPI and component scores, backcast EPI scores for 2002-2012, and time-series source data. The 2014 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 25, 2014. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2014 EPI is at http://epi.yale.edu/. proprietary
-CIESIN_SEDAC_EPI_2016_2016.00 2016 Environmental Performance Index (EPI) SEDAC STAC Catalog 1950-01-01 2016-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1419908204-SEDAC.umm_json The 2016 Environmental Performance Index (EPI) ranks 180 countries on 20 performance indicators in the following 9 policy categories: health impacts, air quality, water and sanitation, water resources, agriculture, forests, fisheries, biodiversity and habitat, and climate and energy. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2016 EPI and component scores, backcast EPI scores for 1950-2016, and time-series source data. The 2016 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 23, 2016. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and Yale Data-Driven Environmental Solutions Group, Yale University, Columbia University Center for International Earth Science Information Network (CIESIN), and the World Economic Forum (WEF). The Interactive Website for the 2016 EPI is at https://epi.yale.edu. proprietary
CIESIN_SEDAC_EPI_2016_2016.00 2016 Environmental Performance Index (EPI) ALL STAC Catalog 1950-01-01 2016-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1419908204-SEDAC.umm_json The 2016 Environmental Performance Index (EPI) ranks 180 countries on 20 performance indicators in the following 9 policy categories: health impacts, air quality, water and sanitation, water resources, agriculture, forests, fisheries, biodiversity and habitat, and climate and energy. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2016 EPI and component scores, backcast EPI scores for 1950-2016, and time-series source data. The 2016 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 23, 2016. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and Yale Data-Driven Environmental Solutions Group, Yale University, Columbia University Center for International Earth Science Information Network (CIESIN), and the World Economic Forum (WEF). The Interactive Website for the 2016 EPI is at https://epi.yale.edu. proprietary
-CIESIN_SEDAC_EPI_2018_2018.00 2018 Environmental Performance Index (EPI) ALL STAC Catalog 1950-01-01 2018-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1604900383-SEDAC.umm_json The 2018 Environmental Performance Index (EPI) ranks 180 countries on 24 performance indicators in the following 10 issue categories: air quality, water and sanitation, heavy metals, biodiversity and habitat, forests, fisheries, climate and energy, air pollution, water resources, and agriculture. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2018 EPI, component scores, and time-series source data. The 2018 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum in January 2018. It is the result of collaboration of the Yale Center for Environmental Law and Policy (YCELP), Yale University, Columbia University Center for International Earth Science Information Network (CIESIN), and the World Economic Forum (WEF). The Interactive Website for the 2018 EPI is at https://epi.envirocenter.yale.edu/. proprietary
+CIESIN_SEDAC_EPI_2016_2016.00 2016 Environmental Performance Index (EPI) SEDAC STAC Catalog 1950-01-01 2016-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1419908204-SEDAC.umm_json The 2016 Environmental Performance Index (EPI) ranks 180 countries on 20 performance indicators in the following 9 policy categories: health impacts, air quality, water and sanitation, water resources, agriculture, forests, fisheries, biodiversity and habitat, and climate and energy. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2016 EPI and component scores, backcast EPI scores for 1950-2016, and time-series source data. The 2016 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum on January 23, 2016. These are the result of collaboration between the Yale Center for Environmental Law and Policy (YCELP) and Yale Data-Driven Environmental Solutions Group, Yale University, Columbia University Center for International Earth Science Information Network (CIESIN), and the World Economic Forum (WEF). The Interactive Website for the 2016 EPI is at https://epi.yale.edu. proprietary
CIESIN_SEDAC_EPI_2018_2018.00 2018 Environmental Performance Index (EPI) SEDAC STAC Catalog 1950-01-01 2018-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1604900383-SEDAC.umm_json The 2018 Environmental Performance Index (EPI) ranks 180 countries on 24 performance indicators in the following 10 issue categories: air quality, water and sanitation, heavy metals, biodiversity and habitat, forests, fisheries, climate and energy, air pollution, water resources, and agriculture. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2018 EPI, component scores, and time-series source data. The 2018 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum in January 2018. It is the result of collaboration of the Yale Center for Environmental Law and Policy (YCELP), Yale University, Columbia University Center for International Earth Science Information Network (CIESIN), and the World Economic Forum (WEF). The Interactive Website for the 2018 EPI is at https://epi.envirocenter.yale.edu/. proprietary
-CIESIN_SEDAC_EPI_2020_2020.00 2020 Environmental Performance Index (EPI) SEDAC STAC Catalog 1950-01-01 2020-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2000613920-SEDAC.umm_json The 2020 Environmental Performance Index (EPI) ranks 180 countries on 32 performance indicators in the following 11 issue categories: air quality, sanitation and drinking water, heavy metals, waste management, biodiversity and habitat, ecosystem services, fisheries, climate change, pollution emissions, agriculture, and water resources. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2020 EPI, component scores, and time-series source data. It is the result of a collaboration of the Yale Center for Environmental Law and Policy (YCELP), Yale University, and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2020 EPI is at https://epi.yale.edu/. proprietary
+CIESIN_SEDAC_EPI_2018_2018.00 2018 Environmental Performance Index (EPI) ALL STAC Catalog 1950-01-01 2018-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1604900383-SEDAC.umm_json The 2018 Environmental Performance Index (EPI) ranks 180 countries on 24 performance indicators in the following 10 issue categories: air quality, water and sanitation, heavy metals, biodiversity and habitat, forests, fisheries, climate and energy, air pollution, water resources, and agriculture. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2018 EPI, component scores, and time-series source data. The 2018 EPI was formally released in Davos, Switzerland, at the annual meeting of the World Economic Forum in January 2018. It is the result of collaboration of the Yale Center for Environmental Law and Policy (YCELP), Yale University, Columbia University Center for International Earth Science Information Network (CIESIN), and the World Economic Forum (WEF). The Interactive Website for the 2018 EPI is at https://epi.envirocenter.yale.edu/. proprietary
CIESIN_SEDAC_EPI_2020_2020.00 2020 Environmental Performance Index (EPI) ALL STAC Catalog 1950-01-01 2020-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2000613920-SEDAC.umm_json The 2020 Environmental Performance Index (EPI) ranks 180 countries on 32 performance indicators in the following 11 issue categories: air quality, sanitation and drinking water, heavy metals, waste management, biodiversity and habitat, ecosystem services, fisheries, climate change, pollution emissions, agriculture, and water resources. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2020 EPI, component scores, and time-series source data. It is the result of a collaboration of the Yale Center for Environmental Law and Policy (YCELP), Yale University, and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2020 EPI is at https://epi.yale.edu/. proprietary
+CIESIN_SEDAC_EPI_2020_2020.00 2020 Environmental Performance Index (EPI) SEDAC STAC Catalog 1950-01-01 2020-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2000613920-SEDAC.umm_json The 2020 Environmental Performance Index (EPI) ranks 180 countries on 32 performance indicators in the following 11 issue categories: air quality, sanitation and drinking water, heavy metals, waste management, biodiversity and habitat, ecosystem services, fisheries, climate change, pollution emissions, agriculture, and water resources. These categories track performance and progress on two broad policy objectives, environmental health and ecosystem vitality. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2020 EPI, component scores, and time-series source data. It is the result of a collaboration of the Yale Center for Environmental Law and Policy (YCELP), Yale University, and the Columbia University Center for International Earth Science Information Network (CIESIN). The Interactive Website for the 2020 EPI is at https://epi.yale.edu/. proprietary
CIESIN_SEDAC_EPI_2022_2022.00 2022 Environmental Performance Index (EPI) SEDAC STAC Catalog 1950-01-01 2022-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2586824658-SEDAC.umm_json The 2022 Environmental Performance Index (EPI) ranks 180 countries on 40 performance indicators in the following 11 issue categories: air quality, sanitation and drinking water, heavy metals, waste management, biodiversity and habitat, ecosystem services, fisheries, acid rain, agriculture, water resources, and climate change mitigation. These categories track performance and progress on three broad policy objectives, environmental health, ecosystem vitality, and climate change. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2022 EPI, component scores, and time-series source data. It is the result of a collaboration of the Yale Center for Environmental Law and Policy (YCELP), Yale University, and the Columbia University Center for International Earth Science Information Network (CIESIN). proprietary
CIESIN_SEDAC_EPI_2022_2022.00 2022 Environmental Performance Index (EPI) ALL STAC Catalog 1950-01-01 2022-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2586824658-SEDAC.umm_json The 2022 Environmental Performance Index (EPI) ranks 180 countries on 40 performance indicators in the following 11 issue categories: air quality, sanitation and drinking water, heavy metals, waste management, biodiversity and habitat, ecosystem services, fisheries, acid rain, agriculture, water resources, and climate change mitigation. These categories track performance and progress on three broad policy objectives, environmental health, ecosystem vitality, and climate change. The EPI's proximity-to-target methodology facilitates cross-country comparisons among economic and regional peer groups. The data set includes the 2022 EPI, component scores, and time-series source data. It is the result of a collaboration of the Yale Center for Environmental Law and Policy (YCELP), Yale University, and the Columbia University Center for International Earth Science Information Network (CIESIN). proprietary
CIESIN_SEDAC_ESI_2000_2000.00 2000 Pilot Environmental Sustainability Index (ESI) ALL STAC Catalog 1978-01-01 1999-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179001887-SEDAC.umm_json The 2000 Pilot Environmental Sustainability Index (ESI) is an exploratory effort to construct an index that measures the ability of a nation's economy to achieve sustainable development, with the long term goal of finding a single indicator for environmental sustainability analagous to that of the Gross Domestic Product (GDP). The index covering 56 countries is a composite measure of the current status of a nation's environmental systems, pressures on those systems, human vulnerability to environmental change, national capacity to respond, and contributions to global environmental stewardship. The index was unveiled at the World Economic Forum's annual meeting, January 2000, Davos, Switzerland. The 2000 Pilot ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN). proprietary
CIESIN_SEDAC_ESI_2000_2000.00 2000 Pilot Environmental Sustainability Index (ESI) SEDAC STAC Catalog 1978-01-01 1999-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179001887-SEDAC.umm_json The 2000 Pilot Environmental Sustainability Index (ESI) is an exploratory effort to construct an index that measures the ability of a nation's economy to achieve sustainable development, with the long term goal of finding a single indicator for environmental sustainability analagous to that of the Gross Domestic Product (GDP). The index covering 56 countries is a composite measure of the current status of a nation's environmental systems, pressures on those systems, human vulnerability to environmental change, national capacity to respond, and contributions to global environmental stewardship. The index was unveiled at the World Economic Forum's annual meeting, January 2000, Davos, Switzerland. The 2000 Pilot ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN). proprietary
CIESIN_SEDAC_ESI_2001_2001.00 2001 Environmental Sustainability Index (ESI) ALL STAC Catalog 1980-01-01 2000-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000220-SEDAC.umm_json The 2001 Environmental Sustainability Index (ESI) utilizes a refined methodology based on the 2000 Pilot ESI effort, to construct an index covering 122 countries that measures the overall progress towards environmental sustainability. The index is a composite measure of the current status of a nation's environmental systems, pressures on those systems, human vulnerability to environmental change, national capacity to respond, and contributions to global environmental stewardship. The refinements included the addition and deletion of indicators, filling gaps in data coverage, new data sets, and the modification of the aggregation scheme. The index was unveiled at the World Economic Forum's annual meeting, January 2001, Davos, Switzerland. The 2001 ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN). proprietary
CIESIN_SEDAC_ESI_2001_2001.00 2001 Environmental Sustainability Index (ESI) SEDAC STAC Catalog 1980-01-01 2000-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000220-SEDAC.umm_json The 2001 Environmental Sustainability Index (ESI) utilizes a refined methodology based on the 2000 Pilot ESI effort, to construct an index covering 122 countries that measures the overall progress towards environmental sustainability. The index is a composite measure of the current status of a nation's environmental systems, pressures on those systems, human vulnerability to environmental change, national capacity to respond, and contributions to global environmental stewardship. The refinements included the addition and deletion of indicators, filling gaps in data coverage, new data sets, and the modification of the aggregation scheme. The index was unveiled at the World Economic Forum's annual meeting, January 2001, Davos, Switzerland. The 2001 ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN). proprietary
-CIESIN_SEDAC_ESI_2002_2002.00 2002 Environmental Sustainability Index (ESI) SEDAC STAC Catalog 1980-01-01 2000-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179001967-SEDAC.umm_json The 2002 Environmental Sustainability Index (ESI) measures overall progress toward environmental sustainability for 142 countries based on environmental systems, stresses, human vulnerability, social and institutional capacity and global stewardship. The addition of a climate change indicator, reduction in number of capacity indicators, and an improved imputation methodology contributed to an improvement from the 2001 ESI. The index was unveiled at the World Economic Forum's annual meeting, January 2002, New York. The 2002 ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN). proprietary
CIESIN_SEDAC_ESI_2002_2002.00 2002 Environmental Sustainability Index (ESI) ALL STAC Catalog 1980-01-01 2000-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179001967-SEDAC.umm_json The 2002 Environmental Sustainability Index (ESI) measures overall progress toward environmental sustainability for 142 countries based on environmental systems, stresses, human vulnerability, social and institutional capacity and global stewardship. The addition of a climate change indicator, reduction in number of capacity indicators, and an improved imputation methodology contributed to an improvement from the 2001 ESI. The index was unveiled at the World Economic Forum's annual meeting, January 2002, New York. The 2002 ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN). proprietary
+CIESIN_SEDAC_ESI_2002_2002.00 2002 Environmental Sustainability Index (ESI) SEDAC STAC Catalog 1980-01-01 2000-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179001967-SEDAC.umm_json The 2002 Environmental Sustainability Index (ESI) measures overall progress toward environmental sustainability for 142 countries based on environmental systems, stresses, human vulnerability, social and institutional capacity and global stewardship. The addition of a climate change indicator, reduction in number of capacity indicators, and an improved imputation methodology contributed to an improvement from the 2001 ESI. The index was unveiled at the World Economic Forum's annual meeting, January 2002, New York. The 2002 ESI is the result of collaboration among the World Economic Forum (WEF), Yale Center for Environmental Law and Policy (YCELP), and the Columbia University Center for International Earth Science Information Network (CIESIN). proprietary
CIESIN_SEDAC_ESI_2005_2005.00 2005 Environmental Sustainability Index (ESI) ALL STAC Catalog 1980-01-01 2000-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179001889-SEDAC.umm_json The 2005 Environmental Sustainability Index (ESI) is a measure of overall progress towards environmental sustainability, developed for 146 countries. The index provides a composite profile of national environmental stewardship based on a compilation of 21 indicators derived from 76 underlying data sets. The 2005 version of the ESI represents a significant update and improvement on earlier versions; the country ESI scores or rankings should not be compared to earlier versions because of changes to the methodology and underlying data. The index was unveiled at the World Economic Forum's annual meeting, January 2005, Davos, Switzerland. The 2005 ESI is a joint product of the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN), in collaboration with the World Economic Forum (WEF) and the Joint Research Centre (JRC), European Commission. proprietary
CIESIN_SEDAC_ESI_2005_2005.00 2005 Environmental Sustainability Index (ESI) SEDAC STAC Catalog 1980-01-01 2000-12-31 -180, -55, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179001889-SEDAC.umm_json The 2005 Environmental Sustainability Index (ESI) is a measure of overall progress towards environmental sustainability, developed for 146 countries. The index provides a composite profile of national environmental stewardship based on a compilation of 21 indicators derived from 76 underlying data sets. The 2005 version of the ESI represents a significant update and improvement on earlier versions; the country ESI scores or rankings should not be compared to earlier versions because of changes to the methodology and underlying data. The index was unveiled at the World Economic Forum's annual meeting, January 2005, Davos, Switzerland. The 2005 ESI is a joint product of the Yale Center for Environmental Law and Policy (YCELP) and the Columbia University Center for International Earth Science Information Network (CIESIN), in collaboration with the World Economic Forum (WEF) and the Joint Research Centre (JRC), European Commission. proprietary
CIESIN_SEDAC_FERMANv1_NAPP_1.00 Global Fertilizer and Manure, Version 1: Nitrogen Fertilizer Application SEDAC STAC Catalog 1994-01-01 2001-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000020-SEDAC.umm_json "The Nitrogen Fertilizer Application data set of the Global Fertilizer and Manure, Version 1 Data Collection represents the amount of nitrogen fertilizer nutrients applied to croplands. The national-level nitrogen fertilizer application rates for crops are from the International Fertilizer Industry Association (IFA) ""Fertilizer Use by Crop 2002"" statistics database that is available by request from the Food and Agriculture Organization (FAO). The number of crop-specific fertilizer application rates reported for each country ranged from 2 crops (Guinea) to over 50 crops (United States), and the years for which the data are reported range from 1994 to 2001. Spatially explicit fertilizer inputs of Nitrogen (N) were computed by fusing national-level statistics on fertilizer use with global maps of harvested area for 175 crops. The data were compiled by Potter et al. (2010) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN)." proprietary
@@ -5187,10 +5188,10 @@ CMS_Soil_CO2_Efflux_1298_1 CMS: Soil CO2 Efflux and Properties, Site Vegetation
CMS_WFEIS_CONUS-AK_1306_1 Annual wildland fire emissions (WFEIS v0.5) for Conterminous US and Alaska, 2001-2013 ORNL_CLOUD STAC Catalog 2001-01-01 2013-12-31 -178.22, 24.2, -65, 71.41 https://cmr.earthdata.nasa.gov/search/concepts/C2389082906-ORNL_CLOUD.umm_json This data set contains annual modeled estimates of wildland fire emissions at 0.01 degree (~1-km) spatial resolution from the Wildland Fire Emissions Information System (WFEIS v0.5) for the conterminous U.S. (CONUS) and Alaska for 2001 through 2013. WFEIS is a web-based tool that provides resources to quantify emissions from past fires and output results as spatial data files (French et al., 2014). The data set includes emissions estimates of carbon (C), carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), other non-methane hydrocarbons (NMHC), and particulate matter (PM) as well as estimates of above-ground biomass, total fuel availability, and consumption estimates. proprietary
CMS_WRF_Footprints_CO2_Signals_1381_1 CMS: CO2 Signals Estimated for Fossil Fuel Emissions and Biosphere Flux, California ORNL_CLOUD STAC Catalog 2010-11-01 2011-05-31 -124.51, 32.2, -115.96, 42.82 https://cmr.earthdata.nasa.gov/search/concepts/C2343166173-ORNL_CLOUD.umm_json This data set provides estimated CO2 emission signals for 16 regions (air quality basins) in California, USA, during the individual months of November 2010 and May 2011. The CO2 signals were predicted from simulated atmospheric CO2 observations and modeled fossil fuel emissions and biosphere CO2 fluxes. Data is also provided for the land surface in the larger modeling domain outside California. CO2 signals refer to the local enhancement or depletion in atmospheric CO2 concentration caused by fossil fuel emissions or biospheric exchange occurring within the region. proprietary
CMS_WRF_Model_Products_1338_1 CMS: Hourly Carbon Dioxide Estimated Using the WRF Model, North America, 2010 ORNL_CLOUD STAC Catalog 2010-01-01 2010-12-31 -151, 13, -41, 63 https://cmr.earthdata.nasa.gov/search/concepts/C2390408273-ORNL_CLOUD.umm_json This data set contains estimated hourly CO2 atmospheric mole fractions and meteorological observations over North America for the year 2010 at a horizontal grid resolution of 27 km and vertical resolution from the surface to 50 hPa. The data are output from the Penn State WRF-Chem version of the Weather Research and Forecasting (WRF) model using lateral boundary conditions and surface fluxes from the CMS-Flux Inversion system. proprietary
-CNDA-ESP_ANT94-0905_LIQ_05 Adaptive strategies of lichen species to cold environments: Antarctica and the Mediterranean high mountains. ALL STAC Catalog 1995-01-19 1995-02-09 -60, -63, -60, -63 https://cmr.earthdata.nasa.gov/search/concepts/C1214613278-SCIOPS.umm_json In English: At the beginning of the 1990's our ecophysiological research on Antarctic lichens was focussed on adaptations to cope with low temperatures. We assumed that low temperatures should play an important limiting role in the growth of the Maritime Antarctic tundra, which is made up of lichens and to a lesser extent of other cryptogams and two species of vascular plants. In different expeditions to the South Shetland Islands, mostly to the Spanish Antarctic Base on Livingston island, we carried out extensive field measurements of gas exchange of representative species of the tundra under natural conditions. We completed these studies with experiments under controlled conditions in the laboratory, exploring the photosynthetic response of these species to light and temperature. We combined gas exchange measurements with chlorophyll fluorescence analyses, with anatomical and ultra structural observations, and with photosynthetic pigments and relations studies. Some of the main specific goals were the comparisons between Antarctic and European populations of certain cosmopolitan lichen species, the tolerance to the simultaneous stresses of high irradiance and low temperatures, and the estimation of the primary production of some lichens during the austral summer. We concluded from these studies that the Antarctic populations were relatively less productive, that both lichens and vascular plants were remarkably resistant to the combination of high irradiances and low temperatures, and that, surprisingly, the austral summer was a period of negative carbon balance for some lichens, which required low temperatures to refrain respiration in order to reach a positive carbon balance. In our opinion, the ecological success of lichens in Antarctica is related not only to the fact that they are well adapted to low temperatures but also to the fact that they can exploit brief, unpredictable, favorable periods during the austral spring and autumn, which it is not the case for vascular plants. These studies left at least two open questions: why are the Antarctic populations so unproductive? And could the temperatures be involved in the limited growth of the Antarctic tundra through their interactions with biogeochemical cycles? The answer to these question is the main goal of our research towards the end of the 90's. Some preliminary results obtained during the 1996/1997 expedition pointed to nutrient availability as an important factor determining maximum rates of photosynthesis and, consequently, potential primary production. Comparisons between characteristic species of the tundra with species growing in the vicinities of penguin colonies or bird perches, which are local sources of nitrogen and phosphorus, confirmed to some extent the overlooked importance of nutrients versus the more commonly addressed role of low temperatures as direct determinant of primary production in terrestrial ecosystems of the maritime Antarctica. En Espanol: Al comienzo de los anos 90 nuestra investigacion ecofisiologica en liquenes antarticos estaba focalizada en las capacidades adaptativas a las bajas temperaturas. Asumimos que las bajas temperaturas jugarian un importante papel limitador en el crecimiento de la tundra antartica maritima, la cual esta formada por liquenes y por una menor cantidad de otras criptogamas y dos especies de plantas vasculares. En diferentes expediciones a las islas Shetland del Sur, la mayoria a la base antartica espanola de la isla Livingston, llevamos a cabo numerosas medidas de campo de intercambio de gases de especies representativas de la tundra bajo condiciones naturales. Completamos estos estudios con experimentos bajo condiciones controladas de laboratorio, explorando la respuesta fotosintetica de estas especies a la luz y la temperatura. Combinamos las medidas de intercambio de gases con analisis de fluorescencia en clorofila, con observaciones anatomicas y ultra estructurales, y con pigmentos fotosinteticos y estudios de relaciones. Algunos de los principales objetivos especificos fueron las comparaciones entre poblaciones Antarticas y europeas de ciertas especies de liquenes cosmopolitas, la tolerancia a la presion simultanea de alta irradiancia y bajas temperaturas, y la estimacion de la produccion primaria de algunos liquenes durante el verano austral. De estos estudios concluimos que las poblaciones antarticas eran relativamente poco productivas, que tanto liquenes como plantas vasculares eran remarcablemente resistentes a la combinacion de altas irradiancias y bajas temperaturas, y que, sorprendentemente, el verano austral era un periodo negativo de balance de carbono para algunos liquenes, los cuales requerian bajas temperaturas para abstenerse de respirar y asi alcanzar un balance de carbono positivo. En nuestra opinion el exito ecologico de los liquenes en la Antartida esta relacionado no solo con la realidad de que estan bien adaptados a las bajas temperaturas sino tambien a que ellos pueden aprovechar los breves e impredecibles, periodos favorables durante la primavera austral y el otono, lo cual no es el caso de las plantas vasculares. Estos estudios dejan al menos dos preguntas abiertas. ?Por que son las poblaciones antarticas tan poco productivas? Y ?podria la temperatura estar implicada en el crecimiento de la tundra antartica a traves de sus interacciones con los ciclos bioquimicos? Las respuestas a estas preguntas es el principal objetivo de nuestra investigacion hacia el final de los anos 90. Algunos resultados preliminares obtenidos durante la expedicion 1996/1997 apuntaban a la disponibilidad de nutrientes como un factor determinante del maximo indice de fotosintesis y, consecuentemente potencial de produccion primaria. Comparaciones entre especies caracteristicas de tundra con especies creciendo en las inmediaciones de las colonias de pinguinos o pedestales de pajaros, los cuales son fuentes locales de nitrogeno y fosforo, confirmaron hasta cierto punto la infravalorada importancia de los nutrientes contra el mas comunmente papel dirigido de las bajas temperaturas como determinante directo de la produccion primaria in ecosistemas terrestres de la Antartida maritima. proprietary
CNDA-ESP_ANT94-0905_LIQ_05 Adaptive strategies of lichen species to cold environments: Antarctica and the Mediterranean high mountains. SCIOPS STAC Catalog 1995-01-19 1995-02-09 -60, -63, -60, -63 https://cmr.earthdata.nasa.gov/search/concepts/C1214613278-SCIOPS.umm_json In English: At the beginning of the 1990's our ecophysiological research on Antarctic lichens was focussed on adaptations to cope with low temperatures. We assumed that low temperatures should play an important limiting role in the growth of the Maritime Antarctic tundra, which is made up of lichens and to a lesser extent of other cryptogams and two species of vascular plants. In different expeditions to the South Shetland Islands, mostly to the Spanish Antarctic Base on Livingston island, we carried out extensive field measurements of gas exchange of representative species of the tundra under natural conditions. We completed these studies with experiments under controlled conditions in the laboratory, exploring the photosynthetic response of these species to light and temperature. We combined gas exchange measurements with chlorophyll fluorescence analyses, with anatomical and ultra structural observations, and with photosynthetic pigments and relations studies. Some of the main specific goals were the comparisons between Antarctic and European populations of certain cosmopolitan lichen species, the tolerance to the simultaneous stresses of high irradiance and low temperatures, and the estimation of the primary production of some lichens during the austral summer. We concluded from these studies that the Antarctic populations were relatively less productive, that both lichens and vascular plants were remarkably resistant to the combination of high irradiances and low temperatures, and that, surprisingly, the austral summer was a period of negative carbon balance for some lichens, which required low temperatures to refrain respiration in order to reach a positive carbon balance. In our opinion, the ecological success of lichens in Antarctica is related not only to the fact that they are well adapted to low temperatures but also to the fact that they can exploit brief, unpredictable, favorable periods during the austral spring and autumn, which it is not the case for vascular plants. These studies left at least two open questions: why are the Antarctic populations so unproductive? And could the temperatures be involved in the limited growth of the Antarctic tundra through their interactions with biogeochemical cycles? The answer to these question is the main goal of our research towards the end of the 90's. Some preliminary results obtained during the 1996/1997 expedition pointed to nutrient availability as an important factor determining maximum rates of photosynthesis and, consequently, potential primary production. Comparisons between characteristic species of the tundra with species growing in the vicinities of penguin colonies or bird perches, which are local sources of nitrogen and phosphorus, confirmed to some extent the overlooked importance of nutrients versus the more commonly addressed role of low temperatures as direct determinant of primary production in terrestrial ecosystems of the maritime Antarctica. En Espanol: Al comienzo de los anos 90 nuestra investigacion ecofisiologica en liquenes antarticos estaba focalizada en las capacidades adaptativas a las bajas temperaturas. Asumimos que las bajas temperaturas jugarian un importante papel limitador en el crecimiento de la tundra antartica maritima, la cual esta formada por liquenes y por una menor cantidad de otras criptogamas y dos especies de plantas vasculares. En diferentes expediciones a las islas Shetland del Sur, la mayoria a la base antartica espanola de la isla Livingston, llevamos a cabo numerosas medidas de campo de intercambio de gases de especies representativas de la tundra bajo condiciones naturales. Completamos estos estudios con experimentos bajo condiciones controladas de laboratorio, explorando la respuesta fotosintetica de estas especies a la luz y la temperatura. Combinamos las medidas de intercambio de gases con analisis de fluorescencia en clorofila, con observaciones anatomicas y ultra estructurales, y con pigmentos fotosinteticos y estudios de relaciones. Algunos de los principales objetivos especificos fueron las comparaciones entre poblaciones Antarticas y europeas de ciertas especies de liquenes cosmopolitas, la tolerancia a la presion simultanea de alta irradiancia y bajas temperaturas, y la estimacion de la produccion primaria de algunos liquenes durante el verano austral. De estos estudios concluimos que las poblaciones antarticas eran relativamente poco productivas, que tanto liquenes como plantas vasculares eran remarcablemente resistentes a la combinacion de altas irradiancias y bajas temperaturas, y que, sorprendentemente, el verano austral era un periodo negativo de balance de carbono para algunos liquenes, los cuales requerian bajas temperaturas para abstenerse de respirar y asi alcanzar un balance de carbono positivo. En nuestra opinion el exito ecologico de los liquenes en la Antartida esta relacionado no solo con la realidad de que estan bien adaptados a las bajas temperaturas sino tambien a que ellos pueden aprovechar los breves e impredecibles, periodos favorables durante la primavera austral y el otono, lo cual no es el caso de las plantas vasculares. Estos estudios dejan al menos dos preguntas abiertas. ?Por que son las poblaciones antarticas tan poco productivas? Y ?podria la temperatura estar implicada en el crecimiento de la tundra antartica a traves de sus interacciones con los ciclos bioquimicos? Las respuestas a estas preguntas es el principal objetivo de nuestra investigacion hacia el final de los anos 90. Algunos resultados preliminares obtenidos durante la expedicion 1996/1997 apuntaban a la disponibilidad de nutrientes como un factor determinante del maximo indice de fotosintesis y, consecuentemente potencial de produccion primaria. Comparaciones entre especies caracteristicas de tundra con especies creciendo en las inmediaciones de las colonias de pinguinos o pedestales de pajaros, los cuales son fuentes locales de nitrogeno y fosforo, confirmaron hasta cierto punto la infravalorada importancia de los nutrientes contra el mas comunmente papel dirigido de las bajas temperaturas como determinante directo de la produccion primaria in ecosistemas terrestres de la Antartida maritima. proprietary
-CNDP_HES_20230103_CHALLENGE_ALS_1.0 Algae sampling of the project CHALLENGE-2 SCIOPS STAC Catalog 2023-01-03 2023-02-28 -70.1938725, -68.1163134, -56.8344988, -61.085064 https://cmr.earthdata.nasa.gov/search/concepts/C2723265658-SCIOPS.umm_json The objective of this sampling is to know the biodiversity of the Antarctic algae communities (macroalgae and microalgae) and their temporal changes along the South Shetland Islands and the Antarctic Peninsula. Another objective of the sampling is to know the molecular biology of certain species of the red algae group and its nuclear patterns. For all this, it is necessary to carry out sampling both in the intertidal zone and in the sublitoral zone. For this study, a total of 54 stations have been sampled. For intertidal communities, 25 x 25cm squares were taken with three replicates per community and a sample was obtained for each different species found throughout the season. For diatoms in the intertidal zone, three sediment falcon tubes were taken from the beach break area. Samples for each species were also collected within the sublitoral zone and in addition to the target species for the molecular study. Samples for each different species were also obtained in the sublitoral area and sampled in addition to the target species for molecular study. Diatoms were obtained by extracting sediment during diving or using multicore and gravity core, in which the first centimetres of sediment were obtained in a falcon tube. On the other hand, samples of epiphyte diatoms, found on benthic animals such as starfish or tunicates, were taken by scraping and later preserved in 70% alcohol. A total of 218 samples of diatoms have been obtained and frozen at -20ºC for preservation. Those taken from sediment and animals have been kept in the refrigerator at 4ºC. A total of 39 samples from squares have been taken. These samples have been classified by taxa at species level and weighed in wet weight. Qualitative biodiversity samples have been 351. These have been stored in zip bags at -20ºC. The samples for molecular studies have been 37 and preserved in three ways each sample; frozen, in Silica gel and in Carnoy (Solution of Ethanol and Glacial Acetic). Analyses and calculations of these results will be carried out later in the Antarctic campaign. proprietary
+CNDA-ESP_ANT94-0905_LIQ_05 Adaptive strategies of lichen species to cold environments: Antarctica and the Mediterranean high mountains. ALL STAC Catalog 1995-01-19 1995-02-09 -60, -63, -60, -63 https://cmr.earthdata.nasa.gov/search/concepts/C1214613278-SCIOPS.umm_json In English: At the beginning of the 1990's our ecophysiological research on Antarctic lichens was focussed on adaptations to cope with low temperatures. We assumed that low temperatures should play an important limiting role in the growth of the Maritime Antarctic tundra, which is made up of lichens and to a lesser extent of other cryptogams and two species of vascular plants. In different expeditions to the South Shetland Islands, mostly to the Spanish Antarctic Base on Livingston island, we carried out extensive field measurements of gas exchange of representative species of the tundra under natural conditions. We completed these studies with experiments under controlled conditions in the laboratory, exploring the photosynthetic response of these species to light and temperature. We combined gas exchange measurements with chlorophyll fluorescence analyses, with anatomical and ultra structural observations, and with photosynthetic pigments and relations studies. Some of the main specific goals were the comparisons between Antarctic and European populations of certain cosmopolitan lichen species, the tolerance to the simultaneous stresses of high irradiance and low temperatures, and the estimation of the primary production of some lichens during the austral summer. We concluded from these studies that the Antarctic populations were relatively less productive, that both lichens and vascular plants were remarkably resistant to the combination of high irradiances and low temperatures, and that, surprisingly, the austral summer was a period of negative carbon balance for some lichens, which required low temperatures to refrain respiration in order to reach a positive carbon balance. In our opinion, the ecological success of lichens in Antarctica is related not only to the fact that they are well adapted to low temperatures but also to the fact that they can exploit brief, unpredictable, favorable periods during the austral spring and autumn, which it is not the case for vascular plants. These studies left at least two open questions: why are the Antarctic populations so unproductive? And could the temperatures be involved in the limited growth of the Antarctic tundra through their interactions with biogeochemical cycles? The answer to these question is the main goal of our research towards the end of the 90's. Some preliminary results obtained during the 1996/1997 expedition pointed to nutrient availability as an important factor determining maximum rates of photosynthesis and, consequently, potential primary production. Comparisons between characteristic species of the tundra with species growing in the vicinities of penguin colonies or bird perches, which are local sources of nitrogen and phosphorus, confirmed to some extent the overlooked importance of nutrients versus the more commonly addressed role of low temperatures as direct determinant of primary production in terrestrial ecosystems of the maritime Antarctica. En Espanol: Al comienzo de los anos 90 nuestra investigacion ecofisiologica en liquenes antarticos estaba focalizada en las capacidades adaptativas a las bajas temperaturas. Asumimos que las bajas temperaturas jugarian un importante papel limitador en el crecimiento de la tundra antartica maritima, la cual esta formada por liquenes y por una menor cantidad de otras criptogamas y dos especies de plantas vasculares. En diferentes expediciones a las islas Shetland del Sur, la mayoria a la base antartica espanola de la isla Livingston, llevamos a cabo numerosas medidas de campo de intercambio de gases de especies representativas de la tundra bajo condiciones naturales. Completamos estos estudios con experimentos bajo condiciones controladas de laboratorio, explorando la respuesta fotosintetica de estas especies a la luz y la temperatura. Combinamos las medidas de intercambio de gases con analisis de fluorescencia en clorofila, con observaciones anatomicas y ultra estructurales, y con pigmentos fotosinteticos y estudios de relaciones. Algunos de los principales objetivos especificos fueron las comparaciones entre poblaciones Antarticas y europeas de ciertas especies de liquenes cosmopolitas, la tolerancia a la presion simultanea de alta irradiancia y bajas temperaturas, y la estimacion de la produccion primaria de algunos liquenes durante el verano austral. De estos estudios concluimos que las poblaciones antarticas eran relativamente poco productivas, que tanto liquenes como plantas vasculares eran remarcablemente resistentes a la combinacion de altas irradiancias y bajas temperaturas, y que, sorprendentemente, el verano austral era un periodo negativo de balance de carbono para algunos liquenes, los cuales requerian bajas temperaturas para abstenerse de respirar y asi alcanzar un balance de carbono positivo. En nuestra opinion el exito ecologico de los liquenes en la Antartida esta relacionado no solo con la realidad de que estan bien adaptados a las bajas temperaturas sino tambien a que ellos pueden aprovechar los breves e impredecibles, periodos favorables durante la primavera austral y el otono, lo cual no es el caso de las plantas vasculares. Estos estudios dejan al menos dos preguntas abiertas. ?Por que son las poblaciones antarticas tan poco productivas? Y ?podria la temperatura estar implicada en el crecimiento de la tundra antartica a traves de sus interacciones con los ciclos bioquimicos? Las respuestas a estas preguntas es el principal objetivo de nuestra investigacion hacia el final de los anos 90. Algunos resultados preliminares obtenidos durante la expedicion 1996/1997 apuntaban a la disponibilidad de nutrientes como un factor determinante del maximo indice de fotosintesis y, consecuentemente potencial de produccion primaria. Comparaciones entre especies caracteristicas de tundra con especies creciendo en las inmediaciones de las colonias de pinguinos o pedestales de pajaros, los cuales son fuentes locales de nitrogeno y fosforo, confirmaron hasta cierto punto la infravalorada importancia de los nutrientes contra el mas comunmente papel dirigido de las bajas temperaturas como determinante directo de la produccion primaria in ecosistemas terrestres de la Antartida maritima. proprietary
CNDP_HES_20230103_CHALLENGE_ALS_1.0 Algae sampling of the project CHALLENGE-2 ALL STAC Catalog 2023-01-03 2023-02-28 -70.1938725, -68.1163134, -56.8344988, -61.085064 https://cmr.earthdata.nasa.gov/search/concepts/C2723265658-SCIOPS.umm_json The objective of this sampling is to know the biodiversity of the Antarctic algae communities (macroalgae and microalgae) and their temporal changes along the South Shetland Islands and the Antarctic Peninsula. Another objective of the sampling is to know the molecular biology of certain species of the red algae group and its nuclear patterns. For all this, it is necessary to carry out sampling both in the intertidal zone and in the sublitoral zone. For this study, a total of 54 stations have been sampled. For intertidal communities, 25 x 25cm squares were taken with three replicates per community and a sample was obtained for each different species found throughout the season. For diatoms in the intertidal zone, three sediment falcon tubes were taken from the beach break area. Samples for each species were also collected within the sublitoral zone and in addition to the target species for the molecular study. Samples for each different species were also obtained in the sublitoral area and sampled in addition to the target species for molecular study. Diatoms were obtained by extracting sediment during diving or using multicore and gravity core, in which the first centimetres of sediment were obtained in a falcon tube. On the other hand, samples of epiphyte diatoms, found on benthic animals such as starfish or tunicates, were taken by scraping and later preserved in 70% alcohol. A total of 218 samples of diatoms have been obtained and frozen at -20ºC for preservation. Those taken from sediment and animals have been kept in the refrigerator at 4ºC. A total of 39 samples from squares have been taken. These samples have been classified by taxa at species level and weighed in wet weight. Qualitative biodiversity samples have been 351. These have been stored in zip bags at -20ºC. The samples for molecular studies have been 37 and preserved in three ways each sample; frozen, in Silica gel and in Carnoy (Solution of Ethanol and Glacial Acetic). Analyses and calculations of these results will be carried out later in the Antarctic campaign. proprietary
+CNDP_HES_20230103_CHALLENGE_ALS_1.0 Algae sampling of the project CHALLENGE-2 SCIOPS STAC Catalog 2023-01-03 2023-02-28 -70.1938725, -68.1163134, -56.8344988, -61.085064 https://cmr.earthdata.nasa.gov/search/concepts/C2723265658-SCIOPS.umm_json The objective of this sampling is to know the biodiversity of the Antarctic algae communities (macroalgae and microalgae) and their temporal changes along the South Shetland Islands and the Antarctic Peninsula. Another objective of the sampling is to know the molecular biology of certain species of the red algae group and its nuclear patterns. For all this, it is necessary to carry out sampling both in the intertidal zone and in the sublitoral zone. For this study, a total of 54 stations have been sampled. For intertidal communities, 25 x 25cm squares were taken with three replicates per community and a sample was obtained for each different species found throughout the season. For diatoms in the intertidal zone, three sediment falcon tubes were taken from the beach break area. Samples for each species were also collected within the sublitoral zone and in addition to the target species for the molecular study. Samples for each different species were also obtained in the sublitoral area and sampled in addition to the target species for molecular study. Diatoms were obtained by extracting sediment during diving or using multicore and gravity core, in which the first centimetres of sediment were obtained in a falcon tube. On the other hand, samples of epiphyte diatoms, found on benthic animals such as starfish or tunicates, were taken by scraping and later preserved in 70% alcohol. A total of 218 samples of diatoms have been obtained and frozen at -20ºC for preservation. Those taken from sediment and animals have been kept in the refrigerator at 4ºC. A total of 39 samples from squares have been taken. These samples have been classified by taxa at species level and weighed in wet weight. Qualitative biodiversity samples have been 351. These have been stored in zip bags at -20ºC. The samples for molecular studies have been 37 and preserved in three ways each sample; frozen, in Silica gel and in Carnoy (Solution of Ethanol and Glacial Acetic). Analyses and calculations of these results will be carried out later in the Antarctic campaign. proprietary
CNDP_JCI_20220103_EPOLAAR_CAM_1.0 All Sky Camera Images, Livingston Island ALL STAC Catalog 2022-01-03 2022-01-29 -60.3904851, -62.6637967, -60.3813871, -62.6617865 https://cmr.earthdata.nasa.gov/search/concepts/C2566384413-SCIOPS.umm_json Images provided by an All Sky Camera installed at the SAS Juan Carlos I on Livingston Island in 2022 proprietary
CNDP_JCI_20220103_EPOLAAR_CAM_1.0 All Sky Camera Images, Livingston Island SCIOPS STAC Catalog 2022-01-03 2022-01-29 -60.3904851, -62.6637967, -60.3813871, -62.6617865 https://cmr.earthdata.nasa.gov/search/concepts/C2566384413-SCIOPS.umm_json Images provided by an All Sky Camera installed at the SAS Juan Carlos I on Livingston Island in 2022 proprietary
CNDP_JCI_20240101_TRIPOLI_CAM_1.0 All Sky Camera Images, Livingston Island (2023) SCIOPS STAC Catalog 2024-01-01 -60.3904851, -62.6637967, -60.3813871, -62.6617865 https://cmr.earthdata.nasa.gov/search/concepts/C3069335901-SCIOPS.umm_json Images provided by an All Sky Camera installed at the SAS Juan Carlos I on Livingston Island since 2023 proprietary
@@ -5200,12 +5201,12 @@ CNES_http__cnes.fr_ark_68059_0b7a761c3e62fd4332cd4f66eff0c845_IDN_1.2 HYDROWEB e
CNES_http__cnes.fr_ark_68059_3bf5d54adb12f57809057d19c2ea4f25_IDN_1.2 HYDROWEB experiment: LAKE PRODUCT CEOS_EXTRA STAC Catalog 1992-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2226555523-CEOS_EXTRA.umm_json The products offered by the Hydroweb project consist of continuous, long-duration time-series of the levels of large lakes with surface areas over 100 km2, reservoirs and the 20 biggest rivers in the world.The operational measurement series are updated no later than 1.5 days after a new altimetry measurement becomes available. They cover about 80 large lakes and 300 measurement points along about 20 major rivers.The research measurement series are updated at regular intervals according to the progress made with processing by the LEGOS laboratory. They cover about 150 large lakes and 1,000 measurement points along about 20 major rivers.Continental waters account for only 0.65% of the total amount of water on Earth, 97% being stored in the oceans and 2.15% in the cryosphere. However, these waters have a significant impact on life on Earth and household needs. They also play a major role in climate variability. Water on Earth is continually recycled through precipitation, evaporation and run-off towards the sea. The increasingly accurate characterisation of the water cycle on land surfaces enables more accurate forecasting of the climate and more careful control of global water resources (human consumption and activities such as agriculture, urbanisation and the production of hydroelectric power, for example). Altimetry missions used are repetitive, i.e. the satellite overflow the same point at a given time interval (10, 17 or 35 days depending on the satellite). The satellite does not deviate from more than +/-1 km across its track. A given lake can be overflown by several satellites, with potentially several passes. The water level and volume time series is operationally updated less than 1.5 working days after the availability of the input altimetry data, for some lakes. Other lakes are also monitored on a research mode basis. [https://www.theia-land.fr/fr/projets/hydroweb] proprietary
CNES_http__cnes.fr_ark_68059_467a7851f76cf868d9568f8d85bd664a_IDN_1.6 JASON 2 experiment: Geophysical products CEOS_EXTRA STAC Catalog 2008-06-20 -180, -66, 180, 66 https://cmr.earthdata.nasa.gov/search/concepts/C2226555596-CEOS_EXTRA.umm_json "The JASON-2 project is a response to the international demand for programs to study and observe oceans and the climate, through a worldwide ocean observation system. It is a continuation to the TOPEX/POSEIDON and JASON-1 altimetry missions developed by CNES and NASA. Altimetry, i.e. the precise measurement of ocean surface topography, has indeed become since 1992 (launch of TOPEX/POSEIDON) an essential tool for the study of oceans on a global scale.JASON-2 is part of cooperation between CNES, EUMETSAT, NASA and NOAA. Space and ground segments of the JASON-2 mission strongly inherit from the JASON-1 mission.Onboard the JASON-2 satellite, which uses a PROTEUS platform, the payload is composed of a Poseidon-3 radar altimeter supplied by CNES, an Advanced Microwave Radiometer (AMR) supplied by NASA/JPL, and a triple system for precise orbit determination: the DORIS instrument (CNES), GPS receiver and a Laser Retroflector Array (LRA) (NASA). Three further onboard instruments (T2L2, LPT, CARMEN-2) will also be included.In order to ensure continuity and optimal inter-calibration of observations over the long term, JASON-2 flies the same orbit as JASON-1 and TOPEX/POSEIDON. Moreover, data processing is integrated into the CNES ground segment ""SALP"" (altimetry and precise positioning system), which already operates the altimetry missions TOPEX/POSEIDON, JASON-1, ENVISAT, GFO, whose data is distributed on the AVISO website.The level 2 data stored at CNES are those addressed in this description. The JASON-2 project is a response to the international demand for programs to study and observe oceans and the climate, through a worldwide ocean observation system. It is a continuation to the TOPEX/POSEIDON and JASON-1 altimetry missions developed by CNES and NASA. Altimetry, i.e. the precise measurement of ocean surface topography, has indeed become since 1992 (launch of TOPEX/POSEIDON) an essential tool for the study of oceans on a global scale.JASON-2 is part of cooperation between CNES, EUMETSAT, NASA and NOAA. Space and ground segments of the JASON-2 mission strongly inherit from the JASON-1 mission.Onboard the JASON-2 satellite, which uses a PROTEUS platform, the payload is composed of a Poseidon-3 radar altimeter supplied by CNES, an Advanced Microwave Radiometer (AMR) supplied by NASA/JPL, and a triple system for precise orbit determination: the DORIS instrument (CNES), GPS receiver and a Laser Retroflector Array (LRA) (NASA). Three further onboard instruments (T2L2, LPT, CARMEN-2) will also be included.In order to ensure continuity and optimal inter-calibration of observations over the long term, JASON-2 will fly the same orbit as JASON-1 and TOPEX/POSEIDON. Moreover, data processing will be integrated into the CNES ground segment ""SALP"" (altimetry and precise positioning system), which already operates the altimetry missions TOPEX/POSEIDON, JASON-1, ENVISAT, GFO, whose data is distributed on the AVISO website.The level 2 data stored at CNES are those addressed in this description. The data described here are part of the European Directive INSPIRE. [http://smsc.cnes.fr/JASON2/] [http://smsc.cnes.fr/JASON2/]" proprietary
CNES_http__cnes.fr_ark_68059_54736f5916e9134386cb9725dbbe67ae_IDN_1.5 JASON 1 experiment: Geophysical products CEOS_EXTRA STAC Catalog 2001-12-07 2013-07-03 -180, -66, 180, 66 https://cmr.earthdata.nasa.gov/search/concepts/C2226555543-CEOS_EXTRA.umm_json "JASON-1 is the follow-on to Topex/Poseidon, whose main features it has inherited (orbit, instruments, measurement accuracy, etc.). JASON-1 is the result of close international cooperation between space agencies (CNES and NASA), industry and data users working to accomplish a benchmark mission in terms of data quality and science and economic return. JASON-1 flies on the same orbit as TOPEX/POSEIDON to ensure a continuity and an optimal inter comparison for long term observations. The data processing is integrated to the CNES ""SALP"" (Systeme d'Altimetrie et de Localisation Precise) Ground Segment, which operates many other missions (TOPEX/POSEIDON, ENVISAT, GFO altimetry missions, JASON-2, SARAL...) whose data are distributed on AVISO web site. The level 2 data stored at CNES are those addressed in this description. JASON-1 is the follow-on to Topex/Poseidon, whose main features it has inherited (orbit, instruments, measurement accuracy, etc.). JASON-1 is the result of close international cooperation between space agencies (CNES and NASA), industry and data users working to accomplish a benchmark mission in terms of data quality and science and economic return. JASON-1 flies on the same orbit as TOPEX/POSEIDON to ensure a continuity and an optimal inter comparison for long term observations. The data processing is integrated to the CNES ""SALP"" (Systeme d'Altimetrie et de Localisation Precise) Ground Segment, which operates many other missions (TOPEX/POSEIDON, ENVISAT, GFO altimetry missions, JASON-2, SARAL...) whose data are distributed on AVISO web site.The level 2 data stored at CNES are those addressed in this description. [http://smsc.cnes.fr/JASON/index.htm] [http://smsc.cnes.fr/JASON/index.htm]" proprietary
-CNNADC_1999_ARCTIC_MAP 1:5000000 map of Arctic Ocean area SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214587206-SCIOPS.umm_json This dataset is maps of Arctic Ocean area,their scales are 1:5000000,1:10000000 and 1:40000000. proprietary
CNNADC_1999_ARCTIC_MAP 1:5000000 map of Arctic Ocean area ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214587206-SCIOPS.umm_json This dataset is maps of Arctic Ocean area,their scales are 1:5000000,1:10000000 and 1:40000000. proprietary
+CNNADC_1999_ARCTIC_MAP 1:5000000 map of Arctic Ocean area SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214587206-SCIOPS.umm_json This dataset is maps of Arctic Ocean area,their scales are 1:5000000,1:10000000 and 1:40000000. proprietary
CNNADC_2006_ZhongshanStation_Antarctica 2006 Zhongshan station earth tide data ALL STAC Catalog 2006-04-01 2006-11-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214587196-SCIOPS.umm_json This is Laseaman hills earth tide data from March to November 2006 by using Lacoste ET gravimeter. proprietary
CNNADC_2006_ZhongshanStation_Antarctica 2006 Zhongshan station earth tide data SCIOPS STAC Catalog 2006-04-01 2006-11-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214587196-SCIOPS.umm_json This is Laseaman hills earth tide data from March to November 2006 by using Lacoste ET gravimeter. proprietary
-CNNADC_2006_ZhongshanStation_Antarctica_2006 2006 Zhongshan station earth tide data - CNNADC_2006_ZhongshanStation_Antarctica_2006 ALL STAC Catalog 2006-04-01 2006-11-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1221420502-SCIOPS.umm_json This is Laseaman hill's earth tide data from March to November 2006 by using Lacoste ET gravimeter. proprietary
CNNADC_2006_ZhongshanStation_Antarctica_2006 2006 Zhongshan station earth tide data - CNNADC_2006_ZhongshanStation_Antarctica_2006 SCIOPS STAC Catalog 2006-04-01 2006-11-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1221420502-SCIOPS.umm_json This is Laseaman hill's earth tide data from March to November 2006 by using Lacoste ET gravimeter. proprietary
+CNNADC_2006_ZhongshanStation_Antarctica_2006 2006 Zhongshan station earth tide data - CNNADC_2006_ZhongshanStation_Antarctica_2006 ALL STAC Catalog 2006-04-01 2006-11-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1221420502-SCIOPS.umm_json This is Laseaman hill's earth tide data from March to November 2006 by using Lacoste ET gravimeter. proprietary
CO2Fluxes_Arctic_Boreal_Domain_2377_1 Machine learning-based Arctic-boreal terrestrial ecosystem CO2 fluxes, 2001-2020 ORNL_CLOUD STAC Catalog 2001-01-01 2020-12-31 -180, 33.68, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3261062541-ORNL_CLOUD.umm_json This dataset provides gridded estimates of gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem CO2 exchange (NEE) across the circumpolar terrestrial Arctic-boreal region at a 1-km spatial resolution. Monthly CO2 flux data from 2001 to 2020 were generated using terrestrial eddy covariance and chamber CO2 flux observations, combined with geospatial meteorological, remote sensing, topographical and soil data, all within a random forest modeling framework. Aggregated average annual NEE, average annual NEE with direct fire emissions added based on the Global Fire Emissions Database (GFED) product, and temporal trends in annual NEE rasters over 2002-2020 are also included. The data are provided in NetCDF and GeoTIFF formats. proprietary
COASTAL_0 COASTAL Project OB_DAAC STAC Catalog 2000-02-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360192-OB_DAAC.umm_json Measurements made along the Eastern Seaboard of the United States, North Atlantic Bight, and Gulf Stream between 2000 and 2010. proprietary
COMEX_AJAX_CO2_CH4_2347_1 COMEX: Flight Information for AJAX Airborne In Situ CO2 and CH4, 2014-2015, USA ORNL_CLOUD STAC Catalog 2014-05-19 2015-08-19 -122.06, 34.13, -116.26, 38.89 https://cmr.earthdata.nasa.gov/search/concepts/C3104478810-ORNL_CLOUD.umm_json This dataset provides information to access NASA Earthdata published flight data and flight information collected by the Alpha Jet Atmospheric eXperiment (AJAX) and associated with the COMEX project in 2014-2015. The file lists information for COMEX-related datasets that has been subsetted from AJAX collections archived through NASA's Atmospheric Science Data Center. AJAX data are not otherwise replicated in this dataset. AJAX is a partnership between NASA's Ames Research Center and H211, L.L.C., which conducted in-situ measurements over California, Nevada, and the coastal Pacific in support of satellite validation. During COMEX data collection, a Picarro greenhouse gas (GHG) sensor was mounted on an Alpha Jet, a tactical strike fighter developed by Dassault-Breguet and Dornier through a German-French NATO collaboration. The GHG sensor made repeat measurements in California and Nevada. In situ data included measurements of CO2, CH4, and H2O at 2 Hz or CH4 and H2O at 10 Hz with a strategy of characterizing atmospheric structure over ocean and land, and vertical profiles to at least 5000 m. Ancillary data, including O3, formaldehyde, and meteorological profiles, were also collected. This dataset provides filenames, spatiotemporal bounds, and download URLs for accessing these in situ data. This information is provided in comma separated values (CSV) format. proprietary
@@ -5229,15 +5230,15 @@ CPEXCV-HALO_DC8_1 CPEX-CV HALO Aerosol and Water Vapor Profiles and Images LARC_
CPEXCV_Cloud_AircraftInSitu_DC8_Data_1 CPEX-CV DC-8 Aircraft In-situ Cloud Data LARC_ASDC STAC Catalog 2022-09-06 2022-09-30 -118.16, 1.84, -14.93, 39.35 https://cmr.earthdata.nasa.gov/search/concepts/C2683359409-LARC_ASDC.umm_json CPEXCV_Cloud_AircraftInSitu_DC8_Data is the in-situ cloud data collected during the Convective Processes Experiment - Cabo Verde (CPEX-CV) onboard the DC-8 aircraft. Data from the Cloud and Aerosol Spectrometer (CAS) instrument is featured in this collection. Data collection for this product is complete. Seeking to better understand atmospheric processes in regions with little data, the Convective Processes Experiment – Cabo Verde (CPEX-CV) campaign conducted by NASA is a continuation of the CPEX – Aerosols & Winds (CPEX-AW) campaign that took place between August to September 2021. The campaign will take place between 1-30 September 2022 and will operate out of Sal Island, Cabo Verde with the primary goal of investigating atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. CPEX-CV will work towards its goal by addressing four main science objectives. The first goal is to improve understanding of the interaction between large-scale environmental forcings such as the Intertropical Convergence Zone (ITCZ), Saharan Air Layer, African easterly waves, and mid-level African easterly jet, and the lifecycle and properties of convective cloud systems, including tropical cyclone precursors, in the tropical East Atlantic region. Next, observations will be made about how local kinematic and thermodynamic conditions, including the vertical structure and variability of the marine boundary layer, relate to the initiation and lifecycle of convective cloud systems and their processes. Third, CPEX-CV will investigate how dynamical and convective processes affect size dependent Saharan dust vertical structure, long-range Saharan dust transport, and boundary layer exchange pathways. The last objective will be to assess the impact of CPEX-CV observations of atmospheric winds, thermodynamics, clouds, and aerosols on the prediction of tropical Atlantic weather systems and validate and interpret spaceborne remote sensors that provide similar measurements. To achieve these objectives, the NASA DC-8 aircraft will be deployed with remote sensing instruments and dropsondes that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. Instruments onboard the aircraft include the Airborne Third Generation Precipitation Radar (APR-3), lidars such as the Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), Advanced Vertical Atmospheric Profiling System (AVAPS) dropsonde system, Cloud Aerosol and Precipitation Spectrometer (CAPS), and the Airborne In-situ and Radio Occultation (AIRO) instrument. Measurements taken by CPEX-CV will assist in moving science forward from previous CPEX and CPEX-AW missions, the calibration and validation of satellite measurements, and the development of airborne sensors, especially those with potential for satellite deployment. proprietary
CPEXCV_Merge_Data_1 CPEX-CV Merge Data Files LARC_ASDC STAC Catalog 2022-09-06 2022-10-02 -118.16, 1.84, -14.93, 39.35 https://cmr.earthdata.nasa.gov/search/concepts/C2683363960-LARC_ASDC.umm_json CPEXCV_Merge_DC8_Data are pre-generated aircraft merge data files created utilizing data collected during the Convective Processes Experiment - Cabo Verde (CPEX-CV) onboard the DC-8 aircraft. Data collection for this product is complete. Seeking to better understand atmospheric processes in regions with little data, the Convective Processes Experiment – Cabo Verde (CPEX-CV) campaign conducted by NASA is a continuation of the CPEX – Aerosols & Winds (CPEX-AW) campaign that took place between August to September 2021. The campaign will take place between 1-30 September 2022 and will operate out of Sal Island, Cabo Verde with the primary goal of investigating atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. CPEX-CV will work towards its goal by addressing four main science objectives. The first goal is to improve understanding of the interaction between large-scale environmental forcings such as the Intertropical Convergence Zone (ITCZ), Saharan Air Layer, African easterly waves, and mid-level African easterly jet, and the lifecycle and properties of convective cloud systems, including tropical cyclone precursors, in the tropical East Atlantic region. Next, observations will be made about how local kinematic and thermodynamic conditions, including the vertical structure and variability of the marine boundary layer, relate to the initiation and lifecycle of convective cloud systems and their processes. Third, CPEX-CV will investigate how dynamical and convective processes affect size dependent Saharan dust vertical structure, long-range Saharan dust transport, and boundary layer exchange pathways. The last objective will be to assess the impact of CPEX-CV observations of atmospheric winds, thermodynamics, clouds, and aerosols on the prediction of tropical Atlantic weather systems and validate and interpret spaceborne remote sensors that provide similar measurements. To achieve these objectives, the NASA DC-8 aircraft will be deployed with remote sensing instruments and dropsondes that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. Instruments onboard the aircraft include the Airborne Third Generation Precipitation Radar (APR-3), lidars such as the Doppler Aerosol WiNd Lidar (DAWN), High Altitude Lidar Observatory (HALO), High Altitude Monolithic Microwave Integrated Circuit (MMIC) Sounding Radiometer (HAMSR), Advanced Vertical Atmospheric Profiling System (AVAPS) dropsonde system, Cloud Aerosol and Precipitation Spectrometer (CAPS), and the Airborne In-situ and Radio Occultation (AIRO) instrument. Measurements taken by CPEX-CV will assist in moving science forward from previous CPEX and CPEX-AW missions, the calibration and validation of satellite measurements, and the development of airborne sensors, especially those with potential for satellite deployment. proprietary
CPEX_DAWN_DC8_1 CPEX DAWN WIND PROFILES LARC_ASDC STAC Catalog 2017-05-27 -97, 16, -69, 29 https://cmr.earthdata.nasa.gov/search/concepts/C1604278273-LARC_ASDC.umm_json During 25 May – 24 June 2017, NASA funded and conducted the Convective Processes Experiment (CPEX) which was based out of Ft. Lauderdale, FL and used a suite of instruments aboard a NASA DC-8 aircraft to investigate convective process and circulations over tropical waters. A main objective of CPEX was to obtain a comprehensive set of temperature, humidity and, particularly, wind observations in the vicinity of scattered and organized deep convection in all phases of the convective life cycle. The featured instrument of the airborne campaign was NASA’s Doppler Aerosol WiNd (DAWN) lidar but also included dropsondes, the Airborne Second Generation Precipitation Radar (APR-2), the High Altitude MMIC Sounding Radiometer (HAMSR), the Microwave Temperature and Humidity Profiler (MTHP), and the Microwave Atmospheric Sounder for Cubesat (MASC). In total, the CPEX campaign flew 16 missions over the Atlantic Ocean, Caribbean Sea and the Gulf of Mexico and included missions investigating undisturbed conditions, scattered convection, organized convection and the environment of a tropical storm. The DAWN (and Dropsonde) wind measurement collected during CPEX have provided a unique set of wind profiles to be used in analysis and model assimilation and prediction studies. CPEX also utilized the High Definition Sounding System (HDSS) dropsonde delivery system developed by Yankee Environmental Services to drop almost 300 dropsondes to obtain additional high-resolution vertical wind profiles during most missions. These dropsondes also provided needed calibration/validation for the much newer DAWN measurements. proprietary
-CPL_ABL_Top_Height_1825_1 ACT-America: CPL-derived Atmospheric Boundary Layer Top Height, Eastern US, 2016-2018 ORNL_CLOUD STAC Catalog 2016-07-18 2018-05-20 -106.49, 27.25, -64, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2677226029-ORNL_CLOUD.umm_json This dataset consists of the atmospheric boundary layer (ABL) top heights and the altitudes of the two additional aerosol layers (in km above mean sea level) derived from Cloud Physics Lidar (CPL) measurements using the Haar wavelet transform method. The CPL instrument was deployed onboard NASA's C-130 aircraft to obtain aerosol backscatter profiles during four ACT-America field campaigns (Summer 2016, Winter 2017, Fall 2017, and Spring 2018). CPL is a backscatter lidar designed to operate simultaneously at three wavelengths. The profiles were collected at 4-second temporal and 30 m vertical resolutions. The time resolution of the provided CPL-derived ABL top heights and other aerosol layers are 8 seconds. proprietary
CPL_ABL_Top_Height_1825_1 ACT-America: CPL-derived Atmospheric Boundary Layer Top Height, Eastern US, 2016-2018 ALL STAC Catalog 2016-07-18 2018-05-20 -106.49, 27.25, -64, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2677226029-ORNL_CLOUD.umm_json This dataset consists of the atmospheric boundary layer (ABL) top heights and the altitudes of the two additional aerosol layers (in km above mean sea level) derived from Cloud Physics Lidar (CPL) measurements using the Haar wavelet transform method. The CPL instrument was deployed onboard NASA's C-130 aircraft to obtain aerosol backscatter profiles during four ACT-America field campaigns (Summer 2016, Winter 2017, Fall 2017, and Spring 2018). CPL is a backscatter lidar designed to operate simultaneously at three wavelengths. The profiles were collected at 4-second temporal and 30 m vertical resolutions. The time resolution of the provided CPL-derived ABL top heights and other aerosol layers are 8 seconds. proprietary
+CPL_ABL_Top_Height_1825_1 ACT-America: CPL-derived Atmospheric Boundary Layer Top Height, Eastern US, 2016-2018 ORNL_CLOUD STAC Catalog 2016-07-18 2018-05-20 -106.49, 27.25, -64, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2677226029-ORNL_CLOUD.umm_json This dataset consists of the atmospheric boundary layer (ABL) top heights and the altitudes of the two additional aerosol layers (in km above mean sea level) derived from Cloud Physics Lidar (CPL) measurements using the Haar wavelet transform method. The CPL instrument was deployed onboard NASA's C-130 aircraft to obtain aerosol backscatter profiles during four ACT-America field campaigns (Summer 2016, Winter 2017, Fall 2017, and Spring 2018). CPL is a backscatter lidar designed to operate simultaneously at three wavelengths. The profiles were collected at 4-second temporal and 30 m vertical resolutions. The time resolution of the provided CPL-derived ABL top heights and other aerosol layers are 8 seconds. proprietary
CRP_0 Remote sensing and field-based studies in the coastal Gulf of Alaska adjacent to the Copper River OB_DAAC STAC Catalog 2010-03-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360197-OB_DAAC.umm_json The coastal marine system of the Gulf of Alaska (GoA) is connected hydrologically, biogeochemically and biologically with the upriver systems of the Copper River basin. Glacially weathered rock yields highly reactive particulate iron (Fe) into rivers that yields an important flux of bioavailable iron to the open ocean. North Pacific deep water is extremely nutrient-rich, and upwelling of deep water in estuaries and at river plumes results in very high biological productivity. The world-renowned fisheries in the vicinity of the Copper River region of the GoA thrive, in part, due to pristine riparian and lacustrine habitats for spawning and rearing. Pacific salmon spawn in the upper reaches of coastal watersheds, and their progeny spend a significant amount of time in freshwater habitats before migrating to the ocean. Prior to making the transition to a fully marine lifestyle, salmon smolts benefit from the enhanced biological productivity at plumes and within estuaries.The coastal GoA region is currently experiencing rapid and accelerating climate change as manifested by rapid recession of glaciers; climate models predict up to a 40% increase in river runoff from Alaska rivers by 2050. Over the coming decades an increase in glacier-dominated river discharge is likely, followed by decreases as glaciers recede. In addition, there will be a change in the seasonality of river discharge. Changes in freshwater discharge are likely to alter the flux of reactive particulate Fe, as well as dissolved organic and inorganic carbon (DIC and DOC) from glacier-dominated rivers, as well as the nitrate flux to surface water from estuarine upwelling, with cascading effects throughout the ecosystem. Furthermore, the freshwater supply of dissolved organic nitrogen (DON) and nitrate may increase over time due to recolonization of deglaciated watersheds by opportunistic nitrogen-fixing plants. New habitats for salmon and other members of the headwater ecosystem are likely to become available as glaciers retreat and as permafrost melts in the upper watershed. Conversely, decreased permafrost and decreased river flows may lead to the loss of habitat as freshwater sources dry seasonally or permanently. In addition, the positive or negative feedbacks to rising atmospheric CO2 concentrations, which are responsible for the warming and the subsequent melting of the glaciers, have not been addressed. As landscapes become ice free, the evolution of vegetation on these areas may act as net C sinks./The specific changes that will be manifested in the Copper River watershed and associated marine systems are difficult to predict and monitor. Using NASA products and a combination of remote sensing and field-based studies, this project seeks to establish a framework to document and monitor physical, biogeochemical biological changes in the coastal Gulf of Alaska adjacent to the Copper River. proprietary
CSA_ortho_1 Casey Station Area Orthophoto AU_AADC STAC Catalog 2000-12-30 2000-12-30 110.507, -66.287, 110.546, -66.274 https://cmr.earthdata.nasa.gov/search/concepts/C1214313443-AU_AADC.umm_json The orthophoto is a rectified georeferenced corrected image of the Casey Station Area. Distortions due to relief and camera have been removed. This orthophoto is shown in a map which is available from the SCAR Map Catalogue. proprietary
CSIRO_AR_GASLAB_1 Concentration and isotopic measurements of radiatively important gases in the southern atmosphere AU_AADC STAC Catalog 1984-05-01 62, -90, 159, -41 https://cmr.earthdata.nasa.gov/search/concepts/C1214308510-AU_AADC.umm_json Australian Antarctic Division project #124 monitors the background level of major greenhouse gases, and related species (carbon dioxide, methane, carbon monoxide, nitrous oxide, hydrogen, and carbon dioxide isotopes, oxygen), at a number of Antarctic sites. Samples of air are collected and returned to CSIRO Atmospheric Research for analysis. Radiocarbon and oxygen are measured by international collaborators. Approximately 4 samples are collected from each station per month. The greenhouse gases released by human activity and most implicated in global climate change, are long lived and well mixed in the atmosphere. The Antarctic regions, remote from industrial and land plant activity are ideally located to measure result of global changes in the gases. The CSIRO sampling network represents the most comprehensive, long running Southern hemisphere program. With continuing innovation in measurement and interpretive models, it is ideally positioned to detect possible climate induced regional changes in carbon uptake, as well as monitor global changes. It also provides essential background information to the new challenge of monitoring integrated emissions from the Australian continent. Data from this project have also been incorporated into State of the Environment Indicator 11, Atmospheric concentrations of greenhouse gas species. See the link below for further details. The download file contains both the individual flask data measurements and also monthly means derived from these. The monthly mean data are presented in the State Of Environment indicator linked below. The monthly mean files are labelled sss_mm.xxx where sss is the site code and xxx is the species identifier. An example for Cape Grim for Methane would be cga_mm.ch4. A number of readme files are also provided in the download for further information. Taken from the 2008-2009 Progress Report: Progress against objectives: Concentrations of CO2, CO, CH4, H2, and N2O, and the isotopes 13C and 18O in CO2, have been made in flask air samples collected at ~2 week intervals at Mawson, Casey, and Macquarie Island. In addition, at Macquarie Island, continuous CO2 measurements and sampling for the O2/N2 ratio and the 14C isotope of CO2 were made. The data have been calibrated and quality controlled for incorporation into global data sets, for use in detecting spatial and temporal trends and in model inversions to infer fluxes. proprietary
CSIRO_Albatross_fish Albatross Bay Fish Data 1986-1988 ALL STAC Catalog 1986-08-11 1988-11-15 141.5, -13, 142, -12.5 https://cmr.earthdata.nasa.gov/search/concepts/C2226653611-CEOS_EXTRA.umm_json "7, four- to five-day cruises were undertaken using the vessel ""Jacqueline D"" in Albatross Bay, Gulf of Carpentaria between August 1986 and November 1988, using a random stratified trawl survey to measure fish species composition and abundance. Four depth zones between 7 and 45 m were sampled during both day and night. Approximately 890,000 fish of 237 species were collected, of which the bulk were made up of 25 species. The dominant families were Leignathidae, Haemulidae and Clupeidae, with Sciaenidae and Dasyatidae important at night. Leiognathus bindus was the most abundant species, while Caranx bucculentus was the most frequently caught (96% of all trawls). The suite of fishes was separately analysed for occurrence of prawn predators. This metadata record is sourced from 'MarLIN', the CSIRO Marine Laboratories Information Network. Information was obtained from http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=1621 . The originating project was the Tropical Fish Ecology Project: Gulf of Carpenteria studies. The Tropical Fish Ecology project over this time period carried out work on fish as peneid prawn predators in Albatross Bay and the Embley Estuary, and as tiger prawn predators at Groote Eylandt; fish surveys of the Gulf of Carpentaria; and the biology of tuna baitfish in the Solomon Islands, Kiribati, and the Maldives (work commissioned by ACIAR)." proprietary
CSIRO_Albatross_fish Albatross Bay Fish Data 1986-1988 CEOS_EXTRA STAC Catalog 1986-08-11 1988-11-15 141.5, -13, 142, -12.5 https://cmr.earthdata.nasa.gov/search/concepts/C2226653611-CEOS_EXTRA.umm_json "7, four- to five-day cruises were undertaken using the vessel ""Jacqueline D"" in Albatross Bay, Gulf of Carpentaria between August 1986 and November 1988, using a random stratified trawl survey to measure fish species composition and abundance. Four depth zones between 7 and 45 m were sampled during both day and night. Approximately 890,000 fish of 237 species were collected, of which the bulk were made up of 25 species. The dominant families were Leignathidae, Haemulidae and Clupeidae, with Sciaenidae and Dasyatidae important at night. Leiognathus bindus was the most abundant species, while Caranx bucculentus was the most frequently caught (96% of all trawls). The suite of fishes was separately analysed for occurrence of prawn predators. This metadata record is sourced from 'MarLIN', the CSIRO Marine Laboratories Information Network. Information was obtained from http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=1621 . The originating project was the Tropical Fish Ecology Project: Gulf of Carpenteria studies. The Tropical Fish Ecology project over this time period carried out work on fish as peneid prawn predators in Albatross Bay and the Embley Estuary, and as tiger prawn predators at Groote Eylandt; fish surveys of the Gulf of Carpentaria; and the biology of tuna baitfish in the Solomon Islands, Kiribati, and the Maldives (work commissioned by ACIAR)." proprietary
-CSIRO_Albatross_primaryprod Albatross Bay Primary Productivity ALL STAC Catalog 1986-01-01 1992-12-31 141.5, -13, 142, -12.5 https://cmr.earthdata.nasa.gov/search/concepts/C2226653609-CEOS_EXTRA.umm_json Yearly sampling from 1986 to 1992 at 4 stations was carried out in Albatross Bay, Gulf of Carpentaria, plus one year with 4 sampling times at 20 stations. Primary productivity in the water column was measured. This metadata record is sourced from 'MarLIN', the CSIRO Marine Laboratories Information Network. Information was obtained from: http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=1664 . The originating project was the Tropical Fish Ecology Project: Gulf of Carpenteria studies. The Tropical Fish Ecology project over this time period carried out work on fish as peneid prawn predators in Albatross Bay and the Embley Estuary, and as tiger prawn predators at Groote Eylandt; fish surveys of the Gulf of Carpentaria; and the biology of tuna baitfish in the Solomon Islands, Kiribati, and the Maldives (work commissioned by ACIAR). proprietary
CSIRO_Albatross_primaryprod Albatross Bay Primary Productivity CEOS_EXTRA STAC Catalog 1986-01-01 1992-12-31 141.5, -13, 142, -12.5 https://cmr.earthdata.nasa.gov/search/concepts/C2226653609-CEOS_EXTRA.umm_json Yearly sampling from 1986 to 1992 at 4 stations was carried out in Albatross Bay, Gulf of Carpentaria, plus one year with 4 sampling times at 20 stations. Primary productivity in the water column was measured. This metadata record is sourced from 'MarLIN', the CSIRO Marine Laboratories Information Network. Information was obtained from: http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=1664 . The originating project was the Tropical Fish Ecology Project: Gulf of Carpenteria studies. The Tropical Fish Ecology project over this time period carried out work on fish as peneid prawn predators in Albatross Bay and the Embley Estuary, and as tiger prawn predators at Groote Eylandt; fish surveys of the Gulf of Carpentaria; and the biology of tuna baitfish in the Solomon Islands, Kiribati, and the Maldives (work commissioned by ACIAR). proprietary
+CSIRO_Albatross_primaryprod Albatross Bay Primary Productivity ALL STAC Catalog 1986-01-01 1992-12-31 141.5, -13, 142, -12.5 https://cmr.earthdata.nasa.gov/search/concepts/C2226653609-CEOS_EXTRA.umm_json Yearly sampling from 1986 to 1992 at 4 stations was carried out in Albatross Bay, Gulf of Carpentaria, plus one year with 4 sampling times at 20 stations. Primary productivity in the water column was measured. This metadata record is sourced from 'MarLIN', the CSIRO Marine Laboratories Information Network. Information was obtained from: http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=1664 . The originating project was the Tropical Fish Ecology Project: Gulf of Carpenteria studies. The Tropical Fish Ecology project over this time period carried out work on fish as peneid prawn predators in Albatross Bay and the Embley Estuary, and as tiger prawn predators at Groote Eylandt; fish surveys of the Gulf of Carpentaria; and the biology of tuna baitfish in the Solomon Islands, Kiribati, and the Maldives (work commissioned by ACIAR). proprietary
CSIRO_PortLincoln Baseline Biological Port Survey - Port Lincoln, May-June 1996 CEOS_EXTRA STAC Catalog 1996-05-27 1996-06-01 135.5, -35, 136, -34.5 https://cmr.earthdata.nasa.gov/search/concepts/C2226653612-CEOS_EXTRA.umm_json This dataset contains the result of a biological baseline survey of the port region of Port Lincoln, South Australia, carried out in May-June 1996 by CSIRO Marine Research Centre for Research on Introduced Marine Pests (CRIMP). Collection methods employed include pylon scrapings, sediment cores, crab traps, plankton nets, and qualitative visual inspection and photographs (both still and video). Voucher specimens have been incorporated into collections of CMR, Hobart. Taxonomic groups surveyed include marine invertebrates, fishes, phytoplankton, macroalgae, and marine vegetation. This dataset forms part of a series of Port Surveys conducted by CRIMP over the period 1996 to present. This metadata record is sourced from 'MarLIN', the CSIRO Marine Laboratories Information Network. Additional information for this dataset may be available via the original MarLIN metadata entry. proprietary
CSIRO_adultprawn Albatross Bay Adult Prawn Data 1986-1992 ALL STAC Catalog 1986-03-01 1992-04-01 141.5, -13, 142, -12.5 https://cmr.earthdata.nasa.gov/search/concepts/C2226653618-CEOS_EXTRA.umm_json Adult prawn species, size, sex, reproductive stage, moult stage, and parasites were measured at 20 stations in Albatross Bay, Gulf of Carpentaria. Sampling was carried out monthly between 1986 and 1992. This metadata record is sourced from 'MarLIN', the CSIRO Marine Laboratories Information Networ Information was obtained from http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=1361 proprietary
CSIRO_adultprawn Albatross Bay Adult Prawn Data 1986-1992 CEOS_EXTRA STAC Catalog 1986-03-01 1992-04-01 141.5, -13, 142, -12.5 https://cmr.earthdata.nasa.gov/search/concepts/C2226653618-CEOS_EXTRA.umm_json Adult prawn species, size, sex, reproductive stage, moult stage, and parasites were measured at 20 stations in Albatross Bay, Gulf of Carpentaria. Sampling was carried out monthly between 1986 and 1992. This metadata record is sourced from 'MarLIN', the CSIRO Marine Laboratories Information Networ Information was obtained from http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=1361 proprietary
@@ -5245,10 +5246,10 @@ CSIRO_phytoplankton Albatross Bay Phytoplankton Data ALL STAC Catalog 1986-03-01
CSIRO_phytoplankton Albatross Bay Phytoplankton Data CEOS_EXTRA STAC Catalog 1986-03-01 1992-04-01 141.5, -13, 142, -12.5 https://cmr.earthdata.nasa.gov/search/concepts/C2226653608-CEOS_EXTRA.umm_json Monthly cruises were carried out between March 1986 and April 1992, at four stations in Albatross Bay, Gulf of Carpentaria. Phytoplankton taxonomic groups were identified. This metadata record is sourced from 'MarLIN', the CSIRO Marine Laboratories Information Network. Information was obtained from: http://www.marine.csiro.au/marq/edd_search.Browse_Citation?txtSession=1582. The originating project was the Tropical Fish Ecology Project: Gulf of Carpenteria studies. The Tropical Fish Ecology project over this time period carried out work on fish as peneid prawn predators in Albatross Bay and the Embley Estuary, and as tiger prawn predators at Groote Eylandt; fish surveys of the Gulf of Carpentaria; and the biology of tuna baitfish in the Solomon Islands, Kiribati, and the Maldives (work commissioned by ACIAR). proprietary
CSIRO_portland Baseline Portland Biological Survey, April-May 1996 CEOS_EXTRA STAC Catalog 1996-04-30 1996-05-05 141.5, -38.5, 142, -38 https://cmr.earthdata.nasa.gov/search/concepts/C2226653622-CEOS_EXTRA.umm_json This dataset contains the result of a biological baseline survey of the port region of Portland, Victoria, carried out in April-May 1996 by CSIRO Marine Research Centre for Research on Introduced Marine Pests (CRIMP). Collection methods employed include pylon scrapings, sediment cores, crab traps, plankton nets, and qualitative visual inspection and photographs (both still and video). Voucher specimens have been incorporated into collections of the Museum of Victoria and CMR, Hobart. Taxonomic groups surveyed include marine invertebrates, fishes, phytoplankton, macroalgae, and marine vegetation. This dataset forms part of a series of Port Surveys conducted by CRIMP over the period 1996 to present. This metadata record is sourced from 'MarLIN', the CSIRO Marine Laboratories Information Network. Additional information for this dataset may be available via the original MarLIN metadata entry (see on-line links). proprietary
CSU Synthetic Attribution Benchmark Dataset_1 CSU Synthetic Attribution Benchmark Dataset MLHUB STAC Catalog 2020-01-01 2023-01-01 -179.5, -89.5, 179.5, 89.5 https://cmr.earthdata.nasa.gov/search/concepts/C2781411899-MLHUB.umm_json This is a synthetic dataset that can be used by users that are interested in benchmarking methods of explainable artificial intelligence (XAI) for geoscientific applications. The dataset is specifically inspired from a climate forecasting setting (seasonal timescales) where the task is to predict regional climate variability given global climate information lagged in time. The dataset consists of a synthetic input X (series of 2D arrays of random fields drawn from a multivariate normal distribution) and a synthetic output Y (scalar series) generated by using a nonlinear function F: R^d -> R.
The synthetic input aims to represent temporally independent realizations of anomalous global fields of sea surface temperature, the synthetic output series represents some type of regional climate variability that is of interest (temperature, precipitation totals, etc.) and the function F is a simplification of the climate system.
Since the nonlinear function F that is used to generate the output given the input is known, we also derive and provide the attribution of each output value to the corresponding input features. Using this synthetic dataset users can train any AI model to predict Y given X and then implement XAI methods to interpret it. Based on the “ground truth” of attribution of F the user can assess the faithfulness of any XAI method.
NOTE: the spatial configuration of the observations in the NetCDF database file conform to the planetocentric coordinate system (89.5N - 89.5S, 0.5E - 359.5E), where longitude is measured in the positive heading east from the prime meridian. proprietary
-CSU_fueltreatment_Fontainebleauwildfirestudy 1999 Fontainebleau Wildfire study SCIOPS STAC Catalog 1970-01-01 -88.71972, 30.401943, -88.71972, 30.401943 https://cmr.earthdata.nasa.gov/search/concepts/C1214620907-SCIOPS.umm_json The data are from the 1999 Fontainebleau wildfire that burned into an area that had previously been treated with 3 prescribed fires (1988, 1992, and 1998) in the Mississippi Sandhill Crane National Wildlife Refuge. Nine plots were established in both the treated area and an adjacent untreated area. Data collected describe stand conditions and fire severity at each plot. The data were collected to assess the effect of repeated prescribed burn treatments on stand conditions and subsequent wildfire severity. proprietary
CSU_fueltreatment_Fontainebleauwildfirestudy 1999 Fontainebleau Wildfire study ALL STAC Catalog 1970-01-01 -88.71972, 30.401943, -88.71972, 30.401943 https://cmr.earthdata.nasa.gov/search/concepts/C1214620907-SCIOPS.umm_json The data are from the 1999 Fontainebleau wildfire that burned into an area that had previously been treated with 3 prescribed fires (1988, 1992, and 1998) in the Mississippi Sandhill Crane National Wildlife Refuge. Nine plots were established in both the treated area and an adjacent untreated area. Data collected describe stand conditions and fire severity at each plot. The data were collected to assess the effect of repeated prescribed burn treatments on stand conditions and subsequent wildfire severity. proprietary
-CSU_fueltreatment_HiMeadow 2000 Hi Meadow Wildfire Study ALL STAC Catalog 1970-01-01 -105.372, 39.368, -105.337, 39.403 https://cmr.earthdata.nasa.gov/search/concepts/C1214620839-SCIOPS.umm_json The data are from the 2000 Hi Meadow wildfire that burned into an area of the Pike National Forest that had received extensive fuel treatments since 1990 that included mechanical thinning and prescribed burning. Twelve plot pairs were established that straddled the fuel treatment boundaries. Data collected describe stand conditions and fire severity at each plot. The data were collected to assess the effect of the fuel treatments on stand conditions and subsequent wildfire severity. proprietary
+CSU_fueltreatment_Fontainebleauwildfirestudy 1999 Fontainebleau Wildfire study SCIOPS STAC Catalog 1970-01-01 -88.71972, 30.401943, -88.71972, 30.401943 https://cmr.earthdata.nasa.gov/search/concepts/C1214620907-SCIOPS.umm_json The data are from the 1999 Fontainebleau wildfire that burned into an area that had previously been treated with 3 prescribed fires (1988, 1992, and 1998) in the Mississippi Sandhill Crane National Wildlife Refuge. Nine plots were established in both the treated area and an adjacent untreated area. Data collected describe stand conditions and fire severity at each plot. The data were collected to assess the effect of repeated prescribed burn treatments on stand conditions and subsequent wildfire severity. proprietary
CSU_fueltreatment_HiMeadow 2000 Hi Meadow Wildfire Study SCIOPS STAC Catalog 1970-01-01 -105.372, 39.368, -105.337, 39.403 https://cmr.earthdata.nasa.gov/search/concepts/C1214620839-SCIOPS.umm_json The data are from the 2000 Hi Meadow wildfire that burned into an area of the Pike National Forest that had received extensive fuel treatments since 1990 that included mechanical thinning and prescribed burning. Twelve plot pairs were established that straddled the fuel treatment boundaries. Data collected describe stand conditions and fire severity at each plot. The data were collected to assess the effect of the fuel treatments on stand conditions and subsequent wildfire severity. proprietary
+CSU_fueltreatment_HiMeadow 2000 Hi Meadow Wildfire Study ALL STAC Catalog 1970-01-01 -105.372, 39.368, -105.337, 39.403 https://cmr.earthdata.nasa.gov/search/concepts/C1214620839-SCIOPS.umm_json The data are from the 2000 Hi Meadow wildfire that burned into an area of the Pike National Forest that had received extensive fuel treatments since 1990 that included mechanical thinning and prescribed burning. Twelve plot pairs were established that straddled the fuel treatment boundaries. Data collected describe stand conditions and fire severity at each plot. The data were collected to assess the effect of the fuel treatments on stand conditions and subsequent wildfire severity. proprietary
CSU_fueltreatments_megramwildfire 1999 Megram Wildfire Study ALL STAC Catalog 1970-01-01 -123.51, 40.95, -123.45, 40.98 https://cmr.earthdata.nasa.gov/search/concepts/C1214620903-SCIOPS.umm_json The data are from the 1999 Megram wildfire that burned into an area of the Six Rivers National Forest that had been affected by a blowdown event in the winter of 1995-96. Surface fuels reduction in a portion of the blowdown area was accomplished via yarding and burning in 1997. Eleven plot pairs were established that straddled the fuel treatment boundaries. Data collected describe stand conditions and fire severity at each plot. proprietary
CSU_fueltreatments_megramwildfire 1999 Megram Wildfire Study SCIOPS STAC Catalog 1970-01-01 -123.51, 40.95, -123.45, 40.98 https://cmr.earthdata.nasa.gov/search/concepts/C1214620903-SCIOPS.umm_json The data are from the 1999 Megram wildfire that burned into an area of the Six Rivers National Forest that had been affected by a blowdown event in the winter of 1995-96. Surface fuels reduction in a portion of the blowdown area was accomplished via yarding and burning in 1997. Eleven plot pairs were established that straddled the fuel treatment boundaries. Data collected describe stand conditions and fire severity at each plot. proprietary
CS_Bibliography_1 A bibliography containing references to contaminated sites from the Antarctic and subantarctic regions AU_AADC STAC Catalog 1992-01-01 2003-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214308509-AU_AADC.umm_json A bibliography of references relating to contaminated sites from the Antarctic and subantarctic regions, dating from 1992 to 2003. The bibliography was compiled by Colin Davis, and contains 17 references. proprietary
@@ -5327,8 +5328,8 @@ CZCS_L3m_PIC_2022.0 Nimbus-7 CZCS Level-3 Global Mapped Particulate Inorganic Ca
CZCS_L3m_POC_2022.0 Nimbus-7 CZCS Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2022.0 OB_CLOUD STAC Catalog 1978-10-30 1986-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300838790-OB_CLOUD.umm_json The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds. proprietary
CZCS_L3m_RRS_2014 Nimbus-7 CZCS Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2014 OB_DAAC STAC Catalog 1978-10-30 1986-06-22 -180, 90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034493-OB_DAAC.umm_json The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds. proprietary
CZCS_L3m_RRS_2022.0 Nimbus-7 CZCS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0 OB_CLOUD STAC Catalog 1978-10-30 1986-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300838810-OB_CLOUD.umm_json The Coastal Zone Color Scanner Experiment (CZCS) was the first instrument devoted to the measurement of ocean color and flown on a spacecraft. Although other instruments flown on other spacecraft had sensed ocean color, their spectral bands, spatial resolution and dynamic range were optimized for land or meteorological use and had limited sensitivity in this area, whereas in CZCS, every parameter was optimized for use over water to the exclusion of any other type of sensing. CZCS had six spectral bands, four of which were used primarily for ocean color. These were of a 20 nanometer bandwidth centered at 443, 520, 550, and 670 nm. Band 5 had a 100 nm bandwidth centered at 750 nm and a dynamic range more suited to land. Band 6 operated in the 10.5 to 12.5 micrometer region and sensed emitted thermal radiance for derivation of equivalent black body temperature. (This thermal band failed within the first year of the mission, and so was not used in the global processing effort.) Bands 1-4 were preset to view water only and saturated when the IFOV was over most types of land surfaces, or clouds. proprietary
-CZM_moris_algonquin_hubline_lng_arc Algonquin Hubline natural gas pipeline, Massachusetts Bay, Massachusetts ALL STAC Catalog 2004-11-04 -70.964935, 42.244022, -70.774414, 42.54302 https://cmr.earthdata.nasa.gov/search/concepts/C1214591612-SCIOPS.umm_json This GIS layer shows the Hubline, an approximately 29.5 mile natural gas pipeline constructed primarily in the ocean along the coast of Massachusetts between Beverly and Weymouth. The route travels in a southerly direction through the communities of Salem, Beverly, Marblehead, Swampscott, Lynn, Nahant, Winthrop, Boston, Hull, Quincy, and Weymouth. This dataset represents an as-built location of the pipeline. Original survey for the bottom position of the pipeline was established by a combination of surface position of the installation vessel using DGPS, diver's surveys, multibeam surveys, and sidescan surveys. The project was surveyed in accordance with the USACOE's minimum standards and techniques as defined in the engineering manual EM 1110-2-1003. proprietary
CZM_moris_algonquin_hubline_lng_arc Algonquin Hubline natural gas pipeline, Massachusetts Bay, Massachusetts SCIOPS STAC Catalog 2004-11-04 -70.964935, 42.244022, -70.774414, 42.54302 https://cmr.earthdata.nasa.gov/search/concepts/C1214591612-SCIOPS.umm_json This GIS layer shows the Hubline, an approximately 29.5 mile natural gas pipeline constructed primarily in the ocean along the coast of Massachusetts between Beverly and Weymouth. The route travels in a southerly direction through the communities of Salem, Beverly, Marblehead, Swampscott, Lynn, Nahant, Winthrop, Boston, Hull, Quincy, and Weymouth. This dataset represents an as-built location of the pipeline. Original survey for the bottom position of the pipeline was established by a combination of surface position of the installation vessel using DGPS, diver's surveys, multibeam surveys, and sidescan surveys. The project was surveyed in accordance with the USACOE's minimum standards and techniques as defined in the engineering manual EM 1110-2-1003. proprietary
+CZM_moris_algonquin_hubline_lng_arc Algonquin Hubline natural gas pipeline, Massachusetts Bay, Massachusetts ALL STAC Catalog 2004-11-04 -70.964935, 42.244022, -70.774414, 42.54302 https://cmr.earthdata.nasa.gov/search/concepts/C1214591612-SCIOPS.umm_json This GIS layer shows the Hubline, an approximately 29.5 mile natural gas pipeline constructed primarily in the ocean along the coast of Massachusetts between Beverly and Weymouth. The route travels in a southerly direction through the communities of Salem, Beverly, Marblehead, Swampscott, Lynn, Nahant, Winthrop, Boston, Hull, Quincy, and Weymouth. This dataset represents an as-built location of the pipeline. Original survey for the bottom position of the pipeline was established by a combination of surface position of the installation vessel using DGPS, diver's surveys, multibeam surveys, and sidescan surveys. The project was surveyed in accordance with the USACOE's minimum standards and techniques as defined in the engineering manual EM 1110-2-1003. proprietary
C_Bibliography_1 A bibliography containing references to Collembola from the Antarctic and subantarctic regions ALL STAC Catalog 1876-01-01 2004-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214308482-AU_AADC.umm_json A bibliography of references relating to Collembola from the Antarctic and subantarctic regions, dating from 1876 to 2004. The bibliography was compiled by Penny Greenslade, and contains 105 references. proprietary
C_Bibliography_1 A bibliography containing references to Collembola from the Antarctic and subantarctic regions AU_AADC STAC Catalog 1876-01-01 2004-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214308482-AU_AADC.umm_json A bibliography of references relating to Collembola from the Antarctic and subantarctic regions, dating from 1876 to 2004. The bibliography was compiled by Penny Greenslade, and contains 105 references. proprietary
C_FluxStocks_CLM5_DART_WestUS_1856_1 CLM5-DART Regional Carbon Fluxes and Stocks over the Western US, 1998-2010 ORNL_CLOUD STAC Catalog 1998-01-01 2010-12-31 -130.62, 25.44, -99.38, 50.89 https://cmr.earthdata.nasa.gov/search/concepts/C2389230395-ORNL_CLOUD.umm_json "This dataset provides monthly estimates of biomass stocks and land-atmosphere carbon exchange across the western United States at 0.95 degrees longitude x 1.25 degrees latitude grid resolution from 1998 through 2010. The data include outputs from two types of model simulations: (1) a ""free"" simulation which used Community Land Model (CLM5.0) simulations forced with meteorology appropriate for complex mountainous terrain, and (2) ""assimilation"" runs using the land surface data assimilation system (CLM5-DART). In assimilation runs, the CLM5 vegetation state is constrained by remotely sensed observations of leaf area index and aboveground biomass, which influenced biomass stocks and carbon fluxes." proprietary
@@ -5337,8 +5338,8 @@ CaTS_0 Caribbean Time Series (CaTS), Puerto Rico, 1993 - 2007 OB_DAAC STAC Catal
CalCOFI_0 California Cooperative Oceanic Fisheries Investigations (CalCOFI) OB_DAAC STAC Catalog 1993-08-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360170-OB_DAAC.umm_json The California Cooperative Oceanic Fisheries Investigations (CalCOFI) proprietary
California_2002_0 Measurements made along the California coast in 2002 OB_DAAC STAC Catalog 2002-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360172-OB_DAAC.umm_json Measurements made along the California coast in 2002. proprietary
CanSIS_Regional_Soils_1347_2 BOREAS CanSIS Regional Soils Data in Vector Format, V2 ORNL_CLOUD STAC Catalog 1982-01-01 1984-12-31 -110, 49, -89, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2767509024-ORNL_CLOUD.umm_json This data set contains soils data from the Canada Soil Information System (CanSIS) in ESRI Shapefile format for the provinces of Saskatchewan and Manitoba. They are provided as part of the BOReal Ecosystem-Atmosphere Study (BOREAS) Staff Science GIS data collection program. Attribute tables provide the various soil data for the polygons. There is one attribute table for Saskatchewan and one for Manitoba. This data product may be useful to someone who is interested in studying this area at a regional scale. proprietary
-Canada_Boreal_Forest_Greenness_1587_1 ABoVE: Peak Greenness for Canadian Boreal Forest from Landsat 5 TM Imagery, 1984-2011 ORNL_CLOUD STAC Catalog 1970-01-01 2014-12-31 -124.47, 45.32, -53.91, 63.44 https://cmr.earthdata.nasa.gov/search/concepts/C2162140027-ORNL_CLOUD.umm_json This dataset provides a 28-year time series of peak greenness (NDVI) data derived from Landsat 5 TM imagery over the boreal forest region of Canada. Landsat 5 TM scenes were collected for 46 selected sidelap sites along gradients in climate, tree cover, and disturbance history from 1984 to 2011. Peak-greenness reflectance was computed for 30-m Landsat pixels using the maximum normalized difference vegetation index (NDVI) along with the normalized burn ratio (NBR) during the period between days of the year (DOY) 180 and 204. To facilitate trend analysis at each site, the NDVI and NBR data of the 30-m Landsat pixels were regridded to the coarser MODIS 500-m (463.3-m) spatial scale to reduce the effects of missing data and to enhance the significance of the trend. The regridded NDVI and NBR 28-year time series data at 500-m resolution are provided for each of the 46 sites. Two trend analyses were run on the 500-m resolution data and are reported for each site. Supplemental site metadata are also provided, including the number of valid Landsat pixels, land cover composition, and disturbance history, for each 500-m pixel. proprietary
Canada_Boreal_Forest_Greenness_1587_1 ABoVE: Peak Greenness for Canadian Boreal Forest from Landsat 5 TM Imagery, 1984-2011 ALL STAC Catalog 1970-01-01 2014-12-31 -124.47, 45.32, -53.91, 63.44 https://cmr.earthdata.nasa.gov/search/concepts/C2162140027-ORNL_CLOUD.umm_json This dataset provides a 28-year time series of peak greenness (NDVI) data derived from Landsat 5 TM imagery over the boreal forest region of Canada. Landsat 5 TM scenes were collected for 46 selected sidelap sites along gradients in climate, tree cover, and disturbance history from 1984 to 2011. Peak-greenness reflectance was computed for 30-m Landsat pixels using the maximum normalized difference vegetation index (NDVI) along with the normalized burn ratio (NBR) during the period between days of the year (DOY) 180 and 204. To facilitate trend analysis at each site, the NDVI and NBR data of the 30-m Landsat pixels were regridded to the coarser MODIS 500-m (463.3-m) spatial scale to reduce the effects of missing data and to enhance the significance of the trend. The regridded NDVI and NBR 28-year time series data at 500-m resolution are provided for each of the 46 sites. Two trend analyses were run on the 500-m resolution data and are reported for each site. Supplemental site metadata are also provided, including the number of valid Landsat pixels, land cover composition, and disturbance history, for each 500-m pixel. proprietary
+Canada_Boreal_Forest_Greenness_1587_1 ABoVE: Peak Greenness for Canadian Boreal Forest from Landsat 5 TM Imagery, 1984-2011 ORNL_CLOUD STAC Catalog 1970-01-01 2014-12-31 -124.47, 45.32, -53.91, 63.44 https://cmr.earthdata.nasa.gov/search/concepts/C2162140027-ORNL_CLOUD.umm_json This dataset provides a 28-year time series of peak greenness (NDVI) data derived from Landsat 5 TM imagery over the boreal forest region of Canada. Landsat 5 TM scenes were collected for 46 selected sidelap sites along gradients in climate, tree cover, and disturbance history from 1984 to 2011. Peak-greenness reflectance was computed for 30-m Landsat pixels using the maximum normalized difference vegetation index (NDVI) along with the normalized burn ratio (NBR) during the period between days of the year (DOY) 180 and 204. To facilitate trend analysis at each site, the NDVI and NBR data of the 30-m Landsat pixels were regridded to the coarser MODIS 500-m (463.3-m) spatial scale to reduce the effects of missing data and to enhance the significance of the trend. The regridded NDVI and NBR 28-year time series data at 500-m resolution are provided for each of the 46 sites. Two trend analyses were run on the 500-m resolution data and are reported for each site. Supplemental site metadata are also provided, including the number of valid Landsat pixels, land cover composition, and disturbance history, for each 500-m pixel. proprietary
Canadian_West_Arctic_Veg_Plots_1543_1 Arctic Vegetation Plots in Northern NWT and YT, Canada, 1965-1966 ORNL_CLOUD STAC Catalog 1965-06-09 1966-08-13 -138.75, 68.22, -135.7, 68.82 https://cmr.earthdata.nasa.gov/search/concepts/C2162120352-ORNL_CLOUD.umm_json This dataset provides vegetation, soil, and plot characteristics for 154 study plots located at three sites across the Richardson Mountains, Northwest Territories (NWT), and the British Mountains, Yukon Territory (YT). Study sites in the NWT included areas near Canoe Lake and Divided Lake; the study site in the YT was near Trout Lake. Specific attributes include dominant vegetation, species cover, and the physical characteristics of the plot areas. A soil pit was dug at each plot and the physical and chemical characteristics were determined for soil horizons. The data were collected in June, July, and August of 1965 and July and August of 1966. proprietary
CapeHatteras2010_0 Measurements near Cape Hatteras in 2010 OB_DAAC STAC Catalog 2010-10-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360173-OB_DAAC.umm_json Measurements made near Cape Hatteras in 2010. proprietary
Cape_Darnley_Bloom_1 Cape Darnley Early-Autumn Phytoplankton Bloom, March 2012 AU_AADC STAC Catalog 2012-03-04 2012-03-07 67.46585, -67.13495, 67.46585, -67.13495 https://cmr.earthdata.nasa.gov/search/concepts/C1214313392-AU_AADC.umm_json These data relate to a large-scale early-autumn phytoplankton bloom that occurred off Cape Darnley, East Antarctica, in March 2012. The bloom was detected by Dr Jan Lieser (Antarctic Climate and Ecosystems Cooperative Research Centre, ACE-CRC) through MODIS satellite and was opportunistically sampled from RSV Aurora Australis using the uncontaminated seawater line. Samples were analysed for protist species and abundances using light and scanning electron microscopy, and pigment analyses were conducted using high performance liquid chromatography. Additional water samples were taken for dissolved nutrient analyses. Specific details of the files are: Cape Darnley Protist Counts Samples were preserved with 1 % vol:vol Lugols iodine and stored in glass bottles in the dark at 4 degrees C. Protists were identified and counted using phase and Nomarski interference optics using Olympus IX71 and IX81 inverted microscopes at 400X to 640X magnification. Bright field optics were also used to discriminate taxa that contained chloroplasts. Protistan taxa were counted in 20 randomly chosen fields of view, except for highly abundant taxa that were counted in a subset of the field of view defined by an ocular quadrant (Whipple grid). Cell biovolumes and carbon conversion statistics were used to calculate the cell biomass of protistan taxa/groups. Cape Darnley Fluorometer Calibration Fluorometer measurements from the ships underway system were calibrated using chlorophyll a readings determined through high performance liquid chromatography. A linear relationship was established between fluorometer v HPLC chlorophyll a measurements at the same sites. The linear equation was then used to convert all underway fluorometry data from the voyage. Cape Darnley Bloom HPLC Pigments CHEMTAX summary Major phytoplankton groups at each site determined through analysis of pigments using high performance liquid chromatography and CHEMTAX. Methods were according to that of Wright et al. (2010). Cape Darnley Bloom Nutrients Dissolved nutrient concentrations. Samples were analysed by the Department of Primary Industries, Parks, Water and Environment, 18 St. Johns Avenue, Newtown, Tasmania 7008. Cape Darnley Underway Data VOYAGE_04_0_201112 Raw underway data from Aurora Australis in the bloom region Cape Darnley Underway Data Maps Maps of the underway data in the bloom region proprietary
@@ -5349,8 +5350,8 @@ Cartagena_Station_0 Antares Cartagena station, Colombia OB_DAAC STAC Catalog 201
CartoSat-1.archive.and.Euro-Maps.3D.Digital.Surface.Model_6.0 CartoSat-1 archive and Euro-Maps 3D Digital Surface Model ESA STAC Catalog 2005-06-01 2019-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336823-ESA.umm_json CartoSat-1 (also known as IRS-P5) archive products are available as PAN-Aft (backward), PAN-Fore (forward) and Stereo (PAN-Aft and PAN-Fore). - Sensor: PAN - Products: PAN-Aft (backward), PAN-Fore (forward), Stereo (PAN-Aft+PAN-Fore) - Type: Panchromatic - Resolution (m): 2.5 - Coverage (km x km): 27 x 27 - System or radiometrically corrected - Ortho corrected (DN) - Neustralitz archive: 2007 - 2016 - Global archive: 2005 - 2019 Note: - Resolution 2.5 m. - Coverage 27 km x 27 km. - System or radiometrically corrected. For Ortho corrected products: If unavailable, user has to supply ground control information and DEM in suitable quality, - For Stereo ortho corrected: only one of the datasets will be ortho corrected. Euro-Maps 3D is a homogeneous, 5 m spaced digital surface model (DSM) semi-automatically derived from 2.5 m in-flight stereo data provided by IRS-P5 CartoSat-1 and developed in cooperation with the German Aerospace Center, DLR. The very detailed and accurate representation of the surface is achieved by using a sophisticated and well adapted algorithm implemented on the basis of the Semi-Global Matching approach. In addition, the final product includes detailed flanking information consisting of several pixel-based quality and traceability layers also including an ortho layer. Product Overview: - Post spacing: 5m - Spatial reference system: DD, UTM or other projections on WGS84 - Height reference system: EGM96 - Absolute vertical accuracy: LE90 5-10 m - Absolute Horizontal Accuracy: CE90 5-10 m - Relative vertical accuracy: LE90 2.5 m - File format: GeoTIFF, 16 bit - Tiling: 0.5° x 0.5° - Ortho Layer Pixel Size: 2.5 m The CartoSat-1 products and Euro-Maps 3D are available as part of the GAF Imagery products from the Indian missions: IRS-1C, IRS-1D, CartoSat-1 (IRS-P5), ResourceSat-1 (IRS-P6) and ResourceSat-2 (IRS-R2) missions. ‘Cartosat-1 archive’ collection has worldwide coverage: for data acquired over Neustrelitz footprint, the users can browse the EOWEB GeoPortal catalogue (http://www.euromap.de/products/serv_003.html) to search archived products; worldwide data (out the Neustrelitz footprint) as well as Euro-Maps 3D DSM products can be requested by contacting GAF user support to check the readiness since no catalogue is available. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. proprietary
Cartosat-1.Euro-Maps.3D_7.0 Cartosat-1 Euro-Maps 3D ESA STAC Catalog 2019-11-12 2022-11-08 -33, 27, 47, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2547572699-ESA.umm_json "A large number of European cities are covered by this dataset; for each city you can find one or more Cartosat-1 ortho image products and one or more Euro-Maps 3D DSM tiles clipped to the extent of the ortho coverage. The Euro-Maps 3D DSM is a homogeneous, 5 m spaced Digital Surface Model semi-automatically derived from 2.5 m Cartosat-1 in-flight stereo data with a vertical accuracy of 10 m. The very detailed and accurate representation of the surface is achieved by using a sophisticated and well adapted algorithm implemented on the basis of the Semi-Global Matching approach. The final product includes several pixel-based quality and traceability layers: The dsm layer (*_dsm.tif) contains the elevation heights as a geocoded raster file The source layer (*_src.tif) contains information about the data source for each height value/pixel The number layer (*_num.tif) contains for each height value/pixel the number of IRS-P5 Cartosat-1 stereo pairs used for the generation of the DEM The quality layer (*_qc.tif) is set to 1 for each height/pixel value derived from IRS-P5 Cartosat-1 data and which meets or exceeds the product specifications The accuracy vertical layer (*_acv.tif) contains the absolute vertical accuracy for each quality controlled height value/pixel. The ortho image is a Panchromatic image at 2.5 m resolution. The following table defines the offered product types. EO-SIP product type Description PAN_PAM_3O IRS-P5 Cartosat-1 ortho image DSM_DEM_3D IRS-P5 Cartosat-1 DSM" proprietary
Casey_Tide_Gauges_2 Casey Tide Gauge Data 1996-2007 AU_AADC STAC Catalog 1996-03-01 2007-11-07 110.49843, -66.29034, 110.54375, -66.2699 https://cmr.earthdata.nasa.gov/search/concepts/C1667368851-AU_AADC.umm_json "Over time there have been a number of tide gauges deployed at Casey Station, Antarctica. The data download files contain further information about the gauges, but some of the information has been summarised here. Note that this metadata record only describes tide gauge data from 1996 to 2007. More recent data are described elsewhere. Old Tide Gauge 2 (TG002_Old) Oldtg02 is a download from the first gauge submerged deployed at Casey in 1992. This gauge was lost but later recovered standing upright in the mud. The gauge overwrote its memory and stopped. The record runs from 02/04/97 to 08/09/99. It is highly probable that the position of the gauge was stable during this period. There is data from the same period from gauge TG06. Tide Gauge 2 (TG002) These folders contain data downloaded from the redeployed gauge TG02. TG02 was redeployed in November 2003. The Record runs from 12/11/03 to 4/3/05. It is expected that data will be downloaded from this gauge for the next 4-5 years. This gauge was deployed after the previously deployed gauge ran out of battery energy. There is therefore a substantial gap in the record prior to 12/11/03. Tide Gauge 6 (TG006) Tg06 was deployed at Casey in March 1996. The battery became exhausted in June 2003. The gauge was replaced by TG02 in Novenber 2003. There is therefore a gap in the data between June and November 2003. Tide Gauges 33, 34 and 36 (TG033, TG034, TGA001, TG036) There are two wharf pressure sensors at Casey separated vertically by 2.007 m. There is also a barometer in the wharf hut. The files in this folder are from the old tide gauge data loggers. There are three loggers, TG33 records pressures from lower water pressure gauge as 30 second average values (absolute pressure mbar). It also records wharf tube water temperatures. This logger also streams 30sec average pressure. TG34 records pressures from upper water pressure gauge. This logger also streams 30sec average values as and 10minute average water pressure data. TGA01 (and later replaced by TG36) records air pressure as 10 minute average values in mbar. Further documentation from the old metadata records: Documentation dated 2001-03-07 Casey Submerged Tide Gauge The gauge used at Casey was designed in 1991/2 by Platypus Engineering, Hobart, Tasmania. It was intended to be submerged in about 7 metres of water in a purpose made concrete mooring in the shape of a truncated pyramid. The gauge measures pressure using a Paroscientific Digiquartz Pressure Transducer with a full scale pressure of 30 psi absolute. The accuracy of the transducer is 1 in 10,000 of full scale over the calibrated temperature. The overall accuracy of the system is better than +/- 3 mm for a known water density. Data is retrieved from the gauges by lowering a coil assembly on the end of a cable over a projecting knob on the top of the gauge and by use of an interface unit, a serial connection can be established to the gauge. Time setting and data retrieval can be then achieved. One of these of these gauges was deployed at Casey in early 1992 in a mooring in Geoffrey Bay. The mooring was apparently moved by sea ice and was later found, but the gauge is missing. A new mooring, one which was originally made for Harry Burton for use in one of the Vestfold Hills lakes, was taken by ship to Casey and was placed in Geoffrey Bay using a collection of 200 litre fuel drum to float the mooring into position. A new gauge was deployed in March 1996. The gauge was lowered into position with the holding grab wired closed to check that the device fitted in the mooring. The gauge became jammed so was left in situ with the grab preventing access to downloading. In April that year Roger Handsworth attached weights to the floating ropes of the grab to sink them out of the way of the freezing surface water. Divers located the mooring and gauge in late 1997 and 22 months of tidal records were retrieved. The gauge was restarted to clear the memory and allow another two years of data to be collected without any problems from a small software bug. Conversion of raw data to tidal records is done as detailed in document DATAFORMAT1.DOC . Levelling In December 1997 a set of water level observations were made by the station leader. These observations have been sent to National Tidal Facility, Flinders University, SA to derive a value for mean sea level. Documentation dated 2008-10-17 There is one submerged bottom mounted gauge at Casey. (TG02) The wharf based tide gauge installation at Casey has been upgraded with 2 Campbell Scientific CR1000 dataloggers. One logger (Main) receives signals from two wharf installed submerged Paroscientific Digiquartz pressure sensors and a barometer. The other logger (Backup) receives signals from only the two submerged sensors. Pressures are recorded in hPa, temperatures from the Digiquartz sensors in degrees C and temperatures from thermistors in the water column in unscaled A/D values. The two submerged pressure sensors are separated vertically by 2.007 metre. The backup logger streams 30 second average pressure values from both submerged sensors. The main datalogger records 3 pressure and 6 temperatures and controls the water heaters. " proprietary
-Catlin_Arctic_Survey_0 2011 R/V Catlin cruise in the Arctic Ocean ALL STAC Catalog 2011-03-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360181-OB_DAAC.umm_json Measurements made in the Arctic Ocean by the RV Catlin in 2011. proprietary
Catlin_Arctic_Survey_0 2011 R/V Catlin cruise in the Arctic Ocean OB_DAAC STAC Catalog 2011-03-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360181-OB_DAAC.umm_json Measurements made in the Arctic Ocean by the RV Catlin in 2011. proprietary
+Catlin_Arctic_Survey_0 2011 R/V Catlin cruise in the Arctic Ocean ALL STAC Catalog 2011-03-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360181-OB_DAAC.umm_json Measurements made in the Arctic Ocean by the RV Catlin in 2011. proprietary
ChesBay_0 Water quality measurements in the Chesapeake Bay OB_DAAC STAC Catalog 2015-07-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360189-OB_DAAC.umm_json Water quality measurements taken in the Chesapeake Bay region of the United States as a joint effort between NASA GSFC and Johns Hopkins University. proprietary
Chesapeake Land Cover_1 Chesapeake Land Cover MLHUB STAC Catalog 2020-01-01 2023-01-01 -80.8092703, 36.5643108, -74.2529408, 43.9973515 https://cmr.earthdata.nasa.gov/search/concepts/C2781412641-MLHUB.umm_json This dataset contains high-resolution aerial imagery from the USDA NAIP program, high-resolution land cover labels from the Chesapeake Conservancy, low-resolution land cover labels from the USGS NLCD 2011 dataset, low-resolution multi-spectral imagery from Landsat 8, and high-resolution building footprint masks from Microsoft Bing, formatted to accelerate machine learning research into land cover mapping. The Chesapeake Conservancy spent over 10 months and $1.3 million creating a consistent six-class land cover dataset covering the Chesapeake Bay watershed. While the purpose of the mapping effort by the Chesapeake Conservancy was to create land cover data to be used in conservation efforts, the same data can be used to train machine learning models that can be applied over even wider areas. proprietary
Chesapeake_Bay_DataFlow_0 Supporting Shellfish Aquaculture in the Chesapeake Bay using Artificial Intelligence to Detect Poor Water Quality through Field Sampling and Remote Sensing OB_DAAC STAC Catalog 2019-11-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2561597300-OB_DAAC.umm_json We are collecting and analyzing biological, chemical, and physical variables in and above the water at target sites and in the lab, looking for hyperspectral proxies that covarying with pollutants. This project is applying an AI model to address water quality, using datasets collected around the Bay in combination with remotely sensed data during targeted field work to support the need to more effectively sort through disparate data sets to identify areas of poor water quality that result in shellfish bed closure. proprietary
@@ -5373,8 +5374,8 @@ Coccolithophore_Fluxes_SAZ_2009-2012_1 Coccolithophore species fluxes in the Aus
Cold_Water_Corals_2.0 Cold Water Corals CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232849262-CEOS_EXTRA.umm_json Habitat Coverage: Seamounts proprietary
Continuous_Lifeform_Maps_CONUS_1809_1 CMS: Vegetative Lifeform Cover from Landsat SR for CONUS, 1984-2018 ORNL_CLOUD STAC Catalog 1984-01-01 2018-12-31 -126.71, 23.27, -65.06, 50.66 https://cmr.earthdata.nasa.gov/search/concepts/C2398099021-ORNL_CLOUD.umm_json This dataset contains estimates of percent cover of tree, shrub, herb, and other (non-vegetation) lifeform classes and uncertainties for the conterminous U.S. (CONUS). The estimates were derived using quantile regression forest models and indicate the percent of ground covered by a vertical projection of each lifeform class ranging from 0 to 100 percent. Model input data included Landsat surface reflectance (SR) data and 165 airborne LiDAR datasets covering eight of the eleven terrestrial biomes of the conterminous U.S. and Alaska. Eighty-six of the LiDAR acquisitions are part of the NASA Goddard's LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) airborne imager data collection; the remaining 79 sites were acquired by the National Science Foundation's National Ecological Observatory Network Airborne Observation Platform (NEON AOP). Acquisitions were selected based on the availability of the SR data for each G-LiHT and NEON dataset. The data are annual estimates from 1984 to 2018 and were tiled (425 tiles) using the CONUS Landsat Analysis Ready Data (ARD) grid scheme. Data are provided in GeoTIFF format. proprietary
Copepods_1 Copepod faecal pellets and carbon flux in the coastal sea-ice zone AU_AADC STAC Catalog 1997-12-03 1998-03-04 77.84363, -68.64456, 78.47534, -68.53226 https://cmr.earthdata.nasa.gov/search/concepts/C1214313441-AU_AADC.umm_json This dataset contains samples collected at O'Gorman Rocks and Ellis Fjord near Davis station from December 1997 to March 1998. Depth-stratified zooplankton samples were obtained for determination of zooplankton abundance and biomass. Water samples were collected for the determination of chlorophyll a concentration, protist identification and abundance, and the concentration of particulate and dissolved organic carbon. Sediment trap material was collected for the analysis of faecal pellets (identification and CHN analyses), protist identification and abundance, and the measurement of particulate organic carbon concentration. Zooplankton grazing experiments were performed in the laboratory at Davis station and zooplankton were also collected for CHN analyses. Data from this project arose from projects ASAC 963 and ASAC 2229. proprietary
-CosRay_Notes_Charts_1959-1986_1 A collection of some comsic ray physics notes and charts from Antarctica in the period 1959-1986 AU_AADC STAC Catalog 1959-01-01 1986-12-31 60, -69, 159, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1703260571-AU_AADC.umm_json Several boxes of paper records belonging to Dr Peter Fenton (a cosmic ray physicist based at the University of Tasmania), were provided to the Australian Antarctic Data Centre by his daughter, Dr Gwen Fenton. The paper records included charts of data collected from Australian Antarctic stations between 1959 and 1986, as well as some explanatory notes, correspondence and administration records. Dr Peter Fenton also had an older brother, Dr Geoff Fenton, who was also a cosmic ray physicist based at the University of Tasmania. The download file contains scanned pdfs of the following items: Charts_1959-1974a.pdf Charts_1959-1974b.pdf Correspondence_1986.pdf Explanatory_Notes_1962-1971.pdf Program_Description_1986.pdf proprietary
CosRay_Notes_Charts_1959-1986_1 A collection of some comsic ray physics notes and charts from Antarctica in the period 1959-1986 ALL STAC Catalog 1959-01-01 1986-12-31 60, -69, 159, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1703260571-AU_AADC.umm_json Several boxes of paper records belonging to Dr Peter Fenton (a cosmic ray physicist based at the University of Tasmania), were provided to the Australian Antarctic Data Centre by his daughter, Dr Gwen Fenton. The paper records included charts of data collected from Australian Antarctic stations between 1959 and 1986, as well as some explanatory notes, correspondence and administration records. Dr Peter Fenton also had an older brother, Dr Geoff Fenton, who was also a cosmic ray physicist based at the University of Tasmania. The download file contains scanned pdfs of the following items: Charts_1959-1974a.pdf Charts_1959-1974b.pdf Correspondence_1986.pdf Explanatory_Notes_1962-1971.pdf Program_Description_1986.pdf proprietary
+CosRay_Notes_Charts_1959-1986_1 A collection of some comsic ray physics notes and charts from Antarctica in the period 1959-1986 AU_AADC STAC Catalog 1959-01-01 1986-12-31 60, -69, 159, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1703260571-AU_AADC.umm_json Several boxes of paper records belonging to Dr Peter Fenton (a cosmic ray physicist based at the University of Tasmania), were provided to the Australian Antarctic Data Centre by his daughter, Dr Gwen Fenton. The paper records included charts of data collected from Australian Antarctic stations between 1959 and 1986, as well as some explanatory notes, correspondence and administration records. Dr Peter Fenton also had an older brother, Dr Geoff Fenton, who was also a cosmic ray physicist based at the University of Tasmania. The download file contains scanned pdfs of the following items: Charts_1959-1974a.pdf Charts_1959-1974b.pdf Correspondence_1986.pdf Explanatory_Notes_1962-1971.pdf Program_Description_1986.pdf proprietary
Cosmic_Ray_NM_1 Cosmic Ray Neutron Monitor Data, Antarctica and Tasmania AU_AADC STAC Catalog 1957-04-01 62, -67, 147.31, -42.97 https://cmr.earthdata.nasa.gov/search/concepts/C1214313426-AU_AADC.umm_json Hourly neutron monitor data both raw data and corrected for atmospheric pressure variations. Data are presently, routinely recorded at Mawson and Kingston, using super neutron monitors. Old data are also available from Wilkes and Casey Stations using standard IGY neutron monitors during earlier operational periods. Details of recording monitors and periods of operation are available on request. proprietary
Cosmic_Ray_PW_1 Cosmic Ray Associated Atmospheric Pressure and Wind Speed Records, Antarctica AU_AADC STAC Catalog 1950-01-01 62, -67, 159, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214313427-AU_AADC.umm_json Records of atmospheric pressure and wind speed used in conjunction with cosmic ray data recorded at corresponding Antarctic cosmic ray observatories. Data has been collected from Australian Antarctic cosmic ray observatories during operational times at Macquarie Island, Casey, Wilkes and Mawson. Individual observatory operating periods are available on request. proprietary
Cosmic_Ray_SM_1 Cosmic Ray Surface Muon Data, Antarctica AU_AADC STAC Catalog 1950-04-01 62, -67, 159, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214308508-AU_AADC.umm_json Data have been collected from muon detectors in cosmic ray observatories/laboratories at Macquarie Island, Wilkes, Casey and Mawson. Data have been collected from varied detectors and detecting systems, specific operational times and detector information are available on request. This dataset contains normalised counting rate records from the combined north/south pointing proportional counter telescopes P2 and P3 in low and high zenith angle modes, corrected for atmospheric pressure variation. No correction has been made for height or temperature of the 125mb level resulting in a significant seasonal variation but no diurnal variation at Antarctic latitudes. The telescope output consists of the sum of the 2-fold coincidence counting rates. Coincidence counts from individual counter pairs are summed into thirteen possible arrival zenith angles and two arrival directions (north or south). The low zenith data are hourly sums of coincidences from the seven lowest zenith angles of arrival (34 degrees -51 degrees) and the high zenith data are hourly sums of coincidences of the remaining six zenith angles of arrival (55 degrees -79 degrees). The accidental rate is removed from the total coincidence rate using a resolving time difference method (See Jacklyn R M and Duldig M L, 20th Int. Cosmic Ray Conf. Papers, Moscow, Vol 4, pp. 380-383, 1987). Further information is also provided in ANARE Research Notes 102, 50 Years of cosmic ray research in Tasmania. A copy of this research note is available for download from the provided URL. proprietary
@@ -5398,8 +5399,8 @@ DB_Antarctic_Artefacts_1 Antarctic Artefacts Database AU_AADC STAC Catalog 1880-
DB_Argos_PTT_Tracking_1 Argos PTT Tracking Database AU_AADC STAC Catalog 1982-03-01 -60, -70, 179, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1214313431-AU_AADC.umm_json A repository of all ARGOS satellite messages from 1982 to present. Trackers have been used on AWS stations, buoys and numerous species of whales, seals and seabirds. ARGOS is a means of sending data back from PTT devices - Position Tracking Terminals. However, the subject does not necessarily have to be moving - as in the case of the Automatic Weather Stations (AWS), which use ARGOS for relaying meteorological data back to Australia. Animal species that have been or are currently monitored by the Australian Antarctic Program using the ARGOS system include: Grey-headed Albatross Black-browed Albatross Light mantled sooty albatross Australian Fur Seal Antarctic Fur Seal Weddell Seal Ross seal Crabeater seal Southern Elephant Seal Emperor Penguin King Penguin Macaroni Penguin Adelie Penguin Pygmy Blue Whale Locations in which the ARGOS system is/was being used by the Australian Antarctic Program are: Admiralty Bay Albatross Island Almagro Auster Rookery Bechervaise Island Cape Gantheaume Caroline Cove Casey Davis Diego Ramirez Dumont d'Urville, Base Edmonson Point Ildefonso Inexpressible Island Macquarie Island Magnetic Island Pedra Branca Scullin Monolith Shirley Island Spit Bay Taylor Rookery Ufs Island Each day, data is retrieved via telnet client from the ARGOS site in France. A batch process parses the data files and inserts into the Data Centre database by 0800 local time. End-users can subscribe to an email describing the recent data uploads. Web-based tools are provided to filter the data by bounding box, time span and type of message quality. Finally a optional velocity filter can be applied to remove spurious positions that should not be reachable by that particular species. For example, seal data can be filtered for positions that would require speeds in excess of 10 km/hr. The same tool ascribes species, gender, age class and breeding status to each set of data. A separate control allows the filtered data to be published to the general public and/or to OBIS and GBIF via web services. Output products include maps, excel spreadsheets and KML files for mapping data on Google Earth. proprietary
DB_Historic_WoV_1 Historic ANARE ship-based wildlife observations from 1947 to 1982. AU_AADC STAC Catalog 1947-06-01 1982-12-31 60, -70, 170, -30 https://cmr.earthdata.nasa.gov/search/concepts/C1214308528-AU_AADC.umm_json Ship-based observations of birds, seals and whales from the original 'ANARE Bird Log' books have been recovered into a single repository of sightings and associated abiotic information. ANARE (Australian National Antarctic Research Expeditions) is the historic acronym for these voyages. A few voyages have been included that were not part of ANARE but have Australian observers or volunteer observers. Voyages start from the 1947/48 austral season up to 1982/83 with an average of 3 voyages per season. There are a few voyages where there is no data. It is not known if either no bird observations were undertaken during this period or that the bird logs exist if observations were undertaken. Current counts are birds, seals and whales Observing platforms include the following ships - Wyatt Earp, Tottan, River Fitzroy, Norsel, Kista Dan, Thala Dan, Magga Dan, Nella Dan, Lady Franklin and Nanok S and a single voyage from the private yacht Solo. The quality and quantity of abiotic data associated with observations such as air temperature, sea ice cover etc vary immensely from voyage to voyage. Where possible this data has been entered. This dataset contains very little information on estimates of survey effort and cannot be used to derive useful presence/absence spatial coverages of species during this period. It is purely sighting data only. proprietary
DB_Marine_Debris_1 Marine debris dataset of Heard Island and Macquarie Island beaches and observed from ships, 1987-2002 AU_AADC STAC Catalog 1987-01-22 2002-02-18 60, -68, 160, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214313415-AU_AADC.umm_json Marine debris records from beaches on Heard and Macquarie Islands and floating debris spotted on voyages. Data were collected by observers surveying beaches either methodically or opportunistically, and by observers spotting debris as it floated past ships. The data were originally collated into a searchable database, but the application is no longer supported by the Australian Antarctic Data Centre. An extract of the data is attached to this metadata record. The extract is in Excel format, and each worksheet is a copy of a database table. proprietary
-DB_Trophic_1 A compilation of dietary and related data from the Southern Ocean ALL STAC Catalog 1960-12-21 2010-03-20 -180, -80, 180, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1214313435-AU_AADC.umm_json 2018-08-10 - these data have been superseded by a new metadata record and dataset - see the provided URL for more details. This record describes a compilation of trophic data from across the Southern Ocean. Data have been drawn from published literature, existing trophic data collections, AADC data sets, and unpublished collections. The database comprises two principal tables. The first table relates to direct sampling methods of dietary assessment, including gut, scat, and bolus content analyses, stomach flushing, and observed feeding. The second table is a compilation of stable isotope values. Each record in these two tables includes details such as the location and date of sampling, predator size and mass, prey size and mass, and estimates of dietary importance. Names have been validated against the World Register of Marine Species (http://www.marinespecies.org/). The schemas of these tables are described below, and a list of the sources used to populate the tables is provided with the data. A range of manual and automated checks were used to ensure that the entered data were as accurate as possible. These included visual checking of transcribed values, checking of row or column sums against known totals, and checking for values outside of allowed ranges. Suspicious entries were re-checked against original source. Apparent errors that could not be resolved were marked as such in the QUALITY_FLAG column, with the reason in the NOTES column. Notes on names 'Sp.' indicates unidentified members of a genus (e.g. 'Pachyptila sp.'). For unidentified taxa at other taxonomic levels, the taxonomic name has been used (e.g. Amphipoda, Myctophidae, Decapoda). Uncertain species identifications (e.g. 'Notothenia rossii?' or 'Gymnoscopelus cf. piabilis') were assigned the genus name (e.g. 'Notothenia sp.'). Original names were retained in a separate column to allow future cross-checking. WoRMS identifiers (APHIA_ID numbers) were recorded with each matched taxon. Grouped prey data in the diet sample table need to be handled with a bit of care. Papers commonly report prey statistics aggregated over groups of prey - e.g. one might give the diet composition by individual cephalopod prey species, and then an overall record for all cephalopod prey. The prey_is_aggregate column identifies such records. This allows us to differentiate grouped data like this from unidentified prey items from a certain prey group - for example, an unidentifiable cephalopod record would be entered as Cephalopoda (the scientific name), with 0 in the prey_is_aggregate column. A record that groups together a number of cephalopod records, possibly including some unidentifiable cephalopods, would also be entered as Cephalopoda, but with a 1 in the prey_is_aggregate column. See the notes on prey_is_aggregate, below. Schema: Diet sample table - LINK_ID: The unique identifier of this record - SOURCE_ID: The reference number of the source of this data record. The list of references is provided with the database and also kept at: http://data.aad.gov.au/aadc/trophic/?tab=3 - LOCATION: The name of the location at which the data was collected. - WEST: The westernmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes) - EAST: The easternmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes) - SOUTH: The southernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes) - NORTH: The northernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes) - OBSERVATION_DATE_START: The start of the sampling period (UTC) - OBSERVATION_DATE_END: The end of the sampling period (UTC). If sampling was carried out over multiple seasons (e.g. during January of 2002 and January of 2003), these dates will indicate the first and last dates (as if the sampling was carried out from 1-Jan-2002 to 31-Jan-2003) - ALTITUDE_MIN: The minimum altitude of the sampling region, in metres (if applicable) - ALTITUDE_MAX: The maximum altitude of the sampling region, in metres (if applicable) - DEPTH_MIN: The shallowest depth of the sampling, in metres (if applicable) - DEPTH_MAX: The deepest depth of the sampling, in metres (if applicable) - PREDATOR_NAME_ORIGINAL: The name of the predator, as it appeared in the original source - PREDATOR_NAME: The scientific name of the predator (corrected, if necessary). - PREDATOR_COMMON_NAME: The common name of the predator (from the WoRMS taxonomic register) - PREDATOR_APHIA_ID: The numeric identifier of the predator in the WoRMS taxonomic register - PREDATOR_LIFE_STAGE: Life stage of the predator. e.g. 'adult', 'chick', 'larva'. Values 'C1'-'C3' refer to calyptopis larval stages of euphausiids. 'F1'-'F6' refer to furcilia larval stages of euphausiids. 'N1'-'N6' refer to nauplius stages of crustaceans. 'Copepodite 1'-'Copepodite 6' refer to developmental stages of copepodites - PREDATOR_BREEDING_STAGE: Stage of the breeding season of the predator, if applicable. e.g. 'brooding', 'chick rearing', 'nonbreeding', 'posthatching' - PREDATOR_SEX: Sex of the predator. 'male', 'female', 'both', or 'unknown' - PREDATOR_SAMPLE_COUNT: The number of predators for which data are given. If (say) 50 predators were caught but only 20 analysed, this column will contain 20. - PREDATOR_TOTAL_COUNT: The total number of predators sampled. If (say) 50 predators were caught but only 20 analysed, this column will contain 50. - PREDATOR_SAMPLE_COUNT: The identifier of this predator sample. PREDATOR_SAMPLE_ID values are unique within a source (i.e. - SOURCE_ID, PREDATOR_SAMPLE_ID pairs are globally unique). Rows with the same SOURCE_ID and PREDATOR_SAMPLE_ID values relate to the same predator individual or population, and so can be combined (e.g. for prey diversity analyses). Subsamples are indicated by a decimal number S.nnn, where S is the parent PREDATOR_SAMPLE_ID, and nnn (001-999) is the subsample number. Studies will often report detailed prey information for a large sample, and also report prey information for various subsamples of that sample (e.g. broken down by predator sex, or sampling season). - PREDATOR_SIZE_MIN: The minimum size of the predators in the sample - PREDATOR_SIZE_MAX: The maximum size of the predators in the sample - PREDATOR_SIZE_MEAN: The mean size of the predators in the sample - PREDATOR_SIZE_SD: The standard deviation of the size of the predators in the sample - PREDATOR_SIZE_UNITS: The units of size. Current values 'mm', 'cm', 'm' - PREDATOR_SIZE_NOTES: Notes on the predator size information, including a definition of what the size value represents (e.g. 'total length', 'standard length') - PREDATOR_MASS_MIN: The minimum mass of the predators in the sample - PREDATOR_MASS_MAX: The maximum mass of the predators in the sample - PREDATOR_MASS_MEAN: The mean mass of the predators in the sample - PREDATOR_MASS_SD: The standard deviation of the mass of the predators in the sample - PREDATOR_MASS_UNITS: The units of mass (e.g. 'g' or 'kg') - PREDATOR_MASS_NOTES: Notes on the predator mass information, including a definition of what the mass value represents (blank implies total body weight). Current values 'g', 'kg', 't' - PREY_NAME_ORIGINAL: The name of the prey item, as it appeared in the original source. - PREY_NAME: The scientific name of the prey item (corrected, if necessary). - PREY_COMMON_NAME: The common name of the prey item (from the WoRMS taxonomic register) - PREY_APHIA_ID: The numeric identifier of the prey in the WoRMS taxonomic register - PREY_IS_AGGREGATE: 'Y' indicates that this row is an aggregation of other rows in this data source. For example, a study might give a number of individual squid species records, and then an overall squid record that encompasses the individual records. Use the PREY_IS_AGGREGATE information to avoid double-counting during analyses. If there no entry in this column, it means that this information is not included anywhere else in the database and can be used freely when aggregating over taxonomic groups, for example - PREY_LIFE_STAGE: Life stage of the prey. e.g. 'adult', 'chick', 'larva' - PREY_SAMPLE_COUNT: The number of prey individuals from which size and mass measurements were made (note: NOT the total number of individuals of this prey type, unless all individuals in the sample were measured) - PREY_SIZE_MIN: The minimum size of the prey in the sample - PREY_SIZE_MAX: The maximum size of the prey in the sample - PREY_SIZE_MEAN: The mean size of the prey in the sample - PREY_SIZE_SD: The standard deviation of the size of the prey in the sample - PREY_SIZE_UNITS: The units of size. Current values 'mm', 'cm', 'm' - PREY_SIZE_NOTES: Notes on the prey size information, including a definition of what the size value represents (e.g. 'total length', 'standard length') - PREY_MASS_MIN: The minimum mass of the prey in the sample - PREY_MASS_MAX: The maximum mass of the prey in the sample - PREY_MASS_MEAN: The mean mass of the prey in the sample - PREY_MASS_SD: The standard deviation of the mass of the prey in the sample - PREY_MASS_UNITS: The units of mass. Current values 'mg', 'g', 'kg' - PREY_MASS_NOTES: Notes on the prey mass information, including a definition of what the mass value represents (blank implies total body weight) - FRACTION_DIET_BY_WEIGHT: The fraction (by weight) of the predator diet that this prey type made up (e.g. if Euphausia superba contributed 50% of the total mass of prey items, this value would be 0.5). Many papers represent very small dietary contributions as 'trace' or sometimes 'less than 0.1%'. These have been entered as -999 - FRACTION_DIET_BY_PREY_ITEMS: The fraction (by number) of prey items that this prey type made up (e.g. if 1000 Euphausia superba were found out of a total of 2000 prey items, this value would be 0.5). Note: many papers represent very small dietary contributions as 'trace' or sometimes 'less than 0.1%'. These have been entered as -999 - FRACTION_OCCURRENCE: The number of times this prey item occurred in a predator sample, as a fraction of the number of non-empty samples (e.g. if Euphausia superba occurred in half of the non-empty stomachs examined, this value would be 0.5). Empty stomachs are ignored for the purposes of calculating fraction of occurrence. For gut content analyses (and any other study types where 'no prey' can occur in a sample), the fraction of empty stomachs is also given (using prey_name 'None' - e.g. if predator_total_count was 10 and 3 stomachs were empty, this will be 0.3). Note: many papers represent very small dietary contributions as 'trace' or sometimes 'less than 0.1%'. These have been entered as -999 - QUALITATIVE_DIETARY_IMPORTANCE: Qualitative description of the dietary importance of this prey item (e.g. from comments about certain prey in the discussion text of an article), if numeric values have not been given. Current values are 'none', 'incidental', 'minor', 'major', 'almost exclusive', 'exclusive' - CONSUMPTION_RATE_MIN: The minimum consumption rate of this prey item - CONSUMPTION_RATE_MAX: The maximum consumption rate of this prey item - CONSUMPTION_RATE_MEAN: The mean consumption rate of this prey item - CONSUMPTION_RATE_SD: The standard deviation of the consumption rate of this prey item - CONSUMPTION_RATE_UNITS: The units of consumption rate (e.g. 'kg/day') - CONSUMPTION_RATE_NOTES: Notes about the consumption rate estimates - IDENTIFICATION_METHOD: How this dietary information was gathered. Multiple values can potentially be entered (separated by commas). Current values include 'scat content' (contents of scats), 'stomach flushing' (physical sampling of the stomach contents by flushing the contents out with water), 'stomach content' (physical sampling of the stomach contents from a dead animal), 'regurgitate content' (physical sampling of the contents of forced or spontaneous regurgitations), 'observed predation', 'bolus content' (physical sampling of the contents of boluses), 'nest detritus', 'unknown' - QUALITY_FLAG: An indicator of the quality of this record. 'Q' indicates that the data are known to be questionable for some reason. The reason should be in the notes column. 'G' indicates good data - IS_SECONDARY_DATA: An indicator of whether this record was entered from its primary source, or from a secondary citation. 'Y' here indicates that the data actually came from another paper and were being reported in this paper as secondary data. Secondary data records are likely to be removed at a later date and replaced with information from the original source. - NOTES: Any other notes - LAST_MODIFIED: The date of last modification of this record Schema: Isotope data table - RECORD_ID: The unique identifier of this record - SOURCE_ID: The reference number of the source of this data record. The list of references is provided with the database and also kept at: http://data.aad.gov.au/aadc/trophic/?tab=3 - LOCATION: The name of the location at which the data was collected. - WEST: The westernmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes) - EAST: The easternmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes) - SOUTH: The southernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes) - NORTH: The northernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes) - OBSERVATION_DATE_START: The start of the sampling period (UTC) - OBSERVATION_DATE_END: The end of the sampling period (UTC). If sampling was carried out over multiple seasons (e.g. during January of 2002 and January of 2003), these dates will indicate the first and last dates (as if the sampling was carried out from 1-Jan-2002 to 31-Jan-2003) - ALTITUDE_MIN: The minimum altitude of the sampling region, in metres (if applicable) - ALTITUDE_MAX: The maximum altitude of the sampling region, in metres (if applicable) - DEPTH_MIN: The shallowest depth of the sampling, in metres (if applicable) - DEPTH_MAX: The deepest depth of the sampling, in metres (if applicable) - TAXON_NAME_ORIGINAL: The name of the taxon, as it appeared in the original source. - TAXON_NAME: The scientific name of the taxon (corrected, if necessary). - TAXON_COMMON_NAME: The common name of the taxon (from the WoRMS taxonomic register) - TAXON_APHIA_ID: The numeric identifier of the taxon in the WoRMS taxonomic register - TAXON_LIFE_STAGE: Life stage of the taxon. e.g. 'adult', 'chick', 'larva'. Values 'C1'-'C3' refer to calyptopis larval stages of euphausiids. 'F1'-'F6' refer to furcilia larval stages of euphausiids. 'N1'-'N6' refer to nauplius stages of crustaceans. 'Copepodite 1'-'Copepodite 6' refer to developmental stages of copepodites - TAXON_BREEDING_STAGE: Stage of the breeding season of the taxon, if applicable. e.g. 'lactating', 'weaning', 'chick rearing' - TAXON_SEX: Sex of the taxon. 'male', 'female', 'both', or 'unknown' - TAXON_SAMPLE_COUNT: The number of samples from which size and stable isotope measurements were made - TAXON_SIZE_MIN: The minimum size of the individuals in the sample - TAXON_SIZE_MAX: The maximum size of the individuals in the sample - TAXON_SIZE_MEAN: The mean size of the individuals in the sample - TAXON_SIZE_SD: The standard deviation of the size of the individuals in the sample - TAXON_SIZE_UNITS: The units of size. Current values 'mm', 'm' - TAXON_SIZE_NOTES: Notes on the size information, including a definition of what the size value represents (e.g. 'total length', 'standard length') - TAXON_MASS_MIN: The minimum mass of the individuals in the sample - TAXON_MASS_MAX: The maximum mass of the individuals in the sample - TAXON_MASS_MEAN: The mean mass of the individuals in the sample - TAXON_MASS_SD: The standard deviation of the mass of the individuals in the sample - TAXON_MASS_UNITS: The units of mass. e.g. 'g', 'kg' - TAXON_MASS_NOTES: Notes on the taxon mass information, including a definition of what the mass value represents (blank implies total body weight) - DELTA_13C_MEAN: The mean of the d13C values from the sample (permil;) - DELTA_13C_VARIABILITY_VALUE: The variability of the d13C values from the sample - DELTA_13C_VARIABILITY_TYPE: The variability type that the DELTA_13C_VARIABILITY_VALUE represents (currently 'SD' standard deviation, or 'SE' standard error) - DELTA_15N_MEAN: The mean of the d15N values from the sample (permil;) - DELTA_15N_VARIABILITY_VALUE: The variability of the d15N values from the sample - DELTA_15N_VARIABILITY_TYPE: The variability type that the DELTA_15N_VARIABILITY_VALUE represents (currently 'SD' standard deviation, or 'SE' standard error) - C_N_RATIO_MEAN: The mean of the C:N ratio values from the sample, expressed as a molar percentage - C_N_RATIO_VARIABILITY_VALUE: The variability of the C:N ratio values from the sample - C_N_RATIO_VARIABILITY_TYPE: The variability type that the C_N_RATIO_VARIABILITY_VALUE represents (currently 'SD' standard deviation, or 'SE' standard error) - ISOTOPES_CARBONATES_EXTRACTED: Were carbonates extracted from the samples prior to isotope analyses? 'Y', 'N', or 'U' (unknown) - ISOTOPES_LIPIDS_EXTRACTED: Were lipids extracted from the samples prior to isotope analyses? 'Y', 'N', or 'U' (unknown) - ISOTOPES_BODY_PART_USED: Which part of the organism was sampled? - QUALITY_FLAG: An indicator of the quality of this record. 'Q' indicates that the data are known to be questionable for some reason. The reason should be in the notes column. 'G' indicates good data - IS_SECONDARY_DATA: An indicator of whether this record was entered from its primary source, or from a secondary citation. 'Y' here indicates that the data actually came from another paper and were being reported in this paper as secondary data. Secondary data records are likely to be removed at a later date and replaced with information from the original source. - NOTES: Any other notes - LAST_MODIFIED: The date of last modification of this record proprietary
DB_Trophic_1 A compilation of dietary and related data from the Southern Ocean AU_AADC STAC Catalog 1960-12-21 2010-03-20 -180, -80, 180, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1214313435-AU_AADC.umm_json 2018-08-10 - these data have been superseded by a new metadata record and dataset - see the provided URL for more details. This record describes a compilation of trophic data from across the Southern Ocean. Data have been drawn from published literature, existing trophic data collections, AADC data sets, and unpublished collections. The database comprises two principal tables. The first table relates to direct sampling methods of dietary assessment, including gut, scat, and bolus content analyses, stomach flushing, and observed feeding. The second table is a compilation of stable isotope values. Each record in these two tables includes details such as the location and date of sampling, predator size and mass, prey size and mass, and estimates of dietary importance. Names have been validated against the World Register of Marine Species (http://www.marinespecies.org/). The schemas of these tables are described below, and a list of the sources used to populate the tables is provided with the data. A range of manual and automated checks were used to ensure that the entered data were as accurate as possible. These included visual checking of transcribed values, checking of row or column sums against known totals, and checking for values outside of allowed ranges. Suspicious entries were re-checked against original source. Apparent errors that could not be resolved were marked as such in the QUALITY_FLAG column, with the reason in the NOTES column. Notes on names 'Sp.' indicates unidentified members of a genus (e.g. 'Pachyptila sp.'). For unidentified taxa at other taxonomic levels, the taxonomic name has been used (e.g. Amphipoda, Myctophidae, Decapoda). Uncertain species identifications (e.g. 'Notothenia rossii?' or 'Gymnoscopelus cf. piabilis') were assigned the genus name (e.g. 'Notothenia sp.'). Original names were retained in a separate column to allow future cross-checking. WoRMS identifiers (APHIA_ID numbers) were recorded with each matched taxon. Grouped prey data in the diet sample table need to be handled with a bit of care. Papers commonly report prey statistics aggregated over groups of prey - e.g. one might give the diet composition by individual cephalopod prey species, and then an overall record for all cephalopod prey. The prey_is_aggregate column identifies such records. This allows us to differentiate grouped data like this from unidentified prey items from a certain prey group - for example, an unidentifiable cephalopod record would be entered as Cephalopoda (the scientific name), with 0 in the prey_is_aggregate column. A record that groups together a number of cephalopod records, possibly including some unidentifiable cephalopods, would also be entered as Cephalopoda, but with a 1 in the prey_is_aggregate column. See the notes on prey_is_aggregate, below. Schema: Diet sample table - LINK_ID: The unique identifier of this record - SOURCE_ID: The reference number of the source of this data record. The list of references is provided with the database and also kept at: http://data.aad.gov.au/aadc/trophic/?tab=3 - LOCATION: The name of the location at which the data was collected. - WEST: The westernmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes) - EAST: The easternmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes) - SOUTH: The southernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes) - NORTH: The northernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes) - OBSERVATION_DATE_START: The start of the sampling period (UTC) - OBSERVATION_DATE_END: The end of the sampling period (UTC). If sampling was carried out over multiple seasons (e.g. during January of 2002 and January of 2003), these dates will indicate the first and last dates (as if the sampling was carried out from 1-Jan-2002 to 31-Jan-2003) - ALTITUDE_MIN: The minimum altitude of the sampling region, in metres (if applicable) - ALTITUDE_MAX: The maximum altitude of the sampling region, in metres (if applicable) - DEPTH_MIN: The shallowest depth of the sampling, in metres (if applicable) - DEPTH_MAX: The deepest depth of the sampling, in metres (if applicable) - PREDATOR_NAME_ORIGINAL: The name of the predator, as it appeared in the original source - PREDATOR_NAME: The scientific name of the predator (corrected, if necessary). - PREDATOR_COMMON_NAME: The common name of the predator (from the WoRMS taxonomic register) - PREDATOR_APHIA_ID: The numeric identifier of the predator in the WoRMS taxonomic register - PREDATOR_LIFE_STAGE: Life stage of the predator. e.g. 'adult', 'chick', 'larva'. Values 'C1'-'C3' refer to calyptopis larval stages of euphausiids. 'F1'-'F6' refer to furcilia larval stages of euphausiids. 'N1'-'N6' refer to nauplius stages of crustaceans. 'Copepodite 1'-'Copepodite 6' refer to developmental stages of copepodites - PREDATOR_BREEDING_STAGE: Stage of the breeding season of the predator, if applicable. e.g. 'brooding', 'chick rearing', 'nonbreeding', 'posthatching' - PREDATOR_SEX: Sex of the predator. 'male', 'female', 'both', or 'unknown' - PREDATOR_SAMPLE_COUNT: The number of predators for which data are given. If (say) 50 predators were caught but only 20 analysed, this column will contain 20. - PREDATOR_TOTAL_COUNT: The total number of predators sampled. If (say) 50 predators were caught but only 20 analysed, this column will contain 50. - PREDATOR_SAMPLE_COUNT: The identifier of this predator sample. PREDATOR_SAMPLE_ID values are unique within a source (i.e. - SOURCE_ID, PREDATOR_SAMPLE_ID pairs are globally unique). Rows with the same SOURCE_ID and PREDATOR_SAMPLE_ID values relate to the same predator individual or population, and so can be combined (e.g. for prey diversity analyses). Subsamples are indicated by a decimal number S.nnn, where S is the parent PREDATOR_SAMPLE_ID, and nnn (001-999) is the subsample number. Studies will often report detailed prey information for a large sample, and also report prey information for various subsamples of that sample (e.g. broken down by predator sex, or sampling season). - PREDATOR_SIZE_MIN: The minimum size of the predators in the sample - PREDATOR_SIZE_MAX: The maximum size of the predators in the sample - PREDATOR_SIZE_MEAN: The mean size of the predators in the sample - PREDATOR_SIZE_SD: The standard deviation of the size of the predators in the sample - PREDATOR_SIZE_UNITS: The units of size. Current values 'mm', 'cm', 'm' - PREDATOR_SIZE_NOTES: Notes on the predator size information, including a definition of what the size value represents (e.g. 'total length', 'standard length') - PREDATOR_MASS_MIN: The minimum mass of the predators in the sample - PREDATOR_MASS_MAX: The maximum mass of the predators in the sample - PREDATOR_MASS_MEAN: The mean mass of the predators in the sample - PREDATOR_MASS_SD: The standard deviation of the mass of the predators in the sample - PREDATOR_MASS_UNITS: The units of mass (e.g. 'g' or 'kg') - PREDATOR_MASS_NOTES: Notes on the predator mass information, including a definition of what the mass value represents (blank implies total body weight). Current values 'g', 'kg', 't' - PREY_NAME_ORIGINAL: The name of the prey item, as it appeared in the original source. - PREY_NAME: The scientific name of the prey item (corrected, if necessary). - PREY_COMMON_NAME: The common name of the prey item (from the WoRMS taxonomic register) - PREY_APHIA_ID: The numeric identifier of the prey in the WoRMS taxonomic register - PREY_IS_AGGREGATE: 'Y' indicates that this row is an aggregation of other rows in this data source. For example, a study might give a number of individual squid species records, and then an overall squid record that encompasses the individual records. Use the PREY_IS_AGGREGATE information to avoid double-counting during analyses. If there no entry in this column, it means that this information is not included anywhere else in the database and can be used freely when aggregating over taxonomic groups, for example - PREY_LIFE_STAGE: Life stage of the prey. e.g. 'adult', 'chick', 'larva' - PREY_SAMPLE_COUNT: The number of prey individuals from which size and mass measurements were made (note: NOT the total number of individuals of this prey type, unless all individuals in the sample were measured) - PREY_SIZE_MIN: The minimum size of the prey in the sample - PREY_SIZE_MAX: The maximum size of the prey in the sample - PREY_SIZE_MEAN: The mean size of the prey in the sample - PREY_SIZE_SD: The standard deviation of the size of the prey in the sample - PREY_SIZE_UNITS: The units of size. Current values 'mm', 'cm', 'm' - PREY_SIZE_NOTES: Notes on the prey size information, including a definition of what the size value represents (e.g. 'total length', 'standard length') - PREY_MASS_MIN: The minimum mass of the prey in the sample - PREY_MASS_MAX: The maximum mass of the prey in the sample - PREY_MASS_MEAN: The mean mass of the prey in the sample - PREY_MASS_SD: The standard deviation of the mass of the prey in the sample - PREY_MASS_UNITS: The units of mass. Current values 'mg', 'g', 'kg' - PREY_MASS_NOTES: Notes on the prey mass information, including a definition of what the mass value represents (blank implies total body weight) - FRACTION_DIET_BY_WEIGHT: The fraction (by weight) of the predator diet that this prey type made up (e.g. if Euphausia superba contributed 50% of the total mass of prey items, this value would be 0.5). Many papers represent very small dietary contributions as 'trace' or sometimes 'less than 0.1%'. These have been entered as -999 - FRACTION_DIET_BY_PREY_ITEMS: The fraction (by number) of prey items that this prey type made up (e.g. if 1000 Euphausia superba were found out of a total of 2000 prey items, this value would be 0.5). Note: many papers represent very small dietary contributions as 'trace' or sometimes 'less than 0.1%'. These have been entered as -999 - FRACTION_OCCURRENCE: The number of times this prey item occurred in a predator sample, as a fraction of the number of non-empty samples (e.g. if Euphausia superba occurred in half of the non-empty stomachs examined, this value would be 0.5). Empty stomachs are ignored for the purposes of calculating fraction of occurrence. For gut content analyses (and any other study types where 'no prey' can occur in a sample), the fraction of empty stomachs is also given (using prey_name 'None' - e.g. if predator_total_count was 10 and 3 stomachs were empty, this will be 0.3). Note: many papers represent very small dietary contributions as 'trace' or sometimes 'less than 0.1%'. These have been entered as -999 - QUALITATIVE_DIETARY_IMPORTANCE: Qualitative description of the dietary importance of this prey item (e.g. from comments about certain prey in the discussion text of an article), if numeric values have not been given. Current values are 'none', 'incidental', 'minor', 'major', 'almost exclusive', 'exclusive' - CONSUMPTION_RATE_MIN: The minimum consumption rate of this prey item - CONSUMPTION_RATE_MAX: The maximum consumption rate of this prey item - CONSUMPTION_RATE_MEAN: The mean consumption rate of this prey item - CONSUMPTION_RATE_SD: The standard deviation of the consumption rate of this prey item - CONSUMPTION_RATE_UNITS: The units of consumption rate (e.g. 'kg/day') - CONSUMPTION_RATE_NOTES: Notes about the consumption rate estimates - IDENTIFICATION_METHOD: How this dietary information was gathered. Multiple values can potentially be entered (separated by commas). Current values include 'scat content' (contents of scats), 'stomach flushing' (physical sampling of the stomach contents by flushing the contents out with water), 'stomach content' (physical sampling of the stomach contents from a dead animal), 'regurgitate content' (physical sampling of the contents of forced or spontaneous regurgitations), 'observed predation', 'bolus content' (physical sampling of the contents of boluses), 'nest detritus', 'unknown' - QUALITY_FLAG: An indicator of the quality of this record. 'Q' indicates that the data are known to be questionable for some reason. The reason should be in the notes column. 'G' indicates good data - IS_SECONDARY_DATA: An indicator of whether this record was entered from its primary source, or from a secondary citation. 'Y' here indicates that the data actually came from another paper and were being reported in this paper as secondary data. Secondary data records are likely to be removed at a later date and replaced with information from the original source. - NOTES: Any other notes - LAST_MODIFIED: The date of last modification of this record Schema: Isotope data table - RECORD_ID: The unique identifier of this record - SOURCE_ID: The reference number of the source of this data record. The list of references is provided with the database and also kept at: http://data.aad.gov.au/aadc/trophic/?tab=3 - LOCATION: The name of the location at which the data was collected. - WEST: The westernmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes) - EAST: The easternmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes) - SOUTH: The southernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes) - NORTH: The northernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes) - OBSERVATION_DATE_START: The start of the sampling period (UTC) - OBSERVATION_DATE_END: The end of the sampling period (UTC). If sampling was carried out over multiple seasons (e.g. during January of 2002 and January of 2003), these dates will indicate the first and last dates (as if the sampling was carried out from 1-Jan-2002 to 31-Jan-2003) - ALTITUDE_MIN: The minimum altitude of the sampling region, in metres (if applicable) - ALTITUDE_MAX: The maximum altitude of the sampling region, in metres (if applicable) - DEPTH_MIN: The shallowest depth of the sampling, in metres (if applicable) - DEPTH_MAX: The deepest depth of the sampling, in metres (if applicable) - TAXON_NAME_ORIGINAL: The name of the taxon, as it appeared in the original source. - TAXON_NAME: The scientific name of the taxon (corrected, if necessary). - TAXON_COMMON_NAME: The common name of the taxon (from the WoRMS taxonomic register) - TAXON_APHIA_ID: The numeric identifier of the taxon in the WoRMS taxonomic register - TAXON_LIFE_STAGE: Life stage of the taxon. e.g. 'adult', 'chick', 'larva'. Values 'C1'-'C3' refer to calyptopis larval stages of euphausiids. 'F1'-'F6' refer to furcilia larval stages of euphausiids. 'N1'-'N6' refer to nauplius stages of crustaceans. 'Copepodite 1'-'Copepodite 6' refer to developmental stages of copepodites - TAXON_BREEDING_STAGE: Stage of the breeding season of the taxon, if applicable. e.g. 'lactating', 'weaning', 'chick rearing' - TAXON_SEX: Sex of the taxon. 'male', 'female', 'both', or 'unknown' - TAXON_SAMPLE_COUNT: The number of samples from which size and stable isotope measurements were made - TAXON_SIZE_MIN: The minimum size of the individuals in the sample - TAXON_SIZE_MAX: The maximum size of the individuals in the sample - TAXON_SIZE_MEAN: The mean size of the individuals in the sample - TAXON_SIZE_SD: The standard deviation of the size of the individuals in the sample - TAXON_SIZE_UNITS: The units of size. Current values 'mm', 'm' - TAXON_SIZE_NOTES: Notes on the size information, including a definition of what the size value represents (e.g. 'total length', 'standard length') - TAXON_MASS_MIN: The minimum mass of the individuals in the sample - TAXON_MASS_MAX: The maximum mass of the individuals in the sample - TAXON_MASS_MEAN: The mean mass of the individuals in the sample - TAXON_MASS_SD: The standard deviation of the mass of the individuals in the sample - TAXON_MASS_UNITS: The units of mass. e.g. 'g', 'kg' - TAXON_MASS_NOTES: Notes on the taxon mass information, including a definition of what the mass value represents (blank implies total body weight) - DELTA_13C_MEAN: The mean of the d13C values from the sample (permil;) - DELTA_13C_VARIABILITY_VALUE: The variability of the d13C values from the sample - DELTA_13C_VARIABILITY_TYPE: The variability type that the DELTA_13C_VARIABILITY_VALUE represents (currently 'SD' standard deviation, or 'SE' standard error) - DELTA_15N_MEAN: The mean of the d15N values from the sample (permil;) - DELTA_15N_VARIABILITY_VALUE: The variability of the d15N values from the sample - DELTA_15N_VARIABILITY_TYPE: The variability type that the DELTA_15N_VARIABILITY_VALUE represents (currently 'SD' standard deviation, or 'SE' standard error) - C_N_RATIO_MEAN: The mean of the C:N ratio values from the sample, expressed as a molar percentage - C_N_RATIO_VARIABILITY_VALUE: The variability of the C:N ratio values from the sample - C_N_RATIO_VARIABILITY_TYPE: The variability type that the C_N_RATIO_VARIABILITY_VALUE represents (currently 'SD' standard deviation, or 'SE' standard error) - ISOTOPES_CARBONATES_EXTRACTED: Were carbonates extracted from the samples prior to isotope analyses? 'Y', 'N', or 'U' (unknown) - ISOTOPES_LIPIDS_EXTRACTED: Were lipids extracted from the samples prior to isotope analyses? 'Y', 'N', or 'U' (unknown) - ISOTOPES_BODY_PART_USED: Which part of the organism was sampled? - QUALITY_FLAG: An indicator of the quality of this record. 'Q' indicates that the data are known to be questionable for some reason. The reason should be in the notes column. 'G' indicates good data - IS_SECONDARY_DATA: An indicator of whether this record was entered from its primary source, or from a secondary citation. 'Y' here indicates that the data actually came from another paper and were being reported in this paper as secondary data. Secondary data records are likely to be removed at a later date and replaced with information from the original source. - NOTES: Any other notes - LAST_MODIFIED: The date of last modification of this record proprietary
+DB_Trophic_1 A compilation of dietary and related data from the Southern Ocean ALL STAC Catalog 1960-12-21 2010-03-20 -180, -80, 180, -40 https://cmr.earthdata.nasa.gov/search/concepts/C1214313435-AU_AADC.umm_json 2018-08-10 - these data have been superseded by a new metadata record and dataset - see the provided URL for more details. This record describes a compilation of trophic data from across the Southern Ocean. Data have been drawn from published literature, existing trophic data collections, AADC data sets, and unpublished collections. The database comprises two principal tables. The first table relates to direct sampling methods of dietary assessment, including gut, scat, and bolus content analyses, stomach flushing, and observed feeding. The second table is a compilation of stable isotope values. Each record in these two tables includes details such as the location and date of sampling, predator size and mass, prey size and mass, and estimates of dietary importance. Names have been validated against the World Register of Marine Species (http://www.marinespecies.org/). The schemas of these tables are described below, and a list of the sources used to populate the tables is provided with the data. A range of manual and automated checks were used to ensure that the entered data were as accurate as possible. These included visual checking of transcribed values, checking of row or column sums against known totals, and checking for values outside of allowed ranges. Suspicious entries were re-checked against original source. Apparent errors that could not be resolved were marked as such in the QUALITY_FLAG column, with the reason in the NOTES column. Notes on names 'Sp.' indicates unidentified members of a genus (e.g. 'Pachyptila sp.'). For unidentified taxa at other taxonomic levels, the taxonomic name has been used (e.g. Amphipoda, Myctophidae, Decapoda). Uncertain species identifications (e.g. 'Notothenia rossii?' or 'Gymnoscopelus cf. piabilis') were assigned the genus name (e.g. 'Notothenia sp.'). Original names were retained in a separate column to allow future cross-checking. WoRMS identifiers (APHIA_ID numbers) were recorded with each matched taxon. Grouped prey data in the diet sample table need to be handled with a bit of care. Papers commonly report prey statistics aggregated over groups of prey - e.g. one might give the diet composition by individual cephalopod prey species, and then an overall record for all cephalopod prey. The prey_is_aggregate column identifies such records. This allows us to differentiate grouped data like this from unidentified prey items from a certain prey group - for example, an unidentifiable cephalopod record would be entered as Cephalopoda (the scientific name), with 0 in the prey_is_aggregate column. A record that groups together a number of cephalopod records, possibly including some unidentifiable cephalopods, would also be entered as Cephalopoda, but with a 1 in the prey_is_aggregate column. See the notes on prey_is_aggregate, below. Schema: Diet sample table - LINK_ID: The unique identifier of this record - SOURCE_ID: The reference number of the source of this data record. The list of references is provided with the database and also kept at: http://data.aad.gov.au/aadc/trophic/?tab=3 - LOCATION: The name of the location at which the data was collected. - WEST: The westernmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes) - EAST: The easternmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes) - SOUTH: The southernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes) - NORTH: The northernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes) - OBSERVATION_DATE_START: The start of the sampling period (UTC) - OBSERVATION_DATE_END: The end of the sampling period (UTC). If sampling was carried out over multiple seasons (e.g. during January of 2002 and January of 2003), these dates will indicate the first and last dates (as if the sampling was carried out from 1-Jan-2002 to 31-Jan-2003) - ALTITUDE_MIN: The minimum altitude of the sampling region, in metres (if applicable) - ALTITUDE_MAX: The maximum altitude of the sampling region, in metres (if applicable) - DEPTH_MIN: The shallowest depth of the sampling, in metres (if applicable) - DEPTH_MAX: The deepest depth of the sampling, in metres (if applicable) - PREDATOR_NAME_ORIGINAL: The name of the predator, as it appeared in the original source - PREDATOR_NAME: The scientific name of the predator (corrected, if necessary). - PREDATOR_COMMON_NAME: The common name of the predator (from the WoRMS taxonomic register) - PREDATOR_APHIA_ID: The numeric identifier of the predator in the WoRMS taxonomic register - PREDATOR_LIFE_STAGE: Life stage of the predator. e.g. 'adult', 'chick', 'larva'. Values 'C1'-'C3' refer to calyptopis larval stages of euphausiids. 'F1'-'F6' refer to furcilia larval stages of euphausiids. 'N1'-'N6' refer to nauplius stages of crustaceans. 'Copepodite 1'-'Copepodite 6' refer to developmental stages of copepodites - PREDATOR_BREEDING_STAGE: Stage of the breeding season of the predator, if applicable. e.g. 'brooding', 'chick rearing', 'nonbreeding', 'posthatching' - PREDATOR_SEX: Sex of the predator. 'male', 'female', 'both', or 'unknown' - PREDATOR_SAMPLE_COUNT: The number of predators for which data are given. If (say) 50 predators were caught but only 20 analysed, this column will contain 20. - PREDATOR_TOTAL_COUNT: The total number of predators sampled. If (say) 50 predators were caught but only 20 analysed, this column will contain 50. - PREDATOR_SAMPLE_COUNT: The identifier of this predator sample. PREDATOR_SAMPLE_ID values are unique within a source (i.e. - SOURCE_ID, PREDATOR_SAMPLE_ID pairs are globally unique). Rows with the same SOURCE_ID and PREDATOR_SAMPLE_ID values relate to the same predator individual or population, and so can be combined (e.g. for prey diversity analyses). Subsamples are indicated by a decimal number S.nnn, where S is the parent PREDATOR_SAMPLE_ID, and nnn (001-999) is the subsample number. Studies will often report detailed prey information for a large sample, and also report prey information for various subsamples of that sample (e.g. broken down by predator sex, or sampling season). - PREDATOR_SIZE_MIN: The minimum size of the predators in the sample - PREDATOR_SIZE_MAX: The maximum size of the predators in the sample - PREDATOR_SIZE_MEAN: The mean size of the predators in the sample - PREDATOR_SIZE_SD: The standard deviation of the size of the predators in the sample - PREDATOR_SIZE_UNITS: The units of size. Current values 'mm', 'cm', 'm' - PREDATOR_SIZE_NOTES: Notes on the predator size information, including a definition of what the size value represents (e.g. 'total length', 'standard length') - PREDATOR_MASS_MIN: The minimum mass of the predators in the sample - PREDATOR_MASS_MAX: The maximum mass of the predators in the sample - PREDATOR_MASS_MEAN: The mean mass of the predators in the sample - PREDATOR_MASS_SD: The standard deviation of the mass of the predators in the sample - PREDATOR_MASS_UNITS: The units of mass (e.g. 'g' or 'kg') - PREDATOR_MASS_NOTES: Notes on the predator mass information, including a definition of what the mass value represents (blank implies total body weight). Current values 'g', 'kg', 't' - PREY_NAME_ORIGINAL: The name of the prey item, as it appeared in the original source. - PREY_NAME: The scientific name of the prey item (corrected, if necessary). - PREY_COMMON_NAME: The common name of the prey item (from the WoRMS taxonomic register) - PREY_APHIA_ID: The numeric identifier of the prey in the WoRMS taxonomic register - PREY_IS_AGGREGATE: 'Y' indicates that this row is an aggregation of other rows in this data source. For example, a study might give a number of individual squid species records, and then an overall squid record that encompasses the individual records. Use the PREY_IS_AGGREGATE information to avoid double-counting during analyses. If there no entry in this column, it means that this information is not included anywhere else in the database and can be used freely when aggregating over taxonomic groups, for example - PREY_LIFE_STAGE: Life stage of the prey. e.g. 'adult', 'chick', 'larva' - PREY_SAMPLE_COUNT: The number of prey individuals from which size and mass measurements were made (note: NOT the total number of individuals of this prey type, unless all individuals in the sample were measured) - PREY_SIZE_MIN: The minimum size of the prey in the sample - PREY_SIZE_MAX: The maximum size of the prey in the sample - PREY_SIZE_MEAN: The mean size of the prey in the sample - PREY_SIZE_SD: The standard deviation of the size of the prey in the sample - PREY_SIZE_UNITS: The units of size. Current values 'mm', 'cm', 'm' - PREY_SIZE_NOTES: Notes on the prey size information, including a definition of what the size value represents (e.g. 'total length', 'standard length') - PREY_MASS_MIN: The minimum mass of the prey in the sample - PREY_MASS_MAX: The maximum mass of the prey in the sample - PREY_MASS_MEAN: The mean mass of the prey in the sample - PREY_MASS_SD: The standard deviation of the mass of the prey in the sample - PREY_MASS_UNITS: The units of mass. Current values 'mg', 'g', 'kg' - PREY_MASS_NOTES: Notes on the prey mass information, including a definition of what the mass value represents (blank implies total body weight) - FRACTION_DIET_BY_WEIGHT: The fraction (by weight) of the predator diet that this prey type made up (e.g. if Euphausia superba contributed 50% of the total mass of prey items, this value would be 0.5). Many papers represent very small dietary contributions as 'trace' or sometimes 'less than 0.1%'. These have been entered as -999 - FRACTION_DIET_BY_PREY_ITEMS: The fraction (by number) of prey items that this prey type made up (e.g. if 1000 Euphausia superba were found out of a total of 2000 prey items, this value would be 0.5). Note: many papers represent very small dietary contributions as 'trace' or sometimes 'less than 0.1%'. These have been entered as -999 - FRACTION_OCCURRENCE: The number of times this prey item occurred in a predator sample, as a fraction of the number of non-empty samples (e.g. if Euphausia superba occurred in half of the non-empty stomachs examined, this value would be 0.5). Empty stomachs are ignored for the purposes of calculating fraction of occurrence. For gut content analyses (and any other study types where 'no prey' can occur in a sample), the fraction of empty stomachs is also given (using prey_name 'None' - e.g. if predator_total_count was 10 and 3 stomachs were empty, this will be 0.3). Note: many papers represent very small dietary contributions as 'trace' or sometimes 'less than 0.1%'. These have been entered as -999 - QUALITATIVE_DIETARY_IMPORTANCE: Qualitative description of the dietary importance of this prey item (e.g. from comments about certain prey in the discussion text of an article), if numeric values have not been given. Current values are 'none', 'incidental', 'minor', 'major', 'almost exclusive', 'exclusive' - CONSUMPTION_RATE_MIN: The minimum consumption rate of this prey item - CONSUMPTION_RATE_MAX: The maximum consumption rate of this prey item - CONSUMPTION_RATE_MEAN: The mean consumption rate of this prey item - CONSUMPTION_RATE_SD: The standard deviation of the consumption rate of this prey item - CONSUMPTION_RATE_UNITS: The units of consumption rate (e.g. 'kg/day') - CONSUMPTION_RATE_NOTES: Notes about the consumption rate estimates - IDENTIFICATION_METHOD: How this dietary information was gathered. Multiple values can potentially be entered (separated by commas). Current values include 'scat content' (contents of scats), 'stomach flushing' (physical sampling of the stomach contents by flushing the contents out with water), 'stomach content' (physical sampling of the stomach contents from a dead animal), 'regurgitate content' (physical sampling of the contents of forced or spontaneous regurgitations), 'observed predation', 'bolus content' (physical sampling of the contents of boluses), 'nest detritus', 'unknown' - QUALITY_FLAG: An indicator of the quality of this record. 'Q' indicates that the data are known to be questionable for some reason. The reason should be in the notes column. 'G' indicates good data - IS_SECONDARY_DATA: An indicator of whether this record was entered from its primary source, or from a secondary citation. 'Y' here indicates that the data actually came from another paper and were being reported in this paper as secondary data. Secondary data records are likely to be removed at a later date and replaced with information from the original source. - NOTES: Any other notes - LAST_MODIFIED: The date of last modification of this record Schema: Isotope data table - RECORD_ID: The unique identifier of this record - SOURCE_ID: The reference number of the source of this data record. The list of references is provided with the database and also kept at: http://data.aad.gov.au/aadc/trophic/?tab=3 - LOCATION: The name of the location at which the data was collected. - WEST: The westernmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes) - EAST: The easternmost longitude of the sampling region, in decimal degrees (negative values indicate western hemisphere longitudes) - SOUTH: The southernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes) - NORTH: The northernmost latitude of the sampling region, in decimal degrees (negative values indicate southern hemisphere latitudes) - OBSERVATION_DATE_START: The start of the sampling period (UTC) - OBSERVATION_DATE_END: The end of the sampling period (UTC). If sampling was carried out over multiple seasons (e.g. during January of 2002 and January of 2003), these dates will indicate the first and last dates (as if the sampling was carried out from 1-Jan-2002 to 31-Jan-2003) - ALTITUDE_MIN: The minimum altitude of the sampling region, in metres (if applicable) - ALTITUDE_MAX: The maximum altitude of the sampling region, in metres (if applicable) - DEPTH_MIN: The shallowest depth of the sampling, in metres (if applicable) - DEPTH_MAX: The deepest depth of the sampling, in metres (if applicable) - TAXON_NAME_ORIGINAL: The name of the taxon, as it appeared in the original source. - TAXON_NAME: The scientific name of the taxon (corrected, if necessary). - TAXON_COMMON_NAME: The common name of the taxon (from the WoRMS taxonomic register) - TAXON_APHIA_ID: The numeric identifier of the taxon in the WoRMS taxonomic register - TAXON_LIFE_STAGE: Life stage of the taxon. e.g. 'adult', 'chick', 'larva'. Values 'C1'-'C3' refer to calyptopis larval stages of euphausiids. 'F1'-'F6' refer to furcilia larval stages of euphausiids. 'N1'-'N6' refer to nauplius stages of crustaceans. 'Copepodite 1'-'Copepodite 6' refer to developmental stages of copepodites - TAXON_BREEDING_STAGE: Stage of the breeding season of the taxon, if applicable. e.g. 'lactating', 'weaning', 'chick rearing' - TAXON_SEX: Sex of the taxon. 'male', 'female', 'both', or 'unknown' - TAXON_SAMPLE_COUNT: The number of samples from which size and stable isotope measurements were made - TAXON_SIZE_MIN: The minimum size of the individuals in the sample - TAXON_SIZE_MAX: The maximum size of the individuals in the sample - TAXON_SIZE_MEAN: The mean size of the individuals in the sample - TAXON_SIZE_SD: The standard deviation of the size of the individuals in the sample - TAXON_SIZE_UNITS: The units of size. Current values 'mm', 'm' - TAXON_SIZE_NOTES: Notes on the size information, including a definition of what the size value represents (e.g. 'total length', 'standard length') - TAXON_MASS_MIN: The minimum mass of the individuals in the sample - TAXON_MASS_MAX: The maximum mass of the individuals in the sample - TAXON_MASS_MEAN: The mean mass of the individuals in the sample - TAXON_MASS_SD: The standard deviation of the mass of the individuals in the sample - TAXON_MASS_UNITS: The units of mass. e.g. 'g', 'kg' - TAXON_MASS_NOTES: Notes on the taxon mass information, including a definition of what the mass value represents (blank implies total body weight) - DELTA_13C_MEAN: The mean of the d13C values from the sample (permil;) - DELTA_13C_VARIABILITY_VALUE: The variability of the d13C values from the sample - DELTA_13C_VARIABILITY_TYPE: The variability type that the DELTA_13C_VARIABILITY_VALUE represents (currently 'SD' standard deviation, or 'SE' standard error) - DELTA_15N_MEAN: The mean of the d15N values from the sample (permil;) - DELTA_15N_VARIABILITY_VALUE: The variability of the d15N values from the sample - DELTA_15N_VARIABILITY_TYPE: The variability type that the DELTA_15N_VARIABILITY_VALUE represents (currently 'SD' standard deviation, or 'SE' standard error) - C_N_RATIO_MEAN: The mean of the C:N ratio values from the sample, expressed as a molar percentage - C_N_RATIO_VARIABILITY_VALUE: The variability of the C:N ratio values from the sample - C_N_RATIO_VARIABILITY_TYPE: The variability type that the C_N_RATIO_VARIABILITY_VALUE represents (currently 'SD' standard deviation, or 'SE' standard error) - ISOTOPES_CARBONATES_EXTRACTED: Were carbonates extracted from the samples prior to isotope analyses? 'Y', 'N', or 'U' (unknown) - ISOTOPES_LIPIDS_EXTRACTED: Were lipids extracted from the samples prior to isotope analyses? 'Y', 'N', or 'U' (unknown) - ISOTOPES_BODY_PART_USED: Which part of the organism was sampled? - QUALITY_FLAG: An indicator of the quality of this record. 'Q' indicates that the data are known to be questionable for some reason. The reason should be in the notes column. 'G' indicates good data - IS_SECONDARY_DATA: An indicator of whether this record was entered from its primary source, or from a secondary citation. 'Y' here indicates that the data actually came from another paper and were being reported in this paper as secondary data. Secondary data records are likely to be removed at a later date and replaced with information from the original source. - NOTES: Any other notes - LAST_MODIFIED: The date of last modification of this record proprietary
DB_Voyages_1 A database of Scientific Voyages of the Australian Antarctic Programme. AU_AADC STAC Catalog 1947-01-01 2014-04-30 20, -70, 160, -30 https://cmr.earthdata.nasa.gov/search/concepts/C1214308530-AU_AADC.umm_json "A register of all voyages that contribute to the science of the Australian Antarctic Programme. It includes voyages that opportunistically collect marine data while underway. Details have been gleaned from historic paper records, publications, voyage situation reports and reports from marine science cruises. Products linked to each voyage include a map, voyage schedule and a list of any science related activities on the voyage. The application links to various external resources within the Antarctic Division such as daily shipping reports, passenger lists and various sets of data. NOTE - Support for this application was put ""on hold"" after the 2013/2014 season. Hence, only voyages up until that season are included in the database. This decision may be revisited at some time in the future." proprietary
DB_Voyages_1 A database of Scientific Voyages of the Australian Antarctic Programme. ALL STAC Catalog 1947-01-01 2014-04-30 20, -70, 160, -30 https://cmr.earthdata.nasa.gov/search/concepts/C1214308530-AU_AADC.umm_json "A register of all voyages that contribute to the science of the Australian Antarctic Programme. It includes voyages that opportunistically collect marine data while underway. Details have been gleaned from historic paper records, publications, voyage situation reports and reports from marine science cruises. Products linked to each voyage include a map, voyage schedule and a list of any science related activities on the voyage. The application links to various external resources within the Antarctic Division such as daily shipping reports, passenger lists and various sets of data. NOTE - Support for this application was put ""on hold"" after the 2013/2014 season. Hence, only voyages up until that season are included in the database. This decision may be revisited at some time in the future." proprietary
DC3_Aerosol_AircraftInSitu_DC8_Data_1 DC3 In-Situ DC-8 Aircraft Aerosol Data LARC_ASDC STAC Catalog 2012-05-04 2012-08-28 -122, 25, -75, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2047155015-LARC_ASDC.umm_json DC3_Aerosol_AircraftInSitu_DC8_Data are in-situ aerosol data collected onboard the DC-8 aircraft during the Deep Convective Clouds and Chemistry (DC3) field campaign. Data collection for this product is complete. The Deep Convective Clouds and Chemistry (DC3) field campaign sought to understand the dynamical, physical, and lightning processes of deep, mid-latitude continental convective clouds and to define the impact of these clouds on upper tropospheric composition and chemistry. DC3 was conducted from May to June 2012 with a base location of Salina, Kansas. Observations were conducted in northeastern Colorado, west Texas to central Oklahoma, and northern Alabama in order to provide a wide geographic sample of storm types and boundary layer compositions, as well as to sample convection. DC3 had two primary science objectives. The first was to investigate storm dynamics and physics, lightning and its production of nitrogen oxides, cloud hydrometeor effects on wet deposition of species, surface emission variability, and chemistry in anvil clouds. Observations related to this objective focused on the early stages of active convection. The second objective was to investigate changes in upper tropospheric chemistry and composition after active convection. Observations related to this objective focused on the 12-48 hours following convection. This objective also served to explore seasonal change of upper tropospheric chemistry. In addition to using the NSF/NCAR Gulfstream-V (GV) aircraft, the NASA DC-8 was used during DC3 to provide in-situ measurements of the convective storm inflow and remotely-sensed measurements used for flight planning and column characterization. DC3 utilized ground-based radar networks spread across its observation area to measure the physical and kinematic characteristics of storms. Additional sampling strategies relied on lightning mapping arrays, radiosondes, and precipitation collection. Lastly, DC3 used data collected from various satellite instruments to achieve its goals, focusing on measurements from CALIOP onboard CALIPSO and CPL onboard CloudSat. In addition to providing an extensive set of data related to deep, mid-latitude continental convective clouds and analyzing their impacts on upper tropospheric composition and chemistry, DC3 improved models used to predict convective transport. DC3 improved knowledge of convection and chemistry, and provided information necessary to understanding the processes relating to ozone in the upper troposphere. proprietary
@@ -5514,8 +5515,8 @@ DISCOVERAQ_Texas_TraceGas_AircraftInSitu_P3B_Data_1 DISCOVER-AQ Texas Deployment
DISCover_land_cover_679_1 LBA Regional Land Cover from AVHRR, 1-km, Version 1.2 (IGBP) ORNL_CLOUD STAC Catalog 1992-04-01 1993-03-31 -85, -25, -30, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2777325823-ORNL_CLOUD.umm_json This data set is a subset of the IGBP DISCover data set, which was derived from the Global Land Cover Characteristics database. The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., latitude 10 deg N to 25 deg S, longitude 30 to 85 W). The data are at 1-km resolution in ASCII GRID format. proprietary
DISP CORONA Satellite Photography USGS_LTA STAC Catalog 1960-07-31 1972-05-31 -180, -85, 180, 85 https://cmr.earthdata.nasa.gov/search/concepts/C1220566178-USGS_LTA.umm_json On February 24, 1995, President Clinton signed an Executive Order, directing the declassification of intelligence imagery acquired by the first generation of United States photo-reconnaissance satellites, including the systems code-named CORONA, ARGON, and LANYARD. More than 860,000 images of the Earth's surface, collected between 1960 and 1972, were declassified with the issuance of this Executive Order. Image collection was driven, in part, by the need to confirm purported developments in then-Soviet strategic missile capabilities. The images also were used to produce maps and charts for the Department of Defense and for other Federal Government mapping programs. In addition to the images, documents and reports (collateral information) are available, pertaining to frame ephemeris data, orbital ephemeris data, and mission performance. Document availability varies by mission; documentation was not produced for unsuccessful missions. proprietary
DLEM_C_N_Export_1699_1 Export and Leaching of Carbon and Nitrogen from Mississippi River Basin, 1901-2099 ORNL_CLOUD STAC Catalog 1901-01-01 2099-12-31 -126, 24.5, -62, 53 https://cmr.earthdata.nasa.gov/search/concepts/C2389021952-ORNL_CLOUD.umm_json This dataset provides estimates for export and leaching of dissolved inorganic carbon (DIC), dissolved organic carbon (DOC), total organic carbon (TOC), particulate organic carbon (POC), ammonium (NH4+), nitrate (NO3-), and total organic nitrogen (TON) from the Mississippi River Basin (MRB) to the Gulf of Mexico. The estimates are provided for a historical period of 1901-2014, and a future period of 2010-2099 (carbon estimates only) under two scenarios of high and low levels of population growth, economy, and energy consumption, respectively. The estimates are from the Dynamic Land Ecosystem Model 2.0 (DLEM 2.0). These data are applicable to studying how changes in multiple environmental factors (e.g., fertilizer application, land-use changes, climate variability, atmospheric CO2, and N deposition) affect the dynamics of leaching and export to the Gulf of Mexico. proprietary
-DLG100K 1:100,000-scale Digital Line Graphs (DLG) from the U.S. Geological Survey ALL STAC Catalog 1987-06-19 -126, 24, -66, 49 https://cmr.earthdata.nasa.gov/search/concepts/C1220566434-USGS_LTA.umm_json Digital line graph (DLG) data are digital representations of cartographic information. DLG's of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1: 100,000 are used. Intermediate-scale DLG's are sold in five categories: (1) Public Land Survey System; (2) boundaries (3) transportation; (4) hydrography; and (5) hypsography. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks. proprietary
DLG100K 1:100,000-scale Digital Line Graphs (DLG) from the U.S. Geological Survey USGS_LTA STAC Catalog 1987-06-19 -126, 24, -66, 49 https://cmr.earthdata.nasa.gov/search/concepts/C1220566434-USGS_LTA.umm_json Digital line graph (DLG) data are digital representations of cartographic information. DLG's of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1: 100,000 are used. Intermediate-scale DLG's are sold in five categories: (1) Public Land Survey System; (2) boundaries (3) transportation; (4) hydrography; and (5) hypsography. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks. proprietary
+DLG100K 1:100,000-scale Digital Line Graphs (DLG) from the U.S. Geological Survey ALL STAC Catalog 1987-06-19 -126, 24, -66, 49 https://cmr.earthdata.nasa.gov/search/concepts/C1220566434-USGS_LTA.umm_json Digital line graph (DLG) data are digital representations of cartographic information. DLG's of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1: 100,000 are used. Intermediate-scale DLG's are sold in five categories: (1) Public Land Survey System; (2) boundaries (3) transportation; (4) hydrography; and (5) hypsography. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks. proprietary
DLG_LARGE Large-scale digital line graph data from the U.S. Geological Survey USGS_LTA STAC Catalog 1970-01-01 -126, 24, -66, 49 https://cmr.earthdata.nasa.gov/search/concepts/C1220566541-USGS_LTA.umm_json Digital line graph (DLG) data are digital representations of cartographic information. DLGs of map features are converted to digital form from maps and related sources. Large-scale DLG data are derived from USGS 1:20,000-, 1: 24,000-, and 1: 25,000-scale 7.5-minute topographic quadrangle maps and are available in nine categories: (1) hypsography, (2) hydrography, (3)vegetative surface cover, (4) non-vegetative features, (5) boundaries, (6)survey control and markers, (7) transportation, (8) manmade features, and (9)Public Land Survey System. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks. proprietary
DMA_DTED Shuttle Radar Topography Mission DTED Level 1 (3-arc second) Data (DTED-1) USGS_LTA STAC Catalog 2000-02-01 2000-02-29 -180, -56, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C1220555800-USGS_LTA.umm_json The Shuttle Radar Topography Mission (SRTM) successfully collected Interferometric Synthetic Aperture Radar (IFSAR) data over 80 percent of the landmass of the Earth between 60 degrees North and 56 degrees South latitudes in February 2000. The mission was co-sponsored by the National Aeronautics and Space Administration (NASA) and National Geospatial-Intelligence Agency (NGA). NASA's Jet Propulsion Laboratory (JPL) performed preliminary processing of SRTM data and forwarded partially finished data directly to NGA for finishing by NGA's contractors and subsequent monthly deliveries to the NGA Digital Products Data Wharehouse (DPDW). All the data products delivered by the contractors conform to the NGA SRTM products and the NGA Digital Terrain Elevation Data (DTED) to the Earth Resources Observation & Science (EROS) Center. The DPDW ingests the SRTM data products, checks them for formatting errors, loads the SRTM DTED into the NGA data distribution system, and ships the public domain SRTM DTED to the U.S. Geological Survey (USGS) Earth Resources Observation & Science (EROS) Center. Two resolutions of finished grade SRTM data are available through EarthExplorer from the collection held in the USGS EROS archive: 1 arc-second (approximately 30-meter) high resolution elevation data are only available for the United States. 3 arc-second (approximately 90-meter) medium resolution elevation data are available for global coverage. The 3 arc-second data were resampled using cubic convolution interpolation for regions between 60° north and 56° south latitude. [Summary provided by the USGS.] proprietary
DMI_OI-DMI-L4-GLOB-v1.0_1.0 GHRSST Level 4 DMI_OI Global Foundation Sea Surface Temperature Analysis (GDS version 2) POCLOUD STAC Catalog 2013-04-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2036881727-POCLOUD.umm_json A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced daily on an operational basis by the Danish Meteorological Institute (DMI) using an optimal interpolation (OI) approach on a global 0.05 degree grid. The analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several satellites. The sensors include the Advanced Very High Resolution Radiometer (AVHRR), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Visible Infrared Imager Radiometer Suite (VIIRS), and the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua. An ice field from the EUMETSAT OSI-SAF is used to mask out areas with ice. This dataset adheres to the version 2 GHRSST Data Processing Specification (GDS). proprietary
@@ -5552,8 +5553,8 @@ DUSTFLEXPART_1 FLEXPART dust aerosol L4 global daily 1 x 1 degrees V1 (DUSTFLEXP
Daily_FineParticulateMatter_AK_2157_1 Simulated Fine Particulate Matter (PM2.5) Estimates over Alaska, 2001-2015 ORNL_CLOUD STAC Catalog 2001-05-10 2015-09-28 -178, 51, -128, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2951624721-ORNL_CLOUD.umm_json The dataset provides simulated PM2.5 concentration estimates over Alaska, U.S. PM2.5 (particulate matter with diameter <= 2.5 microns) concentrations in air (micrograms m-3) are gridded at 0.1-degree resolution for May to September for the years 2001 through 2015. The data were created in a modeling process utilizing the Wildland Fire Emissions Inventory System (WFEIS), the Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset, and multiple models including the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The data are provided in GeoTIFF format. proprietary
Dairy_Methane_CA_V1-2_1902_1.2 Methane Emissions from Dairy Sources (Vista-CA), State of California, USA, 2019 ORNL_CLOUD STAC Catalog 2019-01-01 2019-12-31 -124.29, 32.73, -115.29, 41.93 https://cmr.earthdata.nasa.gov/search/concepts/C2515935936-ORNL_CLOUD.umm_json This dataset provides estimates of methane (CH4) emissions from dairies in California at a resolution of 0.1 degrees (~ 10 km x 10 km) for the year 2019. The mapped sources of dairy CH4 emissions are enteric fermentation and manure management reported in gigagrams per square km per year (Gg km-2 y-1). The sum of the two sources is also provided. These data are in the succession of Vista California (Vista-CA) spatial datasets that have identified and classified potential methane source emitters in California and were created utilizing an assortment of publicly available data sources from local, state, and federal agencies. This dataset can serve as a planning tool for mitigation, a prior for atmospheric observation-based emissions estimates, attribution of emissions to a specific facility, and to validate CH4 emissions reductions from management changes. proprietary
Dalberg Data Insights Crop Type Uganda_1 Dalberg Data Insights Crop Type Uganda MLHUB STAC Catalog 2020-01-01 2023-01-01 33.5466817, 1.4271017, 35.0001342, 3.7269467 https://cmr.earthdata.nasa.gov/search/concepts/C2781412457-MLHUB.umm_json This dataset contains crop types and field boundaries along with other metadata collected in a campaign run by Dalberg Data Insights in the end of September 2017, as close as possible to the harvest period of 2017. GeoODKapps were used to collect approximately four points per field to get widest coverage during two field campaigns.
Post ground data collection, Radiant Earth Foundation conducted a quality control of the polygons using Sentinel-2 imagery of the growing season as well as Google basemap imagery, and removed several polygons that overlapped with infrastructure or built-up areas. Finally, ground reference polygons were matched with corresponding time series data from Sentinel-2 satellites (listed in the source imagery property of each label item). proprietary
-Dall_Sheep_Population_Dynamics_1640_1 ABoVE: Dall Sheep Lamb Recruitment and Climate Data, Alaska and NW Canada, 2000-2015 ALL STAC Catalog 2000-01-01 2015-12-31 -163.28, 59.6, -123.55, 69.71 https://cmr.earthdata.nasa.gov/search/concepts/C2162145802-ORNL_CLOUD.umm_json This dataset contains estimated annual average Dall sheep (Ovis dalli dalli) lamb-to-ewe ratios for each year from 2000-2015 across the full species range in Alaska and Northwestern Canada. Sheep population data are from surveys conducted over the 14 major mountain ranges encompassing the range of Dall sheep. For this study, the mountain ranges were divided into 24 mountain units due to differing climate gradients. Estimated covariate environmental and climate data used to examine the relationship between environmental conditions and Dall sheep population performance (per mountain unit) are also provided and include precipitation, temperature, snow cover, elevation, and distance to the center of the range. proprietary
Dall_Sheep_Population_Dynamics_1640_1 ABoVE: Dall Sheep Lamb Recruitment and Climate Data, Alaska and NW Canada, 2000-2015 ORNL_CLOUD STAC Catalog 2000-01-01 2015-12-31 -163.28, 59.6, -123.55, 69.71 https://cmr.earthdata.nasa.gov/search/concepts/C2162145802-ORNL_CLOUD.umm_json This dataset contains estimated annual average Dall sheep (Ovis dalli dalli) lamb-to-ewe ratios for each year from 2000-2015 across the full species range in Alaska and Northwestern Canada. Sheep population data are from surveys conducted over the 14 major mountain ranges encompassing the range of Dall sheep. For this study, the mountain ranges were divided into 24 mountain units due to differing climate gradients. Estimated covariate environmental and climate data used to examine the relationship between environmental conditions and Dall sheep population performance (per mountain unit) are also provided and include precipitation, temperature, snow cover, elevation, and distance to the center of the range. proprietary
+Dall_Sheep_Population_Dynamics_1640_1 ABoVE: Dall Sheep Lamb Recruitment and Climate Data, Alaska and NW Canada, 2000-2015 ALL STAC Catalog 2000-01-01 2015-12-31 -163.28, 59.6, -123.55, 69.71 https://cmr.earthdata.nasa.gov/search/concepts/C2162145802-ORNL_CLOUD.umm_json This dataset contains estimated annual average Dall sheep (Ovis dalli dalli) lamb-to-ewe ratios for each year from 2000-2015 across the full species range in Alaska and Northwestern Canada. Sheep population data are from surveys conducted over the 14 major mountain ranges encompassing the range of Dall sheep. For this study, the mountain ranges were divided into 24 mountain units due to differing climate gradients. Estimated covariate environmental and climate data used to examine the relationship between environmental conditions and Dall sheep population performance (per mountain unit) are also provided and include precipitation, temperature, snow cover, elevation, and distance to the center of the range. proprietary
Dall_Sheep_Snowpack_1602_1 ABoVE: Dall Sheep Response to Snow and Landscape Covariates, Alaska, 2005-2008 ORNL_CLOUD STAC Catalog 2005-09-01 2008-08-31 -154.53, 59.98, -153.03, 61.05 https://cmr.earthdata.nasa.gov/search/concepts/C2170971503-ORNL_CLOUD.umm_json This dataset provides daily estimates of snow depth and snow density for the study area in Lake Clark National Park and Preserve (LCNPP), Alaska. The data were generated using SnowModel and used as snow covariates along with landscape covariates in modeling efforts to study Dall sheep movements in response to dynamic snow conditions. Thirty adult Dall sheep (12 male, 18 female) were captured and outfitted with global positioning system (GPS) collars programmed to acquire locations every seven hours. Given the individual sheep locations, their distances to land cover (e.g., shrub, forest, glacier), landscape characteristics (e.g., elevation, terrain ruggedness index (TRI), vector ruggedness measure (VRM), slope, and aspect), snow depth and density, MODIS normalized difference snow index (NDSI), and other covariates were determined and are provided in the environmental data file. The snow density and depth data are provided at 25-m, 100-m, 500-m, 2000-m, and 10000-m grid resolutions, at 1-day increments, and cover the period September 1, 2005 through August 31, 2008. The sheep, snow, and landscape data cover the years 2006, 2007, and 2008. proprietary
Dall_Sheep_Snowpack_1602_1 ABoVE: Dall Sheep Response to Snow and Landscape Covariates, Alaska, 2005-2008 ALL STAC Catalog 2005-09-01 2008-08-31 -154.53, 59.98, -153.03, 61.05 https://cmr.earthdata.nasa.gov/search/concepts/C2170971503-ORNL_CLOUD.umm_json This dataset provides daily estimates of snow depth and snow density for the study area in Lake Clark National Park and Preserve (LCNPP), Alaska. The data were generated using SnowModel and used as snow covariates along with landscape covariates in modeling efforts to study Dall sheep movements in response to dynamic snow conditions. Thirty adult Dall sheep (12 male, 18 female) were captured and outfitted with global positioning system (GPS) collars programmed to acquire locations every seven hours. Given the individual sheep locations, their distances to land cover (e.g., shrub, forest, glacier), landscape characteristics (e.g., elevation, terrain ruggedness index (TRI), vector ruggedness measure (VRM), slope, and aspect), snow depth and density, MODIS normalized difference snow index (NDSI), and other covariates were determined and are provided in the environmental data file. The snow density and depth data are provided at 25-m, 100-m, 500-m, 2000-m, and 10000-m grid resolutions, at 1-day increments, and cover the period September 1, 2005 through August 31, 2008. The sheep, snow, and landscape data cover the years 2006, 2007, and 2008. proprietary
Davis_2009_Aerial_Photography_1 High resolution digital aerial surveys of portions of the Vestfold Hills and Rauer Group AU_AADC STAC Catalog 2009-11-17 2009-11-23 77.5833, -68.5833, 78.5833, -68.3333 https://cmr.earthdata.nasa.gov/search/concepts/C1214313429-AU_AADC.umm_json "High resolution digital aerial photography of Adelie penguin colonies, Davis Station, Heidemann Valley, and other various areas, LIDAR scanning of portions of the Vestfold Hills, Rauer Islands and sea ice in front of the Amery Ice Shelf, conducted from 2009/11/17 to 2009/11/23. Some of the aerial photography has been conducted in support of various AAS projects: AAS 3012 (ASAC_3012) AAS 2722 (ASAC_2722) AAS 1034 (ASAC_1034) AAS 3130 (ASAC_3130) A short list of the work carried out: - Long duration over water/sea ice flights for the purposes of ""Investigation of physical and biological processes in the Antarctic sea ice zone during spring using in situ, aircraft and underwater observations"". - Over-flights at 750m over specific islands in the Vestfold Hills and Rauer Islands known to hold Adelie colonies. - Transects of flights were performed over Davis station, at 500m altitude, taking photos and LIDAR measurements. - The evaluation of the APPLS equipment (camera, LIDAR, electronics, software) was performed and in parallel to the other tasks. - Production a digital elevation model of the Heidemann Bay Area. - Aerial photography / LIDAR of moss beds in the Vestfold Hills area. - The Marine Plain area, south east of Davis, was mapped using LIDAR and aerial imagery for the purposes of general Antarctic information. - The Vestfold Lakes, particularly Lake Druzby, Watts Lake, Lake Nicholson and Crooked Lake provide interesting aerial imagery. - The opportunity was taken to visit the plateau skiway (at 'Woop woop') and estimate the effort in opening the skiway later in the season. - Fly over and photograph the length of the resupply fuel hose from the AA to the shore. - The Russian 'Progress 1 and 2', and Chinese Zhong Shan stations were over flown and aerial imagery collected. Taken from the report: This document describes the results of the use of the APPLS (Aerial Photographic Pyrometer Laser System) at Davis during resupply 2009/2010 (November 17 to 24, 2009). This document is primarily for Science Technical Support use. Portions of the report can be used to provide information on the results obtained to other parts of AAD." proprietary
@@ -5582,8 +5583,8 @@ Daymet_SubDaily_Puerto_Rico_1977_1 Sub-daily Climate Forcings for Puerto Rico OR
Daymet_V4_Daily_MonthlyLatency_1904_1 Daymet Version 4 Monthly Latency: Daily Surface Weather Data ORNL_CLOUD STAC Catalog 2021-01-01 2023-03-31 -178.13, 14.07, -53.06, 83.2 https://cmr.earthdata.nasa.gov/search/concepts/C2992264879-ORNL_CLOUD.umm_json This dataset provides Daymet Version 4 daily data on a monthly cycle as 1-km gridded estimates of daily weather variables for minimum temperature (tmin), maximum temperature (tmax), precipitation (prcp), shortwave radiation (srad), vapor pressure (vp), snow water equivalent (swe), and day length. Data are derived from the Daymet version 4 software where the primary inputs are daily observations of near-surface maximum and minimum air temperature and daily total precipitation from weather stations. The main algorithm to estimate primary Daymet variables (tmax, tmin, and prcp) at each Daymet grid is based on a combination of interpolation and extrapolation, using inputs from multiple weather stations and weights that reflect the spatial and temporal relationships between a Daymet grid and the surrounding weather stations. Secondary variables (srad, vp, and swe) are derived from the primary variables (tmax, tmin, and prcp) based on atmospheric theory and empirical relationships. The day length (dayl) estimate is based on geographic location and time of year. Data are available for the Continental North America, Puerto Rico, and Hawaii as separate spatial layers in a Lambert Conformal Conic projection and are distributed in standardized Climate and Forecast (CF)-compliant netCDF file formats. proprietary
Daymet_xval_V4R1_2132_4.1 Daymet: Station-Level Inputs and Cross-Validation for North America, Version 4 R1 ORNL_CLOUD STAC Catalog 1950-01-01 2023-12-31 -178.13, 14.07, -52.67, 82.91 https://cmr.earthdata.nasa.gov/search/concepts/C2531991823-ORNL_CLOUD.umm_json This dataset reports the station-level daily weather observation data and the corresponding cross-validation results for three Daymet model parameters: minimum temperature (tmin), maximum temperature (tmax), and daily total precipitation (prcp) across continental North America (including Canada, the United States, and Mexico), Hawaii, and Puerto Rico. Each data file contains the daily observations and cross-validation results for one parameter for each modeled region and each year, that is, from 1980 to the current calendar year for stations across continental North America and Hawaii and from 1950 to the current year for Puerto Rico. Also included are corresponding station metadata files listing every surface weather station used in Daymet processing for each parameter, region, and year and containing the station name, station identification, latitude, and longitude. The data are provided in netCDF and text formats. In Version 4 R1, all 2020 and 2021 files were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release. proprietary
Decadal_LULC_India_1336_1 Decadal Land Use and Land Cover Classifications across India, 1985, 1995, 2005 ORNL_CLOUD STAC Catalog 1985-01-01 2005-12-31 66.31, 6.71, 98.93, 36.32 https://cmr.earthdata.nasa.gov/search/concepts/C2773245356-ORNL_CLOUD.umm_json This data set provides land use and land cover (LULC) classification products at 100-m resolution for India at decadal intervals for 1985, 1995 and 2005. The data were derived from Landsat 4 and 5 Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Multispectral (MSS) data, India Remote Sensing satellites (IRS) Resourcesat Linear Imaging Self-Scanning Sensor-1 or III (LISS-I, LISS-III) data, ground truth surveys, and visual interpretation. The data were classified according to the International Geosphere-Biosphere Programme (IGBP) classification scheme. proprietary
-Decadal_Water_Maps_1324_1.1 ABoVE: Surface Water Extent, Boreal and Tundra Regions, North America, 1991-2011 ORNL_CLOUD STAC Catalog 1990-01-01 2012-12-31 -177.48, 41.7, -53.94, 82.37 https://cmr.earthdata.nasa.gov/search/concepts/C2162118169-ORNL_CLOUD.umm_json This data set provides the location and extent of surface water (open water not including vegetated wetlands) for the entire Boreal and Tundra regions of North America for three epochs, centered on 1991, 2001, and 2011. Each of the products were generated with at least three years of ice-free Landsat imagery. The data are at 30-m resolution and were derived from time series of Landsat 4 and 5 Thematic Mapper (TM) data and Landsat 7 Enhanced Thematic Mapper (ETM+) covering all of Alaska and all provinces of Canada. The overall goal was to generate a map of the nominal extent of water for a given epoch, where nominal is neither the maximum nor the minimum but rather a representative extent for that time period. proprietary
Decadal_Water_Maps_1324_1.1 ABoVE: Surface Water Extent, Boreal and Tundra Regions, North America, 1991-2011 ALL STAC Catalog 1990-01-01 2012-12-31 -177.48, 41.7, -53.94, 82.37 https://cmr.earthdata.nasa.gov/search/concepts/C2162118169-ORNL_CLOUD.umm_json This data set provides the location and extent of surface water (open water not including vegetated wetlands) for the entire Boreal and Tundra regions of North America for three epochs, centered on 1991, 2001, and 2011. Each of the products were generated with at least three years of ice-free Landsat imagery. The data are at 30-m resolution and were derived from time series of Landsat 4 and 5 Thematic Mapper (TM) data and Landsat 7 Enhanced Thematic Mapper (ETM+) covering all of Alaska and all provinces of Canada. The overall goal was to generate a map of the nominal extent of water for a given epoch, where nominal is neither the maximum nor the minimum but rather a representative extent for that time period. proprietary
+Decadal_Water_Maps_1324_1.1 ABoVE: Surface Water Extent, Boreal and Tundra Regions, North America, 1991-2011 ORNL_CLOUD STAC Catalog 1990-01-01 2012-12-31 -177.48, 41.7, -53.94, 82.37 https://cmr.earthdata.nasa.gov/search/concepts/C2162118169-ORNL_CLOUD.umm_json This data set provides the location and extent of surface water (open water not including vegetated wetlands) for the entire Boreal and Tundra regions of North America for three epochs, centered on 1991, 2001, and 2011. Each of the products were generated with at least three years of ice-free Landsat imagery. The data are at 30-m resolution and were derived from time series of Landsat 4 and 5 Thematic Mapper (TM) data and Landsat 7 Enhanced Thematic Mapper (ETM+) covering all of Alaska and all provinces of Canada. The overall goal was to generate a map of the nominal extent of water for a given epoch, where nominal is neither the maximum nor the minimum but rather a representative extent for that time period. proprietary
DeciduousFractionl_CanopyCover_2296_1 Deciduous Fractional Cover and Tree Canopy Cover for Boreal North America, 1992-2015 ORNL_CLOUD STAC Catalog 1992-01-01 2015-12-31 -179.94, 40, -50, 80.25 https://cmr.earthdata.nasa.gov/search/concepts/C2787699948-ORNL_CLOUD.umm_json This dataset holds deciduous fraction and tree canopy cover at 30-m resolution over the North American boreal domain for 1992 to 2015. Deciduous fraction is the areal percentage of deciduous trees relative to all tree canopy cover within a pixel, and tree canopy cover is the areal percentage of a pixel that is covered by tree canopy. Deciduous fraction values are valid only for pixels with tree canopy cover >25 percent. Normalized difference vegetation index (NDVI)-based median-value image composites were derived from Landsat 5, 7, and 8 Collection 1 surface reflectance datasets for years 1987-1997, 1998-2002, 2003-2007, 2008-2012, and 2013-2018 to create composites for nominal years 1992, 2000, 2005, 2010, and 2015, respectively. These image composites were prepared for early spring, mid-summer, and mid-to-late fall seasons to identify key differences in deciduous and evergreen green-up amplitudes. Random Forest (RF) regression models were used to derive deciduous fraction and tree canopy cover from the image composites. These models were trained with data from in-situ samples across Alaska and Canada from a variety of studies. Seventy percent of the in-situ samples were used for training and 30% for validation. Per-pixel uncertainty for both deciduous fraction and tree canopy cover are included and were based on one standard deviation of output values across all decision trees in the RF regression. These datasets were developed as part of NASA's ABoVE project to capture forest composition changes over the North American boreal domain across the last several decades. The data are provided in GeoTIFF format. proprietary
Declassified_Satellite_Imagery_2_2002 Declassified Satellite Imagery 2 (2002) USGS_LTA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220567575-USGS_LTA.umm_json Declassified satellite images provide an important worldwide record of land-surface change. With the success of the first release of classified satellite photography in 1995, images from U.S. military intelligence satellites KH-7 and KH-9 were declassified in accordance with Executive Order 12951 in 2002. The data were originally used for cartographic information and reconnaissance for U.S. intelligence agencies. Since the images could be of historical value for global change research and were no longer critical to national security, the collection was made available to the public. Keyhole (KH) satellite systems KH-7 and KH-9 acquired photographs of the Earth’s surface with a telescopic camera system and transported the exposed film through the use of recovery capsules. The capsules or buckets were de-orbited and retrieved by aircraft while the capsules parachuted to earth. The exposed film was developed and the images were analyzed for a range of military applications. The KH-7 surveillance system was a high resolution imaging system that was operational from July 1963 to June 1967. Approximately 18,000 black-and-white images and 230 color images are available from the 38 missions flown during this program. Key features for this program were larger area of coverage and improved ground resolution. The cameras acquired imagery in continuous lengthwise sweeps of the terrain. KH-7 images are 9 inches wide, vary in length from 4 inches to 500 feet long, and have a resolution of 2 to 4 feet. The KH-9 mapping program was operational from March 1973 to October 1980 and was designed to support mapping requirements and exact positioning of geographical points for the military. This was accomplished by using image overlap for stereo coverage and by using a camera system with a reseau grid to correct image distortion. The KH-9 framing cameras produced 9 x 18 inch imagery at a resolution of 20-30 feet. Approximately 29,000 mapping images were acquired from 12 missions. The original film sources are maintained by the National Archives and Records Administration (NARA). Duplicate film sources held in the USGS EROS Center archive are used to produce digital copies of the imagery. proprietary
Del_Ches_Bay_Fluorescence_0 Fluorescence measurements along Chesapeake Bay and Delaware coast OB_DAAC STAC Catalog 2008-04-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360200-OB_DAAC.umm_json Measurements made in the Chesapeake Bay and off the Delaware coast in 2008. proprietary
@@ -5654,14 +5655,14 @@ Drone Imagery Classification Training Dataset for Crop Types in Rwanda_1 Drone I
Dunne_545_1 Global Distribution of Plant-Extractable Water Capacity of Soil (Dunne) ORNL_CLOUD STAC Catalog 1996-01-01 1996-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2216862849-ORNL_CLOUD.umm_json Plant-extractable water capacity of soil is the amount of water that can be extracted from the soil to fulfill evapotranspiration demands. This data set provides an estimate of the global distribution of plant-extractable water capacity of soil. proprietary
E06_OCM_GAC_STGO00GND_1.0 EOS-06 OCM Global Area Coverage (GAC) - 1080m resolution Standard Products - Oceansat Series ISRO STAC Catalog 2023-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2866789316-ISRO.umm_json The main objectives of E06 are to study surface winds and ocean surface strata, observation of chlorophyll concentrations, monitoring of phytoplankton blooms, study of atmospheric aerosols and suspended sediments in the water. This has global coverage for every 2 days and sun glint free data for every 13 days. proprietary
E06_OCM_LAC_STGO00GND_1.0 EOS-06 OCM Local Area Coverage (LAC) - 366m Resolution Standard Products - Oceansat Series ISRO STAC Catalog 2023-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2866790547-ISRO.umm_json The main objectives of E06 are to study surface winds and ocean surface strata, observation of chlorophyll concentrations, monitoring of phytoplankton blooms, study of atmospheric aerosols and suspended sediments in the water. proprietary
-EANET Acid Deposition Monitoring Network in East Asia Data (EANET) CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232847617-CEOS_EXTRA.umm_json "Rapid industrialization in the East Asian countries has helped in achieving economic growth. Along with industrialization, primary energy consumption has also rapidly increased in East Asia. In 2002, total primary energy consumption in East Asia was 2.5 billion tons (oil equivalent). The major energy source in East Asia is coal, accounting for 38% of the total in 2002. Oil and natural gas follow at a rate of 33% and 8.7% respectively. The combustion of these fossil fuels is the main source of air pollutants such as sulfur dioxide and nitrogen oxides released into the atmosphere. East Asiafs total primary energy consumption in 2030 is estimated to be 4.7 billion tons (oil equivalent), twice large than in 2002 (international Energy Agency (IEA), World Energy Outlook 2004). If there is no efficient control, the emission of air pollutants will also increase. Sulfur and nitric acids are recognized as major causes of atmospheric acidification. Sulfur dioxide and nitrogen oxides emitted from the burning of coal and oil react in the atmosphere to form sulfuric acid and nitric acid that are deposited on the earth. Sulfuric acid is one of the most important components used to evaluate acid deposition. In some major cities in East Asia the annual deposition of sulfate amounts to more that 100 kg/ha. Sulfuric acid is not only deposited with precipitation in the cities but also transported together with sulfur dioxide and sulfate as well as other acids to surrounding areas and may affect our natural ecosystems. Acid deposition can cause various effects on the ecosystems through acidification of soil and waters as well as damage to buildings and cultural heritage through corrosion of metals, concrete and stone. In order to assess the adverse effects on the ecosystem, it is necessary to identify dose-effect relationship of acid and eutrophic substances in environment. It is also important to quantify the effects on ecosystems, estimate the necessary amount of reduction of emission, and consider the most cost-effective policy options. Determination of emission reduction target may require the identification of the threshold level of acidic and eutrophic substances that do not cause any adverse effect on ecosystems. Acid deposition is not limited by national boundaries and therefore cooperation at the regional and international level is required to effectively address this problem. In Europe, it was successfully achieved through the activities under the Convention on Long-Range Transboundary Air Pollution (CLRTAP). As pointed out in Agenda 21 adopted by the United Nations Conference on Environment and Development in June 1992, ""the programs (in Europe and North America) need to be continued and enhanced, and their experience needs to be shared with other regions of the world"". The Acid Deposition Monitoring Network in East Asia (EANET) was established as a regional cooperative initiative to promote efforts for environmental sustainability and protection of human health in the East Asian region." proprietary
EANET Acid Deposition Monitoring Network in East Asia Data (EANET) ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232847617-CEOS_EXTRA.umm_json "Rapid industrialization in the East Asian countries has helped in achieving economic growth. Along with industrialization, primary energy consumption has also rapidly increased in East Asia. In 2002, total primary energy consumption in East Asia was 2.5 billion tons (oil equivalent). The major energy source in East Asia is coal, accounting for 38% of the total in 2002. Oil and natural gas follow at a rate of 33% and 8.7% respectively. The combustion of these fossil fuels is the main source of air pollutants such as sulfur dioxide and nitrogen oxides released into the atmosphere. East Asiafs total primary energy consumption in 2030 is estimated to be 4.7 billion tons (oil equivalent), twice large than in 2002 (international Energy Agency (IEA), World Energy Outlook 2004). If there is no efficient control, the emission of air pollutants will also increase. Sulfur and nitric acids are recognized as major causes of atmospheric acidification. Sulfur dioxide and nitrogen oxides emitted from the burning of coal and oil react in the atmosphere to form sulfuric acid and nitric acid that are deposited on the earth. Sulfuric acid is one of the most important components used to evaluate acid deposition. In some major cities in East Asia the annual deposition of sulfate amounts to more that 100 kg/ha. Sulfuric acid is not only deposited with precipitation in the cities but also transported together with sulfur dioxide and sulfate as well as other acids to surrounding areas and may affect our natural ecosystems. Acid deposition can cause various effects on the ecosystems through acidification of soil and waters as well as damage to buildings and cultural heritage through corrosion of metals, concrete and stone. In order to assess the adverse effects on the ecosystem, it is necessary to identify dose-effect relationship of acid and eutrophic substances in environment. It is also important to quantify the effects on ecosystems, estimate the necessary amount of reduction of emission, and consider the most cost-effective policy options. Determination of emission reduction target may require the identification of the threshold level of acidic and eutrophic substances that do not cause any adverse effect on ecosystems. Acid deposition is not limited by national boundaries and therefore cooperation at the regional and international level is required to effectively address this problem. In Europe, it was successfully achieved through the activities under the Convention on Long-Range Transboundary Air Pollution (CLRTAP). As pointed out in Agenda 21 adopted by the United Nations Conference on Environment and Development in June 1992, ""the programs (in Europe and North America) need to be continued and enhanced, and their experience needs to be shared with other regions of the world"". The Acid Deposition Monitoring Network in East Asia (EANET) was established as a regional cooperative initiative to promote efforts for environmental sustainability and protection of human health in the East Asian region." proprietary
+EANET Acid Deposition Monitoring Network in East Asia Data (EANET) CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232847617-CEOS_EXTRA.umm_json "Rapid industrialization in the East Asian countries has helped in achieving economic growth. Along with industrialization, primary energy consumption has also rapidly increased in East Asia. In 2002, total primary energy consumption in East Asia was 2.5 billion tons (oil equivalent). The major energy source in East Asia is coal, accounting for 38% of the total in 2002. Oil and natural gas follow at a rate of 33% and 8.7% respectively. The combustion of these fossil fuels is the main source of air pollutants such as sulfur dioxide and nitrogen oxides released into the atmosphere. East Asiafs total primary energy consumption in 2030 is estimated to be 4.7 billion tons (oil equivalent), twice large than in 2002 (international Energy Agency (IEA), World Energy Outlook 2004). If there is no efficient control, the emission of air pollutants will also increase. Sulfur and nitric acids are recognized as major causes of atmospheric acidification. Sulfur dioxide and nitrogen oxides emitted from the burning of coal and oil react in the atmosphere to form sulfuric acid and nitric acid that are deposited on the earth. Sulfuric acid is one of the most important components used to evaluate acid deposition. In some major cities in East Asia the annual deposition of sulfate amounts to more that 100 kg/ha. Sulfuric acid is not only deposited with precipitation in the cities but also transported together with sulfur dioxide and sulfate as well as other acids to surrounding areas and may affect our natural ecosystems. Acid deposition can cause various effects on the ecosystems through acidification of soil and waters as well as damage to buildings and cultural heritage through corrosion of metals, concrete and stone. In order to assess the adverse effects on the ecosystem, it is necessary to identify dose-effect relationship of acid and eutrophic substances in environment. It is also important to quantify the effects on ecosystems, estimate the necessary amount of reduction of emission, and consider the most cost-effective policy options. Determination of emission reduction target may require the identification of the threshold level of acidic and eutrophic substances that do not cause any adverse effect on ecosystems. Acid deposition is not limited by national boundaries and therefore cooperation at the regional and international level is required to effectively address this problem. In Europe, it was successfully achieved through the activities under the Convention on Long-Range Transboundary Air Pollution (CLRTAP). As pointed out in Agenda 21 adopted by the United Nations Conference on Environment and Development in June 1992, ""the programs (in Europe and North America) need to be continued and enhanced, and their experience needs to be shared with other regions of the world"". The Acid Deposition Monitoring Network in East Asia (EANET) was established as a regional cooperative initiative to promote efforts for environmental sustainability and protection of human health in the East Asian region." proprietary
EARTH_CRUST_AEDD_PAC_MAR_GEOL1 Branch of Pacific Marine Geology Sample-oriented Digital Data Base, USGS, Alaska CEOS_EXTRA STAC Catalog 1964-01-01 -180, 53, -130, 74 https://cmr.earthdata.nasa.gov/search/concepts/C2231555378-CEOS_EXTRA.umm_json Summary level describes each U.S. Geological Survey, Office of Pacific Marine Geology cruise. Inventory level describes each individual sampling activity. Sample log details tests on each sample. Data level contains results of tests and assessments (geologic, engineering, biological). Data is from the Pacific Ocean and Arctic Ocean basins. Data base is mostly complete for region offshore of central and northern California. Interactive access from outside the facility is limited at present. proprietary
-EARTH_CRUST_AK_PETROGRAPH_THIN1 Alaskan Rocks - Petrographic Thin Sections; USGS, Anchorage CEOS_EXTRA STAC Catalog 1891-01-01 -179, 50, -140, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231550025-CEOS_EXTRA.umm_json A collection of petrographic thin sections made from rock samples collected by USGS field geologists in Alaska. Many of the sections have corresponding descriptions cards; thin sections number 30,000. Written requests or appointment only. Access to materials restricted to on-site use for non-USGS employees. proprietary
EARTH_CRUST_AK_PETROGRAPH_THIN1 Alaskan Rocks - Petrographic Thin Sections; USGS, Anchorage ALL STAC Catalog 1891-01-01 -179, 50, -140, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231550025-CEOS_EXTRA.umm_json A collection of petrographic thin sections made from rock samples collected by USGS field geologists in Alaska. Many of the sections have corresponding descriptions cards; thin sections number 30,000. Written requests or appointment only. Access to materials restricted to on-site use for non-USGS employees. proprietary
+EARTH_CRUST_AK_PETROGRAPH_THIN1 Alaskan Rocks - Petrographic Thin Sections; USGS, Anchorage CEOS_EXTRA STAC Catalog 1891-01-01 -179, 50, -140, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231550025-CEOS_EXTRA.umm_json A collection of petrographic thin sections made from rock samples collected by USGS field geologists in Alaska. Many of the sections have corresponding descriptions cards; thin sections number 30,000. Written requests or appointment only. Access to materials restricted to on-site use for non-USGS employees. proprietary
EARTH_CRUST_AUS_BMR_Min_Loc_DB Mineral Occurrence Location Data Base; BMR, Australia CEOS_EXTRA STAC Catalog 1989-08-01 110, -45, 155, -10 https://cmr.earthdata.nasa.gov/search/concepts/C2231957528-CEOS_EXTRA.umm_json The Australian Mineral Occurrence Location Database provides information on mineral occurrence and deposit locations in Australia. Currently, data covers 52% of Australia by area. The data base contains the name of mineral occurrences with the geographical coordinates of each occurrence. The spatial resolution: varies from 10m to 10 km, mainly about 500m. Sources of the data are named, such as maps, bibliographies, or correspondence. Commodities associated with the occurrence are delineated. The data is about 32 Megabytes and is rectified to standard coordinates. There are charges for the data. Order form and information about the data set are available from B. Elliott, Project Manager, Mineral Databases (telephone 06-2499502) or BMR Publication Sales (fax 06-2499982). proprietary
-EARTH_CRUST_USGS_AK_NOTEBOOKS1 Alaskan Geologic Field Notebooks; USGS, Anchorage CEOS_EXTRA STAC Catalog 1891-01-01 -179, 50, -140, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231549582-CEOS_EXTRA.umm_json These notebooks are the original field records of geologic observations made by USGS geologists working in Alaska. They contain field stations, sample numbers, rock types and descriptions, terrain conditions and outcrop sketches. Some also report topographic measurements, daily weather, and camp life. A few personal diaries of the early explorers are preserved. The notebooks may be viewed by written requests or appointment only. Access to certain materials may be restricted for non-government employees. Microfilm for these records is stored in Menlo Park, California. The data consists of 3,600 notebooks. proprietary
EARTH_CRUST_USGS_AK_NOTEBOOKS1 Alaskan Geologic Field Notebooks; USGS, Anchorage ALL STAC Catalog 1891-01-01 -179, 50, -140, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231549582-CEOS_EXTRA.umm_json These notebooks are the original field records of geologic observations made by USGS geologists working in Alaska. They contain field stations, sample numbers, rock types and descriptions, terrain conditions and outcrop sketches. Some also report topographic measurements, daily weather, and camp life. A few personal diaries of the early explorers are preserved. The notebooks may be viewed by written requests or appointment only. Access to certain materials may be restricted for non-government employees. Microfilm for these records is stored in Menlo Park, California. The data consists of 3,600 notebooks. proprietary
+EARTH_CRUST_USGS_AK_NOTEBOOKS1 Alaskan Geologic Field Notebooks; USGS, Anchorage CEOS_EXTRA STAC Catalog 1891-01-01 -179, 50, -140, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231549582-CEOS_EXTRA.umm_json These notebooks are the original field records of geologic observations made by USGS geologists working in Alaska. They contain field stations, sample numbers, rock types and descriptions, terrain conditions and outcrop sketches. Some also report topographic measurements, daily weather, and camp life. A few personal diaries of the early explorers are preserved. The notebooks may be viewed by written requests or appointment only. Access to certain materials may be restricted for non-government employees. Microfilm for these records is stored in Menlo Park, California. The data consists of 3,600 notebooks. proprietary
EARTH_CRUST_USGS_COAL_NCRDS_DB National Coal Resources Data System; USGS CEOS_EXTRA STAC Catalog 1966-01-01 -125, 25, -66, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231554417-CEOS_EXTRA.umm_json The basic National Coal Resources Data System (NCRDS) allows users to interactively retrieve information on coal quantity and quality and to build new resource data from ongoing research on the geology of coal by the U.S. Geological Survey and state agencies. The NCRDS is an automated system. Data bases accessed contain some proprietary data. Access to non-U.S. Geological Survey users is limited to nonproprietary data. NCRDS is a user-oriented computerized storage, retrieval, and display system devised by the U.S. Geological Survey to assess the quantity and quality of national coal resources. The U.S. Geological Survey has initiated a 5- to 10-year program to provide point-source coverage for the coal-bearing rocks in the U.S. Cooperative projects with many state geologic agencies have been funded to supplement U.S. Geological Survey work and to amass the volume of data required to assess U.S. coal resources. Currently, files containing summary areal coal tonnage estimates and proximate/ultimate chemical analyses and point-located major, minor, and trace-element analyses are available. The point-source files are used to calculate resource estimates and to depict trends in the occurrence and chemical characteristics of coal. Primary data for measurements, coal-bed outcrop patterns, burned, channeled, and mined-out areas, and geochemical analyses. System software can calculate coal resource estimates, generate overburden or interburden distribution, and delineate areas of coal with selected quality parameters (for example, >1 percent sulfur, <50 ppm Zinc) within specified boundaries, (for example, county, quadrangle, or lease tract). Data may be displayed descriptively by preformated tables, user-determined listings, and selected statistics or graphically by two-and three-dimensional diagrams, trend surfaces, isoline maps, and stratigraphic sections. The system runs on a SUN Network of servers and workstations located in Reston, VA. and Denver, CO. proprietary
EARTH_CRUST_USGS_GeoNames Geologic Names of the U.S., Territories and Possessions, USGS CEOS_EXTRA STAC Catalog 1800-01-01 -125, 25, -66, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231552995-CEOS_EXTRA.umm_json The data base contains an annotated index lexicon of formal geologic nomenclature of the United States, territories, and possessions, with data on location, geologic age, U.S. Geological Survey usage, lithology, geologic province, thickness at type section, location of type section, and naming reference for each geologic unit. proprietary
EARTH_CRUST_USGS_NPRA_GEOCHEM1 National Petroleum Reserve-Alaska Geochemical Data; USGS CEOS_EXTRA STAC Catalog 1977-01-01 1978-12-31 -162, 60, -152, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2231554279-CEOS_EXTRA.umm_json The National Petroleum Reserve in Alaska (NPRA) is located in the primitive wilderness of Alaska's North Slope. The U.S. Geological Survey (USGS) began some geological surveying in this area in the early 1900's, and the U.S. Navy began geological and geophysical surveys and drilling in 1945 to appraise the petroleum potential of the Reserve. GEOCHEMICAL DATA Includes microfilm reels of Phase I, II, and III geochemical analyses of well cores. See also the NPPRA legacy data archive:http://energy.cr.usgs.gov/ proprietary
@@ -5689,12 +5690,12 @@ EARTH_LAND_USGS_AK_Koyukuk1 Koyukuk National Wildlife Refuge Landcover, Topograp
EARTH_LAND_USGS_AK_NOAA_AVHRR NOAA Digital AVHRR Satellite Data; USGS, Alaska CEOS_EXTRA STAC Catalog 1984-01-01 170, 52, -130, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231553312-CEOS_EXTRA.umm_json This digital data set contains selected NOAA 6, 7, 8 and 9 Advanced Very High Resolution Radiometer (AVHRR) imagery of Alaska; AVHRR is carried on NOAA's polar orbiting satellites. Spatial referencing is 1.1 km at nadir. Data source is National Oceanic and Atmospheric Administration (NOAA). The data set includes 47 records with estimated growth rate of 100 records per year. Storage required varies by storage medium and selected scene. The file structure is sequential. Data are available on 9-track, 800 bpi, 1600 bpi, 6250 bpi, unlabeled, unblocked, BCD, fixed record length tape. Subsets and customs formats are available. Limited documentation is available. Data is organized by 7 1/2 ' or 15 ' quads. Uses include fuel mapping, vegetation monitoring, large area mosaic, and monitoring of ice/snow dynamics. proprietary
EARTH_LAND_USGS_AK_NPRA_veg1 National Petroleum Reserve in Alaska (NPRA) - Vegetation Map, USGS CEOS_EXTRA STAC Catalog 1975-01-01 1977-12-31 -180, 53, -132, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2231553282-CEOS_EXTRA.umm_json A vegetation/land cover raster digital data set for the entire National Petroleum Reserve in Alaska (NPR-A) was generated from Landsat multispectral data sets. Included are eleven categories of vegetation and land cover which are derived from all or portions of 10 Landsat MSS scenes. The data set covers all or part of thirteen 1:250,000-scale topographic quadrangles. Data are stored in 50 meter pixels and registered to a UTM base. A full NPR-A mosaic as well as the 1:250,000 topographic series. Data are available in two forms: a digital mosaic of (1) the entire NPR-A coverage, split into two pieces each and registered to a separate UTM zone, or (2) for each 1:250,000-scale topo quadrangle area within the NPR-A. This file is too large to remain online. It is stored on magnetic tape at Moffett Field, CA. proprietary
EARTH_LAND_USGS_AK_Wildlif_Ref1 Arctic National Wildlife Refuge Data; USGS, Alaska CEOS_EXTRA STAC Catalog 1976-08-27 1981-08-05 -151.5, 65.5, -140.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552054-CEOS_EXTRA.umm_json Digital land cover and terrain data of the Arctic National Wildlife Refuge (ANWR) were prduced by the U.S. Geological Survey (USGS) Earth Resources Observation Systems Field Office, Anchorage, Alaska for the U.S. Fish and Wildlife Service (USFWS). These and other environmental data, were incorporated into the USFWS geographic information system to prepare a comprehensive conservation plan for the ANWR and an environmental impact statement which addresses oil and gas development in the Arctic Coastal Plain, and to assist research of the Porcupine Caribou herd. The data set contains land cover classification derived from Landsat MSS data, and elevation, slope and aspect data derived from DEM data. Data can be keyed on a U.S. Geological Survey 1:250,000 quadrangle basis. Spatial referencing is by 50 meter grid cells. Data source is Landsat MSS data, Digital Elevation Model (DEM) data, containing 299 records and the storage required varies by storage medium and selected area; file structure is sequential. Limited documentation and users guide are available. The data is organized by 7 1/2 ' or 15 ' quads. proprietary
-EARTH_LAND_USGS_ALASKA_FOSSILS1 Alaskan Fossil Identification File ALL STAC Catalog 1898-01-01 -180, 53, -130, 74 https://cmr.earthdata.nasa.gov/search/concepts/C2231550059-CEOS_EXTRA.umm_json The data base consists of a compilation of reports made by the U.S. Geological Survey Branch of Paleontology and Stratigraphy concerning the identification of fossils collected in Alaska. Data includes fossil type and age, sample locality, collector, author, and date of report. Reports are grouped together by year, but are not indexed. Written requests or appointment only. Permission for access by non-U.S. Geological Survey employees must be obtained in writing from the U.S. Geological Survey Branch of Paleontology and Stratigraphy. Data consists of 65 notebooks. proprietary
EARTH_LAND_USGS_ALASKA_FOSSILS1 Alaskan Fossil Identification File CEOS_EXTRA STAC Catalog 1898-01-01 -180, 53, -130, 74 https://cmr.earthdata.nasa.gov/search/concepts/C2231550059-CEOS_EXTRA.umm_json The data base consists of a compilation of reports made by the U.S. Geological Survey Branch of Paleontology and Stratigraphy concerning the identification of fossils collected in Alaska. Data includes fossil type and age, sample locality, collector, author, and date of report. Reports are grouped together by year, but are not indexed. Written requests or appointment only. Permission for access by non-U.S. Geological Survey employees must be obtained in writing from the U.S. Geological Survey Branch of Paleontology and Stratigraphy. Data consists of 65 notebooks. proprietary
+EARTH_LAND_USGS_ALASKA_FOSSILS1 Alaskan Fossil Identification File ALL STAC Catalog 1898-01-01 -180, 53, -130, 74 https://cmr.earthdata.nasa.gov/search/concepts/C2231550059-CEOS_EXTRA.umm_json The data base consists of a compilation of reports made by the U.S. Geological Survey Branch of Paleontology and Stratigraphy concerning the identification of fossils collected in Alaska. Data includes fossil type and age, sample locality, collector, author, and date of report. Reports are grouped together by year, but are not indexed. Written requests or appointment only. Permission for access by non-U.S. Geological Survey employees must be obtained in writing from the U.S. Geological Survey Branch of Paleontology and Stratigraphy. Data consists of 65 notebooks. proprietary
EARTH_LAND_USGS_ALASKA_GEODETIC Alaska Geodetic Control Files; USGS ALL STAC Catalog 1890-01-01 -180, 53, -130, 74 https://cmr.earthdata.nasa.gov/search/concepts/C2231552547-CEOS_EXTRA.umm_json Positional (horizontal) central data and elevational (vertical) control data for the state of Alaska. Data may include description of control points and 'to-reach' information. It is issued in 1/2 ' or 15 ' quads, states, and the 1:250,000 topographic series. These maps are not yet available in digital form. proprietary
EARTH_LAND_USGS_ALASKA_GEODETIC Alaska Geodetic Control Files; USGS CEOS_EXTRA STAC Catalog 1890-01-01 -180, 53, -130, 74 https://cmr.earthdata.nasa.gov/search/concepts/C2231552547-CEOS_EXTRA.umm_json Positional (horizontal) central data and elevational (vertical) control data for the state of Alaska. Data may include description of control points and 'to-reach' information. It is issued in 1/2 ' or 15 ' quads, states, and the 1:250,000 topographic series. These maps are not yet available in digital form. proprietary
-EARTH_LAND_USGS_AMES_AIR_PHOTOS Aerial Photographs (from AMES Pilot Land Data System); USGS EDC, Sioux Falls USGS_LTA STAC Catalog 1970-01-01 -180, 20, -60, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1220566371-USGS_LTA.umm_json "The aerial photography inventoried by the Pilot Land Data System (PLDS) at NASA AMES Research Center has been transferred to the USGS EROS Data Center. The photos were obtained from cameras mounted on high and medium altitude aircraft based at the NASA Ames Research Center. Several cameras with varying focal lengths, lenses and film formats are used, but the Wild RC-10 camera with a focal length of 152 millimeters and a 9 by 9 inch film format is most common. The positive transparencies are typically used for ancillary ground checks in conjunctions with digital processing for the same sites. The aircraft flights, specifically requested by scientists performing approved research, often simultaneously collect data using other sensors on board (e.g. Thematic Mapper Simulators (TMS) and Thermal Infrared Multispectral Scanners). High altitude color infrared photography is used regularly by government agencies for such applications as crop yield forecasting, timber inventory and defoliation assessment, water resource management, land use surveys, water pollution monitoring, and natural disaster assessment. To order, specify the latitude and longitude of interest. You will then be given a list of photos available for that location. In some cases, ""flight books"" are available at EDC that describe the nature of the mission during which the photos were taken and other attribute information. The customer service personnel have access to these books for those photo sets for which the books exist." proprietary
EARTH_LAND_USGS_AMES_AIR_PHOTOS Aerial Photographs (from AMES Pilot Land Data System); USGS EDC, Sioux Falls ALL STAC Catalog 1970-01-01 -180, 20, -60, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1220566371-USGS_LTA.umm_json "The aerial photography inventoried by the Pilot Land Data System (PLDS) at NASA AMES Research Center has been transferred to the USGS EROS Data Center. The photos were obtained from cameras mounted on high and medium altitude aircraft based at the NASA Ames Research Center. Several cameras with varying focal lengths, lenses and film formats are used, but the Wild RC-10 camera with a focal length of 152 millimeters and a 9 by 9 inch film format is most common. The positive transparencies are typically used for ancillary ground checks in conjunctions with digital processing for the same sites. The aircraft flights, specifically requested by scientists performing approved research, often simultaneously collect data using other sensors on board (e.g. Thematic Mapper Simulators (TMS) and Thermal Infrared Multispectral Scanners). High altitude color infrared photography is used regularly by government agencies for such applications as crop yield forecasting, timber inventory and defoliation assessment, water resource management, land use surveys, water pollution monitoring, and natural disaster assessment. To order, specify the latitude and longitude of interest. You will then be given a list of photos available for that location. In some cases, ""flight books"" are available at EDC that describe the nature of the mission during which the photos were taken and other attribute information. The customer service personnel have access to these books for those photo sets for which the books exist." proprietary
+EARTH_LAND_USGS_AMES_AIR_PHOTOS Aerial Photographs (from AMES Pilot Land Data System); USGS EDC, Sioux Falls USGS_LTA STAC Catalog 1970-01-01 -180, 20, -60, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1220566371-USGS_LTA.umm_json "The aerial photography inventoried by the Pilot Land Data System (PLDS) at NASA AMES Research Center has been transferred to the USGS EROS Data Center. The photos were obtained from cameras mounted on high and medium altitude aircraft based at the NASA Ames Research Center. Several cameras with varying focal lengths, lenses and film formats are used, but the Wild RC-10 camera with a focal length of 152 millimeters and a 9 by 9 inch film format is most common. The positive transparencies are typically used for ancillary ground checks in conjunctions with digital processing for the same sites. The aircraft flights, specifically requested by scientists performing approved research, often simultaneously collect data using other sensors on board (e.g. Thematic Mapper Simulators (TMS) and Thermal Infrared Multispectral Scanners). High altitude color infrared photography is used regularly by government agencies for such applications as crop yield forecasting, timber inventory and defoliation assessment, water resource management, land use surveys, water pollution monitoring, and natural disaster assessment. To order, specify the latitude and longitude of interest. You will then be given a list of photos available for that location. In some cases, ""flight books"" are available at EDC that describe the nature of the mission during which the photos were taken and other attribute information. The customer service personnel have access to these books for those photo sets for which the books exist." proprietary
EARTH_LAND_USGS_DEM_AK1 Digital Terrain Data Sets for Alaska, USGS CEOS_EXTRA STAC Catalog 1982-01-01 -180, 54, -135, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231550167-CEOS_EXTRA.umm_json This data set contains up to nine types of digital elevation data: 1-1 degree blocks, 2-1 degree x 3 degree mosaic of elevation (latitude/longitude coordinate system), 3-1 degree x 3 degree mosaic of slope, 4-1 degree x 3 degree mosaic of aspect (latitude/longitude coordinate system), 5-1 degree x 3 degree mosaic of filtered elevation (5 x 5 filter), 6-1 degree x 3 degree mosaic of elevation (UTM registered), 7-1 degree x 3 degree mosaic of slope (UTM registered), 8-1 degree x 3 degree mosaic of aspect (UTM registered), 9-1 degree x 3 degree mosaic of shaded relief (latitude/longitude coordinate system). Data coverage is from 1982 to present with work ongoing. Data source is 1:250,000 scale Defense Mapping Agency Digital Terrain Series. The data set currently contains 966 records with estimated growth of 5-15 records per year. Storage required varies by selection on area size. Data are available on: 9-track, 800 bpi, 1600 bpi, 6250 bpi, unlabeled, unblocked, or BCD tape. Subsets on the main file and custom formats as well as limited documentation is available. Data is organized by 7 1/2 ' or 15 ' quads. This data is intended to be used for land cover analysis, wildlife refuge studies, drainage analysis, and land use planning. proprietary
EARTH_LAND_USGS_EDC_AK_Landsat Landsat 1-5 dataset from Alaska Field Office's Dbase; USGS, Alaska CEOS_EXTRA STAC Catalog 1972-01-01 170, 52, -130, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231551714-CEOS_EXTRA.umm_json This data set contains raw unregistered Landsat digital data covering most of Alaska. Data obtained from EROS Data Center in Sioux Falls, South Dakota. Data acquired from 1980 and is ongoing. Some Landsat scenes date back to 1972. The data set currently has 585 records with a growth chart at 5-10 records per year. The amount of storage required varies by medium used or full scene or subscene selection; the file structure is sequential. Spatial referencing of data is by 57 x 59 meter grid cell size-MSS data. Data are available on 9-track, 800 bpi, 1600 bpi, 6250 bpi, unlabeled, unblocked, BCD, fixed record length tape. Subsets and custom formats are available. Limited documentation is available. The data is organized in 7 1/2 ' or 15 ' quads. Data is used for false color composites, land cover analysis, geologic analysis, hydrogeologic analysis, land use planning, basis for update of topographic maps, production of image maps. proprietary
EARTH_LAND_USGS_Water_PKFIL Annual Peak Discharge and Stage of US Surface Water; USGS CEOS_EXTRA STAC Catalog 1900-01-01 -125, 25, -66, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231549666-CEOS_EXTRA.umm_json Originally available as hard copy publication, the Annual Peak Discharge and Stage of US Surface Water data will be made available to the public via the World Wide Web. The URL of the data set is to be announced. For more information, please contact the U.S. Geological Survey, Water Resources Division. The Peak Flow File (PKFIL) of the U.S. Geological Survey/Water Resources Division contains data on Annual maximum (peak) streamflow (discharge) and gage height (stage) values at surface water sites in the U.S. These data are published annually on a state basis in water resources data reports. proprietary
@@ -5702,13 +5703,13 @@ ECA011 Air-Water flux of organochlorine pesticides along the Western Antarctic P
ECA011 Air-Water flux of organochlorine pesticides along the Western Antarctic Peninsula SCIOPS STAC Catalog 2001-10-07 2002-03-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595136-SCIOPS.umm_json Hexachlorobenzene (HCB), heptachlor, α- and γ- HCH and heptachlor epoxide were identified in air, seawater, sea ice, and snow. Samples were collected during the austral winter (September-October 2001) and summer (January-February 2002) along a transect in the Western Antarctic Peninsula. By comparison with previous studie they concluded HCB and HCH levels declined over the past 20 years, with a half-life of 3 28 years in Antarctic air. However, they observed that heptachlor epoxide levels did not decrease in Antarctic air over the past decade, possibly due to continued use of heptachlor in the southern hemisphere. They detected peak heptachlor concentrations in air coincident with air masses moving into the region from lower latitudes. Levels of lindane were 1.2-200 times higher in annual sea ice and snow compared to α HCH, likely due to greater atmospheric input of γ-HCH. On the basis of the ratio of α/γ-HCH <1 in Antarctic air, sea ice and snow they concluded that there is a predominance of influx of lindane versus technical HCH to the regional environment. However, they also observed that the α/γ-HCH in seawater was >1, likely due to more rapid microbial degradation of γ- versus α-HCH. Also this study concluded that the water/air fugacity ratios for HCHs demonstrate continued atmospheric influx of HCHs to coastal Antarctic seas, particularly during late summer proprietary
ECA012 Air-Water Gas Exchange of Hexachlorocycloheane Enamtiomers in the South Atlantic Ocean and Antarctica SCIOPS STAC Catalog 1997-11-30 1998-02-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595146-SCIOPS.umm_json The spatial distribution of α-HCH and the net direction of air/water gas exchange were determined between November 1997 and February 1998. Air and water samples were collected between South Atlantic Ocean (South Africa) and Antarctica SANAE Base (70°S, 3°E). The α-HCH concentrations in air and surface water were much lower than in Arctic regions, consistent with the historically lower usage of technical HCH in the Southern Hemisphere. The water/air fugacity ratios of α-HCH were lower than or equal to 1.0, indicating steady state or net deposition conditions. One analysis of the enantiomeric fractionation was also made The results showed that the α-HCH in water was enantioselectively metabolized and that the two isomers [(-)α-HCH and (+)α-HCH] in the air boundary layer reflected those in surface water, showing the bidirectional nature of gas exchange. proprietary
ECA012 Air-Water Gas Exchange of Hexachlorocycloheane Enamtiomers in the South Atlantic Ocean and Antarctica ALL STAC Catalog 1997-11-30 1998-02-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595146-SCIOPS.umm_json The spatial distribution of α-HCH and the net direction of air/water gas exchange were determined between November 1997 and February 1998. Air and water samples were collected between South Atlantic Ocean (South Africa) and Antarctica SANAE Base (70°S, 3°E). The α-HCH concentrations in air and surface water were much lower than in Arctic regions, consistent with the historically lower usage of technical HCH in the Southern Hemisphere. The water/air fugacity ratios of α-HCH were lower than or equal to 1.0, indicating steady state or net deposition conditions. One analysis of the enantiomeric fractionation was also made The results showed that the α-HCH in water was enantioselectively metabolized and that the two isomers [(-)α-HCH and (+)α-HCH] in the air boundary layer reflected those in surface water, showing the bidirectional nature of gas exchange. proprietary
-ECA014 Air-Water Distribution of POPs Along a North-South Atlantic Transect SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595147-SCIOPS.umm_json To study the transport of POPs from the northern hemisphere to the southern, cruises were carried out collecting aerosol and surface water samples where different classes of organic pollutants were determined. The content of polychlorinated biphenyls (PCBs), hexachlorobenzene (HCB), 1,1-dichloro-2,2-bis(4-chlorophenyl)ethene (4,4′-DDE), and polyaromatic hydrocarbons (PAHs) were determined from the island of Texel (The 29 Netherlands) to Walvis Bay (Namibia) and Cape Town (South Africa).The concentrations of HCB range from 2 to 9 pg L-1 in water and from 56 to 145 pg m-3 in air. Concentrations of 4,4’-DDE in water ranged from 0.3 to 1.4 pg L-1, which is similar to the values found in previous studies carried out in the North Atlantic (0.4–0.6 pg L-1). Atmospheric 4,4’-DDE concentrations range from 0.1 to 0.9 pg m-3 were somewhat smaller than the values of 1.3–6.3 pg m-3 observed in the same area during one cruise carried out in April 1990. During the same cruises the contents of polycyclic aromatic hydrocarbons (PAHs) and one emerging class of pollutants (polychlorinated naphthalenes, PCNs) were determined. The highest PAH concentrations occurred in the European samples, and in samples close to West Africa and South Africa. Consistently low PAH concentrations were measured in the southern hemisphere open ocean samples (190-680 pg/m3). Concentrations showed a diurnal cycle, the day/night ratios of phenanthrene, 1-methylphenanthrene and fluoranthene were typically ~1.5-2.5:1. The mechanisms causing this pattern are not understood at present, but dynamic environmental processes are implicated. The highest PCN concentrations occurred in the European samples, but high values were also detected off the West African coast, and in the sample taken closest to South Africa. proprietary
ECA014 Air-Water Distribution of POPs Along a North-South Atlantic Transect ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595147-SCIOPS.umm_json To study the transport of POPs from the northern hemisphere to the southern, cruises were carried out collecting aerosol and surface water samples where different classes of organic pollutants were determined. The content of polychlorinated biphenyls (PCBs), hexachlorobenzene (HCB), 1,1-dichloro-2,2-bis(4-chlorophenyl)ethene (4,4′-DDE), and polyaromatic hydrocarbons (PAHs) were determined from the island of Texel (The 29 Netherlands) to Walvis Bay (Namibia) and Cape Town (South Africa).The concentrations of HCB range from 2 to 9 pg L-1 in water and from 56 to 145 pg m-3 in air. Concentrations of 4,4’-DDE in water ranged from 0.3 to 1.4 pg L-1, which is similar to the values found in previous studies carried out in the North Atlantic (0.4–0.6 pg L-1). Atmospheric 4,4’-DDE concentrations range from 0.1 to 0.9 pg m-3 were somewhat smaller than the values of 1.3–6.3 pg m-3 observed in the same area during one cruise carried out in April 1990. During the same cruises the contents of polycyclic aromatic hydrocarbons (PAHs) and one emerging class of pollutants (polychlorinated naphthalenes, PCNs) were determined. The highest PAH concentrations occurred in the European samples, and in samples close to West Africa and South Africa. Consistently low PAH concentrations were measured in the southern hemisphere open ocean samples (190-680 pg/m3). Concentrations showed a diurnal cycle, the day/night ratios of phenanthrene, 1-methylphenanthrene and fluoranthene were typically ~1.5-2.5:1. The mechanisms causing this pattern are not understood at present, but dynamic environmental processes are implicated. The highest PCN concentrations occurred in the European samples, but high values were also detected off the West African coast, and in the sample taken closest to South Africa. proprietary
+ECA014 Air-Water Distribution of POPs Along a North-South Atlantic Transect SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595147-SCIOPS.umm_json To study the transport of POPs from the northern hemisphere to the southern, cruises were carried out collecting aerosol and surface water samples where different classes of organic pollutants were determined. The content of polychlorinated biphenyls (PCBs), hexachlorobenzene (HCB), 1,1-dichloro-2,2-bis(4-chlorophenyl)ethene (4,4′-DDE), and polyaromatic hydrocarbons (PAHs) were determined from the island of Texel (The 29 Netherlands) to Walvis Bay (Namibia) and Cape Town (South Africa).The concentrations of HCB range from 2 to 9 pg L-1 in water and from 56 to 145 pg m-3 in air. Concentrations of 4,4’-DDE in water ranged from 0.3 to 1.4 pg L-1, which is similar to the values found in previous studies carried out in the North Atlantic (0.4–0.6 pg L-1). Atmospheric 4,4’-DDE concentrations range from 0.1 to 0.9 pg m-3 were somewhat smaller than the values of 1.3–6.3 pg m-3 observed in the same area during one cruise carried out in April 1990. During the same cruises the contents of polycyclic aromatic hydrocarbons (PAHs) and one emerging class of pollutants (polychlorinated naphthalenes, PCNs) were determined. The highest PAH concentrations occurred in the European samples, and in samples close to West Africa and South Africa. Consistently low PAH concentrations were measured in the southern hemisphere open ocean samples (190-680 pg/m3). Concentrations showed a diurnal cycle, the day/night ratios of phenanthrene, 1-methylphenanthrene and fluoranthene were typically ~1.5-2.5:1. The mechanisms causing this pattern are not understood at present, but dynamic environmental processes are implicated. The highest PCN concentrations occurred in the European samples, but high values were also detected off the West African coast, and in the sample taken closest to South Africa. proprietary
ECA023 A 50-years record of DDT and HCH in lake sediment in King George Island, Antarctic SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595228-SCIOPS.umm_json The Antarctic continent does not have stream–river drainage systems, Antarctic lakes are thus the main sinks for water and solutes from the surrounding environment. Depending on their origin, the presence of a perennial ice cover, exposed rocks and soils in the watershed, seabirds and distance from the sea, the water may show very different characteristics – from almost distilled to salt-rich brine which does not freeze in winter. This dataset regards the accumulation flux profiles and temporal trends of organochlorine pesticides such as DDT and HCH in two lake cores from King George Island, West Antarctica. In the lake core sediments with glacier melt water input, the accumulation flux of DDT shows an abnormal peak around the 1980s in addition to the expected one in the 1960s. In the lake core sediments without glacier melt water input, the accumulation flux of DDT shows a gradual decline trend after the peak in 1960s. This striking difference in the DDT flux profiles between the two lake cores is most likely caused by the regional climate warming and the resulted discharge of the DDT stored in the Antarctic ice cap into the lakes in the Antarctic glacier frontier, as already reported in 1996 for PCBs. proprietary
ECA023 A 50-years record of DDT and HCH in lake sediment in King George Island, Antarctic ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595228-SCIOPS.umm_json The Antarctic continent does not have stream–river drainage systems, Antarctic lakes are thus the main sinks for water and solutes from the surrounding environment. Depending on their origin, the presence of a perennial ice cover, exposed rocks and soils in the watershed, seabirds and distance from the sea, the water may show very different characteristics – from almost distilled to salt-rich brine which does not freeze in winter. This dataset regards the accumulation flux profiles and temporal trends of organochlorine pesticides such as DDT and HCH in two lake cores from King George Island, West Antarctica. In the lake core sediments with glacier melt water input, the accumulation flux of DDT shows an abnormal peak around the 1980s in addition to the expected one in the 1960s. In the lake core sediments without glacier melt water input, the accumulation flux of DDT shows a gradual decline trend after the peak in 1960s. This striking difference in the DDT flux profiles between the two lake cores is most likely caused by the regional climate warming and the resulted discharge of the DDT stored in the Antarctic ice cap into the lakes in the Antarctic glacier frontier, as already reported in 1996 for PCBs. proprietary
ECA051 Anthropogenic Activities in Remote Area: the case study of Admiralty Bay, King George Island CEOS_EXTRA STAC Catalog 1986-01-01 2002-12-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2227456102-CEOS_EXTRA.umm_json The aim of the present work is to characterize the local atmospheric emissions levels and compare them to the component derived from global pollution in a remote site at South Hemisphere (Admiralty Bay located at King George Island in Antarctic Peninsula). Airborne particles, snow and soil/sediments samples were analyzed. Local-produced atmospheric aerosol dispersion was estimated for metals originated by fossil fuel burning from the permanent scientific stations using a simplified Gaussian model. Validation of atmospheric dispersion was established by in situ measurements. Soluble and insoluble particles deposited in freshly snow and airborne particles were analyzed by PIXE (Particle Induced X-Ray Emission) for the determination of the elemental mass concentration and to obtain the Mass Median Aerodynamic Diameter (MMAD). The results showed significant correlation between the concentration of atmospheric aerosol and the freshly deposited particles in the snow, and permitted an estimate of the atmospheric snow deposition factor for K, Cu, Zn, Fe, Pb, and Ti. Results of long-term aerosol data compilation suggest that besides the local aerosol sources, the continental atmospheric transport of airborne particles is not significantly affected by the airborne particles produced by local human impacts at King George Island. proprietary
-ECA060 A 2000-year record of mercury and ancient civilizations in seal hairs from King George Island, West Antarctica ALL STAC Catalog 1999-02-01 2002-02-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214598661-SCIOPS.umm_json The concentrations of total mercury (HgT) and three bio-essential elements (phosphor, potassium, sodium) were analyzed in Antarctic seal hairs from a lake core spanning the past 2000 years and collected from King George Island (63823VS, 57800VW), West Antarctica. The HgT concentration shows a significant fluctuation while the levels of the three bio-essential elements remain almost constant. The rise and fall of the HgT concentration in the seal hairs are found to be closely coincided with ancient activities of gold and silver mining using Hg-amalgamation process around the world, especially in the Southern Hemisphere. Two profiles of HgT in other two lake cores, one affected by seal excrements and the other by penguin droppings, from the same region are similar to the one in seal hairs. The Hg concentration profile in the seal hairs is significantly correlated with the one in a peat bog of Southern Chile near King George Island. Since Hg is existent mainly at the form of methyl-mercury in seal hairs, this correlation supports a relationship and link between atmospheric mercury concentration and methyl-mercury production. Comparing with samples from American and European continents, the Antarctic seal hairs provide an archive of total mercury concentration in surface seawater of the South Ocean less affected by regional human activities, and this archive may provide a good reference for assessing the global Hg emissions, depositions and recycling in the past thousand years. proprietary
ECA060 A 2000-year record of mercury and ancient civilizations in seal hairs from King George Island, West Antarctica SCIOPS STAC Catalog 1999-02-01 2002-02-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214598661-SCIOPS.umm_json The concentrations of total mercury (HgT) and three bio-essential elements (phosphor, potassium, sodium) were analyzed in Antarctic seal hairs from a lake core spanning the past 2000 years and collected from King George Island (63823VS, 57800VW), West Antarctica. The HgT concentration shows a significant fluctuation while the levels of the three bio-essential elements remain almost constant. The rise and fall of the HgT concentration in the seal hairs are found to be closely coincided with ancient activities of gold and silver mining using Hg-amalgamation process around the world, especially in the Southern Hemisphere. Two profiles of HgT in other two lake cores, one affected by seal excrements and the other by penguin droppings, from the same region are similar to the one in seal hairs. The Hg concentration profile in the seal hairs is significantly correlated with the one in a peat bog of Southern Chile near King George Island. Since Hg is existent mainly at the form of methyl-mercury in seal hairs, this correlation supports a relationship and link between atmospheric mercury concentration and methyl-mercury production. Comparing with samples from American and European continents, the Antarctic seal hairs provide an archive of total mercury concentration in surface seawater of the South Ocean less affected by regional human activities, and this archive may provide a good reference for assessing the global Hg emissions, depositions and recycling in the past thousand years. proprietary
+ECA060 A 2000-year record of mercury and ancient civilizations in seal hairs from King George Island, West Antarctica ALL STAC Catalog 1999-02-01 2002-02-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214598661-SCIOPS.umm_json The concentrations of total mercury (HgT) and three bio-essential elements (phosphor, potassium, sodium) were analyzed in Antarctic seal hairs from a lake core spanning the past 2000 years and collected from King George Island (63823VS, 57800VW), West Antarctica. The HgT concentration shows a significant fluctuation while the levels of the three bio-essential elements remain almost constant. The rise and fall of the HgT concentration in the seal hairs are found to be closely coincided with ancient activities of gold and silver mining using Hg-amalgamation process around the world, especially in the Southern Hemisphere. Two profiles of HgT in other two lake cores, one affected by seal excrements and the other by penguin droppings, from the same region are similar to the one in seal hairs. The Hg concentration profile in the seal hairs is significantly correlated with the one in a peat bog of Southern Chile near King George Island. Since Hg is existent mainly at the form of methyl-mercury in seal hairs, this correlation supports a relationship and link between atmospheric mercury concentration and methyl-mercury production. Comparing with samples from American and European continents, the Antarctic seal hairs provide an archive of total mercury concentration in surface seawater of the South Ocean less affected by regional human activities, and this archive may provide a good reference for assessing the global Hg emissions, depositions and recycling in the past thousand years. proprietary
ECCO_L4_ANCILLARY_DATA_V4R4_V4r4 ECCO Ancillary Data (Version 4 Release 4) POCLOUD STAC Catalog 1992-01-01 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2096684707-POCLOUD.umm_json This dataset provides ancillary data for the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate, and is intended for expert users to reproduce the state estimate. The ancillary data include documentation files, files required to initialize the model, forcing fields, binary input grid files, observational data used to constrain the model, model equivalent of observed profiles, files related to atmospheric flux-forced experiments, and some script files. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. proprietary
ECCO_L4_ATM_STATE_05DEG_DAILY_V4R4_V4r4 ECCO Atmosphere Surface Temperature, Humidity, Wind, and Pressure - Daily Mean 0.5 Degree (Version 4 Release 4) POCLOUD STAC Catalog 1992-01-01 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1990404801-POCLOUD.umm_json This dataset contains daily-averaged atmosphere surface temperature, humidity, wind, and pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00. proprietary
ECCO_L4_ATM_STATE_05DEG_MONTHLY_V4R4_V4r4 ECCO Atmosphere Surface Temperature, Humidity, Wind, and Pressure - Monthly Mean 0.5 Degree (Version 4 Release 4) POCLOUD STAC Catalog 1992-01-01 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1990404814-POCLOUD.umm_json This dataset contains monthly-averaged atmosphere surface temperature, humidity, wind, and pressure interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00. proprietary
@@ -5936,13 +5937,13 @@ EO:EUM:DAT:MULT:OSSTNAR_2013-11-20 L3C North Atlantic Regional (NAR) Sea Surface
EO:EUM:DAT:SENTINEL-3:SL_2_WST___NRT_2017-07-05 SLSTR Sea Surface Temperatures (SST) in NRT - Sentinel-3 EUMETSAT STAC Catalog 2017-07-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1588876556-EUMETSAT.umm_json SLSTR SST has a spatial resolution of 1km at nadir. All Sentinel-3 NRT products are available at pick-up point in less than 3h. Skin Sea Surface Temperature following the GHRSST L2P GDS2 format specification, see https://www.ghrsst.org/ . Sentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme. proprietary
EO:EUM:DAT:SENTINEL-3:SL_2_WST___NTC_2017-07-05 SLSTR Sea Surface Temperatures (SST) in NTC - Sentinel-3 EUMETSAT STAC Catalog 2017-07-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1588876559-EUMETSAT.umm_json The SLSTR SST has a spatial resolution of 1km at nadir. All Sentinel-3 Non Time Critical (NTC) products are available at pick-up point in less than 30 days. Skin Sea Surface Temperature following the GHRSST L2P GDS2 format specification, see https://www.ghrsst.org/ . Sentinel-3 is part of a series of Sentinel satellites, under the umbrella of the EU Copernicus programme. proprietary
EOLE1_001 Eole 1 Raw Temperature, Pressure and Location Data Near 200 mbar (EOLE1) at GES DISC GES_DISC STAC Catalog 1971-08-27 1972-07-04 -180, -60, 180, -30 https://cmr.earthdata.nasa.gov/search/concepts/C3031691150-GES_DISC.umm_json The Eole 1 Raw Temperature, Pressure and Location Data Near 200 mbar product was obtained from the experimenter and originally consisted of a BCD tape generated on a CDC 6600 computer, subsequently converted to ASCII characters. The data are arranged sequentially by orbit. Data from each orbit are contained in a single record and consist of a heading giving the orbit number, the number of balloons contacted, and a control number. Following the heading, the balloon number, date of observation, location, and ambient temperature and pressure are listed. A maximum of 25 balloon contacts may appear in a single record. Empty records with no balloon contacts have been omitted. These data were obtained from balloons near 200 mbar and are for the region between 30 deg S and 60 deg S. The upper level wind speed and direction can be generated from these data by comparing individual balloon locations obtained from successive orbits. Eole, also known as the Cooperative Application Satellite (CAS-A), was the the second French experimental relay and meteorological satellite and the first launched by NASA under a cooperative agreement with the Centre National d'Etudes Spatiales (CNES). proprietary
-EOSWEBSTER_CLIMCALC_NE_US A Spatial Model of Atmospheric Deposition For the Northeastern U.S. ALL STAC Catalog 1970-01-01 -77, 38, -66, 48 https://cmr.earthdata.nasa.gov/search/concepts/C1214584276-SCIOPS.umm_json CLIMCALC is a simple model of physical and chemical climate for the northeasten United States (New York and New England) that can be incorporated into a geographic information system (GIS) for integration with ecosystem models presented. The variables include average maximum and minimum daily temperature, precipitation, humidity, and solar radiation, all at a monthly time step, as well as annual wet and dry deposition of sulfur and nitrogen. Regressions on latitude, longitude, and elevation are fitted to regional data bases of these variables The equations are combined with a digital elevation model (DEM) of the region to generate GIS coverages of each variableresults are from a model of atmospheric deposition called CLIMCALC. Spatial patterns of atmospheric deposition across the northeastern United States were evaluated and summarized in a simple model as a function of elevation and geographic position within the region. For wet deposition, 3-11 yr of annual concentration data for the major ions in precipitation were obtained from the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) for 26 sites within the region. Concentration trends were evaluated by regression of annual mean concentrations against latitude and longitude. For nitrate, sulfate, and ammonium concentrations, a more than twofold linear decrease occurs from western New York and Pennsylvania to eastern Maine. These trends were combined with regional and elevational trends or precipitation amount, obtained from 30-yr records of annual precipitation at >300 weather stations, to provide long-term patterns of wet deposition. Regional trends of dry deposition of N and S compounds were determined using 2-3 yrs of particle and gas concentration data collected by the National Dry Deposition Network (NDDN) and several other sources, in combination with estimates of deposition velocities. Contrary to wet deposition trends, the dominant air concentration trends were steep decreases from south to north, creating regional decreases in total deposition (wet + dry) from the southwest to the northeast. This contrast between wet and dry deposition trends suggests that within the northeast the two deposition forms are received in different proportions from different source areas, wet deposited materials primarily from areas to the west and dry deposited materials primarily from urban areas along the southern edge of the region. The equations generated describing spatial patterns of wet and dry depositions within the region were entered into a geographic information system (GIS) containing a digital elevation model (DEM) in order to develop spatially explicit predictions of atmospheric deposition for the region. proprietary
EOSWEBSTER_CLIMCALC_NE_US A Spatial Model of Atmospheric Deposition For the Northeastern U.S. SCIOPS STAC Catalog 1970-01-01 -77, 38, -66, 48 https://cmr.earthdata.nasa.gov/search/concepts/C1214584276-SCIOPS.umm_json CLIMCALC is a simple model of physical and chemical climate for the northeasten United States (New York and New England) that can be incorporated into a geographic information system (GIS) for integration with ecosystem models presented. The variables include average maximum and minimum daily temperature, precipitation, humidity, and solar radiation, all at a monthly time step, as well as annual wet and dry deposition of sulfur and nitrogen. Regressions on latitude, longitude, and elevation are fitted to regional data bases of these variables The equations are combined with a digital elevation model (DEM) of the region to generate GIS coverages of each variableresults are from a model of atmospheric deposition called CLIMCALC. Spatial patterns of atmospheric deposition across the northeastern United States were evaluated and summarized in a simple model as a function of elevation and geographic position within the region. For wet deposition, 3-11 yr of annual concentration data for the major ions in precipitation were obtained from the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) for 26 sites within the region. Concentration trends were evaluated by regression of annual mean concentrations against latitude and longitude. For nitrate, sulfate, and ammonium concentrations, a more than twofold linear decrease occurs from western New York and Pennsylvania to eastern Maine. These trends were combined with regional and elevational trends or precipitation amount, obtained from 30-yr records of annual precipitation at >300 weather stations, to provide long-term patterns of wet deposition. Regional trends of dry deposition of N and S compounds were determined using 2-3 yrs of particle and gas concentration data collected by the National Dry Deposition Network (NDDN) and several other sources, in combination with estimates of deposition velocities. Contrary to wet deposition trends, the dominant air concentration trends were steep decreases from south to north, creating regional decreases in total deposition (wet + dry) from the southwest to the northeast. This contrast between wet and dry deposition trends suggests that within the northeast the two deposition forms are received in different proportions from different source areas, wet deposited materials primarily from areas to the west and dry deposited materials primarily from urban areas along the southern edge of the region. The equations generated describing spatial patterns of wet and dry depositions within the region were entered into a geographic information system (GIS) containing a digital elevation model (DEM) in order to develop spatially explicit predictions of atmospheric deposition for the region. proprietary
+EOSWEBSTER_CLIMCALC_NE_US A Spatial Model of Atmospheric Deposition For the Northeastern U.S. ALL STAC Catalog 1970-01-01 -77, 38, -66, 48 https://cmr.earthdata.nasa.gov/search/concepts/C1214584276-SCIOPS.umm_json CLIMCALC is a simple model of physical and chemical climate for the northeasten United States (New York and New England) that can be incorporated into a geographic information system (GIS) for integration with ecosystem models presented. The variables include average maximum and minimum daily temperature, precipitation, humidity, and solar radiation, all at a monthly time step, as well as annual wet and dry deposition of sulfur and nitrogen. Regressions on latitude, longitude, and elevation are fitted to regional data bases of these variables The equations are combined with a digital elevation model (DEM) of the region to generate GIS coverages of each variableresults are from a model of atmospheric deposition called CLIMCALC. Spatial patterns of atmospheric deposition across the northeastern United States were evaluated and summarized in a simple model as a function of elevation and geographic position within the region. For wet deposition, 3-11 yr of annual concentration data for the major ions in precipitation were obtained from the National Atmospheric Deposition Program/National Trends Network (NADP/NTN) for 26 sites within the region. Concentration trends were evaluated by regression of annual mean concentrations against latitude and longitude. For nitrate, sulfate, and ammonium concentrations, a more than twofold linear decrease occurs from western New York and Pennsylvania to eastern Maine. These trends were combined with regional and elevational trends or precipitation amount, obtained from 30-yr records of annual precipitation at >300 weather stations, to provide long-term patterns of wet deposition. Regional trends of dry deposition of N and S compounds were determined using 2-3 yrs of particle and gas concentration data collected by the National Dry Deposition Network (NDDN) and several other sources, in combination with estimates of deposition velocities. Contrary to wet deposition trends, the dominant air concentration trends were steep decreases from south to north, creating regional decreases in total deposition (wet + dry) from the southwest to the northeast. This contrast between wet and dry deposition trends suggests that within the northeast the two deposition forms are received in different proportions from different source areas, wet deposited materials primarily from areas to the west and dry deposited materials primarily from urban areas along the southern edge of the region. The equations generated describing spatial patterns of wet and dry depositions within the region were entered into a geographic information system (GIS) containing a digital elevation model (DEM) in order to develop spatially explicit predictions of atmospheric deposition for the region. proprietary
EOSWEBSTER_US_County_Data Agricultural, Geographic and Population data for Counties in the Contiguous United States SCIOPS STAC Catalog 1972-01-01 1998-12-31 -124, 26, -66, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1214608658-SCIOPS.umm_json Annual crop data from 1972 to 1998 are now available on EOS-WEBSTER. These data are county-based acreage, production, and yield estimates published by the National Agricultural Statistics Service. We also provide county level livestock, geography, agricultural management, and soil properties derived from datasets from the early 1990s. The National Agricultural Statistics Service (NASS), the statistical arm of the U.S. Department of Agriculture, publishes U.S., state, and county level agricultural statistics for many commodities and data series. In response to our users requests, EOS-WEBSTER now provides 27 years of crop statistics, which can be subset temporally and/or spatially. All data are at the county scale, and are only for the conterminous US (48 states + DC). There are 3111 counties in the database. The list includes 43 cities that are classified as counties: Baltimore City, MD; St. Louis City, MO; and 41 cities in Virginia. In addition, a collection of livestock, geography, agricultural practices, and soil properties variables for 1992 is available through EOS-WEBSTER. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF. The US County data has been divided into seven datasets. US County Data Datasets: 1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties proprietary
EOSWEBSTER_US_County_Data Agricultural, Geographic and Population data for Counties in the Contiguous United States ALL STAC Catalog 1972-01-01 1998-12-31 -124, 26, -66, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1214608658-SCIOPS.umm_json Annual crop data from 1972 to 1998 are now available on EOS-WEBSTER. These data are county-based acreage, production, and yield estimates published by the National Agricultural Statistics Service. We also provide county level livestock, geography, agricultural management, and soil properties derived from datasets from the early 1990s. The National Agricultural Statistics Service (NASS), the statistical arm of the U.S. Department of Agriculture, publishes U.S., state, and county level agricultural statistics for many commodities and data series. In response to our users requests, EOS-WEBSTER now provides 27 years of crop statistics, which can be subset temporally and/or spatially. All data are at the county scale, and are only for the conterminous US (48 states + DC). There are 3111 counties in the database. The list includes 43 cities that are classified as counties: Baltimore City, MD; St. Louis City, MO; and 41 cities in Virginia. In addition, a collection of livestock, geography, agricultural practices, and soil properties variables for 1992 is available through EOS-WEBSTER. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF. The US County data has been divided into seven datasets. US County Data Datasets: 1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties proprietary
EPA0175 National Water Quality Assessment Program (NAWQA) Home Page CEOS_EXTRA STAC Catalog 1991-01-01 -125, 25, -67, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2232411681-CEOS_EXTRA.umm_json "The ""National Water Quality Assessment Program (NAWQA) Home Page"" is an Internet resource that provides information on research dealing with water quality in the United States. This home page provides links to NAWQA activities, selected publications, a bibliography, and summaries of current research projects. The NAWQA program is designed to assess historical, current, and future water-quality conditions in representative river basins and aquifers nationwide. One of the primary objectives of the program is to describe relations between natural factors, human activities, and water quality conditions and to define those factors that most affect water quality in different parts of the Nation. The linkage of water quality to environmental processes is of fundamental importance to water-resource managers, planners, and policy makers. It provides a strong and unbiased basis for better decision making by those responsible for making decisions that affect our water resources, including the United States Congress, Federal, State, and local agencies, environmental groups, and industry. Information from the NAWQA Program also will be useful for guiding research, monitoring, and regulatory activities in cost effective ways. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: National Water Quality Assessment Program Dissemination Media: Online File Format: Size: Memory Requirements: Operating System: Hardware Required: Software Required: Availability Status: On Request Documentation Available:" proprietary
-EPA_AQA Air Quality Atlas ALL STAC Catalog 1970-01-01 -109.35, 25.19, -88.54, 37.43 https://cmr.earthdata.nasa.gov/search/concepts/C1214621333-SCIOPS.umm_json The Air Quality Atlas is a collection of maps prepared by the Air Quality Analysis Section in the Region 6 office of the U.S. Environmental Protection Agency (EPA). The atlas presents a spatial analysis of air quality in EPA Region 6 for 1996, focusing on the six criteria pollutants for which the EPA has set primary and secondary standards to protect public health and welfare. These standards, defined as the National Ambient Air Quality Standards (NAAQS), have been set for the following six pollutants: lead, nitrogen dioxide, carbon monoxide, sulfur dioxide, ozone, and small particles less than or equal to 10 microns in aerodynamic diameter (PM-10). The primary standards are set to protect public health, and the secondary standards are set to protect public welfare, such as buildings, forests, and agricultural crops. The primary and secondary standards are currently identical for all of the criteria pollutants except sulfur dioxide. The sulfur dioxide secondary standard is based on a three hour averaging time, while the primary standard is based on both 24-hour and annual averaging times. The maps show Region 6 air quality levels referenced against the standards set for the six criteria pollutants. The legend for each map, except for the two exceedance day maps, was constructed to show the following information: (1) The blue shade depicts levels less than 10% of the standard; (2) the green shade depicts levels between 10-50% of the standard; (3) the gray shade depicts levels between 50-90% of the standard; (4) the yellow shade depicts levels within 10% of the standard; and (5) the red shade depicts levels over the standard. Counties not shaded (white) either do not contain monitors, or their monitors did not achieve a data capture rate of at least 75% (exception - all ozone site data were reported). The data used to compose each map were obtained from the EPA's Aerometric Information Retrieval System (AIRS) data base. Analysis of the maps reveals that all Region 6 monitors recorded concentrations below the NAAQS set for lead, nitrogen dioxide, and sulfur dioxide. Indeed, a significant amount of areas in Region 6 recorded maximum concentrations well below these standards. Additional map analysis shows that one Region 6 county (El Paso) contained monitors recording measurements above the carbon monoxide 8-hour standard, that two Region 6 counties (El Paso and Dona Ana) contained monitors recording measurements above the PM-10 standards, and that every state except Arkansas had at least one monitor with values above the ozone standard. Following each map displaying the 1996 Region 6 status of particulate and ozone air quality is a map showing the number of days per county in which a monitor recorded concentrations above the PM-10 or ozone standards. [Summary provided by the EPA.] proprietary
EPA_AQA Air Quality Atlas SCIOPS STAC Catalog 1970-01-01 -109.35, 25.19, -88.54, 37.43 https://cmr.earthdata.nasa.gov/search/concepts/C1214621333-SCIOPS.umm_json The Air Quality Atlas is a collection of maps prepared by the Air Quality Analysis Section in the Region 6 office of the U.S. Environmental Protection Agency (EPA). The atlas presents a spatial analysis of air quality in EPA Region 6 for 1996, focusing on the six criteria pollutants for which the EPA has set primary and secondary standards to protect public health and welfare. These standards, defined as the National Ambient Air Quality Standards (NAAQS), have been set for the following six pollutants: lead, nitrogen dioxide, carbon monoxide, sulfur dioxide, ozone, and small particles less than or equal to 10 microns in aerodynamic diameter (PM-10). The primary standards are set to protect public health, and the secondary standards are set to protect public welfare, such as buildings, forests, and agricultural crops. The primary and secondary standards are currently identical for all of the criteria pollutants except sulfur dioxide. The sulfur dioxide secondary standard is based on a three hour averaging time, while the primary standard is based on both 24-hour and annual averaging times. The maps show Region 6 air quality levels referenced against the standards set for the six criteria pollutants. The legend for each map, except for the two exceedance day maps, was constructed to show the following information: (1) The blue shade depicts levels less than 10% of the standard; (2) the green shade depicts levels between 10-50% of the standard; (3) the gray shade depicts levels between 50-90% of the standard; (4) the yellow shade depicts levels within 10% of the standard; and (5) the red shade depicts levels over the standard. Counties not shaded (white) either do not contain monitors, or their monitors did not achieve a data capture rate of at least 75% (exception - all ozone site data were reported). The data used to compose each map were obtained from the EPA's Aerometric Information Retrieval System (AIRS) data base. Analysis of the maps reveals that all Region 6 monitors recorded concentrations below the NAAQS set for lead, nitrogen dioxide, and sulfur dioxide. Indeed, a significant amount of areas in Region 6 recorded maximum concentrations well below these standards. Additional map analysis shows that one Region 6 county (El Paso) contained monitors recording measurements above the carbon monoxide 8-hour standard, that two Region 6 counties (El Paso and Dona Ana) contained monitors recording measurements above the PM-10 standards, and that every state except Arkansas had at least one monitor with values above the ozone standard. Following each map displaying the 1996 Region 6 status of particulate and ozone air quality is a map showing the number of days per county in which a monitor recorded concentrations above the PM-10 or ozone standards. [Summary provided by the EPA.] proprietary
+EPA_AQA Air Quality Atlas ALL STAC Catalog 1970-01-01 -109.35, 25.19, -88.54, 37.43 https://cmr.earthdata.nasa.gov/search/concepts/C1214621333-SCIOPS.umm_json The Air Quality Atlas is a collection of maps prepared by the Air Quality Analysis Section in the Region 6 office of the U.S. Environmental Protection Agency (EPA). The atlas presents a spatial analysis of air quality in EPA Region 6 for 1996, focusing on the six criteria pollutants for which the EPA has set primary and secondary standards to protect public health and welfare. These standards, defined as the National Ambient Air Quality Standards (NAAQS), have been set for the following six pollutants: lead, nitrogen dioxide, carbon monoxide, sulfur dioxide, ozone, and small particles less than or equal to 10 microns in aerodynamic diameter (PM-10). The primary standards are set to protect public health, and the secondary standards are set to protect public welfare, such as buildings, forests, and agricultural crops. The primary and secondary standards are currently identical for all of the criteria pollutants except sulfur dioxide. The sulfur dioxide secondary standard is based on a three hour averaging time, while the primary standard is based on both 24-hour and annual averaging times. The maps show Region 6 air quality levels referenced against the standards set for the six criteria pollutants. The legend for each map, except for the two exceedance day maps, was constructed to show the following information: (1) The blue shade depicts levels less than 10% of the standard; (2) the green shade depicts levels between 10-50% of the standard; (3) the gray shade depicts levels between 50-90% of the standard; (4) the yellow shade depicts levels within 10% of the standard; and (5) the red shade depicts levels over the standard. Counties not shaded (white) either do not contain monitors, or their monitors did not achieve a data capture rate of at least 75% (exception - all ozone site data were reported). The data used to compose each map were obtained from the EPA's Aerometric Information Retrieval System (AIRS) data base. Analysis of the maps reveals that all Region 6 monitors recorded concentrations below the NAAQS set for lead, nitrogen dioxide, and sulfur dioxide. Indeed, a significant amount of areas in Region 6 recorded maximum concentrations well below these standards. Additional map analysis shows that one Region 6 county (El Paso) contained monitors recording measurements above the carbon monoxide 8-hour standard, that two Region 6 counties (El Paso and Dona Ana) contained monitors recording measurements above the PM-10 standards, and that every state except Arkansas had at least one monitor with values above the ozone standard. Following each map displaying the 1996 Region 6 status of particulate and ozone air quality is a map showing the number of days per county in which a monitor recorded concentrations above the PM-10 or ozone standards. [Summary provided by the EPA.] proprietary
EPEA_0 ANTARES monitoring station at the EPEA Station on the Argentina shelf OB_DAAC STAC Catalog 2012-07-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360228-OB_DAAC.umm_json The EPEA (Estación Permanente de Estudios Ambientales) time series station was started in 2000 and since 2003 belongs to ANTARES (www.antares.ws), a network of Latin American time series stations whose main goal is the study of long-term changes in coastal ecosystems to distinguish those due to natural variability from those due to external perurbations (anthropogenic effects).Different research groups at the INIDEP (the National Institute of Fisheries Research and Development of Argentina) sample at the EPEA station, monitoring chemical, environmental and bio-optical variables as well as the bacterioplankton, phytoplankton, zooplankton, and the icthyoplankton communities. EPEA station is located on the Argentine shelf (38°28'S, 57°41'W), 27.0 nautical miles from Mar del Plata city and 13.5 nautical miles from the coast and has a depth of 50m. EPEA is characterized by a temperate regime, with annual sea surface temperatures between 10°C and 21°C and salinity values ranging between 33.5 and 34.1. Occasionally the site can receive less salty waters coming from the North, influenced by the La Plata River, driving salinity values to less than 31.0. Its oceanographic regime is described as the transition between high salinity coastal waters to the medium shelf (Guerrero et al., 1997). proprietary
ERBE_S10N_WFOV_NF_Edition2 Earth Radiation Budget Experiment (ERBE) S-10N (Nonscanner-only) Wide Field of View (WFOV) Numerical Filter (NF) Radiant Flux and Albedo Edition 2 in Native Format LARC_ASDC STAC Catalog 1984-11-01 1999-09-30 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000800-LARC_ASDC.umm_json ERBE_S10N_WFOV_NF_Edition2 is the Earth Radiation Budget Experiment (ERBE) S-10N (Nonscanner-only) Wide Field of View (WFOV) Numerical Filter (NF) Radiant Flux and Albedo Edition 2 in Native Format data product. Data collection for this product is complete. The reprocessed ERBE S10N_WFOV ERBS Edition2 data product contains temporally and spatially averaged shortwave (SW) and longwave (LW) top-of-the-atmosphere (TOA) fluxes derived from one month of Earth Radiation Budget Experiment non-scanning wide field-of-view instruments aboard the Earth Radiation Budget Satellite. Instantaneous TOA fluxes from the ERBE/ERBS S7 product were spatially averaged on a 5° and 10° equal-angle grid using numerical filter and shape factor techniques, respectively. ERBE scanner-independent temporal interpolation algorithms were applied to produce daily, monthly-hourly, and monthly mean fluxes from the instantaneous gridded data. The S10N_WFOV files contain both temporally averaged and instantaneous gridded mean values of TOA total-sky LW flux, total-sky SW flux, and total-sky albedo for each 5° and 10° region observed during the month. The major differences between Edition2 and the original release are in the monthly mean fluxes with (1) the incorporation of stochastic quality assurance algorithms for filtering out monthly-mean data with excessive temporal sample errors and (2) a self-consistent usage of the WFOV data in selecting scene-dependent directional models for temporal interpolation of the ERBE WFOV instantaneous gridded data. proprietary
ERBE_S10N_WFOV_NF_Edition3 Earth Radiation Budget Experiment (ERBE) S-10N (Nonscanner-only) Wide Field of View (WFOV) Numerical Filter (NF) Radiant Flux and Albedo Edition 3 in Native Format LARC_ASDC STAC Catalog 1984-11-01 1999-09-30 180, -60, -180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C1000000820-LARC_ASDC.umm_json ERBE_S10N_WFOV_NF_Edition3 is the Earth Radiation Budget Experiment (ERBE) S-10N (Nonscanner-only) Wide Field of View (WFOV) Numerical Filter (NF) Radiant Flux and Albedo Edition 3 in Native Format data product. Data collection for this product is complete. This data product contains temporally and spatially averaged shortwave (SW) and longwave (LW) top-of-the-atmosphere (TOA) fluxes derived from one month of Earth Radiation Budget Experiment non-scanning wide field-of-view instruments aboard the Earth Radiation Budget Satellite (ERBS). Instantaneous TOA fluxes were spatially averaged on 5° and 10° equal-angle grids using numerical filter and shape factor techniques, respectively. ERBE scanner-independent temporal interpolation algorithms were applied to produce daily, monthly-hourly, and monthly mean fluxes from the instantaneous gridded data. The S10N_WFOV files contain both temporally averaged and instantaneous gridded mean values of TOA total-sky LW flux, total-sky SW flux, and total-sky albedo for each 5° and 10° region observed during the month. The main difference between Edition3 and Edition2 releases is in the treatment of TOA radiative fluxes resulting from changes in the ERBE non-scanner processing algorithm to account for decay in satellite altitude over the data period. proprietary
@@ -6000,14 +6001,14 @@ ERSATSRL1BBrightnessTemperatureRadianceER1AT1RBTER2AT1RBT40_5.0 ERS ATSR L1B Bri
ERS_ALT_2M_6.0 ERS-1/2 Radar Altimeter REAPER METEO Product - [ERS_ALT_2M] ESA STAC Catalog 1991-08-03 2003-07-02 -180, -82, 180, 82 https://cmr.earthdata.nasa.gov/search/concepts/C1965336899-ESA.umm_json "This is a RA Meteo product containing only the 1 Hz parameters for altimeter (surface range, satellite altitude, wind speed and significant wave height at nadir) and MWR/MWS data (brightness temperature at 23.8 GHz and 36.5 GHz, water vapour content, liquid water content) used to correct altimeter measurements. It also contains the full geophysical corrections. This product corresponds to a subset of the REAPER GDR product (ERS_ALT_2_). The REAPER (REprocessing of Altimeter Products for ERS) product is generated by applying a similar processing as for Envisat RA-2 on the Level 1b consolidated waveforms using 4 different re-trackers, RA calibration improvement, new precise orbit solution (POD), new ionospheric corrections (NICO09 until 1998 and GIM up to 2003), ECMWF ERA-interim model and updated SSB tables. This product contains only the low rate of 1Hz data. The REAPER Meteo (ERS_ALT_2M) is a global product including data over ocean, ice and land. It should be noted that this product differs from the Envisat RA2 in the following ways: the product format; which is NetCDF (more details can be found in the Product Handbook https://earth.esa.int/eogateway/documents/20142/37627/reaper-product-handbook-for-ers-altimetry-reprocessed-products.pdf), and not PDS the product is delivered based on orbit acquisition and not per pass (pole-to-pole) This product is extended through Envisat RA-2 data The creation of the Fundamental Data Records (FDR4ALT) datasets _$$released in March 2024$$ https://earth.esa.int/eogateway/news/fdr4alt-esa-unveils-new-cutting-edge-ers-envisat-altimeter-and-microwave-radiometer-dataset , represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are therefore strongly encouraged to make use of these new datasets for optimal results. The records are aimed at different user communities and include the following datasets: 1. _$$Fundamental Data Records for Altimetry$$ https://earth.esa.int/eogateway/catalog/fdr-for-altimetry 2. _$$Fundamental Data Records for Radiometry$$ https://earth.esa.int/eogateway/catalog/fdr-for-radiometry 3. _$$Atmospheric Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-atmosphere 4. _$$Inland Waters Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-inland-water 5. _$$Land Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-land-ice 6. _$$Ocean & Coastal Topography Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-and-coastal-topography 7. _$$Ocean Waves Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-waves 8. _$$Sea Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-sea-ice " proprietary
ERS_ALT_2S_6.0 ERS-1/2 Radar Altimeter REAPER Sensor Geophysical Data Record - SGDR [ERS_ALT_2S] ESA STAC Catalog 1991-08-03 2003-07-02 -180, -82, 180, 82 https://cmr.earthdata.nasa.gov/search/concepts/C1965336901-ESA.umm_json "This is a RA Geophysical Data Record (GDR) product containing radar range, orbital altitude, wind speed, wave height and water vapour from the ATSR/MWR as well as geophysical corrections. The REAPER (REprocessing of Altimeter Products for ERS) product is generated by applying a similar processing as for Envisat RA-2 on the Level 1b consolidated waveforms using 4 different re-trackers, RA calibration improvement, new precise orbit solution (POD), new ionospheric corrections (NICO09 until 1998 and GIM up to 2003), ECMWF ERA-interim model and updated SSB tables. This product contains two data rates: a low rate of 1Hz and a high rate of 20Hz. Most 1Hz data is also represented at 20Hz, while microwave radiometer (ATSR/MWR) data and the atmospheric and geophysical corrections are only given at 1 Hz. The REAPER GDR (ERS_ALT_2_) is a global product including data over ocean, ice and land. It should be noted that this product differs from the Envisat RA2 in the following ways: The product format; which is NetCDF (more details can be found in the Product Handbook, and not PDS The product is delivered based on orbit acquisition and not per pass (pole-to-pole). This product is extended through Envisat RA-2 data. The creation of the Fundamental Data Records (FDR4ALT) datasets _$$released in March 2024$$ https://earth.esa.int/eogateway/news/fdr4alt-esa-unveils-new-cutting-edge-ers-envisat-altimeter-and-microwave-radiometer-dataset , represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are therefore strongly encouraged to make use of these new datasets for optimal results. The records are aimed at different user communities and include the following datasets: 1. _$$Fundamental Data Records for Altimetry$$ https://earth.esa.int/eogateway/catalog/fdr-for-altimetry 2. _$$Fundamental Data Records for Radiometry$$ https://earth.esa.int/eogateway/catalog/fdr-for-radiometry 3. _$$Atmospheric Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-atmosphere 4. _$$Inland Waters Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-inland-water 5. _$$Land Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-land-ice 6. _$$Ocean & Coastal Topography Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-and-coastal-topography 7. _$$Ocean Waves Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-waves 8. _$$Sea Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-sea-ice " proprietary
ERS_ALT_2__6.0 ERS-1/2 Radar Altimeter REAPER Geophysical Data Record - GDR [ERS_ALT_2] ESA STAC Catalog 1991-08-03 2003-07-02 -180, -82, 180, 82 https://cmr.earthdata.nasa.gov/search/concepts/C1965336902-ESA.umm_json "This is a RA Geophysical Data Record (GDR) product containing radar range, orbital altitude, wind speed, wave height and water vapour from the ATSR/MWR as well as geophysical corrections. The REAPER (REprocessing of Altimeter Products for ERS) product is generated by applying a similar processing as for Envisat RA-2 on the Level 1b consolidated waveforms using 4 different re-trackers, RA calibration improvement, new precise orbit solution (POD), new ionospheric corrections (NICO09 until 1998 and GIM up to 2003), ECMWF ERA-interim model and updated SSB tables. This product contains two data rates: a low rate of 1Hz and a high rate of 20Hz. Most 1Hz data is also represented at 20Hz, while microwave radiometer (ATSR/MWR) data and the atmospheric and geophysical corrections are only given at 1 Hz. The REAPER GDR (ERS_ALT_2_) is a global product including data over ocean, ice and land. It should be noted that this product differs from the Envisat RA2 in the following ways: The product format; which is NetCDF (more details can be found in the Product Handbook, and not PDS The product is delivered based on orbit acquisition and not per pass (pole-to-pole). This product is extended through Envisat RA-2 data. The creation of the Fundamental Data Records (FDR4ALT) datasets _$$released in March 2024$$ https://earth.esa.int/eogateway/news/fdr4alt-esa-unveils-new-cutting-edge-ers-envisat-altimeter-and-microwave-radiometer-dataset , represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are therefore strongly encouraged to make use of these new datasets for optimal results. The records are aimed at different user communities and include the following datasets: 1. _$$Fundamental Data Records for Altimetry$$ https://earth.esa.int/eogateway/catalog/fdr-for-altimetry 2. _$$Fundamental Data Records for Radiometry$$ https://earth.esa.int/eogateway/catalog/fdr-for-radiometry 3. _$$Atmospheric Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-atmosphere 4. _$$Inland Waters Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-inland-water 5. _$$Land Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-land-ice 6. _$$Ocean & Coastal Topography Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-and-coastal-topography 7. _$$Ocean Waves Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-ocean-waves 8. _$$Sea Ice Thematic Data Product$$ https://earth.esa.int/eogateway/catalog/tdp-for-sea-ice " proprietary
-ERS_CONT_500_ANT_1 500 metre interval contours of Antarctica derived from ERS radar altimetry data. AU_AADC STAC Catalog 2003-01-01 2003-01-31 -180, -82, 180, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214308545-AU_AADC.umm_json 500 metre interval contours of the Antarctic continent derived from slope corrected orthometric heights that were captured using European Remote Sensing (ERS) radar altimetry. ESA's two European Remote Sensing (ERS) satellites, ERS-1 and 2, were launched into the same orbit in 1991 and 1995 respectively. Their payloads included a synthetic aperture imaging radar, radar altimeter and instruments to measure ocean surface temperature and wind fields. ERS-2 added an additional sensor for atmospheric ozone monitoring. The two satellites acquired a combined data set extending over two decades. The ERS-1 mission ended on 10 March 2000 and ERS-2 was retired on 05 September 2011. proprietary
ERS_CONT_500_ANT_1 500 metre interval contours of Antarctica derived from ERS radar altimetry data. ALL STAC Catalog 2003-01-01 2003-01-31 -180, -82, 180, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214308545-AU_AADC.umm_json 500 metre interval contours of the Antarctic continent derived from slope corrected orthometric heights that were captured using European Remote Sensing (ERS) radar altimetry. ESA's two European Remote Sensing (ERS) satellites, ERS-1 and 2, were launched into the same orbit in 1991 and 1995 respectively. Their payloads included a synthetic aperture imaging radar, radar altimeter and instruments to measure ocean surface temperature and wind fields. ERS-2 added an additional sensor for atmospheric ozone monitoring. The two satellites acquired a combined data set extending over two decades. The ERS-1 mission ended on 10 March 2000 and ERS-2 was retired on 05 September 2011. proprietary
+ERS_CONT_500_ANT_1 500 metre interval contours of Antarctica derived from ERS radar altimetry data. AU_AADC STAC Catalog 2003-01-01 2003-01-31 -180, -82, 180, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214308545-AU_AADC.umm_json 500 metre interval contours of the Antarctic continent derived from slope corrected orthometric heights that were captured using European Remote Sensing (ERS) radar altimetry. ESA's two European Remote Sensing (ERS) satellites, ERS-1 and 2, were launched into the same orbit in 1991 and 1995 respectively. Their payloads included a synthetic aperture imaging radar, radar altimeter and instruments to measure ocean surface temperature and wind fields. ERS-2 added an additional sensor for atmospheric ozone monitoring. The two satellites acquired a combined data set extending over two decades. The ERS-1 mission ended on 10 March 2000 and ERS-2 was retired on 05 September 2011. proprietary
ERS_CONT_MERGED_AMERY_1 Contours for the Amery Region map dated November 2002. AU_AADC STAC Catalog 2002-11-01 2002-11-30 56, -77.1, 80, -67.4 https://cmr.earthdata.nasa.gov/search/concepts/C1214308546-AU_AADC.umm_json Contours for the Amery Region map published by the Australian Antarctic Data Centre in November 2002 (see link below). This contour data were derived from Russian space photography, ERS-1 and ERS-2 Radar altimeter data (BKG, Germany) and the Antarctic Digital Database, Version 2. Refer to the contour source diagram - digital data (refer to metadata record ERS_CONT_SOURCE_AMERY) or view map (see link below). The contour interval is 500 metres from 500 to 3000 metres. There are also 200 metre contours. ESA's two European Remote Sensing (ERS) satellites, ERS-1 and 2, were launched into the same orbit in 1991 and 1995 respectively. Their payloads included a synthetic aperture imaging radar, radar altimeter and instruments to measure ocean surface temperature and wind fields. ERS-2 added an additional sensor for atmospheric ozone monitoring. The two satellites acquired a combined data set extending over two decades. The ERS-1 mission ended on 10 March 2000 and ERS-2 was retired on 05 September 2011. proprietary
ERS_CONT_SOURCE_AMERY_1 Contour source data for the Amery Region map dated November 2002. AU_AADC STAC Catalog 2002-11-01 2002-11-30 56, -77.1, 80, -67.4 https://cmr.earthdata.nasa.gov/search/concepts/C1214308547-AU_AADC.umm_json This polygon shapefile was used in the contour source diagram on the Amery Region Map published by the Australian Antarctic Data Centre in November 2002 (see link). The contours used in the map were derived from a number of different data sources: 1 - Russian Space Photography, ERS-1 Radar Altimeter data and digitised from 1:1 million scale maps produced by National Mapping Australia; 2 - Antarctic Digital Database Version 2; 3 - ERS-1 and ERS-2 Radar Altimeter data (BKG, Germany). This shapefile shows in which part of the map each source was used. proprietary
ERS_DTM_1 Data generated from slope corrected orthometric heights of Antarctica derived from ERS Radar Altimetry AU_AADC STAC Catalog 2003-01-01 2003-01-31 -180, -82, 180, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214313453-AU_AADC.umm_json Data generated from slope corrected orthometric heights derived from ERS radar altimetry as described in the paper 'A Digital Terrain Ice Model of Antarctica derived by ERS Radar Altimeter Data' by J. Ihde, J. Eck, U. Schirmer. The data products (and their metadata records): the original point data as a shapefile (GRI_ORT_SLC_FIN); a shapefile showing data and no data areas for the original point data (ERS_REL_ANT); a triangulated irregular network (TIN) generated from the point data (ERS_DTM_TIN_ANT); 500 m interval contours interpolated from the TIN (ERS_CONT_500_ANT); a raster grid with 5 km cell size interpolated from the TIN (ERS_DTM_GRID_ANT); a contour shapefile for the Amery Region map published by the Australian Antarctic Data Centre in November 2002 - contours sourced from ERS radar altimetry, the Antarctic Digital Database Version 2 and Russian space photography (ERS_CONT_MERGED_AMERY); a shapefile used for the contour source diagram for the Amery Region map (ERS_CONT_SOURCE_AMERY). ESA's two European Remote Sensing (ERS) satellites, ERS-1 and 2, were launched into the same orbit in 1991 and 1995 respectively. Their payloads included a synthetic aperture imaging radar, radar altimeter and instruments to measure ocean surface temperature and wind fields. ERS-2 added an additional sensor for atmospheric ozone monitoring. The two satellites acquired a combined data set extending over two decades. The ERS-1 mission ended on 10 March 2000 and ERS-2 was retired on 05 September 2011. proprietary
ERS_DTM_GRID_ANT_1 Digital terrain model of Antarctica in ESRI Grid format, derived from ERS Radar Altimeter data. AU_AADC STAC Catalog 2003-01-01 2003-01-31 -180, -82, 180, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214308548-AU_AADC.umm_json ESRI formatted raster grid of the Antarctic continental terrain, derived from ERS radar altimeter data. The data is in a Polar Stereographic projection with true scale at 71 degrees South. The grid has 'no data' cells in latitudes south of 82 degrees South and steep areas of the continent, particularly along the coast. ESA's two European Remote Sensing (ERS) satellites, ERS-1 and 2, were launched into the same orbit in 1991 and 1995 respectively. Their payloads included a synthetic aperture imaging radar, radar altimeter and instruments to measure ocean surface temperature and wind fields. ERS-2 added an additional sensor for atmospheric ozone monitoring. The two satellites acquired a combined data set extending over two decades. The ERS-1 mission ended on 10 March 2000 and ERS-2 was retired on 05 September 2011. proprietary
-ERS_DTM_TIN_ANT_1 A digital terrain model of Antarctica in Triangulated Irregular Network (TIN) format, derived from ERS Radar Altimetry. AU_AADC STAC Catalog 2003-01-01 2003-01-31 -180, -82, 180, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214308549-AU_AADC.umm_json An ESRI formatted triangular irregular network (TIN) of the Antarctic continental terrain, derived from ERS radar altimeter data. The data is in a Polar Stereographic projection with true scale at 71 degrees South. The TIN is unreliable in latitudes south of 82 degrees South and steep areas of the continent, particularly along the coast. ESA's two European Remote Sensing (ERS) satellites, ERS-1 and 2, were launched into the same orbit in 1991 and 1995 respectively. Their payloads included a synthetic aperture imaging radar, radar altimeter and instruments to measure ocean surface temperature and wind fields. ERS-2 added an additional sensor for atmospheric ozone monitoring. The two satellites acquired a combined data set extending over two decades. The ERS-1 mission ended on 10 March 2000 and ERS-2 was retired on 05 September 2011. proprietary
ERS_DTM_TIN_ANT_1 A digital terrain model of Antarctica in Triangulated Irregular Network (TIN) format, derived from ERS Radar Altimetry. ALL STAC Catalog 2003-01-01 2003-01-31 -180, -82, 180, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214308549-AU_AADC.umm_json An ESRI formatted triangular irregular network (TIN) of the Antarctic continental terrain, derived from ERS radar altimeter data. The data is in a Polar Stereographic projection with true scale at 71 degrees South. The TIN is unreliable in latitudes south of 82 degrees South and steep areas of the continent, particularly along the coast. ESA's two European Remote Sensing (ERS) satellites, ERS-1 and 2, were launched into the same orbit in 1991 and 1995 respectively. Their payloads included a synthetic aperture imaging radar, radar altimeter and instruments to measure ocean surface temperature and wind fields. ERS-2 added an additional sensor for atmospheric ozone monitoring. The two satellites acquired a combined data set extending over two decades. The ERS-1 mission ended on 10 March 2000 and ERS-2 was retired on 05 September 2011. proprietary
+ERS_DTM_TIN_ANT_1 A digital terrain model of Antarctica in Triangulated Irregular Network (TIN) format, derived from ERS Radar Altimetry. AU_AADC STAC Catalog 2003-01-01 2003-01-31 -180, -82, 180, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214308549-AU_AADC.umm_json An ESRI formatted triangular irregular network (TIN) of the Antarctic continental terrain, derived from ERS radar altimeter data. The data is in a Polar Stereographic projection with true scale at 71 degrees South. The TIN is unreliable in latitudes south of 82 degrees South and steep areas of the continent, particularly along the coast. ESA's two European Remote Sensing (ERS) satellites, ERS-1 and 2, were launched into the same orbit in 1991 and 1995 respectively. Their payloads included a synthetic aperture imaging radar, radar altimeter and instruments to measure ocean surface temperature and wind fields. ERS-2 added an additional sensor for atmospheric ozone monitoring. The two satellites acquired a combined data set extending over two decades. The ERS-1 mission ended on 10 March 2000 and ERS-2 was retired on 05 September 2011. proprietary
ESA_Orthorectified_Map_oriented_Level1_products_6.0 MOS-1/1B ESA Orthorectified Map-oriented Products [MES_GEC_1P] ESA STAC Catalog 1987-09-08 1993-08-20 -120, 19, 95, 87 https://cmr.earthdata.nasa.gov/search/concepts/C3325394868-ESA.umm_json "The ESA Orthorectified Map-oriented (Level 1) Products collection is composed of MOS-1/1B MESSR (Multi-spectral Electronic Self-Scanning Radiometer) data products generated as part of the MOS Bulk Processing Campaign using the MOS Processor v3.02. The products are available in GeoTIFF format and disseminated within EO-SIP packaging. Please refer to the _$$MOS Product Format Specification$$ https://earth.esa.int/eogateway/documents/d/earth-online/mos-product-format-specification for further details. The collection consists of data products of the following type: MES_GEC_1P: Geocoded Ellipsoid GCP Corrected Level 1 MOS-1/1B MESSR products which are the default products generated by the MOS MESSR processor in all cases (where possible), with usage of the latest set of LANDSAT improved GCP (Ground Control Points). These are orthorectified map-oriented products, corresponding to the old MOS-1/1B MES_ORT_1P products with geolocation improvements. MESSR Instrument Characteristics Band Wavelength Range (nm) Spatial Resolution (m) Swath Width (km) 1 (VIS) 510 – 690 50 100 2 (VIS) 610 – 690 50 100 3 (NIR) 720 – 800 50 100 4 (NIR) 800 – 1100 50 100" proprietary
ESA_System_corrected_Level_1_MOS_1_1B_VTIR_product_6.0 MOS-1/1B ESA System Corrected VTIR Products [VTI_SYC_1P] ESA STAC Catalog 1987-09-08 1993-09-30 -120, 19, 95, 87 https://cmr.earthdata.nasa.gov/search/concepts/C3325393706-ESA.umm_json "The ESA System Corrected (Level 1) MOS-1/1B VTIR Products collection is composed of MOS-1/1B VTIR (Visible and Thermal Infrared Radiometer) data products generated as part of the MOS Bulk Processing Campaign using the MOS Processor v3.02. The products are available in GeoTIFF format and disseminated within EO-SIP packaging. Please refer to the MOS Product Format Specification for further details. The collection consists of data products of the following type: VTI_SYC_1P: System corrected Level 1 MOS-1/1B VTIR products in EO-SIP format. Band Wavelength Range (µm) Spatial Resolution (km) Swath Width (km) 1 (VIS) 0.5 – 0.7 0.9 1500 2 (TIR) 6.0 – 7.0 2.7 1500 3 (TIR) 10.5 – 11.5 2.7 1500 4 (TIR) 11.5 – 12.5 2.7 1500" proprietary
ESA_System_corrected_map_oriented_Level_1_products_6.0 MOS-1/1B ESA System Corrected Map-oriented Products [MES_GES_1P] ESA STAC Catalog 1987-09-08 1993-08-20 -120, 19, 95, 87 https://cmr.earthdata.nasa.gov/search/concepts/C3325394286-ESA.umm_json "The ESA System Corrected Map-oriented (Level 1) Products collection is composed of MOS-1/1B MESSR (Multi-spectral Electronic Self-Scanning Radiometer) data products generated as part of the MOS Bulk Processing Campaign using the MOS Processor v3.02. The products are available in GeoTIFF format and disseminated within EO-SIP packaging. Please refer to the _$$MOS Product Format Specification$$ https://earth.esa.int/eogateway/documents/d/earth-online/mos-product-format-specification for further details. The collection consists of data products of the following type: MES_GES_1P: Geocoded Ellipsoid System Corrected Level 1 MOS-1/1B MESSR products as generated by the MOS MESSR processor where the generation of MES_GEC_1P products is not possible. These replace the old MES_SYC_1P products. MESSR Instrument Characteristics Band Wavelength Range (nm) Spatial Resolution (m) Swath Width (km) 1 (VIS) 510 – 690 50 100 2 (VIS) 610 – 690 50 100 3 (NIR) 720 – 800 50 100 4 (NIR) 800 – 1100 50 100" proprietary
@@ -6037,8 +6038,8 @@ EastAnglia10YearMean_549_1 Global 10-Year Mean Monthly Climatology, 1901-1990 (N
EastAnglia30YearMean_550_1 Global 30-Year Mean Monthly Climatology, 1901-1960 (New et al.) ORNL_CLOUD STAC Catalog 1901-01-01 1961-01-01 -180, -60, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2776885667-ORNL_CLOUD.umm_json A data set of 30-year mean monthly surface climate over global land areas, excluding Antarctica. Interpolated from station data to 0.5 degree lat/lon for a range of variables: precipitation, wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and and minimum temperature can be determined), vapour pressure, cloud cover, ground-frost frequency. proprietary
EastAngliaClimate_542_1 Global 30-Year Mean Monthly Climatology, 1961-1990 (New et al.) ORNL_CLOUD STAC Catalog 1961-01-01 1991-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776876780-ORNL_CLOUD.umm_json A data set of mean monthly surface climate over global land areas, excluding Antarctica. Interpolated from station data to 0.5 degrees lat/lon for a range of variables: precipitation and wet-day frequency, mean temperature and diurnal temperature range (from which maximum temperature and minimum temperature can be determined), vapour pressure, sunshine, cloud cover, ground-frost frequency and windspeed. proprietary
EastAngliaPrecip_417_1 Global Monthly Precipitation, 1900-1999 (Hulme) ORNL_CLOUD STAC Catalog 1900-01-01 1999-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776827920-ORNL_CLOUD.umm_json An historical monthly precipitation data set for global land areas from 1900 to January 1, 1999, gridded at two different resolutions (2.5 degrees latitude by 3.75 degrees longitude and 5 degrees latitude/longitude). proprietary
-Ecosystem_Map_SRD_PAD_1947_1 ABoVE: Wetland Type, Slave River and Peace-Athabasca Deltas, Canada, 2007 and 2017 ALL STAC Catalog 2006-06-14 2019-05-28 -115.29, 57.77, -109.64, 61.79 https://cmr.earthdata.nasa.gov/search/concepts/C2240727799-ORNL_CLOUD.umm_json This dataset provides ecosystem-types for the Slave River Delta (SRD) and Peace-Athabasca Delta (PAD), Canada, for the time periods circa 2007 and circa 2017. The image resolution is 12.5 m with 0.2-hectare minimum mapping unit. Included are an 18-class modified Enhanced Wetland Classification (EWC) scheme for wetland, peatland, and upland areas. Classes were derived from a Random Forest classification trained on multi-seasonal moderate-resolution images and synthetic aperture radar (SAR) imagery sourced from aerial and satellite sensors, field data, and calculated indices. Indices included Height Above Nearest Drainage (HAND) and Topographic Position Index (TPI), both derived from a digital elevation model, to differentiate between land cover types. The c. 2007 remote sensing data were comprised of early and late growing season Landsat-5, ERS2, L-Band PALSAR from 2006 to 2010 and growing season Landsat thermal composites. The c. 2017 remote sensing data were comprised of early and late growing season Landsat-8 and L-Band PALSAR-2 from 2017 to 2019, Sentinel-1 June VV and VH mean and standard deviations, and growing season Landsat thermal composites. Elevation indices from multi-resolution TPI and HAND were created from the Japan Aerospace Exploration Agency Advanced Land Observing Satellite 30 m Global Spatial Data Model. Also included are the images used for classification and the classification error matrices for each map and time period. Data are provided in GeoTIFF and GeoPackage file formats. proprietary
Ecosystem_Map_SRD_PAD_1947_1 ABoVE: Wetland Type, Slave River and Peace-Athabasca Deltas, Canada, 2007 and 2017 ORNL_CLOUD STAC Catalog 2006-06-14 2019-05-28 -115.29, 57.77, -109.64, 61.79 https://cmr.earthdata.nasa.gov/search/concepts/C2240727799-ORNL_CLOUD.umm_json This dataset provides ecosystem-types for the Slave River Delta (SRD) and Peace-Athabasca Delta (PAD), Canada, for the time periods circa 2007 and circa 2017. The image resolution is 12.5 m with 0.2-hectare minimum mapping unit. Included are an 18-class modified Enhanced Wetland Classification (EWC) scheme for wetland, peatland, and upland areas. Classes were derived from a Random Forest classification trained on multi-seasonal moderate-resolution images and synthetic aperture radar (SAR) imagery sourced from aerial and satellite sensors, field data, and calculated indices. Indices included Height Above Nearest Drainage (HAND) and Topographic Position Index (TPI), both derived from a digital elevation model, to differentiate between land cover types. The c. 2007 remote sensing data were comprised of early and late growing season Landsat-5, ERS2, L-Band PALSAR from 2006 to 2010 and growing season Landsat thermal composites. The c. 2017 remote sensing data were comprised of early and late growing season Landsat-8 and L-Band PALSAR-2 from 2017 to 2019, Sentinel-1 June VV and VH mean and standard deviations, and growing season Landsat thermal composites. Elevation indices from multi-resolution TPI and HAND were created from the Japan Aerospace Exploration Agency Advanced Land Observing Satellite 30 m Global Spatial Data Model. Also included are the images used for classification and the classification error matrices for each map and time period. Data are provided in GeoTIFF and GeoPackage file formats. proprietary
+Ecosystem_Map_SRD_PAD_1947_1 ABoVE: Wetland Type, Slave River and Peace-Athabasca Deltas, Canada, 2007 and 2017 ALL STAC Catalog 2006-06-14 2019-05-28 -115.29, 57.77, -109.64, 61.79 https://cmr.earthdata.nasa.gov/search/concepts/C2240727799-ORNL_CLOUD.umm_json This dataset provides ecosystem-types for the Slave River Delta (SRD) and Peace-Athabasca Delta (PAD), Canada, for the time periods circa 2007 and circa 2017. The image resolution is 12.5 m with 0.2-hectare minimum mapping unit. Included are an 18-class modified Enhanced Wetland Classification (EWC) scheme for wetland, peatland, and upland areas. Classes were derived from a Random Forest classification trained on multi-seasonal moderate-resolution images and synthetic aperture radar (SAR) imagery sourced from aerial and satellite sensors, field data, and calculated indices. Indices included Height Above Nearest Drainage (HAND) and Topographic Position Index (TPI), both derived from a digital elevation model, to differentiate between land cover types. The c. 2007 remote sensing data were comprised of early and late growing season Landsat-5, ERS2, L-Band PALSAR from 2006 to 2010 and growing season Landsat thermal composites. The c. 2017 remote sensing data were comprised of early and late growing season Landsat-8 and L-Band PALSAR-2 from 2017 to 2019, Sentinel-1 June VV and VH mean and standard deviations, and growing season Landsat thermal composites. Elevation indices from multi-resolution TPI and HAND were created from the Japan Aerospace Exploration Agency Advanced Land Observing Satellite 30 m Global Spatial Data Model. Also included are the images used for classification and the classification error matrices for each map and time period. Data are provided in GeoTIFF and GeoPackage file formats. proprietary
Ecotoxicology_1 Developing environmental quality guidelines for Antarctica: Responses of Antarctic and subantarctic biota to contaminants AU_AADC STAC Catalog 2007-09-30 2012-03-31 110.48, -66.32, 110.56, -66.24 https://cmr.earthdata.nasa.gov/search/concepts/C1575836029-AU_AADC.umm_json Metadata record for data from AAS (ASAC) Project 2933. See the child records for access to the datasets. Public While it is generally thought that Antarctic organisms are highly sensitive to pollution, there is little data to support or disprove this. Such data is essential if realistic environmental guidelines, which take into account unique physical, biological and chemical characteristics of the Antarctic environment, are to be developed. Factors that modify bioavailability, and the effects of common contaminants on a range of Antarctic organisms from micro-algae to macro-invertebrates will be examined. Risk assessment techniques developed will provide the scientific basis for prioritising contaminated site remediation activities in marine environments, and will contribute to the development of guidelines specific to Antarctica. Project objectives: 1. Develop and use toxicity tests to characterise the responses of a range of Antarctic marine invertebrates, micro- and macro-algae to common inorganic and organic contaminants. 2. To examine factors controlling bioavailability and the influence of physical, chemical and biological properties unique to the Antarctic environment on the bioavailability and toxicity of contaminants to biota. 3. To compare the response of Antarctic biota to analogous species in Arctic, temperate and tropical environments in order to determine the applicability of using toxicity data and environmental guidelines developed in other regions of the world for use in the Antarctic. 4. Develop a suite of standard bioassay techniques using Antarctic species to assess the toxicity of mixtures of contaminants (aqueous and sediment-bound) including tip leachates, sewage effluents and contaminated sediments. 5. To establish risk assessment models to predict the potential hazards associated with contaminated sites in Antarctica to marine biota, and to develop Water and Sediment Quality Guidelines for Antarctica to set as targets for the remediation of contaminated marine environments. Taken from the 2008-2009 Progress Report: Progress against objectives: Due to logistical constraints, only a short field season (5 weeks) was conducted at Casey in 2008/09 and no berths were allocated solely to this project. A team of 6 scientists worked together on an intensive marine sampling program under TRENZ (AAS project 2948, CI Stark) in support of 5 different AAS projects, including this one. The lack of adequate personnel dedicated to this project and the limited time that we were allocated on station hindered progress and meant that no experiments on Antarctic organisms were able to be conducted in situ. The airlink was however successfully used to transport marine invertebrates collected at Casey and held in seawater at 0degC back to Hobart on 3 separate flights. These invertebrates are currently being maintained in the cold water ecotoxicology aquarium facilities at Kingston. Once they are sorted and where possible established in cultures, they will be used in toxicity tests. Progress against specific objects are: 1) Much effort and time has been put towards the husbandry and culture of the collected Antarctic marine invertebrates. Some species are now successfully breeding in the laboratory providing new generations and sensitive juvenile stages of invertebrates to work with in toxicity tests. This culturing capability, if able to be developed, will hugely extend opportunities for carrying out research for this project, by giving us access to live material over the winter months and during summer when berths to or space on station in Antarctica is limited. Toxicity tests using some of the common amphipods and gastropods collected in the 0809 season at Casey will commence shortly at Kingston. 2) Toxicity tests to commence shortly using invertebrates collected in the 0809 season now being maintained in the Ecotoxicology aquarium will focus on interactions and potentially synergistic effects of contaminants along with other environmental stressors including increases in temperature and decreases in salinity associated with predicted environmental changes in response to climate change. 3) A phD candidate has recently started on this project and is currently reviewing all available literature on the response of Antarctic species to contaminants and environmental stressors in comparison to related species from lower latitudes. 4) Invertebrates collected in the 0809 season that are being maintained in the Ecotoxicology aquarium will be screened in toxicity tests to commence shortly. Methods will then be developed using the most suitable and sensitive species to form the basis of standard bioassay procedures that can be used to test mixtures such as sewage effluents and tip leachates in the upcoming season. 5) The establishment of risk assessment models and Environmental Quality Guidelines for Antarctica is a long term goal of this project when data from the first 4 objectives can be synthesised and hence has not yet been addressed. Taken from the 2009-2010 Progress Report: Progress against objectives: Objectives 1 and 2: Metal effects on the behaviour and survival of three marine invertebrate species were investigated during the field season. Two replicate toxicity tests were conducted on the larvae of sea urchin Sterechinus neumayeri where combined effects of metal (copper and cadmium) and temperature (-1, 1 and 3 degrees Celsius) were to be investigated on developmental success. However, due to lower than optimal fertilisation success, both tests were terminated before any meaningful results could be derived. Four tests were conducted on the adult amphipod, Paramorea walkeri. Two replicate tests investigated combined metal (copper and cadmium) and temperature (-1, 1 and 3 degrees Celsius) effects, and two tests investigated the effects of copper, cadmium, lead, zinc and nickel exposure at ambient sea water temperature of -1 degrees Celsius. One test was conducted with the micro-gastropod Skenella paludionoides being exposed to copper, cadmium, lead, zinc and nickel at ambient sea water temperature. The larvae of bivalve Laternula sp. were also investigated as a potential test organism for metal toxicity. Strip spawning was conducted a number of times, however, this technique did not provide adequate levels of fertilisation success and as such, the toxicity tests on larval development were not completed. Objective 3: A phD candidate working on this project is in the process of compiling a review of all available date on the response of Antarctic species to contaminants and environmental stressors in comparison to related species from lower latitudes. This literature review will form a major component of her thesis' first chapter Objective 4: Methods for Standard bioassay procedures were developed using the most suitable and sensitive species, the amphipod Paramoera walkeri and the microgastropod Skenella paludionoides. These standard tests were then used to assess the toxicity of sewage effluent at Davis Station (in conjunction with project 3217). Objective 5: Toxicity tests on sewage effluent were conducted as part of a risk assessment to determine hazards associated with the current discharge. The determined toxicity of the sewage effluent will provide a basis for guideline recommendations on the required level of treatment and on what constitutes an adequate or 'safe' dilution factor for dispersal of the effluent discharge to the near shore marine environment. proprietary
Effect_Environment_Moose_1739_1 ABoVE: Environmental Conditions During Fall Moose Hunting Seasons, Alaska, 2000-2016 ALL STAC Catalog 2000-01-01 2016-12-31 -158.53, 64.55, -156.66, 64.93 https://cmr.earthdata.nasa.gov/search/concepts/C2143402663-ORNL_CLOUD.umm_json This dataset provides daily and annual air temperature, river water level, and leaf drop dates coincident with the moose (Alces alces) hunting season (September) for the area surrounding the rural communities of Nulato, Koyukuk, Kaltag, Galena, Ruby, Huslia, and Hughes in interior Alaska, USA, over the period 2000-2016. The main objective of the study was to assess how the environmental conditions impacted the success of hunters who rely on moose as a subsistence resource. proprietary
Effect_Environment_Moose_1739_1 ABoVE: Environmental Conditions During Fall Moose Hunting Seasons, Alaska, 2000-2016 ORNL_CLOUD STAC Catalog 2000-01-01 2016-12-31 -158.53, 64.55, -156.66, 64.93 https://cmr.earthdata.nasa.gov/search/concepts/C2143402663-ORNL_CLOUD.umm_json This dataset provides daily and annual air temperature, river water level, and leaf drop dates coincident with the moose (Alces alces) hunting season (September) for the area surrounding the rural communities of Nulato, Koyukuk, Kaltag, Galena, Ruby, Huslia, and Hughes in interior Alaska, USA, over the period 2000-2016. The main objective of the study was to assess how the environmental conditions impacted the success of hunters who rely on moose as a subsistence resource. proprietary
@@ -6060,8 +6061,8 @@ Eurobis_2_24 Feb 2004 (Version 2.1) AlgaeBase (EUROBIS) SCIOPS STAC Catalog 1970
Eurobis_2_24 Feb 2004 (Version 2.1) AlgaeBase (EUROBIS) ALL STAC Catalog 1970-01-01 -45, 25, 50, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1214589737-SCIOPS.umm_json "AlgaeBase is a database of information on algae that includes terrestrial, marine and freshwater organisms. At present, the data for the marine algae, particularly seaweeds, are the most complete. AlgaeBase is often a compromise of taxonomic opinions that may or may not reflect your particular conclusions. Feel free to use the information and images included on the AlgaeBase web site, but do please cite AlgaeBase in your publications or presentations. This helps to raise money in order to continue maintenance of the service. Please also realise that AlgaeBase is made available in an incomplete form and is purely meant as a aid to taxonomic studies and not a definitive source in its own right. You should always check the information included prior to use. [Source: The information provided in the summary was extracted from the MarBEF Data System at ""http://www.marbef.org/data/eurobisproviders.php""]" proprietary
Eurobis_505_1 A comparison of benthic biodiversity in the North Sea, English Channel and Celtic Seas (EUROBIS) SCIOPS STAC Catalog 1992-05-12 1996-07-09 -7.99, 48.5, 8.39, 58 https://cmr.earthdata.nasa.gov/search/concepts/C1214586057-SCIOPS.umm_json "Data which produced the publications: Rees, H. L. et al. (1999) and Rees, H. L. et al. (2000). See references below. Size reference: 69 stations sampled, 2735 distribution records [Source: The information provided in the summary was extracted from the MarBEF Data System at ""http://www.marbef.org/data/eurobisproviders.php""]" proprietary
Eurobis_505_1 A comparison of benthic biodiversity in the North Sea, English Channel and Celtic Seas (EUROBIS) ALL STAC Catalog 1992-05-12 1996-07-09 -7.99, 48.5, 8.39, 58 https://cmr.earthdata.nasa.gov/search/concepts/C1214586057-SCIOPS.umm_json "Data which produced the publications: Rees, H. L. et al. (1999) and Rees, H. L. et al. (2000). See references below. Size reference: 69 stations sampled, 2735 distribution records [Source: The information provided in the summary was extracted from the MarBEF Data System at ""http://www.marbef.org/data/eurobisproviders.php""]" proprietary
-Eurobis_618_1 70 samples data of Kiel Bay (EUROBIS) SCIOPS STAC Catalog 1995-05-29 10.3944, 54.3814, 10.3944, 54.3814 https://cmr.earthdata.nasa.gov/search/concepts/C1214586110-SCIOPS.umm_json "Marine Benthic data on benthos at station 014 in Kiel Bay representing 1,144 distribution records of 56 taxa from 1 station in Kiel Bay. [Source: The information provided in the summary was extracted from the MarBEF Data System at ""http://www.marbef.org/data/eurobisproviders.php""]" proprietary
Eurobis_618_1 70 samples data of Kiel Bay (EUROBIS) ALL STAC Catalog 1995-05-29 10.3944, 54.3814, 10.3944, 54.3814 https://cmr.earthdata.nasa.gov/search/concepts/C1214586110-SCIOPS.umm_json "Marine Benthic data on benthos at station 014 in Kiel Bay representing 1,144 distribution records of 56 taxa from 1 station in Kiel Bay. [Source: The information provided in the summary was extracted from the MarBEF Data System at ""http://www.marbef.org/data/eurobisproviders.php""]" proprietary
+Eurobis_618_1 70 samples data of Kiel Bay (EUROBIS) SCIOPS STAC Catalog 1995-05-29 10.3944, 54.3814, 10.3944, 54.3814 https://cmr.earthdata.nasa.gov/search/concepts/C1214586110-SCIOPS.umm_json "Marine Benthic data on benthos at station 014 in Kiel Bay representing 1,144 distribution records of 56 taxa from 1 station in Kiel Bay. [Source: The information provided in the summary was extracted from the MarBEF Data System at ""http://www.marbef.org/data/eurobisproviders.php""]" proprietary
Eyes on the Ground Image Data_1 Eyes on the Ground Image Data MLHUB STAC Catalog 2020-01-01 2023-01-01 33.9095879, -2.9922132, 38.7868576, 1.15124 https://cmr.earthdata.nasa.gov/search/concepts/C2781412330-MLHUB.umm_json The 'Eyes on the Ground' project ([lacunafund.org](https://lacunafund.org/ag2020awards/)) is a collaboration between ACRE Africa, the International Food Policy Research Institute (IFPRI), and the Lacuna Fund, to create a large machine learning (ML) dataset of smallholder farmer's fields based upon previous work within the Picture Based Insurance framework (Ceballos, Kramer and Robles, 2019, [https://doi.org/10.1016/j.deveng.2019.100042](https://doi.org/10.1016/j.deveng.2019.100042)). This is a unique dataset of georeferenced crop images along with labels on input use, crop management, phenology, crop damage, and yields, collected across 8 counties in Kenya.The research leading to this dataset was undertaken as part of the CGIAR research program on Policies, Institutions and Markets (PIM) proprietary
FAO_AGL FAO/AGL World River Sediment Yields Database CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -90 https://cmr.earthdata.nasa.gov/search/concepts/C2232283741-CEOS_EXTRA.umm_json "Food and Agricultural Organization of the United Nations (FAO)/AGL World River Sediment Yields Database The World River Sediment Yields database contains data on annual sediment yields in worldwide rivers and reservoirs, searchable by river, country and continent. The database was compiled from different sources by HR Wallingford, UK, on behalf of the FAO Land and Water Development Division. It is currently in a test phase. The database allows its user to enter the name of the river, the country, or the continent for which they would like to see summary sedimentation data. From this data, you can discover explanations of the data and complete sedimentation records Data URL: ""http://www.fao.org/ag/AGL/aglw/sediment/default.asp"" Information taken from ""http://www.fao.org/ag/AGL/aglw/sediment/default.asp""" proprietary
FAO_FIGIS FAO Fisheries Global Information System CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232284263-CEOS_EXTRA.umm_json FAO's major program on Fisheries aims to promote sustainable development of responsible fisheries and contribute to food security. To implement this major program, the Fisheries Department focuses its activities, through programs in Fishery Resources, Fishery Policy, Fishery Industries and Fishery Information on three medium-term strategic objectives, including promotion of responsible fisheries sector management at the global, regional and national levels, promotion of increased contribution of responsible fisheries and aquaculture to world food supplies and food security, and global monitoring and strategic analysis of fisheries The FAO Fisheries Global Information System is a global network of integrated fisheries information. FIGIS is a work in progress - sections are currently under development. Valuable information can be accessed on topics such as aquatic species, marine resources, marine fisheries, and fishing technology. Soon you will be able to access databases on trade and marketing, aquaculture, inland fisheries, and fisheries issues. http://www.fao.org/fishery/figis proprietary
@@ -6069,15 +6070,15 @@ FAOd0008_148 FAO World Soil Resources CEOS_EXTRA STAC Catalog 1970-01-01 -180,
FAOd0018_148 Distribution of Major Soil Types CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232283838-CEOS_EXTRA.umm_json "This CD-ROM is released in conjunction with World Soil Resources Reports No. 94: ""Lecture Notes on the Major Soils of the World"". In addition to the complete (hyperlinked) text of the book, it contains many additional pictures, a slideshow with a virtual tour of soils and landscapes and a typical soil profile for each of the thirty reference soil groups of the World Reference Base for Soil Resources. In total more than 550 slides and pictures illustrate the lecture notes. ""http://www.fao.org/icatalog/search/dett.asp?aries_id=102985""" proprietary
FAOd0019_148 Digital Soil Map of the World and Derived Soil Properties. CEOS_EXTRA STAC Catalog 1970-01-01 6.11, 36.15, 19.33, 47.71 https://cmr.earthdata.nasa.gov/search/concepts/C2232283478-CEOS_EXTRA.umm_json "This CD-ROM contains the Digital Soil Map of the World in various formats, verctor as well as raster, supported by most GIS software. The base material is the FAO/UNESCO Soil Map of the World at an original scale of 1:5 million. Programs and data files give tabular country information on soil characteristics and derived soil properties from the map are included, such as pH, organic carbon content and soil moisture storage capability. In addition programs and data files are included that display derived soil properties. The revision included the adding of a number of user-friendly ArcView files allowing the display of dominant soils by continent and the inclusion of the update of the image of the WRB World Soil Resources Map. ""http://www.fao.org/icatalog/search/dett.asp?aries_id=103540""" proprietary
FAOd0020_148 Hydrological Basins of Africa CEOS_EXTRA STAC Catalog 1970-01-01 -17.3, -34.6, 51.1, 38.2 https://cmr.earthdata.nasa.gov/search/concepts/C2232283043-CEOS_EXTRA.umm_json Hydrological Basins of Africa, with major basins and sub basins, automatically derived from USGS topographic data with some manual corrections in flat areas. Current version completed March 2000. proprietary
-FAUNA_PENGUIN_COLONY_1 A census of penguin colony counts (provided to OBIS) from the year 1900 to 1996 in the Antarctic Region ALL STAC Catalog 1901-01-01 1996-12-31 -180, -80, 180, -45 https://cmr.earthdata.nasa.gov/search/concepts/C1214313468-AU_AADC.umm_json This dataset is a census of penguin colony counts from the year 1900 in the Antarctic region. It forms part of the Inventory of Antarctic seabird breeding sites within the Antarctic and subantarctic islands. The Antarctic and subantarctic fauna database (seabirds) is a database detailing the distribution and abundance of breeding localities for Antarctic and Subantarctic seabirds. Each species' compilation was produced by members of the SCAR Bird Biology Subcommittee. This separate metadata record has been created beacause it represents only the penguin colony counts that have been published to OBIS. Note: The Year (not day or month) date is only relevent in this dataset. The positions that have been published to OBIS include latitude and longitude positions that were not included within the original dataset. The latitude and longitude positions that were not noted by the observer have been created from the locality given by the observer using the Antarctic Composite Gazetteer. Two spreadsheets are available for download, from the URL given below. The original, unmodified spreadsheet is available, as well as a corrected spreadsheet. In the corrected spreadsheet, the AADC has attempted to reconcile the poorly presented localities into a single column. It is possible that some of these localities may not be correct. The fields in this dataset are: SCAR Number Species Region Locality Longitude Latitude Number of Colonies Number of Pairs Type and accuracy of count Data Date References Remarks These data are further referenced in ANARE Research Notes 9 - see reference below. proprietary
FAUNA_PENGUIN_COLONY_1 A census of penguin colony counts (provided to OBIS) from the year 1900 to 1996 in the Antarctic Region AU_AADC STAC Catalog 1901-01-01 1996-12-31 -180, -80, 180, -45 https://cmr.earthdata.nasa.gov/search/concepts/C1214313468-AU_AADC.umm_json This dataset is a census of penguin colony counts from the year 1900 in the Antarctic region. It forms part of the Inventory of Antarctic seabird breeding sites within the Antarctic and subantarctic islands. The Antarctic and subantarctic fauna database (seabirds) is a database detailing the distribution and abundance of breeding localities for Antarctic and Subantarctic seabirds. Each species' compilation was produced by members of the SCAR Bird Biology Subcommittee. This separate metadata record has been created beacause it represents only the penguin colony counts that have been published to OBIS. Note: The Year (not day or month) date is only relevent in this dataset. The positions that have been published to OBIS include latitude and longitude positions that were not included within the original dataset. The latitude and longitude positions that were not noted by the observer have been created from the locality given by the observer using the Antarctic Composite Gazetteer. Two spreadsheets are available for download, from the URL given below. The original, unmodified spreadsheet is available, as well as a corrected spreadsheet. In the corrected spreadsheet, the AADC has attempted to reconcile the poorly presented localities into a single column. It is possible that some of these localities may not be correct. The fields in this dataset are: SCAR Number Species Region Locality Longitude Latitude Number of Colonies Number of Pairs Type and accuracy of count Data Date References Remarks These data are further referenced in ANARE Research Notes 9 - see reference below. proprietary
+FAUNA_PENGUIN_COLONY_1 A census of penguin colony counts (provided to OBIS) from the year 1900 to 1996 in the Antarctic Region ALL STAC Catalog 1901-01-01 1996-12-31 -180, -80, 180, -45 https://cmr.earthdata.nasa.gov/search/concepts/C1214313468-AU_AADC.umm_json This dataset is a census of penguin colony counts from the year 1900 in the Antarctic region. It forms part of the Inventory of Antarctic seabird breeding sites within the Antarctic and subantarctic islands. The Antarctic and subantarctic fauna database (seabirds) is a database detailing the distribution and abundance of breeding localities for Antarctic and Subantarctic seabirds. Each species' compilation was produced by members of the SCAR Bird Biology Subcommittee. This separate metadata record has been created beacause it represents only the penguin colony counts that have been published to OBIS. Note: The Year (not day or month) date is only relevent in this dataset. The positions that have been published to OBIS include latitude and longitude positions that were not included within the original dataset. The latitude and longitude positions that were not noted by the observer have been created from the locality given by the observer using the Antarctic Composite Gazetteer. Two spreadsheets are available for download, from the URL given below. The original, unmodified spreadsheet is available, as well as a corrected spreadsheet. In the corrected spreadsheet, the AADC has attempted to reconcile the poorly presented localities into a single column. It is possible that some of these localities may not be correct. The fields in this dataset are: SCAR Number Species Region Locality Longitude Latitude Number of Colonies Number of Pairs Type and accuracy of count Data Date References Remarks These data are further referenced in ANARE Research Notes 9 - see reference below. proprietary
FDRforAltimetry_6.0 Fundamental Data Records for Altimetry [ALT_FDR___] ESA STAC Catalog 1991-08-03 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3325394451-ESA.umm_json This dataset is a Fundamental Data Record (FDR) resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ . The Fundamental Data Record for Altimetry V1 products contain Level 0 and Level 1 altimeter-related parameters including calibrated radar waveforms and supplementary instrumental parameters describing the altimeter operating status and configuration through the satellite lifetime. The data record consists of data for the ERS-1, ERS-2 and Envisat missions for the period ranging from 1991 to 2012, and bases on the Level 1 data obtained from previous ERS REAPER and ENVISAT V3.0 reprocessing efforts incorporating new algorithms, flags, and corrections to enhance the accuracy and reliability of the data. For many aspects, the Altimetry FDR product has improved compared to the existing individual mission datasets: New neural-network waveform classification, surface type classification, distance to shoreline and surface flag based on GSHHG Instrumental calibration information directly available in the product Improved Orbit solutions Correction of REAPER drawbacks (i.e., time jumps and negative waveforms) The FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used. Information for expert altimetry users is also available in a dedicated NetCDF group within the products. Please consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data. The FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results. proprietary
FDRforAtmosphericCompositionATMOSL1B_4.0 Fundamental Data Record for Atmospheric Composition [ATMOS__L1B] ESA STAC Catalog 1995-06-28 2012-04-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3325394388-ESA.umm_json "The Fundamental Data Record (FDR) for Atmospheric Composition UVN Level 1b v.1.0 dataset is a cross-instrument Level-1 product [ATMOS__L1B] generated in 2023 and resulting from the _$$ESA FDR4ATMOS project$$ https://atmos.eoc.dlr.de/FDR4ATMOS/ . The FDR contains selected Earth Observation Level 1b parameters (irradiance/reflectance) from the nadir-looking measurements of the ERS-2 GOME and Envisat SCIAMACHY missions for the period ranging from 1995 to 2012. The data record offers harmonised cross-calibrated spectra, essential for subsequent trace gas retrieval. The focus lies on spectral windows in the Ultraviolet-Visible-Near Infrared regions the retrieval of critical atmospheric constituents like ozone (O3), sulphur dioxide (SO2), nitrogen dioxide (NO2) column densities, alongside cloud parameters in the NIR spectrum. For many aspects, the FDR product has improved compared to the existing individual mission datasets: • GOME solar irradiances are harmonised using a validated SCIAMACHY solar reference spectrum, solving the problem of the fast-changing etalon present in the original GOME Level 1b data; • Reflectances for both GOME and SCIAMACHY are provided in the FDR product. GOME reflectances are harmonised to degradation-corrected SCIAMACHY values, using collocated data from the CEOS PIC sites; • SCIAMACHY data are scaled to the lowest integration time within the spectral band using high-frequency PMD measurements from the same wavelength range. This simplifies the use of the SCIAMACHY spectra which were split in a complex cluster structure (with own integration time) in the original Level 1b data; • The harmonization process applied mitigates the viewing angle dependency observed in the UV spectral region for GOME data; • Uncertainties are provided. Each FDR product covers three FDRs (irradiance/reflectance for UV-VIS-NIR) for a single day within the same product including information from the individual ERS-2 GOME and Envisat SCIAMACHY orbits therein. FDR has been generated in two formats: Level 1A and Level 1B targeting expert users and nominal applications respectively. The Level 1A [ATMOS__L1A] data include additional parameters such as harmonisation factors, PMD, and polarisation data extracted from the original mission Level 1 products. The ATMOS__L1A dataset is not part of the nominal dissemination to users. In case of specific requirements, please contact _$$EOHelp$$ http://esatellus.service-now.com/csp?id=esa_simple_request&sys_id=f27b38f9dbdffe40e3cedb11ce961958 . The FDR4ATMOS products should be regarded as experimental due to the innovative approach and the current use of a limited-sized test dataset to investigate the impact of harmonization on the Level 2 target species, specifically SO2, O3 and NO2. Presently, this analysis is being carried out within follow-on activities. One of the main aspects of the project was the characterization of Level 1 uncertainties for both instruments, based on metrological best practices. The following documents are provided: 1. General guidance on a metrological approach to Fundamental Data Records (FDR) -> link TBC 2. Uncertainty Characterisation document -> link TBC 3. Effect tables -> link TBC 4. NetCDF files containing example uncertainty propagation analysis and spectral error correlation matrices for SCIAMACHY (Atlantic and Mauretania scene for 2003 and 2010) and GOME (Atlantic scene for 2003) links TBC reflectance_uncertainty_example_FDR4ATMOS_GOME.nc reflectance_uncertainty_example_FDR4ATMOS_SCIA.nc The FDR V1 is currently being extended to include the MetOp GOME-2 series. All the new products are conveniently formatted in NetCDF. Free standard tools, such as _$$Panoply$$ https://www.giss.nasa.gov/tools/panoply/ , can be used to read NetCDF data. Panoply is sourced and updated by external entities. For further details, please consult our _$$Terms and Conditions page$$ https://earth.esa.int/eogateway/terms-and-conditions ." proprietary
FDRforRadiometry_5.0 Fundamental Data Records for Radiometry [MWR_FDR___] ESA STAC Catalog 1991-08-03 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3325393568-ESA.umm_json This dataset is a Fundamental Data Record (FDR) resulting from the _$$ESA FDR4ALT project$$ https://www.fdr4alt.org/ . The Fundamental Data Record for Radiometry V1 products contain intercalibrated Top of the Atmosphere brightness temperatures at 23.8 and 36.5 GHz. The collection covers data for the ERS-1, ERS-2 and Envisat missions, and is built upon a new processing of Level 0 data, incorporating numerous improvements in terms of algorithms, flagging procedures, and corrections. Compared to existing datasets, the Radiometry FDR demonstrates notable improvements in several aspects: New solutions for instrumental effects (ERS Reflector loss, Skyhorn, and Sidelobe corrections) Native sampling rate of 7Hz with enhanced coverage The FDR4ALT products are available in NetCDF format. Free standard tools for reading NetCDF data can be used. Information for expert altimetry users is also available in a dedicated NetCDF group within the products. Please consult the _$$FDR4ALT Product User Guide$$ https://earth.esa.int/eogateway/documents/d/earth-online/fdr4alt-products-user-guide before using the data. The FDR4ALT datasets represent the new reference data for the ERS/Envisat altimetry missions, superseding any previous mission data. Users are strongly encouraged to make use of these datasets for optimal results. proprietary
FEDMAC_AEROSOLS Aerosol Optical Thickness Measurements During the Forest Ecosystem Dynamics - Multisensor Aircraft Campaign ALL STAC Catalog 1990-09-08 1990-09-15 -68, 45, -68, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214600425-SCIOPS.umm_json " Forest Ecosystem Dynamics Multisensor Airborne Campaign (FED MAC): Aerosol Optical Thickness The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem. Measurement of atmospheric attenuation and hence estimate of the aerosol optical thickness were made in the Northern Experimental Forest (NEF) in Howland, Maine, with sunphotometers. This parameter is useful in calibration and correction of other measurements made with remote sensing instruments at FED sites. Measurements were made with the eight channel sun-photometer named SXM-2 (440, 522, 613, 672, 781, 871 and 1030 nm with 10 nm FWHM) located on the ground. It tracks the sun automatically using a 4 quadrant detector. The detector is a silicon photodiode which is kept at a constant temperature. The instrument has a 1.5 degree field-of-view. The FED Home Page is at: ""https://forest.gsfc.nasa.gov/"". " proprietary
FEDMAC_AEROSOLS Aerosol Optical Thickness Measurements During the Forest Ecosystem Dynamics - Multisensor Aircraft Campaign SCIOPS STAC Catalog 1990-09-08 1990-09-15 -68, 45, -68, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214600425-SCIOPS.umm_json " Forest Ecosystem Dynamics Multisensor Airborne Campaign (FED MAC): Aerosol Optical Thickness The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem. Measurement of atmospheric attenuation and hence estimate of the aerosol optical thickness were made in the Northern Experimental Forest (NEF) in Howland, Maine, with sunphotometers. This parameter is useful in calibration and correction of other measurements made with remote sensing instruments at FED sites. Measurements were made with the eight channel sun-photometer named SXM-2 (440, 522, 613, 672, 781, 871 and 1030 nm with 10 nm FWHM) located on the ground. It tracks the sun automatically using a 4 quadrant detector. The detector is a silicon photodiode which is kept at a constant temperature. The instrument has a 1.5 degree field-of-view. The FED Home Page is at: ""https://forest.gsfc.nasa.gov/"". " proprietary
-FEDMAC_ALPS Airborne Laser Polarization Sensor (ALPS) Experiment During the Forest Ecosystem Dynamics - Multisensor Airborne Campaign SCIOPS STAC Catalog 1990-09-09 1990-09-11 -68, 45, -68, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214600409-SCIOPS.umm_json " Forest Ecosystem Dynamics Multisensor Airborne Campaign (FED MAC): Airborne Laser Polarization Experiment The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem. A new remote sensing instrument, the Airborne Laser Polarization Sensor (ALPS), mounted on a helicopter, was used to make multispectral radiometric and polarization measurements of the Earth's surface using a polarized laser light source. The ALPS system consists of a pulsed, polarized laser source, an optical receiver package, a video camera and recorder, and data acquisition and analysis hardware and software. The choice of laser wavelengths is limited to frequencies from the ultraviolet to the near-infrared by the photo-cathode response of the selected photo multiplier tube (PMT) detectors. Twelve PMTs were used corresponding to the 12 channels of data: Channels 1,2,3,4,9 & 10 have 1090 nm bandpass filters. The reminder are for 532 nm. Channels 9 and 11 have no polarization filters. For each wavelength, polarization filters are mounted in front of each PMT at angles relative to the transmitted polarization. A pulsed (7 ns) Nd:YAG laser is employed. It operates in the infrared at 1060 nm and the visible at 532 nm. The 532 nm green wavelength can be seen near the center of the TV screen as it hits the surface in most cases. This is used for ground truth correlation. The spot is about 20 cm in diameter from 100 meters altitude. In these data for ALPS Experiment for the FED MAC 90, the file tabulation refers to data files taken on September 9 and 11. A standard VHS video tape is available (the master tapes are recorded at the SP speed on Super-VHS). The first half of this tape is from a camera coaxial with the laser transmission. Time on the tape correspond to file times while oral comments on the tape supplement the general comments. The second half of the tape consists primarily of site descriptive narration on the ground and some pictures of the helicopter setup. The FED Home Page is at: ""https://forest.gsfc.nasa.gov/"". " proprietary
FEDMAC_ALPS Airborne Laser Polarization Sensor (ALPS) Experiment During the Forest Ecosystem Dynamics - Multisensor Airborne Campaign ALL STAC Catalog 1990-09-09 1990-09-11 -68, 45, -68, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214600409-SCIOPS.umm_json " Forest Ecosystem Dynamics Multisensor Airborne Campaign (FED MAC): Airborne Laser Polarization Experiment The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem. A new remote sensing instrument, the Airborne Laser Polarization Sensor (ALPS), mounted on a helicopter, was used to make multispectral radiometric and polarization measurements of the Earth's surface using a polarized laser light source. The ALPS system consists of a pulsed, polarized laser source, an optical receiver package, a video camera and recorder, and data acquisition and analysis hardware and software. The choice of laser wavelengths is limited to frequencies from the ultraviolet to the near-infrared by the photo-cathode response of the selected photo multiplier tube (PMT) detectors. Twelve PMTs were used corresponding to the 12 channels of data: Channels 1,2,3,4,9 & 10 have 1090 nm bandpass filters. The reminder are for 532 nm. Channels 9 and 11 have no polarization filters. For each wavelength, polarization filters are mounted in front of each PMT at angles relative to the transmitted polarization. A pulsed (7 ns) Nd:YAG laser is employed. It operates in the infrared at 1060 nm and the visible at 532 nm. The 532 nm green wavelength can be seen near the center of the TV screen as it hits the surface in most cases. This is used for ground truth correlation. The spot is about 20 cm in diameter from 100 meters altitude. In these data for ALPS Experiment for the FED MAC 90, the file tabulation refers to data files taken on September 9 and 11. A standard VHS video tape is available (the master tapes are recorded at the SP speed on Super-VHS). The first half of this tape is from a camera coaxial with the laser transmission. Time on the tape correspond to file times while oral comments on the tape supplement the general comments. The second half of the tape consists primarily of site descriptive narration on the ground and some pictures of the helicopter setup. The FED Home Page is at: ""https://forest.gsfc.nasa.gov/"". " proprietary
+FEDMAC_ALPS Airborne Laser Polarization Sensor (ALPS) Experiment During the Forest Ecosystem Dynamics - Multisensor Airborne Campaign SCIOPS STAC Catalog 1990-09-09 1990-09-11 -68, 45, -68, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214600409-SCIOPS.umm_json " Forest Ecosystem Dynamics Multisensor Airborne Campaign (FED MAC): Airborne Laser Polarization Experiment The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem. A new remote sensing instrument, the Airborne Laser Polarization Sensor (ALPS), mounted on a helicopter, was used to make multispectral radiometric and polarization measurements of the Earth's surface using a polarized laser light source. The ALPS system consists of a pulsed, polarized laser source, an optical receiver package, a video camera and recorder, and data acquisition and analysis hardware and software. The choice of laser wavelengths is limited to frequencies from the ultraviolet to the near-infrared by the photo-cathode response of the selected photo multiplier tube (PMT) detectors. Twelve PMTs were used corresponding to the 12 channels of data: Channels 1,2,3,4,9 & 10 have 1090 nm bandpass filters. The reminder are for 532 nm. Channels 9 and 11 have no polarization filters. For each wavelength, polarization filters are mounted in front of each PMT at angles relative to the transmitted polarization. A pulsed (7 ns) Nd:YAG laser is employed. It operates in the infrared at 1060 nm and the visible at 532 nm. The 532 nm green wavelength can be seen near the center of the TV screen as it hits the surface in most cases. This is used for ground truth correlation. The spot is about 20 cm in diameter from 100 meters altitude. In these data for ALPS Experiment for the FED MAC 90, the file tabulation refers to data files taken on September 9 and 11. A standard VHS video tape is available (the master tapes are recorded at the SP speed on Super-VHS). The first half of this tape is from a camera coaxial with the laser transmission. Time on the tape correspond to file times while oral comments on the tape supplement the general comments. The second half of the tape consists primarily of site descriptive narration on the ground and some pictures of the helicopter setup. The FED Home Page is at: ""https://forest.gsfc.nasa.gov/"". " proprietary
FEWS_precip_711_1 SAFARI 2000 FEWS 10-day Rainfall Estimate, 8-Km, 1999-2001 ORNL_CLOUD STAC Catalog 1999-01-01 2001-12-31 20.64, -42.28, 50.52, 10.1 https://cmr.earthdata.nasa.gov/search/concepts/C2788383221-ORNL_CLOUD.umm_json The U.S. Agency for International Development (USAID) Famine Early Warning System (FEWS) has been supporting the production of 10-day Rainfall Estimate (RFE) data for Africa since 1995. The FEWSNET project was established with the goal of reducing the incidence of drought- or flood-induced famine by providing decision makers with timely and accurate information on conditions that may require intervention. RFE data for continental Africa for 1999, 2000, and 2001 were downloaded the from the African Data Dissemination Service (ADDS) site and were subset for southern Africa by the SAFARI 2000 data group. The RFE 1.0 algorithm, implemented from 1995 to 2000, uses an interpolation method to combine Meteosat and Global Telecommunication System (GTS) data, and warm cloud information for the 10-day estimations. The 30-minute geostationary Meteosat-7 satellite infrared data are used to estimate convective rainfall from areas where cloud top temperatures are less than 235K. The RFE 2.0 algorithm, implemented as of January 1, 2001, uses additional techniques to better estimate precipitation while continuing the use of cold cloud duration and station rainfall data. proprietary
FIA_Forest_Biomass_Estimates_1873_1 CMS: Forest Aboveground Biomass from FIA Plots across the Conterminous USA, 2009-2019 ORNL_CLOUD STAC Catalog 2009-01-01 2019-12-31 -125.01, 24.32, -66.69, 49.51 https://cmr.earthdata.nasa.gov/search/concepts/C2345878726-ORNL_CLOUD.umm_json This dataset provides forest biomass estimates for the conterminous United States based on data from the USDA Forest Inventory and Analysis (FIA) program. FIA maintains uniformly measured field plots across the conterminous U.S. This dataset, derived from field survey data from 2009-2019, includes statistical estimates of biomass at the finest scale (64,000-hectare hexagons) allowed by FIA's sample density. Estimates include the mean (and standard error of the mean) biomass for both live and dead trees, calculated using three sets of allometric equations. There is also an estimate of the area of forestland in each hexagon. These data can be useful for assessing the accuracy of remotely sensed biomass estimates. proprietary
FIFE_CD_V3_130_1 FIFE CDROM Vol. 3 Contents: NS001 Thematic Mapper Simulator (TMS) Imagery, 1987-1989 ORNL_CLOUD STAC Catalog 1987-06-04 1989-08-11 -96.58, 39.08, -96.58, 39.08 https://cmr.earthdata.nasa.gov/search/concepts/C2758951357-ORNL_CLOUD.umm_json This data set provides aircraft-based NS001 Thematic Mapper Simulator (TMS) images of the study area associated with The First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) project conducted on the Konza Prairie in Kansas. The images were acquired during June 1987 to August 1989. The images in this data set were originally provided on the FIFE CD-ROM Volume 3. proprietary
@@ -6266,8 +6267,8 @@ F_Bibliography_1 A bibliography containing references to flora from the Antarcti
FieldData_Alaska_Tundra_2177_1 Field Data on Soils, Vegetation, and Fire History for Alaska Tundra Sites, 1972-2020 ORNL_CLOUD STAC Catalog 1972-08-01 2020-08-15 -166.41, 61.14, -141.68, 71.33 https://cmr.earthdata.nasa.gov/search/concepts/C2756289636-ORNL_CLOUD.umm_json This dataset, titled the Synthesized Alaskan Tundra Field Database (SATFiD), provides a comprehensive collection of in-situ field data compiled from 37 existing datasets resulting from field surveys conducted at Alaska tundra sites between 1972 to 2020. The data were harmonized prior to being included in this dataset. The variables include active layer thickness, vegetation cover (by plant functional types), soil moisture and temperatures, as well as the wildfire history. SATFiD provides a unique lens into various long-term ecological processes within the tundra (such as the fire-permafrost-vegetation interactions) under a rapidly changing climate. proprietary
Field_Measurements_868_1 BigFoot Field Data for North American Sites, 1999-2003 ORNL_CLOUD STAC Catalog 1999-01-01 2003-12-31 -156.61, 34.32, -72.25, 71.27 https://cmr.earthdata.nasa.gov/search/concepts/C2751481641-ORNL_CLOUD.umm_json The BigFoot project gathered field data for selected EOS Land Validation Sites in North America from 1999 to 2003. Data collected and derived for varying intervals at the BigFoot sites and archived with this data set include FPAR, nitrogen content, allometry equations, root biomass, LAI, tree biomass, soil respiration, NPP, landcover images, and vegetation inventories.Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, and deciduous broadleaf forest; desert grassland and shrubland. The project collected multi-year, in situ measurements of ecosystem structure and functional characteristics related to the terrestrial carbon cycle at the sites listed in Table 1. Companion files include documentation of measurement data, site and plot locations (Figure 2), and plot photographs for the SEVI and TUND sites (Figure 3).BigFoot Project Background: Reflectance data from MODIS, the Moderate Resolution Imaging Spectrometer onboard NASA's Earth Observing System (EOS) satellites Terra and Aqua ( http://landval.gsfc.nasa.gov/MODIS/index.php ), was used to produce several science products including land cover, leaf area index (LAI), gross primary production (GPP), and net primary production (NPP). The overall goal of the BigFoot Project was to provide validation of these products. To do this, BigFoot combined ground measurements, additional high-resolution remote-sensing data, and ecosystem process models at six flux tower sites representing different biomes to evaluate the effects of the spatial and temporal patterns of ecosystem characteristics on MODIS products. BigFoot characterized up to a 7 x 7 km area (49 1-km MODIS pixels) surrounding the CO2 flux towers located at six of the nine BigFoot sites. The sampling design allowed the Project to examine scales and spatial patterns of these properties, the inter-annual variability and validity of MODIS products, and provided for a field-based ecological characterization of the flux tower footprint. BigFoot was funded by NASA's Terrestrial Ecology Program. proprietary
Fire_Emissions_Indonesia_2118_1 Fire Particulate Emissions from Combined VIIRS and AHI Data for Indonesia, 2015-2020 ORNL_CLOUD STAC Catalog 2015-07-04 2020-12-31 89, -11, 153, 10.1 https://cmr.earthdata.nasa.gov/search/concepts/C2600303267-ORNL_CLOUD.umm_json This dataset provides 10-minute fire emissions within 0.1-degree regularly spaced intervals across Indonesia from July 2015 to December 2020. The dataset was produced with a top-down approach based on fire radiative energy (FRE) and smoke aerosol emission coefficients (Ce) derived from multiple new-generation satellite observations. Specifically, the Ce values of peatland, tropical forest, cropland, or savanna and grassland were derived from fire radiative power (FRP) and emission rates of smoke aerosols based on Visible Infrared Imaging Radiometer Suite (VIIRS) active fire and aerosol products. FRE for each 0.1-degree interval was calculated from the diurnal FRP cycle that was reconstructed by fusing cloud-corrected FRP retrievals from the high temporal-resolution (10 mins) Himawari-8 Advanced Himawari Imager (AHI) with those from high spatial-resolution (375 m) VIIRS. This new dataset was named the Fused AHI-VIIRS based fire Emissions (FAVE). Fire emissions data are provided in comma-separated values (CSV) format with one file per month from July 2015 to December 2020. Each file includes variables of fire observation time, fire geographic location, classification, fire radiative energy, various fire emissions and related standard deviations. proprietary
-Fire_Emissions_NWT_1561_1 ABoVE: Wildfire Carbon Emissions and Burned Plot Characteristics, NWT, CA, 2014-2016 ORNL_CLOUD STAC Catalog 2014-07-02 2016-08-01 -136.13, 56.25, -102, 71.7 https://cmr.earthdata.nasa.gov/search/concepts/C2111710292-ORNL_CLOUD.umm_json This dataset provides estimates of wildfire carbon emissions and uncertainties at 30-m resolution, and measurements collected at burned and unburned field plots from the 2014 wildfire sites near Yellowknife, Northwest Territories (NWT), Canada. Field data were collected at 211 burned plots in 2015 and include site characteristics, tree cover and species, basal area, delta normalized burn ratio (dNBR), plot characteristics, soil carbon, and carbon combusted. Data were collected at 36 unburned plots with characteristics similar to the burned plots in 2016. The emission estimates were derived from a statistical modeling approach based on measurements of carbon consumption at the 211 burned field plots located in seven independent burn scars. Estimates include uncertainty of field observations of aboveground and belowground combustion, as well as prediction uncertainty from a multiplicative regression model. To apply the model across all 2014 NWT fire perimeters, the final model covariates were re-gridded to a common 30-m grid defined by the Arctic Boreal and Vulnerability Experiment (ABoVE) Project. The regression model was then applied to burned pixels defined by a threshold of Landsat-derived differenced Normalized Burn Ratio (dNBR) within fire perimeters. Derived carbon emissions and uncertainty in g/m2 are provided for each 30-m grid cell. The modeled NWT domain encompasses 29 tiles within the ABoVE 30-m reference grid system. proprietary
Fire_Emissions_NWT_1561_1 ABoVE: Wildfire Carbon Emissions and Burned Plot Characteristics, NWT, CA, 2014-2016 ALL STAC Catalog 2014-07-02 2016-08-01 -136.13, 56.25, -102, 71.7 https://cmr.earthdata.nasa.gov/search/concepts/C2111710292-ORNL_CLOUD.umm_json This dataset provides estimates of wildfire carbon emissions and uncertainties at 30-m resolution, and measurements collected at burned and unburned field plots from the 2014 wildfire sites near Yellowknife, Northwest Territories (NWT), Canada. Field data were collected at 211 burned plots in 2015 and include site characteristics, tree cover and species, basal area, delta normalized burn ratio (dNBR), plot characteristics, soil carbon, and carbon combusted. Data were collected at 36 unburned plots with characteristics similar to the burned plots in 2016. The emission estimates were derived from a statistical modeling approach based on measurements of carbon consumption at the 211 burned field plots located in seven independent burn scars. Estimates include uncertainty of field observations of aboveground and belowground combustion, as well as prediction uncertainty from a multiplicative regression model. To apply the model across all 2014 NWT fire perimeters, the final model covariates were re-gridded to a common 30-m grid defined by the Arctic Boreal and Vulnerability Experiment (ABoVE) Project. The regression model was then applied to burned pixels defined by a threshold of Landsat-derived differenced Normalized Burn Ratio (dNBR) within fire perimeters. Derived carbon emissions and uncertainty in g/m2 are provided for each 30-m grid cell. The modeled NWT domain encompasses 29 tiles within the ABoVE 30-m reference grid system. proprietary
+Fire_Emissions_NWT_1561_1 ABoVE: Wildfire Carbon Emissions and Burned Plot Characteristics, NWT, CA, 2014-2016 ORNL_CLOUD STAC Catalog 2014-07-02 2016-08-01 -136.13, 56.25, -102, 71.7 https://cmr.earthdata.nasa.gov/search/concepts/C2111710292-ORNL_CLOUD.umm_json This dataset provides estimates of wildfire carbon emissions and uncertainties at 30-m resolution, and measurements collected at burned and unburned field plots from the 2014 wildfire sites near Yellowknife, Northwest Territories (NWT), Canada. Field data were collected at 211 burned plots in 2015 and include site characteristics, tree cover and species, basal area, delta normalized burn ratio (dNBR), plot characteristics, soil carbon, and carbon combusted. Data were collected at 36 unburned plots with characteristics similar to the burned plots in 2016. The emission estimates were derived from a statistical modeling approach based on measurements of carbon consumption at the 211 burned field plots located in seven independent burn scars. Estimates include uncertainty of field observations of aboveground and belowground combustion, as well as prediction uncertainty from a multiplicative regression model. To apply the model across all 2014 NWT fire perimeters, the final model covariates were re-gridded to a common 30-m grid defined by the Arctic Boreal and Vulnerability Experiment (ABoVE) Project. The regression model was then applied to burned pixels defined by a threshold of Landsat-derived differenced Normalized Burn Ratio (dNBR) within fire perimeters. Derived carbon emissions and uncertainty in g/m2 are provided for each 30-m grid cell. The modeled NWT domain encompasses 29 tiles within the ABoVE 30-m reference grid system. proprietary
Fire_Ignitions_Locations_AK_CA_2316_1 ABoVE: Ignitions of ABoVE-FED Fires in Alaska and Canada ALL STAC Catalog 2001-01-01 2019-12-31 -166.19, 44.91, -52.89, 73.01 https://cmr.earthdata.nasa.gov/search/concepts/C3103956593-ORNL_CLOUD.umm_json This dataset provides daily fire ignition locations and timing for boreal fires in Alaska, U.S., and Canada between 2001 and 2019. The fire ignition locations and timing are extracted from the ABoVE Fire Emission Database; however, the temperate prairies of Canada, the Atlantic Highlands, and Mixed Wood Plains were not included. Fires were detected from Landsat differenced normalized burn ratio (dNBR) and the daily MODIS burned area and active fire products. Detections by dNBR were limited to fire perimeters from national fire databases. Fire ignition locations were retrieved using a local minimum within the fire perimeters. However, when fire locations were confounded due to simultaneous active fire detections, the fire ignition location was set as the centroid of these pixels. A spatial uncertainty equaling the standard deviation of the pixels' coordinates and the nominal nadir of 1000 m was applied to the fire ignition location. The temporal resolution of the ignition timing is within one day. Data is provided in comma separated values (CSV) and shapefile formats. proprietary
Fire_Ignitions_Locations_AK_CA_2316_1 ABoVE: Ignitions of ABoVE-FED Fires in Alaska and Canada ORNL_CLOUD STAC Catalog 2001-01-01 2019-12-31 -166.19, 44.91, -52.89, 73.01 https://cmr.earthdata.nasa.gov/search/concepts/C3103956593-ORNL_CLOUD.umm_json This dataset provides daily fire ignition locations and timing for boreal fires in Alaska, U.S., and Canada between 2001 and 2019. The fire ignition locations and timing are extracted from the ABoVE Fire Emission Database; however, the temperate prairies of Canada, the Atlantic Highlands, and Mixed Wood Plains were not included. Fires were detected from Landsat differenced normalized burn ratio (dNBR) and the daily MODIS burned area and active fire products. Detections by dNBR were limited to fire perimeters from national fire databases. Fire ignition locations were retrieved using a local minimum within the fire perimeters. However, when fire locations were confounded due to simultaneous active fire detections, the fire ignition location was set as the centroid of these pixels. A spatial uncertainty equaling the standard deviation of the pixels' coordinates and the nominal nadir of 1000 m was applied to the fire ignition location. The temporal resolution of the ignition timing is within one day. Data is provided in comma separated values (CSV) and shapefile formats. proprietary
Flight_Environment_Parameters_1909_1 ATom: Flight Dynamics and Environmental Parameters of the NASA DC-8 Aircraft ORNL_CLOUD STAC Catalog 2016-07-29 2018-05-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2677183090-ORNL_CLOUD.umm_json This dataset contains flight dynamics and environmental parameters (often referred to as housekeeping) specific to the DC-8 aircraft as collected from an assortment of instruments across all four ATom campaigns flown from 2016 through 2018. Measurements include aircraft position, altitude, speed, wind parameters, air temperature, and atmospheric and cabin pressure. These data can be used to understand the interior and exterior conditions and positioning of the DC-8 aircraft at 1-second resolution. proprietary
@@ -6286,8 +6287,8 @@ Forest_Inventory_Data_Brazil_1563_1 Forest Inventories at Burned and Unburned Tr
Forest_Inventory_Tapajos_1552_1 Tree Inventory and Biometry Measurements, Tapajos National Forest, Para, Brazil, 2010 ORNL_CLOUD STAC Catalog 2010-08-31 2010-09-16 -54.99, -3.15, -54.95, -2.86 https://cmr.earthdata.nasa.gov/search/concepts/C2764883885-ORNL_CLOUD.umm_json This dataset provides tree inventory, tree height, diameter at breast height (DBH), and estimated crown measurements from 30 plots located in the Tapajos National Forest, Para, Brazil collected in September 2010. The plots were located in primary forest, primary forest subject to reduced-impact selective logging (PFL) between 1999 and 2003, and secondary forest (SF) with different age and disturbance histories. Plots were centered on GLAS (the Geoscience Laser Altimeter System) LiDAR instrument footprints selected along two sensor acquisition tracks spanning a wide range in vertical structure and aboveground biomass. proprietary
Forested_Areas_Amazonas_Brazil_1515_1 LiDAR and DTM Data from Forested Land Near Manaus, Amazonas, Brazil, 2008 ORNL_CLOUD STAC Catalog 2008-06-07 2008-06-24 -60.22, -2.98, -59.76, -2.32 https://cmr.earthdata.nasa.gov/search/concepts/C3012482048-ORNL_CLOUD.umm_json This data set provides LiDAR point clouds and digital terrain models (DTM) from surveys over the K34 tower site in the Cuieiras Biological Reserve, over forest inventory plots in the Adolpho Ducke Forest Reserve, and over sites of the Biological Dynamics of Forest Fragments Project (BDFFP) in Rio Preto da Eva municipality near Manaus, Amazonas, Brazil during June 2008. The surveys encompass the K34 eddy flux tower managed through the Large-scale Biosphere-Atmosphere Experiment in Amazonia, forest inventory plots managed by the Programa de Pesquisa em Biodiversidade (PPBio), and sites managed by the BDFFP. The LiDAR data was collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time. proprietary
Forested_Areas_Para_Brazil_1514_1 LiDAR and DTM Data from Tapajos National Forest in Para, Brazil, 2008 ORNL_CLOUD STAC Catalog 2008-06-25 2008-07-04 -54.98, -3.06, -54.94, -2.85 https://cmr.earthdata.nasa.gov/search/concepts/C2992471915-ORNL_CLOUD.umm_json This data set provides LiDAR point clouds and digital terrain models (DTM) from surveys over the Tapajos National Forest in Belterra municipality, Para, Brazil during late June and early July 2008. The surveys encompass the K67 and K83 eddy flux towers and a deforestation chronosequence managed through the Large-Scale Biosphere-Atmosphere Experiment in Amazonia providing long-term flux measurements of carbon dioxide. The LiDAR data was collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time. proprietary
-Frac_FuelComponent_Maps_Tundra_1761_1 ABoVE: Distribution Maps of Wildland Fire Fuel Components across Alaskan Tundra, 2015 ALL STAC Catalog 2013-01-01 2017-12-31 -170.01, 57.39, -132.49, 72.52 https://cmr.earthdata.nasa.gov/search/concepts/C2143402675-ORNL_CLOUD.umm_json This dataset provides maps of the distribution of three major wildland fire fuel types at 30 m spatial resolution covering the Alaskan arctic tundra, circa 2015. The three fuel components include woody (evergreen and deciduous shrubs), herbaceous (sedges and grasses), and nonvascular species (mosses and lichens). Multi-seasonal and multispectral mosaics were first developed at 30 m resolution using Landsat 8 surface reflectance data collected from 2013 to 2017. The spectral information from Landsat mosaics was combined with field observations from representative tundra vegetation plots collected during multiple field trips to model the fractional cover of fuel type components. An improved vegetation mask for shrub and graminoid-dominated tundra was developed using random forest classification and is also included. The final fractional cover maps were developed using the trained model with the multi-seasonal and multi-spectral Landsat mosaics across the entire Alaskan tundra. proprietary
Frac_FuelComponent_Maps_Tundra_1761_1 ABoVE: Distribution Maps of Wildland Fire Fuel Components across Alaskan Tundra, 2015 ORNL_CLOUD STAC Catalog 2013-01-01 2017-12-31 -170.01, 57.39, -132.49, 72.52 https://cmr.earthdata.nasa.gov/search/concepts/C2143402675-ORNL_CLOUD.umm_json This dataset provides maps of the distribution of three major wildland fire fuel types at 30 m spatial resolution covering the Alaskan arctic tundra, circa 2015. The three fuel components include woody (evergreen and deciduous shrubs), herbaceous (sedges and grasses), and nonvascular species (mosses and lichens). Multi-seasonal and multispectral mosaics were first developed at 30 m resolution using Landsat 8 surface reflectance data collected from 2013 to 2017. The spectral information from Landsat mosaics was combined with field observations from representative tundra vegetation plots collected during multiple field trips to model the fractional cover of fuel type components. An improved vegetation mask for shrub and graminoid-dominated tundra was developed using random forest classification and is also included. The final fractional cover maps were developed using the trained model with the multi-seasonal and multi-spectral Landsat mosaics across the entire Alaskan tundra. proprietary
+Frac_FuelComponent_Maps_Tundra_1761_1 ABoVE: Distribution Maps of Wildland Fire Fuel Components across Alaskan Tundra, 2015 ALL STAC Catalog 2013-01-01 2017-12-31 -170.01, 57.39, -132.49, 72.52 https://cmr.earthdata.nasa.gov/search/concepts/C2143402675-ORNL_CLOUD.umm_json This dataset provides maps of the distribution of three major wildland fire fuel types at 30 m spatial resolution covering the Alaskan arctic tundra, circa 2015. The three fuel components include woody (evergreen and deciduous shrubs), herbaceous (sedges and grasses), and nonvascular species (mosses and lichens). Multi-seasonal and multispectral mosaics were first developed at 30 m resolution using Landsat 8 surface reflectance data collected from 2013 to 2017. The spectral information from Landsat mosaics was combined with field observations from representative tundra vegetation plots collected during multiple field trips to model the fractional cover of fuel type components. An improved vegetation mask for shrub and graminoid-dominated tundra was developed using random forest classification and is also included. The final fractional cover maps were developed using the trained model with the multi-seasonal and multi-spectral Landsat mosaics across the entire Alaskan tundra. proprietary
Freeze_Thaw_NorthernHemisphere_2323_1 Probabilistic Freeze-Thaw Record for the Northern Hemisphere, 2016-2020 ORNL_CLOUD STAC Catalog 2016-01-01 2020-12-31 -180, 0, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2953939783-ORNL_CLOUD.umm_json This dataset provides a probabilistic freeze/thaw (FT) data record from 2016 to 2020 for the Northern Hemisphere derived using a deep learning model (U-Net). The model was informed by satellite multi-frequency microwave brightness temperature retrievals from the NASA SMAP (Soil Moisture Active Passive) and JAXA AMSR2 (Advanced Microwave Scanning Radiometer 2) radiometers, and trained using daily soil temperature observations from Northern Hemisphere weather stations and global reanalysis data (ERA-5). Unlike other available FT data records that provide only a binary classification of frozen or non-frozen conditions, this product includes both binary FT and continuous variable estimates of the probability of thawed conditions. This product is designed to complement other established binary FT data records, including the NASA FT Earth System Data Record and SMAP Level 3 FT operational products, by providing a probabilistic FT variable with enhanced accuracy and sensitivity to near-surface (<=5 cm depth) soil FT condition. The data are provided in cloud optimized GeoTIFF (COG) format. proprietary
Frost_Boils_Veg_Plots_1361_1 Arctic Vegetation Plots at Frost Boil Sites, North Slope, Alaska, 2000-2006 ORNL_CLOUD STAC Catalog 2000-08-16 2006-07-30 -148.99, 69.15, -147.99, 70.38 https://cmr.earthdata.nasa.gov/search/concepts/C2170969622-ORNL_CLOUD.umm_json This data set describes the environment, soil, and vegetation on nonsorted circles and earth hummocks at seven study sites along a N-S-transect from the Arctic Ocean to the Arctic Foothills based on data collected from 2000 to 2006. The study sites are located along the Dalton Highway, beginning in Prudhoe Bay, on the North Slope of Alaska. These frost-boil features are important landscape components of the arctic tundra. Data include the baseline plot information for vegetation, soils, and site factors for 117 study plots subjectively located in areas of homogeneous, representative vegetation on frost-heave features surrounding stable tundra. Nine community types were identified in three bioclimate subzones. Vegetation was classified according to the Braun-Blanquet system. proprietary
Ft_Lauderdale_0 Measurements made by the Lady Pamela research vessel off the south Florida coast OB_DAAC STAC Catalog 2003-08-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360238-OB_DAAC.umm_json Measurements made by the Lady Pamela research vessel off the south Florida coast near Fort Lauderdale in 2003. proprietary
@@ -6341,34 +6342,34 @@ G18-ABI-L2P-ACSPO-v2.90_2.90 GHRSST L2P NOAA/ACSPO GOES-18/ABI West America Regi
G18-ABI-L3C-ACSPO-v2.90_2.90 GHRSST L3C NOAA/ACSPO GOES-18/ABI West America Region Sea Surface Temperature v2.90 dataset POCLOUD STAC Catalog 2022-06-07 163, -60, -77, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2731041317-POCLOUD.umm_json The G18-ABI-L3C-ACSPO-v2.90 dataset produced by the NOAA ACSPO system is used to derive Subskin and Depth Sea Surface Temperature (SST) from the ABI sensor onboard the G18 satellite. NOAA’s G18 (aka GOES-T before launch) was launched on March 1, 2022, replacing G17 as GOES West in Jan'2023. It is the third satellite in the US GOES–R Series, the Western Hemisphere’s most sophisticated weather-observing and environmental-monitoring system. The ABI is the primary instrument on the GOES-R Series for imaging Earth’s weather, oceans, and environment.
The G18-ABI-L3C-ACSPO-v2.90 dataset is a gridded version of the G18-ABI-L2P-ACSPO-v2.90 dataset (https://podaac.jpl.nasa.gov/dataset/G18-ABI-L2P-ACSPO-v2.90). The L3C (Level 3 Collated) outputs 24 hourly granules per day, with a daily volume of 0.7 GB/day. Valid SSTs are found over oceans, sea, lakes or rivers, with fill values reported elsewhere. All valid SSTs in L3C are recommended for users, although data over internal waters may not have enough in situ data to be adequately validated. Per GDS2 specifications, two additional Sensor-Specific Error Statistics layers (bias and standard deviation) are reported in each pixel with valid SST.
The ACSPO G18/ABI L3C product is validated against iQuam in situ data (Xu and Ignatov, 2014) and continuously monitored in the NOAA SQUAM system (Dash et al, 2010). The NRT files are replaced with Delayed Mode (DM) files, with a latency of ~2-months. File names remain unchanged, and DM vs NRT can be identified by different time stamps and global attributes inside the files (MERRA instead of GFS for atmospheric profiles, and same day CMC L4 analyses in DM instead of one-day delayed in NRT processing). proprietary
G5NR_1 GEOS-5 Nature Run data NCCS STAC Catalog 2005-05-15 2007-06-16 -180, 90, 179.9375, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1634215803-NCCS.umm_json This specific GEOS-5 model configuration used to perform a two-year global, non-hydrostatic mesoscale simulation for the period 2005-2007 at 7-km (3.5-km in the future) horizontal resolution. Because this simulation is intended to serve as a reference Nature Run for Observing System Simulation Experiments (OSSEs, e.g., Errico et al., 2012) it will be referred to as the 7-km GEOS-5 Nature Run or 7-km G5NR. This simulation has been performed with the Ganymed version of GEOS- 5, more specifically with CVS Tag wmp-Ganymed-4_0_BETA8. In addition to standard meteorological parameters (wind, temperature, moisture, surface pressure), this simulation includes 15 aerosol tracers (dust, sea-salt, sulfate, black and organic carbon), O3, CO and CO2. This model simulation is driven by prescribed sea-surface temperature and sea-ice, as well as surface emissions and uptake of aerosols and trace gases, including daily volcanic and biomass burning emissions, biogenic sources and sinks of CO2, and high-resolution inventories of anthropogenic sources.The simulation is performed at a horizontal resolution of 7 km using a cubed-sphere horizontal grid with 72 vertical levels, extending up to to 0.01 hPa (~ 80 km). For user convenience, all data products are generated on two logically rectangular longitude-latitude grids: a full-resolution 0.0625o grid that approximately matches the native cubed-sphere resolution, and another 0.5o reduced-resolution grid. The majority of the full-resolution data products are instantaneous with some fields being time-averaged. The reduced-resolution datasets are mostly time-averaged, with some fields being instantaneous. Hourly data intervals are used for the reduced-resolution datasets, while 30-minute intervals are used for the full-resolution products. All full-resolution output is on the model’s native 72-layer hybrid sigma-pressure vertical grid, while the reduced-resolution output is given on native vertical levels and on 48 pressure surfaces extending up to 0.02 hPa. Section 4 presents additional details on horizontal and vertical grids. proprietary
GAMSSA_28km-ABOM-L4-GLOB-v01_1.0 GHRSST Level 4 GAMSSA_28km Global Foundation Sea Surface Temperature Analysis v1.0 dataset (GDS2) POCLOUD STAC Catalog 2008-07-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2036881735-POCLOUD.umm_json A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis, produced daily on an operational basis at the Australian Bureau of Meteorology (BoM) using optimal interpolation (OI) on a global 0.25 degree grid. This Global Australian Multi-Sensor SST Analysis (GAMSSA) v1.0 system blends satellite SST observations from passive infrared and passive microwave radiometers with in situ data from ships, drifting buoys and moorings from the Global Telecommunications System (GTS). SST observations that have experienced recent surface wind speeds less than 6 m/s during the day or less than 2 m/s during night are rejected from the analysis. The processing results in daily foundation SST estimates that are largely free of nocturnal cooling and diurnal warming effects. Sea ice concentrations are supplied by the NOAA/NCEP 12.7 km sea ice analysis. In the absence of observations, the analysis relaxes to the Reynolds and Smith (1994) Monthly 1 degree SST climatology for 1961 - 1990. proprietary
-GB-NERC-BAS-AEDC-00250 AFI 01/27_01 - Dyke intrusions as tracers of continental break-up processes - Rock samples collected in Dronning Maud Land in 2000/2001 ALL STAC Catalog 2000-09-01 2006-12-01 -5.5, -74, 1, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1214594499-SCIOPS.umm_json The style and volume of magmatism varies between margins from large volume flood basalts such as the Parana or Deccan provinces to less volumetric margins such as the southern part of the South Atlantic. This CASE (Collaborative Awards in Science and Engineering) studentship was intended to provide support to study the evolution of the break-up of Africa and East Antarctica which occurred in the early Jurassic. An extended period of magmatism has been suggested for this margin associated with complex extensional tectonics. A combined geochronological / geochemical approach was used to understand the evolution of the crust and sub-continental lithospheric mantle during the break-up of one central portion of the Gondwana super continent. Igneous dykes and sills were collected in Dronning Maud Land during the field season 2000/01. The aim was to measure ages of volcanism during flood basalt events in Dronning Maud Land associated with the breakup of Gondwana. proprietary
GB-NERC-BAS-AEDC-00250 AFI 01/27_01 - Dyke intrusions as tracers of continental break-up processes - Rock samples collected in Dronning Maud Land in 2000/2001 SCIOPS STAC Catalog 2000-09-01 2006-12-01 -5.5, -74, 1, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1214594499-SCIOPS.umm_json The style and volume of magmatism varies between margins from large volume flood basalts such as the Parana or Deccan provinces to less volumetric margins such as the southern part of the South Atlantic. This CASE (Collaborative Awards in Science and Engineering) studentship was intended to provide support to study the evolution of the break-up of Africa and East Antarctica which occurred in the early Jurassic. An extended period of magmatism has been suggested for this margin associated with complex extensional tectonics. A combined geochronological / geochemical approach was used to understand the evolution of the crust and sub-continental lithospheric mantle during the break-up of one central portion of the Gondwana super continent. Igneous dykes and sills were collected in Dronning Maud Land during the field season 2000/01. The aim was to measure ages of volcanism during flood basalt events in Dronning Maud Land associated with the breakup of Gondwana. proprietary
+GB-NERC-BAS-AEDC-00250 AFI 01/27_01 - Dyke intrusions as tracers of continental break-up processes - Rock samples collected in Dronning Maud Land in 2000/2001 ALL STAC Catalog 2000-09-01 2006-12-01 -5.5, -74, 1, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1214594499-SCIOPS.umm_json The style and volume of magmatism varies between margins from large volume flood basalts such as the Parana or Deccan provinces to less volumetric margins such as the southern part of the South Atlantic. This CASE (Collaborative Awards in Science and Engineering) studentship was intended to provide support to study the evolution of the break-up of Africa and East Antarctica which occurred in the early Jurassic. An extended period of magmatism has been suggested for this margin associated with complex extensional tectonics. A combined geochronological / geochemical approach was used to understand the evolution of the crust and sub-continental lithospheric mantle during the break-up of one central portion of the Gondwana super continent. Igneous dykes and sills were collected in Dronning Maud Land during the field season 2000/01. The aim was to measure ages of volcanism during flood basalt events in Dronning Maud Land associated with the breakup of Gondwana. proprietary
GB-NERC-BAS-AEDC-00251 AFI 01/27_02 - Dyke intrusions as tracers of continental break-up processes - Ar-Ar dating, field data and selected geochemical analysis data of rock samples collected in Dronning Maud Land in 2000/2001 SCIOPS STAC Catalog 2000-09-01 2006-12-01 -5.5, -74, 1, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1214594540-SCIOPS.umm_json The style and volume of magmatism varies between margins from large volume flood basalts such as the Parana or Deccan provinces to less volumetric margins such as the southern part of the South Atlantic. This case (Collaborative Awards in Science and Engineering) studentship was intended to provide support to study the evolution of the break-up of Africa and East Antarctica which occurred in the early Jurassic. An extended period of magmatism has been suggested for this margin associated with complex extensional tectonics. A combined geochronological / geochemical approach was used to understand the evolution of the crust and sub-continental lithospheric mantle during the break-up of one central portion of the Gondwana super continent. Igneous dykes and sills were collected in Dronning Maud Land during the field season 2000/01. The aim was to measure ages of volcanism during flood basalt events in Dronning Maud Land associated with the breakup of Gondwana. proprietary
GB-NERC-BAS-AEDC-00251 AFI 01/27_02 - Dyke intrusions as tracers of continental break-up processes - Ar-Ar dating, field data and selected geochemical analysis data of rock samples collected in Dronning Maud Land in 2000/2001 ALL STAC Catalog 2000-09-01 2006-12-01 -5.5, -74, 1, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1214594540-SCIOPS.umm_json The style and volume of magmatism varies between margins from large volume flood basalts such as the Parana or Deccan provinces to less volumetric margins such as the southern part of the South Atlantic. This case (Collaborative Awards in Science and Engineering) studentship was intended to provide support to study the evolution of the break-up of Africa and East Antarctica which occurred in the early Jurassic. An extended period of magmatism has been suggested for this margin associated with complex extensional tectonics. A combined geochronological / geochemical approach was used to understand the evolution of the crust and sub-continental lithospheric mantle during the break-up of one central portion of the Gondwana super continent. Igneous dykes and sills were collected in Dronning Maud Land during the field season 2000/01. The aim was to measure ages of volcanism during flood basalt events in Dronning Maud Land associated with the breakup of Gondwana. proprietary
GB-NERC-BAS-AEDC-00260 AFI 02/30_01 - The status of dark septate fungi in Antarctic plant and soil communities - Fungal cultures, plant and soil samples (live and frozen) collected from the northern Antarctic Peninsula region in 2002/2003 SCIOPS STAC Catalog 2002-01-01 2003-12-31 -68.35, -67.6, -36.48333, -54.28333 https://cmr.earthdata.nasa.gov/search/concepts/C1214594541-SCIOPS.umm_json Three plant species, the leafy liverwort Cephaloziella varians and the angiosperms Deschampsia antarctica and Colobanthus quitensis, were sampled from 12 islands across a 1480 km latitudinal gradient from South Georgia through to Adelaide Island. Samples were collected to determine the abundance of dark septate fungi in Antarctic plant and soil communities and the effects of these organisms on plant growth. Where the target species were found in sufficient numbers to allow sampling, it proved possible to collect at least 10 samples of each species. At least 10 soil samples were collected from each site where Deschampsia was found. Plants, with intact roots and soil, were transported back to the UK using cool and frozen stowage. Additionally, intact live plants were transported to the UK in an illuminated cabinet. Seeds of the two key species (Deschampsia antarctica and Colobanthus quitensis) were also collected at Bird Island and South Georgia. As the exact months of t he data collection were not provided, and the metadata standard requires a YYYY-MM-DD format, this dataset has been dated as 1st January for start date, and 31st December for stop date. proprietary
GB-NERC-BAS-AEDC-00260 AFI 02/30_01 - The status of dark septate fungi in Antarctic plant and soil communities - Fungal cultures, plant and soil samples (live and frozen) collected from the northern Antarctic Peninsula region in 2002/2003 ALL STAC Catalog 2002-01-01 2003-12-31 -68.35, -67.6, -36.48333, -54.28333 https://cmr.earthdata.nasa.gov/search/concepts/C1214594541-SCIOPS.umm_json Three plant species, the leafy liverwort Cephaloziella varians and the angiosperms Deschampsia antarctica and Colobanthus quitensis, were sampled from 12 islands across a 1480 km latitudinal gradient from South Georgia through to Adelaide Island. Samples were collected to determine the abundance of dark septate fungi in Antarctic plant and soil communities and the effects of these organisms on plant growth. Where the target species were found in sufficient numbers to allow sampling, it proved possible to collect at least 10 samples of each species. At least 10 soil samples were collected from each site where Deschampsia was found. Plants, with intact roots and soil, were transported back to the UK using cool and frozen stowage. Additionally, intact live plants were transported to the UK in an illuminated cabinet. Seeds of the two key species (Deschampsia antarctica and Colobanthus quitensis) were also collected at Bird Island and South Georgia. As the exact months of t he data collection were not provided, and the metadata standard requires a YYYY-MM-DD format, this dataset has been dated as 1st January for start date, and 31st December for stop date. proprietary
-GB-NERC-BAS-AEDC-00262 AFI 02/30_02 - The status of dark septate fungi in Antarctic plant and soil communities - Analysis of fungal cultures, plant and soil samples collected from the northern Antarctic Peninsula region in 2002/2003 ALL STAC Catalog 2002-07-01 2005-06-30 -68.35, -67.6, -36.48333, -54.28333 https://cmr.earthdata.nasa.gov/search/concepts/C1214594523-SCIOPS.umm_json "This study investigated the status of dark septate (""DS"") fungi in Antarctic plant and soil communities, with the aim of determining the abundance of DS fungi in plant roots and rhizoids, their taxonomic affinities and their symbiotic status. Abundances of fungal hyphae were recorded in roots and rhizoids, and fungi were isolated and identified. Sequencing of ITS (internal transcribed spacer) regions of rDNA indicated that some isolates share taxonomic affinities with fungi of known symbiotic status. Synthesis experiments assessed the effects of DS fungal isolates, including H. ericae, on the growth and nutrient balance of their host plants. Seeds of Deschampsia antarctica and Colobanthus quitensis were collected for use in ecophysiological experiments." proprietary
GB-NERC-BAS-AEDC-00262 AFI 02/30_02 - The status of dark septate fungi in Antarctic plant and soil communities - Analysis of fungal cultures, plant and soil samples collected from the northern Antarctic Peninsula region in 2002/2003 SCIOPS STAC Catalog 2002-07-01 2005-06-30 -68.35, -67.6, -36.48333, -54.28333 https://cmr.earthdata.nasa.gov/search/concepts/C1214594523-SCIOPS.umm_json "This study investigated the status of dark septate (""DS"") fungi in Antarctic plant and soil communities, with the aim of determining the abundance of DS fungi in plant roots and rhizoids, their taxonomic affinities and their symbiotic status. Abundances of fungal hyphae were recorded in roots and rhizoids, and fungi were isolated and identified. Sequencing of ITS (internal transcribed spacer) regions of rDNA indicated that some isolates share taxonomic affinities with fungi of known symbiotic status. Synthesis experiments assessed the effects of DS fungal isolates, including H. ericae, on the growth and nutrient balance of their host plants. Seeds of Deschampsia antarctica and Colobanthus quitensis were collected for use in ecophysiological experiments." proprietary
-GB-NERC-BAS-AEDC-00272 AFI 04/17_02 - Glacial-interglacial changes in the lost drainage basin on the West Antarctic Ice Sheet - Sediment cores collected in the Bellingshausen Sea, 2004 ALL STAC Catalog 2004-01-23 2004-02-13 -90, -73, -76, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214594542-SCIOPS.umm_json The main aim of this project was to carry out the first systematic investigation of the former ice drainage basin in the southern Bellingshausen Sea, using sediment cores, swath bathymetry by means of the EM120 multibeam echo sounder and the TOPAS sub-bottom profiling system on RRS James Clark Ross (JR104). Reconnaissance data collected on previous cruises JR04 (1993) and cruises of R/V Polarstern in 1994 and 1995 suggested that this area contained the outlet of a very large ice drainage basin during late Quaternary glacial periods. The data and samples collected allowed us to address questions about the timing and rate of grounding line retreat from the continental shelf, the dynamic character of the ice that covered the shelf, and its influence on glaciomarine processes on the adjacent continental slope. proprietary
+GB-NERC-BAS-AEDC-00262 AFI 02/30_02 - The status of dark septate fungi in Antarctic plant and soil communities - Analysis of fungal cultures, plant and soil samples collected from the northern Antarctic Peninsula region in 2002/2003 ALL STAC Catalog 2002-07-01 2005-06-30 -68.35, -67.6, -36.48333, -54.28333 https://cmr.earthdata.nasa.gov/search/concepts/C1214594523-SCIOPS.umm_json "This study investigated the status of dark septate (""DS"") fungi in Antarctic plant and soil communities, with the aim of determining the abundance of DS fungi in plant roots and rhizoids, their taxonomic affinities and their symbiotic status. Abundances of fungal hyphae were recorded in roots and rhizoids, and fungi were isolated and identified. Sequencing of ITS (internal transcribed spacer) regions of rDNA indicated that some isolates share taxonomic affinities with fungi of known symbiotic status. Synthesis experiments assessed the effects of DS fungal isolates, including H. ericae, on the growth and nutrient balance of their host plants. Seeds of Deschampsia antarctica and Colobanthus quitensis were collected for use in ecophysiological experiments." proprietary
GB-NERC-BAS-AEDC-00272 AFI 04/17_02 - Glacial-interglacial changes in the lost drainage basin on the West Antarctic Ice Sheet - Sediment cores collected in the Bellingshausen Sea, 2004 SCIOPS STAC Catalog 2004-01-23 2004-02-13 -90, -73, -76, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214594542-SCIOPS.umm_json The main aim of this project was to carry out the first systematic investigation of the former ice drainage basin in the southern Bellingshausen Sea, using sediment cores, swath bathymetry by means of the EM120 multibeam echo sounder and the TOPAS sub-bottom profiling system on RRS James Clark Ross (JR104). Reconnaissance data collected on previous cruises JR04 (1993) and cruises of R/V Polarstern in 1994 and 1995 suggested that this area contained the outlet of a very large ice drainage basin during late Quaternary glacial periods. The data and samples collected allowed us to address questions about the timing and rate of grounding line retreat from the continental shelf, the dynamic character of the ice that covered the shelf, and its influence on glaciomarine processes on the adjacent continental slope. proprietary
+GB-NERC-BAS-AEDC-00272 AFI 04/17_02 - Glacial-interglacial changes in the lost drainage basin on the West Antarctic Ice Sheet - Sediment cores collected in the Bellingshausen Sea, 2004 ALL STAC Catalog 2004-01-23 2004-02-13 -90, -73, -76, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214594542-SCIOPS.umm_json The main aim of this project was to carry out the first systematic investigation of the former ice drainage basin in the southern Bellingshausen Sea, using sediment cores, swath bathymetry by means of the EM120 multibeam echo sounder and the TOPAS sub-bottom profiling system on RRS James Clark Ross (JR104). Reconnaissance data collected on previous cruises JR04 (1993) and cruises of R/V Polarstern in 1994 and 1995 suggested that this area contained the outlet of a very large ice drainage basin during late Quaternary glacial periods. The data and samples collected allowed us to address questions about the timing and rate of grounding line retreat from the continental shelf, the dynamic character of the ice that covered the shelf, and its influence on glaciomarine processes on the adjacent continental slope. proprietary
GB-NERC-BAS-AEDC-00273 AFI 04/17_01 - Glacial-interglacial changes in the lost drainage basin on the West Antarctic Ice Sheet - EM120 Swath Bathymetry and TOPAS sub-bottom profiler data, Bellingshausen Sea, 2004 ALL STAC Catalog 2004-01-23 2004-02-13 -90, -73, -76, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214594544-SCIOPS.umm_json The main aim of this project was to carry out the first systematic investigation of the former ice drainage basin in the southern Bellingshausen Sea, using sediment cores, swath bathymetry by means of the EM120 multibeam echo sounder and the TOPAS sub-bottom profiling system on RRS James Clark Ross (JR104). Reconnaissance data collected on previous cruises JR04 (1993) and cruises of R/V Polarstern in 1994 and 1995 suggested that this area contained the outlet of a very large ice drainage basin during late Quaternary glacial periods. The data and samples collected allowed us to address questions about the timing and rate of grounding line retreat from the continental shelf, the dynamic character of the ice that covered the shelf, and its influence on glaciomarine processes on the adjacent continental slope. proprietary
GB-NERC-BAS-AEDC-00273 AFI 04/17_01 - Glacial-interglacial changes in the lost drainage basin on the West Antarctic Ice Sheet - EM120 Swath Bathymetry and TOPAS sub-bottom profiler data, Bellingshausen Sea, 2004 SCIOPS STAC Catalog 2004-01-23 2004-02-13 -90, -73, -76, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214594544-SCIOPS.umm_json The main aim of this project was to carry out the first systematic investigation of the former ice drainage basin in the southern Bellingshausen Sea, using sediment cores, swath bathymetry by means of the EM120 multibeam echo sounder and the TOPAS sub-bottom profiling system on RRS James Clark Ross (JR104). Reconnaissance data collected on previous cruises JR04 (1993) and cruises of R/V Polarstern in 1994 and 1995 suggested that this area contained the outlet of a very large ice drainage basin during late Quaternary glacial periods. The data and samples collected allowed us to address questions about the timing and rate of grounding line retreat from the continental shelf, the dynamic character of the ice that covered the shelf, and its influence on glaciomarine processes on the adjacent continental slope. proprietary
-GB-NERC-BAS-AEDC-00276 AFI 02/36_02 - Geochemical Tracing of Pacific-to-Atlantic Mantle Flow through the Drake Passage/Scotia Sea Gateway - Rock samples collected by dredging in the Scotia Sea, February and March 2004 ALL STAC Catalog 2004-02-19 2004-03-03 -55, -58, -40, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214594545-SCIOPS.umm_json Sampling was undertaken within the West Scotia Sea in an attempt to identify the boundary between the Pacific and Bouvet mantle domains and so understand, quantify and document the flow of mantle - which is important for understanding global geodynamics The JR77 cruise aimed to acquire rock samples to constrain the history of the mantle beneath the Scotia Sea, from which the oceanic crust was derived by melting. Twenty days of rock dredging were conducted at fourteen sites in five main areas. Thirteen dredges were successful in recovering oceanic rocks of mixed sizes, up to and including very large boulders and dredge paths of up to 1 km were followed. The cruise also (remarkably) recovered fresh mantle peridotite nodules from the West Scotia Ridge, the first of its type - to our knowledge - from the world's ocean ridge system. proprietary
GB-NERC-BAS-AEDC-00276 AFI 02/36_02 - Geochemical Tracing of Pacific-to-Atlantic Mantle Flow through the Drake Passage/Scotia Sea Gateway - Rock samples collected by dredging in the Scotia Sea, February and March 2004 SCIOPS STAC Catalog 2004-02-19 2004-03-03 -55, -58, -40, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214594545-SCIOPS.umm_json Sampling was undertaken within the West Scotia Sea in an attempt to identify the boundary between the Pacific and Bouvet mantle domains and so understand, quantify and document the flow of mantle - which is important for understanding global geodynamics The JR77 cruise aimed to acquire rock samples to constrain the history of the mantle beneath the Scotia Sea, from which the oceanic crust was derived by melting. Twenty days of rock dredging were conducted at fourteen sites in five main areas. Thirteen dredges were successful in recovering oceanic rocks of mixed sizes, up to and including very large boulders and dredge paths of up to 1 km were followed. The cruise also (remarkably) recovered fresh mantle peridotite nodules from the West Scotia Ridge, the first of its type - to our knowledge - from the world's ocean ridge system. proprietary
+GB-NERC-BAS-AEDC-00276 AFI 02/36_02 - Geochemical Tracing of Pacific-to-Atlantic Mantle Flow through the Drake Passage/Scotia Sea Gateway - Rock samples collected by dredging in the Scotia Sea, February and March 2004 ALL STAC Catalog 2004-02-19 2004-03-03 -55, -58, -40, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214594545-SCIOPS.umm_json Sampling was undertaken within the West Scotia Sea in an attempt to identify the boundary between the Pacific and Bouvet mantle domains and so understand, quantify and document the flow of mantle - which is important for understanding global geodynamics The JR77 cruise aimed to acquire rock samples to constrain the history of the mantle beneath the Scotia Sea, from which the oceanic crust was derived by melting. Twenty days of rock dredging were conducted at fourteen sites in five main areas. Thirteen dredges were successful in recovering oceanic rocks of mixed sizes, up to and including very large boulders and dredge paths of up to 1 km were followed. The cruise also (remarkably) recovered fresh mantle peridotite nodules from the West Scotia Ridge, the first of its type - to our knowledge - from the world's ocean ridge system. proprietary
GB-NERC-BAS-AEDC-00277 AFI 02/36_01 - Geochemical Tracing of Pacific-to-Atlantic Mantle Flow through the Drake Passage/Scotia Sea Gateway - Dredge sampling information from the Scotia Sea collected in February and March 2004 ALL STAC Catalog 2004-02-19 2004-03-03 -55, -58, -40, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214594516-SCIOPS.umm_json The target area was along the eastern segments of the West Scotia Ridge, an ocean spreading centre which stopped spreading about 10 million years ago. The spreading centre has high topographic relief and contains an axial rift, which has flanks that are suitable for dredging. The plan was to map the spreading centre using the swath bathymetry system, and then to use this map to locate the best dredging sites. Thirteen dredges were successful in recovering oceanic rocks of mixed sizes, up to and including very large boulders and dredge paths of up to 1 km were followed. proprietary
GB-NERC-BAS-AEDC-00277 AFI 02/36_01 - Geochemical Tracing of Pacific-to-Atlantic Mantle Flow through the Drake Passage/Scotia Sea Gateway - Dredge sampling information from the Scotia Sea collected in February and March 2004 SCIOPS STAC Catalog 2004-02-19 2004-03-03 -55, -58, -40, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214594516-SCIOPS.umm_json The target area was along the eastern segments of the West Scotia Ridge, an ocean spreading centre which stopped spreading about 10 million years ago. The spreading centre has high topographic relief and contains an axial rift, which has flanks that are suitable for dredging. The plan was to map the spreading centre using the swath bathymetry system, and then to use this map to locate the best dredging sites. Thirteen dredges were successful in recovering oceanic rocks of mixed sizes, up to and including very large boulders and dredge paths of up to 1 km were followed. proprietary
GB-NERC-BAS-AEDC-00278 AFI 02/36_03 - Geochemical Tracing of Pacific-to-Atlantic Mantle Flow through the Drake Passage/Scotia Sea Gateway - Swath Bathymetry conducted in the Scotia Sea, February and March 2004 ALL STAC Catalog 2004-02-19 2004-03-03 -55, -58, -40, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214594517-SCIOPS.umm_json The target area was along the eastern segments of the West Scotia Ridge, an ocean spreading centre which stopped spreading about 10 million years ago. The spreading centre has high topographic relief and contains an axial rift, which has flanks that are suitable for dredging. The fieldwork involved mapping the spreading centre using swath bathymetry, and then using this information to locate the best dredging sites. This meant successfully imaging a significant area of hitherto unsurveyed oceanic crust and recovering rocks at 13 dredge sites. The new bathymetric maps add considerably to knowledge of the West Scotia Ridge. proprietary
GB-NERC-BAS-AEDC-00278 AFI 02/36_03 - Geochemical Tracing of Pacific-to-Atlantic Mantle Flow through the Drake Passage/Scotia Sea Gateway - Swath Bathymetry conducted in the Scotia Sea, February and March 2004 SCIOPS STAC Catalog 2004-02-19 2004-03-03 -55, -58, -40, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214594517-SCIOPS.umm_json The target area was along the eastern segments of the West Scotia Ridge, an ocean spreading centre which stopped spreading about 10 million years ago. The spreading centre has high topographic relief and contains an axial rift, which has flanks that are suitable for dredging. The fieldwork involved mapping the spreading centre using swath bathymetry, and then using this information to locate the best dredging sites. This meant successfully imaging a significant area of hitherto unsurveyed oceanic crust and recovering rocks at 13 dredge sites. The new bathymetric maps add considerably to knowledge of the West Scotia Ridge. proprietary
-GB-NERC-BAS-AEDC-00279 AFI 02/36_04 - Geochemical Tracing of Pacific-to-Atlantic Mantle Flow through the Drake Passage/Scotia Sea Gateway - Geochemical analysis of rock samples collected by dredging in the Scotia Sea, February and March 2004 SCIOPS STAC Catalog 2001-10-01 2005-09-30 -55, -58, -40, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214594518-SCIOPS.umm_json The initial aim of this project was to carry out a higher resolution geochemical study of mantle flow using existing samples. This confirmed flow from the Bouvet domain into the East Scotia Sea and placed constraints on flow pathways. The second stage was to sample further within the West Scotia Sea and to use elemental and isotope (Sr, Nd, Pb, Hf) analyses to fingerprint mantle provenance. The results were used to locate and investigate the nature of the Pacific-South Atlantic mantle domain boundary and thus to contribute to the understanding and quantification of global upper mantle fluxes. proprietary
GB-NERC-BAS-AEDC-00279 AFI 02/36_04 - Geochemical Tracing of Pacific-to-Atlantic Mantle Flow through the Drake Passage/Scotia Sea Gateway - Geochemical analysis of rock samples collected by dredging in the Scotia Sea, February and March 2004 ALL STAC Catalog 2001-10-01 2005-09-30 -55, -58, -40, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214594518-SCIOPS.umm_json The initial aim of this project was to carry out a higher resolution geochemical study of mantle flow using existing samples. This confirmed flow from the Bouvet domain into the East Scotia Sea and placed constraints on flow pathways. The second stage was to sample further within the West Scotia Sea and to use elemental and isotope (Sr, Nd, Pb, Hf) analyses to fingerprint mantle provenance. The results were used to locate and investigate the nature of the Pacific-South Atlantic mantle domain boundary and thus to contribute to the understanding and quantification of global upper mantle fluxes. proprietary
+GB-NERC-BAS-AEDC-00279 AFI 02/36_04 - Geochemical Tracing of Pacific-to-Atlantic Mantle Flow through the Drake Passage/Scotia Sea Gateway - Geochemical analysis of rock samples collected by dredging in the Scotia Sea, February and March 2004 SCIOPS STAC Catalog 2001-10-01 2005-09-30 -55, -58, -40, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214594518-SCIOPS.umm_json The initial aim of this project was to carry out a higher resolution geochemical study of mantle flow using existing samples. This confirmed flow from the Bouvet domain into the East Scotia Sea and placed constraints on flow pathways. The second stage was to sample further within the West Scotia Sea and to use elemental and isotope (Sr, Nd, Pb, Hf) analyses to fingerprint mantle provenance. The results were used to locate and investigate the nature of the Pacific-South Atlantic mantle domain boundary and thus to contribute to the understanding and quantification of global upper mantle fluxes. proprietary
GB-NERC-BAS-AEDC-00284 AFI 01/08 - Imaging the plasmasphere from Antarctica - VLF Doppler (Doppler Radio Receiver) at Rothera 2001-2002 SCIOPS STAC Catalog 2001-12-01 2002-12-01 -68.1297, -67.5675, -68.1297, -67.5675 https://cmr.earthdata.nasa.gov/search/concepts/C1214594519-SCIOPS.umm_json New instrumentation was deployed in the Antarctic Peninsula region to monitor conditions occurring in the region of near-space surrounding the Earth. The opportunity was taken to link into a NASA satellite mission occurring at the same time and with similar goals - to study the dynamics of the Earth-Sun system at a location where the two systems are finely balanced. The experiments have been used to interpret the changes in plasma composition at the same point in space due to solar weather events. A refurbished VLF Doppler receiver was installed at Rothera to measure plasmaspheric electron concentration. The electron number density was determined from analysis of the 15 minute integration providing group delay times, Doppler shift and arrival bearing of whistler-mode signals, of man-made transmissions, from MSK format transmitters from north east America. If you would like more information about the VLF Doppler receiver data that is still being routinely collected a t Rothera please contact the Antarctic Environmental Data Centre (&AEDC&) at the British Antarctic Survey. proprietary
GB-NERC-BAS-AEDC-00284 AFI 01/08 - Imaging the plasmasphere from Antarctica - VLF Doppler (Doppler Radio Receiver) at Rothera 2001-2002 ALL STAC Catalog 2001-12-01 2002-12-01 -68.1297, -67.5675, -68.1297, -67.5675 https://cmr.earthdata.nasa.gov/search/concepts/C1214594519-SCIOPS.umm_json New instrumentation was deployed in the Antarctic Peninsula region to monitor conditions occurring in the region of near-space surrounding the Earth. The opportunity was taken to link into a NASA satellite mission occurring at the same time and with similar goals - to study the dynamics of the Earth-Sun system at a location where the two systems are finely balanced. The experiments have been used to interpret the changes in plasma composition at the same point in space due to solar weather events. A refurbished VLF Doppler receiver was installed at Rothera to measure plasmaspheric electron concentration. The electron number density was determined from analysis of the 15 minute integration providing group delay times, Doppler shift and arrival bearing of whistler-mode signals, of man-made transmissions, from MSK format transmitters from north east America. If you would like more information about the VLF Doppler receiver data that is still being routinely collected a t Rothera please contact the Antarctic Environmental Data Centre (&AEDC&) at the British Antarctic Survey. proprietary
-GB-NERC-BAS-AEDC-00289 AFI 02/48_02 - Ice-rafted debris on the Antarctic continental margin and dynamics of the Antarctic Ice Sheet - Vibro gravity cores, and sediments data collected from the Weddell Sea, Marguerite Bay, Feb - March 2002 ALL STAC Catalog 2002-02-01 2002-03-01 -72, -68.5, -69, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214594547-SCIOPS.umm_json Ice-rafted (Heinrich) layers in the North Atlantic provide clear evidence that basins of large Quaternary ice sheets have, in the past, exhibited major dynamic instabilities. The presence of large ice sheets on the modern Antarctic continent provides an important opportunity to investigate the deposition of ice-rafted debris in a region where the dynamics of the parent drainage basins are known. The aim of the project was to reconstruct the Late Quaternary dynamics of the Antarctic Peninsula Ice Sheet in Marguerite Bay and to compare sedimentation and IRD records with the Larsen Ice Shelf area, on the other side of the Antarctic Peninsula. Two cruises were undertaken to collect the data. The JR71 (2002) cruise builds on the swath bathymetry and TOPAS survey undertaken on the JR59 (2001) cruise. Successful coring and examination of sediments now on and immediately beneath the sea floor, which provided the deforming bed of the former ice stream, enhanced our understanding of conditions beneath ice streams. proprietary
GB-NERC-BAS-AEDC-00289 AFI 02/48_02 - Ice-rafted debris on the Antarctic continental margin and dynamics of the Antarctic Ice Sheet - Vibro gravity cores, and sediments data collected from the Weddell Sea, Marguerite Bay, Feb - March 2002 SCIOPS STAC Catalog 2002-02-01 2002-03-01 -72, -68.5, -69, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214594547-SCIOPS.umm_json Ice-rafted (Heinrich) layers in the North Atlantic provide clear evidence that basins of large Quaternary ice sheets have, in the past, exhibited major dynamic instabilities. The presence of large ice sheets on the modern Antarctic continent provides an important opportunity to investigate the deposition of ice-rafted debris in a region where the dynamics of the parent drainage basins are known. The aim of the project was to reconstruct the Late Quaternary dynamics of the Antarctic Peninsula Ice Sheet in Marguerite Bay and to compare sedimentation and IRD records with the Larsen Ice Shelf area, on the other side of the Antarctic Peninsula. Two cruises were undertaken to collect the data. The JR71 (2002) cruise builds on the swath bathymetry and TOPAS survey undertaken on the JR59 (2001) cruise. Successful coring and examination of sediments now on and immediately beneath the sea floor, which provided the deforming bed of the former ice stream, enhanced our understanding of conditions beneath ice streams. proprietary
+GB-NERC-BAS-AEDC-00289 AFI 02/48_02 - Ice-rafted debris on the Antarctic continental margin and dynamics of the Antarctic Ice Sheet - Vibro gravity cores, and sediments data collected from the Weddell Sea, Marguerite Bay, Feb - March 2002 ALL STAC Catalog 2002-02-01 2002-03-01 -72, -68.5, -69, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214594547-SCIOPS.umm_json Ice-rafted (Heinrich) layers in the North Atlantic provide clear evidence that basins of large Quaternary ice sheets have, in the past, exhibited major dynamic instabilities. The presence of large ice sheets on the modern Antarctic continent provides an important opportunity to investigate the deposition of ice-rafted debris in a region where the dynamics of the parent drainage basins are known. The aim of the project was to reconstruct the Late Quaternary dynamics of the Antarctic Peninsula Ice Sheet in Marguerite Bay and to compare sedimentation and IRD records with the Larsen Ice Shelf area, on the other side of the Antarctic Peninsula. Two cruises were undertaken to collect the data. The JR71 (2002) cruise builds on the swath bathymetry and TOPAS survey undertaken on the JR59 (2001) cruise. Successful coring and examination of sediments now on and immediately beneath the sea floor, which provided the deforming bed of the former ice stream, enhanced our understanding of conditions beneath ice streams. proprietary
GB-NERC-BAS-AEDC-00290 AFI 02/48_01 - Ice-rafted debris on the Antarctic continental margin and dynamics of the Antarctic Ice Sheet - Swath Bathymetry, EM120 and TOPAS data collected from the Weddell Sea and Marguerite Bay, Feb - March 2002 ALL STAC Catalog 2002-02-01 2002-03-01 -72, -68.5, -69, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214594548-SCIOPS.umm_json Ice-rafted (Heinrich) layers in the North Atlantic provide clear evidence that basins of large Quaternary ice sheets have, in the past, exhibited major dynamic instabilities. The presence of large ice sheets on the modern Antarctic continent provides an important opportunity to investigate the deposition of ice-rafted debris in a region where the dynamics of the parent drainage basins are known. The aim of the project was to reconstruct the Late Quaternary dynamics of the Antarctic Peninsula Ice Sheet in Marguerite Bay and to compare sedimentation and IRD records with the Larsen Ice Shelf area, on the other side of the Antarctic Peninsula. Two cruises were undertaken to collect the data. The JR71 (2002) cruise builds on the swath bathymetry and TOPAS survey undertaken on the JR59 (2001) cruise. The mapping of streamlined sedimentary bedforms on the outer shelf has allowed the dimensions of a former fast-flowing ice stream present at the Last Glacial Maximum to be defin ed. This, in turn, enabled estimates of the past magnitude of ice flow through this glacial system to be calculated. Data was collected using Kongsberg-Simrad EM120 multibeam swath bathymetry and a TOPAS sub-bottom profiler. EM120 data was processed using the Kongsberg-Simrad bathymetric processing package &NEPTUNE&. These ice flux estimates were compared with computer-model reconstructions of former ice-sheet dynamics as a robust test of model performance. proprietary
GB-NERC-BAS-AEDC-00290 AFI 02/48_01 - Ice-rafted debris on the Antarctic continental margin and dynamics of the Antarctic Ice Sheet - Swath Bathymetry, EM120 and TOPAS data collected from the Weddell Sea and Marguerite Bay, Feb - March 2002 SCIOPS STAC Catalog 2002-02-01 2002-03-01 -72, -68.5, -69, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214594548-SCIOPS.umm_json Ice-rafted (Heinrich) layers in the North Atlantic provide clear evidence that basins of large Quaternary ice sheets have, in the past, exhibited major dynamic instabilities. The presence of large ice sheets on the modern Antarctic continent provides an important opportunity to investigate the deposition of ice-rafted debris in a region where the dynamics of the parent drainage basins are known. The aim of the project was to reconstruct the Late Quaternary dynamics of the Antarctic Peninsula Ice Sheet in Marguerite Bay and to compare sedimentation and IRD records with the Larsen Ice Shelf area, on the other side of the Antarctic Peninsula. Two cruises were undertaken to collect the data. The JR71 (2002) cruise builds on the swath bathymetry and TOPAS survey undertaken on the JR59 (2001) cruise. The mapping of streamlined sedimentary bedforms on the outer shelf has allowed the dimensions of a former fast-flowing ice stream present at the Last Glacial Maximum to be defin ed. This, in turn, enabled estimates of the past magnitude of ice flow through this glacial system to be calculated. Data was collected using Kongsberg-Simrad EM120 multibeam swath bathymetry and a TOPAS sub-bottom profiler. EM120 data was processed using the Kongsberg-Simrad bathymetric processing package &NEPTUNE&. These ice flux estimates were compared with computer-model reconstructions of former ice-sheet dynamics as a robust test of model performance. proprietary
-GB-NERC-BAS-AEDC-00293 AFI 04/09_01 - Improving ice-core interpretation - AWS data, Rothschild, Latady and Smyley Islands, 2005 SCIOPS STAC Catalog 2005-01-08 2006-02-11 -79, -73, -72.5, -69.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214594528-SCIOPS.umm_json The project was concerned with understanding how air mass origin and meteorology affect the mass accumulation of snow in areas of the Antarctic Peninsula, and how the atmosphere''s properties are preserved in the snow. Three micro-power Automatic Weather Stations with two sonic ranging sensors were deployed at field-sites situated at Rothschild Island, Latady Island and Smyley Island in January 2005. The Automatic Weather Stations instruments included a wind vane and two humicaps on the mast and two sonic ranging sensors mounted on separate horizontal scaffold poles. proprietary
GB-NERC-BAS-AEDC-00293 AFI 04/09_01 - Improving ice-core interpretation - AWS data, Rothschild, Latady and Smyley Islands, 2005 ALL STAC Catalog 2005-01-08 2006-02-11 -79, -73, -72.5, -69.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214594528-SCIOPS.umm_json The project was concerned with understanding how air mass origin and meteorology affect the mass accumulation of snow in areas of the Antarctic Peninsula, and how the atmosphere''s properties are preserved in the snow. Three micro-power Automatic Weather Stations with two sonic ranging sensors were deployed at field-sites situated at Rothschild Island, Latady Island and Smyley Island in January 2005. The Automatic Weather Stations instruments included a wind vane and two humicaps on the mast and two sonic ranging sensors mounted on separate horizontal scaffold poles. proprietary
+GB-NERC-BAS-AEDC-00293 AFI 04/09_01 - Improving ice-core interpretation - AWS data, Rothschild, Latady and Smyley Islands, 2005 SCIOPS STAC Catalog 2005-01-08 2006-02-11 -79, -73, -72.5, -69.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214594528-SCIOPS.umm_json The project was concerned with understanding how air mass origin and meteorology affect the mass accumulation of snow in areas of the Antarctic Peninsula, and how the atmosphere''s properties are preserved in the snow. Three micro-power Automatic Weather Stations with two sonic ranging sensors were deployed at field-sites situated at Rothschild Island, Latady Island and Smyley Island in January 2005. The Automatic Weather Stations instruments included a wind vane and two humicaps on the mast and two sonic ranging sensors mounted on separate horizontal scaffold poles. proprietary
GB-NERC-BAS-AEDC-00294 AFI 04/09_02 - Improving ice-core interpretation - Analysis of Snow/Ice cores collected from Rothschild, Latady & Smyley Islands, 2006 ALL STAC Catalog 2006-01-29 2006-02-11 -79, -73, -72.5, -69.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214594549-SCIOPS.umm_json The project was concerned with understanding how air mass origin and meteorology affect the mass accumulation of snow in areas of the Antarctic Peninsula, and how the atmosphere''s properties are preserved in the snow. Ground truth measurements in the form of snow/ice cores were obtained in 2006 at three sites, Rothschild Island, Latady Island and Smyley Island, where Automatic Weather Stations had been deployed in the previous season. At both the Rothschild Island and Smyley Island sites the AWS, due to an unprecedented amount of snowfall, had been buried therefore two cores, 8m and 12m in length, were obtained from the approximate position of the AWS, in addition to the sampling of a snow pit. At the Latady Island site the top 60cm of the 5m AWS was protruding above the surface, again, due to an unprecedented amount of snowfall. A diagonally descending trench was dug to recover the AWS and two cores were collected at this site. Photographs of the expedition showing the ground layout, the situation of the cores and what was done when they were gathered are available and stored with the data. proprietary
GB-NERC-BAS-AEDC-00294 AFI 04/09_02 - Improving ice-core interpretation - Analysis of Snow/Ice cores collected from Rothschild, Latady & Smyley Islands, 2006 SCIOPS STAC Catalog 2006-01-29 2006-02-11 -79, -73, -72.5, -69.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214594549-SCIOPS.umm_json The project was concerned with understanding how air mass origin and meteorology affect the mass accumulation of snow in areas of the Antarctic Peninsula, and how the atmosphere''s properties are preserved in the snow. Ground truth measurements in the form of snow/ice cores were obtained in 2006 at three sites, Rothschild Island, Latady Island and Smyley Island, where Automatic Weather Stations had been deployed in the previous season. At both the Rothschild Island and Smyley Island sites the AWS, due to an unprecedented amount of snowfall, had been buried therefore two cores, 8m and 12m in length, were obtained from the approximate position of the AWS, in addition to the sampling of a snow pit. At the Latady Island site the top 60cm of the 5m AWS was protruding above the surface, again, due to an unprecedented amount of snowfall. A diagonally descending trench was dug to recover the AWS and two cores were collected at this site. Photographs of the expedition showing the ground layout, the situation of the cores and what was done when they were gathered are available and stored with the data. proprietary
GB-NERC-BAS-AEDC-00296 AFI 04/16_01 - Satellite-Derived Elevation Changes on the Antarctic Peninsula CVaCS-DECAP - Glacier flow vertical motion measurements, Antarctic Peninsula, 2005/07 ALL STAC Catalog 2005-12-01 2007-01-22 -84.25, -75.91667, -64.6667, -66.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214594550-SCIOPS.umm_json Correction, Verification and Context, of Satellite-Derived Elevation Changes on the Antarctic Peninsula CVaCS-DECAP The aim of the project was to measure the various factors that affect altitude of snow surfaces in Antarctica, in order to validate data from satellite altimeters. In particular, it aimed for a better understanding of the factors affecting snowpack compaction rates, by accurate measurement of compaction over a period of several years. At four sites on the Antarctic Peninsula during the 2004-2005 austral summer ice cores were drilled to reveal the history of snowfall, and how the snow gets denser as it is crushed. Loggers designed to measure the compaction of snow were installed in boreholes, these sensors took a measurement every hour and are sensitive to downward movements of less than a millimetre. Automatic weather stations, sonic snow rangers and thermistor strings were also installed at each site, measuring the snow arriving at hourly intervals. A network of stakes was surveyed by GPS to provide horizontal strain rates, of the glacier, at each location. The flow away from the sites was compared with the snowfall from the ice cores to show up any imbalance. proprietary
@@ -6377,16 +6378,16 @@ GB-NERC-BAS-AEDC-00311 AFI 01/01_01 - Biodiversity response to climate change: b
GB-NERC-BAS-AEDC-00311 AFI 01/01_01 - Biodiversity response to climate change: biodiversity and climate significance of Tertiary forest communities of Antarctica - Fossil wood and leaves of Tertiary age, Seymour Island and adjacent, 2001 ALL STAC Catalog 2001-01-01 2001-03-31 -56.75, -64.283, -56.75, -64.283 https://cmr.earthdata.nasa.gov/search/concepts/C1214594530-SCIOPS.umm_json During field work in 2001 over 1600 specimens were collected from four main fossil plant assemblages: the ''Nordenksjold flora'' from the Cross Valley Formation of Late Palaeocene age; and 3 floras from La Meseta Formation i) Flora2 from the Valle De Las Focas allomember, ~late Early Eocene, ii) Wiman Flora, Acantilados allomember, late Early/mid Eocene, iii) Cucullaea 1, Cuculleae 1 allomember Flora, early Late Eocene. In addition smaller collections of fossils from other parts of the La Meseta Formation were collected. The work concentrated on the Late Palaeocene and the Cuculleae 1 floras as these were the best preserved and had sufficient morphotypes for climate analysis. In the Late Palaeocene flora 36 angiosperm leaf morphotypes were identified, along with 2 pteridophytes (ferns), and podocarp and araucarian conifers. Discovery of several new leaf types indicates that the Tertiary floras from Antarctica were more diverse than previously thought. proprietary
GB-NERC-BAS-AEDC-00312 AFI 01/01_02 - Biodiversity response to climate change: biodiversity and climate significance of Tertiary forest communities of Antarctica - Analysis of fossil wood & leaves of Tertiary age, Seymour Island&adjacent, 2001 ALL STAC Catalog 2000-08-14 2003-02-13 -56.75, -64.283, -56.75, -64.283 https://cmr.earthdata.nasa.gov/search/concepts/C1214594531-SCIOPS.umm_json Fossils from Palaeogene strata on Seymour Island, Antarctic Peninsula, were studied to determine the nature of vegetation response to the fundamental change from greenhouse to icehouse climates in Antarctica. Palaeoclimate data was derived using CLAMP (Climate Leaf Analysis Multivariate Program) and several Leaf Margin Analysis (LMA) techniques based on the physiognomic properties of the leaves. Climate interpretation of the fossils produced new data on terrestrial climate change at high latitudes and were used to test and validate climate models, and to establish whether climate-induced changes in biodiversity occurred in a gradual or punctuated manner. proprietary
GB-NERC-BAS-AEDC-00312 AFI 01/01_02 - Biodiversity response to climate change: biodiversity and climate significance of Tertiary forest communities of Antarctica - Analysis of fossil wood & leaves of Tertiary age, Seymour Island&adjacent, 2001 SCIOPS STAC Catalog 2000-08-14 2003-02-13 -56.75, -64.283, -56.75, -64.283 https://cmr.earthdata.nasa.gov/search/concepts/C1214594531-SCIOPS.umm_json Fossils from Palaeogene strata on Seymour Island, Antarctic Peninsula, were studied to determine the nature of vegetation response to the fundamental change from greenhouse to icehouse climates in Antarctica. Palaeoclimate data was derived using CLAMP (Climate Leaf Analysis Multivariate Program) and several Leaf Margin Analysis (LMA) techniques based on the physiognomic properties of the leaves. Climate interpretation of the fossils produced new data on terrestrial climate change at high latitudes and were used to test and validate climate models, and to establish whether climate-induced changes in biodiversity occurred in a gradual or punctuated manner. proprietary
-GB-NERC-BAS-AEDC-00342 AFI 07/02_01 - Subglacial Lake Ellsworth - SEISMIC data, Antarctica, 2007/08 ALL STAC Catalog 2007-11-09 2008-02-03 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594553-SCIOPS.umm_json Seismic reflection data acquired in the region of Subglacial Lake Ellsworth. Recording instrument: Geometrics Geode, 48 channels, active source (explosives). Five single-fold lines. Line length between 7.7 and 2.5 km. In addition, fold increased to 4 for the central part of one line (over the lake itself). Dataset also includes data from a single shallow seismic refraction experiment. proprietary
GB-NERC-BAS-AEDC-00342 AFI 07/02_01 - Subglacial Lake Ellsworth - SEISMIC data, Antarctica, 2007/08 SCIOPS STAC Catalog 2007-11-09 2008-02-03 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594553-SCIOPS.umm_json Seismic reflection data acquired in the region of Subglacial Lake Ellsworth. Recording instrument: Geometrics Geode, 48 channels, active source (explosives). Five single-fold lines. Line length between 7.7 and 2.5 km. In addition, fold increased to 4 for the central part of one line (over the lake itself). Dataset also includes data from a single shallow seismic refraction experiment. proprietary
-GB-NERC-BAS-AEDC-00343 AFI 07/02_02 Subglacial Lake Ellsworth - GPS data, Antarctica, 2007/08 ALL STAC Catalog 2007-11-09 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594535-SCIOPS.umm_json Geographical Positioning System (GPS) data recorded in the region of Subglacial Lake Ellsworth. Recording instruments: Leica geodetic receivers. Four locations with continuous data records; all other locations (~70) occupied for short periods (mostly 1 hour). proprietary
+GB-NERC-BAS-AEDC-00342 AFI 07/02_01 - Subglacial Lake Ellsworth - SEISMIC data, Antarctica, 2007/08 ALL STAC Catalog 2007-11-09 2008-02-03 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594553-SCIOPS.umm_json Seismic reflection data acquired in the region of Subglacial Lake Ellsworth. Recording instrument: Geometrics Geode, 48 channels, active source (explosives). Five single-fold lines. Line length between 7.7 and 2.5 km. In addition, fold increased to 4 for the central part of one line (over the lake itself). Dataset also includes data from a single shallow seismic refraction experiment. proprietary
GB-NERC-BAS-AEDC-00343 AFI 07/02_02 Subglacial Lake Ellsworth - GPS data, Antarctica, 2007/08 SCIOPS STAC Catalog 2007-11-09 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594535-SCIOPS.umm_json Geographical Positioning System (GPS) data recorded in the region of Subglacial Lake Ellsworth. Recording instruments: Leica geodetic receivers. Four locations with continuous data records; all other locations (~70) occupied for short periods (mostly 1 hour). proprietary
+GB-NERC-BAS-AEDC-00343 AFI 07/02_02 Subglacial Lake Ellsworth - GPS data, Antarctica, 2007/08 ALL STAC Catalog 2007-11-09 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594535-SCIOPS.umm_json Geographical Positioning System (GPS) data recorded in the region of Subglacial Lake Ellsworth. Recording instruments: Leica geodetic receivers. Four locations with continuous data records; all other locations (~70) occupied for short periods (mostly 1 hour). proprietary
GB-NERC-BAS-AEDC-00344 AFI 07/02_03 Subglacial Lake Ellsworth - RADAR data, Antarctica, 2007/08 SCIOPS STAC Catalog 2007-11-09 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594554-SCIOPS.umm_json Radar data acquired in the region of Subglacial Lake Ellsworth. Recording instrument: the British Antarctic Survey's (BAS) DELORES 1 and DELORES II radar systems. Line length between 1 and 45 km. Simultaneous GPS data acquired with Leica geodetic GPS receiver at 1 sec intervals. proprietary
GB-NERC-BAS-AEDC-00344 AFI 07/02_03 Subglacial Lake Ellsworth - RADAR data, Antarctica, 2007/08 ALL STAC Catalog 2007-11-09 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594554-SCIOPS.umm_json Radar data acquired in the region of Subglacial Lake Ellsworth. Recording instrument: the British Antarctic Survey's (BAS) DELORES 1 and DELORES II radar systems. Line length between 1 and 45 km. Simultaneous GPS data acquired with Leica geodetic GPS receiver at 1 sec intervals. proprietary
GB-NERC-BAS-AEDC-00347 AFI 07/02_04 - Subglacial Lake Ellsworth - METEOROLOGICAL data, Antarctica, 2007/08 SCIOPS STAC Catalog 2007-11-09 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594536-SCIOPS.umm_json Meteorological data acquired in the region of Subglacial Lake Ellsworth. Recording instrument: HOBO Weather Station (HOBO AWS) recording wind speed, wind direction, temperature, pressure, humidity, solar radiation. HOBO - registered trademark of the Onset Computer Corporation proprietary
GB-NERC-BAS-AEDC-00347 AFI 07/02_04 - Subglacial Lake Ellsworth - METEOROLOGICAL data, Antarctica, 2007/08 ALL STAC Catalog 2007-11-09 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594536-SCIOPS.umm_json Meteorological data acquired in the region of Subglacial Lake Ellsworth. Recording instrument: HOBO Weather Station (HOBO AWS) recording wind speed, wind direction, temperature, pressure, humidity, solar radiation. HOBO - registered trademark of the Onset Computer Corporation proprietary
-GB-NERC-BAS-AEDC-00348 AFI 07/02_05 - Subglacial Lake Ellsworth - ICE CORE samples, Antarctica, 2007/08 SCIOPS STAC Catalog 2007-11-09 2008-02-03 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594555-SCIOPS.umm_json Shallow ice cores collected in the region of Subglacial Lake Ellsworth. Three cores drilled to ~20 m depth. Two cores returned to UK for analysis. One core measured for density-depth in the field, then discarded. One of the two cores returned to UK has been sent to Bristol University for major anion/cation analysis; the other core is at the British Antarctic Survey (BAS) and will be analysed for accumulation rate. Expect no core to remain once analysis has been completed. proprietary
GB-NERC-BAS-AEDC-00348 AFI 07/02_05 - Subglacial Lake Ellsworth - ICE CORE samples, Antarctica, 2007/08 ALL STAC Catalog 2007-11-09 2008-02-03 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594555-SCIOPS.umm_json Shallow ice cores collected in the region of Subglacial Lake Ellsworth. Three cores drilled to ~20 m depth. Two cores returned to UK for analysis. One core measured for density-depth in the field, then discarded. One of the two cores returned to UK has been sent to Bristol University for major anion/cation analysis; the other core is at the British Antarctic Survey (BAS) and will be analysed for accumulation rate. Expect no core to remain once analysis has been completed. proprietary
+GB-NERC-BAS-AEDC-00348 AFI 07/02_05 - Subglacial Lake Ellsworth - ICE CORE samples, Antarctica, 2007/08 SCIOPS STAC Catalog 2007-11-09 2008-02-03 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594555-SCIOPS.umm_json Shallow ice cores collected in the region of Subglacial Lake Ellsworth. Three cores drilled to ~20 m depth. Two cores returned to UK for analysis. One core measured for density-depth in the field, then discarded. One of the two cores returned to UK has been sent to Bristol University for major anion/cation analysis; the other core is at the British Antarctic Survey (BAS) and will be analysed for accumulation rate. Expect no core to remain once analysis has been completed. proprietary
GB-NERC-BAS-AEDC-00349 AFI 07/02_06 - Subglacial Lake Ellsworth - ICE CORE data, Antarctica, 2007/08 ALL STAC Catalog 2007-11-09 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594556-SCIOPS.umm_json Analysis of shallow ice cores collected in the region of Subglacial Lake Ellsworth. Three cores drilled to ~20 m depth. Two cores returned to UK for analysis. One core measured for density-depth in the field, then discarded. One of the two cores returned to UK has been sent to Bristol University for major anion/cation analysis; the other core is at the British Antarctic Survey (BAS) and will be analysed for accumulation rate. Density data is complete. Accumulation and chemical analysis is in progress. proprietary
GB-NERC-BAS-AEDC-00349 AFI 07/02_06 - Subglacial Lake Ellsworth - ICE CORE data, Antarctica, 2007/08 SCIOPS STAC Catalog 2007-11-09 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594556-SCIOPS.umm_json Analysis of shallow ice cores collected in the region of Subglacial Lake Ellsworth. Three cores drilled to ~20 m depth. Two cores returned to UK for analysis. One core measured for density-depth in the field, then discarded. One of the two cores returned to UK has been sent to Bristol University for major anion/cation analysis; the other core is at the British Antarctic Survey (BAS) and will be analysed for accumulation rate. Density data is complete. Accumulation and chemical analysis is in progress. proprietary
GB-NERC-BAS-AEDC-00350 AFI 07/02_07 Subglacial Lake Ellsworth - GRAVITY data, Antarctica, 2007/08 SCIOPS STAC Catalog 2007-11-09 2008-02-03 -91.01667, -79.86667, -89.21667, -79.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214594538-SCIOPS.umm_json Gravity data acquired in the region of Subglacial Lake Ellsworth. Instrument Lacoste and Romberg land gravity meter. Drift control primarily contained within the local area. Single, one-way tie to international gravity base station network (Rothera) Single survey line ~30 km long. Station spacing 2 km, except for 240 m spacing over the lake. Position, elevation, ice- and water-thickness data exist for each station. proprietary
@@ -6397,28 +6398,28 @@ GB-NERC-BAS-AEDC-00361 AFI 01/05_01 - Basal conditions on Rutford Ice Stream, We
GB-NERC-BAS-AEDC-00361 AFI 01/05_01 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Borehole sensors data, 2004/06 SCIOPS STAC Catalog 2004-11-18 2006-02-28 -83.9, -78.14, -83.9, -78.14 https://cmr.earthdata.nasa.gov/search/concepts/C1214594657-SCIOPS.umm_json Approximatively 1MB of ice temperature data acquired during the RABID Project. Measured on a thermistor cable with 10 sensors located at depths between 15 m and 300 m below the surface. Collected between November 2004 and February 2006. proprietary
GB-NERC-BAS-AEDC-00367 AFI 01/05_02 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Drill monitoring data, 2004/06 SCIOPS STAC Catalog 2005-01-08 2005-01-17 -83.9, -78.14, -83.9, -78.14 https://cmr.earthdata.nasa.gov/search/concepts/C1214594619-SCIOPS.umm_json Digital time series data collected for monitoring of drilling during the RABID Project. Water temperature, pressure, flow. Drill depth and hose tension. Instrumentation: SENSORS Flow meter - Kobold Instruments Ltd, L25 axial turbine flow meter. Water pressure - Omega Engineering Ltd, PX222-250GV pressure transducer Water level - GEMS 4000KGB100M2KJ Range 0-10bG immersible pressure transducer Water temperate - Omega Engineering Ltd, K2017 PT100 ceramic element thermometer Hose tension(Load Cell) - Omega Engineering Ltd, LCCB-2K load cell Hose speed and depth - Red Lion, rotary pulse generator LSQS0200 Additional water temperature - FishTag and TinyTalk data loggers proprietary
GB-NERC-BAS-AEDC-00367 AFI 01/05_02 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Drill monitoring data, 2004/06 ALL STAC Catalog 2005-01-08 2005-01-17 -83.9, -78.14, -83.9, -78.14 https://cmr.earthdata.nasa.gov/search/concepts/C1214594619-SCIOPS.umm_json Digital time series data collected for monitoring of drilling during the RABID Project. Water temperature, pressure, flow. Drill depth and hose tension. Instrumentation: SENSORS Flow meter - Kobold Instruments Ltd, L25 axial turbine flow meter. Water pressure - Omega Engineering Ltd, PX222-250GV pressure transducer Water level - GEMS 4000KGB100M2KJ Range 0-10bG immersible pressure transducer Water temperate - Omega Engineering Ltd, K2017 PT100 ceramic element thermometer Hose tension(Load Cell) - Omega Engineering Ltd, LCCB-2K load cell Hose speed and depth - Red Lion, rotary pulse generator LSQS0200 Additional water temperature - FishTag and TinyTalk data loggers proprietary
-GB-NERC-BAS-AEDC-00368 AFI 01/05_03 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - GPS data, 2004/06 SCIOPS STAC Catalog 2004-11-18 2006-02-28 -85, -78.25, -82, -77.75 https://cmr.earthdata.nasa.gov/search/concepts/C1214594659-SCIOPS.umm_json GPS positions from sensors monitoring ice flow during the RABID Project (Leica and Trimble receivers). Five stations on the ice stream, plus one on slow-moving adjacent ice sheet (Fletcher Promontory), and one on a nunatak (unofficial name &Tolly''s Heel&) in the Ellsworth Mountains. Sensors: Leica 1200 GPS receivers Trimble 5200 GPS receivers Trimble 4000 GPS receivers proprietary
GB-NERC-BAS-AEDC-00368 AFI 01/05_03 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - GPS data, 2004/06 ALL STAC Catalog 2004-11-18 2006-02-28 -85, -78.25, -82, -77.75 https://cmr.earthdata.nasa.gov/search/concepts/C1214594659-SCIOPS.umm_json GPS positions from sensors monitoring ice flow during the RABID Project (Leica and Trimble receivers). Five stations on the ice stream, plus one on slow-moving adjacent ice sheet (Fletcher Promontory), and one on a nunatak (unofficial name &Tolly''s Heel&) in the Ellsworth Mountains. Sensors: Leica 1200 GPS receivers Trimble 5200 GPS receivers Trimble 4000 GPS receivers proprietary
+GB-NERC-BAS-AEDC-00368 AFI 01/05_03 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - GPS data, 2004/06 SCIOPS STAC Catalog 2004-11-18 2006-02-28 -85, -78.25, -82, -77.75 https://cmr.earthdata.nasa.gov/search/concepts/C1214594659-SCIOPS.umm_json GPS positions from sensors monitoring ice flow during the RABID Project (Leica and Trimble receivers). Five stations on the ice stream, plus one on slow-moving adjacent ice sheet (Fletcher Promontory), and one on a nunatak (unofficial name &Tolly''s Heel&) in the Ellsworth Mountains. Sensors: Leica 1200 GPS receivers Trimble 5200 GPS receivers Trimble 4000 GPS receivers proprietary
GB-NERC-BAS-AEDC-00369 AFI 01/05_04 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Seismic reflection data, 2004/06 ALL STAC Catalog 2004-11-28 2005-02-06 -85, -78.25, -83, -78 https://cmr.earthdata.nasa.gov/search/concepts/C1214594680-SCIOPS.umm_json Digital seismic reflection data (BISON 9024 seismograph) acquired during the RABID Project. Data collected using 24 channels, active source (explosives). Four single-fold lines. Line length 3.6 km. Instrumentation Data logger: BISON 9024 seismograph Sensors: OYO-Geospace geophones (100 Hz natural frequency) proprietary
GB-NERC-BAS-AEDC-00369 AFI 01/05_04 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Seismic reflection data, 2004/06 SCIOPS STAC Catalog 2004-11-28 2005-02-06 -85, -78.25, -83, -78 https://cmr.earthdata.nasa.gov/search/concepts/C1214594680-SCIOPS.umm_json Digital seismic reflection data (BISON 9024 seismograph) acquired during the RABID Project. Data collected using 24 channels, active source (explosives). Four single-fold lines. Line length 3.6 km. Instrumentation Data logger: BISON 9024 seismograph Sensors: OYO-Geospace geophones (100 Hz natural frequency) proprietary
-GB-NERC-BAS-AEDC-00371 AFI 01/05_05 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Ice core samples, 2004/06 SCIOPS STAC Catalog 2005-01-24 2005-01-26 -83.9, -78.14, -83.9, -78.14 https://cmr.earthdata.nasa.gov/search/concepts/C1214594681-SCIOPS.umm_json Sections of ice core acquired from upper 100 m of the ice stream during the RABID Project. Retrieved using hot-water corer. Cores taken at selected depths in two adjacent holes. Core section length = up to 4 m. Number of core sections = 6. Total length = 20.8 m. Instrumentation: Ice cores drilled using hot-water ice-coring technique. proprietary
GB-NERC-BAS-AEDC-00371 AFI 01/05_05 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Ice core samples, 2004/06 ALL STAC Catalog 2005-01-24 2005-01-26 -83.9, -78.14, -83.9, -78.14 https://cmr.earthdata.nasa.gov/search/concepts/C1214594681-SCIOPS.umm_json Sections of ice core acquired from upper 100 m of the ice stream during the RABID Project. Retrieved using hot-water corer. Cores taken at selected depths in two adjacent holes. Core section length = up to 4 m. Number of core sections = 6. Total length = 20.8 m. Instrumentation: Ice cores drilled using hot-water ice-coring technique. proprietary
-GB-NERC-BAS-AEDC-00373 AFI 01/05_06 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Radar data, 2004/06 SCIOPS STAC Catalog 2005-01-21 2005-02-13 -85, -78.25, -83, -78 https://cmr.earthdata.nasa.gov/search/concepts/C1214594667-SCIOPS.umm_json Ground-Penetrating Radar (GPR) data acquired during the RABID Project with a Mala GPR. proprietary
+GB-NERC-BAS-AEDC-00371 AFI 01/05_05 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Ice core samples, 2004/06 SCIOPS STAC Catalog 2005-01-24 2005-01-26 -83.9, -78.14, -83.9, -78.14 https://cmr.earthdata.nasa.gov/search/concepts/C1214594681-SCIOPS.umm_json Sections of ice core acquired from upper 100 m of the ice stream during the RABID Project. Retrieved using hot-water corer. Cores taken at selected depths in two adjacent holes. Core section length = up to 4 m. Number of core sections = 6. Total length = 20.8 m. Instrumentation: Ice cores drilled using hot-water ice-coring technique. proprietary
GB-NERC-BAS-AEDC-00373 AFI 01/05_06 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Radar data, 2004/06 ALL STAC Catalog 2005-01-21 2005-02-13 -85, -78.25, -83, -78 https://cmr.earthdata.nasa.gov/search/concepts/C1214594667-SCIOPS.umm_json Ground-Penetrating Radar (GPR) data acquired during the RABID Project with a Mala GPR. proprietary
-GB-NERC-BAS-AEDC-00374 AFI 01/05_07 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Weather data, 2004/06 ALL STAC Catalog 2004-12-30 2005-02-20 -83.9, -78.14, -83.9, -78.14 https://cmr.earthdata.nasa.gov/search/concepts/C1214594668-SCIOPS.umm_json Weather data acquired on Rutford Ice Stream during the RABID Project. Wind speed, wind direction, temperature, pressure, humidity, solar radiation recored with an HOBO AWS (Automatic Weather Station: data logger & sensors ); proprietary
+GB-NERC-BAS-AEDC-00373 AFI 01/05_06 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Radar data, 2004/06 SCIOPS STAC Catalog 2005-01-21 2005-02-13 -85, -78.25, -83, -78 https://cmr.earthdata.nasa.gov/search/concepts/C1214594667-SCIOPS.umm_json Ground-Penetrating Radar (GPR) data acquired during the RABID Project with a Mala GPR. proprietary
GB-NERC-BAS-AEDC-00374 AFI 01/05_07 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Weather data, 2004/06 SCIOPS STAC Catalog 2004-12-30 2005-02-20 -83.9, -78.14, -83.9, -78.14 https://cmr.earthdata.nasa.gov/search/concepts/C1214594668-SCIOPS.umm_json Weather data acquired on Rutford Ice Stream during the RABID Project. Wind speed, wind direction, temperature, pressure, humidity, solar radiation recored with an HOBO AWS (Automatic Weather Station: data logger & sensors ); proprietary
+GB-NERC-BAS-AEDC-00374 AFI 01/05_07 - Basal conditions on Rutford Ice Stream, West Antarctica: Hot-water drilling and down-hole instrumentation - Weather data, 2004/06 ALL STAC Catalog 2004-12-30 2005-02-20 -83.9, -78.14, -83.9, -78.14 https://cmr.earthdata.nasa.gov/search/concepts/C1214594668-SCIOPS.umm_json Weather data acquired on Rutford Ice Stream during the RABID Project. Wind speed, wind direction, temperature, pressure, humidity, solar radiation recored with an HOBO AWS (Automatic Weather Station: data logger & sensors ); proprietary
GB-NERC-BAS-AEDC-00396 AFI 01/07_01 - Observations of Antarctic Precipitation processes - Air samples and analyses, 2000/03 SCIOPS STAC Catalog 2000-06-22 2003-11-01 75, -74.63, 75, -74.63 https://cmr.earthdata.nasa.gov/search/concepts/C1214603086-SCIOPS.umm_json The sampling programme was carried out successfully using kites and helium balloon assisted kites to sample in both low and higher winds speeds. Air samples were successfully processed using the Ice Nucleus chamber for a variety of wind directions representing a range of air mass trajectories and source regions. proprietary
GB-NERC-BAS-AEDC-00396 AFI 01/07_01 - Observations of Antarctic Precipitation processes - Air samples and analyses, 2000/03 ALL STAC Catalog 2000-06-22 2003-11-01 75, -74.63, 75, -74.63 https://cmr.earthdata.nasa.gov/search/concepts/C1214603086-SCIOPS.umm_json The sampling programme was carried out successfully using kites and helium balloon assisted kites to sample in both low and higher winds speeds. Air samples were successfully processed using the Ice Nucleus chamber for a variety of wind directions representing a range of air mass trajectories and source regions. proprietary
-GB-NERC-BAS-AEDC-00400 AFI 02/37_01 - Identifying terranes in the Antarctic Peninsula using primitive basalt dykes as lithospheric probes - Rock samples collected from Palmer Land and Graham Land in the 2001/2002 field season. SCIOPS STAC Catalog 2001-11-01 2002-02-28 -65, -73, -63, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214594682-SCIOPS.umm_json Initial work during the 2001/2002 field season commenced with reconnaissance and sampling in northeast Palmer Land. Over a two month period, outcrop from the Welch Mountains to the Eternity Range was visited, the geology described, and mafic dyke samples collected for analysis. This was followed by a further two months based on the ship HMS Endurance, carrying out helicopter assisted sampling of numerous islands and coastal localities along the western and eastern margin of northern Graham Land. Approximately 200 (400kg of dyke and host rock at Palmer land and 80kg at nine localities in Graham Land) rock samples were collected. proprietary
GB-NERC-BAS-AEDC-00400 AFI 02/37_01 - Identifying terranes in the Antarctic Peninsula using primitive basalt dykes as lithospheric probes - Rock samples collected from Palmer Land and Graham Land in the 2001/2002 field season. ALL STAC Catalog 2001-11-01 2002-02-28 -65, -73, -63, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214594682-SCIOPS.umm_json Initial work during the 2001/2002 field season commenced with reconnaissance and sampling in northeast Palmer Land. Over a two month period, outcrop from the Welch Mountains to the Eternity Range was visited, the geology described, and mafic dyke samples collected for analysis. This was followed by a further two months based on the ship HMS Endurance, carrying out helicopter assisted sampling of numerous islands and coastal localities along the western and eastern margin of northern Graham Land. Approximately 200 (400kg of dyke and host rock at Palmer land and 80kg at nine localities in Graham Land) rock samples were collected. proprietary
-GB-NERC-BAS-AEDC-00401 AFI 02/37_02 - Identifying terranes in the Antarctic Peninsula using primitive basalt dykes as lithospheric probes - Geochemical and petrographic analysis of rock samples, 2001/02 ALL STAC Catalog 2001-11-01 2002-02-28 -65, -73, -63, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214594700-SCIOPS.umm_json The chemistry of mafic volcanic rocks and minor intrusions erupted on continents can be used to define the composition and history of subcontinental asthenospheric and lithospheric mantle domains. We have produced new and collated published data for Antarctica in order to identify mantle domains beneath the continent. Suitable material archived at the British Antarctic Survey, Cambridge, the result of previous geological research, was sampled and prepared for petrographic and geochemical analysis in the intervening period between field collection and sample arrival in the United Kingdom. Field information, petrography and raw geochemical data obtained from XRF (X-ray fluorescence), ICPMS (Inductively coupled plasma-mass spectrometer), TIMS (Thermal Ionization Mass Spectrometer), Ar/Ar analysis and Electron Microprobe analysis of rock samples collected from Palmer Land and Graham Land was used to define a geochemical profile of crust/mantle architecture beneath the An tarctic Peninsula. proprietary
+GB-NERC-BAS-AEDC-00400 AFI 02/37_01 - Identifying terranes in the Antarctic Peninsula using primitive basalt dykes as lithospheric probes - Rock samples collected from Palmer Land and Graham Land in the 2001/2002 field season. SCIOPS STAC Catalog 2001-11-01 2002-02-28 -65, -73, -63, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214594682-SCIOPS.umm_json Initial work during the 2001/2002 field season commenced with reconnaissance and sampling in northeast Palmer Land. Over a two month period, outcrop from the Welch Mountains to the Eternity Range was visited, the geology described, and mafic dyke samples collected for analysis. This was followed by a further two months based on the ship HMS Endurance, carrying out helicopter assisted sampling of numerous islands and coastal localities along the western and eastern margin of northern Graham Land. Approximately 200 (400kg of dyke and host rock at Palmer land and 80kg at nine localities in Graham Land) rock samples were collected. proprietary
GB-NERC-BAS-AEDC-00401 AFI 02/37_02 - Identifying terranes in the Antarctic Peninsula using primitive basalt dykes as lithospheric probes - Geochemical and petrographic analysis of rock samples, 2001/02 SCIOPS STAC Catalog 2001-11-01 2002-02-28 -65, -73, -63, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214594700-SCIOPS.umm_json The chemistry of mafic volcanic rocks and minor intrusions erupted on continents can be used to define the composition and history of subcontinental asthenospheric and lithospheric mantle domains. We have produced new and collated published data for Antarctica in order to identify mantle domains beneath the continent. Suitable material archived at the British Antarctic Survey, Cambridge, the result of previous geological research, was sampled and prepared for petrographic and geochemical analysis in the intervening period between field collection and sample arrival in the United Kingdom. Field information, petrography and raw geochemical data obtained from XRF (X-ray fluorescence), ICPMS (Inductively coupled plasma-mass spectrometer), TIMS (Thermal Ionization Mass Spectrometer), Ar/Ar analysis and Electron Microprobe analysis of rock samples collected from Palmer Land and Graham Land was used to define a geochemical profile of crust/mantle architecture beneath the An tarctic Peninsula. proprietary
-GB-NERC-BAS-AEDC-00423 AFI 01/07_02 - Observations of Antarctic Precipitation processes - Ice Nuclei & Meteorological Data, Mount Rex, Antarctica Jan-Feb 2002 SCIOPS STAC Catalog 2002-01-17 2002-02-17 75, -74.63, 75, -74.63 https://cmr.earthdata.nasa.gov/search/concepts/C1214599942-SCIOPS.umm_json The sampling programme was carried out successfully using kites and helium balloon assisted kites to sample in both low and higher winds speeds. Air samples were successfully processed using the Ice Nucleus chamber for a variety of wind directions representing a range of air mass trajectories and source regions. The aim was to sample air that had passed over land (the Peninsula), sea (Bellingshausen and Weddell) or ice (the plateau) and compare the size and quantity of ice crystals transported. Data collected using our own Automatic Weather Station (AWS), also an ADAS tether sonde system, some radiosondes, a sensor and logger attached to the ice-crystal replicator system and an Ice Nucleus chamber. The collection was made during a month in January, February 2002 East of Weatherheaven. proprietary
+GB-NERC-BAS-AEDC-00401 AFI 02/37_02 - Identifying terranes in the Antarctic Peninsula using primitive basalt dykes as lithospheric probes - Geochemical and petrographic analysis of rock samples, 2001/02 ALL STAC Catalog 2001-11-01 2002-02-28 -65, -73, -63, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214594700-SCIOPS.umm_json The chemistry of mafic volcanic rocks and minor intrusions erupted on continents can be used to define the composition and history of subcontinental asthenospheric and lithospheric mantle domains. We have produced new and collated published data for Antarctica in order to identify mantle domains beneath the continent. Suitable material archived at the British Antarctic Survey, Cambridge, the result of previous geological research, was sampled and prepared for petrographic and geochemical analysis in the intervening period between field collection and sample arrival in the United Kingdom. Field information, petrography and raw geochemical data obtained from XRF (X-ray fluorescence), ICPMS (Inductively coupled plasma-mass spectrometer), TIMS (Thermal Ionization Mass Spectrometer), Ar/Ar analysis and Electron Microprobe analysis of rock samples collected from Palmer Land and Graham Land was used to define a geochemical profile of crust/mantle architecture beneath the An tarctic Peninsula. proprietary
GB-NERC-BAS-AEDC-00423 AFI 01/07_02 - Observations of Antarctic Precipitation processes - Ice Nuclei & Meteorological Data, Mount Rex, Antarctica Jan-Feb 2002 ALL STAC Catalog 2002-01-17 2002-02-17 75, -74.63, 75, -74.63 https://cmr.earthdata.nasa.gov/search/concepts/C1214599942-SCIOPS.umm_json The sampling programme was carried out successfully using kites and helium balloon assisted kites to sample in both low and higher winds speeds. Air samples were successfully processed using the Ice Nucleus chamber for a variety of wind directions representing a range of air mass trajectories and source regions. The aim was to sample air that had passed over land (the Peninsula), sea (Bellingshausen and Weddell) or ice (the plateau) and compare the size and quantity of ice crystals transported. Data collected using our own Automatic Weather Station (AWS), also an ADAS tether sonde system, some radiosondes, a sensor and logger attached to the ice-crystal replicator system and an Ice Nucleus chamber. The collection was made during a month in January, February 2002 East of Weatherheaven. proprietary
-GB-NERC-BAS-PDC-00499 ACES-FOCAS: Forcings from the Ocean, Clouds, Atmosphere and Sea-ice SCIOPS STAC Catalog 2008-01-01 2008-02-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214599974-SCIOPS.umm_json ACES will investigate the atmospheric and oceanic links that connect the climate of the Antarctic to that of lower latitudes, and their controlling mechanisms. Specific research topics will include the formation and properties of Antarctic clouds, the complexities of the atmospheric boundary layer, and the importance to the global ocean circulation of cold, dense water masses generated in the Antarctic. By quantifying the role of southern polar processes in the global climate system, ACES will help improve predictions of climate change. Our knowledge of the workings of the climate system is far from complete. We know that atmospheric and oceanic processes in the Antarctic and Southern Ocean influence and are influenced by global climate, but we are unsure of important details. Describing and quantifying the role of the southern polar regions in the global climate system is both important and timely. Delivering the Results ACES will carry out a comprehensive programme of oceanographic measurements from BAS ships in the Weddell and Bellingshausen Seas, and will use BAS's instrument-carrying Twin Otter aircraft to help us study cloud microphysics and air-sea-ice interaction. We will obtain an ice core from the southwestern Antarctic Peninsula to give us a 150-year record of the strength of the circumpolar westerly winds. We will use these observations to test and improve global climate models and a new regional atmosphere-ice-ocean model for the Antarctic. ACES will link with CACHE, GRADES, GEACEP, BIOFLAME, DISCOVERY 2010, and SEC. proprietary
+GB-NERC-BAS-AEDC-00423 AFI 01/07_02 - Observations of Antarctic Precipitation processes - Ice Nuclei & Meteorological Data, Mount Rex, Antarctica Jan-Feb 2002 SCIOPS STAC Catalog 2002-01-17 2002-02-17 75, -74.63, 75, -74.63 https://cmr.earthdata.nasa.gov/search/concepts/C1214599942-SCIOPS.umm_json The sampling programme was carried out successfully using kites and helium balloon assisted kites to sample in both low and higher winds speeds. Air samples were successfully processed using the Ice Nucleus chamber for a variety of wind directions representing a range of air mass trajectories and source regions. The aim was to sample air that had passed over land (the Peninsula), sea (Bellingshausen and Weddell) or ice (the plateau) and compare the size and quantity of ice crystals transported. Data collected using our own Automatic Weather Station (AWS), also an ADAS tether sonde system, some radiosondes, a sensor and logger attached to the ice-crystal replicator system and an Ice Nucleus chamber. The collection was made during a month in January, February 2002 East of Weatherheaven. proprietary
GB-NERC-BAS-PDC-00499 ACES-FOCAS: Forcings from the Ocean, Clouds, Atmosphere and Sea-ice ALL STAC Catalog 2008-01-01 2008-02-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214599974-SCIOPS.umm_json ACES will investigate the atmospheric and oceanic links that connect the climate of the Antarctic to that of lower latitudes, and their controlling mechanisms. Specific research topics will include the formation and properties of Antarctic clouds, the complexities of the atmospheric boundary layer, and the importance to the global ocean circulation of cold, dense water masses generated in the Antarctic. By quantifying the role of southern polar processes in the global climate system, ACES will help improve predictions of climate change. Our knowledge of the workings of the climate system is far from complete. We know that atmospheric and oceanic processes in the Antarctic and Southern Ocean influence and are influenced by global climate, but we are unsure of important details. Describing and quantifying the role of the southern polar regions in the global climate system is both important and timely. Delivering the Results ACES will carry out a comprehensive programme of oceanographic measurements from BAS ships in the Weddell and Bellingshausen Seas, and will use BAS's instrument-carrying Twin Otter aircraft to help us study cloud microphysics and air-sea-ice interaction. We will obtain an ice core from the southwestern Antarctic Peninsula to give us a 150-year record of the strength of the circumpolar westerly winds. We will use these observations to test and improve global climate models and a new regional atmosphere-ice-ocean model for the Antarctic. ACES will link with CACHE, GRADES, GEACEP, BIOFLAME, DISCOVERY 2010, and SEC. proprietary
-GB-NERC-BAS-PDC-00500 ACES-ACCENT: Antarctic Climate Change and Nonlinear Teleconnections SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600003-SCIOPS.umm_json ACES will investigate the atmospheric and oceanic links that connect the climate of the Antarctic to that of lower latitudes, and their controlling mechanisms. Specific research topics will include the formation and properties of Antarctic clouds, the complexities of the atmospheric boundary layer, and the importance to the global ocean circulation of cold, dense water masses generated in the Antarctic. By quantifying the role of southern polar processes in the global climate system, ACES will help improve predictions of climate change. Our knowledge of the workings of the climate system is far from complete. We know that atmospheric and oceanic processes in the Antarctic and Southern Ocean influence and are influenced by global climate, but we are unsure of important details. Describing and quantifying the role of the southern polar regions in the global climate system is both important and timely. Delivering the Results ACES will carry out a comprehensive programme of oceanographic measurements from BAS ships in the Weddell and Bellingshausen Seas, and will use BAS's instrument-carrying Twin Otter aircraft to help us study cloud microphysics and air-sea-ice interaction. We will obtain an ice core from the southwestern Antarctic Peninsula to give us a 150-year record of the strength of the circumpolar westerly winds. We will use these observations to test and improve global climate models and a new regional atmosphere-ice-ocean model for the Antarctic. ACES will link with CACHE, GRADES, GEACEP, BIOFLAME, DISCOVERY 2010, and SEC. proprietary
+GB-NERC-BAS-PDC-00499 ACES-FOCAS: Forcings from the Ocean, Clouds, Atmosphere and Sea-ice SCIOPS STAC Catalog 2008-01-01 2008-02-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214599974-SCIOPS.umm_json ACES will investigate the atmospheric and oceanic links that connect the climate of the Antarctic to that of lower latitudes, and their controlling mechanisms. Specific research topics will include the formation and properties of Antarctic clouds, the complexities of the atmospheric boundary layer, and the importance to the global ocean circulation of cold, dense water masses generated in the Antarctic. By quantifying the role of southern polar processes in the global climate system, ACES will help improve predictions of climate change. Our knowledge of the workings of the climate system is far from complete. We know that atmospheric and oceanic processes in the Antarctic and Southern Ocean influence and are influenced by global climate, but we are unsure of important details. Describing and quantifying the role of the southern polar regions in the global climate system is both important and timely. Delivering the Results ACES will carry out a comprehensive programme of oceanographic measurements from BAS ships in the Weddell and Bellingshausen Seas, and will use BAS's instrument-carrying Twin Otter aircraft to help us study cloud microphysics and air-sea-ice interaction. We will obtain an ice core from the southwestern Antarctic Peninsula to give us a 150-year record of the strength of the circumpolar westerly winds. We will use these observations to test and improve global climate models and a new regional atmosphere-ice-ocean model for the Antarctic. ACES will link with CACHE, GRADES, GEACEP, BIOFLAME, DISCOVERY 2010, and SEC. proprietary
GB-NERC-BAS-PDC-00500 ACES-ACCENT: Antarctic Climate Change and Nonlinear Teleconnections ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600003-SCIOPS.umm_json ACES will investigate the atmospheric and oceanic links that connect the climate of the Antarctic to that of lower latitudes, and their controlling mechanisms. Specific research topics will include the formation and properties of Antarctic clouds, the complexities of the atmospheric boundary layer, and the importance to the global ocean circulation of cold, dense water masses generated in the Antarctic. By quantifying the role of southern polar processes in the global climate system, ACES will help improve predictions of climate change. Our knowledge of the workings of the climate system is far from complete. We know that atmospheric and oceanic processes in the Antarctic and Southern Ocean influence and are influenced by global climate, but we are unsure of important details. Describing and quantifying the role of the southern polar regions in the global climate system is both important and timely. Delivering the Results ACES will carry out a comprehensive programme of oceanographic measurements from BAS ships in the Weddell and Bellingshausen Seas, and will use BAS's instrument-carrying Twin Otter aircraft to help us study cloud microphysics and air-sea-ice interaction. We will obtain an ice core from the southwestern Antarctic Peninsula to give us a 150-year record of the strength of the circumpolar westerly winds. We will use these observations to test and improve global climate models and a new regional atmosphere-ice-ocean model for the Antarctic. ACES will link with CACHE, GRADES, GEACEP, BIOFLAME, DISCOVERY 2010, and SEC. proprietary
+GB-NERC-BAS-PDC-00500 ACES-ACCENT: Antarctic Climate Change and Nonlinear Teleconnections SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600003-SCIOPS.umm_json ACES will investigate the atmospheric and oceanic links that connect the climate of the Antarctic to that of lower latitudes, and their controlling mechanisms. Specific research topics will include the formation and properties of Antarctic clouds, the complexities of the atmospheric boundary layer, and the importance to the global ocean circulation of cold, dense water masses generated in the Antarctic. By quantifying the role of southern polar processes in the global climate system, ACES will help improve predictions of climate change. Our knowledge of the workings of the climate system is far from complete. We know that atmospheric and oceanic processes in the Antarctic and Southern Ocean influence and are influenced by global climate, but we are unsure of important details. Describing and quantifying the role of the southern polar regions in the global climate system is both important and timely. Delivering the Results ACES will carry out a comprehensive programme of oceanographic measurements from BAS ships in the Weddell and Bellingshausen Seas, and will use BAS's instrument-carrying Twin Otter aircraft to help us study cloud microphysics and air-sea-ice interaction. We will obtain an ice core from the southwestern Antarctic Peninsula to give us a 150-year record of the strength of the circumpolar westerly winds. We will use these observations to test and improve global climate models and a new regional atmosphere-ice-ocean model for the Antarctic. ACES will link with CACHE, GRADES, GEACEP, BIOFLAME, DISCOVERY 2010, and SEC. proprietary
GCAM_Land_Cover_2005-2095_1216_1 CMS: Land Cover Projections (5.6-km) from GCAM v3.1 for Conterminous USA, 2005-2095 ORNL_CLOUD STAC Catalog 2005-01-01 2095-12-31 -124.69, 25.25, -67.09, 49.35 https://cmr.earthdata.nasa.gov/search/concepts/C2395504063-ORNL_CLOUD.umm_json The data provided are annual land cover projections for years 2005 through 2095 generated by the Global Change Assessment Model (GCAM) Version 3.1. For the conterminous USA, the GCAM global gridded results were downscaled to ~5.6 km (0.05 degree) resolution. For each 5.6 x 5.6 km area, the annual land cover percentage comprised by each of the nineteen different land cover classes/plant functional types (PFTs) of the Community Land Model (CLM) (Table 1) are provided.Results are reported for GCAM runs of three scenarios of future human efforts towards climate mitigation as related to global carbon emissions, radiative forcing, and land cover change. Specific scenario conditions were 1) a reference scenario with no explicit climate mitigation efforts that reaches a radiative forcing level of over 7 W/m2 in 2100, 2) the 2.6 mitigation pathway (MP) scenario which is a very low emission scenario with a mid-century peak in radiative forcing at ~3 W/m2, declining to 2.6 W/m2 in 2100, and 3) the 4.5 MP scenario which stabilizes radiative forcing at 4.5 W/m2 (~ 650 ppm CO2-equivalent) before 2100.These downscaled land cover projections can be used to derive spatially explicit estimates of potential shifts in croplands, grasslands, shrub lands, and forest lands in each future climate scenario.Data are presented as three NetCDF v4 files (.nc4), one for each future climate scenario -- 2.6 MP, 4.5 MP, and GCAM reference). proprietary
GCIP-GREDS GCIP Reference Data Set (GREDS), U.S. Geological Survey Open-File Report 94-388 CEOS_EXTRA STAC Catalog 1951-01-01 1995-04-01 -125, 24, -66, 52 https://cmr.earthdata.nasa.gov/search/concepts/C2231554529-CEOS_EXTRA.umm_json "The data sets on this compact disc are a compilation of several geographic reference data sets of interest to the global-change research community. The data sets were chosen with input from the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP) Data Committee and the GCIP Hydrometeorology and Atmospheric Subpanels. The data sets include: locations and periods of record for stream gages, reservoir gages, and meteorological stations; a 500-meter-resolution digital elevation model; grid-node locations for the Eta numerical weather-prediction model; and digital map data sets of geology, land use, streams, large reservoirs, average annual runoff, average annual precipitation, average annual temperature, average annual heating and cooling degree days, hydrologic units, and state and county boundaries. Also included are digital index maps for LANDSAT scenes, and for the U.S. Geological Survey 1:250,000, 1:100,000, and 1:24,000-scale map series. Most of the data sets cover the conterminous United States; the digital elevation model also includes part of southern Canada. The stream and reservoir gage and meteorological station files cover all states having area within the Mississippi River Basin plus that part of the Mississippi River Basin lying within Canada. Several data-base retrievals were processed by state, therefore many sites outside the Mississippi River Basin are included. See: ""http://nsdi.usgs.gov"" for a complete desciption of metadata and browse images." proprietary
GCOM-C_SGLI_L1A_SWI_and_TIR_1km_NA GCOM-C/SGLI L1A Shortwave Infrared Thermal Infrared (1km) JAXA STAC Catalog 2018-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698128829-JAXA.umm_json GCOM-C/SGLI L1A Shortwave Infrared Thermal Infrared (1km) dataset is obtained from the SGLI sensor onboard GCOM-C and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-C is Sun-synchronous sub-recurrent Orbit satellite launched on December 23, 2017, which mounts SGLI and conducts long-term global observations of geophysical variables related to the global climate system across 28 items including aerosol and vegetation over 4 areas of atmosphere, land, ocean, and cryosphere. The data will be used to contribute to higher accuracy of global warming prediction. The SGLI has swath of 1150 km in the visible band and 1400 km in the infrared band. GCOM-C/SGLI Level 1A products are using the data from the satellite as inputs and the following processes are applied on the input data: determination scene range, deletion of duplicated packets and filling of missing data with dummy data, calculation of radiometric correction information, calculation of geometric information and creation of missing packet information and quality information. This product is Observation DN value observed by SGLI-IRS Radiometer (Short Wavelength Infrared (SWI: 1.05 micrometer to 2.21 micrometer, 4 channels) and Thermal Infrared (TIR: 10.8 micrometer, 12.0 micrometer, 2 channels)) are stored for each band as image data. The provided format is HDF5. The spatial resolution is 1 km also 250 m are available. The geometry is not corrected, and the observation position of ground of pixel is varied each band. Therefore, the latitude/longitude information of 10 pixels interval in each band is appended. However, there is no interval in AT direction of IRS. The stored geometric information is the center position of the pixel. The current version of the product is Version 2. proprietary
@@ -7117,12 +7118,12 @@ GGD200_1 Borehole temperatures in deep wells of Western Siberia, Russia, 1960-19
GGD222_1 Active layer and permafrost properties, including snow depth, soil temperature, and soil moisture, Barrow, Alaska, Version 1 NSIDCV0 STAC Catalog 1962-01-01 1993-12-31 -156.78872, 71.29058, -156.78872, 71.29058 https://cmr.earthdata.nasa.gov/search/concepts/C1386206550-NSIDCV0.umm_json This data set contains soil temperature, soil moisture, thaw depth, and snow depth data collected at test sites near Barrow, Alaska, during the following years. Soil temperature data - 1963-1966, 1993 Soil moisture data - 1963 Thaw depth - 1962-1968, 1991-1993 Snow depth - 1963-1964 This study focused on characterizing the active soil layer at Barrow, and determining the relationships between and among these physical properties at permafrost sites in the Arctic. This site is U1 of the IPA's Circumpolar Active Layer Monitoring (CALM) Program and later measurements are available at the CALM Web site. proprietary
GGD222_1 Active layer and permafrost properties, including snow depth, soil temperature, and soil moisture, Barrow, Alaska, Version 1 ALL STAC Catalog 1962-01-01 1993-12-31 -156.78872, 71.29058, -156.78872, 71.29058 https://cmr.earthdata.nasa.gov/search/concepts/C1386206550-NSIDCV0.umm_json This data set contains soil temperature, soil moisture, thaw depth, and snow depth data collected at test sites near Barrow, Alaska, during the following years. Soil temperature data - 1963-1966, 1993 Soil moisture data - 1963 Thaw depth - 1962-1968, 1991-1993 Snow depth - 1963-1964 This study focused on characterizing the active soil layer at Barrow, and determining the relationships between and among these physical properties at permafrost sites in the Arctic. This site is U1 of the IPA's Circumpolar Active Layer Monitoring (CALM) Program and later measurements are available at the CALM Web site. proprietary
GGD223_1 Borehole locations and permafrost depths, Alaska, USA, Version 1 NSIDCV0 STAC Catalog 1950-01-01 1989-12-31 -165.767, 62.19, -141.15, 71.189 https://cmr.earthdata.nasa.gov/search/concepts/C1386206551-NSIDCV0.umm_json The methods utilized by the U.S. Geological Survey to measure subsurface temperatures have evolved considerably over the years. Although some of the early measurements were obtained using thermistor strings frozen into permafrost, the vast majority of the measurements were made in fluid-filled holes using a custom temperature sensor. A typical sensor used in Alaska prior to 1989 consisted of a series-parallel network of 20 thermistors; see Sass et al. [1971] for a more detailed description. During a logging experiment, the resistance of the thermistor network was determined using a Wheatstone bridge prior to 1967. After that time, a 4-wire resistance measurement was made using a commercial 5.5-digit multimeter (DMM). Before 1984, boreholes were logged in the 'incremental' or 'stop-and-go' modes; the vertical spacing of the measurements was typically 3-15 m. Beginning in 1984, the depth/resistance measurements were automatically stored on magnetic tape, allowing boreholes to be logged in the 'continuous' mode; the typical data spacing for the continuous temperature logs was 0.3 m (1 ft). Many of the Alaskan boreholes were re-logged several times to quantify the thermal disturbance caused by drilling the holes (see Lachenbruch and Brewer [1959]). A review of current temperature measuring techniques used by the USGS in the polar regions is given by Clow et al. [1996]. Data from 1950-1989 are included on the CAPS CD-ROM Version 1.0, June 1998. proprietary
-GGD239_1 Active layer physical processes at Broeggerhalvoya, western Spitsbergen, Version 1 NSIDCV0 STAC Catalog 1985-07-01 1986-06-30 12.462, 78.958, 12.462, 78.958 https://cmr.earthdata.nasa.gov/search/concepts/C1386206556-NSIDCV0.umm_json These data have been collected from an Arctic desert site (latitude 78o57'29N, longitude 12o27'42E), Broeggerhalvoya in western Spitsbergen, 10 km NW from Ny Alesund, 45 m above sea level, 2 km from the shore. This is a low relief tip of a bedrock peninsula covered with several meters of glacial drift and reworked raised beach ridges. The measurements are obtained in the site of well developed patterned ground, sorted polygons, where the influence of plants, including thermal insulation and transpiration, is negligible. The 1985-1986 period was average. Mean annual air temperature was -6.6 C, 0.4 C colder than the long-term (1975-1990) mean, but well within the mean variability. Mean winter air temperature is relatively warm (mean of coldest month, February, is -14.6 C). Annual precipitation was 17 % greater than the ong-term mean (372 mm); however, the number of rain-on-snow events was less (3) than average (5.5). Overall, the reference period is close to long-term averages. A program of automated soil temperature recordings was initiated in the summer of 1984, at a patterned ground field site Thermistors were placed approximately 0.1 m apart in an epoxy-filled PVC rod (18 mm outside diameter), buried in the center of a fine-grained domain of a sorted circle, down to 1.14 m below the ground surface. The data presented here covers 7/1/85-7/1/86, once a day (6 am), at two levels (0.0 m, 1.145 m below surface). The resolution of the thermistors is 0.004 C, and the accuracy is estimated to be 0.02 C near 0 C. Missing data accounts for less than 7 %. The gaps are filled with simple average of the beginning and end of the gap values. For a detailed description of the field site and data analysis see Putkonen (1997) and Hallet and Prestrud (1986). These data are presented on the CAPS Version 1.0 CD-ROM, June 1998. proprietary
GGD239_1 Active layer physical processes at Broeggerhalvoya, western Spitsbergen, Version 1 ALL STAC Catalog 1985-07-01 1986-06-30 12.462, 78.958, 12.462, 78.958 https://cmr.earthdata.nasa.gov/search/concepts/C1386206556-NSIDCV0.umm_json These data have been collected from an Arctic desert site (latitude 78o57'29N, longitude 12o27'42E), Broeggerhalvoya in western Spitsbergen, 10 km NW from Ny Alesund, 45 m above sea level, 2 km from the shore. This is a low relief tip of a bedrock peninsula covered with several meters of glacial drift and reworked raised beach ridges. The measurements are obtained in the site of well developed patterned ground, sorted polygons, where the influence of plants, including thermal insulation and transpiration, is negligible. The 1985-1986 period was average. Mean annual air temperature was -6.6 C, 0.4 C colder than the long-term (1975-1990) mean, but well within the mean variability. Mean winter air temperature is relatively warm (mean of coldest month, February, is -14.6 C). Annual precipitation was 17 % greater than the ong-term mean (372 mm); however, the number of rain-on-snow events was less (3) than average (5.5). Overall, the reference period is close to long-term averages. A program of automated soil temperature recordings was initiated in the summer of 1984, at a patterned ground field site Thermistors were placed approximately 0.1 m apart in an epoxy-filled PVC rod (18 mm outside diameter), buried in the center of a fine-grained domain of a sorted circle, down to 1.14 m below the ground surface. The data presented here covers 7/1/85-7/1/86, once a day (6 am), at two levels (0.0 m, 1.145 m below surface). The resolution of the thermistors is 0.004 C, and the accuracy is estimated to be 0.02 C near 0 C. Missing data accounts for less than 7 %. The gaps are filled with simple average of the beginning and end of the gap values. For a detailed description of the field site and data analysis see Putkonen (1997) and Hallet and Prestrud (1986). These data are presented on the CAPS Version 1.0 CD-ROM, June 1998. proprietary
+GGD239_1 Active layer physical processes at Broeggerhalvoya, western Spitsbergen, Version 1 NSIDCV0 STAC Catalog 1985-07-01 1986-06-30 12.462, 78.958, 12.462, 78.958 https://cmr.earthdata.nasa.gov/search/concepts/C1386206556-NSIDCV0.umm_json These data have been collected from an Arctic desert site (latitude 78o57'29N, longitude 12o27'42E), Broeggerhalvoya in western Spitsbergen, 10 km NW from Ny Alesund, 45 m above sea level, 2 km from the shore. This is a low relief tip of a bedrock peninsula covered with several meters of glacial drift and reworked raised beach ridges. The measurements are obtained in the site of well developed patterned ground, sorted polygons, where the influence of plants, including thermal insulation and transpiration, is negligible. The 1985-1986 period was average. Mean annual air temperature was -6.6 C, 0.4 C colder than the long-term (1975-1990) mean, but well within the mean variability. Mean winter air temperature is relatively warm (mean of coldest month, February, is -14.6 C). Annual precipitation was 17 % greater than the ong-term mean (372 mm); however, the number of rain-on-snow events was less (3) than average (5.5). Overall, the reference period is close to long-term averages. A program of automated soil temperature recordings was initiated in the summer of 1984, at a patterned ground field site Thermistors were placed approximately 0.1 m apart in an epoxy-filled PVC rod (18 mm outside diameter), buried in the center of a fine-grained domain of a sorted circle, down to 1.14 m below the ground surface. The data presented here covers 7/1/85-7/1/86, once a day (6 am), at two levels (0.0 m, 1.145 m below surface). The resolution of the thermistors is 0.004 C, and the accuracy is estimated to be 0.02 C near 0 C. Missing data accounts for less than 7 %. The gaps are filled with simple average of the beginning and end of the gap values. For a detailed description of the field site and data analysis see Putkonen (1997) and Hallet and Prestrud (1986). These data are presented on the CAPS Version 1.0 CD-ROM, June 1998. proprietary
GGD23_1 Active-Layer and Permafrost Temperatures, Sisimiut (Holsteinsborg), Greenland, Version 1 NSIDCV0 STAC Catalog 1967-09-01 1982-08-31 -53.64, 66.94, -53.64, 66.94 https://cmr.earthdata.nasa.gov/search/concepts/C1386206552-NSIDCV0.umm_json This data set contains active-layer and permafrost temperatures from Sisimiut, west Greenland, recorded from 18 sensors at depths of 0.25 m, 0.5 m, 0.75 m, 1 m, 1.25 m, 1.5 m, 1.75 m, 2 m, 2.5 m, 3 m, 3.5 m, 4 m, 4.5 m, 5 m, 6 m, 7 m, 8 m, and 9 m below the surface. Snow depth, snow extent, and surface air temperature were also recorded. Thermometers recorded temperatures once a day from September 1967 to August 1982; however, this data set only contains bi-weekly averages. Data are in tab-delimited ASCII text format and are available via FTP. proprietary
GGD23_1 Active-Layer and Permafrost Temperatures, Sisimiut (Holsteinsborg), Greenland, Version 1 ALL STAC Catalog 1967-09-01 1982-08-31 -53.64, 66.94, -53.64, 66.94 https://cmr.earthdata.nasa.gov/search/concepts/C1386206552-NSIDCV0.umm_json This data set contains active-layer and permafrost temperatures from Sisimiut, west Greenland, recorded from 18 sensors at depths of 0.25 m, 0.5 m, 0.75 m, 1 m, 1.25 m, 1.5 m, 1.75 m, 2 m, 2.5 m, 3 m, 3.5 m, 4 m, 4.5 m, 5 m, 6 m, 7 m, 8 m, and 9 m below the surface. Snow depth, snow extent, and surface air temperature were also recorded. Thermometers recorded temperatures once a day from September 1967 to August 1982; however, this data set only contains bi-weekly averages. Data are in tab-delimited ASCII text format and are available via FTP. proprietary
-GGD249_1 Active layer thickness and ground temperatures, Svea, Svalbard, Version 1 ALL STAC Catalog 1987-07-01 1996-05-31 16.683, 77.9, 16.683, 77.9 https://cmr.earthdata.nasa.gov/search/concepts/C1386206575-NSIDCV0.umm_json Snow and soil temperature records for January 1988 - May 1996 are presented. Included are snow depth and weight measurements, snow density (calculated), active layer depth in the frost tubes, weight of wet and dried soil samples from unknown depth within the active layer (water content calculated), and soil temperature at the surface (0.05 cm) and to the depths of 3 to 4 meters at 3 sites. The sites are 1) on a road covered by 1 m of gravel underlain by clay; 2) outside a building on piles, (sensors are placed 1 to 2 m from the building wall); and 3) under the building between piles. In addition, air temperature was measured inside the building or between the piles (documentation is not clear on this point.) There are several gaps in temperature measurements (January 1991 to May 1992). These data are presented on the CAPS CD-ROM version 1.0, June 1998. Air temperature, wind direction, and temperature were measured at 5, 20, 50, 100, 150, and 200 cm below the tundra surface at an undisturbed site; and at 5, 20, 50, 100, 150, 200 cm, and 3 m and 8 m below the concrete surface of a building. Incoming radiation, outgoing radiation, temperature of the heat flux instrument, global radiation, heat flux, wind speed, wind speed maximum, average wind speed, and temperature inside the building were measured since 1993 with data loggers. All data are recorded for July 1987 - February 1996. proprietary
GGD249_1 Active layer thickness and ground temperatures, Svea, Svalbard, Version 1 NSIDCV0 STAC Catalog 1987-07-01 1996-05-31 16.683, 77.9, 16.683, 77.9 https://cmr.earthdata.nasa.gov/search/concepts/C1386206575-NSIDCV0.umm_json Snow and soil temperature records for January 1988 - May 1996 are presented. Included are snow depth and weight measurements, snow density (calculated), active layer depth in the frost tubes, weight of wet and dried soil samples from unknown depth within the active layer (water content calculated), and soil temperature at the surface (0.05 cm) and to the depths of 3 to 4 meters at 3 sites. The sites are 1) on a road covered by 1 m of gravel underlain by clay; 2) outside a building on piles, (sensors are placed 1 to 2 m from the building wall); and 3) under the building between piles. In addition, air temperature was measured inside the building or between the piles (documentation is not clear on this point.) There are several gaps in temperature measurements (January 1991 to May 1992). These data are presented on the CAPS CD-ROM version 1.0, June 1998. Air temperature, wind direction, and temperature were measured at 5, 20, 50, 100, 150, and 200 cm below the tundra surface at an undisturbed site; and at 5, 20, 50, 100, 150, 200 cm, and 3 m and 8 m below the concrete surface of a building. Incoming radiation, outgoing radiation, temperature of the heat flux instrument, global radiation, heat flux, wind speed, wind speed maximum, average wind speed, and temperature inside the building were measured since 1993 with data loggers. All data are recorded for July 1987 - February 1996. proprietary
+GGD249_1 Active layer thickness and ground temperatures, Svea, Svalbard, Version 1 ALL STAC Catalog 1987-07-01 1996-05-31 16.683, 77.9, 16.683, 77.9 https://cmr.earthdata.nasa.gov/search/concepts/C1386206575-NSIDCV0.umm_json Snow and soil temperature records for January 1988 - May 1996 are presented. Included are snow depth and weight measurements, snow density (calculated), active layer depth in the frost tubes, weight of wet and dried soil samples from unknown depth within the active layer (water content calculated), and soil temperature at the surface (0.05 cm) and to the depths of 3 to 4 meters at 3 sites. The sites are 1) on a road covered by 1 m of gravel underlain by clay; 2) outside a building on piles, (sensors are placed 1 to 2 m from the building wall); and 3) under the building between piles. In addition, air temperature was measured inside the building or between the piles (documentation is not clear on this point.) There are several gaps in temperature measurements (January 1991 to May 1992). These data are presented on the CAPS CD-ROM version 1.0, June 1998. Air temperature, wind direction, and temperature were measured at 5, 20, 50, 100, 150, and 200 cm below the tundra surface at an undisturbed site; and at 5, 20, 50, 100, 150, 200 cm, and 3 m and 8 m below the concrete surface of a building. Incoming radiation, outgoing radiation, temperature of the heat flux instrument, global radiation, heat flux, wind speed, wind speed maximum, average wind speed, and temperature inside the building were measured since 1993 with data loggers. All data are recorded for July 1987 - February 1996. proprietary
GGD272_1 Cryosolic pedons from Russia and Alaska, Version 1 NSIDCV0 STAC Catalog 1970-01-01 -149, 62, 161, 69 https://cmr.earthdata.nasa.gov/search/concepts/C1386206581-NSIDCV0.umm_json U.S. pedon data on the CAPS Version 1.0 CD-ROM, June 1998, are a sample of the pedon data contained on a CD-ROM produced by the National Soil Survey Center - Soil Survey Laboratory(NSSC-SSL). The data include recent pedons from analyses for soil characterization and research within the National Cooperative Soil Survey. Less-than-complete characterization data are available for many pedons because only selected measurements were planned or because the planned measurements are not yet complete. This database is dynamic-- data for additional pedons are added as they are sampled and analyzed, other information is updated as pedons are classified, suspect measurements are rerun and replaced, and errors are found and corrected. The data on the NSSC-SSL CD-ROM represent a 'snapshot' of the database. The database includes pedons that represent the central concept of a soil series, pedons that represent the central concept of a map unit but not of a series, and pedons sampled to bracket a range of properties within a series or landscape. Thus, attribute data for some data elements may be incomplete or missing for certain portions of the United States. In instances where data are unavailable, a mask should be used to exclude the area from the analysis. For research purposes, all data are retained in the database. Users unfamiliar with a given soil or set of data may want to consult with a research soil scientist at the National Soil Survey Center. A research soil scientist can be reached by telephone at (402) 437-5006, or by writing the Soil Survey Laboratory Head, National Soil Survey Center, Natural Resources Conservation Service, Federal Building, Room 152, 100 Centennial Mall North, Lincoln, NE 68508-3866 USA. Pedons on the CAPS Version 1.0 CD-ROM cover areas in Russia (60 deg 37 min N to 69 deg 27 min N; 159 deg 07 min E to 161 deg 33 min E) and in Alaska (62 deg to 68 deg N; 135 deg to 149 deg W). proprietary
GGD311_1 Cryosolic pedons from Northern Canada, Version 1 NSIDCV0 STAC Catalog 1975-01-01 1996-12-31 -138.997, 63.942, -64.681, 81.832 https://cmr.earthdata.nasa.gov/search/concepts/C1386206775-NSIDCV0.umm_json Pedons included here represent Cryosolic (permafrost-affected) soils from across the Canadian North from Baffin Island in the east, to the lower Mackenzie Valley and northern Yukon in the west, and to Ellesmere Island in the High Arctic. Pedon locations are Pangnirtung Pass, Baffin Island, N.W.T. (8 pedons); Inuvik area, N.W.T. (2 pedons); Mackenzie Delta, N.W.T. (2 pedons); Tanquary Fiord, Ellesmere Island, N.W.T. (4 pedons); Lake Hazen, Ellesmere Island, N.W.T. (4 pedons); Eagle Plains, northern Yukon (3 pedons); Dawson City area, central Yukon (2 pedons). Cryosolic soils, according to the Canadian soil classification, are either mineral or organic materials that have permafrost either within 1 m of the surface (Static and Organic Cryosols) or within 2 m (Turbic Cryosols) if more than one-third of the pedon has been strongly cryoturbated, as indicated by disrupted, mixed, or broken horizons. They have a mean annual temperature below 0 degree C. In the soil profile descriptions, the perennially frozen (permafrost) soil horizons are identified by the letter 'z'. The descriptions and nomenclature used to describe these pedons are according to - Expert Committee on Soil Survey. 1983. The Canada Soil Information System, Manual for describing soil in the field. Agriculture Canada, Ottawa, Canada. Agriculture Canada Expert Committee on Soil Survey. 1987. The Canadian System of Soil Classification. (2nd ed.) Research Branch, Agriculture Canada, Ottawa, Canada. The methods for laboratory analysis are according to - Sheldrick, B.H. (editor). 1984. Analytical Methods Manual. 1984. Land Resource Research Institute, Agriculture Canada, Ottawa, Canada. Additional information relating to these pedons can be obtain by contacting Charles Tarnocai, Agriculture and Agri-Food Canada, Research Branch (ECORC), K.W. Neatby Building, Rm. 1135, 960 Carling Avenue, OTTAWA, Canada, K1A 0C6; Tel.- (613) 759-1857; Fax- (613) 759-1937; E-mail- tarnocaict@em.agr.ca. The data file on the CAPS Version 1.0 CD-ROM contains laboratory analyses of the soil samples, including chemical, physical, mineralogical (clay mineralogy when applicable), and particle size distribution analyses. proprietary
GGD316_1 Catalog of boreholes from Russia and Mongolia, Version 1 NSIDCV0 STAC Catalog 1980-01-01 1993-12-31 53.383, 46.8, 178.683, 75.583 https://cmr.earthdata.nasa.gov/search/concepts/C1386206796-NSIDCV0.umm_json This catalog of boreholes from across Russia and Mongolia includes those published in papers and monographs as well as other literature of limited circulation. The 122 boreholes were used to derive a characterization of the Russian territory according to eight geocryological regions. Five boreholes are included for Mongolia. Data from these boreholes were used in the generation of the Circum-arctic Map of Permafrost and Ground-Ice Conditions (Brown et al., 1997). Data obtained from various sources as noted within each borehole entry. The time period varies for each borehole, but is primarily from the late 1980s to early 1990s. Observation methods include 'Standard logging', a combined natural gamma logging, electric logging and well caliper logging; 'Geothermal observations' which demonstrate the thickness of layer with the temperature below zero (data of Yakutsk Permafrost Institute, Siberian Branch, Academy of Sciences of the USSR); visual observations on ice-content in the core, and depth of appearance of fresh water table; thermologging of the boreholes (studies of 'PGO Yakutskgeologia'); and electric, well caliper and thermal logging in pioneer and exploratory oil and gas wells ('PGO Lenaneftegasgeologia' studies). The permafrost base is exposed by a number of adjacent boreholes; interval of fluctuations of permafrost depth is shown. The data are presented on the CAPS Version 1.0 CD-ROM, June 1998. proprietary
@@ -7135,8 +7136,8 @@ GGD499_1 Borehole permafrost data, Kumtor and Taragai Valleys, Tienshan, Kazakhs
GGD503_1 Canadian Geothermal Data Collection: Deep permafrost temperatures and thickness of permafrost, Version 1 NSIDCV0 STAC Catalog 1965-01-01 1997-12-31 -151, 60, -60, 85 https://cmr.earthdata.nasa.gov/search/concepts/C1386206872-NSIDCV0.umm_json Precision temperature measurements have been made in some 150 deep wells and holes drilled in the course of natural resource exploration in the permafrost regions of Northern Canada. In most cases, holes were logged by lowering a probe containing a regions of Northern Canada. In most cases, holes were logged by lowering a probe containing a thermistor incrementally down the well, in other cases multi-thermistor cables were left in the holes and periodic measurements taken. In the 1990's, a few holes were logged by a automatic quasi- continuous logging system. Most holes were logged annually for 5-10 years after drilling completion, and measured temperatures show the disturbance due to drilling and the gradual recovery to near-undisturbed conditions. Some holes in the collection are of depth less than 125 m. Permafrost thicknesses are estimated at each well or hole from the depth of the 0 degree Celsius isotherm. This data collection provides the highest quality of permafrost temperature and permafrost thickness information available for Northern Canada. Other data are the large number of downhole temperature and permafrost thickness estimates taken during commercial well logging of petroleum exploration wells, and are by nature of lesser quality. These data are not included in this data set, but references to compilations of this data are provided. A short text (2000 words), tables of site locations and permafrost thicknesses with small-scale maps, and an extensive bibliography accompany the data collection. The file structure and contents of each file are well described. The text is sufficient to locate the data of interest, and the file description is adequate for a user to recover the parameters of interest. The data are presented on the CAPS Version 1.0 CD-ROM, June 1998. proprietary
GGD611_1 Air Temperatures at High Altitude, Kanchanjunga Himal, Eastern Nepal, Version 1 NSIDCV0 STAC Catalog 1998-11-04 1999-11-17 87.933, 27.65, 88.067, 27.8 https://cmr.earthdata.nasa.gov/search/concepts/C1386206883-NSIDCV0.umm_json This data set provides air temperature (1.5 m above ground surface) data from the Kanchanjunga Himal, eastern Nepal. Air temperature was monitored from November 1998 to November 1999 at three locations (Tengkoma, Lhonak, and Ghunsa) at altitudes of 3410, 4750 and 6012 m ASL. Although temperature was measured at one-hour intervals, only daily mean values are provided. proprietary
GGD611_1 Air Temperatures at High Altitude, Kanchanjunga Himal, Eastern Nepal, Version 1 ALL STAC Catalog 1998-11-04 1999-11-17 87.933, 27.65, 88.067, 27.8 https://cmr.earthdata.nasa.gov/search/concepts/C1386206883-NSIDCV0.umm_json This data set provides air temperature (1.5 m above ground surface) data from the Kanchanjunga Himal, eastern Nepal. Air temperature was monitored from November 1998 to November 1999 at three locations (Tengkoma, Lhonak, and Ghunsa) at altitudes of 3410, 4750 and 6012 m ASL. Although temperature was measured at one-hour intervals, only daily mean values are provided. proprietary
-GGD622_1 Active-Layer Depth of a Finnish Palsa Bog, Version 1 NSIDCV0 STAC Catalog 1993-09-08 2002-10-14 27.17, 69.82, 27.17, 69.82 https://cmr.earthdata.nasa.gov/search/concepts/C1386206889-NSIDCV0.umm_json This data set contains 76 active-layer depth measurements (cm) of the Vaisjeäggi palsa bog, Finland, from 08 September 1993 to 14 October 2002. Data were collected from a single location at 69 deg 49'16.6' N, 27 deg 10'17.1' E. Data also contain snow depth (cm) when snow cover was present. Data are in tab-delimited ASCII text format, and are available via ftp. proprietary
GGD622_1 Active-Layer Depth of a Finnish Palsa Bog, Version 1 ALL STAC Catalog 1993-09-08 2002-10-14 27.17, 69.82, 27.17, 69.82 https://cmr.earthdata.nasa.gov/search/concepts/C1386206889-NSIDCV0.umm_json This data set contains 76 active-layer depth measurements (cm) of the Vaisjeäggi palsa bog, Finland, from 08 September 1993 to 14 October 2002. Data were collected from a single location at 69 deg 49'16.6' N, 27 deg 10'17.1' E. Data also contain snow depth (cm) when snow cover was present. Data are in tab-delimited ASCII text format, and are available via ftp. proprietary
+GGD622_1 Active-Layer Depth of a Finnish Palsa Bog, Version 1 NSIDCV0 STAC Catalog 1993-09-08 2002-10-14 27.17, 69.82, 27.17, 69.82 https://cmr.earthdata.nasa.gov/search/concepts/C1386206889-NSIDCV0.umm_json This data set contains 76 active-layer depth measurements (cm) of the Vaisjeäggi palsa bog, Finland, from 08 September 1993 to 14 October 2002. Data were collected from a single location at 69 deg 49'16.6' N, 27 deg 10'17.1' E. Data also contain snow depth (cm) when snow cover was present. Data are in tab-delimited ASCII text format, and are available via ftp. proprietary
GGD623_1 Annual Thaw Depths and Water Depths in Tanana Flats, Alaska, Version 1 NSIDCV0 STAC Catalog 1995-08-01 2002-08-01 -147.9, 64.7, -147.9, 64.7 https://cmr.earthdata.nasa.gov/search/concepts/C1386206891-NSIDCV0.umm_json Thaw depths and water depths were monitored at 1 m to 2 m intervals along a 255-m transect across an area of discontinuous and degrading permafrost on the Tanana Flats south of Fairbanks, Alaska. Measurements were taken once a year in late August from 1995 to 2002 to show effects of winter snow depths, climate warming, and vegetation and wetland creation-surface subsidence. Data are in a single tab-delimited ASCII text file, available via FTP. proprietary
GGD632_1 Active-Layer and Permafrost Temperatures, Soendre Stroemfjord, Greenland, Version 1 NSIDCV0 STAC Catalog 1967-09-06 1976-02-15 50.8, 67, 50.8, 67 https://cmr.earthdata.nasa.gov/search/concepts/C1386206903-NSIDCV0.umm_json This data set contains active-layer and permafrost temperatures from two stations in Soendre Stroemfjord, Greenland. Snow depth and snow extent were also recorded. Thermometers at Station A (67 deg N, 50.8 deg W, 50 m asl) recorded temperatures once a day from September 1967 to February 1976. Thermometers at Station B (67 deg N, 50.8 deg W, 38 m asl) recorded temperatures once a day from September 1967 to August 1970; however, only bi-weekly averages are given for Station B. Data are in tab-delimited ASCII text format and are available via FTP. proprietary
GGD632_1 Active-Layer and Permafrost Temperatures, Soendre Stroemfjord, Greenland, Version 1 ALL STAC Catalog 1967-09-06 1976-02-15 50.8, 67, 50.8, 67 https://cmr.earthdata.nasa.gov/search/concepts/C1386206903-NSIDCV0.umm_json This data set contains active-layer and permafrost temperatures from two stations in Soendre Stroemfjord, Greenland. Snow depth and snow extent were also recorded. Thermometers at Station A (67 deg N, 50.8 deg W, 50 m asl) recorded temperatures once a day from September 1967 to February 1976. Thermometers at Station B (67 deg N, 50.8 deg W, 38 m asl) recorded temperatures once a day from September 1967 to August 1970; however, only bi-weekly averages are given for Station B. Data are in tab-delimited ASCII text format and are available via FTP. proprietary
@@ -7158,28 +7159,28 @@ GLAH02_033 GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033 NSIDC_CPRD STAC Ca
GLAH02_033 GLAS/ICESat L1A Global Atmosphere Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991862-NSIDC_ECS.umm_json GLAH02 Level-1A atmospheric data include the normalized relative backscatter for the 532 nm and 1064 nm channels, and low-level instrument corrections such as laser energy (1064 nm and 532 nm), photon coincidence (532 nm), and detector gain correction (1064 nm). Each data granule has an associated browse product. proprietary
GLAH03_033 GLAS/ICESat L1A Global Engineering Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547514-NSIDC_CPRD.umm_json Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02. proprietary
GLAH03_033 GLAS/ICESat L1A Global Engineering Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991863-NSIDC_ECS.umm_json Level-1A global engineering data (GLAH03) include satellite housekeeping data used to calibrate data values for GLA01 and GLA02. proprietary
-GLAH04_033 GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.umm_json Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing. proprietary
GLAH04_033 GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153547635-NSIDC_CPRD.umm_json Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing. proprietary
+GLAH04_033 GLAS/ICESat L1A Global Laser Pointing Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991864-NSIDC_ECS.umm_json Level-1A global laser pointing data (GLAH04) contain two orbits of attitude data from the spacecraft star tracker, instrument star tracker, gyro, and laser reference system, and other spacecraft attitude data required to calculate precise laser pointing. proprietary
GLAH05_034 GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549166-NSIDC_CPRD.umm_json GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product. proprietary
GLAH05_034 GLAS/ICESat L1B Global Waveform-based Range Corrections Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000460-NSIDC_ECS.umm_json GLAH05 Level-1B waveform parameterization data include output parameters from the waveform characterization procedure and other parameters required to calculate surface slope and relief characteristics. GLAH05 contains parameterizations of both the transmitted and received pulses and other characteristics from which elevation and footprint-scale roughness and slope are calculated. The received pulse characterization uses two implementations of the retracking algorithms: one tuned for ice sheets, called the standard parameterization, used to calculate surface elevation for ice sheets, oceans, and sea ice; and another for land (the alternative parameterization). Each data granule has an associated browse product. proprietary
GLAH06_034 GLAS/ICESat L1B Global Elevation Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2033638023-NSIDC_CPRD.umm_json GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product. proprietary
GLAH06_034 GLAS/ICESat L1B Global Elevation Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000445-NSIDC_ECS.umm_json GLAH06 Level-1B Global Elevation is a product that is analogous to the geodetic data records distributed for radar altimetry missions. It contains elevations previously corrected for tides, atmospheric delays, and surface characteristics within the footprint. Elevation is calculated using the ice sheet parameterization. Additional information allows the user to calculate an elevation based on land, sea ice, or ocean algorithms. Each data granule has an associated browse product. proprietary
-GLAH07_033 GLAS/ICESat L1B Global Backscatter Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.umm_json GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product. proprietary
GLAH07_033 GLAS/ICESat L1B Global Backscatter Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991867-NSIDC_ECS.umm_json GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product. proprietary
+GLAH07_033 GLAS/ICESat L1B Global Backscatter Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549420-NSIDC_CPRD.umm_json GLAH07 Level-1B global backscatter data are provided at full instrument resolution. The product includes full 532 nm (41.1 to -1.0 km) and 1064 nm (20 to -1 km) calibrated attenuated backscatter profiles at 5 times per second, and from 10 to -1 km, at 40 times per second for both channels. Also included are calibration coefficient values and molecular backscatter profiles at once per second. Data granules contain approximately 190 minutes (2 orbits) of data. Each data granule has an associated browse product. proprietary
GLAH08_033 GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549511-NSIDC_CPRD.umm_json GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product. proprietary
GLAH08_033 GLAS/ICESat L2 Global Planetary Boundary Layer and Elevated Aerosol Layer Heights (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1631093696-NSIDC_ECS.umm_json GLAH08 Level-2 planetary boundary layer (PBL) and elevated aerosol layer heights data contains PBL heights, ground detection heights, and top and bottom heights of elevated aerosols from -1.5 km to 20.5 km (4 sec sampling rate) and from 20.5 km to 41 km (20 sec sampling rate). Each data granule has an associated browse product. proprietary
-GLAH09_033 GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.umm_json GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product. proprietary
GLAH09_033 GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991869-NSIDC_ECS.umm_json GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product. proprietary
+GLAH09_033 GLAS/ICESat L2 Global Cloud Heights for Multi-layer Clouds (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549579-NSIDC_CPRD.umm_json GLAH09 Level-2 cloud heights for multi-layer clouds contain cloud layer top and bottom height data at sampling rates of 4 sec, 1 sec, 5 Hz, and 40 Hz. Each data granule has an associated browse product. proprietary
GLAH10_033 GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-09-25 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991870-NSIDC_ECS.umm_json GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product. proprietary
GLAH10_033 GLAS/ICESat L2 Global Aerosol Vertical Structure Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-09-25 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549654-NSIDC_CPRD.umm_json GLAH10 Level-2 aerosol vertical structure data contain the attenuation-corrected cloud and aerosol backscatter and extinction profiles at a 4 sec sampling rate for aerosols and a 1 sec rate for clouds. Each data granule has an associated browse product. proprietary
GLAH11_033 GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C189991871-NSIDC_ECS.umm_json GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product. proprietary
GLAH11_033 GLAS/ICESat L2 Global Thin Cloud/Aerosol Optical Depths Data (HDF5) V033 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549738-NSIDC_CPRD.umm_json GLAH11 Level-2 thin cloud/aerosol optical depths data contain thin cloud and aerosol optical depths. A thin cloud is one that does not completely attenuate the lidar signal return, which generally corresponds to clouds with optical depths less than about 2.0. Each data granule has an associated browse product. proprietary
-GLAH12_034 GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
GLAH12_034 GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549818-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
-GLAH13_034 GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000464-NSIDC_ECS.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
+GLAH12_034 GLAS/ICESat L2 Global Antarctic and Greenland Ice Sheet Altimetry Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000461-NSIDC_ECS.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
GLAH13_034 GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153549910-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
-GLAH14_034 GLAS/ICESat L2 Global Land Surface Altimetry Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000443-NSIDC_ECS.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
+GLAH13_034 GLAS/ICESat L2 Sea Ice Altimetry Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000464-NSIDC_ECS.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
GLAH14_034 GLAS/ICESat L2 Global Land Surface Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153551318-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
+GLAH14_034 GLAS/ICESat L2 Global Land Surface Altimetry Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000443-NSIDC_ECS.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
GLAH15_034 GLAS/ICESat L2 Ocean Altimetry Data (HDF5) V034 NSIDC_ECS STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C1000000420-NSIDC_ECS.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
GLAH15_034 GLAS/ICESat L2 Ocean Altimetry Data (HDF5) V034 NSIDC_CPRD STAC Catalog 2003-02-20 2009-10-11 -180, -86, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2153552369-NSIDC_CPRD.umm_json GLAH06 is used in conjunction with GLAH05 to create the Level-2 altimetry products. Level-2 altimetry data provide surface elevations for ice sheets (GLAH12), sea ice (GLAH13), land (GLAH14), and oceans (GLAH15). Data also include the laser footprint geolocation and reflectance, as well as geodetic, instrument, and atmospheric corrections for range measurements. The Level-2 elevation products, are regional products archived at 14 orbits per granule, starting and stopping at the same demarcation (± 50° latitude) as GLAH05 and GLAH06. Each regional product is processed with algorithms specific to that surface type. Surface type masks define which data are written to each of the products. If any data within a given record fall within a specific mask, the entire record is written to the product. Masks can overlap: for example, non-land data in the sea ice region may be written to the sea ice and ocean products. This means that an algorithm may write the same data to more than one Level-2 product. In this case, different algorithms calculate the elevations in their respective products. The surface type masks are versioned and archived at NSIDC, so users can tell which data to expect in each product. Each data granule has an associated browse product. proprietary
GLCHMK_001 G-LiHT Canopy Height Model KML V001 LPCLOUD STAC Catalog 2011-06-30 -170, 10, -50, 73 https://cmr.earthdata.nasa.gov/search/concepts/C2763264695-LPCLOUD.umm_json Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT(https://gliht.gsfc.nasa.gov/)) mission utilizes a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico. The purpose of G-LiHT’s Canopy Height Model Keyhole Markup Language (KML) data product (GLCHMK) is to provide LiDAR-derived maximum canopy height and canopy variability information to aid in the study and analysis of biodiversity and climate change. Scientists at NASA’s Goddard Space Flight Center began collecting data over locally-defined areas in 2011 and that the collection will continue to grow as aerial campaigns are flown and processed. GLCHMK data are processed as a Google Earth overlay KML file at a nominal 1 meter spatial resolution over locally-defined areas. A low resolution browse is also provided showing the canopy height with a color map applied in JPEG format. proprietary
@@ -7247,12 +7248,12 @@ GMAO_M2SCREAM_INST3_CHEM_1 M2-SCREAM: 3d,3-Hourly,Instantaneous,Model-Level,Assi
GMAO_M2SCREAM_MONTH_UNCERT_1 M2-SCREAM: Monthly,Model-Level,Assimilated Constituent Fields uncertainties GES_DISC STAC Catalog 2004-09-01 2024-09-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2311994359-GES_DISC.umm_json The MERRA-2 Stratospheric Composition Reanalysis of Aura MLS (M2-SCREAM) products produced at NASA’s Global Modeling and Assimilation Office (GMAO) are generated by assimilating MLS and OMI retrievals into the GEOS Constituent Data Assimilation System (CoDAS) driven by meteorological fields from MERRA-2. M2-SCREAM assimilates hydrochloric acid (HCl), nitric acid (HNO3), stratospheric water vapor (H2O), nitrous oxide (N2O) and ozone with a system equipped with a version of the GEOS general circulation model and a stratospheric chemistry model, StratChem. Assimilated fields are provided globally at 0.5° by 0.625° resolution at three-hourly frequencies from 2004/09/01 to 2024/09/30. Assimilation uncertainties for each of the assimilated constituents are calculated from the CoDAS statistical output (Wargan et al., 2022) and provided as global full-resolution three-dimensional monthly files. Data product updates in March 2024, as a result of Aura MLS “duty cycle” of 190-GHz measurements, include reduced availability of H2O, N2O and HNO3 retrievals resulting in expected M2-SCREAM data quality degradation. However, preliminary analysis shows that the GEOS CoDAS handles the reduced temporal data coverage well, indicating that the GEOS model accurately propagates information from past observations. Data product updates in June 2024 resulting from MLS version upgrade to v5.0 include discontinuities in assimilated H2O (throughout the stratosphere) and N2O (in the lower stratosphere). To note: MLS water vapor is about 0.5 ppmv lower in v5.0, and the vertical range of assimilated N2O data is 100 hPa, extended down from 68 hPa. GMAO is not aware of discontinuities in HCl, HNO3, and ozone related to the version switch. proprietary
GMI-REMSS-L3U-v8.2a_8.2a GHRSST Level 3U Global Subskin Sea Surface Temperature from GMI onboard GPM satellite POCLOUD STAC Catalog 2014-03-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2036877762-POCLOUD.umm_json The Global Precipitation Measurement (GPM) satellite was launched on February 27th, 2014 with the GPM Microwave Imager (GMI) instrument on board. The GPM mission is a joint effort between NASA, the Japan Aerospace Exploration Agency (JAXA) and other international partners. In march 2005, NASA has chosen the Ball Aerospace and Technologies Corp., Boulder, Colorado to build the GMI instrument on the continued success of the Tropical Rainfall Measuring Mission (TRMM) satellite by expanding current coverage of precipitation from the tropics to the entire world. GMI is a dual-polarization, multi-channel, conical-scanning, passive microwave radiometer with frequent revisit times. One of the primary differences between GPM and other satellites with microwave radiometers is the orbit, which is inclined 65 degrees, allowing a full sampling of all local Earth times repeated approximately every 2 weeks. The GPM platform undergoes yaw maneuvers approximately every 40 days to compensate for the sun's changing position and prevent the side of the spacecraft facing the sun from overheating. Today, the GMI instrument plays an essential role in the worldwide measurement of precipitation and environmental forecasting. Sea Surface Temperature (SST) is one of its major products. The GMI data from the Remote Sensing System (REMSS) have been produced using an updated RTM, Version-8. The V8 brightness temperatures from GMI are slightly different from the V7 brightness temperatures; The SST datasets are available in near-real time (NRT) as they arrive, with a delay of about 3 to 6 hours, including the Daily, 3-Day, Weekly, and Monthly time series products. proprietary
GNATS_0 Gulf of Maine North Atlantic Time Series (GNATS) OB_DAAC STAC Catalog 2001-06-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360260-OB_DAAC.umm_json The Gulf of Maine (GoM) is a highly productive shelf sea that constitutes a large part of the N.E. US Continental Shelf. We have run a time series across the GoM for the last 8 years known as GNATS (Gulf of Maine North Atlantic Time Series). It consists of monthly, cross-Gulf sampling on ships of opportunity, during clear-sky days, so that we are assured concurrent measurements from ship and satellite (ocean color, SST). The power of this strategy is seen in our 95% success rate for being at sea during clear, high quality overpasses (randomly, one would expect a success rate of ~10% due to the GoM cloud climatology). We then can extrapolate our large shipboard data set of carbon cycle parameters to regional scales using synoptic remote sensing. GNATS includes a suite of carbon-specific standing stocks and rate measurements (e.g. POC, PIC [calcite], DOC, primary productivity, and calcification) plus hydrographic, chemical and optical measurements. Through coordinated ship/satellite measurements, we can constrain the major carbon production terms of the Gulf, follow their monthly variation using synoptic remote sensing, and regionally tune satellite algorithms. GNATS documents not only marine carbon pools, but it includes carbon supplied from the terrestrial watershed; this is why the Gulf is optically-dominated by Case II waters. We propose to A) continue GNATS, coordinated ship and satellite measurements for another 3 years, B) provide monthly, regional estimates of the standing stock and production terms for the various particulate and dissolved carbon fractions based on satellite ocean color observations and C) perform a statistical comparison of photoadaptive parameters in the Mid-Atlantic Bight and GoM to examine how broadly we can extrapolate these results along the NE U.S. Continental Shelf. Deliverables of this work will be: ship-based quantification of the various components of the carbon cycle in the GoM (standing stocks of POC, PIC, DOC plus primary production/calcification rates), an improved DOC algorithm, tuning of satellite carbon algorithms for the NE Continental Shelf, and documentation of the long- term biogeochemical and ecological changes occurring in the GoM carbon cycle. Quantification of the variability in the composition and concentration of dissolved and particulate carbon over a wide range of temporal and spatial scales is the first step towards understanding the role of coastal ecosystems in the global carbon cycle. proprietary
-GNVd0188_104 30 arc-second DEM for Africa CEOS_EXTRA STAC Catalog 1996-07-23 1996-07-23 -20, -35, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848851-CEOS_EXTRA.umm_json PLEASE NOTE: This is an updated release of the Africa 30 arc second DEM. Comments from users of this data set are welcome. Please contact Dean Gesch (gesch@dg1.cr.usgs.gov) or Sue Jenson (jenson@dg1.cr.usgs.gov). A digital elevation model (DEM) consists of a sampled array of elevations for ground positions that are normally spaced at regular intervals. To meet the needs of the geospatial data user community for regional and continental scale elevation data, the staff at the U.S. Geological Survey's EROS Data Center (EDC) are developing DEM's at a horizontal grid spacing of 30 arc seconds (approximately 1 kilometer). These data are being made available to the public via electronic distribution and hard media. As of July, 1996 data are available for Africa, Antarctica, Asia, Europe, and North America. Data sets for South America, Australia, New Zealand, the islands of southeast Asia, and Greenland are under development and are scheduled for release before the end of 1996. proprietary
GNVd0188_104 30 arc-second DEM for Africa ALL STAC Catalog 1996-07-23 1996-07-23 -20, -35, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848851-CEOS_EXTRA.umm_json PLEASE NOTE: This is an updated release of the Africa 30 arc second DEM. Comments from users of this data set are welcome. Please contact Dean Gesch (gesch@dg1.cr.usgs.gov) or Sue Jenson (jenson@dg1.cr.usgs.gov). A digital elevation model (DEM) consists of a sampled array of elevations for ground positions that are normally spaced at regular intervals. To meet the needs of the geospatial data user community for regional and continental scale elevation data, the staff at the U.S. Geological Survey's EROS Data Center (EDC) are developing DEM's at a horizontal grid spacing of 30 arc seconds (approximately 1 kilometer). These data are being made available to the public via electronic distribution and hard media. As of July, 1996 data are available for Africa, Antarctica, Asia, Europe, and North America. Data sets for South America, Australia, New Zealand, the islands of southeast Asia, and Greenland are under development and are scheduled for release before the end of 1996. proprietary
+GNVd0188_104 30 arc-second DEM for Africa CEOS_EXTRA STAC Catalog 1996-07-23 1996-07-23 -20, -35, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848851-CEOS_EXTRA.umm_json PLEASE NOTE: This is an updated release of the Africa 30 arc second DEM. Comments from users of this data set are welcome. Please contact Dean Gesch (gesch@dg1.cr.usgs.gov) or Sue Jenson (jenson@dg1.cr.usgs.gov). A digital elevation model (DEM) consists of a sampled array of elevations for ground positions that are normally spaced at regular intervals. To meet the needs of the geospatial data user community for regional and continental scale elevation data, the staff at the U.S. Geological Survey's EROS Data Center (EDC) are developing DEM's at a horizontal grid spacing of 30 arc seconds (approximately 1 kilometer). These data are being made available to the public via electronic distribution and hard media. As of July, 1996 data are available for Africa, Antarctica, Asia, Europe, and North America. Data sets for South America, Australia, New Zealand, the islands of southeast Asia, and Greenland are under development and are scheduled for release before the end of 1996. proprietary
GNVd0189_104 30 arc-second DEM for Antarctica CEOS_EXTRA STAC Catalog 1996-07-17 1996-07-17 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2232848315-CEOS_EXTRA.umm_json "PLEASE NOTE: This is a beta release of the Antarctica DEM. If any data anomalies are noticed, please send an E-mail to either Mike Oimoen at: oimoen@dgl.cr.usgs.gov, or Sue Jenson at: jenson@dg1.cr.usgs.gov. We will look into them, and they may be addressed in the next release. The Antarctica data is provided in two projections. Antarctic DEM in geographic (lat/lon) coordinates: 30 arc-second spacing -ant_dem_lkms1 UL = 180 W, 60S. LR = 90W, 90S (3600 rows x 10800 columns) -ant_dem_lkms2 UL = 90 W, 60S. LR = 0W, 90S (3600 rows x 10800 columns) -ant_dem_1kms3 UL = 0 W, 60S. LR = 90E, 90S (3600 rows x 10800 columns) -ant_dem_1kms4 UL = 90 E, 60S. LR = 180E, 90S (3600 rows x 10800 columns) Antarctic DEM in polar stereographic coordinates (meters) -ant_dem_1kmps UL = -2700000 x 2699000. LR = 2699000 x -2700000 (5400 rows x 5400 columns) Note: Both DEMs are referenced to the WGS84 ellipsoid. The standard latitude of the polar stereographic DEM is 71S, and it central meridian is 0. Data Organization: Data are distributed as 16-bit straight raster image files in a latitude/ longitude coordinate system, and also in a polar stereographic coordinate system. Image files are identified by the .bil.gz extension. Each image file of the Antarctica data set is compressed using the GNU ""gzip"" utility. If you do not have access to gzip, the FTP server will uncompress the file as you retrieve it. To do this, simply leave off the "".gz"" extension when retrieving the file (NOTE: This option is not available through MOSAIC). For example, to retrieve the file ""af_1k_dem1.bil.gz"" without compression just use ""get af_dem_lks1.bil"". Note that the uncompressed files are typically five times larger than the compressed versions and so will take five times longer to transmit. The gzip program is available via anonymous FTP at the following sites: prep.ai.mit.edu:/pub/gnuwuarchive.wustl.edu:/systems/gnu Each image file is accompanied by five ancillary files (header file, world file, statistics file, coordinate file, and data descriptor record ). The format of each ancillary file is described below:" proprietary
GNVd0189_104 30 arc-second DEM for Antarctica ALL STAC Catalog 1996-07-17 1996-07-17 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2232848315-CEOS_EXTRA.umm_json "PLEASE NOTE: This is a beta release of the Antarctica DEM. If any data anomalies are noticed, please send an E-mail to either Mike Oimoen at: oimoen@dgl.cr.usgs.gov, or Sue Jenson at: jenson@dg1.cr.usgs.gov. We will look into them, and they may be addressed in the next release. The Antarctica data is provided in two projections. Antarctic DEM in geographic (lat/lon) coordinates: 30 arc-second spacing -ant_dem_lkms1 UL = 180 W, 60S. LR = 90W, 90S (3600 rows x 10800 columns) -ant_dem_lkms2 UL = 90 W, 60S. LR = 0W, 90S (3600 rows x 10800 columns) -ant_dem_1kms3 UL = 0 W, 60S. LR = 90E, 90S (3600 rows x 10800 columns) -ant_dem_1kms4 UL = 90 E, 60S. LR = 180E, 90S (3600 rows x 10800 columns) Antarctic DEM in polar stereographic coordinates (meters) -ant_dem_1kmps UL = -2700000 x 2699000. LR = 2699000 x -2700000 (5400 rows x 5400 columns) Note: Both DEMs are referenced to the WGS84 ellipsoid. The standard latitude of the polar stereographic DEM is 71S, and it central meridian is 0. Data Organization: Data are distributed as 16-bit straight raster image files in a latitude/ longitude coordinate system, and also in a polar stereographic coordinate system. Image files are identified by the .bil.gz extension. Each image file of the Antarctica data set is compressed using the GNU ""gzip"" utility. If you do not have access to gzip, the FTP server will uncompress the file as you retrieve it. To do this, simply leave off the "".gz"" extension when retrieving the file (NOTE: This option is not available through MOSAIC). For example, to retrieve the file ""af_1k_dem1.bil.gz"" without compression just use ""get af_dem_lks1.bil"". Note that the uncompressed files are typically five times larger than the compressed versions and so will take five times longer to transmit. The gzip program is available via anonymous FTP at the following sites: prep.ai.mit.edu:/pub/gnuwuarchive.wustl.edu:/systems/gnu Each image file is accompanied by five ancillary files (header file, world file, statistics file, coordinate file, and data descriptor record ). The format of each ancillary file is described below:" proprietary
-GNVd0190_104 30 arc-second DEM for Europe CEOS_EXTRA STAC Catalog 1995-09-22 1995-09-22 -25, 35, 22, 85 https://cmr.earthdata.nasa.gov/search/concepts/C2232848511-CEOS_EXTRA.umm_json The European 30 arc-second DEM was compiled from varied data sources. The primary source was a generalization of the Level 1 Digital Terrain Elevation Data. Digital Terrain Elevation Data (DTED) is a 1 degree by 1 degree dataset produced by the US Defense Mapping Agency (DMA) that contains digital data in the form of a uniform matrix of terrain elevation values for most parts of the world. It was originally designed to provide basic quantitative data for military training, planning and operating systems that require terrain elevation, slope and related information. This includes applications such as modeling the influence of terrain on radar line-of-sight, automatic height determination, terrain modeling etc. proprietary
GNVd0190_104 30 arc-second DEM for Europe ALL STAC Catalog 1995-09-22 1995-09-22 -25, 35, 22, 85 https://cmr.earthdata.nasa.gov/search/concepts/C2232848511-CEOS_EXTRA.umm_json The European 30 arc-second DEM was compiled from varied data sources. The primary source was a generalization of the Level 1 Digital Terrain Elevation Data. Digital Terrain Elevation Data (DTED) is a 1 degree by 1 degree dataset produced by the US Defense Mapping Agency (DMA) that contains digital data in the form of a uniform matrix of terrain elevation values for most parts of the world. It was originally designed to provide basic quantitative data for military training, planning and operating systems that require terrain elevation, slope and related information. This includes applications such as modeling the influence of terrain on radar line-of-sight, automatic height determination, terrain modeling etc. proprietary
+GNVd0190_104 30 arc-second DEM for Europe CEOS_EXTRA STAC Catalog 1995-09-22 1995-09-22 -25, 35, 22, 85 https://cmr.earthdata.nasa.gov/search/concepts/C2232848511-CEOS_EXTRA.umm_json The European 30 arc-second DEM was compiled from varied data sources. The primary source was a generalization of the Level 1 Digital Terrain Elevation Data. Digital Terrain Elevation Data (DTED) is a 1 degree by 1 degree dataset produced by the US Defense Mapping Agency (DMA) that contains digital data in the form of a uniform matrix of terrain elevation values for most parts of the world. It was originally designed to provide basic quantitative data for military training, planning and operating systems that require terrain elevation, slope and related information. This includes applications such as modeling the influence of terrain on radar line-of-sight, automatic height determination, terrain modeling etc. proprietary
GO-BGC_0 Global Ocean Biogeochemistry Array OB_DAAC STAC Catalog 2021-03-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2431253176-OB_DAAC.umm_json The Global Ocean Biogeochemistry (GO-BGC) Array is a project funded by the US National Science Foundation (NSF Award 1946578 ) to build a global network of chemical and biological sensors that will monitor ocean health. This grant is being used to build and deploy 500 robotic ocean-monitoring floats around the globe as part of NSF’s Mid-scale Research Infrastructure-2 program. This network of floats is collecting data on the chemistry and the biology of the ocean from the surface to a depth of 2,000 meters, augmenting the existing Argo array that monitors ocean temperature and salinity. The GO-BGC Array is led by Director Ken Johnson and administered by the Monterey Bay Aquarium Research Institute. For questions specific to the HPLC/POC/PON data submitted to SeaBASS please contact Josh Plant at jplant@mbari.org. proprietary
GO-SHIP_0 Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP) OB_DAAC STAC Catalog 2016-11-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360348-OB_DAAC.umm_json Measurements from the GO-SHIP (Global Ocean Ship-based Hydrographic Investigations Program) project, which is a network of sustained hydrographic sections, supporting physical oceanography, the carbon cycle, and marine biogeochemistry and ecosystems. proprietary
GOA97_0 Gulf of Alaska measurements in 1997 OB_DAAC STAC Catalog 1997-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360262-OB_DAAC.umm_json Measurements taken in the Gulf of Alaska during 1997. proprietary
@@ -7511,8 +7512,8 @@ GPM_PRL1KA_07 GPM DPR Ka-band Received Power L1B 1.5 hours 5 km V07 (GPM_PRL1KA)
GPM_PRL1KU_07 GPM DPR Ku-band Received Power L1B 1.5 hours 5 km V07 (GPM_PRL1KU) at GES DISC GES_DISC STAC Catalog 2014-03-08 -180, -70, 180, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2179064680-GES_DISC.umm_json "Version 07 is the current version of the data set. Older versions are no longer available and have been superseded by Version 07. This product contains the calibrated received power from the Ku-band Radar of the Dual-frequency Precipitation Radar (DPR) aboard the core satellite of the Global Precipitation Measurement (GPM) mission. The Ku-radar scan pattern is simpler than that of the Ka-band Radar, and is similar to the TRMM PR. It only has ""Normal Scan"" (NS) swath consisting of 49 footprints cross-track in a scan and the footprint size is about 5 km in diameter. The scan swath is 245 km. " proprietary
GPP_CONUS_TROPOMI_1875_1 CMS: Daily Gross Primary Productivity over CONUS from TROPOMI SIF, 2018-2021 ORNL_CLOUD STAC Catalog 2018-02-15 2021-10-15 -125, 24, -65, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2390701035-ORNL_CLOUD.umm_json This dataset includes estimates of gross primary production (GPP) for the conterminous U.S., for 2018-02-15 to 2021-10-15, based on measurements of solar-induced chlorophyll fluorescence from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5P satellite platform. GPP was estimated from rates of photosynthesis inferred from SIF using a linear model and ecosystem scaling factors from 102 AmeriFlux sites. Knowledge of the spatiotemporal patterns of GPP is necessary for understanding regional and global carbon budgets. Broad-scale estimates of GPP have typically relied upon carbon cycle models linking spatial patterns of vegetation with remotely sensed environmental data. SIF provides a means to directly estimate photosynthetic activity, and therefore, GPP. Recent deployments of satellite platforms that measure SIF provide near-real-time measurements and represent a breakthrough in measuring GPP on a global scale. Regular SIF measurements can detect spatially explicit ecosystem-level responses to climate events such as drought and flooding. This dataset includes spatially explicit estimates of GPP (g m-2 d-1), uncertainty in GPP, and related TROPOMI SIF measurements (mW m-2 sr-1 nm-1) at 500-m resolution. The data are provided in NetCDF format. proprietary
GPP_COS_Conductance_SoilFluxes_2324_1 SiB4 Modeled 0.5-degree Carbonyl Sulfide Vegetation and Soil Fluxes, 2000-2020 ORNL_CLOUD STAC Catalog 2000-01-01 2020-12-31 -180, 53, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3293388915-ORNL_CLOUD.umm_json This dataset provides outputs from the Simple Biosphere Model (v 4.2). Products include hourly 0.5-degree gridded fluxes of gross primary productivity (GPP), respiration, carbonyl sulfide (COS) uptake by vegetation and soil, along with conductance of COS (apparent mesophyll and total), stomatal conductance of water and partial pressure of CO2 in the canopy air space, leaf surface, interior and chloroplast. The data are separated by plant functional type (PFT). Fluxes have dimensions of latitude, longitude, time, and plant functional type. Model output spans 53N to 90N latitude and 180W to 180E longitude over years 2000 to 2020. The data are provided in NetCDF version 4 format. proprietary
-GPP_MODIS_Alaska_Canada_2024_1 ABoVE: Light-Curve Modelling of Gridded GPP Using MODIS MAIAC and Flux Tower Data ALL STAC Catalog 2000-01-01 2018-01-01 -172.08, 50.06, -73.64, 79.75 https://cmr.earthdata.nasa.gov/search/concepts/C2445456434-ORNL_CLOUD.umm_json This dataset contains gridded estimations of daily ecosystem Gross Primary Production (GPP) in grams of carbon per day at a 1 km2 spatial resolution over Alaska and Canada from 2000-01-01 to 2018-01-01. Daily estimates of GPP were derived from a light-curve model that was fitted and validated over a network of ABoVE domain Ameriflux flux towers then upscaled using MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) data to span the extended ABoVE domain. In general, the methods involved three steps; the first step involved collecting and processing mainly carbon-flux site-level data, the second step involved the analysis and correction of site-level MAIAC data, and the final step developed a framework to produce large-scale estimates of GPP. The light-curve parameter model was generated by upscaling from flux tower sub-daily temporal resolution by deconvolving the GPP variable into 3 components: the absorbed photosynthetically active radiation (aPAR), the maximum GPP or maximum photosynthetic capacity (GPPmax), and the photosynthetic limitation or amount of light needed to reach maximum capacity (PPFDmax). GPPmax and PPFDmax were related to satellite reflectance measurements sampled at the daily scale. GPP over the extended ABoVE domain was estimated at a daily resolution from the light-curve parameter model using MODIS MAIAC daily reflectance as input. This framework allows large-scale estimates of phenology and evaluation of ecosystem sensitivity to climate change. proprietary
GPP_MODIS_Alaska_Canada_2024_1 ABoVE: Light-Curve Modelling of Gridded GPP Using MODIS MAIAC and Flux Tower Data ORNL_CLOUD STAC Catalog 2000-01-01 2018-01-01 -172.08, 50.06, -73.64, 79.75 https://cmr.earthdata.nasa.gov/search/concepts/C2445456434-ORNL_CLOUD.umm_json This dataset contains gridded estimations of daily ecosystem Gross Primary Production (GPP) in grams of carbon per day at a 1 km2 spatial resolution over Alaska and Canada from 2000-01-01 to 2018-01-01. Daily estimates of GPP were derived from a light-curve model that was fitted and validated over a network of ABoVE domain Ameriflux flux towers then upscaled using MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) data to span the extended ABoVE domain. In general, the methods involved three steps; the first step involved collecting and processing mainly carbon-flux site-level data, the second step involved the analysis and correction of site-level MAIAC data, and the final step developed a framework to produce large-scale estimates of GPP. The light-curve parameter model was generated by upscaling from flux tower sub-daily temporal resolution by deconvolving the GPP variable into 3 components: the absorbed photosynthetically active radiation (aPAR), the maximum GPP or maximum photosynthetic capacity (GPPmax), and the photosynthetic limitation or amount of light needed to reach maximum capacity (PPFDmax). GPPmax and PPFDmax were related to satellite reflectance measurements sampled at the daily scale. GPP over the extended ABoVE domain was estimated at a daily resolution from the light-curve parameter model using MODIS MAIAC daily reflectance as input. This framework allows large-scale estimates of phenology and evaluation of ecosystem sensitivity to climate change. proprietary
+GPP_MODIS_Alaska_Canada_2024_1 ABoVE: Light-Curve Modelling of Gridded GPP Using MODIS MAIAC and Flux Tower Data ALL STAC Catalog 2000-01-01 2018-01-01 -172.08, 50.06, -73.64, 79.75 https://cmr.earthdata.nasa.gov/search/concepts/C2445456434-ORNL_CLOUD.umm_json This dataset contains gridded estimations of daily ecosystem Gross Primary Production (GPP) in grams of carbon per day at a 1 km2 spatial resolution over Alaska and Canada from 2000-01-01 to 2018-01-01. Daily estimates of GPP were derived from a light-curve model that was fitted and validated over a network of ABoVE domain Ameriflux flux towers then upscaled using MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) data to span the extended ABoVE domain. In general, the methods involved three steps; the first step involved collecting and processing mainly carbon-flux site-level data, the second step involved the analysis and correction of site-level MAIAC data, and the final step developed a framework to produce large-scale estimates of GPP. The light-curve parameter model was generated by upscaling from flux tower sub-daily temporal resolution by deconvolving the GPP variable into 3 components: the absorbed photosynthetically active radiation (aPAR), the maximum GPP or maximum photosynthetic capacity (GPPmax), and the photosynthetic limitation or amount of light needed to reach maximum capacity (PPFDmax). GPPmax and PPFDmax were related to satellite reflectance measurements sampled at the daily scale. GPP over the extended ABoVE domain was estimated at a daily resolution from the light-curve parameter model using MODIS MAIAC daily reflectance as input. This framework allows large-scale estimates of phenology and evaluation of ecosystem sensitivity to climate change. proprietary
GPP_surfaces_749_1 BigFoot GPP Surfaces for North and South American Sites, 2000-2004 ORNL_CLOUD STAC Catalog 2000-01-01 2004-12-31 -156.61, -2.86, -54.96, 71.27 https://cmr.earthdata.nasa.gov/search/concepts/C2751481399-ORNL_CLOUD.umm_json The BigFoot project gathered Gross Primary Production (GPP) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. BigFoot was funded by NASA's Terrestrial Ecology Program.For more details on the BigFoot Project, please visit the website: http://www.fsl.orst.edu/larse/bigfoot/index.html. proprietary
GPROF_precip_716_1 SAFARI 2000 SSM/I GPROF 6.0 Precipitation Data, 0.5-Deg, 1999-2001 ORNL_CLOUD STAC Catalog 1999-01-01 2001-12-31 5, -35, 60, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2788392368-ORNL_CLOUD.umm_json The GPROF 6.0 Pentads data set contains 5-day (pentad) averages of the GPROF 6.0 Gridded Orbits. The GPROF(Goddard Profiling Algorithm) data set contains a suite of 9 products providing instantaneous, gridded values of precipitation totals for each granule of the SSM/I (Special Sensor Microwave/Imager) data over the roughly 14-year period July 1987 through the present. Even though there have been at least two satellites for the entire period, sampling is sufficiently sparse that the data are averaged for pentads, then the pentads are smoothed with a 1-2-3-2-1 time-weighting. The last two pentads are unevenly weighted since the last (or last two) pentads in the average are not yet available. Consequently, the last two pentads must be recomputed when the next pentad becomes available.The data set prepared for SAFARI cover the years 1999, 2000, and 2001.The main refereed citations for the data set are Kummerow et al. (1996)and Olson et al. (1999) proprietary
GPR_MACCA_ANARE53_1 Ground Penetrating Radar data collected at Macquarie Island at the Station, tip and transmitter hut sites AU_AADC STAC Catalog 2000-11-11 2000-11-13 158.76, -54.79, 158.965, -54.48 https://cmr.earthdata.nasa.gov/search/concepts/C1214308576-AU_AADC.umm_json GPR data collected at Macquarie Island at three locations, at the station area, above the abandoned tip and around the ionosonde hut. The instrument used was Ramac GPR with 250 MHz antennas. The station data are positioned, the other two data sets are not, only description of location is available. Data are in .rad and .rd3 format. proprietary
@@ -7553,8 +7554,8 @@ GRC-GFO_GRIDDED_AOD1B_JPL_1-DEG_RL06.3_RL06.3 JPL GRACE/GRACE-FO Gridded-AOD1B W
GRC-GFO_GRIDDED_AOD1B_JPL_MASCON_RL06.3_RL06.3 JPL GRACE/GRACE-FO Gridded-AOD1B Water-Equivalent-Thickness Surface-Mass Anomaly RL06.3 dataset for Tellus Level-3 mascon 0.5-degree grid POCLOUD STAC Catalog 2002-04-04 -180, -89.5, 180, 89.5 https://cmr.earthdata.nasa.gov/search/concepts/C3215162709-POCLOUD.umm_json GRACE non-tidal high-frequency atmospheric and oceanic mass variation models are routinely generated at GFZ as so-called Atmosphere and Ocean De-aliasing Level-1B (AOD1B) products (in terms of corresponding spherical harmonic geopotential coefficients) to be added to the background static gravity model during GRACE monthly gravity field determination. AOD1B products are 3-hourly series of spherical harmonic coefficients up to degree and order 180 which are routinely provided to the GRACE Science Data System and the user community with only a few days time delay. These products reflect spatio-temporal mass variations in the atmosphere and oceans deduced from an operational atmospheric model and corresponding ocean dynamics provided by an ocean model. The variability is derived by subtraction of a long-term mean of vertical integrated atmospheric mass distributions and a corresponding mean of ocean bottom pressure as simulated with the ocean model.
The Gridded AOD1B data sets provided here contain the monthly mean AOD1B data in geolocated gridded form, smoothed or spatially aggregated to be consistent with the GRACE and GRACE-FO Tellus Level-3 data products of land and/or ocean mass anomalies. With these gridded AOD1B Level-3 products, users can remove or add the effects of the modeled mean monthly atmospheric and ocean bottom pressure change (e.g., to compare different models). proprietary
GREENLAND_MASS_TELLUS_MASCON_CRI_TIME_SERIES_RL06.3_V4_RL06.3Mv04 Tellus Level-4 Greenland Mass Anomaly Time Series from JPL GRACE/GRACE-FO Mascon CRI Filtered Release 06.3 version 04 POCLOUD STAC Catalog 2002-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3206299308-POCLOUD.umm_json This dataset is a time series of mass variability averaged over all of the global ocean. It provides the non-steric or mass only sea level changes over time. The mass variability are derived from JPL GRACE Mascon Ocean, Ice, and Hydrology Equivalent Water Height CRI Filtered RL06.3Mv04 dataset, which can be found at https://doi.org/10.5067/TEMSC-3JC634. A more detailed description on the Mascon solution, including the mathematical derivation, implementation of geophysical constraints, and solution validation, please see Watkins et al., 2015, doi: 10.1002/2014JB011547. The mass variability is provided as an ASCII table. proprietary
GRID-INPE GRID-INPE; UNEP Global Resource Information Database - INPE Cooperating Center CEOS_EXTRA STAC Catalog 1993-04-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232849388-CEOS_EXTRA.umm_json This is a collection of data-sets held by GRID-INPE. Please contact the technical contact for further details and data-set breakdown. GRID-INPE is a cooperating center to UNEP's Global Resource Information Database. Grid is a system of cooperating centers within the United Nations Environmental Programme that is dedicated to making environmental information more readily accessible to environmental analysis as well as to international and national decision makers. Its mission is to provide timely and reliable geo-referenced environmental information. Besides acquiring and disseminating integrated, spatially-referenced environmental data, GRID provides decision-support services to environmental analysts and international and national decision makers, and fosters the use of geographic information systems (GIS) and satellite image processing (IP) as tools for environmental analysis. proprietary
-GSI_ABSOLUT_GRAVITY_ANT Absolute gravity measurement SCIOPS STAC Catalog 1992-01-01 39.5, -69, 39.5, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214590222-SCIOPS.umm_json The IAGBN aims to distribute gravity points worldwide and construct a network on which gravity observation is based. There are two kinds of points: A is a point set up in regions with stable crustal structure, and B is a point set up in regions where crustal activity is expected. Syowa Station in Antarctica was among the 36 A points. McMurdo Station of the U.S. is the only point in Antarctica other than Syowa Station that is classified as A. Introduced GSI in 1980, the upcast-type absolute gravity meter (GA60) generally called the Sakuma type, was used in this survey. The 36th JARE (1994) conducted observation using FG5 that the GSI introduced in 1992. Because FG5 measures gravity in a free-fall system, it is characterized by the ability to conduct automatic continuous measurement and allow for many measurements. proprietary
GSI_ABSOLUT_GRAVITY_ANT Absolute gravity measurement ALL STAC Catalog 1992-01-01 39.5, -69, 39.5, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214590222-SCIOPS.umm_json The IAGBN aims to distribute gravity points worldwide and construct a network on which gravity observation is based. There are two kinds of points: A is a point set up in regions with stable crustal structure, and B is a point set up in regions where crustal activity is expected. Syowa Station in Antarctica was among the 36 A points. McMurdo Station of the U.S. is the only point in Antarctica other than Syowa Station that is classified as A. Introduced GSI in 1980, the upcast-type absolute gravity meter (GA60) generally called the Sakuma type, was used in this survey. The 36th JARE (1994) conducted observation using FG5 that the GSI introduced in 1992. Because FG5 measures gravity in a free-fall system, it is characterized by the ability to conduct automatic continuous measurement and allow for many measurements. proprietary
+GSI_ABSOLUT_GRAVITY_ANT Absolute gravity measurement SCIOPS STAC Catalog 1992-01-01 39.5, -69, 39.5, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214590222-SCIOPS.umm_json The IAGBN aims to distribute gravity points worldwide and construct a network on which gravity observation is based. There are two kinds of points: A is a point set up in regions with stable crustal structure, and B is a point set up in regions where crustal activity is expected. Syowa Station in Antarctica was among the 36 A points. McMurdo Station of the U.S. is the only point in Antarctica other than Syowa Station that is classified as A. Introduced GSI in 1980, the upcast-type absolute gravity meter (GA60) generally called the Sakuma type, was used in this survey. The 36th JARE (1994) conducted observation using FG5 that the GSI introduced in 1992. Because FG5 measures gravity in a free-fall system, it is characterized by the ability to conduct automatic continuous measurement and allow for many measurements. proprietary
GSI_JARE_TOPOMAPS 1:50,000 Topographic maps from Japan Antarctic Research Expedition (JARE) ALL STAC Catalog 1989-04-01 23, -73, 28, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1214610482-SCIOPS.umm_json The data set consists of 1:50,000 topographic maps which cover most areas of the Sor-Rondane Mountains, with 21 sheets. The contour interval is 20 m. All maps have been digitalized into raster data and are available in TIFF format. proprietary
GSI_JARE_TOPOMAPS 1:50,000 Topographic maps from Japan Antarctic Research Expedition (JARE) SCIOPS STAC Catalog 1989-04-01 23, -73, 28, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1214610482-SCIOPS.umm_json The data set consists of 1:50,000 topographic maps which cover most areas of the Sor-Rondane Mountains, with 21 sheets. The contour interval is 20 m. All maps have been digitalized into raster data and are available in TIFF format. proprietary
GSJ-DAM Aeromagnetic Reconnaissance Survey Data ALL STAC Catalog 1964-01-01 123, 24, 145, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214608183-SCIOPS.umm_json The Geological Survey of Japan has carried out developments on the exploration and analysis techniques in aeromagnetic survey since 1964, when the research on aeromagnetic exploration was begun on full scale. And since 1969, explorations for various purposes as well as investigations for assessing the deposit of hydrocarbon resources in the continental shelf area surrounding Japan have been carried out. The results were already published as the Aerial Aeromagnetic Map series, and the data were stored in magnetic media in the form of file groups with unified formats. proprietary
@@ -7631,22 +7632,22 @@ Global_Landslide_Nowcast_2.0.0 Global Landslide Nowcast from LHASA L4 1 day 1 km
Global_Litter_Carbon_Nutrients_1244_1 A Global Database of Litterfall Mass and Litter Pool Carbon and Nutrients ALL STAC Catalog 1827-01-01 1997-12-31 -156.7, -54.5, 176.2, 72.5 https://cmr.earthdata.nasa.gov/search/concepts/C2784385713-ORNL_CLOUD.umm_json Measurement data of aboveground litterfall and littermass and litter carbon, nitrogen, and nutrient concentrations were extracted from 685 original literature sources and compiled into a comprehensive database to support the analysis of global patterns of carbon and nutrients in litterfall and litter pools. Data are included from sources dating from 1827 to 1997. The reported data include the literature reference, general site information (description, latitude, longitude, and elevation), site climate data (mean annual temperature and precipitation), site vegetation characteristics (management, stand age, ecosystem and vegetation-type codes), annual quantities of litterfall (by class, kg m-2 yr-1), litter pool mass (by class and litter layer, kg m-2), and concentrations of nitrogen (N), phosphorus (P), and base cations for the litterfall (g m-2 yr-1) and litter pool components (g m-2). The investigators intent was to compile a comprehensive data set of individual direct field measurements as reported by researchers. While the primary emphasis was on acquiring C data, measurements of N, P, and base cations were also obtained, although the database is sparse for elements other than C and N. Each of the 1,497 records in the database represents a measurement site. Replicate measurements were averaged according to conventions described in Section 5 and recorded for each site in the database. The sites were at 575 different locations. proprietary
Global_Litter_Carbon_Nutrients_1244_1 A Global Database of Litterfall Mass and Litter Pool Carbon and Nutrients ORNL_CLOUD STAC Catalog 1827-01-01 1997-12-31 -156.7, -54.5, 176.2, 72.5 https://cmr.earthdata.nasa.gov/search/concepts/C2784385713-ORNL_CLOUD.umm_json Measurement data of aboveground litterfall and littermass and litter carbon, nitrogen, and nutrient concentrations were extracted from 685 original literature sources and compiled into a comprehensive database to support the analysis of global patterns of carbon and nutrients in litterfall and litter pools. Data are included from sources dating from 1827 to 1997. The reported data include the literature reference, general site information (description, latitude, longitude, and elevation), site climate data (mean annual temperature and precipitation), site vegetation characteristics (management, stand age, ecosystem and vegetation-type codes), annual quantities of litterfall (by class, kg m-2 yr-1), litter pool mass (by class and litter layer, kg m-2), and concentrations of nitrogen (N), phosphorus (P), and base cations for the litterfall (g m-2 yr-1) and litter pool components (g m-2). The investigators intent was to compile a comprehensive data set of individual direct field measurements as reported by researchers. While the primary emphasis was on acquiring C data, measurements of N, P, and base cations were also obtained, although the database is sparse for elements other than C and N. Each of the 1,497 records in the database represents a measurement site. Replicate measurements were averaged according to conventions described in Section 5 and recorded for each site in the database. The sites were at 575 different locations. proprietary
Global_Maps_C_Density_2010_1763_1 Global Aboveground and Belowground Biomass Carbon Density Maps for the Year 2010 ORNL_CLOUD STAC Catalog 2010-01-01 2010-12-31 -180, -61.1, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C2764708636-ORNL_CLOUD.umm_json This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at a 300-m spatial resolution. The aboveground biomass map integrates land-cover specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree cover and landcover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided. proprietary
-Global_Microbial_Biomass_C_N_P_1264_1 A Compilation of Global Soil Microbial Biomass Carbon, Nitrogen, and Phosphorus Data ALL STAC Catalog 1977-11-16 2012-06-01 -180, -90, 177.9, 79 https://cmr.earthdata.nasa.gov/search/concepts/C2216863966-ORNL_CLOUD.umm_json This data set provides the concentrations of soil microbial biomass carbon (C), nitrogen (N) and phosphorus (P), soil organic carbon, total nitrogen, and total phosphorus at biome and global scales. The data were compiled from a comprehensive survey of publications from the late 1970s to 2012 and include 3,422 data points from 315 papers. These data are from soil samples collected primarily at 0-15 cm depth with some from 0-30 cm. In addition, data were compiled for soil microbial biomass concentrations from soil profile samples to depths of 100 cm. Sampling site latitude and longitude were available for the majority of the samples that enabled assembling additional soil properties, site characteristics, vegetation distributions, biomes, and long-term climate data from several global sources of soil, land-cover, and climate data. These site attributes are included with the microbial biomass data. This data set contains two *.csv files of the soil microbial biomass C, N, P data. The first provides all compiled results emphasizing the full spatial extent of the data, while the second is a subset that provides only data from a series of profile samples emphasizing the vertical distribution of microbial biomass concentrations.There is a companion file, also in .csv format, of the references for the surveyed publications. A reference_number relates the data to the respective publication.The concentrations of soil microbial biomass, in combination with other soil databases, were used to estimate the global storage of soil microbial biomass C and N in 0-30 cm and 0-100 cm soil profiles. These storage estimates were combined with a spatial map of 12 major biomes (boreal forest, temperate coniferous forest, temperate broadleaf forest, tropical and subtropical forests, mixed forest, grassland, shrub, tundra, desert, natural wetland, cropland, and pasture) at 0.05-degree by 0.5-degree spatial resolution. The biome map and six estimates of C and N storage and C:N ration in soil microbial biomass are provided in a single netCDF format file. proprietary
Global_Microbial_Biomass_C_N_P_1264_1 A Compilation of Global Soil Microbial Biomass Carbon, Nitrogen, and Phosphorus Data ORNL_CLOUD STAC Catalog 1977-11-16 2012-06-01 -180, -90, 177.9, 79 https://cmr.earthdata.nasa.gov/search/concepts/C2216863966-ORNL_CLOUD.umm_json This data set provides the concentrations of soil microbial biomass carbon (C), nitrogen (N) and phosphorus (P), soil organic carbon, total nitrogen, and total phosphorus at biome and global scales. The data were compiled from a comprehensive survey of publications from the late 1970s to 2012 and include 3,422 data points from 315 papers. These data are from soil samples collected primarily at 0-15 cm depth with some from 0-30 cm. In addition, data were compiled for soil microbial biomass concentrations from soil profile samples to depths of 100 cm. Sampling site latitude and longitude were available for the majority of the samples that enabled assembling additional soil properties, site characteristics, vegetation distributions, biomes, and long-term climate data from several global sources of soil, land-cover, and climate data. These site attributes are included with the microbial biomass data. This data set contains two *.csv files of the soil microbial biomass C, N, P data. The first provides all compiled results emphasizing the full spatial extent of the data, while the second is a subset that provides only data from a series of profile samples emphasizing the vertical distribution of microbial biomass concentrations.There is a companion file, also in .csv format, of the references for the surveyed publications. A reference_number relates the data to the respective publication.The concentrations of soil microbial biomass, in combination with other soil databases, were used to estimate the global storage of soil microbial biomass C and N in 0-30 cm and 0-100 cm soil profiles. These storage estimates were combined with a spatial map of 12 major biomes (boreal forest, temperate coniferous forest, temperate broadleaf forest, tropical and subtropical forests, mixed forest, grassland, shrub, tundra, desert, natural wetland, cropland, and pasture) at 0.05-degree by 0.5-degree spatial resolution. The biome map and six estimates of C and N storage and C:N ration in soil microbial biomass are provided in a single netCDF format file. proprietary
+Global_Microbial_Biomass_C_N_P_1264_1 A Compilation of Global Soil Microbial Biomass Carbon, Nitrogen, and Phosphorus Data ALL STAC Catalog 1977-11-16 2012-06-01 -180, -90, 177.9, 79 https://cmr.earthdata.nasa.gov/search/concepts/C2216863966-ORNL_CLOUD.umm_json This data set provides the concentrations of soil microbial biomass carbon (C), nitrogen (N) and phosphorus (P), soil organic carbon, total nitrogen, and total phosphorus at biome and global scales. The data were compiled from a comprehensive survey of publications from the late 1970s to 2012 and include 3,422 data points from 315 papers. These data are from soil samples collected primarily at 0-15 cm depth with some from 0-30 cm. In addition, data were compiled for soil microbial biomass concentrations from soil profile samples to depths of 100 cm. Sampling site latitude and longitude were available for the majority of the samples that enabled assembling additional soil properties, site characteristics, vegetation distributions, biomes, and long-term climate data from several global sources of soil, land-cover, and climate data. These site attributes are included with the microbial biomass data. This data set contains two *.csv files of the soil microbial biomass C, N, P data. The first provides all compiled results emphasizing the full spatial extent of the data, while the second is a subset that provides only data from a series of profile samples emphasizing the vertical distribution of microbial biomass concentrations.There is a companion file, also in .csv format, of the references for the surveyed publications. A reference_number relates the data to the respective publication.The concentrations of soil microbial biomass, in combination with other soil databases, were used to estimate the global storage of soil microbial biomass C and N in 0-30 cm and 0-100 cm soil profiles. These storage estimates were combined with a spatial map of 12 major biomes (boreal forest, temperate coniferous forest, temperate broadleaf forest, tropical and subtropical forests, mixed forest, grassland, shrub, tundra, desert, natural wetland, cropland, and pasture) at 0.05-degree by 0.5-degree spatial resolution. The biome map and six estimates of C and N storage and C:N ration in soil microbial biomass are provided in a single netCDF format file. proprietary
Global_Monthly_GPP_1789_1 Global Monthly GPP from an Improved Light Use Efficiency Model, 1982-2016 ORNL_CLOUD STAC Catalog 1982-01-01 2017-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2764742564-ORNL_CLOUD.umm_json This dataset provides global monthly average gross primary productivity (GPP; g carbon/m2/d) modeled at 8 km spatial resolution for each of the 35 years from 1982-2016. GPP is based on the well-known Monteith light use efficiency (LUE) equation but was improved with optimized spatially and temporally explicit LUE values derived from selected FLUXNET tower site data. Optimized LUE was extrapolated to a consistent 8 km resolution global grid using multiple explanatory variables representing climatic, landscape, and vegetation factors influencing LUE and GPP. Global gridded long-term daily GPP was derived using the optimized LUE, Global Inventory Modeling and Mapping Studies (GIMMS3g) canopy fraction of photosynthetically active radiation (FPAR), and Modern-Era Retrospective analysis for Research and Applications, Version 2, (MERRA-2) meteorological information. These data will improve satellite-based estimation and understanding of GPP using a refined LUE model framework. proprietary
Global_Phosphorus_Dist_Map_1223_1 Global Gridded Soil Phosphorus Distribution Maps at 0.5-degree Resolution ORNL_CLOUD STAC Catalog 1850-01-01 1850-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2216863372-ORNL_CLOUD.umm_json This data set provides estimates of different forms of naturally occurring soil phosphorus (P) including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P on a global scale at 0.5-degree resolution. The data were assembled from chronosequence information and global spatial databases to develop a map of total soil P and the distribution among mineral bound, labile, organic, occluded, and secondary P forms in soils. Uncertainty was calculated for the different forms. The data set has no explicit temporal component -- data were nominally for the pre-industrial period ca. 1850.The estimated global spatial variation and distribution of different soil P forms presented in this study will be useful for global biogeochemistry models that include P as a limiting element in biological production by providing initial estimates of the available soil P for plant uptake and microbial utilization (Yang et al., 2013).There is one netCDF data file (.nc) with this data set. proprietary
-Global_Phosphorus_Hedley_Fract_1230_1 A Global Database of Soil Phosphorus Compiled from Studies Using Hedley Fractionation ORNL_CLOUD STAC Catalog 1985-01-01 2010-12-31 -117.86, -42.5, 117.6, 63.23 https://cmr.earthdata.nasa.gov/search/concepts/C2216863440-ORNL_CLOUD.umm_json This data set provides concentrations of soil phosphorus (P) compiled from the peer-reviewed literature that cited the Hedley fractionation method (Hedley and Stewart, 1982). This database contains estimates of different forms of naturally occurring soil phosphorus, including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P, based on the analyses of the various Hedley soil fractions.The recent literature survey (Yang and Post, 2011) was restricted to studies of natural, unfertilized, and uncultivated soils since 1995. Ninety measurements of soil P fractions were identified. These were added to the 88 values from soils in natural ecosystems that Cross and Schlesinger (1995) had compiled. Cross and Schlesinger provided a comprehensive survey on Hedley P data prior to 1995. Measurement data are provided for studies published from 1985 through 2010. In addition to the Hedley P fraction measurement data Yang and Post (2011) also compiled information on soil order, soil pH, organic carbon and nitrogen content, as well as the geographic location (longitude and latitude) of the measurement sites. proprietary
Global_Phosphorus_Hedley_Fract_1230_1 A Global Database of Soil Phosphorus Compiled from Studies Using Hedley Fractionation ALL STAC Catalog 1985-01-01 2010-12-31 -117.86, -42.5, 117.6, 63.23 https://cmr.earthdata.nasa.gov/search/concepts/C2216863440-ORNL_CLOUD.umm_json This data set provides concentrations of soil phosphorus (P) compiled from the peer-reviewed literature that cited the Hedley fractionation method (Hedley and Stewart, 1982). This database contains estimates of different forms of naturally occurring soil phosphorus, including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P, based on the analyses of the various Hedley soil fractions.The recent literature survey (Yang and Post, 2011) was restricted to studies of natural, unfertilized, and uncultivated soils since 1995. Ninety measurements of soil P fractions were identified. These were added to the 88 values from soils in natural ecosystems that Cross and Schlesinger (1995) had compiled. Cross and Schlesinger provided a comprehensive survey on Hedley P data prior to 1995. Measurement data are provided for studies published from 1985 through 2010. In addition to the Hedley P fraction measurement data Yang and Post (2011) also compiled information on soil order, soil pH, organic carbon and nitrogen content, as well as the geographic location (longitude and latitude) of the measurement sites. proprietary
-Global_RTSG_Flux_1078_1 A Global Database of Gas Fluxes from Soils after Rewetting or Thawing, Version 1.0 ORNL_CLOUD STAC Catalog 1956-01-01 2009-12-31 -149.63, -36.45, 160.52, 74.5 https://cmr.earthdata.nasa.gov/search/concepts/C2216863284-ORNL_CLOUD.umm_json This database contains information compiled from published studies on gas flux from soil following rewetting or thawing. The resulting database includes 222 field and laboratory observations focused on rewetting of dry soils, and 116 field laboratory observations focused on thawing of frozen soils studies conducted from 1956 to 2010. Fluxes of carbon dioxide, methane, nitrous oxide, nitrogen oxide, and ammonia (CO2, CH4, N2O, NO and NH3) were compiled from the literature and the flux rates were normalized for ease of comparison. Field observations of gas flux following rewetting of dry soils include events caused by natural rainfall, simulated rainfall in natural ecosystems, and irrigation in agricultural lands. Similarly, thawing of frozen soils include field observations of natural thawing, simulated freezing-thawing events (i.e., thawing of simulated frozen soil by snow removal), and thawing of seasonal ice in temperate and high latitude regions (Kim et al., 2012). Reported parameters include experiment type, location, site type, vegetation, climate, soil properties, rainfall, soil moisture, soil gas flux after wetting and thawing, peak soil gas flux properties, and the corresponding study references. There is one comma-delimited data file. proprietary
+Global_Phosphorus_Hedley_Fract_1230_1 A Global Database of Soil Phosphorus Compiled from Studies Using Hedley Fractionation ORNL_CLOUD STAC Catalog 1985-01-01 2010-12-31 -117.86, -42.5, 117.6, 63.23 https://cmr.earthdata.nasa.gov/search/concepts/C2216863440-ORNL_CLOUD.umm_json This data set provides concentrations of soil phosphorus (P) compiled from the peer-reviewed literature that cited the Hedley fractionation method (Hedley and Stewart, 1982). This database contains estimates of different forms of naturally occurring soil phosphorus, including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P, based on the analyses of the various Hedley soil fractions.The recent literature survey (Yang and Post, 2011) was restricted to studies of natural, unfertilized, and uncultivated soils since 1995. Ninety measurements of soil P fractions were identified. These were added to the 88 values from soils in natural ecosystems that Cross and Schlesinger (1995) had compiled. Cross and Schlesinger provided a comprehensive survey on Hedley P data prior to 1995. Measurement data are provided for studies published from 1985 through 2010. In addition to the Hedley P fraction measurement data Yang and Post (2011) also compiled information on soil order, soil pH, organic carbon and nitrogen content, as well as the geographic location (longitude and latitude) of the measurement sites. proprietary
Global_RTSG_Flux_1078_1 A Global Database of Gas Fluxes from Soils after Rewetting or Thawing, Version 1.0 ALL STAC Catalog 1956-01-01 2009-12-31 -149.63, -36.45, 160.52, 74.5 https://cmr.earthdata.nasa.gov/search/concepts/C2216863284-ORNL_CLOUD.umm_json This database contains information compiled from published studies on gas flux from soil following rewetting or thawing. The resulting database includes 222 field and laboratory observations focused on rewetting of dry soils, and 116 field laboratory observations focused on thawing of frozen soils studies conducted from 1956 to 2010. Fluxes of carbon dioxide, methane, nitrous oxide, nitrogen oxide, and ammonia (CO2, CH4, N2O, NO and NH3) were compiled from the literature and the flux rates were normalized for ease of comparison. Field observations of gas flux following rewetting of dry soils include events caused by natural rainfall, simulated rainfall in natural ecosystems, and irrigation in agricultural lands. Similarly, thawing of frozen soils include field observations of natural thawing, simulated freezing-thawing events (i.e., thawing of simulated frozen soil by snow removal), and thawing of seasonal ice in temperate and high latitude regions (Kim et al., 2012). Reported parameters include experiment type, location, site type, vegetation, climate, soil properties, rainfall, soil moisture, soil gas flux after wetting and thawing, peak soil gas flux properties, and the corresponding study references. There is one comma-delimited data file. proprietary
+Global_RTSG_Flux_1078_1 A Global Database of Gas Fluxes from Soils after Rewetting or Thawing, Version 1.0 ORNL_CLOUD STAC Catalog 1956-01-01 2009-12-31 -149.63, -36.45, 160.52, 74.5 https://cmr.earthdata.nasa.gov/search/concepts/C2216863284-ORNL_CLOUD.umm_json This database contains information compiled from published studies on gas flux from soil following rewetting or thawing. The resulting database includes 222 field and laboratory observations focused on rewetting of dry soils, and 116 field laboratory observations focused on thawing of frozen soils studies conducted from 1956 to 2010. Fluxes of carbon dioxide, methane, nitrous oxide, nitrogen oxide, and ammonia (CO2, CH4, N2O, NO and NH3) were compiled from the literature and the flux rates were normalized for ease of comparison. Field observations of gas flux following rewetting of dry soils include events caused by natural rainfall, simulated rainfall in natural ecosystems, and irrigation in agricultural lands. Similarly, thawing of frozen soils include field observations of natural thawing, simulated freezing-thawing events (i.e., thawing of simulated frozen soil by snow removal), and thawing of seasonal ice in temperate and high latitude regions (Kim et al., 2012). Reported parameters include experiment type, location, site type, vegetation, climate, soil properties, rainfall, soil moisture, soil gas flux after wetting and thawing, peak soil gas flux properties, and the corresponding study references. There is one comma-delimited data file. proprietary
Global_Reservoirs_Methane_1918_1 Global-Gridded Daily Methane Emissions from Inland Dam-Reservoir Systems ORNL_CLOUD STAC Catalog 2002-01-01 2015-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2515869951-ORNL_CLOUD.umm_json This dataset includes global maps of methane (CH4) emissions from inland dam-reservoir systems at 0.25-degree spatial resolution. Daily emission rates (as grams of CH4 per day per total area of grid cell) were estimated for boreal, temperate, and subtropical-tropical eco-climatic domains and total emissions. The annual duration of the emission season is based on freeze-thaw cycles of these water bodies as applicable. In addition, the dataset includes the total fractional area of reservoirs in each grid cell. These estimates will promote understanding of the current and future role of reservoirs in the global CH4 budget and guide efforts to mitigate reservoir-related CH4 emissions. These emission estimates are climatological; one daily value for each day of year (n=365) is provided for each grid cell. Modeled estimates were based on daily mean inputs, averaged over 2002 to 2015. proprietary
Global_Riverine_N2O_Emissions_1791_1 CMS: Annual Estimates of Global Riverine Nitrous Oxide Emissions, 1900-2016 ORNL_CLOUD STAC Catalog 1900-01-01 2016-12-31 -180, -88.5, 180, 88.5 https://cmr.earthdata.nasa.gov/search/concepts/C2389020006-ORNL_CLOUD.umm_json This dataset provides modeled estimates of annual nitrous oxide (N2O) emissions at a coarse geographic scale (0.5 x 0.5 degree) for two sets of global rivers and streams covering the period of 1900-2016. Emissions (g N2O-N/yr) are provided for higher-order rivers and streams (>=4th order) and headwater streams (<4th order). The estimates were derived from a water transport model, the Model for Scale Adaptive River Transport (MOSART), coupled with the Dynamic Land Ecosystem Model (DLEM) to link hydrology and ecosystem processes pertaining to N2O flux and transport. Factors driving the model included climate, land use and land cover, and nitrogen inputs (i.e., fertilizer, deposition, manure, and sewage). Nitrogen discharges from streams and rivers to the ocean were calibrated from observations from 50 river basins across the globe. proprietary
Global_SIF_OCO2_MODIS_1863_2 High Resolution Global Contiguous SIF Estimates from OCO-2 SIF and MODIS, Version 2 ORNL_CLOUD STAC Catalog 2014-09-01 2020-07-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207986708-ORNL_CLOUD.umm_json This dataset provides spatially-contiguous global mean daily solar-induced chlorophyll fluorescence (SIF) estimates at 0.05 degree (approximately 5 km at the equator) spatial and 16-day temporal resolution from September 2014 through July 2020. This product was derived from Orbiting Carbon Observatory-2 (OCO-2) SIF observations and produced by training an artificial neural network (ANN) on the native OCO-2 SIF observations and MODIS BRDF-corrected seven-band surface reflectance along OCO-2's orbits. The trained ANN model was then applied to predict mean daily SIF (mW/m2/nm/sr) in OCO-2's gap regions based on MODIS reflectance and landcover. This framework was stratified by biomes and 16-day time steps. This dataset's high resolution and global contiguous coverage will greatly enhance the synergy between satellite SIF and photosynthesis measured on the ground at consistent spatial scales. Potential applications of this dataset include advancing dynamic drought monitoring and mitigation, informing agricultural planning and yield estimation, and providing a benchmark for upcoming satellite missions with SIF capabilities at higher spatial resolutions. proprietary
Global_Salt_Marsh_Change_2122_1 Global Salt Marsh Change, 2000-2019 ORNL_CLOUD STAC Catalog 2000-01-01 2019-12-31 -170, -47, 180, 74 https://cmr.earthdata.nasa.gov/search/concepts/C2575421513-ORNL_CLOUD.umm_json This dataset provides global salt marsh change, including loss and gain for five-year periods from 2000-2019. Loss and gain at a 30 m spatial resolution were estimated with Normalized Difference Vegetation Index (NDVI) anomaly algorithm using Landsat 5, 7, and 8 collections within the known extent of salt marshes. The data are provided in cloud-optimized GeoTIFF format. proprietary
Global_Soil_Regolith_Sediment_1304_1 Global 1-km Gridded Thickness of Soil, Regolith, and Sedimentary Deposit Layers ORNL_CLOUD STAC Catalog 1900-01-01 2015-12-31 -180, -60, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2216864025-ORNL_CLOUD.umm_json This data set provides high-resolution estimates of the thickness of the permeable layers above bedrock (soil, regolith, and sedimentary deposits) within a global 30-arcsecond (~1-km) grid using the best available data for topography, climate, and geology as input. These data are modeled to represent estimated thicknesses by landform type for the geological present. proprietary
Global_Veg_Greenness_GIMMS_3G_2187_1 Global Vegetation Greenness (NDVI) from AVHRR GIMMS-3G+, 1981-2022 ORNL_CLOUD STAC Catalog 1982-01-01 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2759076389-ORNL_CLOUD.umm_json This dataset holds the Global Inventory Modeling and Mapping Studies-3rd Generation V1.2 (GIMMS-3G+) data for the Normalized Difference Vegetation Index (NDVI). NDVI was based on corrected and calibrated measurements from Advanced Very High Resolution Radiometer (AVHRR) data with a spatial resolution of 0.0833 degree and global coverage for 1982 to 2022. Maximum NDVI values are reported within twice monthly compositing periods (two values per month). The dataset was assembled from different AVHRR sensors and accounts for various deleterious effects, such as calibration loss, orbital drift, and volcanic eruptions. The data are provided in NetCDF format. proprietary
-Globalsoil_ESM A Global Soil Dataset for Earth System Modeling ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214604044-SCIOPS.umm_json We developed a comprehensive, gridded Global Soil Dataset for use in Earth System Models (GSDE) and other applications as well. GSDE provides soil information including soil particle-size distribution, organic carbon, and nutrients, etc. and quality control information in terms of confidence level. GSDE is based on the Soil Map of the World and various regional and national soil databases, including soil attribute data and soil maps. We used a standardized data structure and data processing procedures to harmonize the data collected from various sources. We then used a soil type linkage method (i.e. taxotransfer rules) and the polygon linkage method to derive the spatial distribution of soil properties. To aggregate the attributes of different compositions of a mapping unit, we used three mapping approaches: area-weighting method, the dominant soil type method and the dominant binned soil attribute method. In the released gridded dataset, we used the area-weighting method as it will meet the demands of most applications. The dataset can be also aggregate to a lower resolution. The resolution is 30 arc-seconds (about 1 km at the equator). The vertical variation of soil property was captured by eight layers to the depth of 2.3 m (i.e. 0- 0.045, 0.045- 0.091, 0.091- 0.166, 0.166- 0.289, 0.289- 0.493, 0.493- 0.829, 0.829- 1.383 and 1.383- 2.296 m). proprietary
Globalsoil_ESM A Global Soil Dataset for Earth System Modeling SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214604044-SCIOPS.umm_json We developed a comprehensive, gridded Global Soil Dataset for use in Earth System Models (GSDE) and other applications as well. GSDE provides soil information including soil particle-size distribution, organic carbon, and nutrients, etc. and quality control information in terms of confidence level. GSDE is based on the Soil Map of the World and various regional and national soil databases, including soil attribute data and soil maps. We used a standardized data structure and data processing procedures to harmonize the data collected from various sources. We then used a soil type linkage method (i.e. taxotransfer rules) and the polygon linkage method to derive the spatial distribution of soil properties. To aggregate the attributes of different compositions of a mapping unit, we used three mapping approaches: area-weighting method, the dominant soil type method and the dominant binned soil attribute method. In the released gridded dataset, we used the area-weighting method as it will meet the demands of most applications. The dataset can be also aggregate to a lower resolution. The resolution is 30 arc-seconds (about 1 km at the equator). The vertical variation of soil property was captured by eight layers to the depth of 2.3 m (i.e. 0- 0.045, 0.045- 0.091, 0.091- 0.166, 0.166- 0.289, 0.289- 0.493, 0.493- 0.829, 0.829- 1.383 and 1.383- 2.296 m). proprietary
+Globalsoil_ESM A Global Soil Dataset for Earth System Modeling ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214604044-SCIOPS.umm_json We developed a comprehensive, gridded Global Soil Dataset for use in Earth System Models (GSDE) and other applications as well. GSDE provides soil information including soil particle-size distribution, organic carbon, and nutrients, etc. and quality control information in terms of confidence level. GSDE is based on the Soil Map of the World and various regional and national soil databases, including soil attribute data and soil maps. We used a standardized data structure and data processing procedures to harmonize the data collected from various sources. We then used a soil type linkage method (i.e. taxotransfer rules) and the polygon linkage method to derive the spatial distribution of soil properties. To aggregate the attributes of different compositions of a mapping unit, we used three mapping approaches: area-weighting method, the dominant soil type method and the dominant binned soil attribute method. In the released gridded dataset, we used the area-weighting method as it will meet the demands of most applications. The dataset can be also aggregate to a lower resolution. The resolution is 30 arc-seconds (about 1 km at the equator). The vertical variation of soil property was captured by eight layers to the depth of 2.3 m (i.e. 0- 0.045, 0.045- 0.091, 0.091- 0.166, 0.166- 0.289, 0.289- 0.493, 0.493- 0.829, 0.829- 1.383 and 1.383- 2.296 m). proprietary
GoMA-Platts_Bank_Aerial_Survey Aerial survey of upper trophic level predators on PLatts Bank, Gulf of Maine SCIOPS STAC Catalog 2005-07-11 2005-07-29 -70.17854, 43.00422, -69.14483, 43.35316 https://cmr.earthdata.nasa.gov/search/concepts/C1214590724-SCIOPS.umm_json The study area is located 50 km from shore in the western Gulf of Maine and covers 1672 km2, including Platts Bank, Three Dory Ridge and surrounding deep water. Platts Bank (43°10N, 069°40W) is a glacial deposit composed primarily of sand and gravel. When defined by the 100 m isobath, the bank is approximately 15 km in its longest dimension and has an area <140 km2. Aerial surveys were flown on ten days from July 11 to 29, 2005 to record the distribution and relative abundance of marine mammals, birds and large fish. Surveys were typically conducted in the morning or early afternoon and consisted of six transects, each 46 km long oriented on an East-West axis to minimize interference from reflected sunlight. Survey legs were flown at 185 km/hr and an altitude of 230 m using a high-wing, twin-engine aircraft. Observation effort (two observers) was concentrated from both sides of the plane perpendicular to the flight path. To estimate the distances of sightings of mammals and fish from the planes flight path, sightings were binned into five groupings corresponding to 15 degrees of arc from 15° (the area directly beneath the plane was not visible) to 90°. When species identification or number of individuals was uncertain, search effort was interrupted while the plane circled to confirm identifications and number of individuals and to obtain a more precise location. Birds were recorded only within a 170 m strip on each side of the aircraft (15° to 45° of arc) during the survey legs. Sightings of birds continued when the plane circled for closer inspection of mammals and fish, but these data were not used in analyses since this would bias bird sightings towards areas where cetaceans were concentrated. Data were recorded by a dedicated data recorder directly onto a computer using software that recorded the time and location from the GPS navigation system aboard the plane at regular intervals throughout the flight and for each recorded sighting. proprietary
GoMA-Platts_Bank_Aerial_Survey Aerial survey of upper trophic level predators on PLatts Bank, Gulf of Maine ALL STAC Catalog 2005-07-11 2005-07-29 -70.17854, 43.00422, -69.14483, 43.35316 https://cmr.earthdata.nasa.gov/search/concepts/C1214590724-SCIOPS.umm_json The study area is located 50 km from shore in the western Gulf of Maine and covers 1672 km2, including Platts Bank, Three Dory Ridge and surrounding deep water. Platts Bank (43°10N, 069°40W) is a glacial deposit composed primarily of sand and gravel. When defined by the 100 m isobath, the bank is approximately 15 km in its longest dimension and has an area <140 km2. Aerial surveys were flown on ten days from July 11 to 29, 2005 to record the distribution and relative abundance of marine mammals, birds and large fish. Surveys were typically conducted in the morning or early afternoon and consisted of six transects, each 46 km long oriented on an East-West axis to minimize interference from reflected sunlight. Survey legs were flown at 185 km/hr and an altitude of 230 m using a high-wing, twin-engine aircraft. Observation effort (two observers) was concentrated from both sides of the plane perpendicular to the flight path. To estimate the distances of sightings of mammals and fish from the planes flight path, sightings were binned into five groupings corresponding to 15 degrees of arc from 15° (the area directly beneath the plane was not visible) to 90°. When species identification or number of individuals was uncertain, search effort was interrupted while the plane circled to confirm identifications and number of individuals and to obtain a more precise location. Birds were recorded only within a 170 m strip on each side of the aircraft (15° to 45° of arc) during the survey legs. Sightings of birds continued when the plane circled for closer inspection of mammals and fish, but these data were not used in analyses since this would bias bird sightings towards areas where cetaceans were concentrated. Data were recorded by a dedicated data recorder directly onto a computer using software that recorded the time and location from the GPS navigation system aboard the plane at regular intervals throughout the flight and for each recorded sighting. proprietary
GozMmlpH2O_1 GOZCARDS Merged Water Vapor 1 month L3 10 degree Zonal Means on a Vertical Pressure Grid V1 (GozMmlpH2O) at GES DISC GES_DISC STAC Catalog 1991-09-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1251051158-GES_DISC.umm_json The GOZCARDS Merged Data for Water Vapor 1 month L3 10 degree Zonal Averages on a Vertical Pressure Grid product (GozMmlpH2O) contains zonal means and related information (standard deviation, minimum/maximum value, etc.), calculated as a result of a merging process that ties together the source datasets, after bias removal and averaging. The merged H2O data are from the following satellite instruments: HALOE (v19; 1991 - 2005), ACE-FTS (v2.2u; 2004 - onward), and Aura MLS (v3.3; 2004 - onward). The vertical pressure range for H2O is from 147 to 0.01 hPa. The input source data used to create this merged product are contained in a separate data product with the short name GozSmlpH2O. The GozMmlpH2O merged data are distributed in netCDF4 format. proprietary
@@ -7663,8 +7664,8 @@ GozSmlpT_1 GOZCARDS Source Temperature 1 month L4 10 degree Zonal Averages on a
Great African Food Company Crop Type Tanzania_1 Great African Food Company Crop Type Tanzania MLHUB STAC Catalog 2020-01-01 2023-01-01 33.5684048, -4.4374314, 37.2124126, -2.0436294 https://cmr.earthdata.nasa.gov/search/concepts/C2781412712-MLHUB.umm_json This dataset contains field boundaries and crop types from farms in Tanzania. Great African Food Company used Farmforce app to collect a point within each field, and recorded other properties including area of the field.
Radiant Earth Foundation team used the point measurements from the ground data collection and the area of each field overlaid on satellite imagery (multiple Sentinel-2 scenes during the growing season, and Google basemap) to draw the polygons for each field. These polygons do not cover the entirety of the field, and are always enclosed within the field. Therefore, they should not be used for field boundary detection, rather as reference polygons for crop type classification. Data points that were not clear if they belong to a neighboring farm (e.g. the point was on the edge of two farms)were removed from the dataset. Finally, ground reference polygons were matched with corresponding time series data from Sentinel-2 satellites (listed in the source imagery property of each label item). proprietary
Great_Belt_0 Great Belt research cruise in the Southern Ocean OB_DAAC STAC Catalog 2011-01-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360350-OB_DAAC.umm_json The Great Belt research cruise investigated the Great Southern Coccolithophore Belt in the Southern Ocean. proprietary
Great_Lakes_0 Water quality measurements from the Great Lakes OB_DAAC STAC Catalog 2008-08-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360351-OB_DAAC.umm_json Water quality measurements taken in the Great Lakes region of the United States. proprietary
-Great_Slave_Lake_Ecosystem_Map_1695_1 ABoVE: Ecosystem Map, Great Slave Lake Area, Northwest Territories, Canada, 1997-2011 ORNL_CLOUD STAC Catalog 1997-09-25 2011-09-14 -123.04, 58.51, -109.46, 65.15 https://cmr.earthdata.nasa.gov/search/concepts/C2143402730-ORNL_CLOUD.umm_json This dataset provides an ecosystem type map at 12.5 meter pixel spacing and 0.2 ha minimum mapping unit for the area surrounding Great Slave Lake, Northwest Territories, Canada for the time period 1997 to 2011. The map includes nine classes for peatland, wetland, and upland areas derived from a Random Forest classification trained on multi-date, multi-sensor remote sensing images across the study extent, and using field data and high-resolution Worldview-2 image interpretation for training and validation. The nine classes are: Water, Marsh, Swamp, Open Fen, Treed Fen, Bog, Upland Deciduous, Upland Conifer, and Sparsely Vegetated. A tenth map class identifies areas of historical fires (prior to 2011) that are currently undergoing post-fire successional revegetation. This dataset provides an ecosystem type map of the area before the large fire season of 2014 to better understand the effects of fires in the area. proprietary
Great_Slave_Lake_Ecosystem_Map_1695_1 ABoVE: Ecosystem Map, Great Slave Lake Area, Northwest Territories, Canada, 1997-2011 ALL STAC Catalog 1997-09-25 2011-09-14 -123.04, 58.51, -109.46, 65.15 https://cmr.earthdata.nasa.gov/search/concepts/C2143402730-ORNL_CLOUD.umm_json This dataset provides an ecosystem type map at 12.5 meter pixel spacing and 0.2 ha minimum mapping unit for the area surrounding Great Slave Lake, Northwest Territories, Canada for the time period 1997 to 2011. The map includes nine classes for peatland, wetland, and upland areas derived from a Random Forest classification trained on multi-date, multi-sensor remote sensing images across the study extent, and using field data and high-resolution Worldview-2 image interpretation for training and validation. The nine classes are: Water, Marsh, Swamp, Open Fen, Treed Fen, Bog, Upland Deciduous, Upland Conifer, and Sparsely Vegetated. A tenth map class identifies areas of historical fires (prior to 2011) that are currently undergoing post-fire successional revegetation. This dataset provides an ecosystem type map of the area before the large fire season of 2014 to better understand the effects of fires in the area. proprietary
+Great_Slave_Lake_Ecosystem_Map_1695_1 ABoVE: Ecosystem Map, Great Slave Lake Area, Northwest Territories, Canada, 1997-2011 ORNL_CLOUD STAC Catalog 1997-09-25 2011-09-14 -123.04, 58.51, -109.46, 65.15 https://cmr.earthdata.nasa.gov/search/concepts/C2143402730-ORNL_CLOUD.umm_json This dataset provides an ecosystem type map at 12.5 meter pixel spacing and 0.2 ha minimum mapping unit for the area surrounding Great Slave Lake, Northwest Territories, Canada for the time period 1997 to 2011. The map includes nine classes for peatland, wetland, and upland areas derived from a Random Forest classification trained on multi-date, multi-sensor remote sensing images across the study extent, and using field data and high-resolution Worldview-2 image interpretation for training and validation. The nine classes are: Water, Marsh, Swamp, Open Fen, Treed Fen, Bog, Upland Deciduous, Upland Conifer, and Sparsely Vegetated. A tenth map class identifies areas of historical fires (prior to 2011) that are currently undergoing post-fire successional revegetation. This dataset provides an ecosystem type map of the area before the large fire season of 2014 to better understand the effects of fires in the area. proprietary
GreenBay_0 2010 Measurements made in Green Bay, Wisconsin OB_DAAC STAC Catalog 2010-09-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360352-OB_DAAC.umm_json Measurements made in Green Bay, Wisconsin in 2010. proprietary
GreenBay_0 2010 Measurements made in Green Bay, Wisconsin ALL STAC Catalog 2010-09-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360352-OB_DAAC.umm_json Measurements made in Green Bay, Wisconsin in 2010. proprietary
Gridded_Biomass_Africa_1777_1 Gridded Estimates of Woody Cover and Biomass across Sub-Saharan Africa, 2000-2004 ORNL_CLOUD STAC Catalog 2000-01-01 2005-01-01 -20.61, -34.81, 61.53, 22.01 https://cmr.earthdata.nasa.gov/search/concepts/C2762262652-ORNL_CLOUD.umm_json This dataset provides maps of woody (tree and shrub) cover and biomass across Sub-Saharan Africa at a resolution of 1 km for the period 2000-2004. Canopy cover observations and remote-sensing data related to woody vegetation were used to predict woody cover across Africa. Predicted woody cover, canopy height, and tree allometry were used to estimate woody biomass for Sub-Saharan Africa. Canopy cover observations were assembled from field measurements and Google Earth imagery collected from 2000-2004. Remote-sensing data related to the structural attributes of woody vegetation were derived from MODIS optical data and Q-SCAT (Quick Scatterometer) microwave measurements. Canopy height estimates were derived from spaceborne lidar and tree allometry equations were retrieved from GlobAllomeTree. proprietary
@@ -7692,8 +7693,8 @@ H3ZFCT_007 HIRDLS/Aura Level 3 Temperature 1deg Lat Zonal Fourier Coefficients V
HABITATCASEY0203_2 Bird habitat surveys conducted in the Windmill Islands during 2002/03 and descriptive information of the terrestrial Environment in the Windmill Islands AU_AADC STAC Catalog 2002-11-12 2003-02-16 110.3, -66.5, 110.75, -66.2333 https://cmr.earthdata.nasa.gov/search/concepts/C1214308579-AU_AADC.umm_json Very little information is available on the geomorphology of areas surrounding Australian Antarctic stations. This type of information is generally collected during geological surveys. This metadata record gathers a range of descriptive geomorphological information of various nature: -Habitat surveys were conducted in the season 2002-2003 in the Windmill Islands in parallel with bird nest mapping (reported in metadata record BIRDSCASEY0203) in order to study selection of nest sites by a range of species. Habitat was described in the survey sites searched for bird nests following various methods (described in BIRDSCASEY0203). Information is stored as GIS files (Arcview 3.2) -polygon shapefile gathering all the geomorphological units. -line shapefile describing habitat along transects used for searching bird nests -polygon shapefile describing habitat in small 25*25m quadrats used for searching bird nests -A collection of 1309 digital photos showing the sites searched for bird nests indexed by grid site number. Plus another set of 194 photos showing region of the Windmill Islands or bird nests more in detail -A set of Digital Elevation Models (DEM) covering the entire Windmill Islands area generated separately for 18 regions. -200m*200m grid created from the coverage of ice-free areas (Aerial photography 93-94) providing site numbers for the photographic database -A series of Black and White aerial Photos (500 m, Zeiss, 1994) scanned at high resolution for the purpose of substrate study. See the word document in the file download for more information. This work has been completed as part of ASAC project 1219 (ASAC_1219). The fields in this dataset are: Date Boulderbig Bouldsmall Baresubst Morsed Scree Snowcover Permice Slope Aspect Photonumber Sitedotid Comments proprietary
HABITATMAWSON04-05_1 Bird habitat surveys conducted during the 2004/05 summer and descriptive information of the terrestrial environment in the Mawson region AU_AADC STAC Catalog 2004-12-10 2005-04-25 62.25, -67.6, 63.5, -67.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214308607-AU_AADC.umm_json Very little information is available on the geomorphology of areas surrounding Australian Antarctic stations. This type of information is generally collected during geological surveys. This metadata record gathers a range of descriptive geomorphological information of various nature: -Habitat surveys were conducted in the season 2004-2005 in the Mawson area in parallel with bird nest mapping (reported in metadata record SNPEMAWSON0405) in order to study selection of nest sites by a range of species. Habitat was described in the survey sites searched for bird nests following various methods (described in). Information is stored as GIS files (Arcview 3.2 or ArcGIS): -polygon shapefile gathering all the geomorphological units describing % substrate cover -A collection of digital photos showing the sites searched for bird nests, most if them indexed by grid site number. The grid sites numbers are located in a shapefile of 200*200m sites, below) -A set of Digital Elevation Models (DEM) covering the entire Mawson area generated for 5 separate regions and the derived slope, aspect, aspect to the prevailing winds, convexity raster files at a 10m resolution -200m*200m grid created from the coverage of ice-free areas (from Aerial photography) providing site boundaries and numbers for the photographic database -A series of Black and White and colour aerial Photos scanned at high resolution for the purpose of substrate study and associated 3D images. This work has been completed as part of ASAC project 2704 (ASAC_2704). The main fields in this dataset are: Date Boulderbig Bouldsmall Baresubst Morsed Scree Snowcover Permice Slope Aspect Sitedotid Comments proprietary
HAB_0 Harmful Algal Blooms (HABs) measurements from multiple sites in 2006 OB_DAAC STAC Catalog 2006-06-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360367-OB_DAAC.umm_json Measurements of Harmful Algal Blooms (HABs) in the Gulf of Mexico, Chesapeake Bay, and Great Lake regions during 2006. proprietary
-HALO_LiDAR_AOP_ML_Heights_1833_1 ACT-America: HALO Lidar Measurements of AOP and ML Heights, 2019 ALL STAC Catalog 2019-06-17 2019-07-28 -102, 28, -73, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2704996986-ORNL_CLOUD.umm_json This dataset provides measurements from the High Altitude Lidar Observatory (HALO) instrument, an airborne multi-function Differential Absorption Lidar (DIAL) and High Spectral Resolution Lidar (HSRL), operating at 532 nm and 1064 nm wavelengths onboard a C-130 aircraft during the June and July 2019 ACT-America campaign. The flights took place over eastern and central North America based from Shreveport, Louisiana; Lincoln, Nebraska; and NASA Wallops Flight Facility located on the eastern shore of Virginia. HALO data were sampled at 0.5 s temporal and 1.25 m vertical resolutions. The data include profiles of aerosol optical properties (AOP), distributions of mixed layer heights (MLH), columns of tropospheric methane, and navigation parameters. The data are provided in HDF5 format along with PNG images and a companion files in Portable Document (*.pdf) format. proprietary
HALO_LiDAR_AOP_ML_Heights_1833_1 ACT-America: HALO Lidar Measurements of AOP and ML Heights, 2019 ORNL_CLOUD STAC Catalog 2019-06-17 2019-07-28 -102, 28, -73, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2704996986-ORNL_CLOUD.umm_json This dataset provides measurements from the High Altitude Lidar Observatory (HALO) instrument, an airborne multi-function Differential Absorption Lidar (DIAL) and High Spectral Resolution Lidar (HSRL), operating at 532 nm and 1064 nm wavelengths onboard a C-130 aircraft during the June and July 2019 ACT-America campaign. The flights took place over eastern and central North America based from Shreveport, Louisiana; Lincoln, Nebraska; and NASA Wallops Flight Facility located on the eastern shore of Virginia. HALO data were sampled at 0.5 s temporal and 1.25 m vertical resolutions. The data include profiles of aerosol optical properties (AOP), distributions of mixed layer heights (MLH), columns of tropospheric methane, and navigation parameters. The data are provided in HDF5 format along with PNG images and a companion files in Portable Document (*.pdf) format. proprietary
+HALO_LiDAR_AOP_ML_Heights_1833_1 ACT-America: HALO Lidar Measurements of AOP and ML Heights, 2019 ALL STAC Catalog 2019-06-17 2019-07-28 -102, 28, -73, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2704996986-ORNL_CLOUD.umm_json This dataset provides measurements from the High Altitude Lidar Observatory (HALO) instrument, an airborne multi-function Differential Absorption Lidar (DIAL) and High Spectral Resolution Lidar (HSRL), operating at 532 nm and 1064 nm wavelengths onboard a C-130 aircraft during the June and July 2019 ACT-America campaign. The flights took place over eastern and central North America based from Shreveport, Louisiana; Lincoln, Nebraska; and NASA Wallops Flight Facility located on the eastern shore of Virginia. HALO data were sampled at 0.5 s temporal and 1.25 m vertical resolutions. The data include profiles of aerosol optical properties (AOP), distributions of mixed layer heights (MLH), columns of tropospheric methane, and navigation parameters. The data are provided in HDF5 format along with PNG images and a companion files in Portable Document (*.pdf) format. proprietary
HAQES_NA_PM25_BC_1 HAQES 3-Hourly Ensemble mean surface PM2.5 Black Carbon concentration, North America V1 (HAQES_NA_PM25_BC) at GES DISC GES_DISC STAC Catalog 2022-11-01 -132, 21, -58.5, 53.5 https://cmr.earthdata.nasa.gov/search/concepts/C2623694321-GES_DISC.umm_json This product provides HAQES 3-hourly ensemble mean surface PM2.5 Black Carbon concentration over the continental United States (CONUS) and surrounding regions. The data is mapped on Lambert projection. The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST). proprietary
HAQES_NA_PM25_BC_CENSUS_1 HAQES 3-Hourly Ensemble mean surface PM2.5 Black Carbon concentration at census level, North America V1 (HAQES_NA_PM25_BC_CENSUS) at GES DISC GES_DISC STAC Catalog 2022-11-01 -132, 21, -58.5, 53.5 https://cmr.earthdata.nasa.gov/search/concepts/C2623694409-GES_DISC.umm_json This product provides HAQES 3-hourly ensemble mean surface PM2.5 Black Carbon concentration at the census level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST). proprietary
HAQES_NA_PM25_BC_COUNTY_1 HAQES 3-Hourly Ensemble mean surface PM2.5 Black Carbon concentration at county level, North America V1 (HAQES_NA_PM25_BC_COUNTY) at GES DISC GES_DISC STAC Catalog 2022-11-01 -132, 21, -58.5, 53.5 https://cmr.earthdata.nasa.gov/search/concepts/C2623694361-GES_DISC.umm_json This product provides HAQES 3-hourly ensemble mean surface PM2.5 Black Carbon concentration at the county level over the continental United States (CONUS). The Hazardous Air Quality Ensemble System (HAQES) is a real-time ensemble forecast of hazardous air quality events, such as wildfires, dust storms, and Volcanic eruptions. Both regional and global models from multiple agencies are used to create the ensemble, including the Goddard Earth Observing System (GEOS) from the National Aeronautics and Space Administration (NASA), the Navy Aerosol Analysis and Prediction System (NAAPS) from Naval Research Laboratory, the Global Ensemble Forecast System Aerosols (GEFS), High-Resolution Rapid Refresh (HRRR), and National Oceanic and Atmospheric Administration-U.S. Environmental Protection Agency (NOAA-EPA) Atmosphere-Chemistry Coupler-Community Multiscale Air Quality model (NACC-CMAQ) from NOAA. The prototypes of HAQES products were developed by the George Mason University Air Quality Laboratory as part of the NASA Health Air Quality Applied Science Team (HAQAST). proprietary
@@ -7876,12 +7877,12 @@ ICId0021_202 IRS Pan 104-052 of 23 Nov 1996 CEOS_EXTRA STAC Catalog 1970-01-01
ICId0023_202 Landsat TM 140-040 of 22 Sep 1992 CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848244-CEOS_EXTRA.umm_json Landsat TM 140-040 of 22 Sep 1992 Satellite image proprietary
ICId0028_202 Landsat TM 141-41 Q2 of 24 Jan 89 CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232849271-CEOS_EXTRA.umm_json Landsat TM 141-41 Q2 of 24 Jan 89 Satellite image proprietary
ICO_Casey_1 In situ chemical oxidisation (ICO) of petroleum hydrocarbons at old Casey Station AU_AADC STAC Catalog 1999-12-01 2001-12-28 110.3, -66.5, 110.75, -66.2333 https://cmr.earthdata.nasa.gov/search/concepts/C1214313554-AU_AADC.umm_json In-situ chemical oxidation (ICO) is a remediation technology that involves the addition of chemicals to the substrate that degrade contaminants through oxidation processes. This series of field experiments conducted at the Old Casey Powerhouse/Workshop investigate the potential for the use of ICO technology in Antarctica on petroleum hydrocarbon contaminated sediments. Surface application was made using 12.5% sodium hyperchlorite, 6.25% sodium hydrechlorite, 30% hydrogen peroxide and Fentons Reagent (sodium hypchlorite with an iron catalyst) on five separate areas of petroleum hydrocarbon contaminated sediments. Sampling was conducted before and after chemical application from the top soil section (0 - 5 cm) and at depth (10 - 15 cm). The data are stored in an excel file. This work was completed as part of ASAC project 1163 (ASAC_1163). The spreadsheet is divided up as follows: The first 51 sheets are the raw GC-FID data for the 99/00 field season, labelled by sample name. These sheets use the same format as the radiometric GC-FID spreadsheet in the metadata record entitled 'Mineralisation results using 14C octadecane at a range of temperatures'. Sample name format consists of a location or experiment indicator (CW=Casey Workshop, BR= Small-scale field trial), the year the sample was collected (00=2000), the sample type (S=Soil) and a sequence number. SUMMARY and PRINTABLE VERSION are the same data in different formats, PRINTABLE VERSION is printer friendly. This summary data includes the hydrocarbon concentrations corrected for dry weight of soil and biodegradation and weathering indices. GRAPHS are graphs. FIELD MEASUREMENTS show the results of the measurements taken in the field and include PID (ppm), Soil temperature (C), Air temperature (C), Ph and MC (moisture content) (%). NOTES shows the chemicals added to each trial, and a short summary of the samples. The next 21 sheets show the raw GC-FID data for the 00/01 field season, labelled to previously explained method. PRINTABLE (0001) is a summary of the raw GC-FID data. The next 3 sheets show the raw GC-FID data for the 01/02 field season, labelled to previously explained method. PRINTABLE (0102) is a summary of the raw GC-FID data. MPN-NOTES shows lab book references and set up summary for the Most Probable Number (MPN) analysis. MPN-DETAILS shows the set up details, calculations and results for each MPN analysis. MPN-RESULTS shows the raw MPN data. MPN-Calculations show the results from the MPN Calculator. The fields in the dataset are: Retention Time Area % Area Height of peak Amount Int Type Units Peak Type Codes proprietary
-ICRAF_AfSIS_AfrHySRTM Africa Soil Information Service (AfSIS): Hydrologically Corrected/Adjusted SRTM DEM (AfrHySRTM) SCIOPS STAC Catalog 1970-01-01 -17.535833, -34.83917, 51.413334, 37.345833 https://cmr.earthdata.nasa.gov/search/concepts/C1214155420-SCIOPS.umm_json The Africa Soil Information Service: Hydrologically Corrected/Adjusted SRTM DEM (AfrHySRTM) is an adjusted elevation raster in which any depressions in the source Digital Elevation Model (DEM) have been eliminated (filled), but allowing for internal drainage since some landscapes contain natural depressions. These landscapes have their own internal drainage systems, which are not connected to adjacent watersheds. Null cells (drains) were placed in depressions exceeding a depth limit of 20 m and with no less than 1000 cells (pixels) during the DEM adjustment process. After filling depressions in the DEM, flowpaths can also be generated. AfrHySRTM uses the CGIAR-CSI SRTM 90m Version 4 as the source DEM The dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya and is distributed by the Africa Soil Information Service. The purpose of the dataset is to serve a wide user community by providing a Digital Elevation Model for the continent of Africa that can be used to predict soil properties as well as for a range of other applications, including erosion and landslide risk. The images and data are available from the Africa Soil Information Service (AfSIS) in Geographic Tagged Image File Format (GeoTIFF) format via download at http://africasoils.net/. proprietary
ICRAF_AfSIS_AfrHySRTM Africa Soil Information Service (AfSIS): Hydrologically Corrected/Adjusted SRTM DEM (AfrHySRTM) ALL STAC Catalog 1970-01-01 -17.535833, -34.83917, 51.413334, 37.345833 https://cmr.earthdata.nasa.gov/search/concepts/C1214155420-SCIOPS.umm_json The Africa Soil Information Service: Hydrologically Corrected/Adjusted SRTM DEM (AfrHySRTM) is an adjusted elevation raster in which any depressions in the source Digital Elevation Model (DEM) have been eliminated (filled), but allowing for internal drainage since some landscapes contain natural depressions. These landscapes have their own internal drainage systems, which are not connected to adjacent watersheds. Null cells (drains) were placed in depressions exceeding a depth limit of 20 m and with no less than 1000 cells (pixels) during the DEM adjustment process. After filling depressions in the DEM, flowpaths can also be generated. AfrHySRTM uses the CGIAR-CSI SRTM 90m Version 4 as the source DEM The dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya and is distributed by the Africa Soil Information Service. The purpose of the dataset is to serve a wide user community by providing a Digital Elevation Model for the continent of Africa that can be used to predict soil properties as well as for a range of other applications, including erosion and landslide risk. The images and data are available from the Africa Soil Information Service (AfSIS) in Geographic Tagged Image File Format (GeoTIFF) format via download at http://africasoils.net/. proprietary
+ICRAF_AfSIS_AfrHySRTM Africa Soil Information Service (AfSIS): Hydrologically Corrected/Adjusted SRTM DEM (AfrHySRTM) SCIOPS STAC Catalog 1970-01-01 -17.535833, -34.83917, 51.413334, 37.345833 https://cmr.earthdata.nasa.gov/search/concepts/C1214155420-SCIOPS.umm_json The Africa Soil Information Service: Hydrologically Corrected/Adjusted SRTM DEM (AfrHySRTM) is an adjusted elevation raster in which any depressions in the source Digital Elevation Model (DEM) have been eliminated (filled), but allowing for internal drainage since some landscapes contain natural depressions. These landscapes have their own internal drainage systems, which are not connected to adjacent watersheds. Null cells (drains) were placed in depressions exceeding a depth limit of 20 m and with no less than 1000 cells (pixels) during the DEM adjustment process. After filling depressions in the DEM, flowpaths can also be generated. AfrHySRTM uses the CGIAR-CSI SRTM 90m Version 4 as the source DEM The dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya and is distributed by the Africa Soil Information Service. The purpose of the dataset is to serve a wide user community by providing a Digital Elevation Model for the continent of Africa that can be used to predict soil properties as well as for a range of other applications, including erosion and landslide risk. The images and data are available from the Africa Soil Information Service (AfSIS) in Geographic Tagged Image File Format (GeoTIFF) format via download at http://africasoils.net/. proprietary
ICRAF_AfSIS_SCA Africa Soil Information Service (AfSIS): Specific Catchment Area (SCA) SCIOPS STAC Catalog 1970-01-01 -17.535833, -34.83917, 51.413334, 37.345833 https://cmr.earthdata.nasa.gov/search/concepts/C1214155401-SCIOPS.umm_json The Africa Soil Information Service (AfSIS): Specific Catchment Area (SCA) is a 90m raster dataset showing local flow accumulation and flow direction using the formula SCA = A/I, where A is unit contributing area of land upslope of a length of contour I. The specific catchment area contributing to flow at any given location can be used to determine relative saturation and water runoff and, together with other topographic factors, can be used to model erosion and landslides. The digital elevation model used to construct this dataset is AfHydSRTM, which is based on the CGIAR-SRTM 90m Version 4. The dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya and is distributed by the Africa Soil Information Service. The specific catchment area is a useful parameter for modeling of runoff, soil erosion and sediment yield.The images and data are available from the Africa Soil Information Service (AfSIS) in Geographic Tagged Image File Format (GeoTIFF) format via download at http://africasoils.net/. proprietary
ICRAF_AfSIS_SCA Africa Soil Information Service (AfSIS): Specific Catchment Area (SCA) ALL STAC Catalog 1970-01-01 -17.535833, -34.83917, 51.413334, 37.345833 https://cmr.earthdata.nasa.gov/search/concepts/C1214155401-SCIOPS.umm_json The Africa Soil Information Service (AfSIS): Specific Catchment Area (SCA) is a 90m raster dataset showing local flow accumulation and flow direction using the formula SCA = A/I, where A is unit contributing area of land upslope of a length of contour I. The specific catchment area contributing to flow at any given location can be used to determine relative saturation and water runoff and, together with other topographic factors, can be used to model erosion and landslides. The digital elevation model used to construct this dataset is AfHydSRTM, which is based on the CGIAR-SRTM 90m Version 4. The dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya and is distributed by the Africa Soil Information Service. The specific catchment area is a useful parameter for modeling of runoff, soil erosion and sediment yield.The images and data are available from the Africa Soil Information Service (AfSIS) in Geographic Tagged Image File Format (GeoTIFF) format via download at http://africasoils.net/. proprietary
-ICRAF_AfSIS_TWI Africa Soil Information Service (AfSIS): Topographic Wetness Index (TWI) SCIOPS STAC Catalog 1970-01-01 -17.535833, -34.83917, 51.413334, 37.345833 https://cmr.earthdata.nasa.gov/search/concepts/C1214155403-SCIOPS.umm_json The Africa Soil Information Service (AfSIS): Topographic Wetness Index (TWI) is a 90m raster dataset showing zones of increased soil moisture where the landscape area contributing runoff is large and slopes are low. The topographic wetness index, originally developed by Beven and Kirkby in 1979, provides a measure of wetness conditions at the catchment scale. This dataset combines local upslope contributing area and slope using the digital elevation model AfHydSRTM, which is based on the CGIAR-SRTM 90m Version 4. The dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya and is distributed by the Africa Soil Information Service. This index is commonly used in soil landscape modeling and in the analysis of vegetation patterns. The images and data are available from the Africa Soil Information Service (AfSIS) in Geographic Tagged Image File Format (GeoTIFF) format via download at http://africasoils.net/. proprietary
ICRAF_AfSIS_TWI Africa Soil Information Service (AfSIS): Topographic Wetness Index (TWI) ALL STAC Catalog 1970-01-01 -17.535833, -34.83917, 51.413334, 37.345833 https://cmr.earthdata.nasa.gov/search/concepts/C1214155403-SCIOPS.umm_json The Africa Soil Information Service (AfSIS): Topographic Wetness Index (TWI) is a 90m raster dataset showing zones of increased soil moisture where the landscape area contributing runoff is large and slopes are low. The topographic wetness index, originally developed by Beven and Kirkby in 1979, provides a measure of wetness conditions at the catchment scale. This dataset combines local upslope contributing area and slope using the digital elevation model AfHydSRTM, which is based on the CGIAR-SRTM 90m Version 4. The dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya and is distributed by the Africa Soil Information Service. This index is commonly used in soil landscape modeling and in the analysis of vegetation patterns. The images and data are available from the Africa Soil Information Service (AfSIS) in Geographic Tagged Image File Format (GeoTIFF) format via download at http://africasoils.net/. proprietary
+ICRAF_AfSIS_TWI Africa Soil Information Service (AfSIS): Topographic Wetness Index (TWI) SCIOPS STAC Catalog 1970-01-01 -17.535833, -34.83917, 51.413334, 37.345833 https://cmr.earthdata.nasa.gov/search/concepts/C1214155403-SCIOPS.umm_json The Africa Soil Information Service (AfSIS): Topographic Wetness Index (TWI) is a 90m raster dataset showing zones of increased soil moisture where the landscape area contributing runoff is large and slopes are low. The topographic wetness index, originally developed by Beven and Kirkby in 1979, provides a measure of wetness conditions at the catchment scale. This dataset combines local upslope contributing area and slope using the digital elevation model AfHydSRTM, which is based on the CGIAR-SRTM 90m Version 4. The dataset was produced at the World Agroforestry Centre (ICRAF) in Nairobi, Kenya and is distributed by the Africa Soil Information Service. This index is commonly used in soil landscape modeling and in the analysis of vegetation patterns. The images and data are available from the Africa Soil Information Service (AfSIS) in Geographic Tagged Image File Format (GeoTIFF) format via download at http://africasoils.net/. proprietary
IDBMG4_5 IceBridge BedMachine Greenland V005 NSIDC_ECS STAC Catalog 1993-01-01 2021-12-31 -80, 60, 10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2420522159-NSIDC_ECS.umm_json This data set contains a bed topography/bathymetry map of Greenland based on mass conservation, multi-beam data, and other techniques. It also includes surface elevation and ice thickness data, as well as an ice/ocean/land mask. proprietary
IDCSI4_1 IceBridge L4 Sea Ice Freeboard, Snow Depth, and Thickness V001 NSIDC_ECS STAC Catalog 2009-03-19 2013-04-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000001460-NSIDC_ECS.umm_json This data set contains derived geophysical data products including sea ice freeboard, snow depth, and sea ice thickness measurements in Greenland and Antarctica retrieved from IceBridge Snow Radar, Digital Mapping System (DMS), Continuous Airborne Mapping By Optical Translator (CAMBOT), and Airborne Topographic Mapper (ATM) data sets. The data were collected as part of Operation IceBridge funded campaigns. proprietary
IDHDT4_1 IceBridge ATM L4 Surface Elevation Rate of Change V001 NSIDC_ECS STAC Catalog 1993-06-23 2018-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000320-NSIDC_ECS.umm_json This data set contains surface elevation rate of change measurements derived from IceBridge and Pre-IceBridge Airborne Topographic Mapper (ATM) widescan elevation measurements data for Arctic and Antarctic missions flown under NASA's Operation IceBridge (OIB) and Arctic Ice Mapping (AIM) projects. proprietary
@@ -7928,13 +7929,13 @@ ILVIS2_2 IceBridge LVIS L2 Geolocated Surface Elevation Product V002 NSIDC_ECS S
IMARPE-Callao_Station_0 Instituto del Mar del Peru (IMARPE)- Callao Station OB_DAAC STAC Catalog 2012-09-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360376-OB_DAAC.umm_json The IMARPE-Callao station is located offshore from the port of Callao, Peru at a water depth of approximately 150 m. This station is operated by the Instituto del Mar del Peru with the objective to study the oceanographic variability off the Peruvian coast, algal blooms and relationships with ENSO. proprietary
IMCS31B_2 IceBridge Scintrex CS-3 Cesium Magnetometer L1B Geolocated Magnetic Anomalies V002 NSIDC_ECS STAC Catalog 2013-11-18 2017-11-25 -180, -90, 180, -63 https://cmr.earthdata.nasa.gov/search/concepts/C1000000840-NSIDC_ECS.umm_json This data set contains magnetic field readings taken over Antarctica using the Scintrex CS-3 Cesium Magnetometer instrument. The data were collected as part of Operation IceBridge funded aircraft survey campaigns. proprietary
IMECOCAL_0 Investigaciones Mexicanas de la Corriente de California (IMECOCAL) OB_DAAC STAC Catalog 2002-01-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360378-OB_DAAC.umm_json Measurements made under the IMECOCAL program (Investigaciones Mexicanas de la Corriente de California, translated: Mexican Research of the California Current) from 2002 to 2004. proprietary
-IMERG_Precip_Canada_Alaska_2097_1 ABoVE: Bias-Corrected IMERG Monthly Precipitation for Alaska and Canada, 2000-2020 ALL STAC Catalog 2000-06-01 2020-12-31 -179.3, 40.8, -48.5, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2550019170-ORNL_CLOUD.umm_json This dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (*.tif) format. Data are provided in the units of mm/day. NASA's IMERG data product is one of the most advanced satellite precipitation products with a 0.1-degree spatial resolution and near global coverage. This dataset bias-corrected IMERG's HQprecipitation precipitation estimates, which are based on passive microwave (PMW)-only retrievals, using a linear regression method. This method utilizes empirical measurements from rain gauge stations from the Global Historical Climatology Network (GHCN) and a digital elevation model. This bias correction approach improves estimates at elevations above 500 m a.s.l., which are typically underestimated. proprietary
IMERG_Precip_Canada_Alaska_2097_1 ABoVE: Bias-Corrected IMERG Monthly Precipitation for Alaska and Canada, 2000-2020 ORNL_CLOUD STAC Catalog 2000-06-01 2020-12-31 -179.3, 40.8, -48.5, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2550019170-ORNL_CLOUD.umm_json This dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (*.tif) format. Data are provided in the units of mm/day. NASA's IMERG data product is one of the most advanced satellite precipitation products with a 0.1-degree spatial resolution and near global coverage. This dataset bias-corrected IMERG's HQprecipitation precipitation estimates, which are based on passive microwave (PMW)-only retrievals, using a linear regression method. This method utilizes empirical measurements from rain gauge stations from the Global Historical Climatology Network (GHCN) and a digital elevation model. This bias correction approach improves estimates at elevations above 500 m a.s.l., which are typically underestimated. proprietary
+IMERG_Precip_Canada_Alaska_2097_1 ABoVE: Bias-Corrected IMERG Monthly Precipitation for Alaska and Canada, 2000-2020 ALL STAC Catalog 2000-06-01 2020-12-31 -179.3, 40.8, -48.5, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2550019170-ORNL_CLOUD.umm_json This dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (*.tif) format. Data are provided in the units of mm/day. NASA's IMERG data product is one of the most advanced satellite precipitation products with a 0.1-degree spatial resolution and near global coverage. This dataset bias-corrected IMERG's HQprecipitation precipitation estimates, which are based on passive microwave (PMW)-only retrievals, using a linear regression method. This method utilizes empirical measurements from rain gauge stations from the Global Historical Climatology Network (GHCN) and a digital elevation model. This bias correction approach improves estimates at elevations above 500 m a.s.l., which are typically underestimated. proprietary
IMFGM1B_1 IceBridge Watson-Gyro Fluxgate Magnetometer L1B Time-Tagged Magnetic Field V001 NSIDC_ECS STAC Catalog 2008-12-29 2013-01-14 -180, -90, 180, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1624663221-NSIDC_ECS.umm_json This data set contains time-registered Level-1B field readings taken over Antarctica using the Watson-Gyro Fluxgate Magnetometer instrument. The data were collected as part of Operation IceBridge funded aircraft survey campaigns. proprietary
IMS1_HYSI_GEO_1.0 IMS-1 HYSI TOA Radiance and Reflectance Product ISRO STAC Catalog 2008-06-22 2012-09-10 -6.0364, -78.8236, 152.6286, 78.6815 https://cmr.earthdata.nasa.gov/search/concepts/C1214622602-ISRO.umm_json The data received from IMS1, HySI which operates in 64 spectral bands in VNIR bands(400-900nm) with 500 meter spatial resolution and swath of 128 kms. proprietary
IN2017_V01_Diatoms_1 Diatom data from voyage 1 of the Investigator, 2017 - PC03 analysis AU_AADC STAC Catalog 2017-01-13 2017-03-05 115.043, -64.4631, 115.0431, -64.463 https://cmr.earthdata.nasa.gov/search/concepts/C2102891805-AU_AADC.umm_json These data were generated by Raffaella Tolotti (raffaella.tolotti@virgilio.it) thanks to a scholarship founded by the Italian P.N.R.A. ‘TYTAN Project (PdR 14_00119): ‘Totten Glacier dYnamics and Southern Ocean circulation impact on deposiTional processes since the mid-lAte CeNozoic’ (Principal Investigator Dr. Donda Federica, Dr. Caburlotto A. - OGS, Trieste) and University of Genova (DISTAV - Prof. Corradi Nicola). These data are based on samples collected during research cruise IN2017_V01 of the RV Investigator, co-chief scientists, Leanne Armand and Phil O’Brien and were collected to provide paleoceanographic and bio/ stratigraphic information on Aurora Basin Antarctic margin evolution. The IN2017-V01post-cruise report is available through open access via the e-document portal through the ANU library. https://openresearch-repository.anu.edu.au/handle/1885/142525 The document DOI: 10.4225/13/5acea64c48693 The preferred citation are: L.K. Armand, P.E. O’Brien and On-board Scientific Party. 2018. Interactions of the Totten Glacier with the Southern Ocean through multiple glacial cycles (IN2017-V01): Post-survey report, Research School of Earth Sciences, Australian National University: Canberra, http://dx.doi.org/10.4225/13/5acea64c48693 Donda F., Leitchenkov, Brancolini G., Romeo R., De Santis L., Escutia C., O'Brien P., Armand L., Caburlotto, A., Cotterle, D., 2020. The influence of Totten Glacier on the Late Cenozoic sedimentary record. Antarctic Science, 1 -3; http://doi:10.1017/S0954102020000188 O’Brien, P.E., Post, A.L., Edwards, S., Martin, T., Carburlotto, A., Donda, F., Leitchenkov, G., Romero, R., Duffy, M., Evangelinos, D., Holder, L., Leventer, A., López-Quirós, A., Opdyke, B.N., and Armand, L.K. in press. Continental slope and rise geomorphology seaward of the Totten Glacier, East Antarctica (112°E-122°E). Marine Geology. Samples for diatom analysis were collected on board ship immediately after core recovery. Sub-samples were sent, according to the Australian standard procedures, to the DISTAV sedimentological laboratory in Genoa (Italy) and prepared for the micro-paleontological analysis according to the laboratory’s protocol (imported and tested from Salamanca University lab.; Referring Prof. Bárcena). Smear-slides and the qualitative-quantitative analyses were performed every 20 cm. Previous onboard smear slides analyses on PC03 highlighted notable variations from the other piston cores, containing some older diatom species. Moreover this core exceptionally did not exhibit a clear cyclicity like the others. It was so assumed to target a condensed sedimentary sequence giving access to older sediments. The further, more in-depth diatom biostratigraphic and quantitative analyses were performed in accordance with the international stratigraphic guide (https://stratigraphy.org/guide/), with the pluri-decennial DSDP and IODP Antarctic diatom biostratigraphic reports and specific papers (see References). Sample preparation, diatom species identification and counting were those described in Schrader and Gersonde (1978), Barde (1981 - modified) and Bodén (1991). Diatom analysis was performed with an immersion 1000x LM Reichert Jung-Polyvar microscope (Wien). Whenever possible, almost 300 diatom valves were counted per slide following the counting methodology presented in Schrader and Gersonde (1978). When diatom concentration proved too low or too concentrated, slides with modified concentrations have been prepared to optimize counting and identification while at least one hundred fields-of-view per poor concentration slide have been analyzed. For samples that were too diatom-poor, the over-concentration of material on the slides resulted in limiting resolution and taxonomic identification of the rare and mostly fragmented valves. Where diatom occurrence was rare only major fragments (>50%) or entire valves were counted. The file (.xls) contains 2 sheets: Sheet: PC03 diatoms dataset. The absolute diatom valve concentration (ADA= Absolute Valves Abundance) was then calculated following Abrantes et al. (2005), Warnock & Scherer (2014) and ADA in Taylor‐Silva & Riesselmann (2018), taking in account initial weights, concentration of the samples and microscope’s characteristics, as the number of valves per gram of dry sediment. Diatoms were identified to species level following Crosta et al. (2005), Armand et al. (2005), Cefarelli et al. (2010) for modern assemblages. Older diatom taxa were identified following Gersonde et Bárcena, 1998, Witkowski et al., 2014; Bohaty et al., 2011; Gombos, 1985; Gombos, 2007; Gersonde et al., 1990; Barron et al., 2004; Harwood et al., 2001; Harwood etal., 1992. Species were considered extinct when observed stratigraphically higher than extinction boundaries as identified by Cody et al. (2008) but the coexistence or the alternation in the stratigraphic sequence of taxa referring to different biostratigraphic age ranges were considered signs of reworking. Sheet: PC03 tephra dataset. During LM microscopic observations some volcanic glass shards were observed first in smear slides and then counted during the activities of microfossils count for diatoms. This allowed to obtain the number of glass shards/g. dry sed. useful to compare with diatom and sediment datasets. Core location: Station_core Longitude Latitude A006_PC03 115.043 -64.463 Depth: The core was taken at Site A006 that was chosen into an overbank deposit on the upper western side of a turbidite channel (Minang-a Canyon) (Fig. 39 – Armand et al., 2017; O’Brien et al., 2020). The setting is at 1862 m depth, shallower respect the other cores. A possible higher energy environment, with a lower sedimentation rate has been first supposed. Temporal coverage: Start date: 2017-01-14 - Stop date: 2018-11-30 References: Armand, L.K., X. Crosta, O. Romero, J. J. Pichon (2005). The biogeography of major diatom taxa in Southern Ocean sediments: 1. Sea ice related species, Paleogeography, Paleoclimatology, Paleoecology, 223, 93-126. Cefarelli, A.O., M. E. Ferrario, G. O. Almandoz, A. G. Atencio, R. Akselman, M. Vernet (2010). Diversity of the diatom genus Fragilariopsis in the Argentine Sea and Antarctic waters: morphology, distribution and abundance, Polar Biology, 33(2), 1463-1484. Cody, R., R. H. Levy, D. M. Harwood, P. M. Sadler (2008). Thinking outside the zone: High-resolution quantitative diatom biochronology for the Antarctic Neogene, Palaeogeography, Palaeoclimatology, Palaeoecology, 260, 92-121; doi:10.1016/j.palaeo.2007.08.020 Crosta, X., O. Romero, L. K. Armand, J. Pichon (2005). The biogeography of major diatom taxa in Southern Ocean sediments: 2. Open ocean related species, Palaeogeography, Palaeoclimatology, Palaeoecology, 223, 66-92. Rebesco, M., E. Domack, F. Zgur, C. Lavoie, A. Leventer, S. Brachfeld, V. Willmott, G. Halverson, M. Truffer, T. Scambos, J. Smith, E. Pettit (2014). Boundary condition of grounding lines prior to collapse, Larsen-B Ice Shelf, Antarctica, Science, 345, 1354-1358. Warnock, J. P., R. P. Scherer (2014). A revised method for determining the absolute abundance of diatoms, J. Paleolimnol.; doi:10.1007/s10933-014-9808-0 Witkowski, J., Bohaty, S.M., McCartney, K., Harwood, D.M., (2012) . Enhanced siliceous plankton productivity in response to middle Eocene warming at Southern Ocean ODP Sites 748 and 749 Palaeogeog., Palaeoclimat., Palaeoecol., 326–328, 78–94; doi:10.1016/j.palaeo.2012.02.006 Witkowski, J., Bohaty, S.M., Edgar, K.M., Harwood, D.M., (2014). Rapid fluctuations in mid-latitude siliceous plankton production during the Middle Eocene Climatic Optimum (ODP Site 1051, Western North Atlantic). Mar. Micropal., 106, 110–129. http://dx.doi.org/10.1016/j.marmicro.2014.01.001 Raffaella Tolotti unpublished data proprietary
-INC_NCMF A Nature Characterization Map of Flanders ALL STAC Catalog 1970-01-01 -5.29, 40.65, 10.4, 51.82 https://cmr.earthdata.nasa.gov/search/concepts/C1214614322-SCIOPS.umm_json The Nature Characterization Map of Flanders is a collection of all available geographic information at the regional level that is considered relevant for nature conservation. The purpose of the map was to compile a database, making it possible to objectively grade the ecological value of a specific location. This grading system is based on three modules?the actual natural value, the abiotic system features, and the legal framework. The actual natural value is primarily derived from the Biological Valuation map, a vegetation and land use map covering the entire Flemish region. Additional information comes from maps of (international) important wildlife areas, biotope rareness, level of habitat fragmentation, and the location of valuable rivers and streams. The abiotic system features are used as a tool to integrate larger areas and to locate potentially valuable systems. The main information source is the soil map, from which several other features are derived. The third module, the legal and policy framework, is important for establishing the feasibility of any proposed conservation projects. It includes information on the legal designation of land use and national and international protection status. In the long term, the aim is to compile the information from the three modules into a single score, based on multicriteria analysis. The system should also allow for expansion and updating when new information becomes available. The map's primary use is to supply policy makers, planners, and nature conservation organizations at the regional and local levels with extensive and objective information. proprietary
INC_NCMF A Nature Characterization Map of Flanders SCIOPS STAC Catalog 1970-01-01 -5.29, 40.65, 10.4, 51.82 https://cmr.earthdata.nasa.gov/search/concepts/C1214614322-SCIOPS.umm_json The Nature Characterization Map of Flanders is a collection of all available geographic information at the regional level that is considered relevant for nature conservation. The purpose of the map was to compile a database, making it possible to objectively grade the ecological value of a specific location. This grading system is based on three modules?the actual natural value, the abiotic system features, and the legal framework. The actual natural value is primarily derived from the Biological Valuation map, a vegetation and land use map covering the entire Flemish region. Additional information comes from maps of (international) important wildlife areas, biotope rareness, level of habitat fragmentation, and the location of valuable rivers and streams. The abiotic system features are used as a tool to integrate larger areas and to locate potentially valuable systems. The main information source is the soil map, from which several other features are derived. The third module, the legal and policy framework, is important for establishing the feasibility of any proposed conservation projects. It includes information on the legal designation of land use and national and international protection status. In the long term, the aim is to compile the information from the three modules into a single score, based on multicriteria analysis. The system should also allow for expansion and updating when new information becomes available. The map's primary use is to supply policy makers, planners, and nature conservation organizations at the regional and local levels with extensive and objective information. proprietary
+INC_NCMF A Nature Characterization Map of Flanders ALL STAC Catalog 1970-01-01 -5.29, 40.65, 10.4, 51.82 https://cmr.earthdata.nasa.gov/search/concepts/C1214614322-SCIOPS.umm_json The Nature Characterization Map of Flanders is a collection of all available geographic information at the regional level that is considered relevant for nature conservation. The purpose of the map was to compile a database, making it possible to objectively grade the ecological value of a specific location. This grading system is based on three modules?the actual natural value, the abiotic system features, and the legal framework. The actual natural value is primarily derived from the Biological Valuation map, a vegetation and land use map covering the entire Flemish region. Additional information comes from maps of (international) important wildlife areas, biotope rareness, level of habitat fragmentation, and the location of valuable rivers and streams. The abiotic system features are used as a tool to integrate larger areas and to locate potentially valuable systems. The main information source is the soil map, from which several other features are derived. The third module, the legal and policy framework, is important for establishing the feasibility of any proposed conservation projects. It includes information on the legal designation of land use and national and international protection status. In the long term, the aim is to compile the information from the three modules into a single score, based on multicriteria analysis. The system should also allow for expansion and updating when new information becomes available. The map's primary use is to supply policy makers, planners, and nature conservation organizations at the regional and local levels with extensive and objective information. proprietary
INDOEX_0 India Ocean Experiment OB_DAAC STAC Catalog 1999-01-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360380-OB_DAAC.umm_json Measurements from the India Ocean Experiment (INDOEX) in 1999. proprietary
INPE_AQUA1_MODIS Aqua 1 MODIS Imagery CEOS_EXTRA STAC Catalog 2005-06-12 -79, -36, -33, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2227456123-CEOS_EXTRA.umm_json Imagery from MODIS sensor, abord Aqua platform, held by INPE. proprietary
INPE_CBERS2B_CCD CCD - High Resolution CCD Camera (CBERS 2B) CEOS_EXTRA STAC Catalog 2007-09-25 2010-03-10 -85, -60, -20, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2227456148-CEOS_EXTRA.umm_json The CCD camera provides images of a 113 km wide strip with 20m spatial resolution. Since this camera has a sideways pointing capability of ± 32 degrees, it is capable of taking stereoscopic images of a certain region. The CCD camera operates in 5 spectral bands that include a panchromatic one from 0.51 to 0.73 µm. A complete coverage cycle of the CCD camera takes 26 days. proprietary
@@ -8029,8 +8030,8 @@ ISCCP_ICESNOW_NAT_1 International Satellite Cloud Climatology Project (ISCCP) Ic
ISCCP_TOVS_NAT_1 International Satellite Cloud Climatology Project (ISCCP) TOVS in Native Data Format LARC_ASDC STAC Catalog 1980-01-01 2009-12-31 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2146825898-LARC_ASDC.umm_json ISCCP_TOVS_NAT_1 is the International Satellite Cloud Climatology Project (ISCCP) TIROS Operational Vertical Sounder (TOVS) data set in the Native Data Format. It is a daily, global description of the ozone, temperature, and humidity distributions obtained from the analysis of data from the TOVS system. The TOVS data set contents include atmosphere and surface data including temperature structure, water, and ozone abundances obtained from the TOVS product and supplemented by two climatologies. The ISCCP_TOVS_NAT data set contains information concerning the atmospheric temperature and humidity profiles as well as the ozone column abundance. Data collection for this data set is complete. This data set is composed of 3 types of data files: CLIM MONTHLY, which contains the monthly climatological data obtained from balloon observations; TOVS MONTHLY, which contains the monthly climatological data computed from the daily TOVS values; and TOVS DAILY, which contains the daily composite of the TOVS Sounding Product. The data was collected on a global equal-area grid with the cell area equivalent to 2.5 degrees latitude/longitude at the equator. The grid began at the South Pole with the intersection of the Greenwich meridian (0 deg. longitude) and the South Pole as a cell corner. The TOVS system flew on the NOAA Operational Polar Orbiting Satellite series. Measurements from the High Resolution Infrared Radiation Sounder (HIRS/2), the Stratospheric Sounding Unit (SSU), and the Microwave Sounding Unit (MSU) were processed by NOAA to produce the TOVS Sounding Product. ISCCP was the first project of the World Climate Research Program (WCRP), to collect and analyze satellite radiance measurements to infer the global distribution of cloud radiative properties and their diurnal and seasonal variations. These data and analysis products were used to improve the understanding and modeling of the effects of clouds on climate. The ISCCP version of the ice/snow data set included only information concerning fractional coverage. The version actually used in the cloud analysis was changed in two ways: reductions to ice/snow presence and creation of margin zones in the data. The first of these was simply the process of converting the coded parameters in the original data set to code values that indicate only the presence or absence of sea ice and/or snow. The latter process filled in nearby grid cells in the data to indicate proximity to snow or sea ice covered locations. The second change is not included in the archived version of this data. proprietary
ISERV_1 International Space Station SERVIR Environmental Research and Visualization System V1 USGS_EROS STAC Catalog 2013-03-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1379906336-USGS_EROS.umm_json Abstract: The ISS SERVIR Environmental Research and Visualization System (ISERV) acquired images of the Earth's surface from the International Space Station (ISS). The goal was to improve automatic image capturing and data transfer. ISERV's main component was the optical assembly which consisted of a 9.25 inch Schmidt-Cassegrain telescope, a focal reducer (field of view enlarger), a digital single lens reflex camera, and a high precision focusing mechanism. A motorized 2-axis pointing mount allowed pointing at targets approximately 23 degrees from nadir in both along- and across-track directions. proprietary
ISLSCP_919_1 Pre-LBA ISLSCP Initiative I Data ORNL_CLOUD STAC Catalog 1987-01-01 1988-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2777741360-ORNL_CLOUD.umm_json This data set contains hydrology, soils, radiation, cloud, and vegetation data from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative I. The ISLSCP data sets should provide LBA modelers with many of the fields required to describe boundary conditions, and to initialize and force a wide range of land-biosphere-atmosphere models. All of the data have been processed to the same global spatial resolution (1 deg. x 1 deg.), using the same land/sea mask and steps have been taken to ensure spatial and temporal continuity of the data. The data sets cover the period 1987-1988 at 1-month time resolution for most of the seasonally varying quantities. For this pre-LBA data set, the ISLSCP I data are provided as global coverages. The companion file illustrations were subset over the LBA study area, from 35-85 deg. W longitude and 20 deg. S to 10 deg. N latitude, as shown in Figure 1.The data files and illustrations are organized into the three groups listed below.1. Hydrology and Soils2. Radiation and Clouds3. VegetationThe data within each of these areas were acquired from a variety of sources including model output, satellites, and ground measurements. The individual data sets were provided in a variety of forms. In some cases, this required the data publication team to regrid and reformat data sets and in others to produce monthly averages from finer resolution data. The specific processing for each data set is detailed in the documentation. The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD-ROMs (Marengo and Victoria, 1998) but are now archived individually. proprietary
-ISPOL2004_AAD_BuoyData_1 AAD buoy data collected during ISPOL 2004, Western Weddell Sea AU_AADC STAC Catalog 2004-11-28 2005-01-01 -54.7666, -68.1666, -54.7666, -68.1666 https://cmr.earthdata.nasa.gov/search/concepts/C1214313558-AU_AADC.umm_json Ice Station POLarstern [ISPOL] was a multi-national, interdisciplinary study coordinated by the Alfred Wegener Institute for Polar and Marine Research, Germany, involving scientists from different institutes and nations across a range of scientific disciplines. ISPOL had been planned as a 50-day drift station in the Western Weddell Sea. Due to particularly heavy sea-ice conditions, the start of the drifting ice station was delayed, so that the drift interval, originating at -68 degrees 10'N, -54 degrees 46'W, lasted only a total of 35 days (28.11.2004 - 01.01.2005). Data and auxiliary information presented here are on the sea-ice drift and deformation experiment, which was a collaborative research program involving the International Arctic Research Center [IARC] at the University of Alaska Fairbanks, the Australian Antarctic Division [AAD], the Finnish Institute of Marine Research [FIMR] and the Alfred Wegener Institute [AWI]. Buoy contributions came from all four institutions listed above. - This metadata record covers only AAD buoy data from the ISPOL 2004 experiment. To estimate the characteristics of the sea-ice drift and dynamics in the Western Weddell Sea a meso-scale array of 26 drifting ice buoys was deployed for about 30 days during late November and December 2004. Sea-ice drift was obtained from the horizontal GPS-derived location measurements, which were made at all buoys but collected at various temporal resolutions and different spatial accuracies. Auxiliary instruments were attached to some of the sea-ice drifters, including temperature probes for air and sea-ice temperatures, and air pressure sensors. Four of the buoys were left in the ice pack after the end of the ISPOL field phase to record the large-scale drift in the region around the ice station from late summer into winter. See the metadata record 'Ice Station Polarstern. Aerial photographs over sea ice taken during the ISLOP project' for more information on the ISPOL project. Also, see the URL given below for the ISPOL home page. proprietary
ISPOL2004_AAD_BuoyData_1 AAD buoy data collected during ISPOL 2004, Western Weddell Sea ALL STAC Catalog 2004-11-28 2005-01-01 -54.7666, -68.1666, -54.7666, -68.1666 https://cmr.earthdata.nasa.gov/search/concepts/C1214313558-AU_AADC.umm_json Ice Station POLarstern [ISPOL] was a multi-national, interdisciplinary study coordinated by the Alfred Wegener Institute for Polar and Marine Research, Germany, involving scientists from different institutes and nations across a range of scientific disciplines. ISPOL had been planned as a 50-day drift station in the Western Weddell Sea. Due to particularly heavy sea-ice conditions, the start of the drifting ice station was delayed, so that the drift interval, originating at -68 degrees 10'N, -54 degrees 46'W, lasted only a total of 35 days (28.11.2004 - 01.01.2005). Data and auxiliary information presented here are on the sea-ice drift and deformation experiment, which was a collaborative research program involving the International Arctic Research Center [IARC] at the University of Alaska Fairbanks, the Australian Antarctic Division [AAD], the Finnish Institute of Marine Research [FIMR] and the Alfred Wegener Institute [AWI]. Buoy contributions came from all four institutions listed above. - This metadata record covers only AAD buoy data from the ISPOL 2004 experiment. To estimate the characteristics of the sea-ice drift and dynamics in the Western Weddell Sea a meso-scale array of 26 drifting ice buoys was deployed for about 30 days during late November and December 2004. Sea-ice drift was obtained from the horizontal GPS-derived location measurements, which were made at all buoys but collected at various temporal resolutions and different spatial accuracies. Auxiliary instruments were attached to some of the sea-ice drifters, including temperature probes for air and sea-ice temperatures, and air pressure sensors. Four of the buoys were left in the ice pack after the end of the ISPOL field phase to record the large-scale drift in the region around the ice station from late summer into winter. See the metadata record 'Ice Station Polarstern. Aerial photographs over sea ice taken during the ISLOP project' for more information on the ISPOL project. Also, see the URL given below for the ISPOL home page. proprietary
+ISPOL2004_AAD_BuoyData_1 AAD buoy data collected during ISPOL 2004, Western Weddell Sea AU_AADC STAC Catalog 2004-11-28 2005-01-01 -54.7666, -68.1666, -54.7666, -68.1666 https://cmr.earthdata.nasa.gov/search/concepts/C1214313558-AU_AADC.umm_json Ice Station POLarstern [ISPOL] was a multi-national, interdisciplinary study coordinated by the Alfred Wegener Institute for Polar and Marine Research, Germany, involving scientists from different institutes and nations across a range of scientific disciplines. ISPOL had been planned as a 50-day drift station in the Western Weddell Sea. Due to particularly heavy sea-ice conditions, the start of the drifting ice station was delayed, so that the drift interval, originating at -68 degrees 10'N, -54 degrees 46'W, lasted only a total of 35 days (28.11.2004 - 01.01.2005). Data and auxiliary information presented here are on the sea-ice drift and deformation experiment, which was a collaborative research program involving the International Arctic Research Center [IARC] at the University of Alaska Fairbanks, the Australian Antarctic Division [AAD], the Finnish Institute of Marine Research [FIMR] and the Alfred Wegener Institute [AWI]. Buoy contributions came from all four institutions listed above. - This metadata record covers only AAD buoy data from the ISPOL 2004 experiment. To estimate the characteristics of the sea-ice drift and dynamics in the Western Weddell Sea a meso-scale array of 26 drifting ice buoys was deployed for about 30 days during late November and December 2004. Sea-ice drift was obtained from the horizontal GPS-derived location measurements, which were made at all buoys but collected at various temporal resolutions and different spatial accuracies. Auxiliary instruments were attached to some of the sea-ice drifters, including temperature probes for air and sea-ice temperatures, and air pressure sensors. Four of the buoys were left in the ice pack after the end of the ISPOL field phase to record the large-scale drift in the region around the ice station from late summer into winter. See the metadata record 'Ice Station Polarstern. Aerial photographs over sea ice taken during the ISLOP project' for more information on the ISPOL project. Also, see the URL given below for the ISPOL home page. proprietary
ISSITGR4_1 ICESat L4 Seasonal Gridded Sea Ice Thickness V001 NSIDC_ECS STAC Catalog 2003-02-20 2008-03-21 -180, 66, 180, 86 https://cmr.earthdata.nasa.gov/search/concepts/C2673518799-NSIDC_ECS.umm_json "This data set reports seasonal gridded winter sea ice thickness across the Arctic Ocean. Sea ice thickness is estimated using ICESat/GLAS L3A Sea Ice Freeboard data and NASA Eulerian Snow On Sea Ice Model (NESOSIM) Version 1.1 snow loading. This data set is a historical complement to ICESat-2 L4 Monthly Gridded Sea Ice Thickness." proprietary
ITPRN5L1_001 ITPR/Nimbus-5 Level 1 Calibrated Radiances V001 (ITPRN5L1) at GES DISC GES_DISC STAC Catalog 1975-02-14 1976-09-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1990675346-GES_DISC.umm_json ITPRN5L1 is the Nimbus-5 Infrared Temperature Profile Radiometer (ITPR) Level-1 Calibrated Radiances data product which contains radiances at 7 infrared spectral regions (2683.0, 899.0, 747.0, 713.8, 689.5, 668.3, and 507.4 cm-1) in a single binary data file. Four are centered near the 15 micron CO2 band, one interval in the water vapor rotational band near 20 microns and two spectral intervals in the atmospheric window regions near 3.7 and 11 microns. The instrument scan sequence consists of three separate grid matrices, to the right, center and left of nadir. Each matrix consists of 10 scan lines with 14 scenes per scan. Each scan footprint is 32 km wide. Due to problems with the instrument, data are limited to three time periods from 14 February 1975 to 1 March 1975 covering East Asia, from 10 May 1976 to 4 June 1976 covering the United States and the Gulf of Mexico, and from 1 September 1976 to 30 September 1976 covering southern Australia and New Zealand. The principal investigator for the ITPR experiment was William L. Smith from NOAA. proprietary
IXBMI2AE_2 MISR L2 Aerosol Product subset for the INTEX-B region V002 LARC STAC Catalog 2006-02-28 2006-04-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000280-LARC.umm_json MISR Level 2 Aerosol Product containing aerosol optical depth and particle type, with associated atmospheric data for the INTEXB_2006 theme. proprietary
@@ -8050,11 +8051,11 @@ Imnavait_Creek_Veg_Plots_1356_1 Arctic Vegetation Plots at Imnavait Creek, Alask
InSAR_Prudhoe_Bay_1267_1 Pre-ABoVE: Remotely Sensed Active Layer Thickness, Prudhoe Bay, Alaska, 1992-2000 ORNL_CLOUD STAC Catalog 1992-01-01 2000-12-31 -149.5, 69.95, -146.99, 70.45 https://cmr.earthdata.nasa.gov/search/concepts/C2170968546-ORNL_CLOUD.umm_json Active layer thickness (ALT) is a critical parameter for monitoring the status of permafrost that is typically measured at specific locations using probing, in situ temperature sensors, or other ground-based observations. The thickness of the active layer is the average annual thaw depth, in permafrost areas, due to solar heating of the surface. This data set includes the mean Remotely Sensed Active Layer Thickness (ReSALT) over years 1992 to 2000 for an area near Prudhoe Bay, Alaska. The data were produced by an Interferometric Synthetic Aperture Radar (InSAR) technique that measures seasonal surface subsidence and infers ALT. ReSALT estimates were validated by comparison with ground-based ALT measurements at multiple sites. These results indicate remote sensing techniques based on InSAR could be an effective way to measure and monitor ALT over large areas on the Arctic coastal plain.These data provide gridded (100-m) estimates of active layer thickness (cm; ALT), seasonal subsidence (cm) and subsidence trend (mm/yr), as well as calculated uncertainty in each of these parameters. This data set was developed in support of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) field campaign.The data are presented in one netCDF (*.nc) file. proprietary
Insitu_Tower_Greenhouse_Gas_1798_1 ACT-America: L1 Raw, Uncalibrated In-Situ CO2, CO, and CH4 Mole Fractions from Towers ORNL_CLOUD STAC Catalog 2015-01-01 2019-12-31 -98.59, 30.2, -76.42, 44.05 https://cmr.earthdata.nasa.gov/search/concepts/C2706327711-ORNL_CLOUD.umm_json This dataset provides Level 1 (L1) in situ atmospheric carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4) concentrations as measured on a network of instrumented communications towers across the central and eastern USA operated by the Atmospheric Carbon and Transport-America (ACT-America) project. There were 11 towers instrumented with cavity ring-down spectrometers (CRDS; Picarro Inc.) with measurements beginning in January 2015 and continuing to October 2019. The measurement period varied by tower site. The Picarro analyzers continuously measured total CH4, isotopic ratio of CH4, CO2, CO, and other greenhouse gas concentrations. Not all species were measured at all sites. Complete tower location, elevation, instrument height, and date/time information are also provided. Determination of greenhouse gas fluxes and uncertainty bounds is essential for the evaluation of the effectiveness of mitigation strategies. These L1 data are raw instrument outputs from the Picarro instruments. A Level 2 (L2) product derived from this L1 data is available and generally would be the preferred data for most use cases. proprietary
Insitu_Tower_Greenhouse_Gas_1798_1 ACT-America: L1 Raw, Uncalibrated In-Situ CO2, CO, and CH4 Mole Fractions from Towers ALL STAC Catalog 2015-01-01 2019-12-31 -98.59, 30.2, -76.42, 44.05 https://cmr.earthdata.nasa.gov/search/concepts/C2706327711-ORNL_CLOUD.umm_json This dataset provides Level 1 (L1) in situ atmospheric carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4) concentrations as measured on a network of instrumented communications towers across the central and eastern USA operated by the Atmospheric Carbon and Transport-America (ACT-America) project. There were 11 towers instrumented with cavity ring-down spectrometers (CRDS; Picarro Inc.) with measurements beginning in January 2015 and continuing to October 2019. The measurement period varied by tower site. The Picarro analyzers continuously measured total CH4, isotopic ratio of CH4, CO2, CO, and other greenhouse gas concentrations. Not all species were measured at all sites. Complete tower location, elevation, instrument height, and date/time information are also provided. Determination of greenhouse gas fluxes and uncertainty bounds is essential for the evaluation of the effectiveness of mitigation strategies. These L1 data are raw instrument outputs from the Picarro instruments. A Level 2 (L2) product derived from this L1 data is available and generally would be the preferred data for most use cases. proprietary
-Interior_Alaska_Subsistence_1725_1 ABoVE: Subsistence Resource Use Areas of Interior Alaskan Communities, 2011-2017 ORNL_CLOUD STAC Catalog 2011-01-01 2017-12-31 -176.65, 51.71, -131.52, 70.15 https://cmr.earthdata.nasa.gov/search/concepts/C2143402732-ORNL_CLOUD.umm_json This dataset provide maps to show the search and harvest areas used by community residents for all subsistence resources combined across Interior Alaska for the years 2011 through 2017. The maps show the extent of areas used by residents for those communities where data collection and research has occurred; it is not a comprehensive use map for the entire area. The maps are a composite of data collected by the Division of Subsistence, Alaska Department of Fish and Game using standardized methods where respondents indicated the search areas for species harvested, the amounts harvested, and the location and months of harvest. These data are important for research, analysis, and regulatory assessment. proprietary
Interior_Alaska_Subsistence_1725_1 ABoVE: Subsistence Resource Use Areas of Interior Alaskan Communities, 2011-2017 ALL STAC Catalog 2011-01-01 2017-12-31 -176.65, 51.71, -131.52, 70.15 https://cmr.earthdata.nasa.gov/search/concepts/C2143402732-ORNL_CLOUD.umm_json This dataset provide maps to show the search and harvest areas used by community residents for all subsistence resources combined across Interior Alaska for the years 2011 through 2017. The maps show the extent of areas used by residents for those communities where data collection and research has occurred; it is not a comprehensive use map for the entire area. The maps are a composite of data collected by the Division of Subsistence, Alaska Department of Fish and Game using standardized methods where respondents indicated the search areas for species harvested, the amounts harvested, and the location and months of harvest. These data are important for research, analysis, and regulatory assessment. proprietary
+Interior_Alaska_Subsistence_1725_1 ABoVE: Subsistence Resource Use Areas of Interior Alaskan Communities, 2011-2017 ORNL_CLOUD STAC Catalog 2011-01-01 2017-12-31 -176.65, 51.71, -131.52, 70.15 https://cmr.earthdata.nasa.gov/search/concepts/C2143402732-ORNL_CLOUD.umm_json This dataset provide maps to show the search and harvest areas used by community residents for all subsistence resources combined across Interior Alaska for the years 2011 through 2017. The maps show the extent of areas used by residents for those communities where data collection and research has occurred; it is not a comprehensive use map for the entire area. The maps are a composite of data collected by the Division of Subsistence, Alaska Department of Fish and Game using standardized methods where respondents indicated the search areas for species harvested, the amounts harvested, and the location and months of harvest. These data are important for research, analysis, and regulatory assessment. proprietary
Interpolated_Met_Products_1876_1 ATom: GEOS-5 Derived Meteorological Conditions and Tagged Tracers Along Flight Tracks ORNL_CLOUD STAC Catalog 2016-07-29 2018-05-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2677009033-ORNL_CLOUD.umm_json "This dataset provides modeled meteorological conditions and tagged-CO tracer concentrations along ATom flight paths derived from the Goddard Earth Observing System Version 5 (GEOS-5) data assimilation products from the Global Modeling and Assimilation Office (GMAO) at NASA's Goddard Space Flight Center. The GMAO ""GEOS fp"" forward processing system ingests satellite, ground-based, and airborne data, using a sophisticated model along with the data's statistical properties to obtain global three-dimensional data gridded fields at regular time intervals. These data are from the GMAO model output that were fitted to the ATom flight tracks by interpolating the GMAO model output to the horizontal ATom flight tracks for each of the 4 ATom Deployments. The dataset also provides tagged-CO tracer concentrations, which represent the contribution of specific regional sources to the total simulated CO. The data products produced are consistent with both the original measurements and the physical laws governing the atmosphere. To provide some meteorological context for the ATom flights, the GEOS5 gridded data are interpolated in space and time to the flight tracks." proprietary
-InundationMap_YkFlats_PeaceAth_1901_1 ABoVE: Wetland Inundation Coverage at Yukon Flats, AK and PA Delta, Canada, 2017-2019 ALL STAC Catalog 2017-05-21 2019-10-26 -146.43, 58.25, -110.92, 66.81 https://cmr.earthdata.nasa.gov/search/concepts/C2482179223-ORNL_CLOUD.umm_json This dataset provides time series of wetland inundation coverage maps and corresponding inundation frequency maps at ~10-meter resolution estimated every 12 days during the free-water period (May to October) for the years 2017-2019 over the Yukon Flats (YK) portion of the Yukon River, Alaska, USA, and the Peace-Athabasca Delta (PAD), Alberta, Canada. Wetland inundation coverage was determined by a two-step modified decision-tree classification approach that first used Sentinel-1 C-band SAR to identify likely inundated areas across a study site and was followed by a decision-tree classification step with C-band SAR backscatter statistics thresholds to distinguish among different inundation components. The result of this process was five classes for each inundation map, namely Open Water (OW), Floating Plants (FP), Emergent Plants (EP), Flooded Vegetation (FV), and Dry Land (DRY). After all the individual (every 12 days) inundation coverage maps were derived for a study site, they were generalized to two-class maps which maintained only inundation status. These generalized maps were then stacked and summarized to produce the inundation frequency map for the site. In these maps, higher values signify more frequently inundated areas, with the maximum value representing permanently inundated pixels. The Sentinel-1 inundation mapping capability demonstrated here provided frequent, broad-scale mapping of different wetland inundation components. Integration of such products with process-based methane (CH4) models would improve simulation of CH4 emissions from wetlands. proprietary
InundationMap_YkFlats_PeaceAth_1901_1 ABoVE: Wetland Inundation Coverage at Yukon Flats, AK and PA Delta, Canada, 2017-2019 ORNL_CLOUD STAC Catalog 2017-05-21 2019-10-26 -146.43, 58.25, -110.92, 66.81 https://cmr.earthdata.nasa.gov/search/concepts/C2482179223-ORNL_CLOUD.umm_json This dataset provides time series of wetland inundation coverage maps and corresponding inundation frequency maps at ~10-meter resolution estimated every 12 days during the free-water period (May to October) for the years 2017-2019 over the Yukon Flats (YK) portion of the Yukon River, Alaska, USA, and the Peace-Athabasca Delta (PAD), Alberta, Canada. Wetland inundation coverage was determined by a two-step modified decision-tree classification approach that first used Sentinel-1 C-band SAR to identify likely inundated areas across a study site and was followed by a decision-tree classification step with C-band SAR backscatter statistics thresholds to distinguish among different inundation components. The result of this process was five classes for each inundation map, namely Open Water (OW), Floating Plants (FP), Emergent Plants (EP), Flooded Vegetation (FV), and Dry Land (DRY). After all the individual (every 12 days) inundation coverage maps were derived for a study site, they were generalized to two-class maps which maintained only inundation status. These generalized maps were then stacked and summarized to produce the inundation frequency map for the site. In these maps, higher values signify more frequently inundated areas, with the maximum value representing permanently inundated pixels. The Sentinel-1 inundation mapping capability demonstrated here provided frequent, broad-scale mapping of different wetland inundation components. Integration of such products with process-based methane (CH4) models would improve simulation of CH4 emissions from wetlands. proprietary
+InundationMap_YkFlats_PeaceAth_1901_1 ABoVE: Wetland Inundation Coverage at Yukon Flats, AK and PA Delta, Canada, 2017-2019 ALL STAC Catalog 2017-05-21 2019-10-26 -146.43, 58.25, -110.92, 66.81 https://cmr.earthdata.nasa.gov/search/concepts/C2482179223-ORNL_CLOUD.umm_json This dataset provides time series of wetland inundation coverage maps and corresponding inundation frequency maps at ~10-meter resolution estimated every 12 days during the free-water period (May to October) for the years 2017-2019 over the Yukon Flats (YK) portion of the Yukon River, Alaska, USA, and the Peace-Athabasca Delta (PAD), Alberta, Canada. Wetland inundation coverage was determined by a two-step modified decision-tree classification approach that first used Sentinel-1 C-band SAR to identify likely inundated areas across a study site and was followed by a decision-tree classification step with C-band SAR backscatter statistics thresholds to distinguish among different inundation components. The result of this process was five classes for each inundation map, namely Open Water (OW), Floating Plants (FP), Emergent Plants (EP), Flooded Vegetation (FV), and Dry Land (DRY). After all the individual (every 12 days) inundation coverage maps were derived for a study site, they were generalized to two-class maps which maintained only inundation status. These generalized maps were then stacked and summarized to produce the inundation frequency map for the site. In these maps, higher values signify more frequently inundated areas, with the maximum value representing permanently inundated pixels. The Sentinel-1 inundation mapping capability demonstrated here provided frequent, broad-scale mapping of different wetland inundation components. Integration of such products with process-based methane (CH4) models would improve simulation of CH4 emissions from wetlands. proprietary
IronEx_0 Iron Fertilization Experiment (IronEx) OB_DAAC STAC Catalog 1995-05-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360383-OB_DAAC.umm_json Measurements made under the IronEx (Iron Fertilization Experiment) in the central eastern Pacific Ocean in 1995. proprietary
IsricWiseGrids_546_1 Global Data Set of Derived Soil Properties, 0.5-Degree Grid (ISRIC-WISE) ORNL_CLOUD STAC Catalog 1950-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2216862923-ORNL_CLOUD.umm_json The World Inventory of Soil Emission Potentials (WISE) database was used to generate a series of uniform data sets of derived soil properties for each of the 106 soil units considered in the Soil Map of the World. These data sets were then used to generate GIS raster image files for the following variables: total available water capacity (mm water per 1 m soil depth); soil organic carbon density (kg C/m**2 for 0-30cm depth range); soil organic carbon density (kg C/m**2 for 0-100cm depth range); soil carbonate carbon density (kg C/m**2 for 0-100cm depth range); soil pH (0-30 cm depth range); and soil pH (30-100 cm depth range). proprietary
IsricWise_547_1 Global Soil Profile Data (ISRIC-WISE) ORNL_CLOUD STAC Catalog 1950-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2216862983-ORNL_CLOUD.umm_json The ISRIC-WISE International soil profile data set consists of a homogenized, global set of 1,125 soil profiles for use by global modelers. These profiles provided the basis for the Global Pedon Database (GPDB) of the International Geosphere-Biosphere Programme (IGBP) - Data and Information System (DIS). The data set includes information on soil classification, site data, soil horizon data, source of data, and methods used for determining analytical data. proprietary
@@ -8104,8 +8105,8 @@ JASON_CS_S6A_L3_ALT_LR_OST_NTC_F08_F08 Sentinel-6A MF Jason-CS L3 P4 Altimeter H
JAXAL2InstChecked_4.0 EarthCARE JAXA L2 Products for Cal/Val Users ESA STAC Catalog 2024-05-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3325394877-ESA.umm_json This EarthCARE collection is restricted, and contains the following data products: · Level 2a: Single-Instrument Geophysical Products These products are derived from individual instrument data onboard EarthCARE. They provide detailed geophysical parameters and properties specific to each instrument's capabilities for example cloud and aerosol properties derived solely from radar or lidar measurements, offering high-resolution insights into atmospheric phenomena. · Level 2b: Synergistic Geophysical Products Level 2b products leverage data from multiple EarthCARE instruments to generate comprehensive, synergistic geophysical datasets. By combining measurements from instruments like radar, lidar, and radiometers, these products offer a more integrated view of cloud-aerosol interactions and atmospheric dynamics. Synergistic products provide enhanced accuracy and depth compared to single-instrument outputs, enabling detailed studies of complex atmospheric processes. proprietary
JAXAL2Products_5.0 EarthCARE JAXA L2 Products for the Commissioning Team ESA STAC Catalog 2024-05-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3325393702-ESA.umm_json This EarthCARE collection contains the following data products: Level 2a: Single-Instrument Geophysical Products These products are derived from individual instrument data onboard EarthCARE. They provide detailed geophysical parameters and properties specific to each instrument's capabilities for example cloud and aerosol properties derived solely from radar or lidar measurements, offering high-resolution insights into atmospheric phenomena. Level 2b: Synergistic Geophysical Products Level 2b products leverage data from multiple EarthCARE instruments to generate comprehensive, synergistic geophysical datasets. By combining measurements from instruments like radar, lidar, and radiometers, these products offer a more integrated view of cloud-aerosol interactions and atmospheric dynamics. Synergistic products provide enhanced accuracy and depth compared to single-instrument outputs, enabling detailed studies of complex atmospheric processes. proprietary
JAXAL2Validated_3.0 EarthCARE JAXA L2 Products ESA STAC Catalog 2024-05-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3325393729-ESA.umm_json This EarthCARE collection contains the following data products: Level 2a: Single-Instrument Geophysical Products These products are derived from individual instrument data onboard EarthCARE. They provide detailed geophysical parameters and properties specific to each instrument's capabilities for example cloud and aerosol properties derived solely from radar or lidar measurements, offering high-resolution insights into atmospheric phenomena. Level 2b: Synergistic Geophysical Products Level 2b products leverage data from multiple EarthCARE instruments to generate comprehensive, synergistic geophysical datasets. By combining measurements from instruments like radar, lidar, and radiometers, these products offer a more integrated view of cloud-aerosol interactions and atmospheric dynamics. Synergistic products provide enhanced accuracy and depth compared to single-instrument outputs, enabling detailed studies of complex atmospheric processes. proprietary
-JCADM_USA_PENGUINS Adelie Penguin ecology SCIOPS STAC Catalog 1995-12-25 2001-01-20 166.17, -77.58, 169.25, -76.92 https://cmr.earthdata.nasa.gov/search/concepts/C1214609023-SCIOPS.umm_json Ecology of Adelie Penguins breeding at colonies in SW Ross Sea. proprietary
JCADM_USA_PENGUINS Adelie Penguin ecology ALL STAC Catalog 1995-12-25 2001-01-20 166.17, -77.58, 169.25, -76.92 https://cmr.earthdata.nasa.gov/search/concepts/C1214609023-SCIOPS.umm_json Ecology of Adelie Penguins breeding at colonies in SW Ross Sea. proprietary
+JCADM_USA_PENGUINS Adelie Penguin ecology SCIOPS STAC Catalog 1995-12-25 2001-01-20 166.17, -77.58, 169.25, -76.92 https://cmr.earthdata.nasa.gov/search/concepts/C1214609023-SCIOPS.umm_json Ecology of Adelie Penguins breeding at colonies in SW Ross Sea. proprietary
JERS-1.OPS.SYC_7.0 JERS-1 OPS (Optical Sensor) Very Near Infrared Radiometer (VNIR) System Corrected Products level 1 ESA STAC Catalog 1992-08-13 1998-10-08 95, -90, -130, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336918-ESA.umm_json The JERS-1 Optical System (OPS) is composed of a Very Near Infrared Radiometer (VNIR) and a Short Wave Infrared Radiometer (SWIR). The instrument has 8 observable spectral bands from visible to short wave infrared. Data acquired by ESA ground stations The JERS-1 OPS products are available in GeoTIFF format. These products are available only for the VNIR sensor. All four bands are corrected. The correction consists in a vertical and horizontal destriping, the radiometry values are expanded from the range [0,63] to the range [0,255]. No geometrical correction is applied on level 1. The pixel size of approximately 18 x 24.2 metres for raw data is newly dimensioned to 18 x 18 metres for System Corrected data using a cubic convolution algorithm. Disclaimer: Cloud coverage for JERS OPS products has not been computed using an algorithm. The cloud cover assignment was performed manually by operators at the acquisition stations. Due to missing attitude information, the Nadir looking band (band 3) and the corresponding forward looking band (band 4) are not well coregistered, resulting in some accuracy limitations. The quality control was not performed systematically for each frame. A subset of the entire JERS Optical dataset was selected and manually checked. As a result of this, users may occasionally encounter issues with some of the individual products. proprietary
JERS-1.SAR.PRI_7.0 JERS-1 SAR Level 1 Precision Image ESA STAC Catalog 1992-07-13 1998-10-08 -95, -90, 130, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336919-ESA.umm_json The JSA_PRI_1P product is comparable to the ESA PRI/IMP images generated for Envisat ASAR and ERS SAR instruments. It is a ground range projected detected image in zero-Doppler SAR coordinates, with a 12.5 metre pixel spacing. It has four overlapping looks in Doppler covering a total bandwidth of 1000Hz, with each look covering a 300Hz bandwidth. Sidelobe reduction is applied to achieve a nominal PSLR of less than -21dB. The image is not geocoded, and terrain distortion (foreshortening and layover) has not been removed. Data acquired by ESA ground stations. proprietary
JERS-1.SAR.SLC_7.0 JERS-1 SAR Level 1 Single Look Complex Image ESA STAC Catalog 1992-07-13 1998-10-08 -95, -90, 130, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336920-ESA.umm_json The JSA_SLC_1P product is comparable to the ESA SLC/IMS images generated for Envisat ASAR and ERS SAR instruments. It is a slant-range projected complex image in zero-Doppler SAR coordinates. The data is sampled in natural units of time in range and along track, with the range pixel spacing corresponding to the reciprocal of the platform ADC rate and the along track spacing to the reciprocal of the PRF. Data is processed to an unweighted Doppler bandwidth of 1000Hz, without sidelobe reduction. The product is suitable for interferometric, calibration and quality analysis applications. Data acquired by ESA ground stations proprietary
@@ -8120,10 +8121,10 @@ JGOFS_0 Joint Global Ocean Flux Study (JGOFS) OB_DAAC STAC Catalog 1986-08-08 -
JGOFS_Arabian_Sea_0 Joint Global Ocean Flux Study (JGOFS) Arabian Sea measurements OB_DAAC STAC Catalog 1994-03-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360384-OB_DAAC.umm_json Joint Global Ocean Flux Study (JGOFS) Arabian Sea measurements from 1994 and 1995. proprietary
JGOFS_BOFS_0 Joint Global Ocean Flux Study (JGOFS) Arabian Sea measurements - Biogeochemical Ocean Flux Study (BOFS) OB_DAAC STAC Catalog 1991-06-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360385-OB_DAAC.umm_json Joint Global Ocean Flux Study (JGOFS) measurements taken by Germany, The Netherlands, and the United Kingdom from 1991. proprietary
JGOFS_EQPAC_0 Joint Global Ocean Flux Study (JGOFS) - Central Equatorial Pacific OB_DAAC STAC Catalog 1992-02-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360386-OB_DAAC.umm_json Joint Global Ocean Flux Study (JGOFS) Central Equatorial Pacific measurements from 1992. proprietary
-JGOFS_EQPAC_CYANOBACT_NANOPLANK Abundance, Biovolume and Biomass of Cyanobacteria and Eukaryotic Pico- and Nanoplankton Measured during the JGOFS Equatorial Pacific Process Study ALL STAC Catalog 1992-02-03 1992-10-21 -140, -17, -140, 12 https://cmr.earthdata.nasa.gov/search/concepts/C1214605622-SCIOPS.umm_json "The Equatorial Pacific Process Study (EQPAC) was conducted along 140 deg W longitude during 1992. Four cruises took place: February 3 - March 9, March 19 - April 15, August 5 - September 18, and September 24 - October 21. A fifth benthic cruise and sediment trap legs were added. During the first cruise (TT007), 15 stations were occupied along 140 deg W longitude from 12 deg N latitude to 12 deg S latitude. During the second cruise (TT008), data were collected at 8 stations along 140 deg W longitude from 9 deg S latitude to 9 deg N latitude. During the third cruise (TT011), data were collected at 15 stations along 140 deg W from 12 deg N latitude to 12 deg S latitude. During the fourth cruise (TT012), data were collected at 5 stations along 140 deg W longitude from 17 deg S latitude to the equator. Abundance, biovolume and biomass of cyanobacteria and eukaryotic plankton were measured at each station in vertical profiles using the CTD rosette water sampler. The cyanobacteria and plankton were enumerated and sized using color image analyzed fluorescence microscopy. The following parameters were measured: abundance of synechococcus-type cyanobacteria biovolume of synechococcus-type cyanobacteria biomass of synechococcus-type cyanobacteria abundance of phototrophic eucaryotic pico- and nanoplankton biovolume of phototrophic eucaryotic pico- and nanoplankton biomass of phototropic eucaryotic pico- and nanoplankton abundance of heterotrophic eucaryotic pico- and nanoplankton biovolume of heterotrophic eucaryotic pico- and nanoplankton biomass of heterotrophic eucaryotic pico- and nanoplankton The abundances are in units of cells/liter; the biovolumes are in units of cubic micrometers; and the biomasses are in units of micrograms of carbon per liter. The data is public domain and can be retrieved on-line at ""http://usjgofs.whoi.edu/jg/dir/jgofs/"" [The information in this summary was derived from the JGOFS World Wide Web pages.]" proprietary
JGOFS_EQPAC_CYANOBACT_NANOPLANK Abundance, Biovolume and Biomass of Cyanobacteria and Eukaryotic Pico- and Nanoplankton Measured during the JGOFS Equatorial Pacific Process Study SCIOPS STAC Catalog 1992-02-03 1992-10-21 -140, -17, -140, 12 https://cmr.earthdata.nasa.gov/search/concepts/C1214605622-SCIOPS.umm_json "The Equatorial Pacific Process Study (EQPAC) was conducted along 140 deg W longitude during 1992. Four cruises took place: February 3 - March 9, March 19 - April 15, August 5 - September 18, and September 24 - October 21. A fifth benthic cruise and sediment trap legs were added. During the first cruise (TT007), 15 stations were occupied along 140 deg W longitude from 12 deg N latitude to 12 deg S latitude. During the second cruise (TT008), data were collected at 8 stations along 140 deg W longitude from 9 deg S latitude to 9 deg N latitude. During the third cruise (TT011), data were collected at 15 stations along 140 deg W from 12 deg N latitude to 12 deg S latitude. During the fourth cruise (TT012), data were collected at 5 stations along 140 deg W longitude from 17 deg S latitude to the equator. Abundance, biovolume and biomass of cyanobacteria and eukaryotic plankton were measured at each station in vertical profiles using the CTD rosette water sampler. The cyanobacteria and plankton were enumerated and sized using color image analyzed fluorescence microscopy. The following parameters were measured: abundance of synechococcus-type cyanobacteria biovolume of synechococcus-type cyanobacteria biomass of synechococcus-type cyanobacteria abundance of phototrophic eucaryotic pico- and nanoplankton biovolume of phototrophic eucaryotic pico- and nanoplankton biomass of phototropic eucaryotic pico- and nanoplankton abundance of heterotrophic eucaryotic pico- and nanoplankton biovolume of heterotrophic eucaryotic pico- and nanoplankton biomass of heterotrophic eucaryotic pico- and nanoplankton The abundances are in units of cells/liter; the biovolumes are in units of cubic micrometers; and the biomasses are in units of micrograms of carbon per liter. The data is public domain and can be retrieved on-line at ""http://usjgofs.whoi.edu/jg/dir/jgofs/"" [The information in this summary was derived from the JGOFS World Wide Web pages.]" proprietary
-JGOFS_EQPAC_DINOFLAG Abundance, Biovolume and Biomass of Heterotrophic Dinoflagellates Measured during the JGOFS Equatorial Pacific Process Study ALL STAC Catalog 1992-02-03 1992-10-21 -140, -17, -140, 12 https://cmr.earthdata.nasa.gov/search/concepts/C1214605584-SCIOPS.umm_json "The Equatorial Pacific Process Study (EQPAC) was conducted along 140 deg W longitude during 1992. Four cruises took place: February 3 - March 9, March 19 - April 15, August 5 - September 18, and September 24 - October 21. A fifth benthic cruise and sediment trap legs were added. During the first cruise (TT007), 15 stations were occupied along 140 deg W longitude from 12 deg N latitude to 12 deg S latitude. During the second cruise (TT008), data were collected at 8 stations along 140 deg W longitude from 9 deg S latitude to 9 deg N latitude. During the third cruise (TT011), data were collected at 15 stations along 140 deg W from 12 deg N latitude to 12 deg S latitude. During the fourth cruise (TT012), data were collected at 5 stations along 140 deg W longitude from 17 deg S latitude to the equator. Samples were collected at each station in a vertical profile using the CTD rosette bottle sampler for the measurement of heterotrophic dinoflagellates. Microzooplankton were enumerated by inverted microscopy of settled samples. Abundance (cells/ml), biovolume (cubic micrometers), and biomass (ugC/l) were measured. The data is public domain and can be retrieved on-line at ""http://usjgofs.whoi.edu/jg/dir/jgofs/"" [The information in this summary was derived from the JGOFS World Wide Web pages.]" proprietary
+JGOFS_EQPAC_CYANOBACT_NANOPLANK Abundance, Biovolume and Biomass of Cyanobacteria and Eukaryotic Pico- and Nanoplankton Measured during the JGOFS Equatorial Pacific Process Study ALL STAC Catalog 1992-02-03 1992-10-21 -140, -17, -140, 12 https://cmr.earthdata.nasa.gov/search/concepts/C1214605622-SCIOPS.umm_json "The Equatorial Pacific Process Study (EQPAC) was conducted along 140 deg W longitude during 1992. Four cruises took place: February 3 - March 9, March 19 - April 15, August 5 - September 18, and September 24 - October 21. A fifth benthic cruise and sediment trap legs were added. During the first cruise (TT007), 15 stations were occupied along 140 deg W longitude from 12 deg N latitude to 12 deg S latitude. During the second cruise (TT008), data were collected at 8 stations along 140 deg W longitude from 9 deg S latitude to 9 deg N latitude. During the third cruise (TT011), data were collected at 15 stations along 140 deg W from 12 deg N latitude to 12 deg S latitude. During the fourth cruise (TT012), data were collected at 5 stations along 140 deg W longitude from 17 deg S latitude to the equator. Abundance, biovolume and biomass of cyanobacteria and eukaryotic plankton were measured at each station in vertical profiles using the CTD rosette water sampler. The cyanobacteria and plankton were enumerated and sized using color image analyzed fluorescence microscopy. The following parameters were measured: abundance of synechococcus-type cyanobacteria biovolume of synechococcus-type cyanobacteria biomass of synechococcus-type cyanobacteria abundance of phototrophic eucaryotic pico- and nanoplankton biovolume of phototrophic eucaryotic pico- and nanoplankton biomass of phototropic eucaryotic pico- and nanoplankton abundance of heterotrophic eucaryotic pico- and nanoplankton biovolume of heterotrophic eucaryotic pico- and nanoplankton biomass of heterotrophic eucaryotic pico- and nanoplankton The abundances are in units of cells/liter; the biovolumes are in units of cubic micrometers; and the biomasses are in units of micrograms of carbon per liter. The data is public domain and can be retrieved on-line at ""http://usjgofs.whoi.edu/jg/dir/jgofs/"" [The information in this summary was derived from the JGOFS World Wide Web pages.]" proprietary
JGOFS_EQPAC_DINOFLAG Abundance, Biovolume and Biomass of Heterotrophic Dinoflagellates Measured during the JGOFS Equatorial Pacific Process Study SCIOPS STAC Catalog 1992-02-03 1992-10-21 -140, -17, -140, 12 https://cmr.earthdata.nasa.gov/search/concepts/C1214605584-SCIOPS.umm_json "The Equatorial Pacific Process Study (EQPAC) was conducted along 140 deg W longitude during 1992. Four cruises took place: February 3 - March 9, March 19 - April 15, August 5 - September 18, and September 24 - October 21. A fifth benthic cruise and sediment trap legs were added. During the first cruise (TT007), 15 stations were occupied along 140 deg W longitude from 12 deg N latitude to 12 deg S latitude. During the second cruise (TT008), data were collected at 8 stations along 140 deg W longitude from 9 deg S latitude to 9 deg N latitude. During the third cruise (TT011), data were collected at 15 stations along 140 deg W from 12 deg N latitude to 12 deg S latitude. During the fourth cruise (TT012), data were collected at 5 stations along 140 deg W longitude from 17 deg S latitude to the equator. Samples were collected at each station in a vertical profile using the CTD rosette bottle sampler for the measurement of heterotrophic dinoflagellates. Microzooplankton were enumerated by inverted microscopy of settled samples. Abundance (cells/ml), biovolume (cubic micrometers), and biomass (ugC/l) were measured. The data is public domain and can be retrieved on-line at ""http://usjgofs.whoi.edu/jg/dir/jgofs/"" [The information in this summary was derived from the JGOFS World Wide Web pages.]" proprietary
+JGOFS_EQPAC_DINOFLAG Abundance, Biovolume and Biomass of Heterotrophic Dinoflagellates Measured during the JGOFS Equatorial Pacific Process Study ALL STAC Catalog 1992-02-03 1992-10-21 -140, -17, -140, 12 https://cmr.earthdata.nasa.gov/search/concepts/C1214605584-SCIOPS.umm_json "The Equatorial Pacific Process Study (EQPAC) was conducted along 140 deg W longitude during 1992. Four cruises took place: February 3 - March 9, March 19 - April 15, August 5 - September 18, and September 24 - October 21. A fifth benthic cruise and sediment trap legs were added. During the first cruise (TT007), 15 stations were occupied along 140 deg W longitude from 12 deg N latitude to 12 deg S latitude. During the second cruise (TT008), data were collected at 8 stations along 140 deg W longitude from 9 deg S latitude to 9 deg N latitude. During the third cruise (TT011), data were collected at 15 stations along 140 deg W from 12 deg N latitude to 12 deg S latitude. During the fourth cruise (TT012), data were collected at 5 stations along 140 deg W longitude from 17 deg S latitude to the equator. Samples were collected at each station in a vertical profile using the CTD rosette bottle sampler for the measurement of heterotrophic dinoflagellates. Microzooplankton were enumerated by inverted microscopy of settled samples. Abundance (cells/ml), biovolume (cubic micrometers), and biomass (ugC/l) were measured. The data is public domain and can be retrieved on-line at ""http://usjgofs.whoi.edu/jg/dir/jgofs/"" [The information in this summary was derived from the JGOFS World Wide Web pages.]" proprietary
JGOFS_EQPAC_MARINE_SNOW Abundance of Particulate Aggregrates (Marine Snow) Measured during the JGOFS Equatorial Pacific Process Study ALL STAC Catalog 1992-03-19 1992-04-15 -140, -17, -140, 12 https://cmr.earthdata.nasa.gov/search/concepts/C1214605602-SCIOPS.umm_json "The Equatorial Pacific Process Study (EQPAC) was conducted along 140 deg W longitude during 1992. Four cruises took place: February 3 - March 9, March 19 - April 15, August 5 - September 18, and September 24 - October 21. A fifth benthic cruise and sediment trap legs were added. During the first cruise (TT007), 15 stations were occupied along 140 deg W longitude from 12 deg N latitude to 12 deg S latitude. During the second cruise (TT008), data were collected at 8 stations along 140 deg W longitude from 9 deg S latitude to 9 deg N latitude. During the third cruise (TT011), data were collected at 15 stations along 140 deg W from 12 deg N latitude to 12 deg S latitude. During the fourth cruise (TT012), data were collected at 5 stations along 140 deg W longitude from 17 deg S latitude to the equator. On the second cruise, a camera and strobelights were used to illuminate aggregate particles. The system was lowered slowly 10-20 m/min through the water column on a trawl wire, exposing frames at a time interval of 7-20 sec calculated to yield 700-800 frames between the surface and the sea floor. Depth was monitored and recorded using a pinger and the ship's precision depth recorder. The parameter measured was the number of aggregates greater than 0.5 mm. The data is public domain and can be retrieved on-line at ""http://usjgofs.whoi.edu/jg/dir/jgofs/"" [The information in this summary was taken from the JGOFS World Wide Web pages.]" proprietary
JGOFS_EQPAC_MARINE_SNOW Abundance of Particulate Aggregrates (Marine Snow) Measured during the JGOFS Equatorial Pacific Process Study SCIOPS STAC Catalog 1992-03-19 1992-04-15 -140, -17, -140, 12 https://cmr.earthdata.nasa.gov/search/concepts/C1214605602-SCIOPS.umm_json "The Equatorial Pacific Process Study (EQPAC) was conducted along 140 deg W longitude during 1992. Four cruises took place: February 3 - March 9, March 19 - April 15, August 5 - September 18, and September 24 - October 21. A fifth benthic cruise and sediment trap legs were added. During the first cruise (TT007), 15 stations were occupied along 140 deg W longitude from 12 deg N latitude to 12 deg S latitude. During the second cruise (TT008), data were collected at 8 stations along 140 deg W longitude from 9 deg S latitude to 9 deg N latitude. During the third cruise (TT011), data were collected at 15 stations along 140 deg W from 12 deg N latitude to 12 deg S latitude. During the fourth cruise (TT012), data were collected at 5 stations along 140 deg W longitude from 17 deg S latitude to the equator. On the second cruise, a camera and strobelights were used to illuminate aggregate particles. The system was lowered slowly 10-20 m/min through the water column on a trawl wire, exposing frames at a time interval of 7-20 sec calculated to yield 700-800 frames between the surface and the sea floor. Depth was monitored and recorded using a pinger and the ship's precision depth recorder. The parameter measured was the number of aggregates greater than 0.5 mm. The data is public domain and can be retrieved on-line at ""http://usjgofs.whoi.edu/jg/dir/jgofs/"" [The information in this summary was taken from the JGOFS World Wide Web pages.]" proprietary
JGOFS_WOCE_0 Joint Global Ocean Flux Study (JGOFS) - World Ocean Circulation Experiment OB_DAAC STAC Catalog 1991-09-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360388-OB_DAAC.umm_json Joint Global Ocean Flux Study (JGOFS) World Ocean Circulation Experiment measurements from 1991. proprietary
@@ -8138,42 +8139,42 @@ K009_1971_1972_NZ_2 A survey of suitable sites in the Wright Valley for borehole
K009_1971_1972_NZ_2 A survey of suitable sites in the Wright Valley for boreholes and a study of Lake Vanda sediments SCIOPS STAC Catalog 1971-11-13 1972-01-07 161.5, -77.5333, 161.5, -77.5333 https://cmr.earthdata.nasa.gov/search/concepts/C1214593255-SCIOPS.umm_json Two weeks were spent in the Wright Valley to survey suitable sites for boreholes to be put down as part of the International Drilling Programme. It was proposed to core the entire thickness of bottom sediments in Lake Vanda to elucidate, among other things, aspects of lake stratigraphy, petrology and hydrology, geothermal gradients in the area and paleoclimates. To locate the best site, a general bathymetric map of the lake and the nature of the bottom surface sediments was conducted. Results of the general reconnaissance are reported in the associated publication including lake depth and lake bottom sediment descriptions. Detailed textural, mineralogical, geochemical and biological investigation of the sediments was conducted. proprietary
K009_1972_1973_NZ_1 A geochemical reconnaisance of the salts in the soils of the Victoria Valley ALL STAC Catalog 1972-12-02 1973-01-19 160, -77.75, 164, -77.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214593216-SCIOPS.umm_json A geochemical reconnaisance of the salts in the Victoria Valley was undertaken in the 1972/73 season. A field camp was set up at Lake Vida and the area from Lake Vida to Lake Vaska, Lake Clarke and up the mountains to the north of Lake Vida were surveyed. Samples of salts were collected where they were visible and a number of soils were collected in closed drainage basins and at the edges of small lakes. A total of 2m of sediments 10m above the lake level were described and calcium carbonate 'biscuit' concretions were collected for 14C and/or U-Th dating. proprietary
K009_1972_1973_NZ_1 A geochemical reconnaisance of the salts in the soils of the Victoria Valley SCIOPS STAC Catalog 1972-12-02 1973-01-19 160, -77.75, 164, -77.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214593216-SCIOPS.umm_json A geochemical reconnaisance of the salts in the Victoria Valley was undertaken in the 1972/73 season. A field camp was set up at Lake Vida and the area from Lake Vida to Lake Vaska, Lake Clarke and up the mountains to the north of Lake Vida were surveyed. Samples of salts were collected where they were visible and a number of soils were collected in closed drainage basins and at the edges of small lakes. A total of 2m of sediments 10m above the lake level were described and calcium carbonate 'biscuit' concretions were collected for 14C and/or U-Th dating. proprietary
-K009_1975_1976_NZ_2 A survey of the Miers and Marshall Valley and Walcott Bay area for dating the formation of the major landforms ALL STAC Catalog 1975-12-09 1977-01-06 161.6666, -77.5166, 161.6666, -77.5166 https://cmr.earthdata.nasa.gov/search/concepts/C1214593295-SCIOPS.umm_json "A few days were spent in the Miers Valley to collect samples of gypsum for geochemical analyses. Surprisingly carbonate ""biscuit"" similar to that found in the Taylor Valley were found. Thus, we noted the elevations of carbonate and gypsum, also in relation to ancient lake levels and moraines. Samples were subjected to geochemical analyses. Kenyte-like boulders in the terrace sequence had been depositied in a tuffaceious matrix. Apparently the boulders had been deposited on the subaqueous part of the delta at a time of higher lake level.The feldspar crystals in these boulders were dated with K-Ar as well as having the glass in the tuffaceous matrix fission-track dated. With dating, we should be able to tie in the age, form and evolution of the old lake levels, deltas and moraines of the Miers Valley with the Taylor Valley. Further samples were collected the following season for dating the formation of the major landforms, especially the moraines and lake levels in the Miers Valley. The Marshall Valley was visited and a massive gypsum vein was sampled and dated. The Walcott Bay was surveyed but no carbonate was found and the shoreline of Mt Discovery was surveyed for carbonates." proprietary
K009_1975_1976_NZ_2 A survey of the Miers and Marshall Valley and Walcott Bay area for dating the formation of the major landforms SCIOPS STAC Catalog 1975-12-09 1977-01-06 161.6666, -77.5166, 161.6666, -77.5166 https://cmr.earthdata.nasa.gov/search/concepts/C1214593295-SCIOPS.umm_json "A few days were spent in the Miers Valley to collect samples of gypsum for geochemical analyses. Surprisingly carbonate ""biscuit"" similar to that found in the Taylor Valley were found. Thus, we noted the elevations of carbonate and gypsum, also in relation to ancient lake levels and moraines. Samples were subjected to geochemical analyses. Kenyte-like boulders in the terrace sequence had been depositied in a tuffaceious matrix. Apparently the boulders had been deposited on the subaqueous part of the delta at a time of higher lake level.The feldspar crystals in these boulders were dated with K-Ar as well as having the glass in the tuffaceous matrix fission-track dated. With dating, we should be able to tie in the age, form and evolution of the old lake levels, deltas and moraines of the Miers Valley with the Taylor Valley. Further samples were collected the following season for dating the formation of the major landforms, especially the moraines and lake levels in the Miers Valley. The Marshall Valley was visited and a massive gypsum vein was sampled and dated. The Walcott Bay was surveyed but no carbonate was found and the shoreline of Mt Discovery was surveyed for carbonates." proprietary
-K009_1979_1980_NZ_1 A study of the glacial history of the McMurdo Oasis by the dating of lacustre carbonates ALL STAC Catalog 1979-12-10 1980-01-15 163.1833, -77.6166, 163.1833, -77.6166 https://cmr.earthdata.nasa.gov/search/concepts/C1214593313-SCIOPS.umm_json Three holes were drilled into frozen sediments around Lake Fryxell. The first was 4ft in depth in frozen silts approx 50m NW of the Fryxell Hut. The second hole was 30m east of the first hole and a depth of 16ft. A third hole was drilled approximately 1km east of the second hole to a depth of 46ft. The cores were analysed and the lacustre carbonates within were dated. This was the first time that diamond drilling was used to drill the cores. proprietary
+K009_1975_1976_NZ_2 A survey of the Miers and Marshall Valley and Walcott Bay area for dating the formation of the major landforms ALL STAC Catalog 1975-12-09 1977-01-06 161.6666, -77.5166, 161.6666, -77.5166 https://cmr.earthdata.nasa.gov/search/concepts/C1214593295-SCIOPS.umm_json "A few days were spent in the Miers Valley to collect samples of gypsum for geochemical analyses. Surprisingly carbonate ""biscuit"" similar to that found in the Taylor Valley were found. Thus, we noted the elevations of carbonate and gypsum, also in relation to ancient lake levels and moraines. Samples were subjected to geochemical analyses. Kenyte-like boulders in the terrace sequence had been depositied in a tuffaceious matrix. Apparently the boulders had been deposited on the subaqueous part of the delta at a time of higher lake level.The feldspar crystals in these boulders were dated with K-Ar as well as having the glass in the tuffaceous matrix fission-track dated. With dating, we should be able to tie in the age, form and evolution of the old lake levels, deltas and moraines of the Miers Valley with the Taylor Valley. Further samples were collected the following season for dating the formation of the major landforms, especially the moraines and lake levels in the Miers Valley. The Marshall Valley was visited and a massive gypsum vein was sampled and dated. The Walcott Bay was surveyed but no carbonate was found and the shoreline of Mt Discovery was surveyed for carbonates." proprietary
K009_1979_1980_NZ_1 A study of the glacial history of the McMurdo Oasis by the dating of lacustre carbonates SCIOPS STAC Catalog 1979-12-10 1980-01-15 163.1833, -77.6166, 163.1833, -77.6166 https://cmr.earthdata.nasa.gov/search/concepts/C1214593313-SCIOPS.umm_json Three holes were drilled into frozen sediments around Lake Fryxell. The first was 4ft in depth in frozen silts approx 50m NW of the Fryxell Hut. The second hole was 30m east of the first hole and a depth of 16ft. A third hole was drilled approximately 1km east of the second hole to a depth of 46ft. The cores were analysed and the lacustre carbonates within were dated. This was the first time that diamond drilling was used to drill the cores. proprietary
-K012_1978_1980_NZ_1 A series of experiments to characterize the neuromuscular transmission in Antarctic fishes (Pagothenia borchgrevinki) and the effects of temperature on these reactions ALL STAC Catalog 1978-11-08 1979-12-06 166.75, -77.85, 166.75, -77.85 https://cmr.earthdata.nasa.gov/search/concepts/C1214591521-SCIOPS.umm_json The low temperature adaptations involved in neuromuscular transmission in Antarctica fish was characterized. An exploratory dissection of Pagothenia borchgrevinki revealed that the inferior oblique ocular muscle was well suited for neuromuscular studies. Visual observations of contraction while stimulating the oculomotor was conducted and the interaction of stimulus frequency and temperature on muscle contraction was monitored. Electromyograms were used to record the muscle contraction at different temperatures and to assess the sensitivity of the neuromuscular junction to curare (tubocurarine - HCl). Photographic records of the EMG experiments were analysed. A sequence of neurophysiological experiments were conducted to further characterize the neuromuscular transmission in fishes including: a) Determination of optimum stimulation frequency and changes with temperature, b) Dose response measurements of acetylcholine and changes with temperature, c) Changes of the resting potential with temperature and d) recording the spontaneous miniature end-plate potentials (MEPP) and temperature induced changes in MEPP size, frequency and rate of decay. Brain and cranial nerves were dissected from five species of fish; P. borchgrevinki, Trematomus bernacchii, T. hansoni, Dissostichus mawsoni and Gymnodraco acuticeps, and preserved in methanol-acetic acid-formalin for anatomical, histological studies and lipid analysis. Glycerated muscle preparations of P. borchgrevinki eye muscles were made to analyse the myosin ATP-ase system responsible for the actual force of the contraction. proprietary
+K009_1979_1980_NZ_1 A study of the glacial history of the McMurdo Oasis by the dating of lacustre carbonates ALL STAC Catalog 1979-12-10 1980-01-15 163.1833, -77.6166, 163.1833, -77.6166 https://cmr.earthdata.nasa.gov/search/concepts/C1214593313-SCIOPS.umm_json Three holes were drilled into frozen sediments around Lake Fryxell. The first was 4ft in depth in frozen silts approx 50m NW of the Fryxell Hut. The second hole was 30m east of the first hole and a depth of 16ft. A third hole was drilled approximately 1km east of the second hole to a depth of 46ft. The cores were analysed and the lacustre carbonates within were dated. This was the first time that diamond drilling was used to drill the cores. proprietary
K012_1978_1980_NZ_1 A series of experiments to characterize the neuromuscular transmission in Antarctic fishes (Pagothenia borchgrevinki) and the effects of temperature on these reactions SCIOPS STAC Catalog 1978-11-08 1979-12-06 166.75, -77.85, 166.75, -77.85 https://cmr.earthdata.nasa.gov/search/concepts/C1214591521-SCIOPS.umm_json The low temperature adaptations involved in neuromuscular transmission in Antarctica fish was characterized. An exploratory dissection of Pagothenia borchgrevinki revealed that the inferior oblique ocular muscle was well suited for neuromuscular studies. Visual observations of contraction while stimulating the oculomotor was conducted and the interaction of stimulus frequency and temperature on muscle contraction was monitored. Electromyograms were used to record the muscle contraction at different temperatures and to assess the sensitivity of the neuromuscular junction to curare (tubocurarine - HCl). Photographic records of the EMG experiments were analysed. A sequence of neurophysiological experiments were conducted to further characterize the neuromuscular transmission in fishes including: a) Determination of optimum stimulation frequency and changes with temperature, b) Dose response measurements of acetylcholine and changes with temperature, c) Changes of the resting potential with temperature and d) recording the spontaneous miniature end-plate potentials (MEPP) and temperature induced changes in MEPP size, frequency and rate of decay. Brain and cranial nerves were dissected from five species of fish; P. borchgrevinki, Trematomus bernacchii, T. hansoni, Dissostichus mawsoni and Gymnodraco acuticeps, and preserved in methanol-acetic acid-formalin for anatomical, histological studies and lipid analysis. Glycerated muscle preparations of P. borchgrevinki eye muscles were made to analyse the myosin ATP-ase system responsible for the actual force of the contraction. proprietary
-K014_1969_1970_NZ_1 A feasibility study of marine investigations at Cape Bird: Plankton sampling, water temperature, conductivity and chlorophyll content ALL STAC Catalog 1969-10-01 1970-02-15 166.6833, -77.1667, 166.6833, -77.1667 https://cmr.earthdata.nasa.gov/search/concepts/C1214592009-SCIOPS.umm_json On arrival at Cape Bird it was found that the pack ice had broken early and sampling had to be limited to inshore waters from ice piers with water depths never greater than about 20 feet. Plankton samples were obtained every third day through the summer to provide records of plankton abundance and composition and chlorophyll content of the water. Records were kept of prevailing sea and weather conditions and sea temperatures and conductivity. proprietary
+K012_1978_1980_NZ_1 A series of experiments to characterize the neuromuscular transmission in Antarctic fishes (Pagothenia borchgrevinki) and the effects of temperature on these reactions ALL STAC Catalog 1978-11-08 1979-12-06 166.75, -77.85, 166.75, -77.85 https://cmr.earthdata.nasa.gov/search/concepts/C1214591521-SCIOPS.umm_json The low temperature adaptations involved in neuromuscular transmission in Antarctica fish was characterized. An exploratory dissection of Pagothenia borchgrevinki revealed that the inferior oblique ocular muscle was well suited for neuromuscular studies. Visual observations of contraction while stimulating the oculomotor was conducted and the interaction of stimulus frequency and temperature on muscle contraction was monitored. Electromyograms were used to record the muscle contraction at different temperatures and to assess the sensitivity of the neuromuscular junction to curare (tubocurarine - HCl). Photographic records of the EMG experiments were analysed. A sequence of neurophysiological experiments were conducted to further characterize the neuromuscular transmission in fishes including: a) Determination of optimum stimulation frequency and changes with temperature, b) Dose response measurements of acetylcholine and changes with temperature, c) Changes of the resting potential with temperature and d) recording the spontaneous miniature end-plate potentials (MEPP) and temperature induced changes in MEPP size, frequency and rate of decay. Brain and cranial nerves were dissected from five species of fish; P. borchgrevinki, Trematomus bernacchii, T. hansoni, Dissostichus mawsoni and Gymnodraco acuticeps, and preserved in methanol-acetic acid-formalin for anatomical, histological studies and lipid analysis. Glycerated muscle preparations of P. borchgrevinki eye muscles were made to analyse the myosin ATP-ase system responsible for the actual force of the contraction. proprietary
K014_1969_1970_NZ_1 A feasibility study of marine investigations at Cape Bird: Plankton sampling, water temperature, conductivity and chlorophyll content SCIOPS STAC Catalog 1969-10-01 1970-02-15 166.6833, -77.1667, 166.6833, -77.1667 https://cmr.earthdata.nasa.gov/search/concepts/C1214592009-SCIOPS.umm_json On arrival at Cape Bird it was found that the pack ice had broken early and sampling had to be limited to inshore waters from ice piers with water depths never greater than about 20 feet. Plankton samples were obtained every third day through the summer to provide records of plankton abundance and composition and chlorophyll content of the water. Records were kept of prevailing sea and weather conditions and sea temperatures and conductivity. proprietary
+K014_1969_1970_NZ_1 A feasibility study of marine investigations at Cape Bird: Plankton sampling, water temperature, conductivity and chlorophyll content ALL STAC Catalog 1969-10-01 1970-02-15 166.6833, -77.1667, 166.6833, -77.1667 https://cmr.earthdata.nasa.gov/search/concepts/C1214592009-SCIOPS.umm_json On arrival at Cape Bird it was found that the pack ice had broken early and sampling had to be limited to inshore waters from ice piers with water depths never greater than about 20 feet. Plankton samples were obtained every third day through the summer to provide records of plankton abundance and composition and chlorophyll content of the water. Records were kept of prevailing sea and weather conditions and sea temperatures and conductivity. proprietary
K014_1970_1971_NZ_5 A general benthic survey of the Cape Bird region: distribution of sediment types, boundaries of faunal zones, bathymetry and current patterns SCIOPS STAC Catalog 1970-12-08 1971-02-01 166.6833, -77.1667, 166.6833, -77.1667 https://cmr.earthdata.nasa.gov/search/concepts/C1214592010-SCIOPS.umm_json A survey of the region from the ice face to McDonald Beach and to a depth of about 300 meters with regard to distribution of sediment types, boundaries of faunal zones and the general bathymetry of the area was completed at Cape Bird. The current pattern around the cape coast was observed and measured and its effect on the local benthic habitat was described. proprietary
K014_1970_1971_NZ_5 A general benthic survey of the Cape Bird region: distribution of sediment types, boundaries of faunal zones, bathymetry and current patterns ALL STAC Catalog 1970-12-08 1971-02-01 166.6833, -77.1667, 166.6833, -77.1667 https://cmr.earthdata.nasa.gov/search/concepts/C1214592010-SCIOPS.umm_json A survey of the region from the ice face to McDonald Beach and to a depth of about 300 meters with regard to distribution of sediment types, boundaries of faunal zones and the general bathymetry of the area was completed at Cape Bird. The current pattern around the cape coast was observed and measured and its effect on the local benthic habitat was described. proprietary
K014_1974_1975_NZ_1 Adelie penguin and skua nest monitoring for the effects of human disturbance on nest success SCIOPS STAC Catalog 1974-10-22 1975-01-25 166.6833, -77.1667, 166.6833, -77.1667 https://cmr.earthdata.nasa.gov/search/concepts/C1214592021-SCIOPS.umm_json Observations were made on the behaviour and breeding success of penguins and skuas in areas of the Cape Bird northern colony subject to interference by man. Interferance being taken as the presence of man and/or man made objects. Areas free from interference except for the observers presence were observed as controls. 300 Adelie penguin and 24 McCormick skua nests were checked daily for eggs and chick success. proprietary
K014_1974_1975_NZ_1 Adelie penguin and skua nest monitoring for the effects of human disturbance on nest success ALL STAC Catalog 1974-10-22 1975-01-25 166.6833, -77.1667, 166.6833, -77.1667 https://cmr.earthdata.nasa.gov/search/concepts/C1214592021-SCIOPS.umm_json Observations were made on the behaviour and breeding success of penguins and skuas in areas of the Cape Bird northern colony subject to interference by man. Interferance being taken as the presence of man and/or man made objects. Areas free from interference except for the observers presence were observed as controls. 300 Adelie penguin and 24 McCormick skua nests were checked daily for eggs and chick success. proprietary
K014_1974_1975_NZ_4 Adelie penguin (Pygoscelis adeliae) and McCormick skua (Catharacta mccormicki) census of the Cape Bird colony 1974 - 1978 SCIOPS STAC Catalog 1974-11-27 1983-12-06 166.6833, -77.1667, 166.6833, -77.1667 https://cmr.earthdata.nasa.gov/search/concepts/C1214592066-SCIOPS.umm_json A population census of the three Adelie penguin colonies in the area of Cape Bird was carried out over several seasons since 1965, between November and December each year. These counts were conducted by ground based observations. Simultaneously, aerial photographs were taken by another study. The total number of birds was counted by 2 people using hand counters. The totals needed to be within 1% of each other or they were recounted. The final number for each colony was determined by averaging all the totals for that colony and rounding to the lower number. Occupied nests were counted with the same technique. Initial maps of the three main colonies were drawn from aerial photographs taken in the late 1960's. Copies of original maps were examined in the field and amendments were made to document changes in the colonies over the years and to update the information for future colony counts. Any penguin or McCormicks skua with bands were read while making the annual colonies count (penguin) or search for during the evenings (skua). The nest sites of skuas were mapped and band numbers of skuas using the nests were recorded in some years. A census of the penguin colonies at Cape Royds was conduction in 1959, 1975, 1977, 1979-1988 using the same methods. proprietary
K014_1974_1975_NZ_4 Adelie penguin (Pygoscelis adeliae) and McCormick skua (Catharacta mccormicki) census of the Cape Bird colony 1974 - 1978 ALL STAC Catalog 1974-11-27 1983-12-06 166.6833, -77.1667, 166.6833, -77.1667 https://cmr.earthdata.nasa.gov/search/concepts/C1214592066-SCIOPS.umm_json A population census of the three Adelie penguin colonies in the area of Cape Bird was carried out over several seasons since 1965, between November and December each year. These counts were conducted by ground based observations. Simultaneously, aerial photographs were taken by another study. The total number of birds was counted by 2 people using hand counters. The totals needed to be within 1% of each other or they were recounted. The final number for each colony was determined by averaging all the totals for that colony and rounding to the lower number. Occupied nests were counted with the same technique. Initial maps of the three main colonies were drawn from aerial photographs taken in the late 1960's. Copies of original maps were examined in the field and amendments were made to document changes in the colonies over the years and to update the information for future colony counts. Any penguin or McCormicks skua with bands were read while making the annual colonies count (penguin) or search for during the evenings (skua). The nest sites of skuas were mapped and band numbers of skuas using the nests were recorded in some years. A census of the penguin colonies at Cape Royds was conduction in 1959, 1975, 1977, 1979-1988 using the same methods. proprietary
-K014_1982_1983_NZ_1 Adelie penguin and skua census and analysis of stomach contents of adelie penguins from Cape Hallett ALL STAC Catalog 1983-01-17 1983-01-22 170.2667, -72.3167, 170.2667, -72.3167 https://cmr.earthdata.nasa.gov/search/concepts/C1214592063-SCIOPS.umm_json In January-February 1983, a four person party spent five weeks at Cape Hallett, Northern Victoria Land, under the auspices of the New Zealand Committee for the International Survey of Antarctic Seabirds (ISAS). The major objectives of this expedition were a census of the Adelie penguin and skua populations and a study of the foods of Adelie penguins. The last penguin census at Cape Hallett prior to this was in 1968. The old Cape Hallett station was abandoned in 1973 and the recovery of the penguin population was checked. All chicks were counted in each colony and their number was compared with counts made in 1961 and aerial photographs from 1982. A skua census was also completed in two separate counts. The feeding ecology of adelie penguins was examined to take the opportunity for making comparisons with results from earlier studies at Cape Hallett. Stomach samples were collected at the creche stage from 76 adult penguins. The penguins were captured as they returned from feeding at sea and stomach contents were sampled using the wet offloading techniqe. The type, abundance and characteristics of the prey species was determine and compared. proprietary
K014_1982_1983_NZ_1 Adelie penguin and skua census and analysis of stomach contents of adelie penguins from Cape Hallett SCIOPS STAC Catalog 1983-01-17 1983-01-22 170.2667, -72.3167, 170.2667, -72.3167 https://cmr.earthdata.nasa.gov/search/concepts/C1214592063-SCIOPS.umm_json In January-February 1983, a four person party spent five weeks at Cape Hallett, Northern Victoria Land, under the auspices of the New Zealand Committee for the International Survey of Antarctic Seabirds (ISAS). The major objectives of this expedition were a census of the Adelie penguin and skua populations and a study of the foods of Adelie penguins. The last penguin census at Cape Hallett prior to this was in 1968. The old Cape Hallett station was abandoned in 1973 and the recovery of the penguin population was checked. All chicks were counted in each colony and their number was compared with counts made in 1961 and aerial photographs from 1982. A skua census was also completed in two separate counts. The feeding ecology of adelie penguins was examined to take the opportunity for making comparisons with results from earlier studies at Cape Hallett. Stomach samples were collected at the creche stage from 76 adult penguins. The penguins were captured as they returned from feeding at sea and stomach contents were sampled using the wet offloading techniqe. The type, abundance and characteristics of the prey species was determine and compared. proprietary
-K014_1982_1983_NZ_3 A distribution of vegetation survey and an environmental assessment carried out to identify any damage caused by previous occupation of the area by man at Cape Hallett's Specially Protected Area No. 7 SCIOPS STAC Catalog 1983-01-01 1983-02-28 170.2667, -72.3167, 170.2667, -72.3167 https://cmr.earthdata.nasa.gov/search/concepts/C1214592043-SCIOPS.umm_json Specially Protected area No.7 is located at the base of Seabee Spit and comprises two major habitat types: a large flat area interrupted by small hummocks and depressions, and adjoining steep scree slopes which form part of the western side of Cape Hallett. In order to provide some up to date information on the current status of the SPA, the distribution of vegetation was surveyed and an environmental assessment carried out to identify any damage caused by previous occupation of the area by man. The adequacy of the present boundaries (1983) was also examined. The algae, mosses and lichens of Cape Hallett were surveyed in two ways: a) A series of photographs was taken to provide overlapping coverage of the SPA and surrounding areas at a small scale. This will allow a sketch map to be made showing broad vegetation distribution patterns, extent of penguin colonies, nature of the topography, occurrence of permanent snow patches and areas of melt water accumulation. b) Three vertical transects were laid across the SPA running west to east over the flat and up the scree slopes. At 5m intervals along each transect the area within a 25 x 25 cm quadrat was examined to provide data on species distribution and cover, the nature of the substrate, slope, aspect, and relative abundance and moisture. The presence/absence of collembola and mites was also recorded as was evidence of the presence of skuas, seals and penguins. A total of 600 quadrats were sampled. proprietary
+K014_1982_1983_NZ_1 Adelie penguin and skua census and analysis of stomach contents of adelie penguins from Cape Hallett ALL STAC Catalog 1983-01-17 1983-01-22 170.2667, -72.3167, 170.2667, -72.3167 https://cmr.earthdata.nasa.gov/search/concepts/C1214592063-SCIOPS.umm_json In January-February 1983, a four person party spent five weeks at Cape Hallett, Northern Victoria Land, under the auspices of the New Zealand Committee for the International Survey of Antarctic Seabirds (ISAS). The major objectives of this expedition were a census of the Adelie penguin and skua populations and a study of the foods of Adelie penguins. The last penguin census at Cape Hallett prior to this was in 1968. The old Cape Hallett station was abandoned in 1973 and the recovery of the penguin population was checked. All chicks were counted in each colony and their number was compared with counts made in 1961 and aerial photographs from 1982. A skua census was also completed in two separate counts. The feeding ecology of adelie penguins was examined to take the opportunity for making comparisons with results from earlier studies at Cape Hallett. Stomach samples were collected at the creche stage from 76 adult penguins. The penguins were captured as they returned from feeding at sea and stomach contents were sampled using the wet offloading techniqe. The type, abundance and characteristics of the prey species was determine and compared. proprietary
K014_1982_1983_NZ_3 A distribution of vegetation survey and an environmental assessment carried out to identify any damage caused by previous occupation of the area by man at Cape Hallett's Specially Protected Area No. 7 ALL STAC Catalog 1983-01-01 1983-02-28 170.2667, -72.3167, 170.2667, -72.3167 https://cmr.earthdata.nasa.gov/search/concepts/C1214592043-SCIOPS.umm_json Specially Protected area No.7 is located at the base of Seabee Spit and comprises two major habitat types: a large flat area interrupted by small hummocks and depressions, and adjoining steep scree slopes which form part of the western side of Cape Hallett. In order to provide some up to date information on the current status of the SPA, the distribution of vegetation was surveyed and an environmental assessment carried out to identify any damage caused by previous occupation of the area by man. The adequacy of the present boundaries (1983) was also examined. The algae, mosses and lichens of Cape Hallett were surveyed in two ways: a) A series of photographs was taken to provide overlapping coverage of the SPA and surrounding areas at a small scale. This will allow a sketch map to be made showing broad vegetation distribution patterns, extent of penguin colonies, nature of the topography, occurrence of permanent snow patches and areas of melt water accumulation. b) Three vertical transects were laid across the SPA running west to east over the flat and up the scree slopes. At 5m intervals along each transect the area within a 25 x 25 cm quadrat was examined to provide data on species distribution and cover, the nature of the substrate, slope, aspect, and relative abundance and moisture. The presence/absence of collembola and mites was also recorded as was evidence of the presence of skuas, seals and penguins. A total of 600 quadrats were sampled. proprietary
+K014_1982_1983_NZ_3 A distribution of vegetation survey and an environmental assessment carried out to identify any damage caused by previous occupation of the area by man at Cape Hallett's Specially Protected Area No. 7 SCIOPS STAC Catalog 1983-01-01 1983-02-28 170.2667, -72.3167, 170.2667, -72.3167 https://cmr.earthdata.nasa.gov/search/concepts/C1214592043-SCIOPS.umm_json Specially Protected area No.7 is located at the base of Seabee Spit and comprises two major habitat types: a large flat area interrupted by small hummocks and depressions, and adjoining steep scree slopes which form part of the western side of Cape Hallett. In order to provide some up to date information on the current status of the SPA, the distribution of vegetation was surveyed and an environmental assessment carried out to identify any damage caused by previous occupation of the area by man. The adequacy of the present boundaries (1983) was also examined. The algae, mosses and lichens of Cape Hallett were surveyed in two ways: a) A series of photographs was taken to provide overlapping coverage of the SPA and surrounding areas at a small scale. This will allow a sketch map to be made showing broad vegetation distribution patterns, extent of penguin colonies, nature of the topography, occurrence of permanent snow patches and areas of melt water accumulation. b) Three vertical transects were laid across the SPA running west to east over the flat and up the scree slopes. At 5m intervals along each transect the area within a 25 x 25 cm quadrat was examined to provide data on species distribution and cover, the nature of the substrate, slope, aspect, and relative abundance and moisture. The presence/absence of collembola and mites was also recorded as was evidence of the presence of skuas, seals and penguins. A total of 600 quadrats were sampled. proprietary
K014_1999_2000_NZ_1 A transplant experiment measuring the effects petroleum derivatives on Trematomus bernacchii from a relatively pristine site and exposing the fish to the waters at Winterquarters Bay and Cape Armitage ALL STAC Catalog 1999-11-24 2000-01-02 166.2, -77.85, 166.6683, -77.5667 https://cmr.earthdata.nasa.gov/search/concepts/C1214591328-SCIOPS.umm_json The impact of petroleum derivatives derived from fuel drums dumped into McMurdo Sound during the period before environmental management practices were regarded was examined on fish in Winterquarters Bay (McMurdo Sound). Experimental fish were captured from a relatively pristine site (Backdoor Bay, Cape Royds) and transported to Winterquarter Bay (heavily polluted) and Cape Armitage (minimally impacted) where they were held in cages. The fish were sampled from both sites after periods of 2 and 4 weeks and examined for physiological condition. Naturally resident fish were also collected from Backdoor Bay and Winterquarters Bay to provide a second, independent set of data. The physical condition of each fish was noted on gross examination and morphometric data was gathered to provide further information on health status. Internal organs (gills and liver) were then sampled for histopathological and biochemical analysis (measurement of cytochrome P450 content and activity). Bile was also removed from the gall bladder for subsequent analysis of petroleum derivative content by fluorimetry. These methods test for correlations between the amount and activity of cytochrome P450 in exposed fish and the quantity of contaminating petroleum contaminants. proprietary
K014_1999_2000_NZ_1 A transplant experiment measuring the effects petroleum derivatives on Trematomus bernacchii from a relatively pristine site and exposing the fish to the waters at Winterquarters Bay and Cape Armitage SCIOPS STAC Catalog 1999-11-24 2000-01-02 166.2, -77.85, 166.6683, -77.5667 https://cmr.earthdata.nasa.gov/search/concepts/C1214591328-SCIOPS.umm_json The impact of petroleum derivatives derived from fuel drums dumped into McMurdo Sound during the period before environmental management practices were regarded was examined on fish in Winterquarters Bay (McMurdo Sound). Experimental fish were captured from a relatively pristine site (Backdoor Bay, Cape Royds) and transported to Winterquarter Bay (heavily polluted) and Cape Armitage (minimally impacted) where they were held in cages. The fish were sampled from both sites after periods of 2 and 4 weeks and examined for physiological condition. Naturally resident fish were also collected from Backdoor Bay and Winterquarters Bay to provide a second, independent set of data. The physical condition of each fish was noted on gross examination and morphometric data was gathered to provide further information on health status. Internal organs (gills and liver) were then sampled for histopathological and biochemical analysis (measurement of cytochrome P450 content and activity). Bile was also removed from the gall bladder for subsequent analysis of petroleum derivative content by fluorimetry. These methods test for correlations between the amount and activity of cytochrome P450 in exposed fish and the quantity of contaminating petroleum contaminants. proprietary
-K017_1967_1968_NZ_2 A study on the siting, establishment and maintenance of territories in the South Polar Skua (Catharacta maccormicki) ALL STAC Catalog 1967-11-10 1968-02-15 166.6833, -77.1667, 166.6833, -77.1667 https://cmr.earthdata.nasa.gov/search/concepts/C1214592026-SCIOPS.umm_json A study of skua territories was conducted by examining siting, establishment and maintenance of territories in two very different conditions including in an area close to the penguins where skuas nest in a tight concentration and in an alpine exposed area of low skua concentration. Direct observations of conflicts and encounters through the summer and the changing position of boundaries was followed in relation to breeding state of the the skua pairs. An independent assessment of a social hierarchy was made to allow investigation of the relation between this hierarchy and territory size, position and breeding success to be concluded. The relation between territory factor and breeding success, especially the survival of the chicks following the displacement of one of the two chicks from the nest that invariable occurs soon after both hatch was also recorded. proprietary
K017_1967_1968_NZ_2 A study on the siting, establishment and maintenance of territories in the South Polar Skua (Catharacta maccormicki) SCIOPS STAC Catalog 1967-11-10 1968-02-15 166.6833, -77.1667, 166.6833, -77.1667 https://cmr.earthdata.nasa.gov/search/concepts/C1214592026-SCIOPS.umm_json A study of skua territories was conducted by examining siting, establishment and maintenance of territories in two very different conditions including in an area close to the penguins where skuas nest in a tight concentration and in an alpine exposed area of low skua concentration. Direct observations of conflicts and encounters through the summer and the changing position of boundaries was followed in relation to breeding state of the the skua pairs. An independent assessment of a social hierarchy was made to allow investigation of the relation between this hierarchy and territory size, position and breeding success to be concluded. The relation between territory factor and breeding success, especially the survival of the chicks following the displacement of one of the two chicks from the nest that invariable occurs soon after both hatch was also recorded. proprietary
-K022_1977_1978_NZ_1 A biological reconnaissance of the photoreceptors of invertebrates and fish from the Ross Sea, identifying the micro fauna and flora of Dry Valley lakes and other organism from the Ross Sea region SCIOPS STAC Catalog 1977-11-22 1978-01-13 160, -78.75, 168, -77 https://cmr.earthdata.nasa.gov/search/concepts/C1214590902-SCIOPS.umm_json A variety of research activities on the organisms in the Ross Dependency was undertaken to determine the biological research potential of the organisms. Most work focused on photoreceptors of different invertebrates and fishes. The studies included work on: a) Glyptonotus antarcticus: The dorsal and ventral eyes of this big isopod were prefixed, postfixed, dehydrated and embedded for transmission electron microscopy (TEM). Additional eyes were prepared for TEM of the inner and outer surfaces. Groups of 4 animals were adapted to 0°C, 5°C and 10°C and their eyes were also prepared for TEM. Another experiment involved painted one eye black and exposing the other to 200 lux for 1 week. Both eyes were analysed with TEM. b) Orchomenella plebs: Freshly caught amphipods were exposed to bright sunlight for 1, 2 and 3 hours. Their eyes, as well as those of fully dark adapted ones were prepared for TEM. This species can also recover when placed in 10°C for 7h and then returned to 0°C water. Eyes of animals adapted to 5°C and 10°C and those that had recovered afterwards in 0°C were prepared for TEM. c) The compound eyes of approx 100 facets belonging to a tiny (1-2mm) parasitic isopod from fish and invertebrate hosts were prepared for TEM. d) Retinae of 3 species of fishes (Trematomus bernacchii, Trematomus brochgrevinkii and Dissostichus mawsoni) were fixed for TEM. The eyes of the Trematomus species were prepared for gas-chromatographical analyses of the fatty acid composition. Observations were carried out on the antifreeze behaviour of D. mawsoni aqueous and vitreous humor. e) The microfauna and flora of Deep Lake and Skua Lake were studied in culture. Numerous drawings of the microorganisms were prepared. f) A number of organisms were collected for identification including benthic marine organism from under the 3-5m thick sea ice, marine mite species, skua egg shells, moss samples (from the top of Mt Erebus) and bacteria which were attempted to be cultured from snow samples. proprietary
+K017_1967_1968_NZ_2 A study on the siting, establishment and maintenance of territories in the South Polar Skua (Catharacta maccormicki) ALL STAC Catalog 1967-11-10 1968-02-15 166.6833, -77.1667, 166.6833, -77.1667 https://cmr.earthdata.nasa.gov/search/concepts/C1214592026-SCIOPS.umm_json A study of skua territories was conducted by examining siting, establishment and maintenance of territories in two very different conditions including in an area close to the penguins where skuas nest in a tight concentration and in an alpine exposed area of low skua concentration. Direct observations of conflicts and encounters through the summer and the changing position of boundaries was followed in relation to breeding state of the the skua pairs. An independent assessment of a social hierarchy was made to allow investigation of the relation between this hierarchy and territory size, position and breeding success to be concluded. The relation between territory factor and breeding success, especially the survival of the chicks following the displacement of one of the two chicks from the nest that invariable occurs soon after both hatch was also recorded. proprietary
K022_1977_1978_NZ_1 A biological reconnaissance of the photoreceptors of invertebrates and fish from the Ross Sea, identifying the micro fauna and flora of Dry Valley lakes and other organism from the Ross Sea region ALL STAC Catalog 1977-11-22 1978-01-13 160, -78.75, 168, -77 https://cmr.earthdata.nasa.gov/search/concepts/C1214590902-SCIOPS.umm_json A variety of research activities on the organisms in the Ross Dependency was undertaken to determine the biological research potential of the organisms. Most work focused on photoreceptors of different invertebrates and fishes. The studies included work on: a) Glyptonotus antarcticus: The dorsal and ventral eyes of this big isopod were prefixed, postfixed, dehydrated and embedded for transmission electron microscopy (TEM). Additional eyes were prepared for TEM of the inner and outer surfaces. Groups of 4 animals were adapted to 0°C, 5°C and 10°C and their eyes were also prepared for TEM. Another experiment involved painted one eye black and exposing the other to 200 lux for 1 week. Both eyes were analysed with TEM. b) Orchomenella plebs: Freshly caught amphipods were exposed to bright sunlight for 1, 2 and 3 hours. Their eyes, as well as those of fully dark adapted ones were prepared for TEM. This species can also recover when placed in 10°C for 7h and then returned to 0°C water. Eyes of animals adapted to 5°C and 10°C and those that had recovered afterwards in 0°C were prepared for TEM. c) The compound eyes of approx 100 facets belonging to a tiny (1-2mm) parasitic isopod from fish and invertebrate hosts were prepared for TEM. d) Retinae of 3 species of fishes (Trematomus bernacchii, Trematomus brochgrevinkii and Dissostichus mawsoni) were fixed for TEM. The eyes of the Trematomus species were prepared for gas-chromatographical analyses of the fatty acid composition. Observations were carried out on the antifreeze behaviour of D. mawsoni aqueous and vitreous humor. e) The microfauna and flora of Deep Lake and Skua Lake were studied in culture. Numerous drawings of the microorganisms were prepared. f) A number of organisms were collected for identification including benthic marine organism from under the 3-5m thick sea ice, marine mite species, skua egg shells, moss samples (from the top of Mt Erebus) and bacteria which were attempted to be cultured from snow samples. proprietary
+K022_1977_1978_NZ_1 A biological reconnaissance of the photoreceptors of invertebrates and fish from the Ross Sea, identifying the micro fauna and flora of Dry Valley lakes and other organism from the Ross Sea region SCIOPS STAC Catalog 1977-11-22 1978-01-13 160, -78.75, 168, -77 https://cmr.earthdata.nasa.gov/search/concepts/C1214590902-SCIOPS.umm_json A variety of research activities on the organisms in the Ross Dependency was undertaken to determine the biological research potential of the organisms. Most work focused on photoreceptors of different invertebrates and fishes. The studies included work on: a) Glyptonotus antarcticus: The dorsal and ventral eyes of this big isopod were prefixed, postfixed, dehydrated and embedded for transmission electron microscopy (TEM). Additional eyes were prepared for TEM of the inner and outer surfaces. Groups of 4 animals were adapted to 0°C, 5°C and 10°C and their eyes were also prepared for TEM. Another experiment involved painted one eye black and exposing the other to 200 lux for 1 week. Both eyes were analysed with TEM. b) Orchomenella plebs: Freshly caught amphipods were exposed to bright sunlight for 1, 2 and 3 hours. Their eyes, as well as those of fully dark adapted ones were prepared for TEM. This species can also recover when placed in 10°C for 7h and then returned to 0°C water. Eyes of animals adapted to 5°C and 10°C and those that had recovered afterwards in 0°C were prepared for TEM. c) The compound eyes of approx 100 facets belonging to a tiny (1-2mm) parasitic isopod from fish and invertebrate hosts were prepared for TEM. d) Retinae of 3 species of fishes (Trematomus bernacchii, Trematomus brochgrevinkii and Dissostichus mawsoni) were fixed for TEM. The eyes of the Trematomus species were prepared for gas-chromatographical analyses of the fatty acid composition. Observations were carried out on the antifreeze behaviour of D. mawsoni aqueous and vitreous humor. e) The microfauna and flora of Deep Lake and Skua Lake were studied in culture. Numerous drawings of the microorganisms were prepared. f) A number of organisms were collected for identification including benthic marine organism from under the 3-5m thick sea ice, marine mite species, skua egg shells, moss samples (from the top of Mt Erebus) and bacteria which were attempted to be cultured from snow samples. proprietary
K024_1996_1997_NZ_3 A vegetation assessment of Beaufort Island SCIOPS STAC Catalog 1997-01-18 1997-01-20 167, -76.9833, 167, -76.9833 https://cmr.earthdata.nasa.gov/search/concepts/C1214593553-SCIOPS.umm_json The vegetation at Beaufort Island was assessed and a report written to ICAIR including a description of the area, species present, comparison to other Dry Valley vegetation, the merits of the vegetation and recommendations of other features worthy of protection. proprietary
K024_1996_1997_NZ_3 A vegetation assessment of Beaufort Island ALL STAC Catalog 1997-01-18 1997-01-20 167, -76.9833, 167, -76.9833 https://cmr.earthdata.nasa.gov/search/concepts/C1214593553-SCIOPS.umm_json The vegetation at Beaufort Island was assessed and a report written to ICAIR including a description of the area, species present, comparison to other Dry Valley vegetation, the merits of the vegetation and recommendations of other features worthy of protection. proprietary
-K029_1999_2000_NZ_1 A molecular analysis of penguin and chewing lice coevolution from Adelie (Pygoscelis adeliae) and Emperor (Aptenodytes forsteri) penguins SCIOPS STAC Catalog 1999-11-08 1999-11-18 166.1655, -77.5555, 169.2705, -77.4541 https://cmr.earthdata.nasa.gov/search/concepts/C1214590412-SCIOPS.umm_json Adelie penguins (Pygoscelis adeliae) at Cape Royds (11-12 November, 1999) were captured and checked for chewing lice. Emperor penguins (Aptenodytes forsteri) at Cape Crozier (15-16 November, 1999) were captured and checked for lice as well. Two species of chewing lice were found, Austrogonioides antarcticus and A. mawsoni on adelies and emperors respectively. The aim of the project was to obtain specimens of all species of lice (15) parasitising penguins (17) and to use molecular and morphological characters to produce a phylogeny for the lice and to compare the lice phylogeny to the penguin phylogeny. PCR was used to allow sequencing of genetic material from the lice, with the sequencing of two gene regions (12s and Cytochrome Oxidase 1). Lice speciation events were dated using molecular data to differentiate between co-speciation and host switching events. proprietary
K029_1999_2000_NZ_1 A molecular analysis of penguin and chewing lice coevolution from Adelie (Pygoscelis adeliae) and Emperor (Aptenodytes forsteri) penguins ALL STAC Catalog 1999-11-08 1999-11-18 166.1655, -77.5555, 169.2705, -77.4541 https://cmr.earthdata.nasa.gov/search/concepts/C1214590412-SCIOPS.umm_json Adelie penguins (Pygoscelis adeliae) at Cape Royds (11-12 November, 1999) were captured and checked for chewing lice. Emperor penguins (Aptenodytes forsteri) at Cape Crozier (15-16 November, 1999) were captured and checked for lice as well. Two species of chewing lice were found, Austrogonioides antarcticus and A. mawsoni on adelies and emperors respectively. The aim of the project was to obtain specimens of all species of lice (15) parasitising penguins (17) and to use molecular and morphological characters to produce a phylogeny for the lice and to compare the lice phylogeny to the penguin phylogeny. PCR was used to allow sequencing of genetic material from the lice, with the sequencing of two gene regions (12s and Cytochrome Oxidase 1). Lice speciation events were dated using molecular data to differentiate between co-speciation and host switching events. proprietary
+K029_1999_2000_NZ_1 A molecular analysis of penguin and chewing lice coevolution from Adelie (Pygoscelis adeliae) and Emperor (Aptenodytes forsteri) penguins SCIOPS STAC Catalog 1999-11-08 1999-11-18 166.1655, -77.5555, 169.2705, -77.4541 https://cmr.earthdata.nasa.gov/search/concepts/C1214590412-SCIOPS.umm_json Adelie penguins (Pygoscelis adeliae) at Cape Royds (11-12 November, 1999) were captured and checked for chewing lice. Emperor penguins (Aptenodytes forsteri) at Cape Crozier (15-16 November, 1999) were captured and checked for lice as well. Two species of chewing lice were found, Austrogonioides antarcticus and A. mawsoni on adelies and emperors respectively. The aim of the project was to obtain specimens of all species of lice (15) parasitising penguins (17) and to use molecular and morphological characters to produce a phylogeny for the lice and to compare the lice phylogeny to the penguin phylogeny. PCR was used to allow sequencing of genetic material from the lice, with the sequencing of two gene regions (12s and Cytochrome Oxidase 1). Lice speciation events were dated using molecular data to differentiate between co-speciation and host switching events. proprietary
K042_1964_1965_NZ_2 A mineralisation survey in the Koettlitz-Blue Glacier and Taylor Valley region to determine the geochemical prospecting of the region SCIOPS STAC Catalog 1964-01-01 1965-01-01 161, -78.5, 165, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214594147-SCIOPS.umm_json A mineralisation survey was conducted in the Koettlitz-Blue Glacier and Taylor Valley region because previous work in these areas mapped Precambrian basement rocks similar to those found in mineralised areas in Australia, South Africa, Canada and Scandinavia. The geological environment in these areas was examined and mineralised boulders in the moraines were investigated. Environments in the area considered most likely to be mineralised are faults, amphibolite-marble-faults contacts, granite-marble contacts (Skarns) and pegmatitie dykes. Very few faults were mapped in the region and none were accessible. Several small faults were examined and found to be barren. Soil samples were collected in the vicinity of faults and examined for copper and zinc. Amphibolite was found to be generally present in minor amounts within metasediments which are mainly marbles but field examination indicated that these were unfavourable for mineralisation. Granite-marble contacts were generally barren, but minor amounts of pyrrhotite and lesser chalcopyrite were found and traces of malachite were present at most localities. Numerous pegmatites were examined but they were invariably small and of a type commonly found in granite but rarely associated with mineralisation. The Koettlitz-Blue Glacier and Taylor Valley region is characterised by a lack of sulphides and must be regarded as generally unfavourable to base metal sulphide mineralisation. No appreciable quantities of industrial minerals were located during the survey, apart from marbles which are abundant and in most cases of apparently high quality. Thirty soil samples were collected in the region and will be analysed for copper and zinc to test the effectiveness of geochemical prospecting in the region. proprietary
K042_1964_1965_NZ_2 A mineralisation survey in the Koettlitz-Blue Glacier and Taylor Valley region to determine the geochemical prospecting of the region ALL STAC Catalog 1964-01-01 1965-01-01 161, -78.5, 165, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214594147-SCIOPS.umm_json A mineralisation survey was conducted in the Koettlitz-Blue Glacier and Taylor Valley region because previous work in these areas mapped Precambrian basement rocks similar to those found in mineralised areas in Australia, South Africa, Canada and Scandinavia. The geological environment in these areas was examined and mineralised boulders in the moraines were investigated. Environments in the area considered most likely to be mineralised are faults, amphibolite-marble-faults contacts, granite-marble contacts (Skarns) and pegmatitie dykes. Very few faults were mapped in the region and none were accessible. Several small faults were examined and found to be barren. Soil samples were collected in the vicinity of faults and examined for copper and zinc. Amphibolite was found to be generally present in minor amounts within metasediments which are mainly marbles but field examination indicated that these were unfavourable for mineralisation. Granite-marble contacts were generally barren, but minor amounts of pyrrhotite and lesser chalcopyrite were found and traces of malachite were present at most localities. Numerous pegmatites were examined but they were invariably small and of a type commonly found in granite but rarely associated with mineralisation. The Koettlitz-Blue Glacier and Taylor Valley region is characterised by a lack of sulphides and must be regarded as generally unfavourable to base metal sulphide mineralisation. No appreciable quantities of industrial minerals were located during the survey, apart from marbles which are abundant and in most cases of apparently high quality. Thirty soil samples were collected in the region and will be analysed for copper and zinc to test the effectiveness of geochemical prospecting in the region. proprietary
K042_1976_1977_NZ_3 A quantitative survey of mosses in the McMurdo Sound region SCIOPS STAC Catalog 1976-10-21 1977-01-12 160, -78.5, 167, -77 https://cmr.earthdata.nasa.gov/search/concepts/C1214594097-SCIOPS.umm_json A quantitative survey of the ecology of mosses in the McMurdo Sound region was conducted in the 1976/77 field season. Moss was found around streams below the Rhone, Hughes and Calkin Glaciers in the Taylor Valley, the moraines below the Hobbs Glacier and in the Salmon, Garwood and Towle Valleys, and in the Scott Base, McMurdo Station areas. Other areas searched where moss was not found included Kennar and Beacon Valleys, the area below La Croix Glacier and the side of the Taylor Valley around Lake Conney not near melt streams below alpine glaciers and the Towle Valley. Algae and lichen were recorded from most of the areas visited. Detailed quantitative surveys of moss were done below the Rhone, Calkin and Hughes Glacier and on the delta below the snout of the Hobbs Glacier. Air spore samples were collected daily, fresh algae was collected from Lake Fryxell and Lake Vanda for C14 dating standards and soils were sampled for tests for microorganisms, pH, carbon and nitrogen content. proprietary
K042_1976_1977_NZ_3 A quantitative survey of mosses in the McMurdo Sound region ALL STAC Catalog 1976-10-21 1977-01-12 160, -78.5, 167, -77 https://cmr.earthdata.nasa.gov/search/concepts/C1214594097-SCIOPS.umm_json A quantitative survey of the ecology of mosses in the McMurdo Sound region was conducted in the 1976/77 field season. Moss was found around streams below the Rhone, Hughes and Calkin Glaciers in the Taylor Valley, the moraines below the Hobbs Glacier and in the Salmon, Garwood and Towle Valleys, and in the Scott Base, McMurdo Station areas. Other areas searched where moss was not found included Kennar and Beacon Valleys, the area below La Croix Glacier and the side of the Taylor Valley around Lake Conney not near melt streams below alpine glaciers and the Towle Valley. Algae and lichen were recorded from most of the areas visited. Detailed quantitative surveys of moss were done below the Rhone, Calkin and Hughes Glacier and on the delta below the snout of the Hobbs Glacier. Air spore samples were collected daily, fresh algae was collected from Lake Fryxell and Lake Vanda for C14 dating standards and soils were sampled for tests for microorganisms, pH, carbon and nitrogen content. proprietary
K042_1979_1980_NZ_3 A gravity survey of the Taylor Valley and Dailey Islands ALL STAC Catalog 1979-12-07 1980-01-15 161, -77.88, 165.1, -77.55 https://cmr.earthdata.nasa.gov/search/concepts/C1214594141-SCIOPS.umm_json A gravity survey of the lower Taylor Valley, from New Harbour to the Suess Glacier was completed in the 1977-1978 field season to tie in with the Dry Valley Drilling Project (DVDP) holes and to trace the bedrock profile as part of the DVDP. In the 1979-1980 season, a gravity survey of the Dry Valleys was designed to compliment sea ice gravity surveys made during the same season and to fill gaps in the existing data measured by Bull (1962, 1964), Smithson (1971), Stern (1978), Hicks (1978) and Hicks and Bennet (1981). A detailed gravity traverse was completed down the Taylor Valley from Northwest Mountain to the sea, with stations at 1 to 3 km intervals. Gravity readings were also made at approximately 10km spacings in the Lower Ferrar and on the Dailey Islands. proprietary
K042_1979_1980_NZ_3 A gravity survey of the Taylor Valley and Dailey Islands SCIOPS STAC Catalog 1979-12-07 1980-01-15 161, -77.88, 165.1, -77.55 https://cmr.earthdata.nasa.gov/search/concepts/C1214594141-SCIOPS.umm_json A gravity survey of the lower Taylor Valley, from New Harbour to the Suess Glacier was completed in the 1977-1978 field season to tie in with the Dry Valley Drilling Project (DVDP) holes and to trace the bedrock profile as part of the DVDP. In the 1979-1980 season, a gravity survey of the Dry Valleys was designed to compliment sea ice gravity surveys made during the same season and to fill gaps in the existing data measured by Bull (1962, 1964), Smithson (1971), Stern (1978), Hicks (1978) and Hicks and Bennet (1981). A detailed gravity traverse was completed down the Taylor Valley from Northwest Mountain to the sea, with stations at 1 to 3 km intervals. Gravity readings were also made at approximately 10km spacings in the Lower Ferrar and on the Dailey Islands. proprietary
-K042_1980_1981_NZ_1 A seismic refraction survey on sea ice near Butter Point, New Harbour, McMurdo Sound SCIOPS STAC Catalog 1980-11-26 1980-12-03 164.12, -77.39, 164.12, -77.39 https://cmr.earthdata.nasa.gov/search/concepts/C1214592047-SCIOPS.umm_json A seismic refraction survey was conducted on sea ice near Butter Point to provide data on sediment thickness for possible further drilling and to investigate the cause of a reported gravity anomaly. 12 vertical geophones were spaced at 29.95m intervals, frozen in to holes chipped in the sea ice and covered by 100-200mm of snow. Two reverse lines were shot, using four shot points. proprietary
K042_1980_1981_NZ_1 A seismic refraction survey on sea ice near Butter Point, New Harbour, McMurdo Sound ALL STAC Catalog 1980-11-26 1980-12-03 164.12, -77.39, 164.12, -77.39 https://cmr.earthdata.nasa.gov/search/concepts/C1214592047-SCIOPS.umm_json A seismic refraction survey was conducted on sea ice near Butter Point to provide data on sediment thickness for possible further drilling and to investigate the cause of a reported gravity anomaly. 12 vertical geophones were spaced at 29.95m intervals, frozen in to holes chipped in the sea ice and covered by 100-200mm of snow. Two reverse lines were shot, using four shot points. proprietary
+K042_1980_1981_NZ_1 A seismic refraction survey on sea ice near Butter Point, New Harbour, McMurdo Sound SCIOPS STAC Catalog 1980-11-26 1980-12-03 164.12, -77.39, 164.12, -77.39 https://cmr.earthdata.nasa.gov/search/concepts/C1214592047-SCIOPS.umm_json A seismic refraction survey was conducted on sea ice near Butter Point to provide data on sediment thickness for possible further drilling and to investigate the cause of a reported gravity anomaly. 12 vertical geophones were spaced at 29.95m intervals, frozen in to holes chipped in the sea ice and covered by 100-200mm of snow. Two reverse lines were shot, using four shot points. proprietary
K042_1982_1983_NZ_2 A seismic refraction survey on sea ice at New Harbour and Dailey Islands ALL STAC Catalog 1982-11-15 1982-12-02 163.83, -77.88, 165.1, -77.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214592049-SCIOPS.umm_json "A seismic refraction survey was conducted on sea ice at New Harbour and the Dailey Islands to provide data on sediment thickness for possible further drilling for Cenozoic investigations in the Western Ross Sea. At New Harbour, two seismic lines, each 8.66km long with shot points at each end and at the centre were laid out in the form of a cross. Water depth was measured at each shot site. At the Dailey Islands, sea bottom depth and dip along the seismic line were determined at each spread by stacking sledge hammer blows on the ice. Two 8.66km lines similar to those at New Harbour were laid out in the for of a ""T"". Four extra shot points were incldued on line A because a complex sea bottom was expected near the islands." proprietary
K042_1982_1983_NZ_2 A seismic refraction survey on sea ice at New Harbour and Dailey Islands SCIOPS STAC Catalog 1982-11-15 1982-12-02 163.83, -77.88, 165.1, -77.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214592049-SCIOPS.umm_json "A seismic refraction survey was conducted on sea ice at New Harbour and the Dailey Islands to provide data on sediment thickness for possible further drilling for Cenozoic investigations in the Western Ross Sea. At New Harbour, two seismic lines, each 8.66km long with shot points at each end and at the centre were laid out in the form of a cross. Water depth was measured at each shot site. At the Dailey Islands, sea bottom depth and dip along the seismic line were determined at each spread by stacking sledge hammer blows on the ice. Two 8.66km lines similar to those at New Harbour were laid out in the for of a ""T"". Four extra shot points were incldued on line A because a complex sea bottom was expected near the islands." proprietary
K042_1990_1991_NZ_2 1:20,000 geological map of Allan Hills SCIOPS STAC Catalog 1990-12-07 1991-01-21 159.4167, -76.8333, -160, -76.5833 https://cmr.earthdata.nasa.gov/search/concepts/C1214592054-SCIOPS.umm_json A 1:20,000 scale geological map of Allan Hills and acompanying text was competed with the Weller Coal Measures being mapped to member level. Additional geographic control was established using a total station and three GPS sites. Three cairns were established near the head of Manhaul Bay and tied into the GPS network. proprietary
@@ -8182,36 +8183,36 @@ K043_1980_1982_NZ_1 A detailed investigation of the paleohydraulic regime (sinuo
K043_1980_1982_NZ_1 A detailed investigation of the paleohydraulic regime (sinuosity, channel width, depth, slope, discharge of the river, etc) during the deposition of the Triassic alluvial plain sequence at Mt Bastion SCIOPS STAC Catalog 1981-10-28 1981-11-21 160.5, -77.3333, 160.5, -77.3333 https://cmr.earthdata.nasa.gov/search/concepts/C1214591166-SCIOPS.umm_json The paleohydraulic Triassic alluvial plain sequence at the head of the Dry Valleys was studied. The Triassic Beacon Supergroup is divided into five stratigraphic units (The Fleming Member of the Feather Conglomerate and the Members A-D of the Lashly Formation) and all are exposed at Mt Bastion where this study was concerned. A detailed investigation of each unit was conducted to determine the paleohydraulic regimes operating during the Triassic deposition. The character of the river system (sinuosity, channel width, depth, slope, discharge, etc) was determined from features of the sedimentary sequence. proprietary
K043_2006_2007_NZ_1 A mathmatical model of population dynamics to explain changes in biodiversity of microorganisms in ice covered marine environments SCIOPS STAC Catalog 1970-01-01 163, -78, 171, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1214591946-SCIOPS.umm_json Physical, geographic and biological data were linked into a mathmatical model of population dynamics to integrate and explain the changes in biodiversity of phytoplankton, bacteria and cyanobacteria in ice covered marine ecosystems at three coastal Antarctic sites (Terra Nova Bay, Granite Harbour and Cape Evans) over several seasons. Data for the model was collected from each site in different seasons. In this way, the model changes with latitude in the relative contributions from each community as well as changes in species composition and distribution. Over the course of study, repeat samplings at each site in different years will facilitate a build of a series of models that describe the biodiversity and health of microbial populations at each site, to enable a better understanding of their ecosystem function and the pressures they may be under. Satellite imagery of ice distributions, thickness and snow cover, and weather patterns were linked with latitudinal variations in biological data, and models of population structure and dynamics were developed. The data that was incorporated into the model included total biomass, chlorophyll content, rates of productivity, species distributions and abundances of microbial organisms within sea ice and in the water beneath. Where possible, variations in local conditions such as snow cover, ice thickness, surface and under ice irradiance were included. proprietary
K043_2006_2007_NZ_1 A mathmatical model of population dynamics to explain changes in biodiversity of microorganisms in ice covered marine environments ALL STAC Catalog 1970-01-01 163, -78, 171, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1214591946-SCIOPS.umm_json Physical, geographic and biological data were linked into a mathmatical model of population dynamics to integrate and explain the changes in biodiversity of phytoplankton, bacteria and cyanobacteria in ice covered marine ecosystems at three coastal Antarctic sites (Terra Nova Bay, Granite Harbour and Cape Evans) over several seasons. Data for the model was collected from each site in different seasons. In this way, the model changes with latitude in the relative contributions from each community as well as changes in species composition and distribution. Over the course of study, repeat samplings at each site in different years will facilitate a build of a series of models that describe the biodiversity and health of microbial populations at each site, to enable a better understanding of their ecosystem function and the pressures they may be under. Satellite imagery of ice distributions, thickness and snow cover, and weather patterns were linked with latitudinal variations in biological data, and models of population structure and dynamics were developed. The data that was incorporated into the model included total biomass, chlorophyll content, rates of productivity, species distributions and abundances of microbial organisms within sea ice and in the water beneath. Where possible, variations in local conditions such as snow cover, ice thickness, surface and under ice irradiance were included. proprietary
-K043_2006_2008_NZ_2 Algal response to transplantation with a ice core flipping experiment, Terra Nova Bay, Ross Sea SCIOPS STAC Catalog 2006-11-03 2006-12-09 164.5, -74.8333, 164.5, -74.8333 https://cmr.earthdata.nasa.gov/search/concepts/C1214590966-SCIOPS.umm_json Three ice cores were drilled in sea ice (2.1 m thick) in the region of Gondwana Station in Terra Nova Bay during the 06-07 season. The cores were stored in black plastic bags and then replaced back within the same hole but in reverse order so that the algae from the bottom of the ice were now at the surface of the ice and the ice at the ice surface were now at the ice water interface at the bottom of the sea ice. An additional three profile cores were also drilled but were replaced back into their original holes in the normal configuration as a control. A further 3 cores were then extracted from the ice and processed for chlorophyll, cell numbers and species composition etc as above. At the end of the deployment period the six cores still in the ice were redrilled and extracted from the ice and samples also taken for chlorophyll, cell numbers and species composition as above. A further 3 cores of undisturbed ice were also taken. proprietary
K043_2006_2008_NZ_2 Algal response to transplantation with a ice core flipping experiment, Terra Nova Bay, Ross Sea ALL STAC Catalog 2006-11-03 2006-12-09 164.5, -74.8333, 164.5, -74.8333 https://cmr.earthdata.nasa.gov/search/concepts/C1214590966-SCIOPS.umm_json Three ice cores were drilled in sea ice (2.1 m thick) in the region of Gondwana Station in Terra Nova Bay during the 06-07 season. The cores were stored in black plastic bags and then replaced back within the same hole but in reverse order so that the algae from the bottom of the ice were now at the surface of the ice and the ice at the ice surface were now at the ice water interface at the bottom of the sea ice. An additional three profile cores were also drilled but were replaced back into their original holes in the normal configuration as a control. A further 3 cores were then extracted from the ice and processed for chlorophyll, cell numbers and species composition etc as above. At the end of the deployment period the six cores still in the ice were redrilled and extracted from the ice and samples also taken for chlorophyll, cell numbers and species composition as above. A further 3 cores of undisturbed ice were also taken. proprietary
-K048_1992_1993_NZ_1 A collection of lithospheric xenoliths from the Executive Committee Range and Mt Murphy Volcanic Complex in West Antarctica and the McMurdo Volcanic Province in McMurdo Sound SCIOPS STAC Catalog 1992-11-14 1992-12-01 -166, -78.4, -166.41667, -75.3667 https://cmr.earthdata.nasa.gov/search/concepts/C1214593948-SCIOPS.umm_json Lithospheric xenoliths are a convenient and relatively cost efficient means of gaining an insight into the petrology of the deep earth. As such, they provide important information on lithospheric structure and processes and can be used to gauge thermal regime and possibly , the timing of events. Lithospheric xenoliths were collected in the 1989/90 and 1990/91 season from Marie Byrd Land, West Antarctica, including Mt Waesche, Mt Sidley, Mt Cumming, Mt Hampton and the USAS Escarpment (Mt Aldaz) in the Executive Committee Range and Mt Murphy in the Mount Murphy Volcanic Complex. Further samples were collected in the 1992/93 season from the McMurdo Volcanic Province at a number of localities on and adjacent to Ross Island (Hut Point Peninsula (Half Moon Crater, Sulphur Cones, Turtle Rock) and Cape Bird), Black Island and in the foothills of the Transantarctic Mountains (Foster Crater on the Koettlitz Glacier). The majority of the samples collected in the 1992/93 season supplemented a collection compiled from the 1982/83 and 1984/85 season. The xenoliths vary from texturally variable, spinel lherzolites and dunites representative of upper mantle assemblages to ultramafic Al-augite kaersutite bearing ultramafic rocks and plagioclase bearing ultramafic to mafic granulites thought to represent the transition zone between upper mantle and lower crust. proprietary
+K043_2006_2008_NZ_2 Algal response to transplantation with a ice core flipping experiment, Terra Nova Bay, Ross Sea SCIOPS STAC Catalog 2006-11-03 2006-12-09 164.5, -74.8333, 164.5, -74.8333 https://cmr.earthdata.nasa.gov/search/concepts/C1214590966-SCIOPS.umm_json Three ice cores were drilled in sea ice (2.1 m thick) in the region of Gondwana Station in Terra Nova Bay during the 06-07 season. The cores were stored in black plastic bags and then replaced back within the same hole but in reverse order so that the algae from the bottom of the ice were now at the surface of the ice and the ice at the ice surface were now at the ice water interface at the bottom of the sea ice. An additional three profile cores were also drilled but were replaced back into their original holes in the normal configuration as a control. A further 3 cores were then extracted from the ice and processed for chlorophyll, cell numbers and species composition etc as above. At the end of the deployment period the six cores still in the ice were redrilled and extracted from the ice and samples also taken for chlorophyll, cell numbers and species composition as above. A further 3 cores of undisturbed ice were also taken. proprietary
K048_1992_1993_NZ_1 A collection of lithospheric xenoliths from the Executive Committee Range and Mt Murphy Volcanic Complex in West Antarctica and the McMurdo Volcanic Province in McMurdo Sound ALL STAC Catalog 1992-11-14 1992-12-01 -166, -78.4, -166.41667, -75.3667 https://cmr.earthdata.nasa.gov/search/concepts/C1214593948-SCIOPS.umm_json Lithospheric xenoliths are a convenient and relatively cost efficient means of gaining an insight into the petrology of the deep earth. As such, they provide important information on lithospheric structure and processes and can be used to gauge thermal regime and possibly , the timing of events. Lithospheric xenoliths were collected in the 1989/90 and 1990/91 season from Marie Byrd Land, West Antarctica, including Mt Waesche, Mt Sidley, Mt Cumming, Mt Hampton and the USAS Escarpment (Mt Aldaz) in the Executive Committee Range and Mt Murphy in the Mount Murphy Volcanic Complex. Further samples were collected in the 1992/93 season from the McMurdo Volcanic Province at a number of localities on and adjacent to Ross Island (Hut Point Peninsula (Half Moon Crater, Sulphur Cones, Turtle Rock) and Cape Bird), Black Island and in the foothills of the Transantarctic Mountains (Foster Crater on the Koettlitz Glacier). The majority of the samples collected in the 1992/93 season supplemented a collection compiled from the 1982/83 and 1984/85 season. The xenoliths vary from texturally variable, spinel lherzolites and dunites representative of upper mantle assemblages to ultramafic Al-augite kaersutite bearing ultramafic rocks and plagioclase bearing ultramafic to mafic granulites thought to represent the transition zone between upper mantle and lower crust. proprietary
+K048_1992_1993_NZ_1 A collection of lithospheric xenoliths from the Executive Committee Range and Mt Murphy Volcanic Complex in West Antarctica and the McMurdo Volcanic Province in McMurdo Sound SCIOPS STAC Catalog 1992-11-14 1992-12-01 -166, -78.4, -166.41667, -75.3667 https://cmr.earthdata.nasa.gov/search/concepts/C1214593948-SCIOPS.umm_json Lithospheric xenoliths are a convenient and relatively cost efficient means of gaining an insight into the petrology of the deep earth. As such, they provide important information on lithospheric structure and processes and can be used to gauge thermal regime and possibly , the timing of events. Lithospheric xenoliths were collected in the 1989/90 and 1990/91 season from Marie Byrd Land, West Antarctica, including Mt Waesche, Mt Sidley, Mt Cumming, Mt Hampton and the USAS Escarpment (Mt Aldaz) in the Executive Committee Range and Mt Murphy in the Mount Murphy Volcanic Complex. Further samples were collected in the 1992/93 season from the McMurdo Volcanic Province at a number of localities on and adjacent to Ross Island (Hut Point Peninsula (Half Moon Crater, Sulphur Cones, Turtle Rock) and Cape Bird), Black Island and in the foothills of the Transantarctic Mountains (Foster Crater on the Koettlitz Glacier). The majority of the samples collected in the 1992/93 season supplemented a collection compiled from the 1982/83 and 1984/85 season. The xenoliths vary from texturally variable, spinel lherzolites and dunites representative of upper mantle assemblages to ultramafic Al-augite kaersutite bearing ultramafic rocks and plagioclase bearing ultramafic to mafic granulites thought to represent the transition zone between upper mantle and lower crust. proprietary
K052_1982_1983_NZ_4 Algae, fungi and actinomycetes from soils of Mt Erebus SCIOPS STAC Catalog 1982-12-04 1982-12-05 167.2833, -77.8833, 167.2833, -77.8833 https://cmr.earthdata.nasa.gov/search/concepts/C1214593380-SCIOPS.umm_json Soil samples were collected from the crater of Mt Erebus. Yeast glucose agar and penicillin and streptomycin was used to culture thermophilic microbes, fungi and actinomycetes. Several thermophilic microbes, fungi and actinomycetes were isolated and established in pure culture. proprietary
K052_1982_1983_NZ_4 Algae, fungi and actinomycetes from soils of Mt Erebus ALL STAC Catalog 1982-12-04 1982-12-05 167.2833, -77.8833, 167.2833, -77.8833 https://cmr.earthdata.nasa.gov/search/concepts/C1214593380-SCIOPS.umm_json Soil samples were collected from the crater of Mt Erebus. Yeast glucose agar and penicillin and streptomycin was used to culture thermophilic microbes, fungi and actinomycetes. Several thermophilic microbes, fungi and actinomycetes were isolated and established in pure culture. proprietary
K052_1982_1983_NZ_5 A hot house experiment at Cape Bird to determine the effects of microclimate on plant establishment ALL STAC Catalog 1982-11-17 1983-01-27 166.405, -77.142, 166.405, -77.142 https://cmr.earthdata.nasa.gov/search/concepts/C1214593365-SCIOPS.umm_json A small perspex frame was placed over bare mineral soil adjacent to the mosses in Keble Valley to examine the effects of humidity, temperature and microclimate on plant establishment. Many green shoots and algae were observed within the frame whilst the control site was bare of vegetation. The area was resurveyed a year later. A six channel temperature probe was used to test the microclimate. proprietary
K052_1982_1983_NZ_5 A hot house experiment at Cape Bird to determine the effects of microclimate on plant establishment SCIOPS STAC Catalog 1982-11-17 1983-01-27 166.405, -77.142, 166.405, -77.142 https://cmr.earthdata.nasa.gov/search/concepts/C1214593365-SCIOPS.umm_json A small perspex frame was placed over bare mineral soil adjacent to the mosses in Keble Valley to examine the effects of humidity, temperature and microclimate on plant establishment. Many green shoots and algae were observed within the frame whilst the control site was bare of vegetation. The area was resurveyed a year later. A six channel temperature probe was used to test the microclimate. proprietary
-K053_1990_1991_NZ_2 Algae cultures from air trap samples, snow samples and algal surveys from Scott Base, the Ross Ice Shelf and Victoria Valley to determine the dispersal of algae by wind within Antarctica ALL STAC Catalog 1990-12-19 1991-01-28 161.5, -77.85, 166.75, -77.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214591606-SCIOPS.umm_json The dispersal of algae by wind within Antarctica was investigated by testing four techniques for detecting viable algae in the air: 1) High through put 'jet' spore samples, 2) Clinical monitors, 3) Liquid impinger and 4) Tauber traps. Air was sampled from Scott Base, the Ross Ice Shelf (at a site east of a line between Cape Crozier on Ross Island and White Island) and Victoria Valley (west end of Lake Vida). Snow drifts were also sampled from the Ross Ice Shelf as they were considered to be natural long term particle traps. Samples were also taken of visible algal growths on soils, in streams and in ponds in the vicinity of Scott Base. Soil samples were removed from the driest surfaces where no vegetation was visible. Cultures established from these were used to indicate the composition of the local algal flora for comparison with airborne species. In Victoria Valley, an extensive survey of the aquatic and terrestrial algae in the valley and along some of the ridges and upper valley sides was completed for knowledge of local sources of airborne propagules for comparison with the air samples. proprietary
K053_1990_1991_NZ_2 Algae cultures from air trap samples, snow samples and algal surveys from Scott Base, the Ross Ice Shelf and Victoria Valley to determine the dispersal of algae by wind within Antarctica SCIOPS STAC Catalog 1990-12-19 1991-01-28 161.5, -77.85, 166.75, -77.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214591606-SCIOPS.umm_json The dispersal of algae by wind within Antarctica was investigated by testing four techniques for detecting viable algae in the air: 1) High through put 'jet' spore samples, 2) Clinical monitors, 3) Liquid impinger and 4) Tauber traps. Air was sampled from Scott Base, the Ross Ice Shelf (at a site east of a line between Cape Crozier on Ross Island and White Island) and Victoria Valley (west end of Lake Vida). Snow drifts were also sampled from the Ross Ice Shelf as they were considered to be natural long term particle traps. Samples were also taken of visible algal growths on soils, in streams and in ponds in the vicinity of Scott Base. Soil samples were removed from the driest surfaces where no vegetation was visible. Cultures established from these were used to indicate the composition of the local algal flora for comparison with airborne species. In Victoria Valley, an extensive survey of the aquatic and terrestrial algae in the valley and along some of the ridges and upper valley sides was completed for knowledge of local sources of airborne propagules for comparison with the air samples. proprietary
+K053_1990_1991_NZ_2 Algae cultures from air trap samples, snow samples and algal surveys from Scott Base, the Ross Ice Shelf and Victoria Valley to determine the dispersal of algae by wind within Antarctica ALL STAC Catalog 1990-12-19 1991-01-28 161.5, -77.85, 166.75, -77.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214591606-SCIOPS.umm_json The dispersal of algae by wind within Antarctica was investigated by testing four techniques for detecting viable algae in the air: 1) High through put 'jet' spore samples, 2) Clinical monitors, 3) Liquid impinger and 4) Tauber traps. Air was sampled from Scott Base, the Ross Ice Shelf (at a site east of a line between Cape Crozier on Ross Island and White Island) and Victoria Valley (west end of Lake Vida). Snow drifts were also sampled from the Ross Ice Shelf as they were considered to be natural long term particle traps. Samples were also taken of visible algal growths on soils, in streams and in ponds in the vicinity of Scott Base. Soil samples were removed from the driest surfaces where no vegetation was visible. Cultures established from these were used to indicate the composition of the local algal flora for comparison with airborne species. In Victoria Valley, an extensive survey of the aquatic and terrestrial algae in the valley and along some of the ridges and upper valley sides was completed for knowledge of local sources of airborne propagules for comparison with the air samples. proprietary
K054_1988_1989_NZ_1 A grafting experiment testing the ability of Antarctic sponges to recognise self from non-self tissue and their immune response SCIOPS STAC Catalog 1988-10-14 1988-11-24 166.6667, -77.85, 166.6667, -77.85 https://cmr.earthdata.nasa.gov/search/concepts/C1214593984-SCIOPS.umm_json A dive site was selected at Cape Armitage to conduct a marine benthos survey. The water was approximately 25m deep and the bottom was found to be rocky and inhabited by sponges. Four sponge species were grafted in an exercise to test the sponges ability to recognise self from non-self tissue and to examine any immune response. The experiments also allowed for the examination of the genetic relatedness among individuals on the reef. Grafter were made by cutting 1cm3 pieces of tissue from a donor sponge and embedding them in replicate host sponges of the same species at varying distances from the donor. Grafters were left in place for up to one week and were monitored daily. At the completion of the experiment, the graft site was excised from the host and frozen for further analysis. proprietary
K054_1988_1989_NZ_1 A grafting experiment testing the ability of Antarctic sponges to recognise self from non-self tissue and their immune response ALL STAC Catalog 1988-10-14 1988-11-24 166.6667, -77.85, 166.6667, -77.85 https://cmr.earthdata.nasa.gov/search/concepts/C1214593984-SCIOPS.umm_json A dive site was selected at Cape Armitage to conduct a marine benthos survey. The water was approximately 25m deep and the bottom was found to be rocky and inhabited by sponges. Four sponge species were grafted in an exercise to test the sponges ability to recognise self from non-self tissue and to examine any immune response. The experiments also allowed for the examination of the genetic relatedness among individuals on the reef. Grafter were made by cutting 1cm3 pieces of tissue from a donor sponge and embedding them in replicate host sponges of the same species at varying distances from the donor. Grafters were left in place for up to one week and were monitored daily. At the completion of the experiment, the graft site was excised from the host and frozen for further analysis. proprietary
-K054_1988_1989_NZ_3 A survey of the density of starfish and sea urchins to determine the grazing pressure of these species on a sponge dominated reef, Cape Armitage ALL STAC Catalog 1988-10-14 1988-11-24 166.6667, -77.85, 166.6667, -77.85 https://cmr.earthdata.nasa.gov/search/concepts/C1214593986-SCIOPS.umm_json In order to determine the grazing pressure of starfish and sea urchin species on the benthic community of a reef at Cape Armitage, a survey was made of these species densities. The survey was stratified by depth. All individuals encountered in five 20m x 1m transects at each depth level were identified and measured. Each animal was examined in order to identify any species. Twelve further 1m x 1m quadrats were examined in detail specifically to look for smaller individuals. proprietary
K054_1988_1989_NZ_3 A survey of the density of starfish and sea urchins to determine the grazing pressure of these species on a sponge dominated reef, Cape Armitage SCIOPS STAC Catalog 1988-10-14 1988-11-24 166.6667, -77.85, 166.6667, -77.85 https://cmr.earthdata.nasa.gov/search/concepts/C1214593986-SCIOPS.umm_json In order to determine the grazing pressure of starfish and sea urchin species on the benthic community of a reef at Cape Armitage, a survey was made of these species densities. The survey was stratified by depth. All individuals encountered in five 20m x 1m transects at each depth level were identified and measured. Each animal was examined in order to identify any species. Twelve further 1m x 1m quadrats were examined in detail specifically to look for smaller individuals. proprietary
-K057_1999_2000_NZ_2 A partitioning experiments to determine the aetiology of x-cell disease SCIOPS STAC Catalog 1999-11-01 1999-12-30 166.75, -77.85, 166.75, -77.85 https://cmr.earthdata.nasa.gov/search/concepts/C1214593003-SCIOPS.umm_json Captured Pagothenia borchgrevinki fish were placed into an aquarium and partitioned into tanks as all healthy, all x-cell or a mixture of the two. Lengths and weights of all fish were measured and the degree of infection was determined for all affected fish. Fish were left in this set up for one month. At the end of the month, the death rate of the fish was measured to help determine unknown factors of the disease such as what the disease is, how is it spread, how quickly does it travel along the gills of individual fish, what happens when 100% of a fishes fills become covered with the disease and does the fish recover? Samples of healthy and x-cell affected tissues were collected for analysis. proprietary
+K054_1988_1989_NZ_3 A survey of the density of starfish and sea urchins to determine the grazing pressure of these species on a sponge dominated reef, Cape Armitage ALL STAC Catalog 1988-10-14 1988-11-24 166.6667, -77.85, 166.6667, -77.85 https://cmr.earthdata.nasa.gov/search/concepts/C1214593986-SCIOPS.umm_json In order to determine the grazing pressure of starfish and sea urchin species on the benthic community of a reef at Cape Armitage, a survey was made of these species densities. The survey was stratified by depth. All individuals encountered in five 20m x 1m transects at each depth level were identified and measured. Each animal was examined in order to identify any species. Twelve further 1m x 1m quadrats were examined in detail specifically to look for smaller individuals. proprietary
K057_1999_2000_NZ_2 A partitioning experiments to determine the aetiology of x-cell disease ALL STAC Catalog 1999-11-01 1999-12-30 166.75, -77.85, 166.75, -77.85 https://cmr.earthdata.nasa.gov/search/concepts/C1214593003-SCIOPS.umm_json Captured Pagothenia borchgrevinki fish were placed into an aquarium and partitioned into tanks as all healthy, all x-cell or a mixture of the two. Lengths and weights of all fish were measured and the degree of infection was determined for all affected fish. Fish were left in this set up for one month. At the end of the month, the death rate of the fish was measured to help determine unknown factors of the disease such as what the disease is, how is it spread, how quickly does it travel along the gills of individual fish, what happens when 100% of a fishes fills become covered with the disease and does the fish recover? Samples of healthy and x-cell affected tissues were collected for analysis. proprietary
+K057_1999_2000_NZ_2 A partitioning experiments to determine the aetiology of x-cell disease SCIOPS STAC Catalog 1999-11-01 1999-12-30 166.75, -77.85, 166.75, -77.85 https://cmr.earthdata.nasa.gov/search/concepts/C1214593003-SCIOPS.umm_json Captured Pagothenia borchgrevinki fish were placed into an aquarium and partitioned into tanks as all healthy, all x-cell or a mixture of the two. Lengths and weights of all fish were measured and the degree of infection was determined for all affected fish. Fish were left in this set up for one month. At the end of the month, the death rate of the fish was measured to help determine unknown factors of the disease such as what the disease is, how is it spread, how quickly does it travel along the gills of individual fish, what happens when 100% of a fishes fills become covered with the disease and does the fish recover? Samples of healthy and x-cell affected tissues were collected for analysis. proprietary
K061_1986_1987_NZ_2 A detailed study of the origin of Olympus Granite Gneiss SCIOPS STAC Catalog 1986-12-15 1987-01-22 161, -77.58, 162.5, -77.41 https://cmr.earthdata.nasa.gov/search/concepts/C1214590869-SCIOPS.umm_json A detailed study of the Olympus Granite Gneiss with particular emphasis on foliation development and its relationship to deformation of Koettlitz Group metasediments, in an attempt to understand its origin was undertaken with a three stage investigation. Firstly, the Olympus Granite Gneiss in the Bull Pass area was studied and sampled with emphasis on its relation to Dais Granite. Secondly, the Koettlitz Group metasediment was studied and sampled looking in detail at anatectic processes associated with deformation of these rocks, including mapping and measuring sections of both Olympus Granite Gneiss/Koettlitz Group contacts. Thirdly, the 'classic locality' of Dais Granite was studied and this rock-types relationship to highly deformed rocks mapped by earlier workers. Laboratory work included detailed structural analysis at all scales, petrographic studies and geochemical analyses. proprietary
K061_1986_1987_NZ_2 A detailed study of the origin of Olympus Granite Gneiss ALL STAC Catalog 1986-12-15 1987-01-22 161, -77.58, 162.5, -77.41 https://cmr.earthdata.nasa.gov/search/concepts/C1214590869-SCIOPS.umm_json A detailed study of the Olympus Granite Gneiss with particular emphasis on foliation development and its relationship to deformation of Koettlitz Group metasediments, in an attempt to understand its origin was undertaken with a three stage investigation. Firstly, the Olympus Granite Gneiss in the Bull Pass area was studied and sampled with emphasis on its relation to Dais Granite. Secondly, the Koettlitz Group metasediment was studied and sampled looking in detail at anatectic processes associated with deformation of these rocks, including mapping and measuring sections of both Olympus Granite Gneiss/Koettlitz Group contacts. Thirdly, the 'classic locality' of Dais Granite was studied and this rock-types relationship to highly deformed rocks mapped by earlier workers. Laboratory work included detailed structural analysis at all scales, petrographic studies and geochemical analyses. proprietary
-K061_1992_1995_NZ_1 A comparative examination of the origin, structure and metamorphism of the Skelton and Koettlitz Group (basement lithologies) in South Victoria Land, Antarctica. ALL STAC Catalog 1992-11-19 1994-12-20 160, -79, 165, -74 https://cmr.earthdata.nasa.gov/search/concepts/C1214591224-SCIOPS.umm_json A comparative examination of the origin, structure and metamorphism of the Skelton and Koettlitz Group (Wilson Terrane) was carried out over three field seasons to determine a) if the two groups could be correlatives, b) the nature of their relationship and c) to account for the difference in strain between them. The effect of plutons on regional and local structure of the Wilson Terrane was examined. The Renegar Glacier was mapped in detail and a study of high strain zones between Koettlitz Group and mafic plutonic bodies was assessed. Samples of plutonic mafic rocks were taken to analyse the chemical and mineralogical response of these rocks to high strain. Detailed mapping of the Skelton Group was carried out around the Cocks Glacier from north of Baronick Glacier to Red Dyke to the SW ridge of Mt Cocks. The lithologies were examined and the stratigraphy at three different localities was established on local and regional scales. North of the Renegar Glacier, the Koettlitz group was also examined. Samples, orientated to distinctive lithogies, were collected. The variation in strain was noted, large bodies of orthogneiss was examined structurally and lithologically and sampled for dating. The outcrop of the Skelton Group was mapped on the east ridge of Mt Kemp and structural relation to the neighbouring rocks was determined. The Williams Peak – Hobbs Peak area was mapped in detail and salmon marble was sampled. The nature of the eastern contact of the Bonney Pluton and the effect of the intrusion of this pluton into the Koettlitz Group was examined. The type section of the Hobbs formation was studied along the east ridge of Hobbs Peak with the degree of strain ascertained. Outcrops and rocks were examined at Radian Ridge, Mount Cocks, Preistly Glacier, Salient Glacier and Substitution Ridge. Field notes and samples were taken along the way to establish the relationships between tectonic and metamorphic sub-areas. Granite, schists, diorite and gabbro were sampled from Panorama Glacier, Marshall Valley, Taylor Valley, Walker Rocks, and Campbell Glacier to propose an indication of the original environment of initial formation of the rocks and provided insight into the processes operating at varying crustal levels during orogenesis. At Mt Dromedary, a sequence was examined for the significant shear zone separating two distinct structural blocks, inferred from pervious mapping. At Teal Island the area was examined and found sediments and rocks which link between the lithologies of the Skelton area. At Mt Huggins a subsidiary ridge was examined finding undeformed metasediments. proprietary
K061_1992_1995_NZ_1 A comparative examination of the origin, structure and metamorphism of the Skelton and Koettlitz Group (basement lithologies) in South Victoria Land, Antarctica. SCIOPS STAC Catalog 1992-11-19 1994-12-20 160, -79, 165, -74 https://cmr.earthdata.nasa.gov/search/concepts/C1214591224-SCIOPS.umm_json A comparative examination of the origin, structure and metamorphism of the Skelton and Koettlitz Group (Wilson Terrane) was carried out over three field seasons to determine a) if the two groups could be correlatives, b) the nature of their relationship and c) to account for the difference in strain between them. The effect of plutons on regional and local structure of the Wilson Terrane was examined. The Renegar Glacier was mapped in detail and a study of high strain zones between Koettlitz Group and mafic plutonic bodies was assessed. Samples of plutonic mafic rocks were taken to analyse the chemical and mineralogical response of these rocks to high strain. Detailed mapping of the Skelton Group was carried out around the Cocks Glacier from north of Baronick Glacier to Red Dyke to the SW ridge of Mt Cocks. The lithologies were examined and the stratigraphy at three different localities was established on local and regional scales. North of the Renegar Glacier, the Koettlitz group was also examined. Samples, orientated to distinctive lithogies, were collected. The variation in strain was noted, large bodies of orthogneiss was examined structurally and lithologically and sampled for dating. The outcrop of the Skelton Group was mapped on the east ridge of Mt Kemp and structural relation to the neighbouring rocks was determined. The Williams Peak – Hobbs Peak area was mapped in detail and salmon marble was sampled. The nature of the eastern contact of the Bonney Pluton and the effect of the intrusion of this pluton into the Koettlitz Group was examined. The type section of the Hobbs formation was studied along the east ridge of Hobbs Peak with the degree of strain ascertained. Outcrops and rocks were examined at Radian Ridge, Mount Cocks, Preistly Glacier, Salient Glacier and Substitution Ridge. Field notes and samples were taken along the way to establish the relationships between tectonic and metamorphic sub-areas. Granite, schists, diorite and gabbro were sampled from Panorama Glacier, Marshall Valley, Taylor Valley, Walker Rocks, and Campbell Glacier to propose an indication of the original environment of initial formation of the rocks and provided insight into the processes operating at varying crustal levels during orogenesis. At Mt Dromedary, a sequence was examined for the significant shear zone separating two distinct structural blocks, inferred from pervious mapping. At Teal Island the area was examined and found sediments and rocks which link between the lithologies of the Skelton area. At Mt Huggins a subsidiary ridge was examined finding undeformed metasediments. proprietary
-K061_2001_2002_NZ_2 A reconstruction of the record of volcanic processes within the vent of a large and explosive basaltic eruption in the Mawson Formation in the Allan Hills SCIOPS STAC Catalog 2001-11-28 2001-12-22 159.65, -78.7333, 159.65, -78.7333 https://cmr.earthdata.nasa.gov/search/concepts/C1214591068-SCIOPS.umm_json The contact relationship between volcanic deposits and surrounding country rocks of the Beacon Supergroup are steep over a large area. Beyond the landslide deposits along the contact between Beacon country rock and Mawson volcaniclastic rocks lies the Mawson itself. An area in which the remains of a single vent of the vent complex was well exposed, on both steep and subhorizontal ground surfaces, was mapped in detail with the geometric relationships between different bodies of volcaniclastic rock examined. The characteristics of the processes that cause one body of debris to be apparently shot through the other was investigated. Standard geological mapping techniques, photographs, scaled sketches and rock samples were used to create a 3-dimensional reconstruction of the record of volcanic processes within the vent of a large and explosive basaltic eruption. proprietary
+K061_1992_1995_NZ_1 A comparative examination of the origin, structure and metamorphism of the Skelton and Koettlitz Group (basement lithologies) in South Victoria Land, Antarctica. ALL STAC Catalog 1992-11-19 1994-12-20 160, -79, 165, -74 https://cmr.earthdata.nasa.gov/search/concepts/C1214591224-SCIOPS.umm_json A comparative examination of the origin, structure and metamorphism of the Skelton and Koettlitz Group (Wilson Terrane) was carried out over three field seasons to determine a) if the two groups could be correlatives, b) the nature of their relationship and c) to account for the difference in strain between them. The effect of plutons on regional and local structure of the Wilson Terrane was examined. The Renegar Glacier was mapped in detail and a study of high strain zones between Koettlitz Group and mafic plutonic bodies was assessed. Samples of plutonic mafic rocks were taken to analyse the chemical and mineralogical response of these rocks to high strain. Detailed mapping of the Skelton Group was carried out around the Cocks Glacier from north of Baronick Glacier to Red Dyke to the SW ridge of Mt Cocks. The lithologies were examined and the stratigraphy at three different localities was established on local and regional scales. North of the Renegar Glacier, the Koettlitz group was also examined. Samples, orientated to distinctive lithogies, were collected. The variation in strain was noted, large bodies of orthogneiss was examined structurally and lithologically and sampled for dating. The outcrop of the Skelton Group was mapped on the east ridge of Mt Kemp and structural relation to the neighbouring rocks was determined. The Williams Peak – Hobbs Peak area was mapped in detail and salmon marble was sampled. The nature of the eastern contact of the Bonney Pluton and the effect of the intrusion of this pluton into the Koettlitz Group was examined. The type section of the Hobbs formation was studied along the east ridge of Hobbs Peak with the degree of strain ascertained. Outcrops and rocks were examined at Radian Ridge, Mount Cocks, Preistly Glacier, Salient Glacier and Substitution Ridge. Field notes and samples were taken along the way to establish the relationships between tectonic and metamorphic sub-areas. Granite, schists, diorite and gabbro were sampled from Panorama Glacier, Marshall Valley, Taylor Valley, Walker Rocks, and Campbell Glacier to propose an indication of the original environment of initial formation of the rocks and provided insight into the processes operating at varying crustal levels during orogenesis. At Mt Dromedary, a sequence was examined for the significant shear zone separating two distinct structural blocks, inferred from pervious mapping. At Teal Island the area was examined and found sediments and rocks which link between the lithologies of the Skelton area. At Mt Huggins a subsidiary ridge was examined finding undeformed metasediments. proprietary
K061_2001_2002_NZ_2 A reconstruction of the record of volcanic processes within the vent of a large and explosive basaltic eruption in the Mawson Formation in the Allan Hills ALL STAC Catalog 2001-11-28 2001-12-22 159.65, -78.7333, 159.65, -78.7333 https://cmr.earthdata.nasa.gov/search/concepts/C1214591068-SCIOPS.umm_json The contact relationship between volcanic deposits and surrounding country rocks of the Beacon Supergroup are steep over a large area. Beyond the landslide deposits along the contact between Beacon country rock and Mawson volcaniclastic rocks lies the Mawson itself. An area in which the remains of a single vent of the vent complex was well exposed, on both steep and subhorizontal ground surfaces, was mapped in detail with the geometric relationships between different bodies of volcaniclastic rock examined. The characteristics of the processes that cause one body of debris to be apparently shot through the other was investigated. Standard geological mapping techniques, photographs, scaled sketches and rock samples were used to create a 3-dimensional reconstruction of the record of volcanic processes within the vent of a large and explosive basaltic eruption. proprietary
-K062_2003_2004_NZ_1 Age determination of the detrital zircon component of crustal slices of Ross Orogen from the Skelton Glacier and Royal Society Ranges areas SCIOPS STAC Catalog 2003-12-04 2004-11-22 161, -79, 163, -78.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214591360-SCIOPS.umm_json It is suggested that the Ross Orogeny is composed of a wide variety of crustal slices that are exotic to their present location and were accreted to the East Antarctic craton during the lower paleozoic Ross Orogeny. To test this hypothesis, rocks (metasedimentary rocks and granite) were sampled from crustal slices in both the Skelton Glacier and Royal Society Ranges including Renegar Glacier area, lower Radian Ridge, Rucker Ridge, Gloomy Hill, the Radian Glacier area, the upper Skelton Glacier area and Stepaside Spur. Samples were crushed and processed through heavy liquids and magnetic separation to isolate detrital grain of zircon and analysed by LAP-ICP-MS and their ages determined. The provenance, or source, of the detrital zircons can also be assessed from the specific characteristics of the age histogram. This enables (a) ready comparison between individual crustal slices to assess whether they originated in the same place prior to accretion and (b) it allows reconstruction of the terranes at the time of sedimentation and (c) it offers the possibility of determining the likely distance of travel of so called exotic terrances prior to accretion. proprietary
+K061_2001_2002_NZ_2 A reconstruction of the record of volcanic processes within the vent of a large and explosive basaltic eruption in the Mawson Formation in the Allan Hills SCIOPS STAC Catalog 2001-11-28 2001-12-22 159.65, -78.7333, 159.65, -78.7333 https://cmr.earthdata.nasa.gov/search/concepts/C1214591068-SCIOPS.umm_json The contact relationship between volcanic deposits and surrounding country rocks of the Beacon Supergroup are steep over a large area. Beyond the landslide deposits along the contact between Beacon country rock and Mawson volcaniclastic rocks lies the Mawson itself. An area in which the remains of a single vent of the vent complex was well exposed, on both steep and subhorizontal ground surfaces, was mapped in detail with the geometric relationships between different bodies of volcaniclastic rock examined. The characteristics of the processes that cause one body of debris to be apparently shot through the other was investigated. Standard geological mapping techniques, photographs, scaled sketches and rock samples were used to create a 3-dimensional reconstruction of the record of volcanic processes within the vent of a large and explosive basaltic eruption. proprietary
K062_2003_2004_NZ_1 Age determination of the detrital zircon component of crustal slices of Ross Orogen from the Skelton Glacier and Royal Society Ranges areas ALL STAC Catalog 2003-12-04 2004-11-22 161, -79, 163, -78.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214591360-SCIOPS.umm_json It is suggested that the Ross Orogeny is composed of a wide variety of crustal slices that are exotic to their present location and were accreted to the East Antarctic craton during the lower paleozoic Ross Orogeny. To test this hypothesis, rocks (metasedimentary rocks and granite) were sampled from crustal slices in both the Skelton Glacier and Royal Society Ranges including Renegar Glacier area, lower Radian Ridge, Rucker Ridge, Gloomy Hill, the Radian Glacier area, the upper Skelton Glacier area and Stepaside Spur. Samples were crushed and processed through heavy liquids and magnetic separation to isolate detrital grain of zircon and analysed by LAP-ICP-MS and their ages determined. The provenance, or source, of the detrital zircons can also be assessed from the specific characteristics of the age histogram. This enables (a) ready comparison between individual crustal slices to assess whether they originated in the same place prior to accretion and (b) it allows reconstruction of the terranes at the time of sedimentation and (c) it offers the possibility of determining the likely distance of travel of so called exotic terrances prior to accretion. proprietary
-K063_1987_1988_NZ_2 Adelie penguin weights before and after foraging trips from three groups of penguins: control, single egg removed and penned females SCIOPS STAC Catalog 1987-11-01 1989-02-06 166.68, -77.17, 166.68, -77.17 https://cmr.earthdata.nasa.gov/search/concepts/C1214591134-SCIOPS.umm_json As an index of physiological condition and success of foraging, penguins were weighed early in the season when they were flipper banded and then re-weighed when they returned from their foraging trip. Three groups were compared: a control group that was left undisturbed except for the weighing, the removal group which the first egg from the nest was removed and the penned group where the female were prevented from going to sea for their first foraging trip by being placed in a pen for 4 days. These observations will contribute to the determination of any annual fluctuations in the success of penguin foraging. proprietary
+K062_2003_2004_NZ_1 Age determination of the detrital zircon component of crustal slices of Ross Orogen from the Skelton Glacier and Royal Society Ranges areas SCIOPS STAC Catalog 2003-12-04 2004-11-22 161, -79, 163, -78.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214591360-SCIOPS.umm_json It is suggested that the Ross Orogeny is composed of a wide variety of crustal slices that are exotic to their present location and were accreted to the East Antarctic craton during the lower paleozoic Ross Orogeny. To test this hypothesis, rocks (metasedimentary rocks and granite) were sampled from crustal slices in both the Skelton Glacier and Royal Society Ranges including Renegar Glacier area, lower Radian Ridge, Rucker Ridge, Gloomy Hill, the Radian Glacier area, the upper Skelton Glacier area and Stepaside Spur. Samples were crushed and processed through heavy liquids and magnetic separation to isolate detrital grain of zircon and analysed by LAP-ICP-MS and their ages determined. The provenance, or source, of the detrital zircons can also be assessed from the specific characteristics of the age histogram. This enables (a) ready comparison between individual crustal slices to assess whether they originated in the same place prior to accretion and (b) it allows reconstruction of the terranes at the time of sedimentation and (c) it offers the possibility of determining the likely distance of travel of so called exotic terrances prior to accretion. proprietary
K063_1987_1988_NZ_2 Adelie penguin weights before and after foraging trips from three groups of penguins: control, single egg removed and penned females ALL STAC Catalog 1987-11-01 1989-02-06 166.68, -77.17, 166.68, -77.17 https://cmr.earthdata.nasa.gov/search/concepts/C1214591134-SCIOPS.umm_json As an index of physiological condition and success of foraging, penguins were weighed early in the season when they were flipper banded and then re-weighed when they returned from their foraging trip. Three groups were compared: a control group that was left undisturbed except for the weighing, the removal group which the first egg from the nest was removed and the penned group where the female were prevented from going to sea for their first foraging trip by being placed in a pen for 4 days. These observations will contribute to the determination of any annual fluctuations in the success of penguin foraging. proprietary
+K063_1987_1988_NZ_2 Adelie penguin weights before and after foraging trips from three groups of penguins: control, single egg removed and penned females SCIOPS STAC Catalog 1987-11-01 1989-02-06 166.68, -77.17, 166.68, -77.17 https://cmr.earthdata.nasa.gov/search/concepts/C1214591134-SCIOPS.umm_json As an index of physiological condition and success of foraging, penguins were weighed early in the season when they were flipper banded and then re-weighed when they returned from their foraging trip. Three groups were compared: a control group that was left undisturbed except for the weighing, the removal group which the first egg from the nest was removed and the penned group where the female were prevented from going to sea for their first foraging trip by being placed in a pen for 4 days. These observations will contribute to the determination of any annual fluctuations in the success of penguin foraging. proprietary
K065_1996_1998_NZ_1 Adelie penguin liver P450 enzymes ALL STAC Catalog 1996-12-07 1998-01-17 166, -78, 170, -77 https://cmr.earthdata.nasa.gov/search/concepts/C1214590806-SCIOPS.umm_json Animlas can be harmed by artificially introduced chemicals either through the food chain or directly. This study aimed to determine how penguins detoxify chemical pollutants they may be exposed to. Liver samples were collected from Adelie penguins from Cape Bird, Cape Royds and Cape Crozier, both adults (10) and chicks (20). The samples were analysed for liver enzymes with the aim to characterize different P450 enzymes involved in biotransformation and detoxification of chemical pollutants. The aim is to determine the susceptibility of Antarctic penguins to environmental chemicals. proprietary
K065_1996_1998_NZ_1 Adelie penguin liver P450 enzymes SCIOPS STAC Catalog 1996-12-07 1998-01-17 166, -78, 170, -77 https://cmr.earthdata.nasa.gov/search/concepts/C1214590806-SCIOPS.umm_json Animlas can be harmed by artificially introduced chemicals either through the food chain or directly. This study aimed to determine how penguins detoxify chemical pollutants they may be exposed to. Liver samples were collected from Adelie penguins from Cape Bird, Cape Royds and Cape Crozier, both adults (10) and chicks (20). The samples were analysed for liver enzymes with the aim to characterize different P450 enzymes involved in biotransformation and detoxification of chemical pollutants. The aim is to determine the susceptibility of Antarctic penguins to environmental chemicals. proprietary
-K081_1983_1986_NZ_1 Algal composition, physico-chemical features, photosynthetic carbon metabolism, nitrogen cycling and the structure and metabolic properties of algal mats in lakes and streams of southern Victoria Land SCIOPS STAC Catalog 1983-11-05 1986-02-03 161, -78.25, 167, -77.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214591569-SCIOPS.umm_json A three year study of lakes and stream of southern Victoria Land was conducted from 1983-1986. In the first season, the algal composition and physico-chemical characteristics of South Victoria Land Streams was investigated. Four rivers were visited in the coure of the summer, once early in the season before they had begun to flow, and then several weeks later when discharge was near to its annual maximum. An additional 8 streams were examined less intensively in the course of the season. These all include Adams, Whangamata, Onyx, Bird, Salmon, Bartley, Fryxell, Commonwealth, Stream CC1 and CC2, Harrison and Miers. Specific studies included 1) Overwintering algal biomass: naturally freeze-dried algal mats were quantified by transect analysis and by chlorophyll a samples, 2) Chlorophyll a biomass levels: stream samples were taken from areas with visually maximum biomass at each site during early and late season and assayed by fluorometer or spectrophotometer, 3) Algal community structure: taxonomic analysis of the stream periphyton, 4) Algal growth and production: artificial substrates deployed and processed for chlorophyll a analysis, 5) Metabolic responses by Antarctic stream algae: recovery from freeze dry conditions (early season) and nutrient uptake by developed communities, 6) Dissolved organic carbon: measured from water samples, 7) Nutrient extraction from stream bed soils: nitrogen and phosphorous released after soaking for 12 hours in glacier melt water, 8) Stream nutrient levels: chemical analysis of water samples, 9) Diurnal studies on variability in nutrient concentrations: monitoring stream parameters every three hours for a 26 hour period, 10) Lake Miers studies: a broad range of limnological measurements made at Lake Miers, possibly the southern most meromictic waterbody no the continent. In the second season, studies were further extended on the epilithic algal and bacterial communities of southern Victoria Land streams to follow respiratory and photosynthetic carbon metabolism by communities at two select stream sites. Nitrogen cycling and photosynthetic metabolism in Lake Fryxell and Lake Vanda was also examined. In the third and final season, preliminary analysis of waters on the McMurdo Ice Shelf and the structure and metabolic properties of the stream algal mats, with special reference to temperature, light and nutrient effects and factors controlling nitrogen cycling, and photosynthesis in Dry Valley lakes (Lake Fryxell, Lake Vanda and Lake Miers), with particular attention to the deep chlorophyll maximum was studied. proprietary
K081_1983_1986_NZ_1 Algal composition, physico-chemical features, photosynthetic carbon metabolism, nitrogen cycling and the structure and metabolic properties of algal mats in lakes and streams of southern Victoria Land ALL STAC Catalog 1983-11-05 1986-02-03 161, -78.25, 167, -77.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214591569-SCIOPS.umm_json A three year study of lakes and stream of southern Victoria Land was conducted from 1983-1986. In the first season, the algal composition and physico-chemical characteristics of South Victoria Land Streams was investigated. Four rivers were visited in the coure of the summer, once early in the season before they had begun to flow, and then several weeks later when discharge was near to its annual maximum. An additional 8 streams were examined less intensively in the course of the season. These all include Adams, Whangamata, Onyx, Bird, Salmon, Bartley, Fryxell, Commonwealth, Stream CC1 and CC2, Harrison and Miers. Specific studies included 1) Overwintering algal biomass: naturally freeze-dried algal mats were quantified by transect analysis and by chlorophyll a samples, 2) Chlorophyll a biomass levels: stream samples were taken from areas with visually maximum biomass at each site during early and late season and assayed by fluorometer or spectrophotometer, 3) Algal community structure: taxonomic analysis of the stream periphyton, 4) Algal growth and production: artificial substrates deployed and processed for chlorophyll a analysis, 5) Metabolic responses by Antarctic stream algae: recovery from freeze dry conditions (early season) and nutrient uptake by developed communities, 6) Dissolved organic carbon: measured from water samples, 7) Nutrient extraction from stream bed soils: nitrogen and phosphorous released after soaking for 12 hours in glacier melt water, 8) Stream nutrient levels: chemical analysis of water samples, 9) Diurnal studies on variability in nutrient concentrations: monitoring stream parameters every three hours for a 26 hour period, 10) Lake Miers studies: a broad range of limnological measurements made at Lake Miers, possibly the southern most meromictic waterbody no the continent. In the second season, studies were further extended on the epilithic algal and bacterial communities of southern Victoria Land streams to follow respiratory and photosynthetic carbon metabolism by communities at two select stream sites. Nitrogen cycling and photosynthetic metabolism in Lake Fryxell and Lake Vanda was also examined. In the third and final season, preliminary analysis of waters on the McMurdo Ice Shelf and the structure and metabolic properties of the stream algal mats, with special reference to temperature, light and nutrient effects and factors controlling nitrogen cycling, and photosynthesis in Dry Valley lakes (Lake Fryxell, Lake Vanda and Lake Miers), with particular attention to the deep chlorophyll maximum was studied. proprietary
+K081_1983_1986_NZ_1 Algal composition, physico-chemical features, photosynthetic carbon metabolism, nitrogen cycling and the structure and metabolic properties of algal mats in lakes and streams of southern Victoria Land SCIOPS STAC Catalog 1983-11-05 1986-02-03 161, -78.25, 167, -77.25 https://cmr.earthdata.nasa.gov/search/concepts/C1214591569-SCIOPS.umm_json A three year study of lakes and stream of southern Victoria Land was conducted from 1983-1986. In the first season, the algal composition and physico-chemical characteristics of South Victoria Land Streams was investigated. Four rivers were visited in the coure of the summer, once early in the season before they had begun to flow, and then several weeks later when discharge was near to its annual maximum. An additional 8 streams were examined less intensively in the course of the season. These all include Adams, Whangamata, Onyx, Bird, Salmon, Bartley, Fryxell, Commonwealth, Stream CC1 and CC2, Harrison and Miers. Specific studies included 1) Overwintering algal biomass: naturally freeze-dried algal mats were quantified by transect analysis and by chlorophyll a samples, 2) Chlorophyll a biomass levels: stream samples were taken from areas with visually maximum biomass at each site during early and late season and assayed by fluorometer or spectrophotometer, 3) Algal community structure: taxonomic analysis of the stream periphyton, 4) Algal growth and production: artificial substrates deployed and processed for chlorophyll a analysis, 5) Metabolic responses by Antarctic stream algae: recovery from freeze dry conditions (early season) and nutrient uptake by developed communities, 6) Dissolved organic carbon: measured from water samples, 7) Nutrient extraction from stream bed soils: nitrogen and phosphorous released after soaking for 12 hours in glacier melt water, 8) Stream nutrient levels: chemical analysis of water samples, 9) Diurnal studies on variability in nutrient concentrations: monitoring stream parameters every three hours for a 26 hour period, 10) Lake Miers studies: a broad range of limnological measurements made at Lake Miers, possibly the southern most meromictic waterbody no the continent. In the second season, studies were further extended on the epilithic algal and bacterial communities of southern Victoria Land streams to follow respiratory and photosynthetic carbon metabolism by communities at two select stream sites. Nitrogen cycling and photosynthetic metabolism in Lake Fryxell and Lake Vanda was also examined. In the third and final season, preliminary analysis of waters on the McMurdo Ice Shelf and the structure and metabolic properties of the stream algal mats, with special reference to temperature, light and nutrient effects and factors controlling nitrogen cycling, and photosynthesis in Dry Valley lakes (Lake Fryxell, Lake Vanda and Lake Miers), with particular attention to the deep chlorophyll maximum was studied. proprietary
K089_2001_2008_NZ_1 5 minute sea level, air temperature and barometric pressure data from a monitoring station near Scott Base since 2001 ALL STAC Catalog 2001-01-01 166.75, -77.85, 166.75, -77.85 https://cmr.earthdata.nasa.gov/search/concepts/C1214591348-SCIOPS.umm_json In January 2001, a sea level monitoring station was installed near to the reverse osmosis intake near Scott Base. The data are transmitted from the sensor, to a data logger at Scott Base. Data is logged and archived including 5 minute sea level, air temperature and barometric pressure data. proprietary
K089_2001_2008_NZ_1 5 minute sea level, air temperature and barometric pressure data from a monitoring station near Scott Base since 2001 SCIOPS STAC Catalog 2001-01-01 166.75, -77.85, 166.75, -77.85 https://cmr.earthdata.nasa.gov/search/concepts/C1214591348-SCIOPS.umm_json In January 2001, a sea level monitoring station was installed near to the reverse osmosis intake near Scott Base. The data are transmitted from the sensor, to a data logger at Scott Base. Data is logged and archived including 5 minute sea level, air temperature and barometric pressure data. proprietary
K089_2001_2012_NZ_1 5 minute sea level, air temperature and barometric pressure data from a monitoring station near Scott Base since 2001 - K089_2001_2012_NZ_1 ALL STAC Catalog 2001-01-01 166.75, -77.85, 166.75, -77.85 https://cmr.earthdata.nasa.gov/search/concepts/C1221420598-SCIOPS.umm_json In January 2001, a sea level monitoring station was installed near to the reverse osmosis intake near Scott Base. The data are transmitted from the sensor, to a data logger at Scott Base. Data is logged and archived including 5 minute sea level, air temperature and barometric pressure data. The tide gauge records data at 5 minute intervals. Annually LINZ (Land Information New Zealand)calibrate the tide gauge over four tide cycles. A geodetic grade GPS receiver is set up on the sea ice near the tide gauge and another is set up on a permanent reference mark ashore. The GPS “observes” the rise and fall of the tide by measuring the changing height of the sea ice. A hole is drilled through the sea ice to enable the height of the reference point of the GPS receiver above the sea surface to be determined. The relationship of the height of the shore-based reference mark and the zero of the sea level sensor is known. These connections enable the height of the sea surface as determined by the sea level sensor to be compared to the height as determined by the GPS measurements. proprietary
@@ -8223,8 +8224,8 @@ K112_1990_1991_NZ_1 1:25,000 geological mapping of the St Johns Range from the c
K112_1990_1991_NZ_1 1:25,000 geological mapping of the St Johns Range from the central Wright Valley to the Mackay Glacier and from the Miller Glacier to west of Victoria Valley ALL STAC Catalog 1990-11-30 1991-01-16 160, -77.45, 164, -76.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214594062-SCIOPS.umm_json The DSIRGEO mapping programme in the 1990/91 season was designed to link the area covered in 1989/90 (Convoy Range) with that covered in 1988/89 (Thundergut Sheet). The eventual aim of the programme is to produce a revised geology of Southern Victoria Land at a scale of 1:250,000. All rock types in the area between the central Wright Valley and the Mackay Glacier, from the Miller Glacier to west of the Victoria Valley were mapped at 1:50,000. The resulting St Johns map sheet will also incorporate previous studies. Field work aimed to establish the extent and intrusive relationships of the various granitoid plutons known to exist in the area and relate them to the area mapping in 1988/89 season to the south. The extent and nature of the small areas of Beacon sediments was also covered. Five major rock groups were mapped including Koettlitz Group metasediments and associated orthogneisses, granitoid plutons and related dikes, Beacon Supergroup sediments, Ferrar Group dolerites and surficial glacial and fluvioglacial deposits. proprietary
K122_2004_2005_NZ_4 Aerial photographs and ground counts for assessing breeding success of Adelie penguin (Pygoscelis adeliae) rookeries on Ross Island ALL STAC Catalog 1983-11-24 166.3, -77.53, 169.55, -77.2166 https://cmr.earthdata.nasa.gov/search/concepts/C1214590789-SCIOPS.umm_json In conjunction with aerial photographs of the colonies ground truth counts were made since the 1983-1984 season at the Ross Island colonies. The number of occupied nests, nests with eggs, nests with both adults present and total penguins at the colony were censused to be able to check for accuracy of the counts from aerial photographs and to assess the breeding status and condition of the birds for that year. Since 1990, ground counts of chicks at each rookey were conducted in late January to measure breeding success (number of chicks/breeding pair). Approximately 100 chicks were selected randomly at each site and they had their weight and flipper length measured to calculate a chick condition index which is comparable between years and between the rookeries. proprietary
K122_2004_2005_NZ_4 Aerial photographs and ground counts for assessing breeding success of Adelie penguin (Pygoscelis adeliae) rookeries on Ross Island SCIOPS STAC Catalog 1983-11-24 166.3, -77.53, 169.55, -77.2166 https://cmr.earthdata.nasa.gov/search/concepts/C1214590789-SCIOPS.umm_json In conjunction with aerial photographs of the colonies ground truth counts were made since the 1983-1984 season at the Ross Island colonies. The number of occupied nests, nests with eggs, nests with both adults present and total penguins at the colony were censused to be able to check for accuracy of the counts from aerial photographs and to assess the breeding status and condition of the birds for that year. Since 1990, ground counts of chicks at each rookey were conducted in late January to measure breeding success (number of chicks/breeding pair). Approximately 100 chicks were selected randomly at each site and they had their weight and flipper length measured to calculate a chick condition index which is comparable between years and between the rookeries. proprietary
-K138_1992_1993_NZ_1 A study of global (Very Low Frequency) VLF propagation with emphasis on the effects of stratospheric ionisation and glacial ice in Antarctica ALL STAC Catalog 1992-11-10 1992-12-05 165, -78, 175, -43 https://cmr.earthdata.nasa.gov/search/concepts/C1214593938-SCIOPS.umm_json The performance of GPS navigation equipment for possible future deployment on Antarctic resupply flights was investigated. In addition, using Hercules C-130 aircrafts fitted with GPS, VLF propagation studies in the Antarctic region and studies of antipodally propagating VLF signals during flights to Antarctica was investigated. VLF/GPS receivers were installed on the RNZAF resupply aircrafts and recordings were made on all available New Zealand flights to the Antarctic. proprietary
K138_1992_1993_NZ_1 A study of global (Very Low Frequency) VLF propagation with emphasis on the effects of stratospheric ionisation and glacial ice in Antarctica SCIOPS STAC Catalog 1992-11-10 1992-12-05 165, -78, 175, -43 https://cmr.earthdata.nasa.gov/search/concepts/C1214593938-SCIOPS.umm_json The performance of GPS navigation equipment for possible future deployment on Antarctic resupply flights was investigated. In addition, using Hercules C-130 aircrafts fitted with GPS, VLF propagation studies in the Antarctic region and studies of antipodally propagating VLF signals during flights to Antarctica was investigated. VLF/GPS receivers were installed on the RNZAF resupply aircrafts and recordings were made on all available New Zealand flights to the Antarctic. proprietary
+K138_1992_1993_NZ_1 A study of global (Very Low Frequency) VLF propagation with emphasis on the effects of stratospheric ionisation and glacial ice in Antarctica ALL STAC Catalog 1992-11-10 1992-12-05 165, -78, 175, -43 https://cmr.earthdata.nasa.gov/search/concepts/C1214593938-SCIOPS.umm_json The performance of GPS navigation equipment for possible future deployment on Antarctic resupply flights was investigated. In addition, using Hercules C-130 aircrafts fitted with GPS, VLF propagation studies in the Antarctic region and studies of antipodally propagating VLF signals during flights to Antarctica was investigated. VLF/GPS receivers were installed on the RNZAF resupply aircrafts and recordings were made on all available New Zealand flights to the Antarctic. proprietary
K1VHR_L02_HEM KALPANA-1 VHRR Level-2B Precipitation Using Hydroestimator Technique ISRO STAC Catalog 2012-11-05 0.843296, -81.04153, 163.15671, 81.04153 https://cmr.earthdata.nasa.gov/search/concepts/C1214622559-ISRO.umm_json Kalpana-1 VHRR Level-2B Precipitation using Hydroestimator Technique in HDF-5 Format proprietary
K1VHR_L02_OLR KALPANA-1 Level-2B Outgoing Longwave Radiation ISRO STAC Catalog 2008-05-06 0.843296, -81.04153, 163.15671, 81.04153 https://cmr.earthdata.nasa.gov/search/concepts/C1214622569-ISRO.umm_json Kalpana-1 VHRR Level-2B Outgoing Longwave Radation (OLR) in HDF-5 Format proprietary
K1VHR_L02_SGP KALPANA-1 VHRR Level-1C Sector Product ISRO STAC Catalog 2010-05-05 20, -50, 130, 50 https://cmr.earthdata.nasa.gov/search/concepts/C1214622583-ISRO.umm_json KALPANA-1 VHRR Level-1C Sector Product (Geocoded, all pixels at same resolution) contains 3 channels data in HDF-5 Format proprietary
@@ -8238,33 +8239,33 @@ KFDBAM_ANU_1 Macquarie Island Baseline Invertebrate Survey 1994 AU_AADC STAC Cat
KILVOLC_FlowerKahn2021_1 MISR Derived Case Study Data for Kilauea Volcanic Eruptions Including Geometric Plume Height and Qualitative Radiometric Particle Property Information LARC_ASDC STAC Catalog 2000-10-25 2018-08-01 -161, 14, -150, 25 https://cmr.earthdata.nasa.gov/search/concepts/C2134682585-LARC_ASDC.umm_json The KILVOLC_FlowerKahn2021_1 dataset is the MISR Derived Case Study Data for Kilauea Volcanic Eruptions Including Geometric Plume Height and Qualitative Radiometric Particle Property Information version 1 dataset. It comprises MISR-derived output from a comprehensive analysis of Kilauea volcanic eruptions (2000-2018). Data collection for this dataset is complete. The data presented here are analyzed and discussed in the following paper: Flower, V.J.B., and R.A. Kahn, 2021. Twenty years of NASA-EOS multi-sensor satellite observations at Kīlauea volcano (2000-2019). J. Volc. Geo. Res. (in press). The data is subdivided by date and MISR orbit number. Within each case folder, there are up to 11 files relating to an individual MISR overpass. Files include plume height records (from both the red and blue spectral bands) derived from the MISR INteractive eXplorer (MINX) program, displayed in: map view, downwind profile plot (along with the associated wind vectors retrieved at plume elevation), a histogram of retrieved plume heights and a text file containing the digital plume height values. An additional JPG is included delineating the plume analysis region, start point for assessing downwind distance, and input wind direction used to initialize the MINX retrieval. A final two files are generated from the MISR Research Aerosol (RA) retrieval algorithm (Limbacher, J.A., and R.A. Kahn, 2014. MISR Research-Aerosol-Algorithm: Refinements For Dark Water Retrievals. Atm. Meas. Tech. 7, 1-19, doi:10.5194/amt-7-1-2014). These files include the RA model output in HDF5, and an associated JPG of key derived variables (e.g. Aerosol Optical Depth, Angstrom Exponent, Single Scattering Albedo, Fraction of Non-Spherical components, model uncertainty classifications and example camera views). File numbers per folder vary depending on the retrieval conditions of specific observations. RA plume retrievals are limited when cloud cover was widespread or the solar radiance was insufficient to run the RA. In these cases the RA files are not included in the individual folders. In cases where activity was observed from multiple volcanic zones in a single overpass, individual folders containing data relating to a single region, are included, and defined by a qualifier (e.g. '_1'). proprietary
KOMPSAT-2 KOMPSAT-2 Panchromatic and multispectral imagery CEOS_EXTRA STAC Catalog 2006-07-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2229454502-CEOS_EXTRA.umm_json The KOMPSAT-2 allows the generation of high resolution images with a GSD of better than 1 m for PAN data and 4 m for MS data with nadir viewing condition at the nominal altitude of 685 km. The MSC has a single PAN spectral band between 500 - 900 nm and 4 MS spectral bands between 450-900 nm. PAN imaging and MS imaging can be operated simultaneously during mission operations. The swath width is greater than or equal to 15 km at the mission altitude for PAN data and MS data. The system is equipped with a solid state recorder to record images not less than 1,000km long at the end of life. The satellite can be rolled up to ±30 degrees off-nadir to pre-position the MSC swath. The KOMPSAT-2 can provide across-track stereo images by multiple passes of the satellite using off-nadir pointing capability. The satellite is compatible with daily revisit operation by off-nadir pointing with degraded GSD. Also, the image products according to the requested products quality standard can be made within one (1) day after satellite passes over the KGS. proprietary
KOMPSAT-2.ESA.archive_9.0 KOMPSAT-2 ESA archive ESA STAC Catalog 2007-04-18 2014-03-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336921-ESA.umm_json Kompsat-2 ESA archive collection is composed by bundle (Panchromatic and Multispectral separated images) products from the Multi-Spectral Camera (MSC) onboard KOMPSAT-2 acquired from 2007 to 2014: 1m resolution for PAN, 4m resolution for MS. Spectral Bands: • Pan: 500 - 900 nm (locate, identify and measure surface features and objects primarily by their physical appearance) • MS1 (blue): 450 - 520 nm (mapping shallow water, differentiating soil from vegetation) • MS2 (green): 520 - 600 nm (differentiating vegetation by health) • MS3 (red): 630 - 690 nm (differentiating vegetation by species) • MS4 (near-infrared): 760 - 900 nm (mapping vegetation, mapping vegetation vigor/health, Differentiating vegetation by species) proprietary
-KOPRI-KPDC-00000001_1 2007 Seismic Data, Antarctica ALL STAC Catalog 2007-12-08 2007-12-11 -63.593556, -62.777306, -61.092444, -61.466739 https://cmr.earthdata.nasa.gov/search/concepts/C2244294500-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in northern sea area of the South Shetland Islands. The research period was from 07 Dec. to 14 Dec. (7 days) in 2007. Geophysical research including acquisition of multi-channel seismic data was preceded. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) and 7 researcher in the cruise." proprietary
KOPRI-KPDC-00000001_1 2007 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 2007-12-08 2007-12-11 -63.593556, -62.777306, -61.092444, -61.466739 https://cmr.earthdata.nasa.gov/search/concepts/C2244294500-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in northern sea area of the South Shetland Islands. The research period was from 07 Dec. to 14 Dec. (7 days) in 2007. Geophysical research including acquisition of multi-channel seismic data was preceded. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) and 7 researcher in the cruise." proprietary
+KOPRI-KPDC-00000001_1 2007 Seismic Data, Antarctica ALL STAC Catalog 2007-12-08 2007-12-11 -63.593556, -62.777306, -61.092444, -61.466739 https://cmr.earthdata.nasa.gov/search/concepts/C2244294500-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in northern sea area of the South Shetland Islands. The research period was from 07 Dec. to 14 Dec. (7 days) in 2007. Geophysical research including acquisition of multi-channel seismic data was preceded. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) and 7 researcher in the cruise." proprietary
KOPRI-KPDC-00000002_1 2004 Seismic Data, Antarctica ALL STAC Catalog 2004-11-29 2004-12-05 -51.372194, -61.652222, -47.042, -60.203167 https://cmr.earthdata.nasa.gov/search/concepts/C2244294814-AMD_KOPRI.umm_json "Korean Antarctic survey carried out the fifth year project as step 3 project in the last annual of ‘The Antarctic Undersea Geological Survey’ was conducted in the northern Fowell Basin of the Weddell Sea. The research period was from 25 Nov. to 9 Dec. (15 days) in 2004. Geophysical research including acquisition of multi-channel seismic data was preceded. According to the results of seismic investigation, the drilling investigation was conducted at the coring point. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) and 12 researcher in the cruise." proprietary
KOPRI-KPDC-00000002_1 2004 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 2004-11-29 2004-12-05 -51.372194, -61.652222, -47.042, -60.203167 https://cmr.earthdata.nasa.gov/search/concepts/C2244294814-AMD_KOPRI.umm_json "Korean Antarctic survey carried out the fifth year project as step 3 project in the last annual of ‘The Antarctic Undersea Geological Survey’ was conducted in the northern Fowell Basin of the Weddell Sea. The research period was from 25 Nov. to 9 Dec. (15 days) in 2004. Geophysical research including acquisition of multi-channel seismic data was preceded. According to the results of seismic investigation, the drilling investigation was conducted at the coring point. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) and 12 researcher in the cruise." proprietary
KOPRI-KPDC-00000003_1 2003 Seismic Data, Antarctica ALL STAC Catalog 2003-12-14 2003-12-17 -49.883889, -61.230056, -46.487694, -59.500833 https://cmr.earthdata.nasa.gov/search/concepts/C2244294883-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as part of step 3 project in year 4 of ‘The Antarctic Undersea Geological Survey’ was conducted in the Powell Basin (IV region) of the northern Weddell Sea, Antarctica. Because Korea doesn't have an icebreaker for Antarctic research, during the Antarctic site survey period, research ships are secured and conducted through a chartering. The available chartering are limited. It's because the duration of the chartering is concentrated in the summer season like any other country. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) used on lease by NOAA in the United States as in other years. It was used from November to December, just before the NOAA use period. The research period was from 24 Nov. to 9 Dec. (8 days) in 2003. After geophysical research including acquisition of multichannel seismic data, a drilling investigation was conducted in coring point was decided from combined geophysical data. 12 researchers from KOPRI, Seoul University etc. participated in the cruise as field investigation personnel." proprietary
KOPRI-KPDC-00000003_1 2003 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 2003-12-14 2003-12-17 -49.883889, -61.230056, -46.487694, -59.500833 https://cmr.earthdata.nasa.gov/search/concepts/C2244294883-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as part of step 3 project in year 4 of ‘The Antarctic Undersea Geological Survey’ was conducted in the Powell Basin (IV region) of the northern Weddell Sea, Antarctica. Because Korea doesn't have an icebreaker for Antarctic research, during the Antarctic site survey period, research ships are secured and conducted through a chartering. The available chartering are limited. It's because the duration of the chartering is concentrated in the summer season like any other country. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) used on lease by NOAA in the United States as in other years. It was used from November to December, just before the NOAA use period. The research period was from 24 Nov. to 9 Dec. (8 days) in 2003. After geophysical research including acquisition of multichannel seismic data, a drilling investigation was conducted in coring point was decided from combined geophysical data. 12 researchers from KOPRI, Seoul University etc. participated in the cruise as field investigation personnel." proprietary
-KOPRI-KPDC-00000004_1 2002 Seismic Data, Antarctica ALL STAC Catalog 2002-12-18 2002-12-21 -50.500417, -60.016, -47.001556, -59.247 https://cmr.earthdata.nasa.gov/search/concepts/C2244294924-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as part of step 3 project in year 3 of ‘The Antarctic Undersea Geological Survey’ was conducted in the Powell Basin(Ⅲ) of the northern Weddell Sea, Antarctica. The research period was from 16 Dec. to 23 Dec. (8 days) in 2002. After geophysical research including acquisition of multi-channel seismic data as well as geomagnatic data, a drilling investigation was conducted in coring point was decided from combined geophysical data. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) and 7 researchers from ‘Korea Ocean Research and Development Institute’ participated in the cruise." proprietary
KOPRI-KPDC-00000004_1 2002 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 2002-12-18 2002-12-21 -50.500417, -60.016, -47.001556, -59.247 https://cmr.earthdata.nasa.gov/search/concepts/C2244294924-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as part of step 3 project in year 3 of ‘The Antarctic Undersea Geological Survey’ was conducted in the Powell Basin(Ⅲ) of the northern Weddell Sea, Antarctica. The research period was from 16 Dec. to 23 Dec. (8 days) in 2002. After geophysical research including acquisition of multi-channel seismic data as well as geomagnatic data, a drilling investigation was conducted in coring point was decided from combined geophysical data. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) and 7 researchers from ‘Korea Ocean Research and Development Institute’ participated in the cruise." proprietary
-KOPRI-KPDC-00000005_1 2001 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 2001-12-15 2001-12-19 -52.37845, -62.5604, -49.249567, -59.814483 https://cmr.earthdata.nasa.gov/search/concepts/C2244294933-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as part of step 3 project in year 2 of ‘The Antarctic Undersea Geological Survey’ was conducted in the Powell Basin of the northern Weddell Sea, Antarctica. The research period was from 15 Dec. to 21 Dec. (7 days) in 2001. After geophysical research including acquisition of multichannel seismic data as well as geomagnatic data, a drilling investigation was conducted in coring point was decided from combined geophysical data. 10 researchers from ‘Korea Ocean Research and Development Institute’ and an out-of-the-way researcher participated in the cruise. We took on lease Russian ""Yuzhmorgeologiya""." proprietary
+KOPRI-KPDC-00000004_1 2002 Seismic Data, Antarctica ALL STAC Catalog 2002-12-18 2002-12-21 -50.500417, -60.016, -47.001556, -59.247 https://cmr.earthdata.nasa.gov/search/concepts/C2244294924-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as part of step 3 project in year 3 of ‘The Antarctic Undersea Geological Survey’ was conducted in the Powell Basin(Ⅲ) of the northern Weddell Sea, Antarctica. The research period was from 16 Dec. to 23 Dec. (8 days) in 2002. After geophysical research including acquisition of multi-channel seismic data as well as geomagnatic data, a drilling investigation was conducted in coring point was decided from combined geophysical data. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) and 7 researchers from ‘Korea Ocean Research and Development Institute’ participated in the cruise." proprietary
KOPRI-KPDC-00000005_1 2001 Seismic Data, Antarctica ALL STAC Catalog 2001-12-15 2001-12-19 -52.37845, -62.5604, -49.249567, -59.814483 https://cmr.earthdata.nasa.gov/search/concepts/C2244294933-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as part of step 3 project in year 2 of ‘The Antarctic Undersea Geological Survey’ was conducted in the Powell Basin of the northern Weddell Sea, Antarctica. The research period was from 15 Dec. to 21 Dec. (7 days) in 2001. After geophysical research including acquisition of multichannel seismic data as well as geomagnatic data, a drilling investigation was conducted in coring point was decided from combined geophysical data. 10 researchers from ‘Korea Ocean Research and Development Institute’ and an out-of-the-way researcher participated in the cruise. We took on lease Russian ""Yuzhmorgeologiya""." proprietary
+KOPRI-KPDC-00000005_1 2001 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 2001-12-15 2001-12-19 -52.37845, -62.5604, -49.249567, -59.814483 https://cmr.earthdata.nasa.gov/search/concepts/C2244294933-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as part of step 3 project in year 2 of ‘The Antarctic Undersea Geological Survey’ was conducted in the Powell Basin of the northern Weddell Sea, Antarctica. The research period was from 15 Dec. to 21 Dec. (7 days) in 2001. After geophysical research including acquisition of multichannel seismic data as well as geomagnatic data, a drilling investigation was conducted in coring point was decided from combined geophysical data. 10 researchers from ‘Korea Ocean Research and Development Institute’ and an out-of-the-way researcher participated in the cruise. We took on lease Russian ""Yuzhmorgeologiya""." proprietary
KOPRI-KPDC-00000006_1 Rock samples of Prince Albert Mountains, Antarctica, 2011-12 season AMD_KOPRI STAC Catalog 2012-01-03 2012-01-13 158.1, -75.833, 159.3, -75.733 https://cmr.earthdata.nasa.gov/search/concepts/C2244294985-AMD_KOPRI.umm_json This entry is for the rock samples of Prince Albert Mountains, Antarctica collected in 2011-12 austral summer season. The collection includes volcanic rocks (basalt, dolerite, hyaloclasite, and tuff) from Ferrar Supergroup and sedimentary rocks (sandstone, siltstone) from Ferrar Supergroup and Beacon Supergroup. A few plant fossil fragments and fragmentd of coals, most likely from the Beacon Supergroup, are also listed in this entry. The samples were collected in order to understand the lithologic characters of basement rocks underneath the David Glacier. Information on the stratigraphy of the volcanics and sedimentary succession will be helpful for understanding geological processes and paleoenvironments of the Victoria Land. proprietary
-KOPRI-KPDC-00000007_1 2000 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 2000-12-04 2000-12-08 -52.378444, -62.5604, -49.249567, -59.814639 https://cmr.earthdata.nasa.gov/search/concepts/C2244292500-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as part of step 3 project in year 1 of ‘The Antarctic Undersea Geological Survey’ was conducted in the Powell Basin of the northern Weddell Sea, Antarctica. The research period was from 3 Dec. to 11 Dec. (9 days) in 2000. After geophysical research including acquisition of seismic data, submarine topography, geomagnatic data was conducted in coring point was decided from combined geophysical data. We took on lease Russian icebreaker ""Yuzhmorgeologiya"" and 13 researcher from ‘Korea Ocean Research and Development Institute’ including a field winter researcher in the cruise. Due to a lot of icebergs and floating ice in the area, the originally planned survey of the side lines is impossible. A survey was conducted on the modified side lines." proprietary
KOPRI-KPDC-00000007_1 2000 Seismic Data, Antarctica ALL STAC Catalog 2000-12-04 2000-12-08 -52.378444, -62.5604, -49.249567, -59.814639 https://cmr.earthdata.nasa.gov/search/concepts/C2244292500-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as part of step 3 project in year 1 of ‘The Antarctic Undersea Geological Survey’ was conducted in the Powell Basin of the northern Weddell Sea, Antarctica. The research period was from 3 Dec. to 11 Dec. (9 days) in 2000. After geophysical research including acquisition of seismic data, submarine topography, geomagnatic data was conducted in coring point was decided from combined geophysical data. We took on lease Russian icebreaker ""Yuzhmorgeologiya"" and 13 researcher from ‘Korea Ocean Research and Development Institute’ including a field winter researcher in the cruise. Due to a lot of icebergs and floating ice in the area, the originally planned survey of the side lines is impossible. A survey was conducted on the modified side lines." proprietary
+KOPRI-KPDC-00000007_1 2000 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 2000-12-04 2000-12-08 -52.378444, -62.5604, -49.249567, -59.814639 https://cmr.earthdata.nasa.gov/search/concepts/C2244292500-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as part of step 3 project in year 1 of ‘The Antarctic Undersea Geological Survey’ was conducted in the Powell Basin of the northern Weddell Sea, Antarctica. The research period was from 3 Dec. to 11 Dec. (9 days) in 2000. After geophysical research including acquisition of seismic data, submarine topography, geomagnatic data was conducted in coring point was decided from combined geophysical data. We took on lease Russian icebreaker ""Yuzhmorgeologiya"" and 13 researcher from ‘Korea Ocean Research and Development Institute’ including a field winter researcher in the cruise. Due to a lot of icebergs and floating ice in the area, the originally planned survey of the side lines is impossible. A survey was conducted on the modified side lines." proprietary
KOPRI-KPDC-00000008_1 1998 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 1998-12-07 1998-12-11 -66.266667, -64.616667, -64.416667, -62.995 https://cmr.earthdata.nasa.gov/search/concepts/C2244292774-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as part of step 2 project in year 2 of 'the Antarctic Undersea Geological Survey' was conducted in the Ⅱ region around the northwestern continent of the Antarctic Peninsula. This area is northwest of Anvers Island, including areas around the pericontinent from the continental shelf to the continental rise zone. The investigation period for this project took a total of 8 days for moving navigation, the survey of the side lines and drilling investigation. After seismic investigation, a surface drilling investigation was conducted in coring point was decided from the reference seismic section. 10 researcher from ‘Korea Ocean Research and Development Institute’ participated in the field survey. We took on lease Russian icebreaker ""Yuzhmorgeologiya""." proprietary
KOPRI-KPDC-00000008_1 1998 Seismic Data, Antarctica ALL STAC Catalog 1998-12-07 1998-12-11 -66.266667, -64.616667, -64.416667, -62.995 https://cmr.earthdata.nasa.gov/search/concepts/C2244292774-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as part of step 2 project in year 2 of 'the Antarctic Undersea Geological Survey' was conducted in the Ⅱ region around the northwestern continent of the Antarctic Peninsula. This area is northwest of Anvers Island, including areas around the pericontinent from the continental shelf to the continental rise zone. The investigation period for this project took a total of 8 days for moving navigation, the survey of the side lines and drilling investigation. After seismic investigation, a surface drilling investigation was conducted in coring point was decided from the reference seismic section. 10 researcher from ‘Korea Ocean Research and Development Institute’ participated in the field survey. We took on lease Russian icebreaker ""Yuzhmorgeologiya""." proprietary
KOPRI-KPDC-00000009_1 1997 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 1997-12-23 1997-12-28 -64.699722, -63.525, -62.157778, -62.041389 https://cmr.earthdata.nasa.gov/search/concepts/C2244293126-AMD_KOPRI.umm_json Korean Antarctic survey carried out as part of step 2 project in year 1 of ‘The Antarctic Undersea Geological Survey’ in 1997 was conducted in a continental shelf in the northwestern part of the Antarctic Peninsula. The research period took a total of 8 days, including 6 days for the seismic survey and 2 days for the drilling investigation. We took on lease Norway R/V 'Polar Duke' and 10 researchers from ‘Korea Ocean Research and Development Institute’ participated as field investigation personnel. The Teac single-channel recorder, EPC Recorder, Q/C MicroMax system etc. was used mainly by Sleeve gun used as a sound source, compressor for creating compressed air, DFS-V Recorder for multi-channel Seismic record, 12 –channel geophone of seismic streamers. Additional Gravity Core was used for sediment research through drilling. proprietary
KOPRI-KPDC-00000009_1 1997 Seismic Data, Antarctica ALL STAC Catalog 1997-12-23 1997-12-28 -64.699722, -63.525, -62.157778, -62.041389 https://cmr.earthdata.nasa.gov/search/concepts/C2244293126-AMD_KOPRI.umm_json Korean Antarctic survey carried out as part of step 2 project in year 1 of ‘The Antarctic Undersea Geological Survey’ in 1997 was conducted in a continental shelf in the northwestern part of the Antarctic Peninsula. The research period took a total of 8 days, including 6 days for the seismic survey and 2 days for the drilling investigation. We took on lease Norway R/V 'Polar Duke' and 10 researchers from ‘Korea Ocean Research and Development Institute’ participated as field investigation personnel. The Teac single-channel recorder, EPC Recorder, Q/C MicroMax system etc. was used mainly by Sleeve gun used as a sound source, compressor for creating compressed air, DFS-V Recorder for multi-channel Seismic record, 12 –channel geophone of seismic streamers. Additional Gravity Core was used for sediment research through drilling. proprietary
KOPRI-KPDC-00000010_1 CTD Data surrounding Chukchi Borderland and Mendeleev Ridge of Arctic in 2011 AMD_KOPRI STAC Catalog 2011-08-02 2011-08-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295142-AMD_KOPRI.umm_json An intensive oceanographic survey was conducted during 21 days from 2011 July 31 to August 20 by IBRV ARAON to measure the spatial and temporal variation of water masses in the Chukchi Borderland/Mendeleev Ridge. The profiles of temperature, salinity and depth were obtained using CTD/Rosette system at 18 stations. To investigate the variability in spatial and temporal distribution of water masses and and understand its transformation along the pathways and relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge. proprietary
-KOPRI-KPDC-00000011_1 1996 Seismic Data, Antarctica ALL STAC Catalog 1996-12-17 1996-12-26 -62.766667, -63.583333, -60.233333, -62.733333 https://cmr.earthdata.nasa.gov/search/concepts/C2244293499-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as in year 3 project of 'the Antarctic Undersea Geological Survey' was conducted in the basin region of western part of the Bransfeed Strait between the Antarctic Peninsula and the South Shetland Islands . During the field investigation, the seismic investigation and the drilling investigation was conducted at the same time. The investigation period took 9 days. 10 researchers from ‘Korea Ocean Research and Development Institute’ and 3 academic personnel participated in the cruise as field investigation personnel. We took on lease Russian R/V ""Yuzhmorgeologiya"" which is marine geology, geophysical survey vessel and Icebreaker." proprietary
KOPRI-KPDC-00000011_1 1996 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 1996-12-17 1996-12-26 -62.766667, -63.583333, -60.233333, -62.733333 https://cmr.earthdata.nasa.gov/search/concepts/C2244293499-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as in year 3 project of 'the Antarctic Undersea Geological Survey' was conducted in the basin region of western part of the Bransfeed Strait between the Antarctic Peninsula and the South Shetland Islands . During the field investigation, the seismic investigation and the drilling investigation was conducted at the same time. The investigation period took 9 days. 10 researchers from ‘Korea Ocean Research and Development Institute’ and 3 academic personnel participated in the cruise as field investigation personnel. We took on lease Russian R/V ""Yuzhmorgeologiya"" which is marine geology, geophysical survey vessel and Icebreaker." proprietary
+KOPRI-KPDC-00000011_1 1996 Seismic Data, Antarctica ALL STAC Catalog 1996-12-17 1996-12-26 -62.766667, -63.583333, -60.233333, -62.733333 https://cmr.earthdata.nasa.gov/search/concepts/C2244293499-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as in year 3 project of 'the Antarctic Undersea Geological Survey' was conducted in the basin region of western part of the Bransfeed Strait between the Antarctic Peninsula and the South Shetland Islands . During the field investigation, the seismic investigation and the drilling investigation was conducted at the same time. The investigation period took 9 days. 10 researchers from ‘Korea Ocean Research and Development Institute’ and 3 academic personnel participated in the cruise as field investigation personnel. We took on lease Russian R/V ""Yuzhmorgeologiya"" which is marine geology, geophysical survey vessel and Icebreaker." proprietary
KOPRI-KPDC-00000012_1 1995 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 1995-12-13 1995-12-18 -58.335, -62.984444, -54.101944, -61.301111 https://cmr.earthdata.nasa.gov/search/concepts/C2244291641-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as in year 2 project of ""Antarctic submarine topography and sediment investigation"", The Field Survey of Antarctica was conducted at the end of 1995 was conducted the multi-channel Seismic Investigation and the drilling Investigation in the eastern part of the Bransfield Strait between the Antarctic Peninsula and the South Shetland Islands and near Sejong Station. We took on lease Russian R/V ""Yuzhmorgeologiya"" which is marine geology, geophysical survey vessel and Icebreaker for field investigation." proprietary
KOPRI-KPDC-00000012_1 1995 Seismic Data, Antarctica ALL STAC Catalog 1995-12-13 1995-12-18 -58.335, -62.984444, -54.101944, -61.301111 https://cmr.earthdata.nasa.gov/search/concepts/C2244291641-AMD_KOPRI.umm_json "Korean Antarctic survey carried out as in year 2 project of ""Antarctic submarine topography and sediment investigation"", The Field Survey of Antarctica was conducted at the end of 1995 was conducted the multi-channel Seismic Investigation and the drilling Investigation in the eastern part of the Bransfield Strait between the Antarctic Peninsula and the South Shetland Islands and near Sejong Station. We took on lease Russian R/V ""Yuzhmorgeologiya"" which is marine geology, geophysical survey vessel and Icebreaker for field investigation." proprietary
KOPRI-KPDC-00000013_1 Trace elements in Vostok Antarctic Ice AMD_KOPRI STAC Catalog 2011-08-26 2011-08-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244291453-AMD_KOPRI.umm_json Lead (Pb), cadmium (Cd), copper (Cu) and zinc (Zn) have been measured by electrothermal atomic absorption spectrometry in various sections of the 3623m deep ice core drilled at Vostok, in central East Antarctica. The sections were dated from 240 to 410 kyear BP (Marine Isotopic Stages (MIS) 7.5 to 11.3), which corresponds to the 3rd and 4th glacial interglacial cycles before present. Concentrations are found to have varied greatly during this 170 kyear time period, with high concentration values during the coldest climatic stages such as MIS 8.4 and 10.2 and much lower concentration values during warmer periods, such as the interglacials MIS 7.5, 9.3 and 11.3. Rock and soil dust were the dominant sources for Pb, whatever the period, and for Zn and Cu and possibly Cd during cold climatic stages. The contribution from volcanic emissions was important for Cd during all periods and might have beensignificant for Cu and Zn during warm periods. proprietary
-KOPRI-KPDC-00000014_1 1994 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 1994-12-19 1994-12-27 -59.352778, -63.060278, -56.167778, -62.030833 https://cmr.earthdata.nasa.gov/search/concepts/C2244291414-AMD_KOPRI.umm_json Korean Antarctic survey carried out as in year 1 of 'the Antarctic Undersea Geological Survey' was conducted at the end of 1994 was conducted Multi-channel Seismic Investgation and Drilling investigation in the central basin of the Bransfield Strait was located in between the Antarctic Peninsula and the South Shetland Islands and the Maxwell Bay area near Sejong Station. The field research was conducted wih other research at the same time. The research period was from 11 Dec. in 1994 to 23 Jan. in 1995 (13 days). - Korean Antarctic survey carried out as part of step 1 project in year 1 to investigate the possibility of oil resources in the Bransfield Strait of Antarctica. - Securing data for tectonic settings research in the same region. - Obtaining basic data for understanding marine geology and sedimentary layers in the same region. proprietary
KOPRI-KPDC-00000014_1 1994 Seismic Data, Antarctica ALL STAC Catalog 1994-12-19 1994-12-27 -59.352778, -63.060278, -56.167778, -62.030833 https://cmr.earthdata.nasa.gov/search/concepts/C2244291414-AMD_KOPRI.umm_json Korean Antarctic survey carried out as in year 1 of 'the Antarctic Undersea Geological Survey' was conducted at the end of 1994 was conducted Multi-channel Seismic Investgation and Drilling investigation in the central basin of the Bransfield Strait was located in between the Antarctic Peninsula and the South Shetland Islands and the Maxwell Bay area near Sejong Station. The field research was conducted wih other research at the same time. The research period was from 11 Dec. in 1994 to 23 Jan. in 1995 (13 days). - Korean Antarctic survey carried out as part of step 1 project in year 1 to investigate the possibility of oil resources in the Bransfield Strait of Antarctica. - Securing data for tectonic settings research in the same region. - Obtaining basic data for understanding marine geology and sedimentary layers in the same region. proprietary
-KOPRI-KPDC-00000015_1 1999 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 1999-12-29 2000-01-01 -69.238889, -65.787222, -66.314722, -63.994444 https://cmr.earthdata.nasa.gov/search/concepts/C2244293812-AMD_KOPRI.umm_json Korean Antarctic survey carried out as part of step 2 project in year 3 of 'The Antarctic Undersea Geological Survey' in 1999 was conducted in the periphery of the continent near Anvers Island in the northwestern part of the Antarctic Peninsula. The research period was from 27 Dec. in 1999 to 3 Jan. in 2000 (8 days). After a geophysical survey was conducted to obtain data such as seismic, submarine topography, gravity, terrestrial magnetism, drilling investigation was conducted in the coring point was decided from combined geophysics data. 13 researchers from ‘Korea Ocean Research and Development Institute’ and an out-of-the-way researcher participated for field investigation members. We used a 'Onnuri', of 'the Korea Ocean Research Institute' to be used for Antarctic research since 1993. proprietary
+KOPRI-KPDC-00000014_1 1994 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 1994-12-19 1994-12-27 -59.352778, -63.060278, -56.167778, -62.030833 https://cmr.earthdata.nasa.gov/search/concepts/C2244291414-AMD_KOPRI.umm_json Korean Antarctic survey carried out as in year 1 of 'the Antarctic Undersea Geological Survey' was conducted at the end of 1994 was conducted Multi-channel Seismic Investgation and Drilling investigation in the central basin of the Bransfield Strait was located in between the Antarctic Peninsula and the South Shetland Islands and the Maxwell Bay area near Sejong Station. The field research was conducted wih other research at the same time. The research period was from 11 Dec. in 1994 to 23 Jan. in 1995 (13 days). - Korean Antarctic survey carried out as part of step 1 project in year 1 to investigate the possibility of oil resources in the Bransfield Strait of Antarctica. - Securing data for tectonic settings research in the same region. - Obtaining basic data for understanding marine geology and sedimentary layers in the same region. proprietary
KOPRI-KPDC-00000015_1 1999 Seismic Data, Antarctica ALL STAC Catalog 1999-12-29 2000-01-01 -69.238889, -65.787222, -66.314722, -63.994444 https://cmr.earthdata.nasa.gov/search/concepts/C2244293812-AMD_KOPRI.umm_json Korean Antarctic survey carried out as part of step 2 project in year 3 of 'The Antarctic Undersea Geological Survey' in 1999 was conducted in the periphery of the continent near Anvers Island in the northwestern part of the Antarctic Peninsula. The research period was from 27 Dec. in 1999 to 3 Jan. in 2000 (8 days). After a geophysical survey was conducted to obtain data such as seismic, submarine topography, gravity, terrestrial magnetism, drilling investigation was conducted in the coring point was decided from combined geophysics data. 13 researchers from ‘Korea Ocean Research and Development Institute’ and an out-of-the-way researcher participated for field investigation members. We used a 'Onnuri', of 'the Korea Ocean Research Institute' to be used for Antarctic research since 1993. proprietary
+KOPRI-KPDC-00000015_1 1999 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 1999-12-29 2000-01-01 -69.238889, -65.787222, -66.314722, -63.994444 https://cmr.earthdata.nasa.gov/search/concepts/C2244293812-AMD_KOPRI.umm_json Korean Antarctic survey carried out as part of step 2 project in year 3 of 'The Antarctic Undersea Geological Survey' in 1999 was conducted in the periphery of the continent near Anvers Island in the northwestern part of the Antarctic Peninsula. The research period was from 27 Dec. in 1999 to 3 Jan. in 2000 (8 days). After a geophysical survey was conducted to obtain data such as seismic, submarine topography, gravity, terrestrial magnetism, drilling investigation was conducted in the coring point was decided from combined geophysics data. 13 researchers from ‘Korea Ocean Research and Development Institute’ and an out-of-the-way researcher participated for field investigation members. We used a 'Onnuri', of 'the Korea Ocean Research Institute' to be used for Antarctic research since 1993. proprietary
KOPRI-KPDC-00000016_1 CTD measurements from Antarctic Peninsula region (KARP 1996-2006) AMD_KOPRI STAC Catalog 2011-12-08 2011-12-08 -66, -69, -44, -59 https://cmr.earthdata.nasa.gov/search/concepts/C2244292241-AMD_KOPRI.umm_json This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 1998 to 2006. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors. proprietary
KOPRI-KPDC-00000017_1 Sedimentological and geochemical analyses of gravity cores from Antarctic Peninsula region (KARP 1996-2006) AMD_KOPRI STAC Catalog 2011-08-26 2011-08-26 -66, -69, -44, -59 https://cmr.earthdata.nasa.gov/search/concepts/C2244291498-AMD_KOPRI.umm_json This dataset includes sedimentological and geochemical analyses of more than 80 gravity cores retrieved from Antarctic Peninsula region during the KARP (Korea Antarctic Research Program) cruise from 1996 to 2006. The cores are generally shorter than 10 m and represent late Pleistocene and Holocene sedimentation. The following data were obtained for all cores: magnetic susceptibility, X-radiographs, granulometry, total carbon and nitrogen content, and total organic and inorganic carbon content. Chronology of the cores were determined by AMS radiocarbon dating method. For selected cores, diatom assemblage, trace and rare earth element concentration, stable and radiogenic isotope compositions were analyzed. proprietary
KOPRI-KPDC-00000018_1 Lichen samples from Terra Nova Bay collected in 2010 AMD_KOPRI STAC Catalog 2010-02-07 2010-02-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295200-AMD_KOPRI.umm_json Lichen samples from Terra Nova Bay collected in 2010. Locality, habitat information for 31 lichen samples proprietary
@@ -8292,10 +8293,10 @@ KOPRI-KPDC-00000039_1 Morphology and phylogenetic relationships of some psychrop
KOPRI-KPDC-00000040_1 Morphology and phylogenetic relationships of polar brown algae AMD_KOPRI STAC Catalog 2005-06-15 11.8895, 62.219167, 168.938167, 70.003167 https://cmr.earthdata.nasa.gov/search/concepts/C2244295631-AMD_KOPRI.umm_json Diversity and biogeography of representative brown algae, the Desmarestiales and the Laminariales in the Arctic, the Antarctic and their neighbour regions including North Atlantic, southern Chile, Tasmania and South Africa were investigated. We recognized eight desmarestialean and 15 laminarialean entities based on their morphological characteristics. The aim of the current investigation has been to survey on diversity and DNA barcoding of brown algae around Dasan Station in Svalbard (Spitsbergen), the Arctic, and King Sejong Station in King George Island, the Antarctic based on morphology and DNA barcoding. proprietary
KOPRI-KPDC-00000041_1 Morphology and phylogenetic relationships of polar Bangiales AMD_KOPRI STAC Catalog 2003-02-21 11.8895, 62.938167, 116.938167, 78.003167 https://cmr.earthdata.nasa.gov/search/concepts/C2244295645-AMD_KOPRI.umm_json Each six sequences were newly determined in this study. Molecular data from over 56 taxa of the Bangiales worldwide including previously published sequences, indicated that monophyly for the genera Bangia and Porphyra is not supported, as in previous molecular studies. Nuclear SSU rDNA, plastid rbcL and mitochondrial cox1 gene sequences were investigated for the Bangiales from the Antarctica and its adjacent waters. proprietary
KOPRI-KPDC-00000042_1 De novo transcriptome analysis of Arctic microalga ArF0006 AMD_KOPRI STAC Catalog 2003-02-21 11.8895, 62.2255, 168.938167, 78.003167 https://cmr.earthdata.nasa.gov/search/concepts/C2244291413-AMD_KOPRI.umm_json Each six sequences were newly determined in this study. Molecular data from over 56 taxa of the Bangiales worldwide including previously published sequences, indicated that monophyly for the genera Bangia and Porphyra is not supported, as in previous molecular studies. Nuclear SSU rDNA, plastid rbcL and mitochondrial cox1 gene sequences were investigated for the Bangiales from the Antarctica and its adjacent waters. proprietary
-KOPRI-KPDC-00000043_1 2000 Sediment Core, Antarctica ALL STAC Catalog 2000-12-08 2000-12-10 -68.527222, -65.264722, -66.956111, -64.021389 https://cmr.earthdata.nasa.gov/search/concepts/C2244294957-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the northern Powell Basin of the Weddell Sea. The research period was from 3 Nov. to 11 Dec. (9 days) in 2000. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 12 researchers participated in the cruise, including the acquisition of multichannel seismic, bathymetry, and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
KOPRI-KPDC-00000043_1 2000 Sediment Core, Antarctica AMD_KOPRI STAC Catalog 2000-12-08 2000-12-10 -68.527222, -65.264722, -66.956111, -64.021389 https://cmr.earthdata.nasa.gov/search/concepts/C2244294957-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the northern Powell Basin of the Weddell Sea. The research period was from 3 Nov. to 11 Dec. (9 days) in 2000. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 12 researchers participated in the cruise, including the acquisition of multichannel seismic, bathymetry, and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
-KOPRI-KPDC-00000044_1 2001 Sediment Core, Antarctica ALL STAC Catalog 2001-12-19 2001-12-21 -58.026667, -61.925556, -52.468056, -60.802778 https://cmr.earthdata.nasa.gov/search/concepts/C2244294981-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the northern Powell Basin of the Weddell Sea. The research period was from 15 Dec. to 21 Dec. (7 days) in 2001. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 11 researchers participated in the cruise, including acquisition of multichannel seismic and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
+KOPRI-KPDC-00000043_1 2000 Sediment Core, Antarctica ALL STAC Catalog 2000-12-08 2000-12-10 -68.527222, -65.264722, -66.956111, -64.021389 https://cmr.earthdata.nasa.gov/search/concepts/C2244294957-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the northern Powell Basin of the Weddell Sea. The research period was from 3 Nov. to 11 Dec. (9 days) in 2000. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 12 researchers participated in the cruise, including the acquisition of multichannel seismic, bathymetry, and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
KOPRI-KPDC-00000044_1 2001 Sediment Core, Antarctica AMD_KOPRI STAC Catalog 2001-12-19 2001-12-21 -58.026667, -61.925556, -52.468056, -60.802778 https://cmr.earthdata.nasa.gov/search/concepts/C2244294981-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the northern Powell Basin of the Weddell Sea. The research period was from 15 Dec. to 21 Dec. (7 days) in 2001. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 11 researchers participated in the cruise, including acquisition of multichannel seismic and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
+KOPRI-KPDC-00000044_1 2001 Sediment Core, Antarctica ALL STAC Catalog 2001-12-19 2001-12-21 -58.026667, -61.925556, -52.468056, -60.802778 https://cmr.earthdata.nasa.gov/search/concepts/C2244294981-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the northern Powell Basin of the Weddell Sea. The research period was from 15 Dec. to 21 Dec. (7 days) in 2001. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 11 researchers participated in the cruise, including acquisition of multichannel seismic and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
KOPRI-KPDC-00000045_1 2002 Sediment Core, Antarctica AMD_KOPRI STAC Catalog 2002-12-21 2002-12-22 -51.625833, -62.175, -49.593889, -60.658889 https://cmr.earthdata.nasa.gov/search/concepts/C2244294992-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the Powell Basin (III region) of the northern Weddell Sea. The research period was from 16 Dec. to 23 Dec. (8 days) in 2002. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 7 researchers participated in the cruise, including acquisition of multichannel seismic as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
KOPRI-KPDC-00000045_1 2002 Sediment Core, Antarctica ALL STAC Catalog 2002-12-21 2002-12-22 -51.625833, -62.175, -49.593889, -60.658889 https://cmr.earthdata.nasa.gov/search/concepts/C2244294992-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the Powell Basin (III region) of the northern Weddell Sea. The research period was from 16 Dec. to 23 Dec. (8 days) in 2002. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 7 researchers participated in the cruise, including acquisition of multichannel seismic as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
KOPRI-KPDC-00000046_1 2003 Sediment Core, Antarctica AMD_KOPRI STAC Catalog 2003-12-18 2003-12-19 -49.607778, -59.492778, -49.607778, -59.492778 https://cmr.earthdata.nasa.gov/search/concepts/C2244295005-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the Powell Basin (IV region) of the northern Weddell Sea. The research period was from 24 Nov. to 9 Dec. (15 days) in 2003. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 12 researchers participated in the cruise, including acquisition of multichannel seismic as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
@@ -8308,28 +8309,28 @@ KOPRI-KPDC-00000049_1 2005 Seismic Data, Antarctica ALL STAC Catalog 2005-12-22
KOPRI-KPDC-00000049_1 2005 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 2005-12-22 2005-12-26 -58.878333, -61.634139, -56.167083, -60.433083 https://cmr.earthdata.nasa.gov/search/concepts/C2244291450-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in northern sea area of the south Shetland Islands. The research period was from 16 Dec. to 30 Dec. (15 days) in 2005. Geophysical research including acquisition of multi-channel seismic data was preceded. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) and 12 researcher." proprietary
KOPRI-KPDC-00000050_1 2006 Seismic Data, Antarctica AMD_KOPRI STAC Catalog 2006-12-06 2006-12-10 -61.33825, -62.045389, -58.481333, -60.755389 https://cmr.earthdata.nasa.gov/search/concepts/C2244291500-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in northern sea area of the South Shetland Islands. The research period was from 05 Dec. to 12 Dec. (8 days) in 2006. Geophysical research including acquisition of multi-channel seismic data was preceded. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) and 12 researcher." proprietary
KOPRI-KPDC-00000050_1 2006 Seismic Data, Antarctica ALL STAC Catalog 2006-12-06 2006-12-10 -61.33825, -62.045389, -58.481333, -60.755389 https://cmr.earthdata.nasa.gov/search/concepts/C2244291500-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in northern sea area of the South Shetland Islands. The research period was from 05 Dec. to 12 Dec. (8 days) in 2006. Geophysical research including acquisition of multi-channel seismic data was preceded. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) and 12 researcher." proprietary
-KOPRI-KPDC-00000051_1 1994 Sediment Core, Antarctica AMD_KOPRI STAC Catalog 1994-12-31 1995-01-02 -58.026667, -62.42, -57.739722, -62.32 https://cmr.earthdata.nasa.gov/search/concepts/C2244291543-AMD_KOPRI.umm_json "For the first year of study ""The Antarctic Undersea Geological Survey"", The Field Survey of Antarctica was conducted at the end of 1994 was conducted multi-channel seismic Investigation and drilling Investigation in the central basin of the Bransfield Strait was located in between the south Shetland Islands and the Antarctic peninsula and Maxwell bay area near Sejong Station. The field investigation was conducted research projects at the same time took 13 days from 11 Dec. in 1994 to 23 Jan. in 1995. - Korean Antarctic survey carried out as part of step 1 project in year 1 to investigate the possibility of oil resources in the Bransfield Strait of Antarctica. - Securing data for tectonic settings research in the same region. - Obtaining basic data for understanding marine geology and sedimentary layers in the same region." proprietary
KOPRI-KPDC-00000051_1 1994 Sediment Core, Antarctica ALL STAC Catalog 1994-12-31 1995-01-02 -58.026667, -62.42, -57.739722, -62.32 https://cmr.earthdata.nasa.gov/search/concepts/C2244291543-AMD_KOPRI.umm_json "For the first year of study ""The Antarctic Undersea Geological Survey"", The Field Survey of Antarctica was conducted at the end of 1994 was conducted multi-channel seismic Investigation and drilling Investigation in the central basin of the Bransfield Strait was located in between the south Shetland Islands and the Antarctic peninsula and Maxwell bay area near Sejong Station. The field investigation was conducted research projects at the same time took 13 days from 11 Dec. in 1994 to 23 Jan. in 1995. - Korean Antarctic survey carried out as part of step 1 project in year 1 to investigate the possibility of oil resources in the Bransfield Strait of Antarctica. - Securing data for tectonic settings research in the same region. - Obtaining basic data for understanding marine geology and sedimentary layers in the same region." proprietary
+KOPRI-KPDC-00000051_1 1994 Sediment Core, Antarctica AMD_KOPRI STAC Catalog 1994-12-31 1995-01-02 -58.026667, -62.42, -57.739722, -62.32 https://cmr.earthdata.nasa.gov/search/concepts/C2244291543-AMD_KOPRI.umm_json "For the first year of study ""The Antarctic Undersea Geological Survey"", The Field Survey of Antarctica was conducted at the end of 1994 was conducted multi-channel seismic Investigation and drilling Investigation in the central basin of the Bransfield Strait was located in between the south Shetland Islands and the Antarctic peninsula and Maxwell bay area near Sejong Station. The field investigation was conducted research projects at the same time took 13 days from 11 Dec. in 1994 to 23 Jan. in 1995. - Korean Antarctic survey carried out as part of step 1 project in year 1 to investigate the possibility of oil resources in the Bransfield Strait of Antarctica. - Securing data for tectonic settings research in the same region. - Obtaining basic data for understanding marine geology and sedimentary layers in the same region." proprietary
KOPRI-KPDC-00000052_1 1995 Sediment Core, Antarctica AMD_KOPRI STAC Catalog 1995-12-19 1995-12-23 -55.951111, -61.969167, -55.051111, -61.951111 https://cmr.earthdata.nasa.gov/search/concepts/C2244291581-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the east basin of the Bransfield Strait between the Antarctic peninsula and south Shetland Islands and Maxwell Bay located at Sejong Station was conducted multi-channel seismic investigation and drilling investigation. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) which is marine geology, geophysical survey vessel and Icebreaker for field investigation." proprietary
KOPRI-KPDC-00000052_1 1995 Sediment Core, Antarctica ALL STAC Catalog 1995-12-19 1995-12-23 -55.951111, -61.969167, -55.051111, -61.951111 https://cmr.earthdata.nasa.gov/search/concepts/C2244291581-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the east basin of the Bransfield Strait between the Antarctic peninsula and south Shetland Islands and Maxwell Bay located at Sejong Station was conducted multi-channel seismic investigation and drilling investigation. We took on lease Russian ""Yuzhmorgeologiya""(5500 ton, ice strengthed vessel) which is marine geology, geophysical survey vessel and Icebreaker for field investigation." proprietary
-KOPRI-KPDC-00000053_1 1996 Sediment Core, Antarctica AMD_KOPRI STAC Catalog 1996-12-16 1996-12-16 -60.151944, -62.100278, -59.717778, -62.051389 https://cmr.earthdata.nasa.gov/search/concepts/C2244291950-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in west of the Bransfeed Strait, a basin between the Antarctic Peninsula and the south Shetland Islands. It tooks 9 days. seismic investigation and drilling investigation were conducted at the same time during the field survey. We took on lease Russian R/V ""Yuzhmorgeologiya"" which is marine geology, geophysical survey vessel and Icebreaker and 10 researchers from ‘Korea Ocean Research and Development Institute’ and 3 academic personnel participated in the cruise as field investigation personnel." proprietary
KOPRI-KPDC-00000053_1 1996 Sediment Core, Antarctica ALL STAC Catalog 1996-12-16 1996-12-16 -60.151944, -62.100278, -59.717778, -62.051389 https://cmr.earthdata.nasa.gov/search/concepts/C2244291950-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in west of the Bransfeed Strait, a basin between the Antarctic Peninsula and the south Shetland Islands. It tooks 9 days. seismic investigation and drilling investigation were conducted at the same time during the field survey. We took on lease Russian R/V ""Yuzhmorgeologiya"" which is marine geology, geophysical survey vessel and Icebreaker and 10 researchers from ‘Korea Ocean Research and Development Institute’ and 3 academic personnel participated in the cruise as field investigation personnel." proprietary
+KOPRI-KPDC-00000053_1 1996 Sediment Core, Antarctica AMD_KOPRI STAC Catalog 1996-12-16 1996-12-16 -60.151944, -62.100278, -59.717778, -62.051389 https://cmr.earthdata.nasa.gov/search/concepts/C2244291950-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in west of the Bransfeed Strait, a basin between the Antarctic Peninsula and the south Shetland Islands. It tooks 9 days. seismic investigation and drilling investigation were conducted at the same time during the field survey. We took on lease Russian R/V ""Yuzhmorgeologiya"" which is marine geology, geophysical survey vessel and Icebreaker and 10 researchers from ‘Korea Ocean Research and Development Institute’ and 3 academic personnel participated in the cruise as field investigation personnel." proprietary
KOPRI-KPDC-00000054_1 1997 Sediment Core, Antarctica ALL STAC Catalog 1997-12-28 1997-12-29 -63.396667, -63.886111, -62.700833, -62.536389 https://cmr.earthdata.nasa.gov/search/concepts/C2244292254-AMD_KOPRI.umm_json Korean Antarctic survey was conducted in 1997 carried out in a continental shelf in the northwestern part of the Antarctic Peninsula. It took 2 days. We took on lease Norway R/V 'Polar Duke' and 11 researchers from ‘Korea Ocean Research and Development Institute’ participated as field investigation personnel. The Teac single-channel recorder, EPC Recorder, Q/C MicroMax system etc. was used mainly by Sleeve gun used as a sound source, compressor for creating compressed air, DFS-V Recorder for multi-channel Seismic record, 12-channel geophone of seismic streamers. Additional Gravity Core was used for sediment research through drilling. proprietary
KOPRI-KPDC-00000054_1 1997 Sediment Core, Antarctica AMD_KOPRI STAC Catalog 1997-12-28 1997-12-29 -63.396667, -63.886111, -62.700833, -62.536389 https://cmr.earthdata.nasa.gov/search/concepts/C2244292254-AMD_KOPRI.umm_json Korean Antarctic survey was conducted in 1997 carried out in a continental shelf in the northwestern part of the Antarctic Peninsula. It took 2 days. We took on lease Norway R/V 'Polar Duke' and 11 researchers from ‘Korea Ocean Research and Development Institute’ participated as field investigation personnel. The Teac single-channel recorder, EPC Recorder, Q/C MicroMax system etc. was used mainly by Sleeve gun used as a sound source, compressor for creating compressed air, DFS-V Recorder for multi-channel Seismic record, 12-channel geophone of seismic streamers. Additional Gravity Core was used for sediment research through drilling. proprietary
-KOPRI-KPDC-00000055_1 1998 Sediment Core, Antarctica ALL STAC Catalog 1998-12-11 1998-12-12 -66.32, -63.95, -63.47, -62.943333 https://cmr.earthdata.nasa.gov/search/concepts/C2244294165-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the continental margin (II region) of the northwestern Antarctic Peninsula. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 10 researchers participated in the cruise, including acquisition of multichannel seismic, gravity, and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
KOPRI-KPDC-00000055_1 1998 Sediment Core, Antarctica AMD_KOPRI STAC Catalog 1998-12-11 1998-12-12 -66.32, -63.95, -63.47, -62.943333 https://cmr.earthdata.nasa.gov/search/concepts/C2244294165-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the continental margin (II region) of the northwestern Antarctic Peninsula. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 10 researchers participated in the cruise, including acquisition of multichannel seismic, gravity, and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
+KOPRI-KPDC-00000055_1 1998 Sediment Core, Antarctica ALL STAC Catalog 1998-12-11 1998-12-12 -66.32, -63.95, -63.47, -62.943333 https://cmr.earthdata.nasa.gov/search/concepts/C2244294165-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the continental margin (II region) of the northwestern Antarctic Peninsula. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 10 researchers participated in the cruise, including acquisition of multichannel seismic, gravity, and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
KOPRI-KPDC-00000056_1 1999 Sediment Core, Antarctica AMD_KOPRI STAC Catalog 2000-01-01 2000-01-03 -66.32, -63.95, -63.47, -62.943333 https://cmr.earthdata.nasa.gov/search/concepts/C2244294945-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the continental margin off the Anvers Island of the northwestern Antarctic Peninsula. The research period was from 25 Nov. in 1999 to 3 Jan. in 2000 (8 days). We took on Korean R/V ""Onnuri"" (KORDI) and 13 researchers participated in the cruise, including acquisition of multichannel seismic, gravity, and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic, SBP, gravity, and magnetometer surveys) 2. Paleoceanographic researches" proprietary
KOPRI-KPDC-00000056_1 1999 Sediment Core, Antarctica ALL STAC Catalog 2000-01-01 2000-01-03 -66.32, -63.95, -63.47, -62.943333 https://cmr.earthdata.nasa.gov/search/concepts/C2244294945-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the continental margin off the Anvers Island of the northwestern Antarctic Peninsula. The research period was from 25 Nov. in 1999 to 3 Jan. in 2000 (8 days). We took on Korean R/V ""Onnuri"" (KORDI) and 13 researchers participated in the cruise, including acquisition of multichannel seismic, gravity, and magnetometer as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic, SBP, gravity, and magnetometer surveys) 2. Paleoceanographic researches" proprietary
-KOPRI-KPDC-00000057_1 2005 Sediment Core, Antarctica AMD_KOPRI STAC Catalog 2005-12-26 2005-12-28 -57.808611, -61.3075, -56.389722, -60.925833 https://cmr.earthdata.nasa.gov/search/concepts/C2244295068-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the northern region off South Shetland Islands. The research period was from 16 Dec. to 30 Dec. (15 days) in 2005. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 12 researchers participated in the cruise, including acquisition of multichannel seismic as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
KOPRI-KPDC-00000057_1 2005 Sediment Core, Antarctica ALL STAC Catalog 2005-12-26 2005-12-28 -57.808611, -61.3075, -56.389722, -60.925833 https://cmr.earthdata.nasa.gov/search/concepts/C2244295068-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the northern region off South Shetland Islands. The research period was from 16 Dec. to 30 Dec. (15 days) in 2005. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 12 researchers participated in the cruise, including acquisition of multichannel seismic as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
-KOPRI-KPDC-00000058_1 2006 Sediment Core, Antarctica ALL STAC Catalog 2006-12-10 2006-12-11 -61.138333, -61.503333, -58.722222, -61.284444 https://cmr.earthdata.nasa.gov/search/concepts/C2244295115-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the northern region off South Shetland Islands. The research period was from 5 Dec. to 12 Dec. (8 days) in 2006. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 8 researchers participated in the cruise, including acquisition of multichannel seismic as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
+KOPRI-KPDC-00000057_1 2005 Sediment Core, Antarctica AMD_KOPRI STAC Catalog 2005-12-26 2005-12-28 -57.808611, -61.3075, -56.389722, -60.925833 https://cmr.earthdata.nasa.gov/search/concepts/C2244295068-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the northern region off South Shetland Islands. The research period was from 16 Dec. to 30 Dec. (15 days) in 2005. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 12 researchers participated in the cruise, including acquisition of multichannel seismic as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
KOPRI-KPDC-00000058_1 2006 Sediment Core, Antarctica AMD_KOPRI STAC Catalog 2006-12-10 2006-12-11 -61.138333, -61.503333, -58.722222, -61.284444 https://cmr.earthdata.nasa.gov/search/concepts/C2244295115-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the northern region off South Shetland Islands. The research period was from 5 Dec. to 12 Dec. (8 days) in 2006. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 8 researchers participated in the cruise, including acquisition of multichannel seismic as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
+KOPRI-KPDC-00000058_1 2006 Sediment Core, Antarctica ALL STAC Catalog 2006-12-10 2006-12-11 -61.138333, -61.503333, -58.722222, -61.284444 https://cmr.earthdata.nasa.gov/search/concepts/C2244295115-AMD_KOPRI.umm_json "Korean Antarctic survey was conducted in the northern region off South Shetland Islands. The research period was from 5 Dec. to 12 Dec. (8 days) in 2006. We took on lease Russian R/V ""Yuzhmorgeologiya"" (5500 ton, ice strengthed vessel) and 8 researchers participated in the cruise, including acquisition of multichannel seismic as well as a detailed samplings (box cores, gravity cores, and grab samples). 1. Geophysical researches (Multichannel seismic and SBP surveys) 2. Paleoceanographic researches" proprietary
KOPRI-KPDC-00000059_1 2010 Sediment Core, Antarctica (LARISSA) AMD_KOPRI STAC Catalog 2011-12-06 2011-12-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244291545-AMD_KOPRI.umm_json For USA-Korea collaborative studies we took RV Palmer to get core sediments in 2010. After we obtained X-radiographs, gray scale analysis was conducted from core sediments. Paleoceanographic researches (LARISSA program) proprietary
KOPRI-KPDC-00000059_1 2010 Sediment Core, Antarctica (LARISSA) ALL STAC Catalog 2011-12-06 2011-12-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244291545-AMD_KOPRI.umm_json For USA-Korea collaborative studies we took RV Palmer to get core sediments in 2010. After we obtained X-radiographs, gray scale analysis was conducted from core sediments. Paleoceanographic researches (LARISSA program) proprietary
KOPRI-KPDC-00000060_1 2010 Sediment Core, Antarctica (K-Polar) ALL STAC Catalog 2009-12-10 2010-03-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244291583-AMD_KOPRI.umm_json Antarctic survey were conducted in Bransfiedl Strait and off Joinville Island for 2010 K-Polar project. We took RV Araon to obtain gravity core sediments for paleoceanographic studies. Paleoceanographic studies proprietary
KOPRI-KPDC-00000060_1 2010 Sediment Core, Antarctica (K-Polar) AMD_KOPRI STAC Catalog 2009-12-10 2010-03-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244291583-AMD_KOPRI.umm_json Antarctic survey were conducted in Bransfiedl Strait and off Joinville Island for 2010 K-Polar project. We took RV Araon to obtain gravity core sediments for paleoceanographic studies. Paleoceanographic studies proprietary
-KOPRI-KPDC-00000061_1 2012 Sediment Core, Antarctica (Amundsen Sea Project) AMD_KOPRI STAC Catalog 2011-12-07 2011-12-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244291671-AMD_KOPRI.umm_json Korean Antarctic survey was conducted off Amundsen Sea, West Antarctica. We took RV Araon in 2012 to obtain gravity core sediments for K-Polar Amundsen Sea project. Paleoceanographic researches proprietary
KOPRI-KPDC-00000061_1 2012 Sediment Core, Antarctica (Amundsen Sea Project) ALL STAC Catalog 2011-12-07 2011-12-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244291671-AMD_KOPRI.umm_json Korean Antarctic survey was conducted off Amundsen Sea, West Antarctica. We took RV Araon in 2012 to obtain gravity core sediments for K-Polar Amundsen Sea project. Paleoceanographic researches proprietary
+KOPRI-KPDC-00000061_1 2012 Sediment Core, Antarctica (Amundsen Sea Project) AMD_KOPRI STAC Catalog 2011-12-07 2011-12-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244291671-AMD_KOPRI.umm_json Korean Antarctic survey was conducted off Amundsen Sea, West Antarctica. We took RV Araon in 2012 to obtain gravity core sediments for K-Polar Amundsen Sea project. Paleoceanographic researches proprietary
KOPRI-KPDC-00000062_1 2012 Sediment Core, Antarctica (LARISSA) AMD_KOPRI STAC Catalog 2011-12-07 2011-12-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244291969-AMD_KOPRI.umm_json For USA-Korea collaborative studies we took RV Palmer and obtained core sediments in 2012. After that, X-radiography and non-destructive XRF of core sediments were conducted. Paleoceanographic researches (LARISSA program) proprietary
KOPRI-KPDC-00000062_1 2012 Sediment Core, Antarctica (LARISSA) ALL STAC Catalog 2011-12-07 2011-12-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244291969-AMD_KOPRI.umm_json For USA-Korea collaborative studies we took RV Palmer and obtained core sediments in 2012. After that, X-radiography and non-destructive XRF of core sediments were conducted. Paleoceanographic researches (LARISSA program) proprietary
KOPRI-KPDC-00000063_1 CTD measurements from Antarctic Peninsula region (KARP 1996) AMD_KOPRI STAC Catalog 2011-12-09 2011-12-09 -66, -69, -44, -59 https://cmr.earthdata.nasa.gov/search/concepts/C2244292467-AMD_KOPRI.umm_json This dataset includes CTD measurements of open water and fjords of Antarctic Peninsula during the KARP (Korea Antarctic Research Program) cruise from 1996. Data obtained are water temperature, salinity, density, dissolved oxygen concentration, turbidity, chlorophyl a, and current velocity. For fjord surveys, tide gauge was moored over the year with temperature and conductivity sensors. proprietary
@@ -8396,22 +8397,22 @@ KOPRI-KPDC-00000123_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E),
KOPRI-KPDC-00000123_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2002 ALL STAC Catalog 2002-09-03 2002-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244294948-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
KOPRI-KPDC-00000124_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2003 ALL STAC Catalog 2011-12-20 2011-12-20 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244294964-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
KOPRI-KPDC-00000124_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2003 AMD_KOPRI STAC Catalog 2011-12-20 2011-12-20 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244294964-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
-KOPRI-KPDC-00000125_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2004 AMD_KOPRI STAC Catalog 2004-01-01 2004-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244294984-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
KOPRI-KPDC-00000125_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2004 ALL STAC Catalog 2004-01-01 2004-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244294984-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
+KOPRI-KPDC-00000125_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2004 AMD_KOPRI STAC Catalog 2004-01-01 2004-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244294984-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
KOPRI-KPDC-00000126_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2005 AMD_KOPRI STAC Catalog 2005-01-01 2005-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244294994-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
KOPRI-KPDC-00000126_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2005 ALL STAC Catalog 2005-01-01 2005-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244294994-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
KOPRI-KPDC-00000127_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2006 AMD_KOPRI STAC Catalog 2006-01-01 2006-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295008-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
KOPRI-KPDC-00000127_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2006 ALL STAC Catalog 2006-01-01 2006-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295008-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
KOPRI-KPDC-00000128_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2007 AMD_KOPRI STAC Catalog 2007-01-01 2007-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295029-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
KOPRI-KPDC-00000128_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2007 ALL STAC Catalog 2007-01-01 2007-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295029-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
-KOPRI-KPDC-00000129_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2008 ALL STAC Catalog 2008-01-01 2008-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295067-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
KOPRI-KPDC-00000129_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2008 AMD_KOPRI STAC Catalog 2008-01-01 2008-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295067-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
-KOPRI-KPDC-00000130_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2009 ALL STAC Catalog 2009-01-01 2009-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295108-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
+KOPRI-KPDC-00000129_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2008 ALL STAC Catalog 2008-01-01 2008-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295067-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
KOPRI-KPDC-00000130_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2009 AMD_KOPRI STAC Catalog 2009-01-01 2009-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295108-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
-KOPRI-KPDC-00000131_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2010 ALL STAC Catalog 2010-01-01 2010-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295155-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
+KOPRI-KPDC-00000130_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2009 ALL STAC Catalog 2009-01-01 2009-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295108-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
KOPRI-KPDC-00000131_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2010 AMD_KOPRI STAC Catalog 2010-01-01 2010-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295155-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
-KOPRI-KPDC-00000132_1 Advanced Microwave Scanning Radiometer from EOS (AMSR-E), 2011 ALL STAC Catalog 2011-01-01 2011-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295202-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer from EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
+KOPRI-KPDC-00000131_1 Advanced Microwave Scanning Radiometer form EOS (AMSR-E), 2010 ALL STAC Catalog 2010-01-01 2010-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295155-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer form EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
KOPRI-KPDC-00000132_1 Advanced Microwave Scanning Radiometer from EOS (AMSR-E), 2011 AMD_KOPRI STAC Catalog 2011-01-01 2011-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295202-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer from EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
+KOPRI-KPDC-00000132_1 Advanced Microwave Scanning Radiometer from EOS (AMSR-E), 2011 ALL STAC Catalog 2011-01-01 2011-12-31 180, -84.959305, 0.5, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295202-AMD_KOPRI.umm_json The Advanced Microwave Scanning Radiometer from EOS (AMSR-E) is a twelve-channel, six-frequency, total power passive-microwave radiometer system. It measures brightness temperatures at 6.925, 10.65, 18.7, 23.8, 36.5 and 89.0 GHz. Vertically and horizontally polarized measurements are taken at all channels. Spatial resolution of the individual measurements varies from 5.4km at 89.0GHz to 56km at 6.9GHz The Earth-emitted microwave radiation is collected by an offset parabolic reflector 1.6 meters in diameter that scans across the Earth along an imaginary conical surface, maintaining a constant Earth incidence angle of 55 and providing a swath width array of six feedhorns which then carry the radiation to radiometers for measurement. Calibration is accomplished with observations of cosmic background radiation and an on-board warm target. proprietary
KOPRI-KPDC-00000133_1 Geo-stationary Ocean Color Imager in Korea Peninsula, 2011 AMD_KOPRI STAC Catalog 2011-04-01 2011-12-31 122.321289, 32.62087, 132.473633, 43.739352 https://cmr.earthdata.nasa.gov/search/concepts/C2244295221-AMD_KOPRI.umm_json Geo-stationary Ocean Color Imager (GOCI) is completing development to provide a monitoring of ocean color at the Korean Peninsula from a geo-stationary platform. GOCI will be carried by the Communication, Ocean, and Meteorological Satellite (COMS) of Korea. The GOCI is designed to provide multi-spectral data to detect, monitor, quantify, and predict short-term changes of coastal ocean environment for marine science research and application purpose. proprietary
KOPRI-KPDC-00000134_1 The Ocean Color and Temperature Scanner in Antarctica, 1996-1997 AMD_KOPRI STAC Catalog 1996-11-02 1997-06-30 0.5, -84.959305, 180, -58.813742 https://cmr.earthdata.nasa.gov/search/concepts/C2244295234-AMD_KOPRI.umm_json Ocean Color and Temperature Scanner (OCTS) is an optical radiometer to achieve highly sensitive spectral measurement with 12 bands covering visible and thermal infrared region. In the visible and near-infrared bands, the ocean conditions are observed by taking advantage of spectral reflectance of the dissolved substances in the water and phytoplankton. OCTS mainly serves as an observation sensor of the ocean conditions, including chlorophyll and dissolved substances in the water, temperature profile and cloud formation processes. proprietary
KOPRI-KPDC-00000135_1 The Ocean Color and Temperature Scanner in Arctic, 1996-1997 AMD_KOPRI STAC Catalog 1996-11-02 1997-06-30 -88.945312, 59.431074, -1.921875, 84.574702 https://cmr.earthdata.nasa.gov/search/concepts/C2244295245-AMD_KOPRI.umm_json Ocean Color and Temperature Scanner (OCTS) is an optical radiometer to achieve highly sensitive spectral measurement with 12 bands covering visible and thermal infrared region. In the visible and near-infrared bands, the ocean conditions are observed by taking advantage of spectral reflectance of the dissolved substances in the water and phytoplankton. OCTS mainly serves as an observation sensor of the ocean conditions, including chlorophyll and dissolved substances in the water, temperature profile and cloud formation processes. proprietary
@@ -8503,12 +8504,12 @@ KOPRI-KPDC-00000220_1 Neutral winds and temperature data in the MLT region at Ki
KOPRI-KPDC-00000221_1 Neutral winds and temperature data in the MLT region at King Sejong Station, Antarctica at 2011 AMD_KOPRI STAC Catalog 2011-01-01 2011-12-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294908-AMD_KOPRI.umm_json Neutral winds and temperature measurements around 70~110 km altitude obtained from the meteor observations at King Sejong Station, Antarctica. Long-term monitoring of the neutral winds and temperature changes over the southern high-latitude region for the study of upper atmospheric thermal structure and dynamics in the MLT region. proprietary
KOPRI-KPDC-00000222_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2008 AMD_KOPRI STAC Catalog 2008-05-07 2008-10-30 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294928-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica. Study of the atmospheric wave activities in the southern high-latitude MLT region. proprietary
KOPRI-KPDC-00000222_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2008 ALL STAC Catalog 2008-05-07 2008-10-30 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294928-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica. Study of the atmospheric wave activities in the southern high-latitude MLT region. proprietary
-KOPRI-KPDC-00000223_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2009 AMD_KOPRI STAC Catalog 2009-02-21 2009-04-18 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294939-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica. Study of the atmospheric wave activities in the southern high-latitude MLT region. proprietary
KOPRI-KPDC-00000223_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2009 ALL STAC Catalog 2009-02-21 2009-04-18 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294939-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica. Study of the atmospheric wave activities in the southern high-latitude MLT region. proprietary
-KOPRI-KPDC-00000224_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2010 ALL STAC Catalog 2010-02-15 2010-10-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294950-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica. Study of the atmospheric wave activities in the southern high-latitude MLT region. proprietary
+KOPRI-KPDC-00000223_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2009 AMD_KOPRI STAC Catalog 2009-02-21 2009-04-18 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294939-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica. Study of the atmospheric wave activities in the southern high-latitude MLT region. proprietary
KOPRI-KPDC-00000224_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2010 AMD_KOPRI STAC Catalog 2010-02-15 2010-10-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294950-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica. Study of the atmospheric wave activities in the southern high-latitude MLT region. proprietary
-KOPRI-KPDC-00000225_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2011 AMD_KOPRI STAC Catalog 2011-03-08 2011-10-28 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294970-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica. Study of the atmospheric wave activities in the southern high-latitude MLT region. proprietary
+KOPRI-KPDC-00000224_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2010 ALL STAC Catalog 2010-02-15 2010-10-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294950-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica. Study of the atmospheric wave activities in the southern high-latitude MLT region. proprietary
KOPRI-KPDC-00000225_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2011 ALL STAC Catalog 2011-03-08 2011-10-28 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294970-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica. Study of the atmospheric wave activities in the southern high-latitude MLT region. proprietary
+KOPRI-KPDC-00000225_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2011 AMD_KOPRI STAC Catalog 2011-03-08 2011-10-28 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294970-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica. Study of the atmospheric wave activities in the southern high-latitude MLT region. proprietary
KOPRI-KPDC-00000226_1 Distributions and diversities of viruses and bacteria in the Larsen A in the Anatarctic Weddell Sea AMD_KOPRI STAC Catalog 2012-03-11 2012-04-19 -60.171462, -65.065271, -57.538784, -64.707333 https://cmr.earthdata.nasa.gov/search/concepts/C2244294995-AMD_KOPRI.umm_json The collapse of the Larsen A Ice Shelve at the eastern coast of the Antarctic Peninsula occurred in 1995. However, no information is available on the spatial distributions of abundances and compositions of viruses and bacteria in the Larsen A area. During the NBP cruise from March 11 to April 19 in 2012, we collected seawater samples for microbial ecology at 7 stations in the study area. For the first time, we will provide the data on the distributions and compositions for marine microbes in the Lasen A area. To investigate distributions and diversities of viruses and bacteria in Larsen A in the Weddell Sea proprietary
KOPRI-KPDC-00000227_1 Isolation, identification, and diversity analysis of bacteria able to degrade humic substances from Alaska tundra soil AMD_KOPRI STAC Catalog 2011-08-13 2011-08-20 -163.711067, 64.847, -163.711067, 64.847 https://cmr.earthdata.nasa.gov/search/concepts/C2244295006-AMD_KOPRI.umm_json Three different kinds of soil humic substances were extracted from soil and plant debris samples in cold environments of Alaska tundra region. A total of 143 cold-adapted bacterial strains having an ability to degrade or bioconvert humic substances were isolated from the samples. The isolates were identified through the analysis of their 16S rRNA genes and the bacterial diversity was analyzed to be simple. The objective is to isolate bacterial strains able to degrade humic substances from cold environments in the Arctic region and to analyze their microbial diversity. Also, a functional genomic study on the microbial degradative pathway(s) for soil humic substances is an another main purpose. proprietary
KOPRI-KPDC-00000228_1 Atmospheric carbon dioxide concentration measurement at the Antarctic King Sejong Station in 2010 AMD_KOPRI STAC Catalog 2010-01-01 2010-12-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295022-AMD_KOPRI.umm_json Atmospheric CO2 concentration measurement started using a Wavelength-Scanned Cavity Ring Down Spectroscopy(WS-CRDS) at the Antarctic King Sejong Station in January of 2010. In October of 2010, CO2 concentration was involved as one of key constituents at the King Sejong station as GAW regional station. For quality assurance, routine managements of the system and its regular calibration using national standard CO2 gases of two-levels have been made at the site. In addition, dehumidification device is used to remove water vapor in the air sample before the sample arrives in the CRDS. Continuous monitoring of accurate and precision atmospheric CO2 concentration at King Sejong Station near the Antarctic Peninsula proprietary
@@ -8565,8 +8566,8 @@ KOPRI-KPDC-00000274_1 soil sampling from glacier-retreat regions in Svalbard in
KOPRI-KPDC-00000275_1 Multibeam data of around KOPRIdge, Antarctic ocean. March, 2011. AMD_KOPRI STAC Catalog 2011-02-24 2011-03-13 156, -63, 161, -61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2244294996-AMD_KOPRI.umm_json During March, 2011, KOPRI conducted KOPRI ridge(KOPRIdge) survey in the longitude 160 degree east, Antarctic ocean. During the cruise, we collected multibeam data. An accurate bathymetry survey for the unknown area. Bathymetric data collected using a MBES during marine scientific survey is essential for geologic and oceanographic. proprietary
KOPRI-KPDC-00000276_1 Multibeam data of around KOPRIdge, Antarctic ocean. December, 2011. AMD_KOPRI STAC Catalog 2011-12-06 2011-12-12 156, -63, 161, -61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2244295009-AMD_KOPRI.umm_json During December, 2011, KOPRI conducted KOPRI ridge(KOPRIdge) survey in the longitude 160 degree east, Antarctic ocean. During the cruise, we collected multibeam data. An accurate bathymetry survey for the unknown area. Bathymetric data collected using a MBES during marine scientific survey is essential for geologic and oceanographic. proprietary
KOPRI-KPDC-00000277_1 Korea Seismic Line 2010 AMD_KOPRI STAC Catalog 2010-12-10 2010-12-13 -60, -63, -54, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2244295030-AMD_KOPRI.umm_json Korea Seismic Line 2010 that are multi-channel seismic data were collected during the 2010-11 austral summer with IBRV Araon in the South Shetland Continental Margin, Antarctica. Purpose of this survey is to investigate the characteristics and distribution of BSR and to analysis geological structure using the multi-channel seismic data from Drake Passage and Bransfield Strait. proprietary
-KOPRI-KPDC-00000278_1 Aerosol Scattering Coefficients in the Antarctic ocean, 2011-2012 ALL STAC Catalog 2011-11-15 2012-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295118-AMD_KOPRI.umm_json Aerosol scattering coefficients for three different wavelengths (λ=450, 550, and 700nm) are measured almost continuously by a nephelometer in the Antarctic ocean. To determine the optical properties of aerosols in the Antarctic ocean. proprietary
KOPRI-KPDC-00000278_1 Aerosol Scattering Coefficients in the Antarctic ocean, 2011-2012 AMD_KOPRI STAC Catalog 2011-11-15 2012-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295118-AMD_KOPRI.umm_json Aerosol scattering coefficients for three different wavelengths (λ=450, 550, and 700nm) are measured almost continuously by a nephelometer in the Antarctic ocean. To determine the optical properties of aerosols in the Antarctic ocean. proprietary
+KOPRI-KPDC-00000278_1 Aerosol Scattering Coefficients in the Antarctic ocean, 2011-2012 ALL STAC Catalog 2011-11-15 2012-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295118-AMD_KOPRI.umm_json Aerosol scattering coefficients for three different wavelengths (λ=450, 550, and 700nm) are measured almost continuously by a nephelometer in the Antarctic ocean. To determine the optical properties of aerosols in the Antarctic ocean. proprietary
KOPRI-KPDC-00000279_1 Aerosol Number Concentration Observed in the Arctic Ocean, 2012. ALL STAC Catalog 2012-07-29 2012-09-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295164-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 10nm in diameter for CPC3772 and >2.5nm for CPC3776. To study aerosol formation and growth in Arctic-Antarctic Ocean. proprietary
KOPRI-KPDC-00000279_1 Aerosol Number Concentration Observed in the Arctic Ocean, 2012. AMD_KOPRI STAC Catalog 2012-07-29 2012-09-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295164-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 10nm in diameter for CPC3772 and >2.5nm for CPC3776. To study aerosol formation and growth in Arctic-Antarctic Ocean. proprietary
KOPRI-KPDC-00000280_1 Aerosol Number Concentration Observed in the Antarctic Ocean, 2011-2012. ALL STAC Catalog 2011-11-15 2012-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295204-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 10nm in diameter for CPC3772 and > 2.5nm for CPC3776. To study aerosol formation and growth in Antarctic Ocean. proprietary
@@ -8594,15 +8595,15 @@ KOPRI-KPDC-00000300_1 Helicopter-borne and ground-towed radar surveys of the Fou
KOPRI-KPDC-00000301_1 Seismic and radar investigations of Fourcade Glacier on King George Island, Antarctica AMD_KOPRI STAC Catalog 2013-02-21 2013-02-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244291563-AMD_KOPRI.umm_json To determine P- and S-wave velocities, elastic properties and subglacial topography of the polythermal Fourcade Glacier, surface seismic and radar surveys were conducted along a 470-m profile in November 2006. P- and S-wave velocity structures were determined by travel-time tomography and inversion of Rayleigh wave dispersion curves, respectively. The average P- and S-wave velocities of ice are 3466 and 1839 m s-1, respectively. Radar velocities were obtained by migration velocity analysis of 112 diffraction events. An estimate of 920 kg m-3 for the bulk density of wet ice corresponds to water contents of 5.1 and 3.2%, which were derived from the average P-wave and radar velocities, respectively. Using this density and the average P- and S-wave velocities, we estimate that the corresponding incompressibility and rigidity of the ice are 6.925 and 3.119 GPa, respectively. Synergistic interpretation of the radar profile and P- and S-wave velocities indicates the presence of a fracture zone above a subglacial high. Here, the P- and S-wave velocities are approximately 5 and 3% less than in the ice above a subglacial valley, respectively. The S-wave velocities indicate that warmer and less rigid ice underlies 10–15 m of colder ice near the surface of the glacier. Such layering is characteristic of polythermal glaciers. As a relatively simple non-invasive approach, integration of P-wave tomography, Rayleigh wave inversion and ground-towed radar is effective for various glaciological studies, including the elastic properties of englacial and subglacial materials, cold/warm ice interfaces, topography of a glacier bed and location of fracture zones. proprietary
KOPRI-KPDC-00000302_2 Climate Measurement Around the King Sejong Station, Antarctica in 2012 AMD_KOPRI STAC Catalog 2012-01-01 2012-12-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244307190-AMD_KOPRI.umm_json Since then, some meteorological data reports which are observed and analyzed at the station are published through an annual report. Goals of this study are to arrange those scraps systematically, and to understand characteristics of meteorological phenomena at the station. Automatical observation elements are composed of wind, air temperature, station level air pressure, relative humidity, dewpoint temperature, horizontal global solar radiation, precipitation. These data are calculated as type of average, maximum, minimum, occurrence time of daily data. Elements of visual observations are consisted of meteorological phenomena of visibility, snow, fog, rain, cloud, blizzard, etc, In this study, meteorological data are collected and checked during 2012 Annual meteorological observation proprietary
KOPRI-KPDC-00000303_1 An englacial image and water pathways of the Fourcade glacier on King George Island, Antarctic Peninsula, inferred from ground-penetrating radar AMD_KOPRI STAC Catalog 2013-02-21 2013-02-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244291599-AMD_KOPRI.umm_json The distribution of small fractures and water content of the Fourcade glacier on King George Island, Antarctica, was investi- gated in November 2006 and December 2007 by two ground-based (470- and 490-m-long profiles) and one helicopter-borne (470-m-long profile) ground-penetrating radar (GPR) surveys using 50-, 100-, and 500-MHz antennas. Radar images in the pre-migrated GPR sections are characterized by a smooth ice surface and irregular bed topography, numerous diffraction hy- perbolas in the ice and at the glacier bed, strong scattering noise, and near-surface folded layers. Scattering noise above a mound in the center of the profiles is associated with an area of dense fractures extending down from the ice surface that has relatively low reflection strength. Near the northeast ends of the profiles where few englacial fractures occur, scattering noise may result from the presence of warmer ice. A water-filled conduit and an air-filled cavity are interpreted as the source of two distinct hyperbolas in sub-glacial valleys based on the polarity of the reflections. Through migration velocity analysis on 106 hyperbolas, radar velocities were obtained for the 100-MHz ground-based profile. Using the velocities and Paren’s mixture formula, we calculated the water content of the ice to have been in the range of 0.00–0.09. High water content occurs near the glacier margin, in sub-glacial valleys, and in zones of scattering noise. proprietary
-KOPRI-KPDC-00000304_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2012 ALL STAC Catalog 2012-02-14 2012-11-04 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244291808-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica. Study of the atmospheric wave activities in the southern high-latitude MLT region. proprietary
KOPRI-KPDC-00000304_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2012 AMD_KOPRI STAC Catalog 2012-02-14 2012-11-04 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244291808-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica. Study of the atmospheric wave activities in the southern high-latitude MLT region. proprietary
+KOPRI-KPDC-00000304_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2012 ALL STAC Catalog 2012-02-14 2012-11-04 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244291808-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica. Study of the atmospheric wave activities in the southern high-latitude MLT region. proprietary
KOPRI-KPDC-00000305_1 Neutral winds and temperature data in the MLT region at King Sejong Station, Antarctica at 2012 AMD_KOPRI STAC Catalog 2012-01-01 2012-12-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244292096-AMD_KOPRI.umm_json Neutral winds and temperature measurements around 70~110 km altitude obtained from the meteor observations at King Sejong Station, Antarctica. Long-term monitoring of the neutral winds and temperature changes over the southern high-latitude region for the study of upper atmospheric thermal structure and dynamics in the MLT region. proprietary
KOPRI-KPDC-00000306_1 Upper atmospheric temperature data obtained from OH and O2 emissions at King Sejong Station, Antarctica at 2012 AMD_KOPRI STAC Catalog 2012-03-01 2012-10-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244292356-AMD_KOPRI.umm_json Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region. Long-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region. proprietary
KOPRI-KPDC-00000307_1 Upper atmospheric temperature data obtained from OH emission in Esrange Space Center, Kiruna, Sweden at 2012 AMD_KOPRI STAC Catalog 2012-01-01 2012-12-31 21.05, 67.883333, 21.05, 67.883333 https://cmr.earthdata.nasa.gov/search/concepts/C2244292679-AMD_KOPRI.umm_json Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region. Long-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region. proprietary
KOPRI-KPDC-00000308_1 Upper atmospheric temperature data obtained from OH emission at Dasan Station, Arctic at 2012 AMD_KOPRI STAC Catalog 2012-01-01 2012-12-31 11.933333, 78.916667, 11.933333, 78.916667 https://cmr.earthdata.nasa.gov/search/concepts/C2244293005-AMD_KOPRI.umm_json Upper atmospheric temperature measurements around 87 km altitude in the mesosphere and lower thermosphere (MLT) region. Long-term monitoring of the upper atmospheric temperature changes over the northern high latitude region for the study of thermal structure and dynamics in the MLT region. proprietary
KOPRI-KPDC-00000309_1 Turbulent fluxes at the Antarctic King Sejong Station in 2003 AMD_KOPRI STAC Catalog 2003-01-01 2003-12-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244293328-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured to December in 2003 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz. Turbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases. proprietary
-KOPRI-KPDC-00000310_1 Air-sea turbulent fluxes on the Arctic in the summer of 2004 AMD_KOPRI STAC Catalog 2004-01-01 2004-12-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244293695-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2004 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz. Turbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases. proprietary
KOPRI-KPDC-00000310_1 Air-sea turbulent fluxes on the Arctic in the summer of 2004 ALL STAC Catalog 2004-01-01 2004-12-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244293695-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2004 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz. Turbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases. proprietary
+KOPRI-KPDC-00000310_1 Air-sea turbulent fluxes on the Arctic in the summer of 2004 AMD_KOPRI STAC Catalog 2004-01-01 2004-12-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244293695-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2004 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz. Turbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases. proprietary
KOPRI-KPDC-00000311_1 Turbulent fluxes at the Antarctic King Sejong Station in 2005 AMD_KOPRI STAC Catalog 2005-01-01 2005-12-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294038-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2005 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz. Turbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases. proprietary
KOPRI-KPDC-00000312_1 Turbulent fluxes at the Antarctic King Sejong Station in 2006 AMD_KOPRI STAC Catalog 2006-01-01 2006-12-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294399-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2006 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz. Turbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases. proprietary
KOPRI-KPDC-00000313_1 Turbulent fluxes at the Antarctic King Sejong Station in 2007 AMD_KOPRI STAC Catalog 2007-01-01 2007-12-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294744-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2007 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz. Turbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases. proprietary
@@ -8613,8 +8614,8 @@ KOPRI-KPDC-00000317_1 Turbulent fluxes at the Antarctic King Sejong Station in 2
KOPRI-KPDC-00000318_1 Turbulent fluxes at the Antarctic King Sejong Station in 2012 AMD_KOPRI STAC Catalog 2012-01-01 2012-12-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294954-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured from January to December in 2012 at a coastal region of the Antarctic King Sejong station. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 20 Hz. Turbulent flux measurements are used to better understand 1) the air-ocean-land-sea ice energy exchanges and 2) water and carbon dioxide gases. proprietary
KOPRI-KPDC-00000319_1 Multibeam data of around KOPRIdge, Antarctic ocean, January-Februray, 2013 AMD_KOPRI STAC Catalog 2013-01-26 2013-02-05 156, -63, 161, -61.5 https://cmr.earthdata.nasa.gov/search/concepts/C2244294978-AMD_KOPRI.umm_json During January to February, 2013, KOPRI conducted KOPRI ridge(KOPRIdge) survey in the longitude 160 degree east, Antarctic ocean. During the cruise, we collected multibeam data. An accurate bathymetry survey for the unknown area. Bathymetric data collected using a MBES during marine scientific survey is essential for geologic and oceanographic. proprietary
KOPRI-KPDC-00000320_1 Korea Seismic Line 2012 AMD_KOPRI STAC Catalog 2013-02-22 2013-02-23 176, -74, 180, -72 https://cmr.earthdata.nasa.gov/search/concepts/C2244294989-AMD_KOPRI.umm_json Korea Seismic Line 2012, multi-channel seismic data, were collected during the 2012-2013 austral summer with RV Araon in the Continental margin of Ross Sea. The major purpose of this survey is to investigate stratigraphy and sedimentary structure of the continental slope of Ross Sea, Antarctica. proprietary
-KOPRI-KPDC-00000321_2 2013 CTD Data, Ross Sea of Antarctic AMD_KOPRI STAC Catalog 2013-01-27 2013-02-19 163.0785, -76.478667, 179.505833, -71.866667 https://cmr.earthdata.nasa.gov/search/concepts/C2244301436-AMD_KOPRI.umm_json In order to understand the role of CDW (Circumpolar Deep Water) in controlling the hydrodynamics and related biochemical processes on the continental shelf of the Ross Sea, the oceanographic research was conducted from 2013 January 19 to March 02. The vertical temperature, salinity and depth were obtained at 41 stations using CTD and Rosette water sampler. In order to identify the temporal and spatial distribution of CDW on the Ross shelf and estimate the heat transport and its effect on the melting of ice shelves by CDW intrusion. A total of the oceanographic investigation was conducted using ship (ARAON) in 2013. proprietary
KOPRI-KPDC-00000321_2 2013 CTD Data, Ross Sea of Antarctic ALL STAC Catalog 2013-01-27 2013-02-19 163.0785, -76.478667, 179.505833, -71.866667 https://cmr.earthdata.nasa.gov/search/concepts/C2244301436-AMD_KOPRI.umm_json In order to understand the role of CDW (Circumpolar Deep Water) in controlling the hydrodynamics and related biochemical processes on the continental shelf of the Ross Sea, the oceanographic research was conducted from 2013 January 19 to March 02. The vertical temperature, salinity and depth were obtained at 41 stations using CTD and Rosette water sampler. In order to identify the temporal and spatial distribution of CDW on the Ross shelf and estimate the heat transport and its effect on the melting of ice shelves by CDW intrusion. A total of the oceanographic investigation was conducted using ship (ARAON) in 2013. proprietary
+KOPRI-KPDC-00000321_2 2013 CTD Data, Ross Sea of Antarctic AMD_KOPRI STAC Catalog 2013-01-27 2013-02-19 163.0785, -76.478667, 179.505833, -71.866667 https://cmr.earthdata.nasa.gov/search/concepts/C2244301436-AMD_KOPRI.umm_json In order to understand the role of CDW (Circumpolar Deep Water) in controlling the hydrodynamics and related biochemical processes on the continental shelf of the Ross Sea, the oceanographic research was conducted from 2013 January 19 to March 02. The vertical temperature, salinity and depth were obtained at 41 stations using CTD and Rosette water sampler. In order to identify the temporal and spatial distribution of CDW on the Ross shelf and estimate the heat transport and its effect on the melting of ice shelves by CDW intrusion. A total of the oceanographic investigation was conducted using ship (ARAON) in 2013. proprietary
KOPRI-KPDC-00000322_1 2013 LADCP Data, Antarctic AMD_KOPRI STAC Catalog 2013-01-27 2013-02-19 -179.505833, -76.478667, -158.396833, -61.75 https://cmr.earthdata.nasa.gov/search/concepts/C2244294999-AMD_KOPRI.umm_json In order to understand the role of CDW (Circumpolar Deep Water) in controlling the hydrodynamics and related biochemical processes on the continental shelf of the Ross Sea, the oceanographic research was conducted from 2013 January 19 to March 02. A lowered acoustic Doppler current profiler (LADCP) was attached to the CTD frame to measure the full profile of current velocities. In order to identify the temporal and spatial distribution of CDW on the Ross Sea shelf and estimate the heat transport and its effect on the melting of ice shelves by CDW intrusion. A total of the oceanographic investigation was conducted by using ship (ARAON) in 2013. proprietary
KOPRI-KPDC-00000322_1 2013 LADCP Data, Antarctic ALL STAC Catalog 2013-01-27 2013-02-19 -179.505833, -76.478667, -158.396833, -61.75 https://cmr.earthdata.nasa.gov/search/concepts/C2244294999-AMD_KOPRI.umm_json In order to understand the role of CDW (Circumpolar Deep Water) in controlling the hydrodynamics and related biochemical processes on the continental shelf of the Ross Sea, the oceanographic research was conducted from 2013 January 19 to March 02. A lowered acoustic Doppler current profiler (LADCP) was attached to the CTD frame to measure the full profile of current velocities. In order to identify the temporal and spatial distribution of CDW on the Ross Sea shelf and estimate the heat transport and its effect on the melting of ice shelves by CDW intrusion. A total of the oceanographic investigation was conducted by using ship (ARAON) in 2013. proprietary
KOPRI-KPDC-00000323_1 Single-Particle Characterization of Summertime Antarctic Aerosols Collected at King George Island Using Quantitative Energy-Dispersive Electron Probe X-ray Microanalysis and Attenuated Total Reflection Fourier Transform-Infrared Imaging Techniques AMD_KOPRI STAC Catalog 2009-03-12 2009-03-16 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295013-AMD_KOPRI.umm_json - Low-Z particle EPMA measurements were carried out on a JEOL JSM-6390 SEM equipped with an Oxford Link SATW ultrathin window energy-dispersive X-ray (EDX) detector. X-ray spectra were recorded under the control of INCA software (Oxford). An accelerating voltage of 10 kV, beam current of 0.5 nA, and a typical measuring time of 15 s were employed. - ATR-FT-IR imaging measurements were performed using a Perkin-Elmer Spectrum 100 FT-IR spectrometer interfaced to a Spectrum Spotlight 400 FT-IR microscope. For ATR imaging, an ATR accessory employing a germanium hemispherical internal reflection element (IRE) crystal with a diameter of 600 μm was used. A new single particle analytical methodology that combines low-Z particle EPMA and ATR-FT-IR imaging technique will be developed to obtain the full description for the micro-physicochemical properties of the same individual Antarctic aerosol particles. proprietary
@@ -8667,8 +8668,8 @@ KOPRI-KPDC-00000366_1 Concentration of atmospheric and oceanic carbon dioxide: 2
KOPRI-KPDC-00000367_1 Concentration of atmospheric and oceanic carbon dioxide: 2011 Chuckchi Sea AMD_KOPRI STAC Catalog 2011-07-30 2011-08-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244294946-AMD_KOPRI.umm_json Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from July 30 to August 19 by the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from Nome, Alaska, to Nome, Alaska, carrying out a series of expeditions in the Chuckchi Sea. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth. Underway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea–air CO2 flux and its time–space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change. proprietary
KOPRI-KPDC-00000368_1 Concentration of atmospheric and oceanic carbon dioxide: 2012 Northwest Pacific Transect AMD_KOPRI STAC Catalog 2012-07-14 2012-09-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244294959-AMD_KOPRI.umm_json Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from July 14 to July 29 by the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from Incheon to Nome, Alaska, carrying out a series of expeditions in the Northwest Pacific Transect. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth. Underway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea–air CO2 flux and its time–space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change. proprietary
KOPRI-KPDC-00000369_1 Concentration of atmospheric and oceanic carbon dioxide: 2012 Arctic Ocean AMD_KOPRI STAC Catalog 2012-08-01 2012-09-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244294980-AMD_KOPRI.umm_json Atmospheric and oceanic carbon dioxide in the marine boundary layer was monitored from August 1 to September 10 by the Non-Dispersive Infrared Analyser along the cruise track of R/V Araon from Nome, Alaska, to Nome, Alaska, carrying out a series of expeditions in the Arctic Ocean. The air inlet to the instrument was located at 29 m asl and the sea water inlet to the instrument was located at 7 m depth. Underway measurement of CO2 in the surface ocean and the air above enables us to estimate ocean uptake and emission of CO2 in global scale. Accurate assessment of the sea–air CO2 flux and its time–space variability is important information for the improvement of our understanding of the global carbon cycle and the prognosis for the future atmospheric CO2 concentration and future enviromental change. proprietary
-KOPRI-KPDC-00000370_1 A study on the distribution characteristics of dissolved inorganic carbon (DIC) in the Amundsen Sea in 2011. ALL STAC Catalog 2010-12-20 2011-01-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244294991-AMD_KOPRI.umm_json To investigate the controls that affect inorganic carbon in the water column of the Amundsen Sea, hydrographic survey using IBRV Araon was carried out from December 20, 2010 to January 22, 2011. At each site, 377 samples for dissolved inorganic carbon (DIC) were collected from Niskin bottle to 500 ml boro-silicate glass bottles on board. And 374 samples for seawater pH were drawn to 250 ml polypropylene bottle. In addition to these investigation, to understand the distribution of the various component of carbonic system, underway observation of CO2 parameters was carried out along the cruise track from King George Island to Christchurch. 141 samples were taken from the water inlet that was about 7m below the surface for the IBRV Araon. Accurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted. proprietary
KOPRI-KPDC-00000370_1 A study on the distribution characteristics of dissolved inorganic carbon (DIC) in the Amundsen Sea in 2011. AMD_KOPRI STAC Catalog 2010-12-20 2011-01-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244294991-AMD_KOPRI.umm_json To investigate the controls that affect inorganic carbon in the water column of the Amundsen Sea, hydrographic survey using IBRV Araon was carried out from December 20, 2010 to January 22, 2011. At each site, 377 samples for dissolved inorganic carbon (DIC) were collected from Niskin bottle to 500 ml boro-silicate glass bottles on board. And 374 samples for seawater pH were drawn to 250 ml polypropylene bottle. In addition to these investigation, to understand the distribution of the various component of carbonic system, underway observation of CO2 parameters was carried out along the cruise track from King George Island to Christchurch. 141 samples were taken from the water inlet that was about 7m below the surface for the IBRV Araon. Accurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted. proprietary
+KOPRI-KPDC-00000370_1 A study on the distribution characteristics of dissolved inorganic carbon (DIC) in the Amundsen Sea in 2011. ALL STAC Catalog 2010-12-20 2011-01-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244294991-AMD_KOPRI.umm_json To investigate the controls that affect inorganic carbon in the water column of the Amundsen Sea, hydrographic survey using IBRV Araon was carried out from December 20, 2010 to January 22, 2011. At each site, 377 samples for dissolved inorganic carbon (DIC) were collected from Niskin bottle to 500 ml boro-silicate glass bottles on board. And 374 samples for seawater pH were drawn to 250 ml polypropylene bottle. In addition to these investigation, to understand the distribution of the various component of carbonic system, underway observation of CO2 parameters was carried out along the cruise track from King George Island to Christchurch. 141 samples were taken from the water inlet that was about 7m below the surface for the IBRV Araon. Accurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted. proprietary
KOPRI-KPDC-00000371_1 A study on the distribution characteristics of dissolved inorganic carbon (DIC) in the Amundsen Sea in 2012. ALL STAC Catalog 2012-01-22 2012-03-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295004-AMD_KOPRI.umm_json In order to study for the effects on the processes controlling inorganic CO2 system during the Antarctic summer ice-free condition, an intensive oceanographic survey using the IBRV Araon from January 22 to March 11 was performed. At each hydrographic station, 628 samples for dissolved inorganic carbon (DIC) were collected from Niskin bottle on board. In addition to these investigation, to understand the distribution of the various component of carbonic system in the surface seawaters, underway observation of CO2 parameters was carried out along the cruise track. 271 samples were taken from the water inlet that was about 7m below the surface for the IBRV Araon. Accurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted. proprietary
KOPRI-KPDC-00000371_1 A study on the distribution characteristics of dissolved inorganic carbon (DIC) in the Amundsen Sea in 2012. AMD_KOPRI STAC Catalog 2012-01-22 2012-03-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295004-AMD_KOPRI.umm_json In order to study for the effects on the processes controlling inorganic CO2 system during the Antarctic summer ice-free condition, an intensive oceanographic survey using the IBRV Araon from January 22 to March 11 was performed. At each hydrographic station, 628 samples for dissolved inorganic carbon (DIC) were collected from Niskin bottle on board. In addition to these investigation, to understand the distribution of the various component of carbonic system in the surface seawaters, underway observation of CO2 parameters was carried out along the cruise track. 271 samples were taken from the water inlet that was about 7m below the surface for the IBRV Araon. Accurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted. proprietary
KOPRI-KPDC-00000372_1 A study on the distribution characteristics of dissolved inorganic carbon (DIC) in the Southern Ocean in summer 2009/2010. ALL STAC Catalog 2009-11-26 2010-01-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295017-AMD_KOPRI.umm_json To investigate the controls that affect inorganic carbon in the water column of the Southern Ocean, hydrographic survey using R/V Polarstern was carried out from November 26, 2009 to January 20, 2010. At 28 stations, 325 samples for dissolved inorganic carbon (DIC) were collected from Niskin bottle to 500 ml boro-silicate glass bottles on board. In addition to these investigation, to understand the distribution of the various component of carbonic system, underway observation of CO2 parameters was carried out along the cruise track from Punta Arenas, Chile, to Wellington, New Zealand. 576 samples were taken from the water inlet that was about 7m below the surface for the IBRV Araon. Accurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted. proprietary
@@ -8730,8 +8731,8 @@ KOPRI-KPDC-00000429_1 Concentration of atmospheric ozone: 2012 Arctic Ocean AMD_
KOPRI-KPDC-00000430_2 Concentration of atmospheric ozone: September 2012 Northwestern Pacific Oceans Transect AMD_KOPRI STAC Catalog 2012-09-12 2012-09-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244307216-AMD_KOPRI.umm_json Measuring atmospheric ozone in the marine boundary layer was monitored from September 12 to September 24 by the Thermo 49i O3 analyzer along the cruise track of R/V Araon from Nome, Alaska to Incheon, carrying out a series of expeditions in the Northwestern Pacific Oceans. The air inlet to the instrument wa located at 29 m asl and the O3 was measured at 0.017 Hz. Tropospheric ozone (O3) is a strong greenhouse gas and it has been significantly increasing during the last century. Moreover it is an important air pollutant which adversely influence to the human health and ecosystems. Therefore our continuous shipborne measurement will help understand global radiative forcing and anthropogenic influences in the atmosphere. proprietary
KOPRI-KPDC-00000431_1 A study on the distribution characteristics of pH in the Amundsen Sea in 2011. AMD_KOPRI STAC Catalog 2010-12-20 2011-01-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295111-AMD_KOPRI.umm_json To investigate the controls that affect inorganic carbon in the water column of the Amundsen Sea, hydrographic survey using IBRV Araon was carried out from December 20, 2010 to January 22, 2011. At each site, 374 samples for pH were collected from Niskin bottle to 250 ml polypropylene bottles on board. Accurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted. proprietary
KOPRI-KPDC-00000431_1 A study on the distribution characteristics of pH in the Amundsen Sea in 2011. ALL STAC Catalog 2010-12-20 2011-01-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295111-AMD_KOPRI.umm_json To investigate the controls that affect inorganic carbon in the water column of the Amundsen Sea, hydrographic survey using IBRV Araon was carried out from December 20, 2010 to January 22, 2011. At each site, 374 samples for pH were collected from Niskin bottle to 250 ml polypropylene bottles on board. Accurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted. proprietary
-KOPRI-KPDC-00000432_1 A study on the distribution characteristics of pH in the Amundsen Sea in 2012. AMD_KOPRI STAC Catalog 2012-01-22 2012-03-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295154-AMD_KOPRI.umm_json In order to study for the effects on the processes controlling inorganic CO2 system during the Antarctic summer ice-free condition, an intensive oceanographic survey using the IBRV Araon from January 22 to March 11 was performed. At each hydrographic station, 628 samples for seawater pH were collected from Niskin bottle on board. Accurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted. proprietary
KOPRI-KPDC-00000432_1 A study on the distribution characteristics of pH in the Amundsen Sea in 2012. ALL STAC Catalog 2012-01-22 2012-03-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295154-AMD_KOPRI.umm_json In order to study for the effects on the processes controlling inorganic CO2 system during the Antarctic summer ice-free condition, an intensive oceanographic survey using the IBRV Araon from January 22 to March 11 was performed. At each hydrographic station, 628 samples for seawater pH were collected from Niskin bottle on board. Accurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted. proprietary
+KOPRI-KPDC-00000432_1 A study on the distribution characteristics of pH in the Amundsen Sea in 2012. AMD_KOPRI STAC Catalog 2012-01-22 2012-03-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295154-AMD_KOPRI.umm_json In order to study for the effects on the processes controlling inorganic CO2 system during the Antarctic summer ice-free condition, an intensive oceanographic survey using the IBRV Araon from January 22 to March 11 was performed. At each hydrographic station, 628 samples for seawater pH were collected from Niskin bottle on board. Accurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Amundsen Sea of the Southern Ocean has known as sink for atmospheric CO2 owing to high biological production. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted. proprietary
KOPRI-KPDC-00000433_1 Distribution of pH in the Chukchi Sea of the Arctic Ocean in summer 2012. AMD_KOPRI STAC Catalog 2012-08-01 2012-09-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295194-AMD_KOPRI.umm_json As a part of the 2012 Korea-Polar Ocean Rapid Transition (K-PORT) program, the IBRV Araon occupied 44 hydrographic stations in the Chukchi Borderland and Medeleev Ridge of the Arctic Ocean from August 1 to September 10. In order to study for rapid climate changes and its impact on distribution of the inorganic CO2 system, 81 samples for pH analysis were taken from Niskin bottle to 100 ml boro-silicate glass bottles on board. Accurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Arctic Ocean has known as sink for atmospheric CO2. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted. proprietary
KOPRI-KPDC-00000434_1 Distribution of total alkalinity in the Chukchi Sea of the Arctic Ocean in summer 2011. AMD_KOPRI STAC Catalog 2011-07-30 2011-08-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295216-AMD_KOPRI.umm_json In order to study for the effects on the processes controlling inorganic CO2 system during the summer ice-free condition in the Arctic Ocean, an intensive oceanographic survey using the IBRV Araon from July 30 to August 19 was performed in Mendeleyev Ridge, East Siberian Sea, and Chuckchi Borderland. At each site, 259 samples for total alkalinity (TA) were taken from Niskin bottle to 500 ml boro-silicate glass bottles on board. Accurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the Arctic Ocean has known as sink for atmospheric CO2. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted. proprietary
KOPRI-KPDC-00000435_1 Distribution of total alkalinity (TA) in the Northwestern Pacific in summer 2012. AMD_KOPRI STAC Catalog 2012-07-14 2012-07-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295228-AMD_KOPRI.umm_json As a part of the 2012 SHIp borne Pole-to-Pole Observations program, the IBRV Araon occupied 12 hydrographic stations in the Northwestern Pacific from July 14 to July 29. In order to study for rapid climate changes and its impact on distribution of the inorganic CO2 system, 225 samples for total alkalinity (TA) were taken from Niskin bottle to 500 ml boro-silicate glass bottles on board. Accurate measurement concerning spatio-temporal variability in CO2 in the water column is highly important to understanding of global carbon cycle and reliable prediction for the future atmospheric concentration of CO2. From flux study perspective of CO2, the North Pacific Ocean has known as sink for atmospheric CO2. But there wasn't enough data, and sea ice have an ambiguous role of air-sea CO2 exchange so that further research must be conducted. proprietary
@@ -8764,24 +8765,24 @@ KOPRI-KPDC-00000461_1 Annual variation of phytoplankton at the Marian Cove, King
KOPRI-KPDC-00000462_1 Marine algae samples from Antarctic Ocean collected in 2011 AMD_KOPRI STAC Catalog 2012-12-16 2012-12-16 -71.116667, -65.7, -71.116667, -65.7 https://cmr.earthdata.nasa.gov/search/concepts/C2244292440-AMD_KOPRI.umm_json Microalgae from Antarctic Ocean collected in 2012 using the habitat information for marine microalgae samples proprietary
KOPRI-KPDC-00000463_1 Air-sea turbulent fluxes on the Arctic in the summer of 2013 ALL STAC Catalog 2013-08-20 2013-09-05 -174, 74, -158, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2244292978-AMD_KOPRI.umm_json On board turbulent fluxes of CO2, CH4 and energy were measured during the cruise in the Chukchi Borderland/Mendeleev Ridge/Beaufort Sea in boreal summer of 2013. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer and closed-path cavity ring-down spectrometer was used for the measurement. Motion sensor was added to the flux system to correct the effect of ship motion on the fluxes. Data were recorded on a data logger with sampling rate of 10 Hz. Turbulent flux measurements are used to 1) better understand the air-sea energy exchanges and 2) evaluate how much the Chukchi sea absorbs or emits green house gases such as CO2 and CH4 in the Chukchi sea, the Arctic in summer proprietary
KOPRI-KPDC-00000463_1 Air-sea turbulent fluxes on the Arctic in the summer of 2013 AMD_KOPRI STAC Catalog 2013-08-20 2013-09-05 -174, 74, -158, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2244292978-AMD_KOPRI.umm_json On board turbulent fluxes of CO2, CH4 and energy were measured during the cruise in the Chukchi Borderland/Mendeleev Ridge/Beaufort Sea in boreal summer of 2013. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer and closed-path cavity ring-down spectrometer was used for the measurement. Motion sensor was added to the flux system to correct the effect of ship motion on the fluxes. Data were recorded on a data logger with sampling rate of 10 Hz. Turbulent flux measurements are used to 1) better understand the air-sea energy exchanges and 2) evaluate how much the Chukchi sea absorbs or emits green house gases such as CO2 and CH4 in the Chukchi sea, the Arctic in summer proprietary
-KOPRI-KPDC-00000464_1 2013 CTD Data, in Chukchi Borderland/Mendeleev Ridge of Arctic ALL STAC Catalog 2013-08-21 2013-09-25 -179.715167, 69.988167, -134.155167, 77.500667 https://cmr.earthdata.nasa.gov/search/concepts/C2244293267-AMD_KOPRI.umm_json An intensive oceanographic survey was conducted during 19 days from 2013 September 7 to September 27 by IBRV ARAON to measure the spatial and temporal variation of water masses in the Chukchi Borderland/Mendeleev Ridge. The profiles of temperature, salinity and depth were obtained using CTD/Rosette system at 16 stations. To investigate the variability in spatial and temporal distribution of water masses and understand its transformation along the pathways and relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge. proprietary
KOPRI-KPDC-00000464_1 2013 CTD Data, in Chukchi Borderland/Mendeleev Ridge of Arctic AMD_KOPRI STAC Catalog 2013-08-21 2013-09-25 -179.715167, 69.988167, -134.155167, 77.500667 https://cmr.earthdata.nasa.gov/search/concepts/C2244293267-AMD_KOPRI.umm_json An intensive oceanographic survey was conducted during 19 days from 2013 September 7 to September 27 by IBRV ARAON to measure the spatial and temporal variation of water masses in the Chukchi Borderland/Mendeleev Ridge. The profiles of temperature, salinity and depth were obtained using CTD/Rosette system at 16 stations. To investigate the variability in spatial and temporal distribution of water masses and understand its transformation along the pathways and relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge. proprietary
-KOPRI-KPDC-00000465_1 2013 LADCP Data, in Chukchi Borderland, Arctic ALL STAC Catalog 2013-08-21 2013-09-25 179.715167, 69.988167, 178.9955, 77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2244293569-AMD_KOPRI.umm_json An intensive oceanographic survey was conducted during 15 days from 2013 August 21 to September 4 by IBRV ARAON to measure the spatial and temporal variation of water masses in the Chukchi Borderland/Mendeleev Ridge. The seawater temperature patten was observed using LADCP and ADCP. To investigate the variability in spatial and temporal distribution of water masses and and understand its transformation along the pathways and relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge. proprietary
+KOPRI-KPDC-00000464_1 2013 CTD Data, in Chukchi Borderland/Mendeleev Ridge of Arctic ALL STAC Catalog 2013-08-21 2013-09-25 -179.715167, 69.988167, -134.155167, 77.500667 https://cmr.earthdata.nasa.gov/search/concepts/C2244293267-AMD_KOPRI.umm_json An intensive oceanographic survey was conducted during 19 days from 2013 September 7 to September 27 by IBRV ARAON to measure the spatial and temporal variation of water masses in the Chukchi Borderland/Mendeleev Ridge. The profiles of temperature, salinity and depth were obtained using CTD/Rosette system at 16 stations. To investigate the variability in spatial and temporal distribution of water masses and understand its transformation along the pathways and relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge. proprietary
KOPRI-KPDC-00000465_1 2013 LADCP Data, in Chukchi Borderland, Arctic AMD_KOPRI STAC Catalog 2013-08-21 2013-09-25 179.715167, 69.988167, 178.9955, 77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2244293569-AMD_KOPRI.umm_json An intensive oceanographic survey was conducted during 15 days from 2013 August 21 to September 4 by IBRV ARAON to measure the spatial and temporal variation of water masses in the Chukchi Borderland/Mendeleev Ridge. The seawater temperature patten was observed using LADCP and ADCP. To investigate the variability in spatial and temporal distribution of water masses and and understand its transformation along the pathways and relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge. proprietary
+KOPRI-KPDC-00000465_1 2013 LADCP Data, in Chukchi Borderland, Arctic ALL STAC Catalog 2013-08-21 2013-09-25 179.715167, 69.988167, 178.9955, 77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2244293569-AMD_KOPRI.umm_json An intensive oceanographic survey was conducted during 15 days from 2013 August 21 to September 4 by IBRV ARAON to measure the spatial and temporal variation of water masses in the Chukchi Borderland/Mendeleev Ridge. The seawater temperature patten was observed using LADCP and ADCP. To investigate the variability in spatial and temporal distribution of water masses and and understand its transformation along the pathways and relationship with sea ice melting in the Chukchi Borderland/Mendeleev Ridge. proprietary
KOPRI-KPDC-00000466_1 The mitochondrial genome of Lepas australis (Crustacea: Maxillopoda: Cirripedia) AMD_KOPRI STAC Catalog 2012-01-18 2012-01-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244293840-AMD_KOPRI.umm_json The complete mitochondrial genome sequence (15,502 nt) of Lepas australis (Crustacea, Maxillopoda, Cirripedia) was determinated. L. australis was collected from Marian Cove near King Sejong station in Antarctica. It consists of the usual 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, and a control region (444 nt). To analyze the mitogenome of the cirriped, we obtained the sequences of CO1, 12S, 16S, CO3 and Cytb using universal primers newly designed in our group and then, amplified the complete mitogenome of using long-PCR by specific primers and genome walking techniques. Mitochondrial genomes contain the most informative sequences and gene arrangement for deeper phylogenetic analyses and they reflect evolutionary relationships and biogeography in the metazoans. We analyzed mitochondrial genome of the Antarctic cirriped L. australis. proprietary
KOPRI-KPDC-00000467_1 Seismic data in David Glacier, 2012 AMD_KOPRI STAC Catalog 2012-01-28 2012-11-20 159.688492, -75.618156, 162.54475, -75.132325 https://cmr.earthdata.nasa.gov/search/concepts/C2244294132-AMD_KOPRI.umm_json Continuous data of a broadband seismometer installed in David Glacier, Antarctica for the period of 2012/1/28~2012/11/20. monitoring icequakes and earthquakes proprietary
KOPRI-KPDC-00000468_1 Sea surface temperature at the Marian Cove, King George Island, Antactica in 2013 AMD_KOPRI STAC Catalog 2013-01-01 2013-12-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244294427-AMD_KOPRI.umm_json Sea surface temperature observed at coast of King Sejong Station, Antarctica in 2013. Infrared sensor (Apogee) was used to measure SST. During sea-ice period, measured temperature represent sea-ice surface temperature not SST. Data interval has been obtained continuously at 30-minute interval. Surface temperature plays critical role in determining air-sea-seaice heat flux. SST (or Sea-ice surface temperature) is used to interpret measured turbulent heat flux. proprietary
KOPRI-KPDC-00000469_1 Zooplankton data in Amundsen Sea, Antarctic, 2012 AMD_KOPRI STAC Catalog 2010-12-27 2011-01-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244294736-AMD_KOPRI.umm_json The rapid melting of glaciers as well as the loss of sea ice in the Amundsen Sea makes it an ideal environmental setting for the investigation of the impacts of climate change in the Antarctic on the distribution and production of mesozooplankton. Mesozooplankton samples were collected with a Bongo net (mesh apertures 330 and 505 lm) at 15 selected stations. The net was towed twice vertically or obliquely within the upper 200 m of the water column. Tow speed and duration were about 1.5–2 knots and 15–20 min, respectively. The primary objectives were to describe the mesozooplankton community and to investigate the linkages between major environmental factors and the mesozooplankton community in the Amundsen Sea. proprietary
-KOPRI-KPDC-00000470_1 2011 Mooring Data, Antarctic ALL STAC Catalog 2010-12-31 2012-03-02 -117.72235, -73.271333, -114.9705, -72.402717 https://cmr.earthdata.nasa.gov/search/concepts/C2244294870-AMD_KOPRI.umm_json In order to understand the role of CDW (Circumpolar Deep Water) in controlling the hydrodynamics and related biochemical processes on the continental shelf of the Amundsen Sea, the oceanographic research was conducted from 2012 January 31 to March 20. During the 2012 Amundsen Sea cruise, a total of 2 moorings were successfully recovered. In order to identify the temporal and spatial distribution of CDW on the Amundsen shelf and estimate the heat transport and its effect on the melting of ice shelves by CDW intrusion. A total of the oceanographic investigation was conducted by using ship (ARAON) in 2012. proprietary
KOPRI-KPDC-00000470_1 2011 Mooring Data, Antarctic AMD_KOPRI STAC Catalog 2010-12-31 2012-03-02 -117.72235, -73.271333, -114.9705, -72.402717 https://cmr.earthdata.nasa.gov/search/concepts/C2244294870-AMD_KOPRI.umm_json In order to understand the role of CDW (Circumpolar Deep Water) in controlling the hydrodynamics and related biochemical processes on the continental shelf of the Amundsen Sea, the oceanographic research was conducted from 2012 January 31 to March 20. During the 2012 Amundsen Sea cruise, a total of 2 moorings were successfully recovered. In order to identify the temporal and spatial distribution of CDW on the Amundsen shelf and estimate the heat transport and its effect on the melting of ice shelves by CDW intrusion. A total of the oceanographic investigation was conducted by using ship (ARAON) in 2012. proprietary
+KOPRI-KPDC-00000470_1 2011 Mooring Data, Antarctic ALL STAC Catalog 2010-12-31 2012-03-02 -117.72235, -73.271333, -114.9705, -72.402717 https://cmr.earthdata.nasa.gov/search/concepts/C2244294870-AMD_KOPRI.umm_json In order to understand the role of CDW (Circumpolar Deep Water) in controlling the hydrodynamics and related biochemical processes on the continental shelf of the Amundsen Sea, the oceanographic research was conducted from 2012 January 31 to March 20. During the 2012 Amundsen Sea cruise, a total of 2 moorings were successfully recovered. In order to identify the temporal and spatial distribution of CDW on the Amundsen shelf and estimate the heat transport and its effect on the melting of ice shelves by CDW intrusion. A total of the oceanographic investigation was conducted by using ship (ARAON) in 2012. proprietary
KOPRI-KPDC-00000471_2 Estimation of POC and Biogenic silica export fluxes using 234Th/238U disequilibrium in the Amundsen sea, Antarctic (2012) AMD_KOPRI STAC Catalog 2012-02-10 2012-03-04 -133.988167, -75.087333, -101.759167, -71.580667 https://cmr.earthdata.nasa.gov/search/concepts/C2244305958-AMD_KOPRI.umm_json Seawaters in 14 water columns were collected during February and March 2012, and analyzed for total and dissolved 234Th, and particulate organic carbon and biogenic silica. 234Th activities were analyzed using a gas-flow proportional β-spectrometer manufactured by Risø National Laboratories (Roskilde, Denmark) following methods described in Buesseler et al. (2001). The export fluxes of particulate organic carbon (POC) play an important role in the transfer of carbon between the atmosphere and the ocean. Accurate estimates of POC export fluxes are critical for constraining models of the global carbon cycle. Over the past few decades, the radioisotope pair 238U and 234Th has been increasingly used to estimate POC export fluxes from the euphotic zone. This method is based on the uptake of 234Th onto biogenic particles in the euphotic zone and the subsequent sinking of particles into deep water. The POC export flux is determined by multiplying the depth-integrated 234Th sinking flux by the POC/234Th ratio on sinking particles. This study aims to estimate the POC export fluxes in the Amundsen Sea using 234Th/238U disequilibrium method. proprietary
KOPRI-KPDC-00000472_1 Marine sediment core samples from the western Arctic expedition in 2013 AMD_KOPRI STAC Catalog 2013-08-24 2013-09-25 -178.761503, 69.970997, -134.604662, 77.503838 https://cmr.earthdata.nasa.gov/search/concepts/C2244294915-AMD_KOPRI.umm_json Marine geology program is conducted during the 4th ARAON Arctic Expedition in 2013. Geological stations were selected based on the study objectives, and their locations were specified using multi-beam bathymetric mapping and sub-bottom profiles. Coring was carried out using several devices. To retrieve the sediment cores at selected geological and oceanographic stations we used different coring gears such as box corer, multiple corers and gravity corer. A box corer (BOX) (50x50x60cm) and a multiple corer (MUC) with 8 tubes were used to obtain surface sediments. For relatively long sediment cores, we used a gravity corer (GC) with 3 or 6-m long barrel. Once retrieved on deck, gravity cores were cut up in lengths of 1.5m and labeled. Overall goal of marine geology for the 4th ARAON Arctic cruise is to take new and undisturbed sediment cores from the selected research target areas including the East Siberian-Chukchi Sea and Beaufort Sea in the western Arctic Ocean. To achieve the study objectives we employed the following geological/geophysical methods: 1) coring seafloor sediment with a gravity corer for sediment composition and stratigraphy (up to ~5 m deep), 2) coring with a multiple corer/box corer for modern/recent seafloor processes. proprietary
KOPRI-KPDC-00000473_1 Araon-based Antarctic Peninsula expedition, 2013 AMD_KOPRI STAC Catalog 2013-04-04 2013-05-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244294929-AMD_KOPRI.umm_json We conducted Araon-based expedition on the western and eastern Antarctic Peninsula and build up ice-shelf monitoring system. Main coring equipment is gravity corer and after half-cut of sediment cores we measured MS, XRF from ITRAX core scanner on cruise. During the expedition, we have some chance to take new geophysical information at Bigo & Leroux bays and off Larsen C ice shelf. (1) to establish an monitoring system for ice shelf movements (2) to reconstruct the environmental changes caused by past climatic changes in the ice shelf area (West Antarctica). proprietary
KOPRI-KPDC-00000474_1 Primary productivity in the Amundsen Sea, 2012 AMD_KOPRI STAC Catalog 2012-02-10 2012-03-10 -137.183, -75.087, -101.759, -71.417 https://cmr.earthdata.nasa.gov/search/concepts/C2244294941-AMD_KOPRI.umm_json To estimate carbon and nitrogen uptake of phytoplankton at different locations, productivity experiments were executed by incubating phytoplankton in the incubators on the deck for 3-4 hours after stable isotopes (13C, 15NO3, and 15NH4) as tracers were inoculated into each bottle. Total 18 productivity experiments were completed during this cruise. At every CTD station, the productivity samples were collected by CTD rosette water samplers at 6 different light depths (100, 30, 12, 5 and 1%). To understand the spatial distribution of phytoplankton productivity and to assess effect of climate change on ocean ecosystem through studying ecological and physiological for phytoplankton in the Amundsen Sea, Antarctica proprietary
KOPRI-KPDC-00000475_1 Air-sea turbulent fluxes on the Arctic of 2010 (ARA01B) ALL STAC Catalog 2010-07-16 2010-08-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244294998-AMD_KOPRI.umm_json On board turbulent fluxes of CO2 and energy were measured during the cruise in the Chukchi Borderland in boreal summer of 2010. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer was used for the measurement. Motion sensor was added to the flux system to remove the effect of ship motion on the fluxes. Data were recorded on a data logger with sampling rate of 10 Hz. To better understanding the role of Chukchi sea in global climate change and to quantify air-sea flux of energy and green house gases by direct measurement of turbulent fluxes. proprietary
KOPRI-KPDC-00000475_1 Air-sea turbulent fluxes on the Arctic of 2010 (ARA01B) AMD_KOPRI STAC Catalog 2010-07-16 2010-08-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244294998-AMD_KOPRI.umm_json On board turbulent fluxes of CO2 and energy were measured during the cruise in the Chukchi Borderland in boreal summer of 2010. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer was used for the measurement. Motion sensor was added to the flux system to remove the effect of ship motion on the fluxes. Data were recorded on a data logger with sampling rate of 10 Hz. To better understanding the role of Chukchi sea in global climate change and to quantify air-sea flux of energy and green house gases by direct measurement of turbulent fluxes. proprietary
-KOPRI-KPDC-00000476_1 Air-sea turbulent fluxes on the Amundsen Sea of 2011 (ANA01C) ALL STAC Catalog 2010-12-21 2011-01-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295011-AMD_KOPRI.umm_json On board turbulent fluxes of CO2 and energy were measured during the cruise in the Amundsen Sea in summer of 2011. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer was used for the measurement. Motion sensor was added to the flux system to remove the effect of ship motion on the fluxes. Data were recorded on a data logger with sampling rate of 10 Hz. To better understanding the role of Amundsen sea in global climate change and to quantify air-sea flux of energy and green house gases by direct measurement of turbulent fluxes. proprietary
KOPRI-KPDC-00000476_1 Air-sea turbulent fluxes on the Amundsen Sea of 2011 (ANA01C) AMD_KOPRI STAC Catalog 2010-12-21 2011-01-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295011-AMD_KOPRI.umm_json On board turbulent fluxes of CO2 and energy were measured during the cruise in the Amundsen Sea in summer of 2011. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer was used for the measurement. Motion sensor was added to the flux system to remove the effect of ship motion on the fluxes. Data were recorded on a data logger with sampling rate of 10 Hz. To better understanding the role of Amundsen sea in global climate change and to quantify air-sea flux of energy and green house gases by direct measurement of turbulent fluxes. proprietary
+KOPRI-KPDC-00000476_1 Air-sea turbulent fluxes on the Amundsen Sea of 2011 (ANA01C) ALL STAC Catalog 2010-12-21 2011-01-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295011-AMD_KOPRI.umm_json On board turbulent fluxes of CO2 and energy were measured during the cruise in the Amundsen Sea in summer of 2011. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer was used for the measurement. Motion sensor was added to the flux system to remove the effect of ship motion on the fluxes. Data were recorded on a data logger with sampling rate of 10 Hz. To better understanding the role of Amundsen sea in global climate change and to quantify air-sea flux of energy and green house gases by direct measurement of turbulent fluxes. proprietary
KOPRI-KPDC-00000477_2 O2/Ar of surface water measured using an equilibrator inlet mass spectrometer (2010.12-2011.01) AMD_KOPRI STAC Catalog 2010-12-26 2011-01-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244307200-AMD_KOPRI.umm_json O2/Ar in seawater, pumped from the intake at 7 m below sea level, was measured using an equilibrator inlet mass spectrometer. The mass spectrometer measured a series of dissolved gases including O2 and Ar every 10 seconds. The data contain ion currents of those gases and total pressure in the mass spectrometer. Net community production (NCP), defined as the difference between autotrophic photosynthesis and (autrophic and heterotrophic) respiration, produces O2 proportional to the amount of net carbon. By measuring chemically and biologically inert Ar together with O2, it is possible to isolate O2 variation by physical processes (e.g., air temperature and pressure change and mixing of water masses) and deduce O2 variation by biological processes. To determine the net community (oxygen) production underway, we measured continuous O2/ Ar measurement system using an equilibrator inlet mass spectrometer. proprietary
KOPRI-KPDC-00000478_1 EK60 Data in Amundsen Sea, Antarctic in 2014 AMD_KOPRI STAC Catalog 2014-01-08 2014-01-12 -119, -74.5, -111, -73 https://cmr.earthdata.nasa.gov/search/concepts/C2244295035-AMD_KOPRI.umm_json Acoustic survey was conducted to understand the variability of krill distribution along two representative ice shelves in the Amundsen Sea: Dotson ice shelf and Getz ice shelf.Acoustic data were collected from surface to 500-m depths using a scientific echo sounder (EK60, Simrad) configured with down-looking 38, 120, and 200 kHz split-beam transducers mounted in the hull of IBRV Araon. - To identify the horizontal and vertical distribution of krill from Dotson ice shelf to Getz ice shelf. - To reveal the main forcing that affects the variability of krill distribution, proprietary
KOPRI-KPDC-00000479_1 EK60 Data surrounding Chukchi Sea of Arctic in 2014 AMD_KOPRI STAC Catalog 2014-07-31 2014-08-24 -180, 65.5, -150, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2244295079-AMD_KOPRI.umm_json Acoustic survey was conducted to observe the distribution and density of zooplankton in the Chukchi sea of Arctic from July to August in 2014. Acoustic data were collected from surface to 400 m using split-beam transducers of 38, 120 and 200 kHZ (Simard EK60 scientific echosounder) during the survey. Observation of the spatial distribution (horizontal and vertical) and density of zooplankton using the acoustic system to understand their variability around Chukchi sea, Arctic. proprietary
@@ -8830,14 +8831,14 @@ KOPRI-KPDC-00000519_1 RS15_LC47 AMD_KOPRI STAC Catalog 2015-09-30 2015-09-30 -18
KOPRI-KPDC-00000520_1 RS15_LC48 AMD_KOPRI STAC Catalog 2015-09-30 2015-09-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244300681-AMD_KOPRI.umm_json Longcore drilling for exploration of the Ross Sea in Antarctica in 2015 proprietary
KOPRI-KPDC-00000521_1 RS15_LC62 AMD_KOPRI STAC Catalog 2015-09-30 2015-09-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244300699-AMD_KOPRI.umm_json Longcore drilling for exploration of the Ross Sea in Antarctica in 2015 proprietary
KOPRI-KPDC-00000522_1 Soil and Fresh/Sea water samples from Barton Peninsular collected in 2014-2015 AMD_KOPRI STAC Catalog 2015-01-18 2015-02-18 -58.76666, -62.21666, -58.76666, -62.21666 https://cmr.earthdata.nasa.gov/search/concepts/C2244300714-AMD_KOPRI.umm_json Analysis of microbial community structure and diversity in soil and water samples from Barton Peninsular in Antarctica Investigation to the terrestrial biodiversity in Barton Peninsular for the monitoring by environment change proprietary
-KOPRI-KPDC-00000523_2 2015 ARAON Arctic geological expedition: Box Core(BC) sediment data ALL STAC Catalog 2015-08-27 2015-09-06 178.870742, 73.620362, 176.540425, 76.602687 https://cmr.earthdata.nasa.gov/search/concepts/C2244307210-AMD_KOPRI.umm_json A total of 7 geological stations were chosen to obtain box core sediments based on previously collected data for geophysical records, providing age control, and investigating paleoceanographic environments. Retrieved cores were described, photographed, and logged on the Geotek Multi-Sensor Core Logger. Detailed analyses such as stable isotopes of planktonic and benthic foraminifers, organic geochemistr0y, biogenic opal contents, microfossils and biomarkers will be performed after this expedition. The overall objective of coring using the box core during cruise ARA06C was to obtain sediment records to constrain, and thus better understand, the timing and chronology of marine glaciations along the East-Siberian and Chukchi continental margins. proprietary
KOPRI-KPDC-00000523_2 2015 ARAON Arctic geological expedition: Box Core(BC) sediment data AMD_KOPRI STAC Catalog 2015-08-27 2015-09-06 178.870742, 73.620362, 176.540425, 76.602687 https://cmr.earthdata.nasa.gov/search/concepts/C2244307210-AMD_KOPRI.umm_json A total of 7 geological stations were chosen to obtain box core sediments based on previously collected data for geophysical records, providing age control, and investigating paleoceanographic environments. Retrieved cores were described, photographed, and logged on the Geotek Multi-Sensor Core Logger. Detailed analyses such as stable isotopes of planktonic and benthic foraminifers, organic geochemistr0y, biogenic opal contents, microfossils and biomarkers will be performed after this expedition. The overall objective of coring using the box core during cruise ARA06C was to obtain sediment records to constrain, and thus better understand, the timing and chronology of marine glaciations along the East-Siberian and Chukchi continental margins. proprietary
+KOPRI-KPDC-00000523_2 2015 ARAON Arctic geological expedition: Box Core(BC) sediment data ALL STAC Catalog 2015-08-27 2015-09-06 178.870742, 73.620362, 176.540425, 76.602687 https://cmr.earthdata.nasa.gov/search/concepts/C2244307210-AMD_KOPRI.umm_json A total of 7 geological stations were chosen to obtain box core sediments based on previously collected data for geophysical records, providing age control, and investigating paleoceanographic environments. Retrieved cores were described, photographed, and logged on the Geotek Multi-Sensor Core Logger. Detailed analyses such as stable isotopes of planktonic and benthic foraminifers, organic geochemistr0y, biogenic opal contents, microfossils and biomarkers will be performed after this expedition. The overall objective of coring using the box core during cruise ARA06C was to obtain sediment records to constrain, and thus better understand, the timing and chronology of marine glaciations along the East-Siberian and Chukchi continental margins. proprietary
KOPRI-KPDC-00000524_2 2015 ARAON Arctic geological expedition: Multi Core(MUC) sediment data ALL STAC Catalog 2015-08-27 2015-09-06 178.870742, 73.620362, -161.168018, 76.602687 https://cmr.earthdata.nasa.gov/search/concepts/C2244307215-AMD_KOPRI.umm_json A total of 6 geological stations were chosen to obtain multi core sediments based on previously collected data for geophysical records, providing age control, and investigating paleoceanographic environments. Retrieved cores were described, photographed, and logged on the Geotek Multi-Sensor Core Logger. Detailed analyses such as stable isotopes of planktonic and benthic foraminifers, organic geochemistry, biogenic opal contents, microfossils and biomarkers will be performed after this expedition. The overall objective of coring using the multi core during cruise ARA06C was to obtain sediment records to constrain, and thus better understand, the timing and chronology of marine glaciations along the East-Siberian and Chukchi continental margins. proprietary
KOPRI-KPDC-00000524_2 2015 ARAON Arctic geological expedition: Multi Core(MUC) sediment data AMD_KOPRI STAC Catalog 2015-08-27 2015-09-06 178.870742, 73.620362, -161.168018, 76.602687 https://cmr.earthdata.nasa.gov/search/concepts/C2244307215-AMD_KOPRI.umm_json A total of 6 geological stations were chosen to obtain multi core sediments based on previously collected data for geophysical records, providing age control, and investigating paleoceanographic environments. Retrieved cores were described, photographed, and logged on the Geotek Multi-Sensor Core Logger. Detailed analyses such as stable isotopes of planktonic and benthic foraminifers, organic geochemistry, biogenic opal contents, microfossils and biomarkers will be performed after this expedition. The overall objective of coring using the multi core during cruise ARA06C was to obtain sediment records to constrain, and thus better understand, the timing and chronology of marine glaciations along the East-Siberian and Chukchi continental margins. proprietary
-KOPRI-KPDC-00000525_2 2015 ARAON Arctic geological expedition: Gravity Core(GC) sediment data ALL STAC Catalog 2015-08-27 2015-09-06 -166.51978, 73.620935, -166.432032, 73.634698 https://cmr.earthdata.nasa.gov/search/concepts/C2244307223-AMD_KOPRI.umm_json A total of 3 geological stations were chosen to obtain box core sediments based on previously collected data for geophysical records, providing age control, and investigating paleoceanographic environments. Retrieved cores were described, photographed, and logged on the Geotek Multi-Sensor Core Logger. Detailed analyses such as stable isotopes of planktonic and benthic foraminifers, organic geochemistr0y, biogenic opal contents, microfossils and biomarkers will be performed after this expedition. The overall objective of coring using the box core during cruise ARA06C was to obtain sediment records to constrain, and thus better understand, the timing and chronology of marine glaciations along the East-Siberian and Chukchi continental margins. proprietary
KOPRI-KPDC-00000525_2 2015 ARAON Arctic geological expedition: Gravity Core(GC) sediment data AMD_KOPRI STAC Catalog 2015-08-27 2015-09-06 -166.51978, 73.620935, -166.432032, 73.634698 https://cmr.earthdata.nasa.gov/search/concepts/C2244307223-AMD_KOPRI.umm_json A total of 3 geological stations were chosen to obtain box core sediments based on previously collected data for geophysical records, providing age control, and investigating paleoceanographic environments. Retrieved cores were described, photographed, and logged on the Geotek Multi-Sensor Core Logger. Detailed analyses such as stable isotopes of planktonic and benthic foraminifers, organic geochemistr0y, biogenic opal contents, microfossils and biomarkers will be performed after this expedition. The overall objective of coring using the box core during cruise ARA06C was to obtain sediment records to constrain, and thus better understand, the timing and chronology of marine glaciations along the East-Siberian and Chukchi continental margins. proprietary
-KOPRI-KPDC-00000526_2 2015 ARAON Arctic geological expedition: Jumbo piston core (JPC) sediment data AMD_KOPRI STAC Catalog 2015-08-27 2015-09-06 178.734385, 73.620362, -161.168018, 76.602687 https://cmr.earthdata.nasa.gov/search/concepts/C2244307203-AMD_KOPRI.umm_json A total of 4 geological stations were chosen to obtain Jumbo piston core sediments based on previously collected data for geophysical records, providing age control, and investigating paleoceanographic environments. Retrieved cores were described, photographed, and logged on the Geotek Multi-Sensor Core Logger. Detailed analyses such as stable isotopes of planktonic and benthic foraminifers, organic geochemistr0y, biogenic opal contents, microfossils and biomarkers will be performed after this expedition. The overall objective of coring using the Jumbo Piston Corer during cruise ARA06C was to obtain longer records of sediments to constrain, and thus better understand, the timing and chronology of marine glaciations along the East-Siberian and Chukchi continental margins. proprietary
+KOPRI-KPDC-00000525_2 2015 ARAON Arctic geological expedition: Gravity Core(GC) sediment data ALL STAC Catalog 2015-08-27 2015-09-06 -166.51978, 73.620935, -166.432032, 73.634698 https://cmr.earthdata.nasa.gov/search/concepts/C2244307223-AMD_KOPRI.umm_json A total of 3 geological stations were chosen to obtain box core sediments based on previously collected data for geophysical records, providing age control, and investigating paleoceanographic environments. Retrieved cores were described, photographed, and logged on the Geotek Multi-Sensor Core Logger. Detailed analyses such as stable isotopes of planktonic and benthic foraminifers, organic geochemistr0y, biogenic opal contents, microfossils and biomarkers will be performed after this expedition. The overall objective of coring using the box core during cruise ARA06C was to obtain sediment records to constrain, and thus better understand, the timing and chronology of marine glaciations along the East-Siberian and Chukchi continental margins. proprietary
KOPRI-KPDC-00000526_2 2015 ARAON Arctic geological expedition: Jumbo piston core (JPC) sediment data ALL STAC Catalog 2015-08-27 2015-09-06 178.734385, 73.620362, -161.168018, 76.602687 https://cmr.earthdata.nasa.gov/search/concepts/C2244307203-AMD_KOPRI.umm_json A total of 4 geological stations were chosen to obtain Jumbo piston core sediments based on previously collected data for geophysical records, providing age control, and investigating paleoceanographic environments. Retrieved cores were described, photographed, and logged on the Geotek Multi-Sensor Core Logger. Detailed analyses such as stable isotopes of planktonic and benthic foraminifers, organic geochemistr0y, biogenic opal contents, microfossils and biomarkers will be performed after this expedition. The overall objective of coring using the Jumbo Piston Corer during cruise ARA06C was to obtain longer records of sediments to constrain, and thus better understand, the timing and chronology of marine glaciations along the East-Siberian and Chukchi continental margins. proprietary
+KOPRI-KPDC-00000526_2 2015 ARAON Arctic geological expedition: Jumbo piston core (JPC) sediment data AMD_KOPRI STAC Catalog 2015-08-27 2015-09-06 178.734385, 73.620362, -161.168018, 76.602687 https://cmr.earthdata.nasa.gov/search/concepts/C2244307203-AMD_KOPRI.umm_json A total of 4 geological stations were chosen to obtain Jumbo piston core sediments based on previously collected data for geophysical records, providing age control, and investigating paleoceanographic environments. Retrieved cores were described, photographed, and logged on the Geotek Multi-Sensor Core Logger. Detailed analyses such as stable isotopes of planktonic and benthic foraminifers, organic geochemistr0y, biogenic opal contents, microfossils and biomarkers will be performed after this expedition. The overall objective of coring using the Jumbo Piston Corer during cruise ARA06C was to obtain longer records of sediments to constrain, and thus better understand, the timing and chronology of marine glaciations along the East-Siberian and Chukchi continental margins. proprietary
KOPRI-KPDC-00000527_1 Shallow ice core drilled at Styx glacier, Antarctic, in 2015 AMD_KOPRI STAC Catalog 2015-10-06 2015-10-06 163.687, -73.851667, 163.687, -73.851667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300726-AMD_KOPRI.umm_json Shallow ice cores drilled from the Styx glacier about 85 km north of the Jang Bogo station in the 2014-2015 summer season, and a 210.5 m long ice core was taken. The age at the bottom of the ice core was estimated to be 1.36 ka based on the depth-density profile and on the temperature at 15 m depth. Reconstruction of past climate and environmental change such as Ross sea ice extent and greenhouse gases proprietary
KOPRI-KPDC-00000528_1 Structural basis for the ligand-binding specificity of fatty acid-binding proteins (pFABP4 and pFABP5) in gentoo penguin AMD_KOPRI STAC Catalog 2015-10-06 2015-10-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244300787-AMD_KOPRI.umm_json The fatty acid-binding proteins (FABPs) are involved in transporting hydrophobic fatty acids between various aqueous compartments of the cell by direct binding of ligands inside their β-barrel cavities. Here, we report the crystal structures of ligand-unbound pFABP4, linoleate-bound pFABP4 and palmitate-bound pFABP5 from the gentoo penguin (Pygoscelis papua) at 2.1, 2.2, and 2.3 Å resolutions, respectively. The pFABP4 and pFABP5 proteins comprise a canonical β-barrel structure with two short α-helices forming a cap region and fatty acid ligand binds in the hydrophobic cavity inside the β-barrel structure. The two linoleate-bound pFABP4 and palmitate-bound pFABP5 structures shows a different ligand-binding mode and a unique ligand-binding pocket caused by several sequence differences (A76/L78, T30/M32, underlining used to indicate pFABP4 residues). Structural comparison also shows a significantly different conformation change in the β3-β4 loop region (residues 57-62) of pFABP5 as well as flipped Phe60 residue (the corresponding residue in pFABP4 is Phe58). Moreover, a ligand-binding study using fluorophore displacement assays indicated that pFABP4 has a relatively strong affinity to linoleate compared with pFABP5. In contrast, pFABP5 clearly exhibits higher affinity for the palmitate compared with pFABP4. Conclusively, our high-resolution structures and ligand-binding study provide useful insights into the ligand-binding preferences of pFABPs based on key protein-ligand interactions. To investigate mechanism of fatty acid transfer, we have carried out structural studies. As the first step toward its structural elucidation, we report the results of preliminary X-ray crystallographic experiments with pFABP4, pFABP4-Linoleate and pFABP5-Palmitate. proprietary
KOPRI-KPDC-00000529_1 Crystal structure of UbiX, an aromatic acid decarboxylase from the psychrophilic bacterium Colwellia psychrerythraea that undergoes FMN-induced conformational changes AMD_KOPRI STAC Catalog 2015-10-06 2015-10-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244300802-AMD_KOPRI.umm_json The ubiX gene of Colwellia psychrerythraea strain 34H encodes a 3-octaprenyl-4-hydroxybenzoate carboxylase (CpsUbiX, UniProtKB code: Q489U8) that is involved in the third step of the ubiquinone biosynthesis pathway and uses flavin mononucleotide (FMN) as a cofactor. Here, we report the crystal structures of two forms of CpsUbiX: an FMN-bound wild type form and an FMN-unbound V47S mutant form. CpsUbiX is a dodecameric enzyme, and each monomer possesses a typical Rossmann-fold structure. However, to our knowledge, the architecture of the FMN-binding domain formed by three neighboring subunits described here is novel and unique to UbiX. The highly conserved Gly15, Ser41, Val47, and Tyr171 residues play important roles in FMN binding. Structural comparison of the FMN-bound wild type form with the FMN-free form revealed a significant conformational difference in the C-terminal loop region (comprising residues 170–177 and 195–206). Subsequent computational modeling and liposome binding assay both suggested that the conformational change observed in the C-terminal loops upon FMN binding plays an important role in substrate binding. The crystal structures presented in this work provide structural framework and insights into the catalytic mechanism of CpsUbiX. To investigate FMN binding mechanism, we have carried out structural studies. As the first step toward its structural elucidation, we report the results of preliminary X-ray crystallographic experiments with CpsUbiX with or without (V47S) cofactor FMN. proprietary
@@ -8879,8 +8880,8 @@ KOPRI-KPDC-00000564_1 Upper atmospheric temperature data obtained from OH emissi
KOPRI-KPDC-00000565_1 Upper atmospheric temperature data obtained from OH and O2 emissions at King Sejong Station, Antarctica at 2013 AMD_KOPRI STAC Catalog 2013-02-27 2013-10-31 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244299516-AMD_KOPRI.umm_json Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region Long-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region proprietary
KOPRI-KPDC-00000566_1 Upper atmospheric temperature data obtained from OH and O2 emissions at King Sejong Station, Antarctica at 2015 AMD_KOPRI STAC Catalog 2015-02-16 2015-09-30 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244299874-AMD_KOPRI.umm_json Upper atmospheric temperature measurements around 87~95 km altitude in the mesosphere and lower thermosphere (MLT) region Long-term monitoring of the upper atmospheric temperature changes over the southern high latitude region for the study of thermal structure and dynamics in the MLT region proprietary
KOPRI-KPDC-00000567_1 Neutral winds and temperature data in the MLT region at King Sejong Station, Antarctica at 2015 AMD_KOPRI STAC Catalog 2015-01-01 2015-09-30 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244300211-AMD_KOPRI.umm_json Neutral winds and temperature measurements around 70~110 km altitude obtained from the meteor observations at King Sejong Station, Antarctica Long-term monitoring of the neutral winds and temperature changes over the southern high-latitude region for the study of upper atmospheric thermal structure and dynamics in the MLT region proprietary
-KOPRI-KPDC-00000568_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2013 ALL STAC Catalog 2013-03-01 2013-10-31 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244300442-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high-latitude MLT region proprietary
KOPRI-KPDC-00000568_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2013 AMD_KOPRI STAC Catalog 2013-03-01 2013-10-31 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244300442-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high-latitude MLT region proprietary
+KOPRI-KPDC-00000568_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2013 ALL STAC Catalog 2013-03-01 2013-10-31 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244300442-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high-latitude MLT region proprietary
KOPRI-KPDC-00000569_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2015 AMD_KOPRI STAC Catalog 2015-02-16 2015-09-30 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244300546-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high-latitude MLT region proprietary
KOPRI-KPDC-00000569_1 All-Sky image data of the airglow emissions at King Sejong Station, Antarctica at 2015 ALL STAC Catalog 2015-02-16 2015-09-30 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244300546-AMD_KOPRI.umm_json All-Sky image data of OI 557.7nm, OI 630.0nm, Na 589.7nm, and OH Meinel band airglow emissions obtained at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high-latitude MLT region proprietary
KOPRI-KPDC-00000570_1 Neutral wind data from FPI installed at Jang Bogo Station, Antarctica at 2014 AMD_KOPRI STAC Catalog 2014-03-10 2014-10-11 164.14, -74.37, 164.14, -74.37 https://cmr.earthdata.nasa.gov/search/concepts/C2244300605-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, 250km measured from FPI instrument at Jang Bogo Station, Antarctica Study of the atmospheric wave activities in MLT and thermosphere regions over the southern high-latitude proprietary
@@ -8902,14 +8903,14 @@ KOPRI-KPDC-00000585_1 Soil moisture and temperature data collected from climate
KOPRI-KPDC-00000586_1 Permafrost core samples in Council, Alaska, USA in 2014 AMD_KOPRI STAC Catalog 2015-12-21 2015-12-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244301145-AMD_KOPRI.umm_json Nine permafrost core samples were collected in Council, Alaska. Three sampling sites were determined by soil resistivity test, and three replicates were collected in each site. Soil core was about 1.1 – 1.5 m in length. Soil microbial community and physical and chemical properties will be analyzed. To investigate the differences of microbial community structure and soil physical and chemical properties 1) between active and permafrost layers and 2) among soils showing different resistivity. proprietary
KOPRI-KPDC-00000587_1 Eddy covariance data of Alaska permafrost site in 2014 AMD_KOPRI STAC Catalog 2014-04-01 2014-11-01 -163.705333, 64.843333, -163.705333, 64.843333 https://cmr.earthdata.nasa.gov/search/concepts/C2244295657-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured during summertime in 2014 at Council, Alaska. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz. To monitor and understand energy/water/green-house-gas flux over permafrost region proprietary
KOPRI-KPDC-00000588_1 Methane flux data of Alaska permafrost site in 2014 AMD_KOPRI STAC Catalog 2014-07-10 2014-07-23 -163.705333, 64.843333, -163.705333, 64.843333 https://cmr.earthdata.nasa.gov/search/concepts/C2244295661-AMD_KOPRI.umm_json High-frequency methane concentration was measured in July 2014 at Council, Alaska. Along with atmospheric turbulence data from 3-D sonic anemometer, methane flux was obtained at 30-minute interval. To monitor and understand methane flux over permafrost region proprietary
-KOPRI-KPDC-00000589_1 Air temperature and humidity in Cambridge Bay, Canada in 2012 ALL STAC Catalog 2012-07-11 2013-08-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295675-AMD_KOPRI.umm_json Air temperature and humidity at 25 cm above the surface from the climate manipulation plots (increasing temperature and precipitation) To monitor the changes in micro-climate properties in air by increasing temperature and precipitation proprietary
KOPRI-KPDC-00000589_1 Air temperature and humidity in Cambridge Bay, Canada in 2012 AMD_KOPRI STAC Catalog 2012-07-11 2013-08-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295675-AMD_KOPRI.umm_json Air temperature and humidity at 25 cm above the surface from the climate manipulation plots (increasing temperature and precipitation) To monitor the changes in micro-climate properties in air by increasing temperature and precipitation proprietary
+KOPRI-KPDC-00000589_1 Air temperature and humidity in Cambridge Bay, Canada in 2012 ALL STAC Catalog 2012-07-11 2013-08-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295675-AMD_KOPRI.umm_json Air temperature and humidity at 25 cm above the surface from the climate manipulation plots (increasing temperature and precipitation) To monitor the changes in micro-climate properties in air by increasing temperature and precipitation proprietary
KOPRI-KPDC-00000590_1 Soil samples after one year of climate manipulation AMD_KOPRI STAC Catalog 2013-07-31 2013-08-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295694-AMD_KOPRI.umm_json Soil samples from climate manipulation plots after one year of warming and increasing precipitation To determine the effects of climate change on soil properties and microbial diversity proprietary
KOPRI-KPDC-00000591_1 Soil samples after three years of climate manipulation AMD_KOPRI STAC Catalog 2015-07-29 2015-08-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295728-AMD_KOPRI.umm_json Soil samples from climate manipulation plots after three years of warming and increasing precipitation To determine the effects of climate change on soil properties and microbial structure and function proprietary
KOPRI-KPDC-00000592_1 Air temperature and humidity in Cambridge Bay, Canada in 2013 ALL STAC Catalog 2013-08-01 2014-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295766-AMD_KOPRI.umm_json Air temperature and humidity at 25 cm above the surface from the climate manipulation plots (increasing temperature and precipitation in 2013 To monitor the changes in micro-climate properties in air by increasing temperature and precipitation proprietary
KOPRI-KPDC-00000592_1 Air temperature and humidity in Cambridge Bay, Canada in 2013 AMD_KOPRI STAC Catalog 2013-08-01 2014-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295766-AMD_KOPRI.umm_json Air temperature and humidity at 25 cm above the surface from the climate manipulation plots (increasing temperature and precipitation in 2013 To monitor the changes in micro-climate properties in air by increasing temperature and precipitation proprietary
-KOPRI-KPDC-00000593_1 Air temperature and humidity in Cambridge Bay, Canada in 2014 AMD_KOPRI STAC Catalog 2014-06-01 2015-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244296042-AMD_KOPRI.umm_json Air temperature and humidity at 25 cm above the surface from the climate manipulation plots (increasing temperature and precipitation) in 2014 To monitor the changes in micro-climate properties in air by increasing temperature and precipitation proprietary
KOPRI-KPDC-00000593_1 Air temperature and humidity in Cambridge Bay, Canada in 2014 ALL STAC Catalog 2014-06-01 2015-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244296042-AMD_KOPRI.umm_json Air temperature and humidity at 25 cm above the surface from the climate manipulation plots (increasing temperature and precipitation) in 2014 To monitor the changes in micro-climate properties in air by increasing temperature and precipitation proprietary
+KOPRI-KPDC-00000593_1 Air temperature and humidity in Cambridge Bay, Canada in 2014 AMD_KOPRI STAC Catalog 2014-06-01 2015-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244296042-AMD_KOPRI.umm_json Air temperature and humidity at 25 cm above the surface from the climate manipulation plots (increasing temperature and precipitation) in 2014 To monitor the changes in micro-climate properties in air by increasing temperature and precipitation proprietary
KOPRI-KPDC-00000594_1 Soil moisture and temperature data collected from climate manipulation plots in Cambridge Bay, Canada in 2013 AMD_KOPRI STAC Catalog 2013-08-01 2014-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244296351-AMD_KOPRI.umm_json Soil volumetric moisture content and temperature for 5 cm depth from climate manipulation (combination of warming and precipitation) plots in 2013 To monitor the changes in micro-climate properties in soil by increasing temperature by open top chambers and increasing precipitation proprietary
KOPRI-KPDC-00000595_1 Soil moisture and temperature data collected from climate manipulation plots in Cambridge Bay, Canada in 2014 AMD_KOPRI STAC Catalog 2014-06-01 2015-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244296625-AMD_KOPRI.umm_json Soil moisture and temperature data collected from climate manipulation plots in Cambridge Bay, Canada in 2014 proprietary
KOPRI-KPDC-00000596_1 Fossil specimens of Northern Victoria Land, 2014-2015 season AMD_KOPRI STAC Catalog 2015-12-30 2015-12-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244296805-AMD_KOPRI.umm_json This entry is for the fossil specimens of Northern Victoria Land (NVL), Antarctica collected in 2014-15 austral summer season. The collection includes trilobites of the Lower Paleozoic Bowers Supergroup and plant fossils of the Beacon Supergroup. Information from the fossils will be helpful for understanding geological processes and paleoenvironments of the Northern Victoria Land. proprietary
@@ -8936,8 +8937,8 @@ KOPRI-KPDC-00000616_1 Benthos image data from transect line, coastal of Jang Bog
KOPRI-KPDC-00000617_1 Black Carbon data at Jang Bogo station, 2015 AMD_KOPRI STAC Catalog 2015-02-14 164.228333, -74.623333, 164.228333, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244298913-AMD_KOPRI.umm_json The aethalometer is used to measure atmospheric black carbon concentration every 5 minute over Jang Bogo station. Monitoring of Black Carbon concentration over Jang Bogo station proprietary
KOPRI-KPDC-00000618_1 Soil and Fresh/Sea water samples from Barton Peninsular collected in 2015-2016 AMD_KOPRI STAC Catalog 2016-01-18 2016-02-21 -58.80624, -62.24449, -58.69884, -62.20679 https://cmr.earthdata.nasa.gov/search/concepts/C2244299282-AMD_KOPRI.umm_json Analysis of microbial community structure and diversity in soil and fresh/sea water samples from Barton Peninsular in Antarctica Investigation to the terrestrial biodiversity in Barton Peninsular for the monitoring by environment change proprietary
KOPRI-KPDC-00000619_1 Environmental data about King George Islands collected in 2016 AMD_KOPRI STAC Catalog 2015-01-31 2015-02-21 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244299653-AMD_KOPRI.umm_json Microclimate data from King George Islands collected in 2016. Investigate relationship between biota proprietary
-KOPRI-KPDC-00000620_1 2015-2016 JBS_micro-climate data_HOBO_soil temp.,PAR,air temp.,relative humidity AMD_KOPRI STAC Catalog 2015-02-09 2015-02-13 164.191389, -74.632806, 164.229972, -74.613 https://cmr.earthdata.nasa.gov/search/concepts/C2244300021-AMD_KOPRI.umm_json Micro-climate data set from The Jang Bogo Station in Terra Nova Bay collected during 1 year, 2015 proprietary
KOPRI-KPDC-00000620_1 2015-2016 JBS_micro-climate data_HOBO_soil temp.,PAR,air temp.,relative humidity ALL STAC Catalog 2015-02-09 2015-02-13 164.191389, -74.632806, 164.229972, -74.613 https://cmr.earthdata.nasa.gov/search/concepts/C2244300021-AMD_KOPRI.umm_json Micro-climate data set from The Jang Bogo Station in Terra Nova Bay collected during 1 year, 2015 proprietary
+KOPRI-KPDC-00000620_1 2015-2016 JBS_micro-climate data_HOBO_soil temp.,PAR,air temp.,relative humidity AMD_KOPRI STAC Catalog 2015-02-09 2015-02-13 164.191389, -74.632806, 164.229972, -74.613 https://cmr.earthdata.nasa.gov/search/concepts/C2244300021-AMD_KOPRI.umm_json Micro-climate data set from The Jang Bogo Station in Terra Nova Bay collected during 1 year, 2015 proprietary
KOPRI-KPDC-00000621_1 Soil and Fresh water samples of the Antarctic Jang Bogo Station from Terra Nova Bay collected in 2016 AMD_KOPRI STAC Catalog 2016-01-07 2016-02-21 164.192056, -74.633361, 164.23725, -74.612056 https://cmr.earthdata.nasa.gov/search/concepts/C2244300323-AMD_KOPRI.umm_json Analysis of microbial community structure and diversity in soil and water samples of the Antarctic Jang Bogo Station from Terra Nova Bay in Antarctica Investigation to the terrestrial biodiversity in Terra Nova Bay for the monitoring by environment change proprietary
KOPRI-KPDC-00000622_1 Sampling activity for identification between biotic (ciliate) and abiotic data from Barton Peninsular in Antarctica during the summer season in 2015/2016. AMD_KOPRI STAC Catalog 2015-12-04 2015-12-18 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300508-AMD_KOPRI.umm_json Identification of ciliate biota and environmental data of habitats from Antarctica (Barton Peninsular) Identification of the relationship between biotic sample and abiotic data proprietary
KOPRI-KPDC-00000623_1 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2015 ALL STAC Catalog 2015-03-01 2016-02-01 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244300569-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2015 Long term monitoring proprietary
@@ -9043,8 +9044,8 @@ KOPRI-KPDC-00000721_1 Lichen samples from South Shetland Islands collected in 20
KOPRI-KPDC-00000722_1 Lichen samples from Punta Arenas in Chile collected in 2014 AMD_KOPRI STAC Catalog 2014-10-08 2014-10-08 -71.416667, -53.6, -71.416667, -53.6 https://cmr.earthdata.nasa.gov/search/concepts/C2244295591-AMD_KOPRI.umm_json Lichen samples from Chile collected in 2014. Locality, habitat information for 165 lichen samples Investigation to diversity, morphology and phylogeography in lichen proprietary
KOPRI-KPDC-00000723_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2012 ALL STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295614-AMD_KOPRI.umm_json Yearly air temperature data from Barton Peninsular collected in 2012 Long term monitoring proprietary
KOPRI-KPDC-00000723_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2012 AMD_KOPRI STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295614-AMD_KOPRI.umm_json Yearly air temperature data from Barton Peninsular collected in 2012 Long term monitoring proprietary
-KOPRI-KPDC-00000724_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2013 ALL STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295626-AMD_KOPRI.umm_json Yearly air temperauter and relative humidity data from Barton Peninsular collected in 2013 Long term monitoring proprietary
KOPRI-KPDC-00000724_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2013 AMD_KOPRI STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295626-AMD_KOPRI.umm_json Yearly air temperauter and relative humidity data from Barton Peninsular collected in 2013 Long term monitoring proprietary
+KOPRI-KPDC-00000724_1 Air temperature and relative humidity data from Barton Peninsular in South Shetland Islands collected in 2013 ALL STAC Catalog 2014-10-08 2014-10-08 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295626-AMD_KOPRI.umm_json Yearly air temperauter and relative humidity data from Barton Peninsular collected in 2013 Long term monitoring proprietary
KOPRI-KPDC-00000725_1 Water isotope composition in a GV7 3-m snow pit (2013-2014) AMD_KOPRI STAC Catalog 2014-10-10 2014-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295745-AMD_KOPRI.umm_json A 3 m snow pit was collected at GV7 (Antarctica) in the 2013-2014 summer season. Its water isotope composition (dD, d18O) was determined using cavity ringdown spectroscopy (PICARRO). To detect annual (seasonal) layering of snowpack. proprietary
KOPRI-KPDC-00000726_1 NEEM project_ice core AMD_KOPRI STAC Catalog 2014-10-10 2014-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295831-AMD_KOPRI.umm_json We obtained ice cores after participating the North Greenland Eemian Ice Drilling program. We reconstruct the high-resolution ice record of a shift of mineral dust sources in response to climate transition between the Last Glacial Maximum(~25,000 yr BP) and Holocene(8,000 yr BP) by analyzing trace elements including rare earth elements from a Greenland NEEM ice core. proprietary
KOPRI-KPDC-00000727_1 ARA05C BC AMD_KOPRI STAC Catalog 2014-10-10 2014-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244296153-AMD_KOPRI.umm_json ARA05C BC proprietary
@@ -9057,8 +9058,8 @@ KOPRI-KPDC-00000733_1 ARA04B/C SP AMD_KOPRI STAC Catalog 2014-10-14 2014-10-14 -
KOPRI-KPDC-00000734_1 ARA02B X-ray AMD_KOPRI STAC Catalog 2014-10-14 2014-10-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244299281-AMD_KOPRI.umm_json Collect column sediments using acrylic plates 1cm long and 30cm deep, work to see clearly sedimentary environment of X-rayed data core. proprietary
KOPRI-KPDC-00000735_1 Expedition ARA05C funded by PE14061 AMD_KOPRI STAC Catalog 2014-08-30 2014-09-10 -139.6567, 69.6114, -133.5922, 71.2306 https://cmr.earthdata.nasa.gov/search/concepts/C2244299682-AMD_KOPRI.umm_json ARA05C Cruise report Summary Y. K. Jin, R. Gwiazda, and S. Dallimore Research activities conducted and preliminary findings The Expedition ARA05C was a highly multidisciplinary undertaking in the Beaufort Sea, carried out in an international collaboration between the Korea Polar Research Institute (KOPRI Korea), the Geological Survey of Canada (GSC), the Monterey Bay Aquarium Research Institute (MBARI, USA), the Department of Fisheries and Ocean (DFO, Canada) and Bremen University (BARUM, Germany). During the ARA05C expedition in the Beaufort Sea (Figures S1 and S2), on the IBRV Araon from August 30 to September 19 2014, multiple research activities were undertaken to study geological processes related to the degrading permafrost, fluid flow and degassing and associated geohazards, the seismostratigraphy of the Beaufort shelf and slope region, as well as physical and chemical oceanography studies of the Arctic Ocean, coupled with continuous atmospheric monitoring studies. The expedition focused on two main research areas in the Canadian Beaufort Sea: the eastern shelf and slope areas of the Mackenzie Trough from August 30 to September 10, 2014, and the Mackenzie Trough area from September 11 to September 15, 2014. Figure S1. Overview map of the ship track and stations of expedition ARA05C. The expedition was split into two main research areas in the Canadian Beaufort Sea: the eastern shelf and slope areas of the Mackenzie Trough from August 30 to September 10, 2014 and the Mackenzie Trough area from September 11 to September 15, 2014. Figure S2. Details of the ship track and stations for Expedition ARA05C Figure S3. Map showing seismic survey lines. Multi-channel seismic data were collected in support of drilling proposals, in particular IODP pre-proposal #806 (Dallimore et al., 2012) and #753 (O’Regan, 2010), and to verify the distribution and internal structures of offshore permafrost occurrences (Figure S3). The multi-channel seismic data were acquired on the outer continental shelf and slope of the Canadian Beaufort Sea, totaling 20 lines with ~1,000 line-kilometers and ~20,000 shot gathers from September 1 to September 13, 2014 (see Chapter 3 for more details). The multichannel seismic data will be processed post-expedition at KOPRI and at GSC. The seismic and OBS data obtained in the 2013 and 2014 Araon cruises will allow us: 1) to investigate the permafrost signature in the shelf area through detailed velocity analyses, and identify and map zones of high-velocity sediments which would be indicative of the presence of ice along the four seismic main lines crossing the OBS stations, and 2) to conduct detailed analysis of the deep structures of the mud volcanoes (fluid expulsion structures) in the slope area. Continuous sub-bottom profiler (SBP) and multibeam data were collected along all ship tracks for detailed subsurface imaging of sediment structures and permafrost, as well as for core-site location verification (see Chapter 5 and 6 for more details). During expedition ARA05C, more than 3,000 line-kilometers of SBP data were collected, co-located with multibeam and backscatter data. These data are an essential part of the study of the sub-seafloor permafrost distribution, and they will provide further insights into sediment dynamics in areas underlaid by permafrost, and at critical boundaries, especially at the shelf edge region. In the shelf, the occurrence of mounds and pingo-like features (PLFs) result in a characteristically rugged landscape with lots of mounds, knolls and PLFs piercing through otherwise laminated sediments. Multibeam and backscatter data were collected along all ship tracks, adding to the database of existing information gathered through previous expeditions in the study region. Heat flow measurements were undertaken at a total of 5 stations and thermal conductivity measurements were also carried out in 5 gravity cores to study the distribution of sub-seafloor permafrost and the thermal structure of fluid expulsion features, as well as the heat flow regime of slope background areas (see Chapter 7 for details). A very important finding was the observation that seafloor temperatures at the mud volcano in 740 m water depth are much higher than those measured in all other stations. Geological sampling using gravity coring and box-coring was performed at strategic sites supporting ongoing international research linked to IODP pre-proposals #753 (O'Regan et al., 2010) and #806 (Dallimore et al., 2012), and at sites of regional interest to define key seismo-stratigraphic horizons critical for the understanding of geohazards in the region (see Chapter 8 for details). In total, 10 gravity cores at 9 sites and 22 box-cores were taken (Figure S1). Most sediment analyses on the recovered cores will be performed post- expedition at various labs in KOPRI, MBARI, and in laboratories of other University-based collaborators in Korea. Onboard, sub-samples were taken from all gravity cores. On selected cores from the Canadian Beaufort study region pore-waters were extracted using rhizones, after logging of physical properties. These samples will be analyzed post-expedition by research collaborators at MBARI. The coring program undertaken augments and complements the database of gravity, piston and vibra-cores collected by the CCGS S.W. Laurier and the IBRV Araon in previous years expeditions in the Beaufort Sea. One of the highlights of this expedition was the first documentation and collection of gas-hydrates from the mud volcano at 740 m water depth. Another important finding was the first documented presence of freshwater ice in the Cyan unit. This unit underlies most of the upper seismo-stratigraphic units of Holocene and late-Glacial age in the Beaufort Sea shelf and slope in the eastern margin of the Mackenzie Trough. In sub-bottom profiles it displays a plastic behavior, with upwards flowing structures that pierce through the overlying units, but reach the seafloor only on a few limited locations. The successful targeting via gravity coring of the small exposure of this unit and the collection of sediments and ice from it was only possible due to the dynamic positioning capabilities of the Araon, which allows it to position the gravity corer within a few meters of the desired target. Water sampling and Conductivity-Temperature-Depth (CTD) profiling was undertaken at most core sites to study the physical and chemical properties of seawater (Figure S1). These station-measurements were complemented by continuous water-properties and atmospheric measurements when the Araon was underway. Seawater samples will be analyzed for DIC/TA, nutrients, DOC, and POC post- expedition at KOPRI. Accurate measurements of the pH of seawater, and the underway continuous stream of measurements of seawater and atmospheric pCO2, CH4, and N2O, required a variety of seawater/air physical properties to be considered in the calculation. Methane was also measured with a methane sensor attached to the CTD tool and at the mud volcanoes in 290 m, 420 m, and 740 m water depths. The methane plumes emanating from these volcanoes were also acoustically imaged with the echo sounder systems onboard the IBRV Araon. Further details on the water sampling and atmospheric measurements are given in Chapter 10 and 11. References Dallimore, S.R., Paull, C.K., Collett, T.S., Jin, Y.K., Mienert, J., Mangelsdorf, K., Riedel, M., 2012. Drilling to investigate methane release and geologic processes associated with warming permafrost and gas hydrate deposits beneath the Beaufort Sea Shelf. IODP Pre-Proposal 806, available online at http://iodp.org/ O’Regan, M., de Vernal, A., Hill, P., Hillaire-Marcel, C., Jakobsson, M., Moran, K., Rochon, A., St-Onge, G., 2010. Late quaternary paleoceanography and glacial dynamics in the Beaufort Sea, IODP pre-proposal #753, available online at http://iodp.org/. During the Expedition ARA05C, multiple research activities were undertaken to study geological processes related to the degrading permafrost, fluid flow and degassing and associated geohazards, the seismostratigraphy of the Beaufort shelf and slope region, as well as physical and chemical oceanography studies of the Arctic Ocean, coupled with continuous atmospheric monitoring studies. The expedition focused on two main research areas in the Canadian Beaufort Sea: the eastern shelf and slope areas of the Mackenzie Trough from August 30 to September 10, 2014, and the Mackenzie Trough area from September 11 to September 15, 2014. proprietary
KOPRI-KPDC-00000736_1 Community respiration data of Amundsen Sea cruise in 201 AMD_KOPRI STAC Catalog 2013-12-31 2014-01-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244300096-AMD_KOPRI.umm_json During the 2014 Amundsen Sea cruise, Community respiration were measured decreasing oxygen concentration by time. The objective of this study is to investigate the fate of organic carbon produced by primary producer in the water column. proprietary
-KOPRI-KPDC-00000737_1 2014-15 Jang Bogo Station micro climate data_HOBO_soil temp.,PAR,air temp.,relative humidity ALL STAC Catalog 2015-10-06 2015-10-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295662-AMD_KOPRI.umm_json 2014-15 Jang Bogo Station micro climate data_HOBO_soil temp.,PAR,air temp.,relative humidity proprietary
KOPRI-KPDC-00000737_1 2014-15 Jang Bogo Station micro climate data_HOBO_soil temp.,PAR,air temp.,relative humidity AMD_KOPRI STAC Catalog 2015-10-06 2015-10-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295662-AMD_KOPRI.umm_json 2014-15 Jang Bogo Station micro climate data_HOBO_soil temp.,PAR,air temp.,relative humidity proprietary
+KOPRI-KPDC-00000737_1 2014-15 Jang Bogo Station micro climate data_HOBO_soil temp.,PAR,air temp.,relative humidity ALL STAC Catalog 2015-10-06 2015-10-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244295662-AMD_KOPRI.umm_json 2014-15 Jang Bogo Station micro climate data_HOBO_soil temp.,PAR,air temp.,relative humidity proprietary
KOPRI-KPDC-00000738_1 Eddy covariance data at DASAN Station in 2013 AMD_KOPRI STAC Catalog 2012-12-13 2014-03-30 11.865833, 78.921944, 11.865833, 78.921944 https://cmr.earthdata.nasa.gov/search/concepts/C2244296857-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2013 at Ny-Alesund where Arctic DASAN station is located. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz. To monitor and understand energy/water/green-house-gas flux at DASAN Station proprietary
KOPRI-KPDC-00000739_1 Eddy covariance data at DASAN Station in 2014 AMD_KOPRI STAC Catalog 2014-04-03 2015-02-08 11.865833, 78.921944, 11.865833, 78.921944 https://cmr.earthdata.nasa.gov/search/concepts/C2244296868-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2014 at Ny-Alesund where Arctic DASAN station is located. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz. To monitor and understand energy/water/green-house-gas flux at DASAN Station proprietary
KOPRI-KPDC-00000740_1 Eddy covariance data at DASAN Station in 2015 AMD_KOPRI STAC Catalog 2015-02-08 2016-04-03 11.865833, 78.921944, 11.865833, 78.921944 https://cmr.earthdata.nasa.gov/search/concepts/C2244296880-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, and CO2 had been measured in 2015 at Ny-Alesund where Arctic DASAN station is located. Eddy covariance system, consisting of 3-D sonic anemometer and open-path CO2/H2O gas analyzer was used for the measurement. Data were recorded on a data logger with sampling rate of 10 Hz. To monitor and understand energy/water/green-house-gas flux at DASAN Station proprietary
@@ -9081,26 +9082,26 @@ KOPRI-KPDC-00000756_1 Gravity cores from Antarctic Weddell Sea(JV10-GC01) AMD_KO
KOPRI-KPDC-00000757_1 Physical and chemical properties of soil cores from Council, Alaska in 2016 AMD_KOPRI STAC Catalog 2017-06-01 2017-09-20 -163.7, 64.85, -163.7, 64.85 https://cmr.earthdata.nasa.gov/search/concepts/C2244299727-AMD_KOPRI.umm_json Several soil physical and chemical properties (moisture content, bulk density, C and N content, etc.) were analyzed from soil samples acquired in tussock and inter-tussock areas in August. 2016. To use for the basic information in the laboratory incubation study and to understand the site characteristics proprietary
KOPRI-KPDC-00000758_1 Crystal structure and functional characterization of an isoaspartyl dipeptidase (CpsIadA) from Colwellia psychrerythraea strain 34H AMD_KOPRI STAC Catalog 2017-06-21 2017-06-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244300669-AMD_KOPRI.umm_json Isoaspartyl dipeptidase (IadA) is an enzyme that catalyzes the hydrolysis of an isoaspartyl dipeptide-like moiety, which can be inappropriately formed in proteins, between the β-carboxyl group side chain of Asp and the amino group of the following amino acid. Here, we have determined the structures of an isoaspartyl dipeptidase (CpsIadA) from Colwellia psychrerythraea, both ligand-free and that complexed with β-isoaspartyl lysine, at 1.85-Å and 2.33-Å resolution, respectively. In both structures, CpsIadA formed an octamer with two Zn ions in the active site. A structural comparison with Escherichia coli isoaspartyl dipeptidase (EcoIadA) revealed a major difference in the structure of the active site. For metal ion coordination, CpsIadA has a Glu166 residue in the active site, whereas EcoIadA has a post-translationally carbamylated-lysine 162 residue. Site-directed mutagenesis studies confirmed that the Glu166 residue is critical for CpsIadA enzymatic activity. This residue substitution from lysine to glutamate induces the protrusion of the β12-α8 loop into the active site to compensate for the loss of length of the side chain. In addition, the α3-β9 loop of CpsIadA adopts a different conformation compared to EcoIadA, which induces a change in the structure of the substrate-binding pocket. Despite CpsIadA having a different active-site residue composition and substrate-binding pocket, there is only a slight difference in CpsIadA substrate specificity compared with EcoIadA. Comparative sequence analysis classified IadA-containing bacteria and archaea into two groups based on the active-site residue composition, with Type I IadAs having a glutamate residue and Type II IadAs having a carbamylated-lysine residue. CpsIadA has maximal activity at pH 8±8.5 and 45ÊC, and was completely inactivated at 60ÊC. Despite being isolated from a psychrophilic bacteria, CpsIadA is thermostable probably owing to its octameric structure. This is the first conclusive description of the structure and properties of a Type I IadA. To determine the structures of an isoaspartyl dipeptidase IadA from a psychrophilic bacterium Colwellia psychrerythraea strain 34H (CpsIadA) in both the ligand-free form and that complexed with β-isoaspartyl lysine proprietary
KOPRI-KPDC-00000759_1 X-ray diffraction data of EaEST AMD_KOPRI STAC Catalog 2016-04-03 2016-04-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244300649-AMD_KOPRI.umm_json A novel microbial esterase, EaEST, from a psychrophilic bacterium Exiguobacterium antarcticum B7, was identified and characterized. To our knowledge, this is the first report describing structural analysis and biochemical characterization of an esterase isolated from the genus Exiguobacterium. Crystal structure of EaEST, determined at a resolution of 1.9 Å, showed that the enzyme has a canonical α/β hydrolase fold with an α-helical cap domain and a catalytic triad consisting of Ser96, Asp220, and His248. Interestingly, the active site of the structure of EaEST is occupied by a peracetate molecule, which is the product of perhydrolysis of acetate. This result suggests that EaEST may have perhydrolase activity. The activity assay showed that EaEST has significant perhydrolase and esterase activity with respect to short-chain p-nitrophenyl esters (≤C8), naphthyl derivatives, phenyl acetate, and glyceryl tributyrate. However, the S96A single mutant had low esterase and perhydrolase activity. Moreover, the L30A mutant showed low levels of protein expression and solubility as well as preference for different substrates. On conducting an enantioselectivity analysis using R- and S-methyl-3-hydroxy-2-methylpropionate, a preference for R-enantiomers was observed. Surprisingly, immobilized EaEST was found to not only retain 200% of its initial activity after incubation for 1 h at 80°C, but also retained more than 60% of its initial activity after 20 cycles of reutilization. This research will serve as basis for future engineering of this esterase for biotechnological and industrial applications. Our goal was to identify a novel cold-active esterase from a polar microorganism. We identified and characterized a novel esterase, EaEST, from a psychrophilic bacterium Exiguobacterium antarcticum B7. Further structural and functional analysis indicated that EaEST had dual activity of a perhydrolase and an esterase. It is known that perhydrolysis is a side activity of esterases and it may be useful in industrial and organic synthesis. Moreover, the peracetate-bound EaEST structure reported in our study provides the first snapshot of the peracetate binding mode, and a comparison of the structure of EaEST with that of PfEST (PDB code 3HI4) reveals a comprehensive structural basis for the conformational changes of this enzyme induced by binding of different substrates. proprietary
-KOPRI-KPDC-00000760_1 Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2016-2017 ALL STAC Catalog 2016-12-28 2017-02-15 153.936483, -75.389942, 159.216086, -75.059956 https://cmr.earthdata.nasa.gov/search/concepts/C2244300682-AMD_KOPRI.umm_json David glacier area ice surface / bed elevation ice surface / bed elevation proprietary
KOPRI-KPDC-00000760_1 Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2016-2017 AMD_KOPRI STAC Catalog 2016-12-28 2017-02-15 153.936483, -75.389942, 159.216086, -75.059956 https://cmr.earthdata.nasa.gov/search/concepts/C2244300682-AMD_KOPRI.umm_json David glacier area ice surface / bed elevation ice surface / bed elevation proprietary
+KOPRI-KPDC-00000760_1 Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2016-2017 ALL STAC Catalog 2016-12-28 2017-02-15 153.936483, -75.389942, 159.216086, -75.059956 https://cmr.earthdata.nasa.gov/search/concepts/C2244300682-AMD_KOPRI.umm_json David glacier area ice surface / bed elevation ice surface / bed elevation proprietary
KOPRI-KPDC-00000761_1 Comparison of diversity of ciliate between Barton peninsula in Antarctica and Korea using NGS technique. AMD_KOPRI STAC Catalog 2017-05-04 2017-06-18 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300615-AMD_KOPRI.umm_json Identification of ciliate diversity from Korea and Antarctica (Barton Peninsular) Comparison of both data to know the specific ciliate in Antarctica proprietary
KOPRI-KPDC-00000762_1 Greenland NEEM 2009S1 shallow ice core trace elements concentrations AMD_KOPRI STAC Catalog 2017-09-27 2017-09-27 -51.06, 77.45, -51.06, 77.45 https://cmr.earthdata.nasa.gov/search/concepts/C2244300703-AMD_KOPRI.umm_json The first high resolution records of atmospherc trace metals for 1711~1969 were recovered from Greenland NEEM shallow ice core together with ions records. These records reveal increases in various atmospheric metals since the Industrial Revolution. Also, the comparion between these records and those from other Greenland ice cores represents regional differences in anthropogenic contributions. Researches for changes in atmospheric trace element over Greenland after the Industrial Revolution and contributions from natural/anthropogenic sources proprietary
KOPRI-KPDC-00000763_1 CPS2 AMD_KOPRI STAC Catalog 2013-02-20 2013-02-27 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244300790-AMD_KOPRI.umm_json CPS2 is termed as cell-protection substances 2 capable of protection of the cells and lowering freezing points below melting points. Antarctic freshwater green microalga, Chloromonas sp. was reported to produce and secrete CPS2. CPS2 genes will be utilized to protect the skin and tissue cells by applying any valuable products. proprietary
KOPRI-KPDC-00000764_1 Fatty acid content of polar microalgae and mesophilic Chlamydomonas CC125 using Gas Chromatography AMD_KOPRI STAC Catalog 2017-05-05 2017-06-04 -58.783333, -62.216667, 11.933333, 78.916667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300805-AMD_KOPRI.umm_json Fatty acid content of polar microalgae and mesophilic microalga Comparison and analysis of fatty acid content of both microalagae proprietary
KOPRI-KPDC-00000765_2 Climate Measurement Around the King Sejong Station, Antarctica in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244305675-AMD_KOPRI.umm_json Meteorological observation was carried out at the King Sejong Station in 2017. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, solar radiation, longwave radiation, UV radiation, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctic Peninsula. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report. To understand weather phenomema and to monitor at Antarctic Peninsula proprietary
KOPRI-KPDC-00000766_1 Soil samples of the Antarctic King Sejong Station from Barton Peninsular collected in 2017 AMD_KOPRI STAC Catalog 2017-01-12 2017-01-27 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244300827-AMD_KOPRI.umm_json Analysis of microbial community structure and diversity in soil samples of the Antarctic King Sejong Station from Barton Peninsular in Antarctica Investigation to the terrestrial biodiversity in Barton peninsular for the monitoring by environment change proprietary
-KOPRI-KPDC-00000767_1 2016-2017 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity AMD_KOPRI STAC Catalog 2016-01-14 2017-01-27 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244300860-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2016 Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2016 proprietary
KOPRI-KPDC-00000767_1 2016-2017 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity ALL STAC Catalog 2016-01-14 2017-01-27 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244300860-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2016 Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2016 proprietary
+KOPRI-KPDC-00000767_1 2016-2017 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity AMD_KOPRI STAC Catalog 2016-01-14 2017-01-27 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244300860-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2016 Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2016 proprietary
KOPRI-KPDC-00000768_1 Rn gas data measured at KSG during 2013.2-2016.11 AMD_KOPRI STAC Catalog 2013-02-01 2016-11-24 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244300905-AMD_KOPRI.umm_json Monitoring of Rn gas at KSG, Antarctica Investigation of air mass path moving to the KSG, Antarctica proprietary
KOPRI-KPDC-00000769_1 Simulated Atmospheric Wind at 850 hPa by Boundary Conditions during Last Glacial Maximum AMD_KOPRI STAC Catalog 2017-09-28 2017-09-28 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244301166-AMD_KOPRI.umm_json Atmospheric wind climatology at 850 hPa from the preindustrial simulation, Last Glacial Maximum simulation, LGM-SST simulation, LGM-SEAICE simulation, and LGM-topography simulation. To examine the responses of SH westerly winds to LGM boundary conditions using the state-of-the-art numerical model. To evaluate which boundary conditions are more important in the position and strength of SH westerly winds. proprietary
-KOPRI-KPDC-00000770_1 Aerosol Number Concentration (>10nm) from King Sejong Station collected in 2010-2016. ALL STAC Catalog 2010-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244298407-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 10nm in diameter Monitoring of Aerosol Number Concentration (>10nm) from King Sejong Station. proprietary
KOPRI-KPDC-00000770_1 Aerosol Number Concentration (>10nm) from King Sejong Station collected in 2010-2016. AMD_KOPRI STAC Catalog 2010-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244298407-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 10nm in diameter Monitoring of Aerosol Number Concentration (>10nm) from King Sejong Station. proprietary
+KOPRI-KPDC-00000770_1 Aerosol Number Concentration (>10nm) from King Sejong Station collected in 2010-2016. ALL STAC Catalog 2010-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244298407-AMD_KOPRI.umm_json Condensation particle counter measures the number of aerosol condensation particles of > 10nm in diameter Monitoring of Aerosol Number Concentration (>10nm) from King Sejong Station. proprietary
KOPRI-KPDC-00000771_1 Italian Seismic Line 2017 AMD_KOPRI STAC Catalog 2017-02-02 2017-03-01 170.15625, -76.980149, -165.498047, -72.127936 https://cmr.earthdata.nasa.gov/search/concepts/C2244295712-AMD_KOPRI.umm_json Italian Seismic Line 2017, single channel seismic data, were collected during the 2016-2017 austral summer with the RV OGS Explora in the Ross Sea continental margin, Antarctica The major purpose of this survey is to investigate stratigraphy and sedimentary structure of the Ross Sea continental margin, Antarctica proprietary
KOPRI-KPDC-00000772_1 List of marine benthic invertebrate animal species around King Sejong Station (2017) AMD_KOPRI STAC Catalog 2017-09-29 2017-09-29 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244295664-AMD_KOPRI.umm_json Survey of marine benthic invertebrate biota by diving around King Sejong Station Diversity of marine benthic invertebrates proprietary
KOPRI-KPDC-00000773_2 Comparison of diversity of ciliate between Jang Bogo Station in Antarctica and Korea using NGS technique (Site261_2014) AMD_KOPRI STAC Catalog 2021-08-02 2021-08-02 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244305169-AMD_KOPRI.umm_json Identification of ciliate diversity from Korea and Antarctica (Jang Bogo Station) Comparison of both data to know the specific ciliate in Antarctica proprietary
KOPRI-KPDC-00000774_1 ANA07C Multi-Channel Seismic Survey Lines AMD_KOPRI STAC Catalog 2017-02-04 2017-02-05 166, -75, 170, -74.5 https://cmr.earthdata.nasa.gov/search/concepts/C2244295683-AMD_KOPRI.umm_json Multi-Channel seismic data were collected during the 2016-2017 ANA07C cruise in the Ross Sea, Antarctic Ocean The major purpose of this survey is to investigate stratography and the structure of sediments across the Terror Rift, Antarctica. proprietary
-KOPRI-KPDC-00000775_1 Aerosol Size Distribution from King Sejong Station collected in 2010-2016. AMD_KOPRI STAC Catalog 2010-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244298745-AMD_KOPRI.umm_json SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016. Monitoring of aerosol size distribution from King Sejong Station. proprietary
KOPRI-KPDC-00000775_1 Aerosol Size Distribution from King Sejong Station collected in 2010-2016. ALL STAC Catalog 2010-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244298745-AMD_KOPRI.umm_json SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016. Monitoring of aerosol size distribution from King Sejong Station. proprietary
+KOPRI-KPDC-00000775_1 Aerosol Size Distribution from King Sejong Station collected in 2010-2016. AMD_KOPRI STAC Catalog 2010-01-01 2016-12-31 -58.78, -62.22, -58.78, -62.22 https://cmr.earthdata.nasa.gov/search/concepts/C2244298745-AMD_KOPRI.umm_json SMPS(Scanning Mobility Particle Sizer) measures the aerosol size distribution from King Sejong Station in 2010-2016. Monitoring of aerosol size distribution from King Sejong Station. proprietary
KOPRI-KPDC-00000776_1 Meterological data at BearPeninsula in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-04-11 -115.56512, -74.1877, -115.56512, -74.1877 https://cmr.earthdata.nasa.gov/search/concepts/C2244300521-AMD_KOPRI.umm_json Meterological observation at BearPeninsula DATA. Continuous meteorological monitoring is required for deep understanding long-term trend of climate change in Antarctic region. Primary climate factors including solar radiation wind speed and direction, air temperature, pressure and relative humidity has been monitored using automatic weather monitoring system at Bear Peninsula. One hourly averaged data are stored at a data logger and an Argos Satellite transmitter is used to transmit daily data. The objectives of this monitoring are to record the past and current climate change through continuous operation of AWS, and to understand characteristics of meteorological phenomena at Bear Peninsula. Monitoring on meteorology at Bear Peninsula. proprietary
KOPRI-KPDC-00000777_2 Fossils from North Greenland (2016) AMD_KOPRI STAC Catalog 2016-07-25 2016-08-12 -42.228333, 82.793333, -42.228333, 82.793333 https://cmr.earthdata.nasa.gov/search/concepts/C2244305474-AMD_KOPRI.umm_json This entry includes the Early Cambrian fossils from Sirius Passet, North Greenland, collected by 2016 KOPRI expedition. The collections include various kinds of marine invertebrates, representing morphology of the early stage of animal evolution. Total of ca. 600 kg of fossils were collected during 2016 expedition. The Early Cambrian fossils will help us understand the rise of the first animals during the Cambrian Explosion. proprietary
KOPRI-KPDC-00000778_1 GV7_S2_dust data AMD_KOPRI STAC Catalog 2017-10-10 2017-10-10 158.85, -70.683333, 158.85, -70.683333 https://cmr.earthdata.nasa.gov/search/concepts/C2244300372-AMD_KOPRI.umm_json GV7_S2_dust data MS4_GV7 S22 dust data proprietary
@@ -9142,8 +9143,8 @@ KOPRI-KPDC-00000813_2 Geomagnetic field, Jang Bogo Station, Antarctica, 2017 AMD
KOPRI-KPDC-00000814_2 All-sky aurora (proton) image, Jang Bogo Station, 2017 ALL STAC Catalog 2017-01-01 2017-12-31 164.14, -74.37, 164.14, -74.37 https://cmr.earthdata.nasa.gov/search/concepts/C2244306721-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at JBS, Antarctica Study of the aurora (proton) characteristics in the northern high latitude proprietary
KOPRI-KPDC-00000814_2 All-sky aurora (proton) image, Jang Bogo Station, 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 164.14, -74.37, 164.14, -74.37 https://cmr.earthdata.nasa.gov/search/concepts/C2244306721-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at JBS, Antarctica Study of the aurora (proton) characteristics in the northern high latitude proprietary
KOPRI-KPDC-00000815_1 Genes involved in metabolites production (2017) AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 -58.783333, -62.216667, -58.783333, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244299848-AMD_KOPRI.umm_json Amino acid and DNA sequences for the production of metabolites in Antarctic copepod T. kingsejongensis Genetic information to understand mechanism of useful metabolites proprietary
-KOPRI-KPDC-00000816_2 All-sky aurora (proton) Image, Longyearbyen, Norway, 2017 ALL STAC Catalog 2017-01-01 2017-02-28 16.12, 78.48, 16.12, 78.48 https://cmr.earthdata.nasa.gov/search/concepts/C2244306732-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at Kjell Henriksen Observatory, Longyearbyen, Norway Study of the aurora characteristics in the northern high latitude proprietary
KOPRI-KPDC-00000816_2 All-sky aurora (proton) Image, Longyearbyen, Norway, 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-02-28 16.12, 78.48, 16.12, 78.48 https://cmr.earthdata.nasa.gov/search/concepts/C2244306732-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at Kjell Henriksen Observatory, Longyearbyen, Norway Study of the aurora characteristics in the northern high latitude proprietary
+KOPRI-KPDC-00000816_2 All-sky aurora (proton) Image, Longyearbyen, Norway, 2017 ALL STAC Catalog 2017-01-01 2017-02-28 16.12, 78.48, 16.12, 78.48 https://cmr.earthdata.nasa.gov/search/concepts/C2244306732-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at Kjell Henriksen Observatory, Longyearbyen, Norway Study of the aurora characteristics in the northern high latitude proprietary
KOPRI-KPDC-00000817_3 Neutral wind and temperature from FPI, Jang Bogo Station, Antarctica, 2016 AMD_KOPRI STAC Catalog 2016-01-01 2016-10-12 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244306006-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at Jang Bogo Station (JBS), Antarctica Study of the atmosphere wave activities in the upper atmosphere in the southern high-latitude proprietary
KOPRI-KPDC-00000818_2 Neutron count, Jang Bogo Station, Antarctica, 2016 AMD_KOPRI STAC Catalog 2015-12-16 2016-04-10 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244306805-AMD_KOPRI.umm_json Cosmic ray origin neutron count measured from neutron monitor at Jang Bogo Station, Antarctica Study of the variation of neutron count in the southern high latitude proprietary
KOPRI-KPDC-00000819_2 Ionospheric scintillation, Jang Bogo Station, Antarctica, 2016 AMD_KOPRI STAC Catalog 2016-01-04 2016-06-29 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244306751-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Jang Bogo Station, Antarctica Study of the ionospheric irregularity in the southern high latitude proprietary
@@ -9276,8 +9277,8 @@ KOPRI-KPDC-00000944_1 Moderate Resolution Imaging Spectroradiometer in Arctic (M
KOPRI-KPDC-00000945_1 Moderate Resolution Imaging Spectroradiometer in Antarctic (MODIS) / Aqua, 2015 AMD_KOPRI STAC Catalog 2015-01-01 2015-12-31 180, -90, -180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2244297229-AMD_KOPRI.umm_json MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Antarctic. The first MODIS instrument was launched on board the Terra satellite in December 1999, and the second was launched on Aqua in May 2002. Derive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans. proprietary
KOPRI-KPDC-00000946_1 Advanced TIROS Operational Vertical Sounder (ATOVS) around the Jang Bogo Station, 2015-2016 ALL STAC Catalog 2015-03-01 2016-02-15 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297301-AMD_KOPRI.umm_json The Advanced TIROS Operational Vertical Sounder (ATOVS) consists of High Resolution Infrared Radiation Sounder (HIRS), the Advanced Microwave Sounding Unit-A (AMSU-A) and AMSU-B for retrieving temperature, humidity and ozone sounding in all weather conditions. The data were obtained around the Jang Bogo Station in Antarctic. To derive products including cloud, ozone, surface elevation, surface pressure, temperature around the Jang Bogo Station. proprietary
KOPRI-KPDC-00000946_1 Advanced TIROS Operational Vertical Sounder (ATOVS) around the Jang Bogo Station, 2015-2016 AMD_KOPRI STAC Catalog 2015-03-01 2016-02-15 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297301-AMD_KOPRI.umm_json The Advanced TIROS Operational Vertical Sounder (ATOVS) consists of High Resolution Infrared Radiation Sounder (HIRS), the Advanced Microwave Sounding Unit-A (AMSU-A) and AMSU-B for retrieving temperature, humidity and ozone sounding in all weather conditions. The data were obtained around the Jang Bogo Station in Antarctic. To derive products including cloud, ozone, surface elevation, surface pressure, temperature around the Jang Bogo Station. proprietary
-KOPRI-KPDC-00000947_1 Advanced Very High Resolution Radiometer (AVHRR) around the Jang Bogo Station, 2015-2016 AMD_KOPRI STAC Catalog 2015-03-03 2016-02-15 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297622-AMD_KOPRI.umm_json The AVHRR is a six channel scanning radiometer providing three solar channels in the visible-near infrared region and three thermal infrared channels and obtained data around the Jang Bogo Station in Antarctic. To derive products including cloud cover, surface temperature, land-water boundaries, snow and ice detection around the Jang Bogo Station. proprietary
KOPRI-KPDC-00000947_1 Advanced Very High Resolution Radiometer (AVHRR) around the Jang Bogo Station, 2015-2016 ALL STAC Catalog 2015-03-03 2016-02-15 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297622-AMD_KOPRI.umm_json The AVHRR is a six channel scanning radiometer providing three solar channels in the visible-near infrared region and three thermal infrared channels and obtained data around the Jang Bogo Station in Antarctic. To derive products including cloud cover, surface temperature, land-water boundaries, snow and ice detection around the Jang Bogo Station. proprietary
+KOPRI-KPDC-00000947_1 Advanced Very High Resolution Radiometer (AVHRR) around the Jang Bogo Station, 2015-2016 AMD_KOPRI STAC Catalog 2015-03-03 2016-02-15 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297622-AMD_KOPRI.umm_json The AVHRR is a six channel scanning radiometer providing three solar channels in the visible-near infrared region and three thermal infrared channels and obtained data around the Jang Bogo Station in Antarctic. To derive products including cloud cover, surface temperature, land-water boundaries, snow and ice detection around the Jang Bogo Station. proprietary
KOPRI-KPDC-00000948_1 Moderate Resolution Imaging Spectroradiometer (MODIS) around the Jang Bogo Station, 2015-2016 AMD_KOPRI STAC Catalog 2015-03-30 2016-02-03 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244297939-AMD_KOPRI.umm_json MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data around the Jang Bogo Station in Antarctic. To derive products including vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans around the Jang Bogo Station. proprietary
KOPRI-KPDC-00000949_1 Medium Resolution Spectral Imager (MERSI) around the Jang Bogo Station, 2015-2016 AMD_KOPRI STAC Catalog 2015-10-31 2015-11-09 164.233333, -74.616667, 164.233333, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244298276-AMD_KOPRI.umm_json MERSI is a scanner carried aboard the third FengYun (FY-3) series of meteorological satellites launched by China and obtained data around the Jang Bogo Station in Antarctic. To derive products including cloud, vegetation, snow and ice, ocean color around the Jang Bogo Station. proprietary
KOPRI-KPDC-00000952_1 Moderate Resolution Imaging Spectroradiometer in the Arctic (MODIS) / Aqua, 2012 AMD_KOPRI STAC Catalog 2012-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244298621-AMD_KOPRI.umm_json MODIS is a 36-band spectroradiometer measuring visible and infrared radiation and obtaining data in Arctic. The first MODIS instrument was launched on board the Aqua satellite in May 2002. Derive products ranging from vegetation, land surface cover, and ocean chlorophyll fluorescence to cloud and aerosol properties, fire occurrence, snow cover on the land, and sea ice cover on the oceans. proprietary
@@ -9337,8 +9338,8 @@ KOPRI-KPDC-00001004_1 Stable water isotope composition of the Styx ice core (v3)
KOPRI-KPDC-00001005_1 GV7_S2_Pu AMD_KOPRI STAC Catalog 2018-10-04 2018-10-04 158.85, -70.683333, 158.85, -70.683333 https://cmr.earthdata.nasa.gov/search/concepts/C2244300677-AMD_KOPRI.umm_json "Atmospheric nuclear explosions during the period from the 1940s to the 1980s are the major anthropogenic source of plutonium (Pu) in the environment. In this work, we analyzed fg g-1 levels of artificial Pu, released predominantly by atmospheric nuclear weapons tests. We measured 351 samples which collected a 78 m-depth fire core at the site of GV7 (S 70°41 ´17.1"", E 158°51´48.9"", 1950 m a.s.l.), Northern Victoria Land, East Antarctica. To determine the Pu concentration in the samples, we used an inductively coupled plasma sector field mass spectrometry coupled with an Apex high-efficiency sample introduction system, which has the advantages of small sample consumption and simple sample preparation." proprietary
KOPRI-KPDC-00001006_1 Styx_Pu AMD_KOPRI STAC Catalog 2018-10-04 2018-10-04 163.683333, -73.85, 163.683333, -73.85 https://cmr.earthdata.nasa.gov/search/concepts/C2244300691-AMD_KOPRI.umm_json Atmospheric nuclear explosions during the period from the 1940s to the 1980s are the major anthropogenic source of plutonium (Pu) in the environment. In this work, we analyzed fg g-1 levels of artificial Pu, released predominantly by atmospheric nuclear weapons tests. proprietary
KOPRI-KPDC-00001007_1 Ubi:DaGolS2, rice transgenic line overexpressing DaGolS2 from Deschampsia antarctica AMD_KOPRI STAC Catalog 2018-01-01 2018-12-31 -58.791222, -62.2365, -58.719472, -62.224972 https://cmr.earthdata.nasa.gov/search/concepts/C2244300474-AMD_KOPRI.umm_json Deschampsia antarctica is an Antarctic hairgrass that grows on the west coast of the Antarctic peninsula. In this report, we have identified and characterized DaGolS2, that is a member of the galactinol synthase group 2. To investigate its possible cellular role in cold tolerance, a transgenic rice system was employed. DaGolS2-overexpressing transgenic rice plants (Ubi:DaGolS2) exhibited markedly increased tolerance to cold and drought stress compared to wild-type plants without growth defects; however, overexpression of DaGolS2 exerted little effect on tolerance to salt stress. These results suggest that overexpression of DaGolS2 directly and indirectly confers enhanced tolerance to cold and drought stresses. proprietary
-KOPRI-KPDC-00001008_2 2018 KOPRI North Greenland Sirius Passet collection 1 ALL STAC Catalog 2021-08-02 2021-08-02 -42.228333, 82.793333, -42.228333, 82.793333 https://cmr.earthdata.nasa.gov/search/concepts/C2244301304-AMD_KOPRI.umm_json This entry includes the Early Cambrian fossils from Sirius Passet, North Greenland, collected by 2016-2018 KOPRI expedition. The collections include various kinds of marine invertebrates, representing morphology of the early stage of animal evolution. Total of ca. 2000 kg of fossils were collected during 2016-2018 expedition. The Early Cambrian fossils will help us understand the rise of the first animals during the Cambrian Explosion. proprietary
KOPRI-KPDC-00001008_2 2018 KOPRI North Greenland Sirius Passet collection 1 AMD_KOPRI STAC Catalog 2021-08-02 2021-08-02 -42.228333, 82.793333, -42.228333, 82.793333 https://cmr.earthdata.nasa.gov/search/concepts/C2244301304-AMD_KOPRI.umm_json This entry includes the Early Cambrian fossils from Sirius Passet, North Greenland, collected by 2016-2018 KOPRI expedition. The collections include various kinds of marine invertebrates, representing morphology of the early stage of animal evolution. Total of ca. 2000 kg of fossils were collected during 2016-2018 expedition. The Early Cambrian fossils will help us understand the rise of the first animals during the Cambrian Explosion. proprietary
+KOPRI-KPDC-00001008_2 2018 KOPRI North Greenland Sirius Passet collection 1 ALL STAC Catalog 2021-08-02 2021-08-02 -42.228333, 82.793333, -42.228333, 82.793333 https://cmr.earthdata.nasa.gov/search/concepts/C2244301304-AMD_KOPRI.umm_json This entry includes the Early Cambrian fossils from Sirius Passet, North Greenland, collected by 2016-2018 KOPRI expedition. The collections include various kinds of marine invertebrates, representing morphology of the early stage of animal evolution. Total of ca. 2000 kg of fossils were collected during 2016-2018 expedition. The Early Cambrian fossils will help us understand the rise of the first animals during the Cambrian Explosion. proprietary
KOPRI-KPDC-00001009_2 Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations, Arctic, 2016 AMD_KOPRI STAC Catalog 2016-01-01 2016-12-31 180, 30, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244306767-AMD_KOPRI.umm_json This Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations (NRTSI) data set provides sea ice concentrations for both the Northern and Southern Hemispheres. The near-real-time passive microwave brightness temperature data that are used as input to this data set are acquired with the Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) satellites. Starting with 1 April 2016, data from DMSP-F18 are used. The SSMIS instrument is the next generation Special Sensor Microwave/Imager (SSM/I) instrument. SSMIS data are received daily from the Comprehensive Large Array-data Stewardship System (CLASS) at the National Oceanic and Atmospheric Administration (NOAA) and are gridded onto a polar stereographic grid. Investigators generate sea ice concentrations from these data using the NASA Team algorithm. proprietary
KOPRI-KPDC-00001010_2 Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations, Arctic, 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 180, 30, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244306790-AMD_KOPRI.umm_json This Near-Real-Time DMSP SSMIS Daily Polar Gridded Sea Ice Concentrations (NRTSI) data set provides sea ice concentrations for both the Northern and Southern Hemispheres. The near-real-time passive microwave brightness temperature data that are used as input to this data set are acquired with the Special Sensor Microwave Imager/Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) satellites. Starting with 1 April 2016, data from DMSP-F18 are used. The SSMIS instrument is the next generation Special Sensor Microwave/Imager (SSM/I) instrument. SSMIS data are received daily from the Comprehensive Large Array-data Stewardship System (CLASS) at the National Oceanic and Atmospheric Administration (NOAA) and are gridded onto a polar stereographic grid. Investigators generate sea ice concentrations from these data using the NASA Team algorithm. proprietary
KOPRI-KPDC-00001011_2 Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1, Arctic, 2006 AMD_KOPRI STAC Catalog 2006-01-01 2006-12-31 180, 30.98, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244306537-AMD_KOPRI.umm_json This data set is generated from brightness temperature data and is designed to provide a consistent time series of sea ice concentrations spanning the coverage of several passive microwave instruments.The data are provided in the polar stereographic projection at a grid cell size of 25 x 25 km. This product is designed to provide a consistent time series of sea ice concentrations (the fraction, or percentage, of ocean area covered by sea ice) spanning the coverage of several passive microwave instruments. To aid in this goal, sea ice algorithm coefficients are changed to reduce differences in sea ice extent and area as estimated using the SMMR and SSM/I sensors. proprietary
@@ -9445,8 +9446,8 @@ KOPRI-KPDC-00001108_4 All-sky aurora (proton) image at Jang Bogo Station, Antarc
KOPRI-KPDC-00001109_4 Geomagnetic field, Jang Bogo Station, Antarctica, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-09-30 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244306279-AMD_KOPRI.umm_json Variation of geomagnetic field measured from search-coil magnetometer (SCM) at Jang Bogo Station, antarctica Study of the activity of ultra low frequency (ULF) wave in the southern high latitude proprietary
KOPRI-KPDC-00001110_4 Neutral wind and temperature from FPI, Dasan Station, Arctic, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-03-22 11.836, 78.938, 11.836, 78.938 https://cmr.earthdata.nasa.gov/search/concepts/C2244307214-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, 250km measured from Febry-Perot interferometer (FPI) at Dasan station, Arctic Study of the atmosphere wave activities in the upper atmosphere in the southern/northern high-latitude proprietary
KOPRI-KPDC-00001111_4 Ionospheric scintillation, Dasan Station, Arctic, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-10-08 11.932, 78.9233, 11.932, 78.9233 https://cmr.earthdata.nasa.gov/search/concepts/C2244306245-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Dasan Station, Arctica Study of the ionospheric irregularity in the northern high latitude proprietary
-KOPRI-KPDC-00001112_4 All-sky aurora (proton) image, Longyearbyen, Norway, 2018 ALL STAC Catalog 2018-01-01 2018-02-28 16.040746, 78.147909, 16.040746, 78.147909 https://cmr.earthdata.nasa.gov/search/concepts/C2244306694-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at Kjell Henriksen Observatory (KHO), Longyearbyen, Norway Study of the aurora (proton) characteristics in the northern high latitude proprietary
KOPRI-KPDC-00001112_4 All-sky aurora (proton) image, Longyearbyen, Norway, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-02-28 16.040746, 78.147909, 16.040746, 78.147909 https://cmr.earthdata.nasa.gov/search/concepts/C2244306694-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at Kjell Henriksen Observatory (KHO), Longyearbyen, Norway Study of the aurora (proton) characteristics in the northern high latitude proprietary
+KOPRI-KPDC-00001112_4 All-sky aurora (proton) image, Longyearbyen, Norway, 2018 ALL STAC Catalog 2018-01-01 2018-02-28 16.040746, 78.147909, 16.040746, 78.147909 https://cmr.earthdata.nasa.gov/search/concepts/C2244306694-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at Kjell Henriksen Observatory (KHO), Longyearbyen, Norway Study of the aurora (proton) characteristics in the northern high latitude proprietary
KOPRI-KPDC-00001113_3 Mesospheric temperature, Kiruna, Sweden, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-10-01 21.03, 67.872, 21.03, 67.872 https://cmr.earthdata.nasa.gov/search/concepts/C2244306621-AMD_KOPRI.umm_json Mesospheric temperature and airglow intensity measured from Fourier Transform Spectrometer (FTS) at Kiruna, Sweden Study of the long-term trend of mesospheric temperature in the northern high latitude proprietary
KOPRI-KPDC-00001114_4 Neutral wind and temperature from FPI, Kiruna, Sweden, 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-10-01 21.03, 67.872, 21.03, 67.872 https://cmr.earthdata.nasa.gov/search/concepts/C2244307306-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, 250km measured from Fabry-Perot Interferometer (FPI) at Kiruna, Sweden Study of the atmosphere wave activities in the upper atmosphere in the northern high-latitude proprietary
KOPRI-KPDC-00001115_2 Ionospheric total electron content monitoring system over Kiruna, Sweden at 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-10-01 21.03, 67.53, 21.03, 67.53 https://cmr.earthdata.nasa.gov/search/concepts/C2244305498-AMD_KOPRI.umm_json Total electron content in the ionosphere over Kiruna, Sweden Study of the statistical characteristics of ionosphere in northern high latitude proprietary
@@ -9458,14 +9459,14 @@ KOPRI-KPDC-00001120_1 CTD data in the Kongfjorden, Svalbard in May, 2017 AMD_KOP
KOPRI-KPDC-00001121_1 CTD data in the Kongfjorden, Svalbard in October, 2017 AMD_KOPRI STAC Catalog 2017-10-18 2017-10-20 11.65, 78.907, 12.385, 78.985 https://cmr.earthdata.nasa.gov/search/concepts/C2244300720-AMD_KOPRI.umm_json In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Kongsfjorden, Svalbard, an extensive oceanographic survey was conducted on the October, 2017. To investigate the temporal and spatial variation of water mass and ocean circulation in the Kongfjorden, Svalbard. proprietary
KOPRI-KPDC-00001122_1 CTD data in the Kongfjorden, Svalbard in April, 2018 AMD_KOPRI STAC Catalog 2018-04-12 2018-04-14 11.65, 78.907, 12.385, 78.985 https://cmr.earthdata.nasa.gov/search/concepts/C2244300782-AMD_KOPRI.umm_json In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Kongsfjorden, Svalbard, an extensive oceanographic survey was conducted on the April, 2018. To investigate the temporal and spatial variation of water mass and ocean circulation in the Kongfjorden, Svalbard. proprietary
KOPRI-KPDC-00001123_1 CTD data in the Kongfjorden, Svalbard in June, 2018 AMD_KOPRI STAC Catalog 2018-06-07 2018-06-09 11.65, 78.907, 12.385, 78.985 https://cmr.earthdata.nasa.gov/search/concepts/C2244300794-AMD_KOPRI.umm_json In order to monitor the temporal and spatial variation of water mass and ocean circulation in the Kongsfjorden, Svalbard, an extensive oceanographic survey was conducted on the June, 2018. To investigate the temporal and spatial variation of water mass and ocean circulation in the Kongfjorden, Svalbard. proprietary
-KOPRI-KPDC-00001124_4 All-sky aurora (electron) image, Jang Bogo Station, Antarctica, 2018 ALL STAC Catalog 2018-03-01 2018-10-31 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244307161-AMD_KOPRI.umm_json Aurora (electron) image measured from all-sky camera at Jang Bogo Station (JBS), Antarctica Study of the aurora characteristics in the southern high latitude proprietary
KOPRI-KPDC-00001124_4 All-sky aurora (electron) image, Jang Bogo Station, Antarctica, 2018 AMD_KOPRI STAC Catalog 2018-03-01 2018-10-31 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244307161-AMD_KOPRI.umm_json Aurora (electron) image measured from all-sky camera at Jang Bogo Station (JBS), Antarctica Study of the aurora characteristics in the southern high latitude proprietary
+KOPRI-KPDC-00001124_4 All-sky aurora (electron) image, Jang Bogo Station, Antarctica, 2018 ALL STAC Catalog 2018-03-01 2018-10-31 164.2273, -74.6202, 164.2273, -74.6202 https://cmr.earthdata.nasa.gov/search/concepts/C2244307161-AMD_KOPRI.umm_json Aurora (electron) image measured from all-sky camera at Jang Bogo Station (JBS), Antarctica Study of the aurora characteristics in the southern high latitude proprietary
KOPRI-KPDC-00001125_4 NanoSMPS particle number concentration in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 11.894, 78.908, 11.894, 78.908 https://cmr.earthdata.nasa.gov/search/concepts/C2244301545-AMD_KOPRI.umm_json The nano-SMPS (nano-Scanning Mobility Particle Sizer) involving Classifier (3080, TSI), nano-DMA (Differential Mobility Analyzer) (3081, TSI, USA), and UCPC (Ultra-Condensation Particle Counter) (3776, TSI, USA) is an important instrument to measure nano-size aerosols (3 to 60 nm). From Oct 2016 to Feb 2020, the nano-SMPS has been operating successfully at Zeppelin Mt, Ny-Alesund in Norway. Based-on the size distribution with particle number concentration in range of 3-60 nm of nanoSMPS, we will invest time-variation of the new particle formation, Climatological circle, and so on in Arctic region. proprietary
KOPRI-KPDC-00001126_5 NanoSMPS particle number concentration in 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-12-31 11.894, 78.908, 11.894, 78.908 https://cmr.earthdata.nasa.gov/search/concepts/C2244301557-AMD_KOPRI.umm_json The nano-SMPS (nano-Scanning Mobility Particle Sizer) involving Classifier (3080, TSI), nano-DMA (Differential Mobility Analyzer) (3081, TSI, USA), and UCPC (Ultra-Condensation Particle Counter) (3776, TSI, USA) is an important instrument to measure nano-size aerosols (3 to 60 nm). From Oct 2016 to Feb 2020, the nano-SMPS has been operating successfully at Zeppelin Mt, Ny-Alesund in Norway. Based-on the size distribution with particle number concentration in range of 3-60 nm of nanoSMPS, we will invest time-variation of the new particle formation, Climatological circle, and so on in Arctic region. proprietary
KOPRI-KPDC-00001127_3 NanoSMPS particle number concentration in 2016 AMD_KOPRI STAC Catalog 2016-10-01 2016-12-31 11.894, 78.908, 11.894, 78.908 https://cmr.earthdata.nasa.gov/search/concepts/C2244301534-AMD_KOPRI.umm_json The nano-SMPS (nano-Scanning Mobility Particle Sizer) involving Classifier (3080, TSI), nano-DMA (Differential Mobility Analyzer) (3081, TSI, USA), and UCPC (Ultra-Condensation Particle Counter) (3776, TSI, USA) is an important instrument to measure nano-size aerosols (3 to 60 nm). From Oct 2016 to Feb 2020, the nano-SMPS has been operating successfully at Zeppelin Mt, Ny-Alesund in Norway. Based-on the size distribution with particle number concentration in range of 3-60 nm of nanoSMPS, we will invest time-variation of the new particle formation, Climatological circle, and so on in Arctic region. proprietary
KOPRI-KPDC-00001128_1 Soil moisture and soil temperature data collected from climate manipulation plots in Cambridge Bay, Canada in 2018 AMD_KOPRI STAC Catalog 2017-06-19 2018-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300912-AMD_KOPRI.umm_json Micro-climate data (soil volumetric content and temperature for 5 cm depth) from climate manipulation (combination of warming and precipitation) plots for 1 year (2017.06 ~ 2018. 06) were collected. To monitor the changes in micro-climate properties in soil by increasing temperature by open top chambers and increasing precipitation proprietary
-KOPRI-KPDC-00001129_1 Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2018 AMD_KOPRI STAC Catalog 2017-06-19 2018-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300871-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2017.06~2018.06) were collected To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary
KOPRI-KPDC-00001129_1 Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2018 ALL STAC Catalog 2017-06-19 2018-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300871-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2017.06~2018.06) were collected To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary
+KOPRI-KPDC-00001129_1 Air temperature and humidity data collected from climate manipulation plots in Cambridge Bay, Canada in 2018 AMD_KOPRI STAC Catalog 2017-06-19 2018-06-18 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300871-AMD_KOPRI.umm_json Micro-climate data (air temperature and humidity for 20 cm height) from climate manipulation (combination of warming and precipitation) plots for 1 year (2017.06~2018.06) were collected To monitor the changes in micro-climate properties of air by increasing temperature by open top chambers and increasing precipitation proprietary
KOPRI-KPDC-00001130_1 Atmospheric DMS mixing ratio measured from Storhofdi, Iceland in 2017-2018. AMD_KOPRI STAC Catalog 2017-04-04 2018-08-18 -20.29, 63.4, -20.29, 63.4 https://cmr.earthdata.nasa.gov/search/concepts/C2244300807-AMD_KOPRI.umm_json Custum-made DMS analyzer was installed at the Storhofdi observatory, Iceland, and monitored the atmospheric DMS mixing ratio in 2017-208. Analyzing in-situ DMs mixing ratio Storhofdi, Iceland. proprietary
KOPRI-KPDC-00001131_1 NDVI data collected from climate manipulation plots in Cambridge Bay, Canada in 2018 AMD_KOPRI STAC Catalog 2018-07-04 2018-09-05 -105.133333, 69.1, -105.133333, 69.1 https://cmr.earthdata.nasa.gov/search/concepts/C2244300832-AMD_KOPRI.umm_json NDVI(Normalized Difference Vegetation Index) from climate manipulation (increasing snow cover) plot for 2 months (2018.7.4 ~ 9.5) were collected proprietary
KOPRI-KPDC-00001132_1 Eddy covariance data of Canada permafrost site in 2017 AMD_KOPRI STAC Catalog 2017-01-01 2017-12-31 -105.058917, 69.13025, -105.058917, 69.13025 https://cmr.earthdata.nasa.gov/search/concepts/C2244301100-AMD_KOPRI.umm_json Turbulent fluxes of momentum, heat, water vapor, CO2 had been measured during summertime in 2017 at Cambridge bay, Canada. Eddy covariance system, consisting of 3-D sonic anemometer, open-path CO2/H2O gas analyzer and open-path CH4 gas analyzer was used for the measurement. Data were recorded with CR3000 logger with sampling rate of 10 Hz. To monitor and understand energy/water/green-house-gas flux over permafrost region proprietary
@@ -9512,8 +9513,8 @@ KOPRI-KPDC-00001173_5 Surface temperature, Humidity, Pressure at the GPS station
KOPRI-KPDC-00001174_3 Ice Sheet monitoring system(AMIGOS)data at Drygalski Ice tongue, Nansen Ice Sheet, and Campbell Glacier in 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-12-31 163.427, -75.351, 164.345, -75.072 https://cmr.earthdata.nasa.gov/search/concepts/C2244298390-AMD_KOPRI.umm_json Remotely operating weather station and digital camera Investigation of the behaviour of ice sheet proprietary
KOPRI-KPDC-00001175_4 Ice sheet monitoring GPS data around the Jang Bogo Station in 2018 AMD_KOPRI STAC Catalog 2018-01-01 2018-12-31 155.34, -75.555, 164.509, -74.268 https://cmr.earthdata.nasa.gov/search/concepts/C2244301615-AMD_KOPRI.umm_json Remotely operating GPS system Investigation of the behaviour of ice sheet proprietary
KOPRI-KPDC-00001176_4 Small phytoplankton contribution to the total primary production during three cruises (ANA02C, ANA04B, ANA06B) in the Amundsen Sea, Antarctica AMD_KOPRI STAC Catalog 2012-02-01 2016-02-29 -127.9108, -75.058905, -101.759, -69.999937 https://cmr.earthdata.nasa.gov/search/concepts/C2244302354-AMD_KOPRI.umm_json To estimate carbon and nitrogen uptake of phytoplankton at different locations, productivity experiments were conducted by incubating phytoplankton in the incubators on the deck for 3-4 hours after adding stable isotopes (13C, 15NO3, and 15NH4) as tracers into each bottle. Productivity experiments were completed during three cruises. The samples for productivity were collected by CTD rosette water samplers at 6 different light depths (100, 50, 30, 12, 5 and 1%). To understand the spatial distribution of phytoplankton productivity and to assess effect of climate change on ocean ecosystem, productivity experiments were executed in the Amundsen Sea, Antarctica. proprietary
-KOPRI-KPDC-00001177_3 Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2018 ALL STAC Catalog 2018-11-18 2019-01-14 154.838627, -75.536572, 155.93514, -75.246428 https://cmr.earthdata.nasa.gov/search/concepts/C2244300863-AMD_KOPRI.umm_json David glacier area ice surface / bed elevation proprietary
KOPRI-KPDC-00001177_3 Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2018 AMD_KOPRI STAC Catalog 2018-11-18 2019-01-14 154.838627, -75.536572, 155.93514, -75.246428 https://cmr.earthdata.nasa.gov/search/concepts/C2244300863-AMD_KOPRI.umm_json David glacier area ice surface / bed elevation proprietary
+KOPRI-KPDC-00001177_3 Air borne Ice radar survey data of Korean route from David glacier, Antarctica in 2018 ALL STAC Catalog 2018-11-18 2019-01-14 154.838627, -75.536572, 155.93514, -75.246428 https://cmr.earthdata.nasa.gov/search/concepts/C2244300863-AMD_KOPRI.umm_json David glacier area ice surface / bed elevation proprietary
KOPRI-KPDC-00001178_2 Carbon and nitrogen uptake rates of pico-phytoplankton during two survey periods (2017 and 2018) in the Kongsfjorden, Svalbard AMD_KOPRI STAC Catalog 2017-05-04 2018-04-14 11.65, 78.918, 12.373, 78.955 https://cmr.earthdata.nasa.gov/search/concepts/C2244302365-AMD_KOPRI.umm_json To estimate carbon and nitrogen uptake of phytoplankton at different locations, productivity experiments were conducted by incubating phytoplankton in the incubators for 4-5 hours after adding stable isotopes (13C, 15NO3, and 15NH4) as tracers into each bottle. The purposes of this study were to estimate the carbon and nitrogen uptake rates of pico-phytoplanktontwo survey periods (2017 and 2018) in Kongsfjorden, Svalbard. proprietary
KOPRI-KPDC-00001179_2 Carbon and nitrogen uptake rates of phytoplankton during April 2018 in Kongsfjorden, Svalbard. AMD_KOPRI STAC Catalog 2018-04-12 2018-04-14 11.65, 78.918, 12.373, 78.955 https://cmr.earthdata.nasa.gov/search/concepts/C2244302326-AMD_KOPRI.umm_json The purposes of this study were to investigate spatial variation in total carbon and nitrogen uptake rates of phytoplankton during April in Kongsfjorden, Svalbard. proprietary
KOPRI-KPDC-00001180_2 Carbon and nitrogen uptake rates of phytoplankton during May, 2017 in the Kongsfjorden, Svalbard AMD_KOPRI STAC Catalog 2017-05-04 2017-05-08 11.65, 78.918, 12.373, 78.955 https://cmr.earthdata.nasa.gov/search/concepts/C2244302375-AMD_KOPRI.umm_json The purposes of this study were to investigate spatial variation in total carbon and nitrogen uptake rates of phytoplankton during the spring period in Kongsfjorden, Svalbard. proprietary
@@ -9595,8 +9596,8 @@ KOPRI-KPDC-00001261_1 Phytoplankton abundance in the Sea water of the Kongsfjord
KOPRI-KPDC-00001262_4 Ionospheric scintillation, Kiruna Sweden, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 21.06242, 67.87654, 21.06242, 67.87654 https://cmr.earthdata.nasa.gov/search/concepts/C2244306224-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Kiruna, Sweden Study of the ionospheric irregularity in the northern high latitude proprietary
KOPRI-KPDC-00001263_3 Neutral wind and temperature, Kiruna Sweden, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-04-15 21.06242, 67.87654, 21.06242, 67.87654 https://cmr.earthdata.nasa.gov/search/concepts/C2244306088-AMD_KOPRI.umm_json Horizontal neutral wind around 250km measured from Fabry-Perot Interferometer (FPI) at Esrange Space Center, Kiruna, Sweden Study of the atmosphere wave activities in the upper atmosphere in the northern high-latitude proprietary
KOPRI-KPDC-00001264_4 Mesospheric temperature, Kiruna Sweden, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-04-15 21.06242, 67.87654, 21.06242, 67.87654 https://cmr.earthdata.nasa.gov/search/concepts/C2244306165-AMD_KOPRI.umm_json Mesospheric temperature and airglow intensity measured from Fourier Transform Spectrometer (FTS) at Kiruna, Study of the long-term trend of mesospheric temperature in the northern high latitude proprietary
-KOPRI-KPDC-00001265_3 All-sky aurora (proton) image, KHO Longyearbyen, 2019 ALL STAC Catalog 2019-01-01 2019-04-15 16.03412, 78.15174, 16.03412, 78.15174 https://cmr.earthdata.nasa.gov/search/concepts/C2244305996-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen Study of the aurora characteristics in thenorthern high latitude proprietary
KOPRI-KPDC-00001265_3 All-sky aurora (proton) image, KHO Longyearbyen, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-04-15 16.03412, 78.15174, 16.03412, 78.15174 https://cmr.earthdata.nasa.gov/search/concepts/C2244305996-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen Study of the aurora characteristics in thenorthern high latitude proprietary
+KOPRI-KPDC-00001265_3 All-sky aurora (proton) image, KHO Longyearbyen, 2019 ALL STAC Catalog 2019-01-01 2019-04-15 16.03412, 78.15174, 16.03412, 78.15174 https://cmr.earthdata.nasa.gov/search/concepts/C2244305996-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen Study of the aurora characteristics in thenorthern high latitude proprietary
KOPRI-KPDC-00001266_4 Ionospheric scintillation, Dasan Station, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-12-31 11.9342, 78.9272, 11.9342, 78.9272 https://cmr.earthdata.nasa.gov/search/concepts/C2244306538-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Dasan station, Arctic Study of the ionospheric irregularity in the northern high latitude proprietary
KOPRI-KPDC-00001267_3 Neutral wind and temperature, Dasan Station, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-04-15 11.9333, 78.9167, 11.9333, 78.9167 https://cmr.earthdata.nasa.gov/search/concepts/C2244306103-AMD_KOPRI.umm_json Horizontal neutral wind around 250km measured from Fabry-Perot Interferometer (FPI) at Dasan station, Arctic region Study of the atmosphere wave activities in the upper atmosphere in the northern high-latitude proprietary
KOPRI-KPDC-00001268_2 The measurement of geomagnetic field at Jang Bogo Station, Antarctica at 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 164.14, -74.37, 164.14, -74.37 https://cmr.earthdata.nasa.gov/search/concepts/C2244301263-AMD_KOPRI.umm_json The value of geomagnetic field intensity observed at Jang Bogo Station, Antarctica To investigate the interaction between ionosphere and geomagnetic disturbances proprietary
@@ -9606,8 +9607,8 @@ KOPRI-KPDC-00001271_2 Ionospheric total electron content monitoring system over
KOPRI-KPDC-00001272_2 Neutron Monitor installed at Jang Bogo Station, Antarctica at 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 164.14, -74.37, 164.14, -74.37 https://cmr.earthdata.nasa.gov/search/concepts/C2244301215-AMD_KOPRI.umm_json The Neutron Monitor observes the neutron flux incoming from space to earth's atmosphere over JBS, Antarctica. To study the variation of neutron flux with the strength of solar activity and the relationship between neutron flux and atmospheric constituents. proprietary
KOPRI-KPDC-00001273_2 Neutral wind data from FPI installed at Jang Bogo Station, Antarctica at 2019 AMD_KOPRI STAC Catalog 2019-03-11 2019-09-30 164.14, -74.37, 164.14, -74.37 https://cmr.earthdata.nasa.gov/search/concepts/C2244301235-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, 250km measured from FPI instrument at JBS station, Antarctica Study of the atmospheric wave activities in MLT and thermosphere regions over the southern high-latitude proprietary
KOPRI-KPDC-00001274_2 Plasma density and drift velocity in ionoephre over Jang Bogo Station, Antarctica at 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 164.14, -74.37, 164.14, -74.37 https://cmr.earthdata.nasa.gov/search/concepts/C2244305912-AMD_KOPRI.umm_json Ionospheric plasma density and drift velocity measured from VIPIR at JBS station, Antarctica Comprehensive study of ionosphere on plasma-neutral interaction over the southern high-latitude proprietary
-KOPRI-KPDC-00001275_3 All-sky airglow image, King Sejong Station, 2019 ALL STAC Catalog 2019-03-11 2019-09-30 -58.78804, -62.22268, -58.78804, -62.22268 https://cmr.earthdata.nasa.gov/search/concepts/C2244306051-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary
KOPRI-KPDC-00001275_3 All-sky airglow image, King Sejong Station, 2019 AMD_KOPRI STAC Catalog 2019-03-11 2019-09-30 -58.78804, -62.22268, -58.78804, -62.22268 https://cmr.earthdata.nasa.gov/search/concepts/C2244306051-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary
+KOPRI-KPDC-00001275_3 All-sky airglow image, King Sejong Station, 2019 ALL STAC Catalog 2019-03-11 2019-09-30 -58.78804, -62.22268, -58.78804, -62.22268 https://cmr.earthdata.nasa.gov/search/concepts/C2244306051-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary
KOPRI-KPDC-00001276_3 Neutral wind and temperature, King Sejong Station, 2019 AMD_KOPRI STAC Catalog 2019-03-11 2019-09-30 -58.78804, -62.22268, -58.78804, -62.22268 https://cmr.earthdata.nasa.gov/search/concepts/C2244306024-AMD_KOPRI.umm_json Horizontal neutral wind around 87km, 97km, and 250km measured from Fabry-Perot Interferometer (FPI) at King Sejong Station Study of the atmosphere wave activities in the upper atmosphere in the southern high-latitude proprietary
KOPRI-KPDC-00001277_3 Ionospheric scintillation, King Sejong Station, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 -58.78804, -62.22268, -58.78804, -62.22268 https://cmr.earthdata.nasa.gov/search/concepts/C2244306035-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at King Sejong Station Study of the ionospheric irregularity in the southern high latitude proprietary
KOPRI-KPDC-00001278_4 Neutral wind and temperature (MR), King Sejong Station, 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-09-30 -58.78462, -62.2238, -58.78462, -62.2238 https://cmr.earthdata.nasa.gov/search/concepts/C2244306123-AMD_KOPRI.umm_json Neutral wind (80 – 100 km) and temperature (~90 km) measured from Meteor Radar (MR) at King Sejong Station, Antarctica Study of the atmosphere wave activities in the mesosphere and lower-thermosphere (MLT) over the southern high-latitude proprietary
@@ -9756,8 +9757,8 @@ KOPRI-KPDC-00001418_1 Eddy covariance data at DASAN Station in 2019 AMD_KOPRI ST
KOPRI-KPDC-00001420_2 Marine heat flow in Chukchi Plateau and East Siberian shelf areas on Arctic ocean 2019 AMD_KOPRI STAC Catalog 2019-09-01 2019-09-17 165.5, 72.9, -162.5, 77.2 https://cmr.earthdata.nasa.gov/search/concepts/C2244307184-AMD_KOPRI.umm_json Heat flow measurements in Chukchi Plateau and East Siberian shelf areas on Arctic ocean Investigation to the thermal structure in Chukchi Plateau and East Siberian shelf areas on Arctic ocean proprietary
KOPRI-KPDC-00001421_1 Hydrocasting observation of conductivity, temperature, and depth (CTD) AMD_KOPRI STAC Catalog 2019-08-30 2019-09-20 165.640667, 73.456833, -169.736, 77.132 https://cmr.earthdata.nasa.gov/search/concepts/C2244304657-AMD_KOPRI.umm_json Warming the Arctic surface ocean due to influx of warm Pacific water not only leads to the declining of the sea ice extent but also triggers melting gas hydrate stored in the Arctic Sea floor of the continental shelf areas. Methane (CH4) is the most abundant hydrocarbon in the atmosphere, where it plays a much more effective role as the greenhouse gas than carbon dioxide (CO2). To understand the behavior of gas hydrate in the sediment and to estimate the CH4 fluxes from the sediment through the water column to the atmosphere, we obtained data on water temperature, salinity, density and fluorescence in the water column. proprietary
KOPRI-KPDC-00001422_2 Surface observation of CH4 in the atmosphere and ocean AMD_KOPRI STAC Catalog 2019-08-30 2019-09-20 165.640667, 64.49025, -156.825778, 77.132 https://cmr.earthdata.nasa.gov/search/concepts/C2244305666-AMD_KOPRI.umm_json Warming the Arctic surface ocean due to influx of warm Pacific water not only leads to the declining of the sea ice extent but also triggers melting gas hydrate stored in the Arctic Sea floor of the continental shelf areas. Methane (CH4) is the most abundant hydrocarbon in the atmosphere, where it plays a much more effective role as the greenhouse gas than carbon dioxide (CO2). We study to estimate the CH4 fluxes on the interface of air and seawater. The CH4 in the ambient air and the surface water were quantitatively measured along the ship track. proprietary
-KOPRI-KPDC-00001423_2 2019 Arctic Araon Cruise (ARA10C) sediment cores (multiple, gravity, and box cores) ALL STAC Catalog 2019-08-29 2019-09-20 167.676767, 73.69587, 179.98125, 77.132017 https://cmr.earthdata.nasa.gov/search/concepts/C2244305039-AMD_KOPRI.umm_json Sediment cores during ARA10C were collected for various scientific research including methane cycle, sedimentology, paleontology, microbiology, organic geochemistry, etc. proprietary
KOPRI-KPDC-00001423_2 2019 Arctic Araon Cruise (ARA10C) sediment cores (multiple, gravity, and box cores) AMD_KOPRI STAC Catalog 2019-08-29 2019-09-20 167.676767, 73.69587, 179.98125, 77.132017 https://cmr.earthdata.nasa.gov/search/concepts/C2244305039-AMD_KOPRI.umm_json Sediment cores during ARA10C were collected for various scientific research including methane cycle, sedimentology, paleontology, microbiology, organic geochemistry, etc. proprietary
+KOPRI-KPDC-00001423_2 2019 Arctic Araon Cruise (ARA10C) sediment cores (multiple, gravity, and box cores) ALL STAC Catalog 2019-08-29 2019-09-20 167.676767, 73.69587, 179.98125, 77.132017 https://cmr.earthdata.nasa.gov/search/concepts/C2244305039-AMD_KOPRI.umm_json Sediment cores during ARA10C were collected for various scientific research including methane cycle, sedimentology, paleontology, microbiology, organic geochemistry, etc. proprietary
KOPRI-KPDC-00001424_1 Manganese nodule samples in the East siberian shelf (2019 ARA10C cruise) AMD_KOPRI STAC Catalog 2019-08-29 2019-11-20 176.338742, 74.921332, 179.055023, 75.799365 https://cmr.earthdata.nasa.gov/search/concepts/C2244305407-AMD_KOPRI.umm_json We collected the manganese nodule by dredge to study the distribution of manganese nodule in the East siberian sea, Arctic Ocean. proprietary
KOPRI-KPDC-00001425_1 Ship-borne radiosonde observation data over the Arctic Ocean in the 2016 Araon summer expedition(ARA07B,ARA07C) AMD_KOPRI STAC Catalog 2016-08-06 2016-09-08 179.619, 66.819, 179.024, 78.547 https://cmr.earthdata.nasa.gov/search/concepts/C2244301446-AMD_KOPRI.umm_json The radiosonde balloon sounding observations were performed from 6 August 2016 to 8 September 2016 to obtain the Arctic Ocean high-resolution atmospheric vertical profiles along the IBRV Araon cruise track at four times daily intervals(00,06,12, and 18UTC). The data include vertical profiles of temperature, humidity, pressure, wind speed, and wind direction up to about 30km. The data have been used for the data assimilation of the KOPRI Arctic weather forecast system. proprietary
KOPRI-KPDC-00001426_1 Ship-borne radiosonde observation data over the Arctic Ocean in the 2017 Araon summer expedition(ARA08B,ARA08C) AMD_KOPRI STAC Catalog 2017-08-07 2017-09-13 179.183, 65.174, 179.086, 77.991 https://cmr.earthdata.nasa.gov/search/concepts/C2244301491-AMD_KOPRI.umm_json The radiosonde balloon sounding observations were performed from 7 August 2017 to 13 September 2017 to obtain the Arctic Ocean high-resolution atmospheric vertical profiles along the IBRV Araon cruise track at four times daily intervals(00,06,12, and 18UTC). The data include vertical profiles of temperature, humidity, pressure, wind speed, and wind direction up to about 30km. The data have been used for the data assimilation of the KOPRI Arctic weather forecast system. proprietary
@@ -9830,20 +9831,20 @@ KOPRI-KPDC-00001494_2 Ionospheric scintillation, King Sejong Station, 2020 AMD_K
KOPRI-KPDC-00001495_3 Metamorphic pressure (P)-temperature (T) condition of the Dessent Ridge amphibolite from the Mountaineer Range, northern Victoria Land, Antarctica AMD_KOPRI STAC Catalog 2020-05-01 2020-08-31 166.575833, -73.391667, 166.575833, -73.391667 https://cmr.earthdata.nasa.gov/search/concepts/C2244306301-AMD_KOPRI.umm_json The metamorphic P-T condition of the Dessent Ridge (Mountaineer Range) amphibolite (SB171119-3B) was calculated in order to investigate the history of tectonic evolution in northern Victoria Land, Antarctica. proprietary
KOPRI-KPDC-00001496_3 SHRIMP U-Pb age data for the Mt. Murchison migmatitic gneiss (four samples) from the Mountaineer Range, northern Victoria Land, Antarctica AMD_KOPRI STAC Catalog 2020-05-01 2020-08-31 166.432778, -73.407778, 166.432778, -73.407778 https://cmr.earthdata.nasa.gov/search/concepts/C2244306320-AMD_KOPRI.umm_json The SHRIMP U-Pb age of the Mt. Murchison (Mountaineer Range) gneiss was measured in order to examine the history of tectonic evolution in northern Victoria Land, Antarctica. The metamorphic and detrital ages of the migmatitic gneiss SB171122-3 (four different parts) were obtained. proprietary
KOPRI-KPDC-00001497_2 Lichen samples from King George Island collected in 2020 AMD_KOPRI STAC Catalog 2020-01-10 2020-01-19 -58.766667, -62.216667, -58.766667, -62.216667 https://cmr.earthdata.nasa.gov/search/concepts/C2244306335-AMD_KOPRI.umm_json Lichen samples from King George Island collected in 2020 Ecophysiological study of lichen proprietary
-KOPRI-KPDC-00001498_2 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2019 AMD_KOPRI STAC Catalog 2019-01-19 2020-01-26 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244306346-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2019 Long term monitoring proprietary
KOPRI-KPDC-00001498_2 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2019 ALL STAC Catalog 2019-01-19 2020-01-26 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244306346-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2019 Long term monitoring proprietary
+KOPRI-KPDC-00001498_2 Air temperature, air humidity, PAR, substrate temperature, and substrate humidity data from Barton Peninsular in King George Island collected in 2019 AMD_KOPRI STAC Catalog 2019-01-19 2020-01-26 -58.789338, -62.240538, -58.721474, -62.220364 https://cmr.earthdata.nasa.gov/search/concepts/C2244306346-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in King George Island collected during 1 year, 2019 Long term monitoring proprietary
KOPRI-KPDC-00001501_2 Temporal variation of marine phytoplankton in the surface water of the Antarctic Jang Bogo Station in Terra Nova Bay, January 2020- September 2020 AMD_KOPRI STAC Catalog 2020-01-01 2020-09-30 164.2, -74.616667, 164.2, -74.616667 https://cmr.earthdata.nasa.gov/search/concepts/C2244303668-AMD_KOPRI.umm_json As a research on the ecology of phytoplankton in the coastal waters of the Jang Bogo Station in Antarctica, the community of phytoplankton and the temporal influences of environmental factors. The temporal influences of environmental factors on marine phytoplankton community were investigated in the Jang Bogo Station in Antarctica. Investigation of marine phytoplankton biomass in the coastal waters around the Jang Bogo Station in Antarctica for the monitoring by environmental change in the surface sea water conducted. proprietary
KOPRI-KPDC-00001502_4 Soil physicochemical data from Barton and Weaver peninsula in King George Island at 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-01-31 -58.8, -62.233333, -58.766664, -62.2 https://cmr.earthdata.nasa.gov/search/concepts/C2244301429-AMD_KOPRI.umm_json Physicochemical data (pH, EC, TC, TIC, TN and soil texture) of glacier foreland soil samples obtained from Barton and Weaver Peninsula in King George Island at 2019 proprietary
KOPRI-KPDC-00001503_4 Fungal NGS data from Barton and Weaver peninsula in King George Island at 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-01-31 -58.8, -62.233333, -58.766664, -62.2 https://cmr.earthdata.nasa.gov/search/concepts/C2244301480-AMD_KOPRI.umm_json These data were obtained to examine fungal community structure and reveal the correlation between soil physicochemical factors and soil fungal composition in glacial foreland of the Antarctic. proprietary
KOPRI-KPDC-00001504_1 Soil and freshwater samples of the Antarctic King Sejong Station from Barton Peninsular collected in 2019 AMD_KOPRI STAC Catalog 2020-01-10 2020-01-21 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244301324-AMD_KOPRI.umm_json Analysis of microbial community structure and diversity in soil and freshwater samples of the Antarctic King Sejong Station from Barton Peninsular in Antarctica Investigation to the terrestrial biodiversity in Barton peninsular for the monitoring by environment change proprietary
-KOPRI-KPDC-00001505_5 All-sky airglow image, King Sejong Station, 2020 AMD_KOPRI STAC Catalog 2020-02-18 2020-09-23 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244307204-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary
KOPRI-KPDC-00001505_5 All-sky airglow image, King Sejong Station, 2020 ALL STAC Catalog 2020-02-18 2020-09-23 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244307204-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary
+KOPRI-KPDC-00001505_5 All-sky airglow image, King Sejong Station, 2020 AMD_KOPRI STAC Catalog 2020-02-18 2020-09-23 -58.47, -62.13, -58.47, -62.13 https://cmr.earthdata.nasa.gov/search/concepts/C2244307204-AMD_KOPRI.umm_json Airglow(OI 557.7nm, OI 630.0nm, and OH Meinel band) image measured from all-sky camera at King Sejong Station, Antarctica Study of the atmospheric wave activities in the southern high latitude proprietary
KOPRI-KPDC-00001506_6 Ionospheric scintillation, Kiruna Sweden, 2020 AMD_KOPRI STAC Catalog 2020-01-01 2020-10-20 21.03, 67.53, 21.03, 67.53 https://cmr.earthdata.nasa.gov/search/concepts/C2244307220-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Kiruna, Sweden Study of the ionospheric irregularity in the northern high latitude proprietary
KOPRI-KPDC-00001507_6 Ionospheric scintillation, Dasan Station, 2020 AMD_KOPRI STAC Catalog 2020-01-01 2020-12-31 11.9342, 78.9272, 11.9342, 78.9272 https://cmr.earthdata.nasa.gov/search/concepts/C2244306380-AMD_KOPRI.umm_json Amplitude and phase scintillations of GPS signal measured from scintillation monitor at Dasan station, Arctic Study of the ionospheric irregularity in the northern high latitude proprietary
KOPRI-KPDC-00001508_4 All-sky aurora (proton) image, KHO Longyearbyen, 2020 ALL STAC Catalog 2020-01-01 2020-10-19 16.12, 78.48, 16.12, 78.48 https://cmr.earthdata.nasa.gov/search/concepts/C2244307127-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen Study of the aurora characteristics in thenorthern high latitude proprietary
KOPRI-KPDC-00001508_4 All-sky aurora (proton) image, KHO Longyearbyen, 2020 AMD_KOPRI STAC Catalog 2020-01-01 2020-10-19 16.12, 78.48, 16.12, 78.48 https://cmr.earthdata.nasa.gov/search/concepts/C2244307127-AMD_KOPRI.umm_json Aurora (proton) image measured from all-sky camera at KHO, Longyearbyen Study of the aurora characteristics in thenorthern high latitude proprietary
-KOPRI-KPDC-00001509_1 2019-2020 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity AMD_KOPRI STAC Catalog 2019-01-19 2020-01-26 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244301374-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2019 proprietary
KOPRI-KPDC-00001509_1 2019-2020 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity ALL STAC Catalog 2019-01-19 2020-01-26 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244301374-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2019 proprietary
+KOPRI-KPDC-00001509_1 2019-2020 Barton Peninsular micro-climate data_HOBO soil temp., PAR, air temp., relative humidity AMD_KOPRI STAC Catalog 2019-01-19 2020-01-26 -58.788436, -62.240056, -58.719694, -62.218583 https://cmr.earthdata.nasa.gov/search/concepts/C2244301374-AMD_KOPRI.umm_json Micro-climate data set from Barton Peninsular in Antarctica collected during 1 year, 2019 proprietary
KOPRI-KPDC-00001510_2 Snow cover map of the Barton Peninsula, King George Island, Antarctica AMD_KOPRI STAC Catalog 1986-01-28 2020-01-19 -58.747839, -62.229025, -58.747839, -62.229025 https://cmr.earthdata.nasa.gov/search/concepts/C2244306359-AMD_KOPRI.umm_json Snow cover on the Barton Peninsula, Antarctica extracted from time-series Landsat satellite data proprietary
KOPRI-KPDC-00001511_3 Bacterial NGS data from Barton and Weaver peninsula in King George Island at 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-01-31 -58.8, -62.233333, -58.766664, -62.2 https://cmr.earthdata.nasa.gov/search/concepts/C2244306368-AMD_KOPRI.umm_json These data were obtained to examine bacterial community structure and reveal the correlation between soil physicochemical factors and soil bacterial composition in glacial foreland of the Antarctic. proprietary
KOPRI-KPDC-00001512_2 2019/20 season Korean Route Traverse based GPS GIS data ALL STAC Catalog 2019-11-07 2020-01-18 149.040453, -77.04815, 164.228789, -74.62405 https://cmr.earthdata.nasa.gov/search/concepts/C2244306379-AMD_KOPRI.umm_json GOAL ○ Development of Korean route and infrastructure such as research camp to approach the Antarctic inland ○ Establishment of support system for the Antarctic inland researches RESEARCH CONTENTS ○ A safe and reliable route expedition to the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researches ○ Construction of logistic camps at the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researches proprietary
@@ -9954,8 +9955,8 @@ KOPRI-KPDC-00001628_3 Weather forecasts over the Arctic region AMD_KOPRI STAC Ca
KOPRI-KPDC-00001629_1 Foraging trips of Chinstrap penguin and Gentoo penguin breeding at Narebski Point from 2006 to 2019 AMD_KOPRI STAC Catalog 2006-12-17 2020-01-02 -58.766667, -62.233333, -58.766667, -62.233333 https://cmr.earthdata.nasa.gov/search/concepts/C2244301271-AMD_KOPRI.umm_json This dataset is the foraging trips of the chick-guarding period penguin obtained by attaching a GPS logger and a time depth recorder device to Chinstrap penguin and Gentoo penguin at Narebski Point from December 2006 to January 2020. In sheet1 and sheet2, the coordinates are recorded in the foraging dive. Separate sheet2 has metadata and parameters of tested penguin. proprietary
KOPRI-KPDC-00001630_1 Foraging trips of Adélie penguin breeding at Inexpressible Island on December 2018 AMD_KOPRI STAC Catalog 2018-12-15 2018-12-17 163.65, -74.9, 163.65, -74.9 https://cmr.earthdata.nasa.gov/search/concepts/C2244301300-AMD_KOPRI.umm_json This dataset is the foraging trips of the chick-guarding period penguin obtained by attaching a GPS logger and a time depth recorder device to Adélie penguin at Inexpressible Island on December 2018. In sheet1, the coordinates are recorded in the foraging dive. Separate sheet2 has metadata and parameters of tested penguin. proprietary
KOPRI-KPDC-00001631_2 Foraging trips of Adélie penguin breeding at Adélie Cove on December 2018 AMD_KOPRI STAC Catalog 2018-12-31 2019-01-02 164, -74.75, 164, -74.75 https://cmr.earthdata.nasa.gov/search/concepts/C2244306008-AMD_KOPRI.umm_json This dataset is the foraging trips of the chick-guarding period penguin obtained by attaching a GPS logger and a time depth recorder device to Adélie penguin at Adélie Cove from December 2018 to January 2019. In sheet1, the coordinates are recorded in the foraging dive. Separate sheet2 has metadata and parameters of tested penguin. proprietary
-KOPRI-KPDC-00001632_1 A study on the distribution characteristics of stable oxygen isotope in the Amundsen Sea in 2011 AMD_KOPRI STAC Catalog 2010-12-20 2011-01-20 -145, -74.6, -112, -72.5 https://cmr.earthdata.nasa.gov/search/concepts/C2244301322-AMD_KOPRI.umm_json To investigate the controls that affect inorganic carbon in the water column of the Amundsen Sea, hydrographic survey using IBRV Araon was carried out from December 20, 2010 to January 22, 2011. Oxygen-18 isotopes were analyzed at 21 stations. proprietary
KOPRI-KPDC-00001632_1 A study on the distribution characteristics of stable oxygen isotope in the Amundsen Sea in 2011 ALL STAC Catalog 2010-12-20 2011-01-20 -145, -74.6, -112, -72.5 https://cmr.earthdata.nasa.gov/search/concepts/C2244301322-AMD_KOPRI.umm_json To investigate the controls that affect inorganic carbon in the water column of the Amundsen Sea, hydrographic survey using IBRV Araon was carried out from December 20, 2010 to January 22, 2011. Oxygen-18 isotopes were analyzed at 21 stations. proprietary
+KOPRI-KPDC-00001632_1 A study on the distribution characteristics of stable oxygen isotope in the Amundsen Sea in 2011 AMD_KOPRI STAC Catalog 2010-12-20 2011-01-20 -145, -74.6, -112, -72.5 https://cmr.earthdata.nasa.gov/search/concepts/C2244301322-AMD_KOPRI.umm_json To investigate the controls that affect inorganic carbon in the water column of the Amundsen Sea, hydrographic survey using IBRV Araon was carried out from December 20, 2010 to January 22, 2011. Oxygen-18 isotopes were analyzed at 21 stations. proprietary
KOPRI-KPDC-00001633_1 Observed CTD data and dissolved noble gases along the Dotson Trough, Amundsen Sea, Antarctica in 2011 AMD_KOPRI STAC Catalog 2010-12-26 2011-01-02 -117.6895, -74.2067, -112.4962, -72.4145 https://cmr.earthdata.nasa.gov/search/concepts/C2244301379-AMD_KOPRI.umm_json This dataset is dissolved noble gases obtained during ANA01C cruise. The dataset also contain potential temperature, salinity and dissolved oxygen obtained by CTD rosette system. The dataset constituted 5 station along the Dotson Trough, Amundsen Sea. proprietary
KOPRI-KPDC-00001634_2 Lowered Acoustic Doppler Current Profiler (LADCP) data - August 2016, western Arctic Ocean (4 CTD stations) AMD_KOPRI STAC Catalog 2016-08-08 2016-08-27 -175.895, 76.575, -164.155, 77.864 https://cmr.earthdata.nasa.gov/search/concepts/C2244306113-AMD_KOPRI.umm_json The data are the Lowered Acoustic Doppler Current Profiler (LADCP) data obtained from R/V Icebreaker ARAON in August 2016. The dataset contains LADCP data from surface to 100 m depth (5-m interval) at 4 CTD stations (Sts. 23, 24, 29, and 30) aiming at measuring instantaneous current profiles. proprietary
KOPRI-KPDC-00001635_2 Meteorological data at the Jang Bogo Station, Antarctica in 2020 AMD_KOPRI STAC Catalog 2020-01-01 2020-12-31 164.228333, -74.623333, 164.228333, -74.623333 https://cmr.earthdata.nasa.gov/search/concepts/C2244306204-AMD_KOPRI.umm_json Meteorological observation was carried out at the Jang Bogo Station in 2020. Observational elements are composed of wind, air temperature, relative humidity, station level atmospheric pressure, visibility, snow depth, cloud height, and precipitation. Goals of this observation are 1) to understand meteorological phenomena and 2) to monitor climate change at Antarctica. These data are recorded automatically then examined by meteorological expert at the station to be produced as a daily, monthly, and annual report. To understand weather phenomena and to monitor climate variation at Jang Bogo Station, Antarctica proprietary
@@ -9993,10 +9994,10 @@ KOPRI-KPDC-00001666_2 Wind data on ARAON DaDis for Antarctic cruise, 2020/2021 A
KOPRI-KPDC-00001667_2 Upper O3 observation data at Jang Bogo Station in 2019 AMD_KOPRI STAC Catalog 2019-01-17 2019-11-28 164.232072, -74.623811, 164.232072, -74.623811 https://cmr.earthdata.nasa.gov/search/concepts/C2244306388-AMD_KOPRI.umm_json Regular upper O3 observation is made once a week from SEP to NOV, and a month except for the preceding period. O3 is sampled and recorded every a second. Monitoring of changes in meteorological variables (O3) with altitude over Jang Bogo station. proprietary
KOPRI-KPDC-00001668_2 Upper O3 observation data at Jang Bogo Station in 2020 AMD_KOPRI STAC Catalog 2020-01-16 2020-12-17 164.232072, -74.623811, 164.232072, -74.623811 https://cmr.earthdata.nasa.gov/search/concepts/C2244306563-AMD_KOPRI.umm_json Regular upper O3 observation is made once a week from SEP to NOV, and a month except for the preceding period. O3 is sampled and recorded every a second. Monitoring of changes in meteorological variables (O3) with altitude over Jang Bogo station. proprietary
KOPRI-KPDC-00001669_2 Upper O3 observation data at Jang Bogo Station in 2021 AMD_KOPRI STAC Catalog 2021-01-02 2021-06-10 164.232072, -74.623811, 164.232072, -74.623811 https://cmr.earthdata.nasa.gov/search/concepts/C2244306666-AMD_KOPRI.umm_json Regular upper O3 observation is made once a week from SEP to NOV, and a month except for the preceding period. O3 is sampled and recorded every a second. Monitoring of changes in meteorological variables (O3) with altitude over Jang Bogo station. proprietary
-KOPRI-KPDC-00001671_3 2018&19 Multibeam data of Terra Nova Bay (around Jang Bogo station) ALL STAC Catalog 2019-02-14 2019-02-15 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306725-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2018&19 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary
KOPRI-KPDC-00001671_3 2018&19 Multibeam data of Terra Nova Bay (around Jang Bogo station) AMD_KOPRI STAC Catalog 2019-02-14 2019-02-15 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306725-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2018&19 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary
-KOPRI-KPDC-00001672_3 2016&17 Multibeam data of Terra Nova Bay (around Jang Bogo station) ALL STAC Catalog 2017-01-29 2017-02-06 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306756-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2016&17 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary
+KOPRI-KPDC-00001671_3 2018&19 Multibeam data of Terra Nova Bay (around Jang Bogo station) ALL STAC Catalog 2019-02-14 2019-02-15 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306725-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2018&19 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary
KOPRI-KPDC-00001672_3 2016&17 Multibeam data of Terra Nova Bay (around Jang Bogo station) AMD_KOPRI STAC Catalog 2017-01-29 2017-02-06 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306756-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2016&17 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary
+KOPRI-KPDC-00001672_3 2016&17 Multibeam data of Terra Nova Bay (around Jang Bogo station) ALL STAC Catalog 2017-01-29 2017-02-06 163.984928, -74.73604, 164.57053, -74.610485 https://cmr.earthdata.nasa.gov/search/concepts/C2244306756-AMD_KOPRI.umm_json The necessity of supplying charts for securing the safety required for navigation and berthing of small ships for research activities and support around the Jang Bogo station increased. Accordingly, in the 2016&17 season, the bathymetry survey was conducted in Terra Noval Bay using the IBRV Araon / Multibeam equipment is EM122 of Kongsberg proprietary
KOPRI-KPDC-00001673_2 Multibeam data, Australian-Antarctic Ridge (AAR) and the Pacific-Antarctic Ridge (PAR), 2020/21 season AMD_KOPRI STAC Catalog 2020-11-28 2020-11-29 -179.79775, -66.58295, -176.64499, -64.11792 https://cmr.earthdata.nasa.gov/search/concepts/C2244306908-AMD_KOPRI.umm_json During 2020/2021 summer season, due to sea ice, we obtained high resolution bathymetric data and marine magnetic data for only one short spreading-segment in “large-scaled spreading and fracture zones (or leaky transform faults)” located between the Australian-Antarctic Ridge (AAR) and the Pacific-Antarctic Ridge (PAR). It is expected that it will be able to contribute to the investigations for the tectonic evolution of the Antarctica related to the Australian-Pacific-Antarctic plates and the evolution of the Zealandia-Antarctic mantle, through the bathymetric and magnetic data that will be accumulated in the future. proprietary
KOPRI-KPDC-00001674_4 WRF model namelist input for Arctic winter climate change studies AMD_KOPRI STAC Catalog 2021-01-01 2021-01-01 180, 56, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2244307132-AMD_KOPRI.umm_json "Attached is a namelist for polar region optimized version of WRF model. It was used for the study ""Short-term Atmospheric Response to Recent Arctic Sea Ice Loss"" (submitted to Geophysical Research Letters)." proprietary
KOPRI-KPDC-00001675_2 Flux of individual lipid biomarkers in the KAMS1 and KAMS2 which collected from August 2017 to August 2018. AMD_KOPRI STAC Catalog 2017-08-18 2018-08-13 177.056033, 75.244383, -171.981267, 75.79935 https://cmr.earthdata.nasa.gov/search/concepts/C2244306291-AMD_KOPRI.umm_json Flux of individual lipid biomarkers in the KAMS1 and KAMS2 which collected from August 2017 to August 2018. Table 1 contained TMF, POC, SIC and Chla data set. Table 2 and 3 contained individual lipid biomarkers data in the KAMS1 and KAMS2, respectively. proprietary
@@ -10097,8 +10098,8 @@ KOPRI-KPDC-00001773_2 Genes involved in adaptation to marine environment in Ceta
KOPRI-KPDC-00001774_1 Weddell Seal hair sample (196296) AMD_KOPRI STAC Catalog 2020-12-21 2020-12-21 164.225806, -74.624389, 164.225806, -74.624389 https://cmr.earthdata.nasa.gov/search/concepts/C2244302383-AMD_KOPRI.umm_json Hair samples were collected to study breeding ecology and adaptation of Weddell seals in Antarctic. proprietary
KOPRI-KPDC-00001776_4 CTD data (2011-2019), XCTD data (2017-2019) AMD_KOPRI STAC Catalog 2011-08-01 2019-08-31 177, 72.5, -150, 80 https://cmr.earthdata.nasa.gov/search/concepts/C2244306846-AMD_KOPRI.umm_json To investigate variations of water masses in the Chukchi Borderland proprietary
KOPRI-KPDC-00001777_2 Soil physicochemical data from two long-term chronosequences (Ardley Island and King George Island) in 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-01-31 -58.93333, -62.233333, -58.766667, -62.216666 https://cmr.earthdata.nasa.gov/search/concepts/C2244306274-AMD_KOPRI.umm_json Physicochemical data (pH, EC, TC, SOC, TIC, TN and soil texture) of glacier foreland soil samples obtained from Ardley and King George Island at 2019 proprietary
-KOPRI-KPDC-00001778_2 2020/21 season Korean Route Traverse based GPS GIS data AMD_KOPRI STAC Catalog 2020-12-01 2020-12-31 164.2362, -74.6281, 164.2362, -74.6281 https://cmr.earthdata.nasa.gov/search/concepts/C2244306293-AMD_KOPRI.umm_json GOAL ○ Development of Korean route and infrastructure such as research camp to approach the Antarctic inland ○ Establishment of support system for the Antarctic inland researches RESEARCH CONTENTS ○ A safe and reliable route expedition to the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researches ○ Construction of logistic camps at the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researche proprietary
KOPRI-KPDC-00001778_2 2020/21 season Korean Route Traverse based GPS GIS data ALL STAC Catalog 2020-12-01 2020-12-31 164.2362, -74.6281, 164.2362, -74.6281 https://cmr.earthdata.nasa.gov/search/concepts/C2244306293-AMD_KOPRI.umm_json GOAL ○ Development of Korean route and infrastructure such as research camp to approach the Antarctic inland ○ Establishment of support system for the Antarctic inland researches RESEARCH CONTENTS ○ A safe and reliable route expedition to the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researches ○ Construction of logistic camps at the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researche proprietary
+KOPRI-KPDC-00001778_2 2020/21 season Korean Route Traverse based GPS GIS data AMD_KOPRI STAC Catalog 2020-12-01 2020-12-31 164.2362, -74.6281, 164.2362, -74.6281 https://cmr.earthdata.nasa.gov/search/concepts/C2244306293-AMD_KOPRI.umm_json GOAL ○ Development of Korean route and infrastructure such as research camp to approach the Antarctic inland ○ Establishment of support system for the Antarctic inland researches RESEARCH CONTENTS ○ A safe and reliable route expedition to the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researches ○ Construction of logistic camps at the sites of the Subglacial Lake and Deep Ice Core drilling for Antarctic inland researche proprietary
KOPRI-KPDC-00001779_3 LoopSeq amplicon sequencing data of microbial 16S-18S-ITS long reads from King George Island in 2019 AMD_KOPRI STAC Catalog 2019-01-01 2019-01-31 -58.93333, -62.216666, -58.93333, -62.216666 https://cmr.earthdata.nasa.gov/search/concepts/C2244306309-AMD_KOPRI.umm_json Loopseq long sequencing read data amplified 16S-18S, 18S-ITS region through synthetic long-read (SLR) sequencing technology to identify microbial species in glacial forelands of the Antarctic. proprietary
KOPRI-KPDC-00001780_7 Multibeam data (around Orca seamount in Bransfield strait) / 2020&21 season ANA11B AMD_KOPRI STAC Catalog 2021-01-23 2021-01-28 -58.8341, -62.55474, -57.84559, -62.38568 https://cmr.earthdata.nasa.gov/search/concepts/C2244306329-AMD_KOPRI.umm_json Since last year, the frequency of earthquakes has increased in the vicinity of Orca seamount in the Bransfield Strait. Accordingly, in order to confirm the change of the submarine topography due to the earthquake, a side line was set in the epicenter where earthquakes mainly occur and the area covering the Orca seamount, and multi-beam survey was conducted. The survey area shows a distribution of water depth of -300 to -2000m. The observation results that have been post-processed will be used as basic data to analyze geological and geophysical characteristics of the region in the future. proprietary
KOPRI-KPDC-00001781_5 KPSN Seismic Data at Victoria Land, Antarctic 2020 AMD_KOPRI STAC Catalog 2020-01-01 2020-12-31 159.085, -75.605, 165.7361, -74.137 https://cmr.earthdata.nasa.gov/search/concepts/C2244306339-AMD_KOPRI.umm_json To monitor the activites of Mt. Melbourne and glacial movements proprietary
@@ -10117,8 +10118,8 @@ KOPRI-KPDC-00001793_2 SHRIMP zircon U-Pb age data for the Abbott alkali feldspar
KOPRI-KPDC-00001794_2 Ship-borne radiosonde observation data over the Arctic Ocean in the 2021 Araon summer expedition(ARA12B,ARA12C) AMD_KOPRI STAC Catalog 2021-07-19 2021-09-12 179.974635, 58.66413, 179.741158, 80.002337 https://cmr.earthdata.nasa.gov/search/concepts/C2244304238-AMD_KOPRI.umm_json The radiosonde balloon sounding observations were performed from 18 July 2021 to 12 September 2021 to obtain the Arctic Ocean high-resolution atmospheric vertical profiles along the IBRV Araon cruise track at two times daily intervals(00 and 12UTC). The data include vertical profiles of temperature, humidity, pressure, wind speed, and wind direction up to about 30km. The data have been used for the data assimilation of the KOPRI Arctic weather forecast system. proprietary
KOPRI-KPDC-00001795_2 Ship-borne radiosonde observation data over the Arctic Ocean in the 2021 Araon summer expedition(ARA12A) AMD_KOPRI STAC Catalog 2021-07-08 2021-07-14 158.04125, 45.86753, -173.457097, 56.675897 https://cmr.earthdata.nasa.gov/search/concepts/C2244304627-AMD_KOPRI.umm_json The radiosonde balloon sounding observations were performed from 8 July 2021 to 14 July 2021 to obtain the Bering Sea high-resolution atmospheric vertical profiles along the IBRV Araon cruise track at four times daily intervals(00,06,12, and 18UTC). The data include vertical profiles of temperature, humidity, pressure, wind speed, and wind direction up to about 30km. The data have been used for the data assimilation of the KOPRI Arctic weather forecast system. proprietary
KOPRI-KPDC-00001796_2 miRNA sequencing data of Field and lab culture Sanionia uncinata AMD_KOPRI STAC Catalog 2015-02-01 2021-08-30 126.646833, -62.219722, -58.767778, 37.368722 https://cmr.earthdata.nasa.gov/search/concepts/C2244306533-AMD_KOPRI.umm_json To investigate miRNA profiling of antartic moss Sanionia uncinata during seasonal changes Using field samples and lab cultre samples proprietary
-KOPRI-KPDC-00001797_2 Age characteristics of Antarctic scallops (Adamussium colbecki) AMD_KOPRI STAC Catalog 2019-02-21 2019-03-01 164.243867, -74.627661, 164.243867, -74.627661 https://cmr.earthdata.nasa.gov/search/concepts/C2244306570-AMD_KOPRI.umm_json Age measurement of Antarctic scallops by shell height proprietary
KOPRI-KPDC-00001797_2 Age characteristics of Antarctic scallops (Adamussium colbecki) ALL STAC Catalog 2019-02-21 2019-03-01 164.243867, -74.627661, 164.243867, -74.627661 https://cmr.earthdata.nasa.gov/search/concepts/C2244306570-AMD_KOPRI.umm_json Age measurement of Antarctic scallops by shell height proprietary
+KOPRI-KPDC-00001797_2 Age characteristics of Antarctic scallops (Adamussium colbecki) AMD_KOPRI STAC Catalog 2019-02-21 2019-03-01 164.243867, -74.627661, 164.243867, -74.627661 https://cmr.earthdata.nasa.gov/search/concepts/C2244306570-AMD_KOPRI.umm_json Age measurement of Antarctic scallops by shell height proprietary
KOPRI-KPDC-00001798_2 Fast Ice Map in the Terra Nova Bay AMD_KOPRI STAC Catalog 2017-05-14 2018-01-09 164.259926, -74.65589, 164.259926, -74.65589 https://cmr.earthdata.nasa.gov/search/concepts/C2244306604-AMD_KOPRI.umm_json Extraction of fast ice area using satellite data proprietary
KOPRI-KPDC-00001800_2 Species list and coverage of benthic animals in Ross Sea, Antarctica AMD_KOPRI STAC Catalog 2017-11-01 2019-11-30 168.024447, -77.84013, 168.024447, -77.84013 https://cmr.earthdata.nasa.gov/search/concepts/C2244306640-AMD_KOPRI.umm_json Species list and coverage of benthic animals in Ross Sea, Antarctica proprietary
KOPRI-KPDC-00001801_2 Ecological index of benthic animals in Ross Sea, Antarctica AMD_KOPRI STAC Catalog 2017-11-01 2019-11-30 168.024447, -77.84013, 168.024447, -77.84013 https://cmr.earthdata.nasa.gov/search/concepts/C2244306667-AMD_KOPRI.umm_json Biodiversity analysis of benthic animals in Ross Sea, Antarctica proprietary
@@ -10265,12 +10266,12 @@ Krill_Technical_Reports_1 Krill Ecology - Technical Reports and Systems Guides A
Krill_growth_rates_1 Experimental studies into growth and ageing of krill 1993-2003 AU_AADC STAC Catalog 1993-03-11 2003-03-17 60, -67, 110, -53 https://cmr.earthdata.nasa.gov/search/concepts/C1214313583-AU_AADC.umm_json Metadata record for data from ASAC Project 2337 See the link below for public details on this project. ---- Public Summary from Project ---- The experimental krill research program is focused on obtaining life history information of use in managing the krill fishery - the largest Antarctic fishery. In particular, the program will concentrate on studies into schooling, growth and ageing of krill. From the abstracts of some of the referenced papers: Nucleic acid contents of tissue were determined from field-caught Antarctic krill to determine whether they could be used as an alternative estimator of individual growth rates which can currently only be obtained by labour intensive on-board incubations. Krill from contrasting growth regimes from early and late summer exhibited differences in RNA-based indices. There was a significant correlation between the independently measured individual growth rates and the RNA-based indices. There was a significant correlation between the independently measured individual growth rates and the RNA:DNA ratio and also the RNA concentration of krill tissue, although the strength of the relationship was only modest. DNA concentration, on average, was relatively constant, irrespective of the growth rates. The moult stage did not appear to have a significant effect on the nucleic acid contents of tissue. Overall, the amount of both nucleic acids varied considerably between individuals. Nucleic acid-based indicators may provide information concerning the recent growth and nutritional status of krill and further experimentation under controlled conditions is warranted. The are, however, reasonably costly and time-consuming measurements. Growth rates of Antarctic krill Euphausia superba Dana in the Indian Ocean sector of the Southern Ocean were measured in 4 summers. Growth rate was measured using an 'instantaneous growth rate' technique which involved measuring the mean change in length if the uropods at moulting. In the first 4 days following collection mean growth rates ranged from 0.35 to 7.34% per moult in adults and 2.42 to 9.05% in juveniles. Mean growth rates of adult and juvenile krill differed between areas and between the different years of the investigation. When food was restricted under experimental conditions, individual krill began to shrink immediately and mean population growth rates decreased gradually, becoming negative after as little as 7 days. Populations of krill which exhibited initial growth rates began to shrink later than those which had initially been growing more slowly. Data were collected on growth rates of krill. These data were collected as part of ASAC projects 34, 1074, 2220 and 2337. ASAC_34 - Ecophysiology of Antarctic Krill 'Euphausia superba' ASAC_1074 - Seasonal growth in krill ASAC_2220 - Collection of live Antarctic krill ASAC_2337 - Experimental studies into growth and ageing of krill The fields in this dataset are: Field season (eg FS9596 = Field Season 1995-1996) Area (eg Indian Ocean) Cruise Month Date Latitude Longitude Total Number of Krill Dead Krill Moulted Krill Experiment ID Station ID Sample ID Sex Growth (IGR%) (% growth at time of moulting) Uropod Size (mm) Days after capture (when moulted) Standard length proprietary
Kuparuk_Veg_Maps_1378_1 Maps of Vegetation Types and Physiographic Features, Kuparuk River Basin, Alaska ORNL_CLOUD STAC Catalog 1976-08-04 2008-12-31 -151.2, 68.29, -148.09, 70.54 https://cmr.earthdata.nasa.gov/search/concepts/C2170969950-ORNL_CLOUD.umm_json This data set provides a collection of vegetation, landscape, geobotanical, elevation, hydrology, and geologic maps for the Kuparuk River Basin, North Slope, Alaska. The maps cover either (1) the entire Kuparuk River Basin, from the headwaters on the north side of the Brooks Range to the Beaufort Sea coast, or (2) the selected Upper Kuparuk River Region including the Toolik Lake and Imnavait Creek research areas. The maps were produced from imagery and existing geobotanical maps covering the period 1976-08-04 to 2008-12-31. proprietary
Kuroshio_Area_0 Measurements in the Kuroshio current OB_DAAC STAC Catalog 1997-11-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360413-OB_DAAC.umm_json Measurements in the Kuroshio, western boundary current in the North Pacific Ocean, from 1997. proprietary
-Kyle-Ferrar_Igneous_Province 40Ar/39Ar dates of Jurassic igneous rocks from Antarctica SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C1214612994-SCIOPS.umm_json Plagioclase mineral separates from basaltic extrusive (lavas) and instrusive (dolerite and gabbro) samples from the Dronning Maud Land area of Antarctica were dated by the incremental heating 40Ar/39Ar method. 32 individual samples were dated with 11 samples having duplicate analyses. proprietary
Kyle-Ferrar_Igneous_Province 40Ar/39Ar dates of Jurassic igneous rocks from Antarctica ALL STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C1214612994-SCIOPS.umm_json Plagioclase mineral separates from basaltic extrusive (lavas) and instrusive (dolerite and gabbro) samples from the Dronning Maud Land area of Antarctica were dated by the incremental heating 40Ar/39Ar method. 32 individual samples were dated with 11 samples having duplicate analyses. proprietary
+Kyle-Ferrar_Igneous_Province 40Ar/39Ar dates of Jurassic igneous rocks from Antarctica SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C1214612994-SCIOPS.umm_json Plagioclase mineral separates from basaltic extrusive (lavas) and instrusive (dolerite and gabbro) samples from the Dronning Maud Land area of Antarctica were dated by the incremental heating 40Ar/39Ar method. 32 individual samples were dated with 11 samples having duplicate analyses. proprietary
L1B_Wind_Products_3.0 Aeolus preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers ESA STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689596-ESA.umm_json The Level 1B wind product of the Aeolus mission contains the preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers, which are generated in Near Real Time. Standard atmospheric correction (Rayleigh channel), receiver response and bias correction is applied. The product is generated within 3 hours after data acquisition. proprietary
L1B_Wind_Products_3.0 Aeolus preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers ALL STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689596-ESA.umm_json The Level 1B wind product of the Aeolus mission contains the preliminary HLOS (horizontal line-of-sight) wind observations for Rayleigh and Mie receivers, which are generated in Near Real Time. Standard atmospheric correction (Rayleigh channel), receiver response and bias correction is applied. The product is generated within 3 hours after data acquisition. proprietary
-L2B_Wind_Products_3.0 Aeolus Scientific L2B Rayleigh/Mie wind product ESA STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689544-ESA.umm_json The Level 2B wind product of the Aeolus mission is a geo-located consolidated HLOS (horizontal line-of-sight) wind observation with actual atmospheric correction applied to Rayleigh channel. The product is generated by within 3 hours after data acquisition. proprietary
L2B_Wind_Products_3.0 Aeolus Scientific L2B Rayleigh/Mie wind product ALL STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689544-ESA.umm_json The Level 2B wind product of the Aeolus mission is a geo-located consolidated HLOS (horizontal line-of-sight) wind observation with actual atmospheric correction applied to Rayleigh channel. The product is generated by within 3 hours after data acquisition. proprietary
+L2B_Wind_Products_3.0 Aeolus Scientific L2B Rayleigh/Mie wind product ESA STAC Catalog 2020-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689544-ESA.umm_json The Level 2B wind product of the Aeolus mission is a geo-located consolidated HLOS (horizontal line-of-sight) wind observation with actual atmospheric correction applied to Rayleigh channel. The product is generated by within 3 hours after data acquisition. proprietary
L2C_Wind_products_5.0 Aeolus Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction assimilation processing ESA STAC Catalog 2020-07-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2619280864-ESA.umm_json The Level 2C wind product of the Aeolus mission provides ECMWF analysis horizontal wind vectors at the geolocations of assimilated L2B HLOS wind components. The L2C can therefore be described as an Aeolus-assisted horizontal wind vector product. The L2C is a distinct product, however the L2C and L2B share a common Earth Explorer file template, with the L2C being a superset of the L2B. The L2C consists of extra datasets appended to the L2B product with information which are relevant to the data assimilation of the L2B winds. proprietary
L2C_Wind_products_5.0 Aeolus Level 2C assisted wind fields resulting from NWP Numerical Weather Prediction assimilation processing ALL STAC Catalog 2020-07-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2619280864-ESA.umm_json The Level 2C wind product of the Aeolus mission provides ECMWF analysis horizontal wind vectors at the geolocations of assimilated L2B HLOS wind components. The L2C can therefore be described as an Aeolus-assisted horizontal wind vector product. The L2C is a distinct product, however the L2C and L2B share a common Earth Explorer file template, with the L2C being a superset of the L2B. The L2C consists of extra datasets appended to the L2B product with information which are relevant to the data assimilation of the L2B winds. proprietary
L2SW_Open_3.0 SMOS NRT L2 Swath Wind Speed ESA STAC Catalog 2018-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689620-ESA.umm_json SMOS retrieved surface wind speed gridded maps (with a spatial sampling of 1/4 x 1/4 degrees) are available in NetCDF format. Each product contains parts of ascending and descending orbits and it is generated by Ifremer, starting from the SMOS L1B data products, in Near Real Time i.e. within 4 to 6 hours from sensing time. Before using this dataset, please check the read-me-first note available in the Resources section below. proprietary
@@ -10290,8 +10291,8 @@ LADSII_hydrographic_survey_1 Hydrographic survey LADSII by the RAN Australian Hy
LAI_Africa_2325_1 MODIS-derived Aggregate, Woody and Herbaceous Leaf Area Index for Africa, 2002-2022 ORNL_CLOUD STAC Catalog 2002-07-05 2022-07-29 -21.28, -40.02, 63.86, 20.02 https://cmr.earthdata.nasa.gov/search/concepts/C2954717391-ORNL_CLOUD.umm_json This dataset provides leaf area index (LAI) estimates for Sub-Saharan Africa for woody, herbaceous, and aggregate vegetation types. The estimates were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 4 and the native MODIS LAI product (MCD15A2H Version 6.1), which provides LAI measurements every 8 days at 500-m pixel size. Data from the MCD15A2H product were processed further to generate three layers including: a smoothed and gap filled LAI layer referred to as aggregate leaf area index and two additional layers processed to separate woody LAI (tree and shrubs) and herbaceous LAI (grass and forbs). The data include 31 MODIS 10-degree tiles and cover 2002 to 2022. The data are provided in NetCDF format. proprietary
LAI_Canada_816_1 Leaf Area Index Maps at 30-m Resolution, Selected Sites, Canada ORNL_CLOUD STAC Catalog 2000-01-01 2001-12-31 -135.05, 44.23, -65, 63.14 https://cmr.earthdata.nasa.gov/search/concepts/C2737900059-ORNL_CLOUD.umm_json This data set provides local LAI maps for the selected measured sites in Canada. These derived maps may also be useful for validating other LAI maps over these same sites given that the areas are protected from disturbance. The maps should be used for the given period of validity. The LAI data are suitable for use in modeling the carbon, water, energy, energy and trace gas exchange between the land surface and the atmosphere at regional scales. The data set may also be useful for monitoring changes in the land surface.The Leaf Area Index (LAI) maps are at 30-m resolution for the selected sites. LAI is defined here as half the total (all-sided) live foliage area per unit horizontal projected ground surface area. Overstory LAI corresponds to all tree foliage except for treeless areas where it corresponds to total foliage. The algorithms were developed from ground measurements and Landsat TM and ETM+ images (Fernandes et. al., 2003). A mask was developed using the Landsat ETM+/TM5 image and available land cover map to identify only those areas with land cover belonging to the sample land cover classes and with Landsat ETM+/TM5 spectral reflectance values that fell within the convex hull of the spectral reflectance values over the plots. LAI was mapped within the masked region using the Landsat ETM+/TM5 image and the developed transfer function. The final LAI map was scaled by a factor of 20 (offset 0). The LAI maps are in Tagged Image File Format (TIFF). proprietary
LAI_VALERI_Canada_829_1 Leaf Area Index Maps at 30-m Resolution, VALERI Site, Larose, Canada ORNL_CLOUD STAC Catalog 2003-05-08 2003-08-19 -75.24, 45.37, -75.2, 45.39 https://cmr.earthdata.nasa.gov/search/concepts/C2737900380-ORNL_CLOUD.umm_json This data set provide local LAI maps for the Larose (Ontario) site in Canada. These derived maps may also be useful for validating other LAI maps over this same site given that the area is protected from disturbance. The maps should be used for the given period of validity. The LAI data are suitable for use in modeling the carbon, water, energy, energy and trace gas exchange between the land surface and the atmosphere at regional scales. The dataset may also be useful for monitoring changes in the land surface. A complete description of producing the maps for the Larose site and the ground measurement campaign is provided in the companion document Larose2003FTReport.pdf. proprietary
-LAI_Woody_Plants_1231_1 A Global Database of Field-observed Leaf Area Index in Woody Plant Species, 1932-2011 ALL STAC Catalog 1932-01-01 2011-12-31 -164.78, -54.2, 175.62, 78.42 https://cmr.earthdata.nasa.gov/search/concepts/C2784385653-ORNL_CLOUD.umm_json This data set provides global leaf area index (LAI) values for woody species. The data are a compilation of field-observed data from 1,216 locations obtained from 554 literature sources published between 1932 and 2011. Only site-specific maximum LAI values were included from the sources; values affected by significant artificial treatments (e.g. continuous fertilization and/or irrigation) and LAI values that were low due to drought or disturbance (e.g. intensive thinning, wildfire, or disease), or because vegetation was immature or old/declining, were excluded (Lio et al., 2014). To maximize the generic applicability of the data, original LAI values from source literature and values standardized using the definition of half of total surface area (HSA) are included. Supporting information, such as geographical coordinates of plot, altitude, stand age, name of dominant species, plant functional types, and climate data are also provided in the data file. There is one data file in comma-separated (.csv) format with this data set and one companion file which provides the data sources. proprietary
LAI_Woody_Plants_1231_1 A Global Database of Field-observed Leaf Area Index in Woody Plant Species, 1932-2011 ORNL_CLOUD STAC Catalog 1932-01-01 2011-12-31 -164.78, -54.2, 175.62, 78.42 https://cmr.earthdata.nasa.gov/search/concepts/C2784385653-ORNL_CLOUD.umm_json This data set provides global leaf area index (LAI) values for woody species. The data are a compilation of field-observed data from 1,216 locations obtained from 554 literature sources published between 1932 and 2011. Only site-specific maximum LAI values were included from the sources; values affected by significant artificial treatments (e.g. continuous fertilization and/or irrigation) and LAI values that were low due to drought or disturbance (e.g. intensive thinning, wildfire, or disease), or because vegetation was immature or old/declining, were excluded (Lio et al., 2014). To maximize the generic applicability of the data, original LAI values from source literature and values standardized using the definition of half of total surface area (HSA) are included. Supporting information, such as geographical coordinates of plot, altitude, stand age, name of dominant species, plant functional types, and climate data are also provided in the data file. There is one data file in comma-separated (.csv) format with this data set and one companion file which provides the data sources. proprietary
+LAI_Woody_Plants_1231_1 A Global Database of Field-observed Leaf Area Index in Woody Plant Species, 1932-2011 ALL STAC Catalog 1932-01-01 2011-12-31 -164.78, -54.2, 175.62, 78.42 https://cmr.earthdata.nasa.gov/search/concepts/C2784385653-ORNL_CLOUD.umm_json This data set provides global leaf area index (LAI) values for woody species. The data are a compilation of field-observed data from 1,216 locations obtained from 554 literature sources published between 1932 and 2011. Only site-specific maximum LAI values were included from the sources; values affected by significant artificial treatments (e.g. continuous fertilization and/or irrigation) and LAI values that were low due to drought or disturbance (e.g. intensive thinning, wildfire, or disease), or because vegetation was immature or old/declining, were excluded (Lio et al., 2014). To maximize the generic applicability of the data, original LAI values from source literature and values standardized using the definition of half of total surface area (HSA) are included. Supporting information, such as geographical coordinates of plot, altitude, stand age, name of dominant species, plant functional types, and climate data are also provided in the data file. There is one data file in comma-separated (.csv) format with this data set and one companion file which provides the data sources. proprietary
LAI_surfaces_747_1 BigFoot Leaf Area Index Surfaces for North and South American Sites, 2000-2003 ORNL_CLOUD STAC Catalog 2000-01-01 2003-12-31 -156.61, -2.86, -54.96, 71.27 https://cmr.earthdata.nasa.gov/search/concepts/C2751481204-ORNL_CLOUD.umm_json The BigFoot project gathered leaf area index (LAI) data for nine EOS Land Validation Sites located from Alaska to Brazil from 2000 to 2003. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest. LAI was measured at plots within each site for at least two years using standard direct and optical methods at each site. BigFoot was funded by NASA's Terrestrial Ecology Program. proprietary
LAMONT_ATL_0 Lamont-Doherty Earth Observatory measurements from the South Atlantic Ocean (ATL) OB_DAAC STAC Catalog 2015-09-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360421-OB_DAAC.umm_json Measurements from the South Atlantic Ocean (ATL) made by researchers at Columbia Universitys Lamont-Doherty Earth Observatory (LDEO). proprietary
LAMONT_GOM_0 Lamont-Doherty Earth Observatory measurements from the Gulf of Mexico (GOM) OB_DAAC STAC Catalog 2010-08-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360423-OB_DAAC.umm_json Measurements from the Gulf of Mexico (GOM) made by researchers at Columbia University's Lamont-Doherty Earth Observatory (LDEO). proprietary
@@ -10415,8 +10416,8 @@ LEOLSTCMG30_002 Low Earth Orbit Land Surface Temperature Monthly Global Gridded
LEO_0 Long-term Ecosystem Observatory (LEO) oceanographic and meteorological data collection system OB_DAAC STAC Catalog 2001-07-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360429-OB_DAAC.umm_json Measurements from the LEO station off the Atlantic Coast of New Jersey in 2001. proprietary
LEVEL_1C__3_5.0 Proba-V 1Km, 333m, and 100m products ESA STAC Catalog 2013-11-28 -180, -56, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C1965336924-ESA.umm_json The Proba-V VEGETATION Raw products (Level 1C/P) and synthesis products (Level 3, S1 = daily, S5 = 5 days, S10 = decade) ensure coverage of all significant landmasses worldwide with, in the case of a 10-day synthesis product, a minimum effect of cloud cover, resulting from selection of cloud-free acquisitions during the 10-day period. It ensures a daily coverage between Lat. 35°N and 75°N, and between 35°S and 56°S, and a full coverage every two days at equator. The VEGETATION instrument is pre-programmed with an indefinite repeated sequence of acquisitions. This nominal acquisition scenario allows a continuous series of identical products to be generated, aiming to map land cover and vegetation growth across the entire planet every two days.Products overview • Projection: Plate carrée projection • Spectral bands: All 4 + NDVI • Format: HDF5 & GeoTiFF The Proba-V VEGETATION Level 3 synthesis products are divided into so called granules, each measuring 10 degrees x 10 degrees, each granule being delivered as a single file. Level 3 products are: - Syntesys S1, with resolution 100m (TOA, TOC and TOC NDVI reflectance), 333m (TOA and TOC reflectance) and 1km (TOA and TOC reflectance) - Syntesys S5, with resolution 100m (TOA, TOC and TOC NDVI reflectance) - Syntesys S10, with resolution 333m (TOC and TOC NDVI reflectance) and 1km (TOC and TOC NDVI reflectance) proprietary
LF_Bibliography_1 Bibliography of papers relevant to longline fishing. AU_AADC STAC Catalog 1972-01-01 -180, -80, 180, 85 https://cmr.earthdata.nasa.gov/search/concepts/C1214313596-AU_AADC.umm_json The bibliography covers a wealth of published, 'grey', and unpublished literature addressing the effects of longline fishing on seabird mortality. The scope is global, but with a special emphasis on the Southern Ocean. Information on longline methodology is included and attention is given to materials that cover the various mitigation methods in use, tested or proposed. Further, information on the relevant aspects of the ecology of affected seabird species is covered, especially that dealing with mortality levels, at-sea distributions and population and conservation biology. Data sources covered include the scientific literature, popular publications, newspaper articles, videos, brochures, maps and posters, as well as government, NGO and IGO reports. proprietary
-LGB_10m_traverse_1 10 m firn temperature data: LGB traverses 1990-95 ALL STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313574-AU_AADC.umm_json The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. Ten metre depth (10 m) firn temperatures, as a proxy indicator of annual mean surface temperature at a site, were recorded approximately every 30 km along the 2014 km main traverse route from LGB00 (68.6543 S, 61.1201 E) near Mawson Station, to LGB72 (69.9209 S, 76.4933 E) near Davis Station. 10 m depth firn temperatures were recorded manually in field notebooks and the data transferred to spreadsheet files (MS Excel). Summary data (30 km spatial resolution) can be obtained from CRC Research Note No.09 'Surface Mass Balance and Snow Surface Properties from the Lambert Glacier Basin Traverses 1990-94'. This work was completed as part of ASAC projects 3 and 2216. Some of this data have been stored in a very old format. The majority of files have been updated to current formats, but some files (kaleidograph files in particular) were not able to be modified due to a lack of appropriate software. However, these files are simply figures, and can be regenerated from the raw data (also provided). The fields in this dataset are: Latitutde Longitude Height Cane Distance Elevation Density Mass Accumulation Year Delta Oxygen-18 Grain Size Ice Crusts Depth Hoar proprietary
LGB_10m_traverse_1 10 m firn temperature data: LGB traverses 1990-95 AU_AADC STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313574-AU_AADC.umm_json The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. Ten metre depth (10 m) firn temperatures, as a proxy indicator of annual mean surface temperature at a site, were recorded approximately every 30 km along the 2014 km main traverse route from LGB00 (68.6543 S, 61.1201 E) near Mawson Station, to LGB72 (69.9209 S, 76.4933 E) near Davis Station. 10 m depth firn temperatures were recorded manually in field notebooks and the data transferred to spreadsheet files (MS Excel). Summary data (30 km spatial resolution) can be obtained from CRC Research Note No.09 'Surface Mass Balance and Snow Surface Properties from the Lambert Glacier Basin Traverses 1990-94'. This work was completed as part of ASAC projects 3 and 2216. Some of this data have been stored in a very old format. The majority of files have been updated to current formats, but some files (kaleidograph files in particular) were not able to be modified due to a lack of appropriate software. However, these files are simply figures, and can be regenerated from the raw data (also provided). The fields in this dataset are: Latitutde Longitude Height Cane Distance Elevation Density Mass Accumulation Year Delta Oxygen-18 Grain Size Ice Crusts Depth Hoar proprietary
+LGB_10m_traverse_1 10 m firn temperature data: LGB traverses 1990-95 ALL STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313574-AU_AADC.umm_json The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. Ten metre depth (10 m) firn temperatures, as a proxy indicator of annual mean surface temperature at a site, were recorded approximately every 30 km along the 2014 km main traverse route from LGB00 (68.6543 S, 61.1201 E) near Mawson Station, to LGB72 (69.9209 S, 76.4933 E) near Davis Station. 10 m depth firn temperatures were recorded manually in field notebooks and the data transferred to spreadsheet files (MS Excel). Summary data (30 km spatial resolution) can be obtained from CRC Research Note No.09 'Surface Mass Balance and Snow Surface Properties from the Lambert Glacier Basin Traverses 1990-94'. This work was completed as part of ASAC projects 3 and 2216. Some of this data have been stored in a very old format. The majority of files have been updated to current formats, but some files (kaleidograph files in particular) were not able to be modified due to a lack of appropriate software. However, these files are simply figures, and can be regenerated from the raw data (also provided). The fields in this dataset are: Latitutde Longitude Height Cane Distance Elevation Density Mass Accumulation Year Delta Oxygen-18 Grain Size Ice Crusts Depth Hoar proprietary
LGB_Del_traverse_1 Delta Oxygen-18 isotope data: LGB traverses 1989-95 AU_AADC STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313576-AU_AADC.umm_json The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. Several shallow depth ice cores (15-60 m) were drilled at selected sites along 2014 km of the main traverse track from LGB00 (68.6543 S, 61.1201 E) near Mawson Station to LGB72 (69.9209 S,76.4933 E) near Davis Station, and at selected sites along a western traverse line from LGB00 toward Enderby Land. Surface cores (2 m) were collected at 30 km intervals along the entire route from LGB00-LGB72. Ice cores have been kept in cool storage at a local cold room storage facility. Isotope data from the cores have been saved in various spreadsheet files (mainly MS Excel). Initial summary data can be obtained from CRC Research Note No.09 'Surface mass balance and snow surface properties from the Lambert Glacier Basin Traverses 1990-94'. This work was completed as part of ASAC projects 3 and 2216. Some of this data have been stored in a very old format. The majority of files have been updated to current formats, but some files (kaleidograph files in particular) were not able to be modified due to a lack of appropriate software. However, these files are simply figures, and can be regenerated from the raw data (also provided). The fields in this dataset are: Latitutde Longitude Height Cane Distance Elevation Density Mass Accumulation Year Delta Oxygen-18 Grain Size Ice Crusts Depth Hoar proprietary
LGB_Gra_traverse_1 Earth gravity field for ice thickness data: LGB traverses 1989-95 AU_AADC STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313598-AU_AADC.umm_json The Lambert Glacier Basin (LGB) series of five oversnow traverses were conducted from 1989-95. LaCoste and Romberg gravity meters were used to record measurements of the Earth's gravity field approximately every 2 km along the 2014 km main traverse route from LGB00 (68.6543 S, 61.1201 E) near Mawson Station, to LGB72 (69.9209 S, 76.4933 E) near Davis Station. Gravity readings were also obtained at 5 km intervals along a 516 km upper western offset track (50 km parallel upslope from main route) from LGBUW485 (68.6458 S, 60.0272 E) to LGBUW000 (72.6508 S, 55.9275 E). Raw data were stored as meter readings in field notebooks, transferred manually to spreadsheet files (MS Excel). Processed data were stored in spreadsheet files (MS Excel). The data available at the url below are stored in various formats. Summary data (2 km spatial resolution) can be obtained from CRC Research Note No.27 'Ice Thicknesses and Surface and Bedrock Elevations from the Lambert Glacier Basin Traverses 1990-95'. Documents providing archive details of the logbooks are available for download from the provided URL. This work was completed as part of ASAC projects 3 and 2216. Logbook(s): - Gravity Meter Log 89/90 - LGBT Gravity #2 1992-93 - Glaciology Gravity Readings LGBT 1990-91 proprietary
LGB_Ht_traverse_1 Ice sheet surface elevation data: LGB traverses 1989-95 AU_AADC STAC Catalog 1989-11-01 1995-02-28 54, -77, 78, -69 https://cmr.earthdata.nasa.gov/search/concepts/C1214313577-AU_AADC.umm_json The ANARE Lambert Glacier Basin (LGB) series of oversnow traverses were conducted during the period 1989-95. Field operations were carried out along the proximity of the 2500 m elevation contour around the interior basin between Mawson and Davis stations. The main traverse route covered some 2014 km of track from LGB00 at 68.6543 S, 61.1201 E, and LGB72 at 69.9209 S, 76.4933 E. An offset route (50 km upslope) parallels the main traverse track around the western half of the basin. Raw data were stored in binary files containing pressure, temperature, navigational position and a variety of other parameters at an approximately 10 m spacing associated with each 2 km long section of track. Processed data were stored as 2 km averaged ice sheet surface elevation spreadsheet files (MS Excel). The data available at the url below are stored in various formats. Summary data (2 km spatial average) can be obtained from CRC Research Note No. 27 'Ice Thicknesses and Surface and Bedrock Elevations from the Lambert Glacier Basin Traverses 1989-95'. This work was completed as part of ASAC projects 3 and 2216. proprietary
@@ -10524,8 +10525,8 @@ Lab96_0 Labrador Sea measurements in 1996 OB_DAAC STAC Catalog 1996-10-20 -180,
LakeBathymetry_Model_NSlope_AK_2243_1 Lake Bathymetry Maps derived from Landsat and Random Forest Modeling, North Slope, AK ORNL_CLOUD STAC Catalog 2016-07-01 2018-08-31 -177.47, 56.09, -128.2, 77.26 https://cmr.earthdata.nasa.gov/search/concepts/C2837050574-ORNL_CLOUD.umm_json This dataset provides lake bathymetry maps derived from Landsat surface reflectance products for a portion of the North Slope area of Alaska. A random forest regression algorithm was used to generate depths for each point identified as being part of a lake, creating depth prediction files for each Landsat scene available for the study period: 2016-07-01 to 2018-08-31. These products are fitted to the ABoVE standard projection and reference grid to make them easily scalable and geometrically compatible with other products in the ABoVE study domain. The data are provided in cloud-optimized GeoTIFF (COG) format. proprietary
LakeSuperior_0 University of Rhode Island measurements in Lake Superior OB_DAAC STAC Catalog 2013-05-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360420-OB_DAAC.umm_json Measurements made in Lake Superior by researchers at the University of Rhode Island. proprietary
Lake_MI_2012_WaterQual_0 Water quality monitoring program in Lake Michigan and Green Bay OB_DAAC STAC Catalog 2012-06-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360419-OB_DAAC.umm_json Measurements taken in Lake Michigan and Green Bay in 2012 as part of a water quality monitoring program. proprietary
-Lake_Wetland_Classes_UAVSAR_1883_1 ABoVE: Lake and Wetland Classification from L-band SAR, Alaska and Canada, 2017-2019 ORNL_CLOUD STAC Catalog 2017-01-01 2019-09-19 -149.16, 53.71, -107.86, 67.91 https://cmr.earthdata.nasa.gov/search/concepts/C2192619280-ORNL_CLOUD.umm_json This dataset contains a high-resolution land cover classification focused on water and wetland vegetation classes over three NASA ABoVE Campaign regions: Yukon Flats, Alaska, USA; the Peace-Athabasca Delta, Alberta; and the Canadian Shield, Northwest Territories (NWT), Canada. The product was derived from L-band synthetic aperture radar (SAR) acquisitions from the airborne NASA UAVSAR instrument in 2017-2019. The classification was trained and validated from field visits, UAV images, satellite imagery as well as other ABoVE datasets. Classifications in all regions are provided as both preliminary 13-class versions and final, simplified 5-class versions. Training and test data used for the classifier are also included as well as characteristics of lakes in the study area. This land cover classification was developed to support a project focusing on potential methane emissions from the shallow near-shore, or littoral, regions of lakes. The emergent aquatic vegetation classes can be used as a proxy for these littoral zones. Wetland vegetation classifications are provided as gridded raster files with an approximately 5-meter spatial resolution and aligned with the original UAVSAR footprints. Composite mosaics that aggregate these UAVSAR scenes by region and day of acquisition, if applicable, are also provided. Classifications in all regions are provided as both preliminary 13-class versions and final 5-class versions. proprietary
Lake_Wetland_Classes_UAVSAR_1883_1 ABoVE: Lake and Wetland Classification from L-band SAR, Alaska and Canada, 2017-2019 ALL STAC Catalog 2017-01-01 2019-09-19 -149.16, 53.71, -107.86, 67.91 https://cmr.earthdata.nasa.gov/search/concepts/C2192619280-ORNL_CLOUD.umm_json This dataset contains a high-resolution land cover classification focused on water and wetland vegetation classes over three NASA ABoVE Campaign regions: Yukon Flats, Alaska, USA; the Peace-Athabasca Delta, Alberta; and the Canadian Shield, Northwest Territories (NWT), Canada. The product was derived from L-band synthetic aperture radar (SAR) acquisitions from the airborne NASA UAVSAR instrument in 2017-2019. The classification was trained and validated from field visits, UAV images, satellite imagery as well as other ABoVE datasets. Classifications in all regions are provided as both preliminary 13-class versions and final, simplified 5-class versions. Training and test data used for the classifier are also included as well as characteristics of lakes in the study area. This land cover classification was developed to support a project focusing on potential methane emissions from the shallow near-shore, or littoral, regions of lakes. The emergent aquatic vegetation classes can be used as a proxy for these littoral zones. Wetland vegetation classifications are provided as gridded raster files with an approximately 5-meter spatial resolution and aligned with the original UAVSAR footprints. Composite mosaics that aggregate these UAVSAR scenes by region and day of acquisition, if applicable, are also provided. Classifications in all regions are provided as both preliminary 13-class versions and final 5-class versions. proprietary
+Lake_Wetland_Classes_UAVSAR_1883_1 ABoVE: Lake and Wetland Classification from L-band SAR, Alaska and Canada, 2017-2019 ORNL_CLOUD STAC Catalog 2017-01-01 2019-09-19 -149.16, 53.71, -107.86, 67.91 https://cmr.earthdata.nasa.gov/search/concepts/C2192619280-ORNL_CLOUD.umm_json This dataset contains a high-resolution land cover classification focused on water and wetland vegetation classes over three NASA ABoVE Campaign regions: Yukon Flats, Alaska, USA; the Peace-Athabasca Delta, Alberta; and the Canadian Shield, Northwest Territories (NWT), Canada. The product was derived from L-band synthetic aperture radar (SAR) acquisitions from the airborne NASA UAVSAR instrument in 2017-2019. The classification was trained and validated from field visits, UAV images, satellite imagery as well as other ABoVE datasets. Classifications in all regions are provided as both preliminary 13-class versions and final, simplified 5-class versions. Training and test data used for the classifier are also included as well as characteristics of lakes in the study area. This land cover classification was developed to support a project focusing on potential methane emissions from the shallow near-shore, or littoral, regions of lakes. The emergent aquatic vegetation classes can be used as a proxy for these littoral zones. Wetland vegetation classifications are provided as gridded raster files with an approximately 5-meter spatial resolution and aligned with the original UAVSAR footprints. Composite mosaics that aggregate these UAVSAR scenes by region and day of acquisition, if applicable, are also provided. Classifications in all regions are provided as both preliminary 13-class versions and final 5-class versions. proprietary
LandCoverNet Africa_1 LandCoverNet Africa MLHUB STAC Catalog 2020-01-01 2023-01-01 -15.9378605, -31.6878376, 46.873921, 31.3398255 https://cmr.earthdata.nasa.gov/search/concepts/C2781412437-MLHUB.umm_json LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Africa contains data across Africa, which accounts for ~1/5 of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.
There are a total of 1980 image chips of 256 x 256 pixels in LandCoverNet Africa V1.0 spanning 66 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files): * Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution * Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution * Landsat-8 surface reflectance product from Collection 2 Level-2
Radiant Earth Foundation designed and generated this dataset with a grant from [Schmidt Futures](https://schmidtfutures.com/) with additional support from [NASA ACCESS](https://earthdata.nasa.gov/esds/competitive-programs/access/radiant-mlhub), [Microsoft AI for Earth](https://www.microsoft.com/en-us/ai/ai-for-earth) and in kind technology support from [Sinergise](https://www.sinergise.com/). proprietary
LandCoverNet Asia_1 LandCoverNet Asia MLHUB STAC Catalog 2020-01-01 2023-01-01 33.0205908, -7.3097478, 160.7091112, 58.6174213 https://cmr.earthdata.nasa.gov/search/concepts/C2781412195-MLHUB.umm_json LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Asia contains data across Asia, which accounts for ~31% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.
There are a total of 2753 image chips of 256 x 256 pixels in LandCoverNet South America V1.0 spanning 92 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2
Radiant Earth Foundation designed and generated this dataset with a grant from [Schmidt Futures](https://schmidtfutures.com/) with additional support from [NASA ACCESS](https://earthdata.nasa.gov/esds/competitive-programs/access/radiant-mlhub), [Microsoft AI for Earth](https://www.microsoft.com/en-us/ai/ai-for-earth) and in kind technology support from [Sinergise](https://www.sinergise.com/). proprietary
LandCoverNet Australia_1 LandCoverNet Australia MLHUB STAC Catalog 2020-01-01 2023-01-01 123.0069917, -46.1160741, 172.3714334, -14.4766436 https://cmr.earthdata.nasa.gov/search/concepts/C2781412728-MLHUB.umm_json LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Australia contains data across Australia, which accounts for ~7% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.
There are a total of 600 image chips of 256 x 256 pixels in LandCoverNet Australia V1.0 spanning 20 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2
Radiant Earth Foundation designed and generated this dataset with a grant from [Schmidt Futures](https://schmidtfutures.com/) with additional support from [NASA ACCESS](https://earthdata.nasa.gov/esds/competitive-programs/access/radiant-mlhub), [Microsoft AI for Earth](https://www.microsoft.com/en-us/ai/ai-for-earth) and in kind technology support from [Sinergise](https://www.sinergise.com/). proprietary
@@ -10549,15 +10550,15 @@ Landsat_RBV_8.0 Landsat RBV ESA STAC Catalog 1978-11-01 2018-08-01 20, -90, 50,
Large_River_DOC_Export_0 Export of dissolved organic carbon (DOC) by large rivers OB_DAAC STAC Catalog 2015-05-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360426-OB_DAAC.umm_json Measurements taken as a part of a project to quanitfy and assess the export of dissolved organic carbon by large rivers. proprietary
Last_Day_Spring_Snow_1528_1 ABoVE: Last Day of Spring Snow, Alaska, USA, and Yukon Territory, Canada, 2000-2016 ORNL_CLOUD STAC Catalog 2000-04-01 2016-07-02 -175.76, 52.17, -97.95, 68.97 https://cmr.earthdata.nasa.gov/search/concepts/C2162119017-ORNL_CLOUD.umm_json "This dataset provides the last day of spring snow cover for most of Alaska and the Yukon Territory for 2000 through 2016. The data are based on the MODIS daily snow cover fraction product (MODSCAG) and are provided at 500-m resolution. Pixels in the daily snow cover fraction grids from April 1 through July 31 were flagged as ""Snow"" if the snow fraction exceeded 0.15, resulting in a time series of binary daily snow cover grids for each year. The annual last day of spring snow for each pixel was identified by day of the year ranging from 91 (April 1) to 183 (July 2)." proprietary
Last_Day_Spring_Snow_1528_1 ABoVE: Last Day of Spring Snow, Alaska, USA, and Yukon Territory, Canada, 2000-2016 ALL STAC Catalog 2000-04-01 2016-07-02 -175.76, 52.17, -97.95, 68.97 https://cmr.earthdata.nasa.gov/search/concepts/C2162119017-ORNL_CLOUD.umm_json "This dataset provides the last day of spring snow cover for most of Alaska and the Yukon Territory for 2000 through 2016. The data are based on the MODIS daily snow cover fraction product (MODSCAG) and are provided at 500-m resolution. Pixels in the daily snow cover fraction grids from April 1 through July 31 were flagged as ""Snow"" if the snow fraction exceeded 0.15, resulting in a time series of binary daily snow cover grids for each year. The annual last day of spring snow for each pixel was identified by day of the year ranging from 91 (April 1) to 183 (July 2)." proprietary
-Leaf_Carbon_Nutrients_1106_1 A Global Database of Carbon and Nutrient Concentrations of Green and Senesced Leaves ALL STAC Catalog 1970-01-01 2009-12-31 -159.7, -50, 176.9, 68.5 https://cmr.earthdata.nasa.gov/search/concepts/C2784383820-ORNL_CLOUD.umm_json This data set provides carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in green and senesced leaves. Vegetation characteristics reported include species growth habit, leaf area, mass, and mass loss with senescence. The data were compiled from 86 selected studies in 31 countries, and resulted in approximately 1,000 data points for both green and senesced leaves from woody and non-woody vegetation as described in Vergutz et al (2012). The studies were conducted from 1970-2009. There are two comma-delimited data files with this data set. proprietary
Leaf_Carbon_Nutrients_1106_1 A Global Database of Carbon and Nutrient Concentrations of Green and Senesced Leaves ORNL_CLOUD STAC Catalog 1970-01-01 2009-12-31 -159.7, -50, 176.9, 68.5 https://cmr.earthdata.nasa.gov/search/concepts/C2784383820-ORNL_CLOUD.umm_json This data set provides carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in green and senesced leaves. Vegetation characteristics reported include species growth habit, leaf area, mass, and mass loss with senescence. The data were compiled from 86 selected studies in 31 countries, and resulted in approximately 1,000 data points for both green and senesced leaves from woody and non-woody vegetation as described in Vergutz et al (2012). The studies were conducted from 1970-2009. There are two comma-delimited data files with this data set. proprietary
+Leaf_Carbon_Nutrients_1106_1 A Global Database of Carbon and Nutrient Concentrations of Green and Senesced Leaves ALL STAC Catalog 1970-01-01 2009-12-31 -159.7, -50, 176.9, 68.5 https://cmr.earthdata.nasa.gov/search/concepts/C2784383820-ORNL_CLOUD.umm_json This data set provides carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in green and senesced leaves. Vegetation characteristics reported include species growth habit, leaf area, mass, and mass loss with senescence. The data were compiled from 86 selected studies in 31 countries, and resulted in approximately 1,000 data points for both green and senesced leaves from woody and non-woody vegetation as described in Vergutz et al (2012). The studies were conducted from 1970-2009. There are two comma-delimited data files with this data set. proprietary
Leaf_Photosynthesis_Traits_1224_1 A Global Data Set of Leaf Photosynthetic Rates, Leaf N and P, and Specific Leaf Area ALL STAC Catalog 1993-01-01 2010-12-31 -122.4, -43.2, 176.13, 58.42 https://cmr.earthdata.nasa.gov/search/concepts/C2784384781-ORNL_CLOUD.umm_json This global data set of photosynthetic rates and leaf nutrient traits was compiled from a comprehensive literature review. It includes estimates of Vcmax (maximum rate of carboxylation), Jmax (maximum rate of electron transport), leaf nitrogen content (N), leaf phosphorus content (P), and specific leaf area (SLA) data from both experimental and ambient field conditions, for a total of 325 species and treatment combinations. Both the original published Vcmax and Jmax values as well as estimates at standard temperature are reported. The maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax) are primary determinants of photosynthetic rates in plants, and modeled carbon fluxes are highly sensitive to these parameters. Previous studies have shown that Vcmax and Jmax correlate with leaf nitrogen across species and regions, and locally across species with leaf phosphorus and specific leaf area, yet no universal relationship suitable for global-scale models is currently available. These data are suitable for exploring the general relationships of Vcmax and Jmax with each other and with leaf N, P and SLA. This data set contains one *.csv file. proprietary
Leaf_Photosynthesis_Traits_1224_1 A Global Data Set of Leaf Photosynthetic Rates, Leaf N and P, and Specific Leaf Area ORNL_CLOUD STAC Catalog 1993-01-01 2010-12-31 -122.4, -43.2, 176.13, 58.42 https://cmr.earthdata.nasa.gov/search/concepts/C2784384781-ORNL_CLOUD.umm_json This global data set of photosynthetic rates and leaf nutrient traits was compiled from a comprehensive literature review. It includes estimates of Vcmax (maximum rate of carboxylation), Jmax (maximum rate of electron transport), leaf nitrogen content (N), leaf phosphorus content (P), and specific leaf area (SLA) data from both experimental and ambient field conditions, for a total of 325 species and treatment combinations. Both the original published Vcmax and Jmax values as well as estimates at standard temperature are reported. The maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax) are primary determinants of photosynthetic rates in plants, and modeled carbon fluxes are highly sensitive to these parameters. Previous studies have shown that Vcmax and Jmax correlate with leaf nitrogen across species and regions, and locally across species with leaf phosphorus and specific leaf area, yet no universal relationship suitable for global-scale models is currently available. These data are suitable for exploring the general relationships of Vcmax and Jmax with each other and with leaf N, P and SLA. This data set contains one *.csv file. proprietary
-Level_2A_aerosol_cloud_optical_products_3.0 Aeolus L2A Aerosol/Cloud optical product ESA STAC Catalog 2021-05-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207498185-ESA.umm_json The Level 2A aerosol/cloud optical products of the Aeolus mission include geo-located consolidated backscatter and extinction profiles, backscatter-to-extinction coefficient, LIDAR ratio, scene classification, heterogeneity index and attenuated backscatter signals. Resolution - Horizontal resolution of L2A optical properties at observation scale (~87 km); Exceptions are group properties (horizontal accumulation of measurements from ~3 km to ~87 km) and attenuated backscatters (~3 km); Note: the resolution of "groups" in the L2A can only go down to 5 measurements at the moment, i.e. ~15 km horizontal resolution. This could be configured to go to 1 measurement - Vertical resolution 250-2000 m (Defined by Range Bin Settings https://earth.esa.int/eogateway/instruments/aladin/overview-of-the-main-wind-rbs-changes). proprietary
Level_2A_aerosol_cloud_optical_products_3.0 Aeolus L2A Aerosol/Cloud optical product ALL STAC Catalog 2021-05-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207498185-ESA.umm_json The Level 2A aerosol/cloud optical products of the Aeolus mission include geo-located consolidated backscatter and extinction profiles, backscatter-to-extinction coefficient, LIDAR ratio, scene classification, heterogeneity index and attenuated backscatter signals. Resolution - Horizontal resolution of L2A optical properties at observation scale (~87 km); Exceptions are group properties (horizontal accumulation of measurements from ~3 km to ~87 km) and attenuated backscatters (~3 km); Note: the resolution of "groups" in the L2A can only go down to 5 measurements at the moment, i.e. ~15 km horizontal resolution. This could be configured to go to 1 measurement - Vertical resolution 250-2000 m (Defined by Range Bin Settings https://earth.esa.int/eogateway/instruments/aladin/overview-of-the-main-wind-rbs-changes). proprietary
+Level_2A_aerosol_cloud_optical_products_3.0 Aeolus L2A Aerosol/Cloud optical product ESA STAC Catalog 2021-05-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207498185-ESA.umm_json The Level 2A aerosol/cloud optical products of the Aeolus mission include geo-located consolidated backscatter and extinction profiles, backscatter-to-extinction coefficient, LIDAR ratio, scene classification, heterogeneity index and attenuated backscatter signals. Resolution - Horizontal resolution of L2A optical properties at observation scale (~87 km); Exceptions are group properties (horizontal accumulation of measurements from ~3 km to ~87 km) and attenuated backscatters (~3 km); Note: the resolution of "groups" in the L2A can only go down to 5 measurements at the moment, i.e. ~15 km horizontal resolution. This could be configured to go to 1 measurement - Vertical resolution 250-2000 m (Defined by Range Bin Settings https://earth.esa.int/eogateway/instruments/aladin/overview-of-the-main-wind-rbs-changes). proprietary
LiDAR_Forest_Inventory_Brazil_1644_1 LiDAR Surveys over Selected Forest Research Sites, Brazilian Amazon, 2008-2018 ORNL_CLOUD STAC Catalog 2008-01-01 2018-12-31 -68.3, -26.7, -39.06, -1.58 https://cmr.earthdata.nasa.gov/search/concepts/C2398128915-ORNL_CLOUD.umm_json This dataset provides the complete catalog of point cloud data collected during LiDAR surveys over selected forest research sites across the Amazon rainforest in Brazil between 2008 and 2018 for the Sustainable Landscapes Brazil Project. Flight lines were selected to overfly key field research sites in the Brazilian states of Acre, Amazonas, Bahia, Goias, Mato Grosso, Para, Rondonia, Santa Catarina, and Sao Paulo. The point clouds have been georeferenced, noise-filtered, and corrected for misalignment of overlapping flight lines. They are provided in 1 km2 tiles. The data were collected to measure forest canopy structure across Amazonian landscapes to monitor the effects of selective logging on forest biomass and carbon balance, and forest recovery over time. proprietary
-LiDAR_Tundra_Forest_AK_1782_1 ABoVE: Terrestrial Lidar Scanning Forest-Tundra Ecotone, Brooks Range, Alaska, 2016 ORNL_CLOUD STAC Catalog 2016-06-14 2016-06-25 -149.76, 67.97, -149.71, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401877-ORNL_CLOUD.umm_json This dataset provides terrestrial lidar scanning (TLS) point cloud data collected at 10 research plots along the forest-tundra ecotone (FTE) in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. Data were collected in mid-June 2016. Data were acquired for each plot from multiple scan positions with a Leica ScanStation C10 green wavelength laser instrument. After processing the point spacing is < 1 cm. TLS enables resolution of 3-dimensional landscape features that can be used to derive ecologically important metrics of canopy structure and surface topography at high spatial resolution. proprietary
LiDAR_Tundra_Forest_AK_1782_1 ABoVE: Terrestrial Lidar Scanning Forest-Tundra Ecotone, Brooks Range, Alaska, 2016 ALL STAC Catalog 2016-06-14 2016-06-25 -149.76, 67.97, -149.71, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401877-ORNL_CLOUD.umm_json This dataset provides terrestrial lidar scanning (TLS) point cloud data collected at 10 research plots along the forest-tundra ecotone (FTE) in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. Data were collected in mid-June 2016. Data were acquired for each plot from multiple scan positions with a Leica ScanStation C10 green wavelength laser instrument. After processing the point spacing is < 1 cm. TLS enables resolution of 3-dimensional landscape features that can be used to derive ecologically important metrics of canopy structure and surface topography at high spatial resolution. proprietary
+LiDAR_Tundra_Forest_AK_1782_1 ABoVE: Terrestrial Lidar Scanning Forest-Tundra Ecotone, Brooks Range, Alaska, 2016 ORNL_CLOUD STAC Catalog 2016-06-14 2016-06-25 -149.76, 67.97, -149.71, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401877-ORNL_CLOUD.umm_json This dataset provides terrestrial lidar scanning (TLS) point cloud data collected at 10 research plots along the forest-tundra ecotone (FTE) in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. Data were collected in mid-June 2016. Data were acquired for each plot from multiple scan positions with a Leica ScanStation C10 green wavelength laser instrument. After processing the point spacing is < 1 cm. TLS enables resolution of 3-dimensional landscape features that can be used to derive ecologically important metrics of canopy structure and surface topography at high spatial resolution. proprietary
LiDAR_Veg_Ht_Idaho_1532_1 LiDAR Data, DEM, and Maximum Vegetation Height Product from Southern Idaho, 2014 ORNL_CLOUD STAC Catalog 2014-08-23 2014-08-31 -116.89, 42.28, -114.68, 43.33 https://cmr.earthdata.nasa.gov/search/concepts/C2767326506-ORNL_CLOUD.umm_json This dataset provides the point cloud data derived from small footprint waveform LiDAR data collected in August 2014 over Reynolds Creek Experimental Watershed and Hollister in southern Idaho. The LiDAR data have been georeferenced, noise-filtered, and corrected for misalignment for overlapping flight lines and are provided in 1 km tiles. High resolution digital elevation models and maps of maximum vegetation height derived from the LiDAR data are provided for each site. proprietary
Lidar_Bibliography_1 A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments AU_AADC STAC Catalog 1961-01-01 62, -68, 159, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214313620-AU_AADC.umm_json A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments - the bibliography has been compiled by Andrew Klekociuk of the Australian Antarctic Division (Space and Atmospheric Sciences section of the Ice, Oceans Atmosphere and Climate Program). At the 4th of June, 2007, the bibliography contained 996 references. The bibliography can also be searched via the scientific bibliographies database available at the URL given below. The fields in this dataset are: year author title journal proprietary
Lidar_Bibliography_1 A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments ALL STAC Catalog 1961-01-01 62, -68, 159, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214313620-AU_AADC.umm_json A bibliography detailing references related to Light Detection and Ranging (LIDAR) instruments - the bibliography has been compiled by Andrew Klekociuk of the Australian Antarctic Division (Space and Atmospheric Sciences section of the Ice, Oceans Atmosphere and Climate Program). At the 4th of June, 2007, the bibliography contained 996 references. The bibliography can also be searched via the scientific bibliographies database available at the URL given below. The fields in this dataset are: year author title journal proprietary
@@ -10836,7 +10837,7 @@ MCD19A1N_6.1NRT MODIS/Terra+Aqua Land Surface BRF Daily L2G Global 500m and 1km
MCD19A1_061 MODIS/Terra+Aqua Land Surface BRF Daily L2G Global 500m and 1km SIN Grid V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2484086031-LPCLOUD.umm_json The MCD19A1 Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Land Surface Bidirectional Reflectance Factor (BRF) gridded Level 2 product produced daily at 500 meter (m) and 1 kilometer (km) pixel resolutions. The MCD19A1 product is corrected for atmospheric gases and aerosols using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The MODIS MAIAC Land Surface BRF products provide an estimate of the surface spectral reflectance as it would be measured at ground-level in the absence of atmospheric scattering or absorption. The MCD19A1 MAIAC Surface Reflectance data product includes 31 Science Dataset (SDS) layers: surface reflectance for bands 1-12, BRF uncertainty for bands 1-2, Quality Assessment (QA) bits at 1 km, surface reflectance for bands 1-7 at 500 m, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, solar azimuth angle, view azimuth angle, glint angle, RossThick/Li-Sparse (RTLS) volumetric kernel, and RTLS geometric kernel at 5 km. A low-resolution browse image is also included showing surface reflectance band combination 1, 4, 3 created using a composite of all available orbits. Each SDS layer within each MCD19A1 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the MCD19A1 data product. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The MCD19 Version 6.1 products have added 250 m resolution bands. * The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product. * MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. * Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D. * There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG. proprietary
MCD19A2CMG_061 MODIS/Terra+Aqua AOD and Water Vapor from MAIAC, Daily L3 Global 0.05Deg CMG V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565807733-LPCLOUD.umm_json The MCD19A2CMG Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) and Water Vapor Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A2CMG product provides the atmospheric properties and view geometry used to calculate the MAIAC Surface Reflectance data products (MCD19A1CMGL (https://doi.org/10.5067/MODIS/MCD19A1CMGL.061) and MCD19A1CMGO (https://doi.org/10.5067/MODIS/MCD19A1CMGO.061)). The MCD19A2CMG AOD data product contains the following Science Dataset (SDS) layers: blue band AOD at 0.47 µm, green band AOD at 0.55 µm, AOD uncertainty, column water vapor for Terra, column water vapor for Aqua, average cloud fraction, available AOD, satellite overpass times, line and sample number, offset, and number of AOD records. A low-resolution browse image is also included showing AOD of the blue band at 0.47 µm created using a composite of all available orbits. proprietary
MCD19A2N_6.1NRT MODIS/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1km SIN Grid NRT LANCEMODIS STAC Catalog 2022-08-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2407807500-LANCEMODIS.umm_json The Moderate Resolution Imaging Spectroradiometer (MODIS) Near Real Time (NRT) Combined Terra and Aqua Multi-Angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth gridded Level 2 product (MCD19A2N) produced daily at 1 kilometer (km) pixel resolutions. The MCD19A2N product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1N product. The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). proprietary
-MCD19A2_006 MODIS/Terra+Aqua Land Aerosol Optical Thickness Daily L2G Global 1km SIN Grid V006 LPCLOUD STAC Catalog 2000-02-26 2023-02-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763289461-LPCLOUD.umm_json The MCD19A2 Version 6 data product was decommissioned on July 31, 2023. The MCD19A2 Version 6 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 1 kilometer (km) pixel resolution. The MCD19A2 product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1 product. The MCD19A2 AOD data product contains the following Science Dataset (SDS) layers: blue band AOD at 0.47 µm, green band AOD at 0.55 µm, AOD uncertainty, fine mode fraction over water, column water vapor over land and clouds (in cm), smoke injection height (m above ground), AOD QA, AOD model at 1km, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, and glint angle at 5km. A low-resolution browse image is also included showing AOD of the blue band at 0.47 µm created using a composite of all available orbits. Each SDS layer within each MCD19A2 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer. proprietary
+MCD19A2_006 MODIS/Terra+Aqua Land Aerosol Optical Thickness Daily L2G Global 1km SIN Grid V006 LPCLOUD STAC Catalog 2000-02-26 2023-02-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763289461-LPCLOUD.umm_json The MCD19A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MCD19A2 Version 6.1 data product (https://doi.org/10.5067/MODIS/MCD19A2.061). The MCD19A2 Version 6 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 1 kilometer (km) pixel resolution. The MCD19A2 product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1 product. The MCD19A2 AOD data product contains the following Science Dataset (SDS) variables: blue band AOD at 0.47 micron, green band AOD at 0.55 micron, AOD uncertainty, fine mode fraction over water, column water vapor over land and clouds (in cm), smoke injection height (m above ground), AOD QA, AOD model at 1 km, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, and glint angle at 5 km. A low-resolution browse image is also included showing AOD of the blue band at 0.47 micron created using a composite of all available orbits. Each SDS variable within each MCD19A2 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS variable. Improvements/Changes from Previous Versions *New product for MODIS Version 6. proprietary
MCD19A2_061 MODIS/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1km SIN Grid V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2324689816-LPCLOUD.umm_json The MCD19A2 Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 1 kilometer (km) pixel resolution. The MCD19A2 product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1 product. The MCD19A2 AOD data product contains the following Science Dataset (SDS) layers: blue band AOD at 0.47 µm, green band AOD at 0.55 µm, AOD uncertainty, fine mode fraction over water, column water vapor over land and clouds (in cm), smoke injection height (m above ground), AOD QA, AOD model at 1km, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, and glint angle at 5km. A low-resolution browse image is also included showing AOD of the blue band at 0.47 µm created using a composite of all available orbits. Each SDS layer within each MCD19A2 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer. Validation at stage 1 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) has been achieved for the AOD SDS layers. Further details regarding MODIS land product validation for MCD19 data products are available from the MODIS Land Team Validation site (https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD19). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * The MCD19 Version 6.1 products have added 250 m resolution bands. * The previous BRDF product (MCD19A3) was reported once every eight days and the new MCD19A3D is a daily product. * MCD19A3D introduces gap-filled NDVI and gap-filled 250 m NBAR. * Snow Fraction, Snow Fit, and Snow Grain size layers were moved from MCD19A1 to the MCD19A3D. * There are four additional Climate Modeling Grid (CMG) products: MCD19A1CMGL, MCD19A1GO, MCD19A2CMG, and MCD19A3CMG. proprietary
MCD19A3CMG_061 MODIS/Terra+Aqua Vegetation Index from MAIAC, Daily L3 Global 0.05Deg CMG V061 LPCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565807736-LPCLOUD.umm_json The MCD19A3CMG Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Vegetation Index Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A3CMG product provides Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) at ground level in the absence of atmospheric scattering or absorption. The MCD19A3CMG Vegetation Index data product contains the following Science Dataset (SDS) layers: NDVI, NDVI normalized to a fixed geometry of solar zenith angle at 45° and nadir view, gap-filled NDVI, EVI, and EVI normalized to a fixed geometry of solar zenith angle at 45° and nadir view. A low-resolution browse image is also included showing NDVI created using a composite of all available orbits. proprietary
MCD19A3DN_6.1NRT MODIS/Terra+Aqua BRDF Model Parameters Daily NRT L3 Global 1km SIN Grid LANCEMODIS STAC Catalog 2023-09-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2407808961-LANCEMODIS.umm_json The MODIS Near Real Time (NRT) Combined Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Bidirectional Reflectance Distribution Function (BRDF) Model Parameters gridded Level 3 product (MCD19A3DN) produced daily at 1 kilometer (km) pixel resolutions. The MCD19A3DN product provides three coefficients (weights) of the RossThick/Li-Sparse (RTLS) BRDF model that can be used to describe the anisotropy of each pixel. The retrievals represent cloud-free and low aerosol conditions. The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). proprietary
@@ -10921,7 +10922,7 @@ MCD43D65_061 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Ref Band4 Daily L3
MCD43D66_061 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Ref Band5 Daily L3 Global 30ArcSec CMG V061 LPCLOUD STAC Catalog 2000-02-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2540275742-LPCLOUD.umm_json The MCD43D66 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. MCD43D66 is the NBAR for MODIS band 5. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). proprietary
MCD43D67_061 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Ref Band6 Daily L3 Global 30ArcSec CMG V061 LPCLOUD STAC Catalog 2000-02-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2540275748-LPCLOUD.umm_json The MCD43D67 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. MCD43D67 is the NBAR for MODIS band 6. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). proprietary
MCD43D68_061 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Ref Band7 Daily L3 Global 30ArcSec CMG V061 LPCLOUD STAC Catalog 2000-02-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2540275753-LPCLOUD.umm_json The MCD43D68 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at 30 arc second (1,000 meter (m)) resolution. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name. This Climate Modeling Grid (CMG) product covers the entire globe for use in climate simulation models. Due to the large file size, each MCD43D product contains just one data layer. MCD43D62 through MCD43D68 are the NBAR products of the MCD43D BRDF/Albedo product suite for MODIS bands 1 through 7. The NBAR algorithm removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. MCD43D68 is the NBAR for MODIS band 7. Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). proprietary
-MCD43GF_006 MODIS/Terra+Aqua BRDF/Albedo Gap-Filled Snow-Free Daily L3 Global 30ArcSec CMG V006 LPDAAC_ECS STAC Catalog 2000-03-03 2017-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1623882456-LPDAAC_ECS.umm_json "The Daily Moderate Resolution Imaging Spectroradiometer (MODIS) (Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6 product suite, with additional processing to provide a gap-filled, snow-free product. The highest quality full inversion values were used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the quality layer for each band. To generate a snow-free product, pixels with ephemeral snow were removed using the MCD43D41 (https://doi.org/10.5067/MODIS/MCD43D41.006) snow flags. The underlying MCD43D utilizes a BRDF model derived from all available high quality cloud clear reflectance data over a 16 day moving window centered on and emphasizing the daily day of interest (the ninth day of each retrieval period as reflected in the Julian date in the filename). This 30arc second BRDF model is then used to produce the Albedo and NBAR products (MCD43D). These BRDF model parameters are computed for MODIS spectral bands 1 through 7 (0.47 um, 0.55 um, 0.67 um, 0.86 um, 1.24 um, 1.64 um, 2.1 um), as well as the shortwave infrared band (0.3-5.0um), visible band (0.3-0.7 um), and near-infrared (0.7-5.0 um) broad bands. The MCD43GF product includes 67 layers containing black-sky albedo (BSA) at local solar noon, isotropic (ISO), volumetric (VOL), geometric (GEO), quality (QA), Nadir BRDF-Adjusted Reflectance (NBAR) at local solar noon, and white-sky albedo (WSA). Due to the large file size, each data layer is distributed as a separate HDF file. Users are encouraged to download the quality layers for each of the 10 bands to check quality assessment information before using the BRDF/Albedo data. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications (https://www.umb.edu/spectralmass/terra_aqua_modis/v006). The MCD43 product is not recommended for solar zenith angles beyond 70 degrees. The MODIS BRDF/Albedo products have achieved stage 3 (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/newPage.cgi?fileName=maturity) validation. Improvements/Changes from Previous Versions Observations are weighted to estimate the BRDF/Albedo on the ninth day of the 16-day period. * MCD43 products use the snow status weighted to the ninth day instead of the majority snow/no-snow observations from the 16-day period. * Better quality at high latitudes from use of all available observations for the acquisition period. Collection 5 used only four observations per day. * The MCD43 products use L2G-lite surface reflectance as input. * In cases where insufficient high-quality reflectances are obtained, a database with archetypal BRDF parameters is used to supplement the observational data and perform a lower quality magnitude inversion. This database is continually updated with the latest full inversion retrieval for each pixel. * CMG Albedo is estimated using all the clear-sky observations within the 1,000 m grid as opposed to aggregating from the 500 m albedo. Important Quality Information The incorrect representation of the aerosol quantities (low average high) in the C6 MYD09 and MOD09 surface reflectance products may have impacted down stream products particularly over arid bright surfaces (https://landweb.modaps.eosdis.nasa.gov/cgi-bin/QA_WWW/displayCase.cgi?esdt=MOD09&caseNum=PM_MOD09_20010&caseLocation=cases_data&type=C6). This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. " proprietary
+MCD43GF_006 MODIS/Terra+Aqua BRDF/Albedo Gap-Filled Snow-Free Daily L3 Global 30ArcSec CMG V006 LPDAAC_ECS STAC Catalog 2000-03-03 2017-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1623882456-LPDAAC_ECS.umm_json "The Daily Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6 product suite, with additional processing to provide a gap-filled, snow-free product. The highest quality full inversion values were used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the quality layer for each band. To generate a snow-free product, pixels with ephemeral snow were removed using the MCD43D41 (https://doi.org/10.5067/MODIS/MCD43D41.006) snow flags. The underlying MCD43D utilizes a BRDF model derived from all available high quality cloud clear reflectance data over a 16 day moving window centered on and emphasizing the daily day of interest (the ninth day of each retrieval period as reflected in the Julian date in the filename). This 30 arc second BRDF model is then used to produce the Albedo and NBAR products (MCD43D). These BRDF model parameters are computed for MODIS spectral bands 1 through 7 (0.47 um, 0.55 um, 0.67 um, 0.86 um, 1.24 um, 1.64 um, 2.1 um), as well as the shortwave infrared band (0.3-5.0 um), visible band (0.3-0.7 um), and near-infrared (0.7-5.0 um) broad bands. The MCD43GF product includes 67 variables containing black-sky albedo (BSA) at local solar noon, isotropic model parameter (ISO), volumetric model parameter (VOL), geometric model parameter (GEO), quality (QA), Nadir BRDF-Adjusted Reflectance (NBAR) at local solar noon, and white-sky albedo (WSA). Due to the large file size, each data variable is distributed as a separate HDF file. Users are encouraged to download the quality variable for each of the 10 bands to check quality assessment information before using the BRDF/Albedo data. The MCD43 product is not recommended for solar zenith angles beyond 70 degrees. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide. Improvements/Changes from Previous Versions * Observations are weighted to estimate the BRDF/Albedo on the ninth day of the 16-day period. * MCD43 products use the snow status weighted to the ninth day instead of the majority snow/no-snow observations from the 16-day period. * Better quality at high latitudes from use of all available observations for the acquisition period. Collection 5 used only four observations per day. * The MCD43 products use L2G-lite surface reflectance as input. * In cases where insufficient high-quality reflectances are obtained, a database with archetypal BRDF parameters is used to supplement the observational data and perform a lower quality magnitude inversion. This database is continually updated with the latest full inversion retrieval for each pixel. * CMG Albedo is estimated using all the clear-sky observations within the 1,000 m grid as opposed to aggregating from the 500 m albedo. Important Quality Information The incorrect representation of the aerosol quantities (low average high) in the C6 MYD09 and MOD09 surface reflectance products may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products. " proprietary
MCD43GF_061 MODIS/Terra+Aqua BRDF/Albedo Gap-Filled Snow-Free Daily L3 Global 30ArcSec CMG V061 LPCLOUD STAC Catalog 2000-03-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3216719281-LPCLOUD.umm_json The Daily Moderate Resolution Imaging Spectroradiometer (MODIS) (Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) 30 arc second, Global Gap-Filled, Snow-Free, (MCD43GF) Version 6.1 is derived from the 30 arc second Climate Modeling Grid (CMG) MCD43D Version 6.1 product suite, with additional processing to provide a gap-filled, snow-free product. The highest quality full inversion values were used for the temporal fitting effort and supplemented with lower quality pixels, spatial fitting, and spatial smoothing as needed. The status of each pixel can be found in the quality layer for each band. To generate a snow-free product, pixels with ephemeral snow were removed using the [MCD43D41](https://doi.org/10.5067/MODIS/MCD43D41.061) snow flags. The underlying MCD43D utilizes a BRDF model derived from all available high quality cloud clear reflectance data over a 16 day moving window centered on and emphasizing the daily day of interest (the ninth day of each retrieval period as reflected in the Julian date in the filename). This 30 arc second BRDF model is then used to produce the Albedo and NBAR products (MCD43D). These BRDF model parameters are computed for MODIS spectral bands 1 through 7 (0.47 um, 0.55 um, 0.67 um, 0.86 um, 1.24 um, 1.64 um, 2.1 um), as well as the shortwave infrared band (0.3-5.0 um), visible band (0.3-0.7 um), and near-infrared (0.7-5.0 um) broad bands. The MCD43GF product includes 67 layers containing black-sky albedo (BSA) at local solar noon, isotropic model parameter (ISO), volumetric model parameter (VOL), geometric model parameter (GEO), quality (QA), Nadir BRDF-Adjusted Reflectance (NBAR) at local solar noon, and white-sky albedo (WSA). Due to the large file size, each data layer is distributed as a separate HDF file. Users are encouraged to download the quality layers for each of the 10 bands to check quality assessment information before using the BRDF/Albedo data. The MCD43 product is not recommended for solar zenith angles beyond 70 degrees. Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the [User Guide](https://www.umb.edu/spectralmass/modis-user-guide-v006-and-v0061/mcd43gf-cmg-gap-filled-snow-free-products/). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). * In Version 6.1 reprocessing, the QA values for the MCD43GF product will change to reflect the band 5 and 6 dead detector issues. proprietary
MCD64A1_061 MODIS/Terra+Aqua Direct Broadcast Burned Area Monthly L3 Global 500m SIN Grid V061 LPCLOUD STAC Catalog 2000-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2565786756-LPCLOUD.umm_json The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500 meter (m) product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance imagery coupled with 1 kilometer (km) MODIS active fire observations. The algorithm uses a burn sensitive Vegetation Index (VI) to create dynamic thresholds that are applied to the composite data. The VI is derived from MODIS shortwave infrared atmospherically corrected surface reflectance bands 5 and 7 with a measure of temporal texture. The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS tile. The date is encoded in a single data layer as the ordinal day of the calendar year on which the burn occurred with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells. The data layers provided in the MCD64A1 product include Burn Date, Burn Data Uncertainty, Quality Assurance, along with First Day and Last Day of reliable change detection of the year. Validation at stage 3 ( https://modis-land.gsfc.nasa.gov/MODLAND_val.html) has been achieved for the MODIS Burned Area product. Further details regarding MODIS land product validation for the MCD64A1 data product is available from the MODIS Land Team Validation site ( https://modis-land.gsfc.nasa.gov/ValStatus.php?ProductID=MOD64). Improvements/Changes from Previous Versions * The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017. * A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). proprietary
MCDAODHD_6.1NRT MODIS/Terra+Aqua L3 Value-added Aerosol Optical Depth - NRT LANCEMODIS STAC Catalog 2017-10-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1426395436-LANCEMODIS.umm_json MODIS was launched aboard the Terra satellite on December 18, 1999 (10:30 am equator crossing time) as part of NASA's Earth Observing System (EOS) mission. MODIS with its 2330 km viewing swath width provides almost daily global coverage. It acquires data in 36 high spectral resolution bands between 0.415 to 14.235 micron with spatial resolutions of 250m(2 bands), 500m(5 bands),and 1000m (29 bands). MODIS sensor counts, calibrated radiances, geolocation products and all derived geophysical atmospheric and ocean products are archived at various DAACs and has been made available to public since April 2000. The shortname for this level-3 MODIS aerosol product is MCDAODHD. The Naval Research Laboratory and the University of North Dakota developed this value-added aerosol optical depth dataset based on MODIS Level 2 aerosol products. MCDAODHD is a gridded product and is specifically designed for quantitative applications including data assimilation and model validation. It is available through LANCE-MODIS. It offers several enhancements over the MODIS Level 2 data on which it is based. These enhancements include stringent filtering to reduce outliers, eliminate cloud contamination, and exclude conditions where aerosol detection is likely to be inaccurate; reduction of systematic biases over land and ocean by empirical corrections; reduction of random variation in AOD values by spatial averaging; quantitative estimation of uncertainty for each AOD data point. The MxDAODHD granules are produced every six hours, and time-stamped 00:00, 06:00, 12:00, and 18:00 (all times UTC). Each granule includes MODIS observations from +/-3 hours from the timestamp (e.g. 12:00 product includes MODIS data from 09:00-15:00 UTC). Production is initiated as soon as the Level 2 inputs become available in the LANCE system. See the LANCE-MODIS page for more dataset information: https://earthdata.nasa.gov/earth-observation-data/near-real-time/download-nrt-data/modis-nrt proprietary
@@ -10983,8 +10984,8 @@ MER_FRS_1P_8.0 Envisat MERIS Full Resolution - Level 1 [MER_FRS_1P/ME_1_FRG] ESA
MER_FRS_2P_8.0 Envisat MERIS Full Resolution - Level 2 [MER_FRS_2P/ME_2_FRG] ESA STAC Catalog 2002-05-17 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207506787-ESA.umm_json MERIS FR Level 2 is a Full-Resolution Geophysical product for Ocean, Land and Atmosphere. Each MERIS Level 2 geophysical product is derived from a MERIS Level 1 product and auxiliary parameter files specific to the MERIS Level 2 processing. The MERIS FR Level 2 product has Sentinel 3-like format starting from the 4th reprocessing data released to users in July 2020. The data package is composed of NetCDF 4 files containing instrumental and scientific measurements, and a Manifest file which contains metadata information related to the description of the product. A Level 2 product is composed of 64 measurement files containing: 13 files containing Water-leaving reflectance, 13 files containing Land surface reflectance and 13 files containing the TOA reflectance (for all bands except those dedicated to measurement of atmospheric gas - M11 and M15), and several files containing additional measurement on Ocean, Land and Atmospheric parameters and annotation. The Auxiliary data used are listed in the Manifest file associated to each product. The Level 2 FR product covers the complete instrument swath. The product duration is not fixed and it can span up to the time interval of the input Level 0/Level 1. Thus the estimated size of the Level 2 FR is dependent on the start/stop time of the acquired segment. During the Envisat mission, acquisition of MERIS Full Resolution data was subject to dedicated planning based on on-demand ordering and coverage of specific areas according to operational recommendations and considerations. See yearly and global density maps to get a better overview of the MERIS FR coverage. proprietary
MESSR_MOS-1_L2_Data_NA MESSR/MOS-1 L2 Data JAXA STAC Catalog 1987-02-24 1995-11-21 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698130302-JAXA.umm_json MESSR/MOS-1 L2 Data is obtained from the MESSR sensor onboard MOS-1, Japan's first marine observation satellite, and produced by the National Space Development Agency of Japan:NASDA. MOS-1, Japan's first marine observation satellite, is Sun-synchronous sub-recurrent Orbit satellite launched on February 19, 1987 as a link in a global satellite observation system for more effective natural resource utilization and for environmental protection. The MESSR is multi-spectral radiometers and has swath of 100 km. This dataset includes radiometric and geometric corrected applied raw data.Map projection is UTM, SOM, PS. The provided format is CEOS. The spatial resolution is 50 m. proprietary
MESSR_MOS-1b_L2_Data_NA MESSR/MOS-1b L2 Data JAXA STAC Catalog 1990-03-09 1996-04-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2698133853-JAXA.umm_json MESSR/MOS-1b L2 Data is obtained from the MESSR sensor onboard MOS-1b, Japan's first marine observation satellite, and produced by the National Space Development Agency of Japan:NASDA. MOS-1b which has the same functions as MOS-1 is Sun-synchronous sub-recurrent Orbit satellite launched on February 7, 1990 as a link in a global satellite observation system for more effective natural resource utilization and for environmental protection. The MESSR is multi-spectral radiometers and has swath of 100 km. This dataset includes radiometric and geometric corrected applied raw data.Map projction is UTM, SOM, PS. The provided format is CEOS. The spatial resolution is 50 m. proprietary
-MFLL_CO2_Weighting_Functions_1891_1 ACT-America: L2 Weighting Functions for Airborne Lidar Column-avg CO2, Eastern USA ORNL_CLOUD STAC Catalog 2016-05-27 2018-05-20 -106.05, 27.23, -71.91, 49.11 https://cmr.earthdata.nasa.gov/search/concepts/C2704977536-ORNL_CLOUD.umm_json This dataset provides vertical weighting function coefficients of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. The MFLL-measured column-averaged CO2 values have certain distinct vertical weights on CO2 profiles depending on the meteorological conditions and the wavelengths used at the measurement time and location. This product includes the instrument location at the time of measurement in geographic coordinates and altitude, along with a vector of weighting function values representing conditions along the nadir direction. proprietary
MFLL_CO2_Weighting_Functions_1891_1 ACT-America: L2 Weighting Functions for Airborne Lidar Column-avg CO2, Eastern USA ALL STAC Catalog 2016-05-27 2018-05-20 -106.05, 27.23, -71.91, 49.11 https://cmr.earthdata.nasa.gov/search/concepts/C2704977536-ORNL_CLOUD.umm_json This dataset provides vertical weighting function coefficients of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. The MFLL-measured column-averaged CO2 values have certain distinct vertical weights on CO2 profiles depending on the meteorological conditions and the wavelengths used at the measurement time and location. This product includes the instrument location at the time of measurement in geographic coordinates and altitude, along with a vector of weighting function values representing conditions along the nadir direction. proprietary
+MFLL_CO2_Weighting_Functions_1891_1 ACT-America: L2 Weighting Functions for Airborne Lidar Column-avg CO2, Eastern USA ORNL_CLOUD STAC Catalog 2016-05-27 2018-05-20 -106.05, 27.23, -71.91, 49.11 https://cmr.earthdata.nasa.gov/search/concepts/C2704977536-ORNL_CLOUD.umm_json This dataset provides vertical weighting function coefficients of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. The MFLL-measured column-averaged CO2 values have certain distinct vertical weights on CO2 profiles depending on the meteorological conditions and the wavelengths used at the measurement time and location. This product includes the instrument location at the time of measurement in geographic coordinates and altitude, along with a vector of weighting function values representing conditions along the nadir direction. proprietary
MFLL_XCO2_Range_10Hz_1892_1 ACT-America: L2 Remotely Sensed Column-avg CO2 by Airborne Lidar, Lite, Eastern USA ALL STAC Catalog 2016-05-27 2018-05-20 -106.05, 27.23, -71.91, 49.11 https://cmr.earthdata.nasa.gov/search/concepts/C2704971204-ORNL_CLOUD.umm_json This dataset provides a direct subset (i.e., the Lite version) of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1-second column CO2 reporting frequency, is included, but not limited to, latitude, longitude, altitude, and attitude. proprietary
MFLL_XCO2_Range_10Hz_1892_1 ACT-America: L2 Remotely Sensed Column-avg CO2 by Airborne Lidar, Lite, Eastern USA ORNL_CLOUD STAC Catalog 2016-05-27 2018-05-20 -106.05, 27.23, -71.91, 49.11 https://cmr.earthdata.nasa.gov/search/concepts/C2704971204-ORNL_CLOUD.umm_json This dataset provides a direct subset (i.e., the Lite version) of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1-second column CO2 reporting frequency, is included, but not limited to, latitude, longitude, altitude, and attitude. proprietary
MI03_resp_nutrients_GC1_1 GC-FID analysis of soil respirometery experiment. Soil from Macquarie Island, sampled in 2003. AU_AADC STAC Catalog 2003-01-01 2003-12-31 158.76892, -54.78406, 158.96667, -54.47802 https://cmr.earthdata.nasa.gov/search/concepts/C1214313661-AU_AADC.umm_json Field samples were collected from the Main Power House at Macquarie Island - coordinates.... The soil sample used for the respirometer trial was made up as a composite of 8 cores, namely: MPH1, MPH3, MPH4, MPH5, MPH7, MPH8 and MPH9. Each core was analysed for petroleum hydrocarbons (PHCs) at 0.05 m intervals. Intervals containing between 2500 and 5000 mg/kg PHC were then combined into a bulked sample used in the respirometer test. The sample was homogenised by placing all the soil (4.5 kg) into a large mixing bowl and stirring with a flat stirrer. The respirometer experiment was conducted by Jim Walworth and Andrew Pond at the University of Arizona. The objective was to optimise the nutrient status for microbial degradation of PHC's. The respirometer used was an N-Con closed system, with 24 flasks. There were 5 treatments and a control, each run in quadriplate. The control was unammended while treatments were 125, 250, 375, 500, and 625 mg nitrogen/kg of soil (on a dry soil weight basis). See: Sheet 'Sample details' for sample barcode, user ID and sample mass summary. Sheet 'GC-FID Data', cells A1-A18 = sample ID, GC injection file and processing notes Sheet 'GC-FID Data', Rows 10 and 11 contain TPH estimates and estimated standard uncertainty for the TPH value Sheet 'GC-FID Data', cells A21-A125 = compounds or GC elution windows measured Sheet 'GC-FID Data', cells B21-B56 = compound [CAS numbers] Sheet 'GC-FID Data', cells C21-AL125 = GC-FID area responses Sheet 'GC-FID Data', cells C128-AL232 = Estimated standard uncertainties for all GC-FID area responses (from blank drifts,local signal/noise etc) Chemical analysis details........Sample Extraction A 0.5mL volume of internal standard solution containing a mixture of compounds (cyclo-octane at c.1000mg/L, d8-naphthalene at 100mg/L, p-terphenyl at 100 mg/L and 1-bromoeicosane at 1000mg/L) dissolved in hexane, was pipetted onto the soil with a calibrated positive displacement pipette. This was followed by the addition of 10mL of hexane and 10mL of water. The vials were then tumbled end over end (50rpm) overnight and centrifuged at 1500 rpm. 1.8mL of the clear hexane layer was transferred by Pasteur pipette into a 2mL vial for Gas Chromatography Flame Ionisation Detector (GC-FID) analysis Chemical analysis details........GC-FID parameters The download file also includes a paper produced from this data. This work was completed as part of ASAC project 1163 (ASAC_1163). proprietary
@@ -11034,8 +11035,8 @@ MI3QCLDN_002 MISR Level 3 Global Cloud public Product in netCDF format covering
MI3QCMVN_002 MISR Level 3 Cloud Motion Vector quarterly Product in netCDF format V002 LARC STAC Catalog 1999-12-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C194517135-LARC.umm_json This file contains the MISR Level 3 Cloud Motion Vector quarterly Product in netCDF format proprietary
MI3YCLDN_002 MISR Level 3 Global Cloud public Product in netCDF format covering a year V002 LARC STAC Catalog 1999-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C108919911-LARC.umm_json This file contains the MISR Level 3 Global Cloud public Product in netCDF format covering a year proprietary
MI3YCMVN_2 MISR Level 3 Cloud Motion Vector yearly Product in netCDF format V002 LARC STAC Catalog 1999-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C194517136-LARC.umm_json MI3YCMVN_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 Cloud Motion Vector yearly Product in netCDF format version 2. It contains retrievals of cloud motion determined by geometrically triangulating the position and motion of cloud features observed by MISR from multiple perspectives and times during the overpass of the Terra platform over each cloud scene. Estimates of cloud motion are a valuable proxy observation of the horizontal atmospheric wind field at the retrieved altitude of the cloud. Data collection for this product is ongoing. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. MISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. proprietary
-MIANACP_1 MISR Aerosol Climatology Product V001 LARC STAC Catalog 1999-11-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C185127378-LARC.umm_json MIANACP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Aerosol Climatology Product version 1. It is 1) the microphysical and scattering characteristics of pure aerosol upon which routine retrievals are based; 2) mixtures of pure aerosol to be compared with MISR observations; and 3) likelihood value assigned to each mode geographically. The ACP describes mixtures of up to three component aerosol types from a list of eight components, in varying proportions. ACP component aerosol particle data quality depends on the ACP input data, which are based on aerosol particles described in the literature, and consider MISR-specific sensitivity to particle size, single-scattering albedo, and shape, and shape - roughly: small, medium and large; dirty and clean; spherical and nonspherical [Kahn et al. , 1998; 2001]. Also reported in the ACP are the mixtures of these components used by the retrieval algorithm. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. proprietary
-MIANCAGP_1 MISR Ancillary Geographic Product V001 LARC STAC Catalog 1999-11-07 2005-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C183897339-LARC.umm_json MIANCAGP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Geographic Product version 1. It is a set of 233 pre-computed files. Each AGP file pertains to a single Terra orbital path. MISR production software relies on information in the AGP, such as digital terrain elevation, as input to the algorithms which generate MISR products. The AGP contains eleven fields of geographical data. This product consists primarily of geolocation data on a Space Oblique Mercator (SOM) Grid. It has 233 parts, corresponding to the 233 repeat orbits of the EOS-AM1 Spacecraft. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. proprietary
+MIANACP_1 MISR Aerosol Climatology Product V001 LARC STAC Catalog 1999-11-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C185127378-LARC.umm_json MIANACP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Aerosol Climatology Product version 1. It is 1) the microphysical and scattering characteristics of pure aerosol upon which routine retrievals are based, 2) mixtures of pure aerosol to be compared with MISR observations, and 3) the likelihood value assigned to each mode geographically. The ACP describes mixtures of up to three component aerosol types from a list of eight components in varying proportions. ACP component aerosol particle data quality depends on the ACP input data, which are based on aerosol particles described in the literature and consider MISR-specific sensitivity to particle size, single-scattering albedo, and shape, and shape - roughly: small, medium, and large; dirty and clean; spherical and nonspherical [Kahn et al., 1998; 2001]. Also reported in the ACP are the mixtures of these components used by the retrieval algorithm. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. proprietary
+MIANCAGP_1 MISR Ancillary Geographic Product V001 LARC STAC Catalog 1999-11-07 2005-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C183897339-LARC.umm_json MIANCAGP_1 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Geographic Product version 1. It is a set of 233 pre-computed files. Each AGP file pertains to a single Terra orbital path. MISR production software relies on information in the AGP, such as digital terrain elevation, as input to the algorithms that generate MISR products. The AGP contains eleven fields of geographical data. This product consists primarily of geolocation data on a Space Oblique Mercator (SOM) Grid. It has 233 parts, corresponding to the 233 repeat orbits of the EOS-AM1 Spacecraft. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. proprietary
MIANCARP_2 MISR Ancillary Radiometric Product V002 LARC STAC Catalog 1999-12-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179031521-LARC.umm_json MIANCARP_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Ancillary Radiometric Product version 2. It is composed of 4 files covering instrument characterization data, pre-flight calibration data, in-flight calibration data, and configuration parameters. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm. proprietary
MIANTASC_002 MISR TASC dataset V002 LARC STAC Catalog 1999-12-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179031528-LARC.umm_json This is the Terrestrial Atmosphere and Surface Climatology used in Level 2 Processing. It is produced by the MISR SCF and shipped to the DAAC for generating MISR Level 2 products. proprietary
MIB1LM_002 MISR Level 1B1 Local Mode Radiance Data V002 LARC STAC Catalog 1999-12-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C179031461-LARC.umm_json This is the Local Mode Level 1B1 Product containing the DNs radiometrically scaled to radiances with no geometric resampling proprietary
@@ -11131,8 +11132,8 @@ MITgcm_LLC4320_Pre-SWOT_JPL_L4_Yongala_v1.0_1.0 Yongala Pre-SWOT Level-4 Hourly
MI_Azorella_PA_201011_update_1 Macquarie Island Azorella presence/absence data. From island wide plant survey 2010-11 AU_AADC STAC Catalog 2010-10-01 2011-03-31 158.7983, -54.7726, 158.9439, -54.4918 https://cmr.earthdata.nasa.gov/search/concepts/C1532636007-AU_AADC.umm_json This data set contains point location data for the presence or absence of Azorella macquariensis on Macquarie Island. The data were collected during an island wide alien plant survey during the 2010-11 season. This dataset was updated on 2016-08-10 and a new dataset DOI created. proprietary
MI_Azorella_dieback_5x5m_1 Macquarie Is. Azorella dieback 5m x 5m quadrats 2008-2012 AU_AADC STAC Catalog 2008-11-01 2011-12-10 158.77, -54.78, 158.95, -54.48 https://cmr.earthdata.nasa.gov/search/concepts/C1214313644-AU_AADC.umm_json This data set comprises data on Azorella macquariensis dieback from four summer seasons at a range of sites across Macquarie Island: 2008-09, 2009-10, 2010-11, 2011-12. Data on the proportion of healthy and dead or dying Azorella was collected from a 5 x 5m quadrat at each site. In some years data on the health of moss in the quadrats is also provided. The file is in the form of an Excel workbook with a separate worksheet for each year. In addition there are photographs of the sites spanning up to 4 years 2008-09 to - 2011 -12. Most photographic suites contain a North West and a South East site photographs and most are within 5- 10 m of the GPS point for the site. The site codes identify the 5 x 5m quadrats. proprietary
MI_Orchids_1976-2009_1 Biology and population studies of two endemic orchid species on sub-Antarctic Macquarie Island AU_AADC STAC Catalog 1976-01-01 2009-01-01 158.75793, -54.78643, 158.96118, -54.47483 https://cmr.earthdata.nasa.gov/search/concepts/C2102891822-AU_AADC.umm_json Two endemic orchid species, Nematoceras dienemum and N. sulcatum, are known from sub-Antarctic Macquarie Island. Several additional orchid populations on the island are reported and cleistogamy is documented in N. dienemum for the first time. The known population sizes, habitats and locations for both orchid species are documented here, and new information on their biology and population ecology is provided. These data are available from the biodiversity database. There are 20 observations in the data collection. proprietary
-MI_alk_clones_1 Alkane mono-oxygenase clone library from Macquarie Island soil ALL STAC Catalog 2008-01-01 2008-03-30 158.93, -54.491, 158.931, -54.49 https://cmr.earthdata.nasa.gov/search/concepts/C1214311195-AU_AADC.umm_json This dataset consists of 81 DNA sequences of the alkane mono-oxygenase gene. The sequence data are in FASTA format which can be opened with any word-processing or sequence analysis software. The clone library was created using the primers described by Kloos et al. (2006, Journal Microbiological Methods 66:486-496) F: AAYACNGCNCAYGARCTNGGNCAYAA and R:GCRTGRTGRTCNGARTGNCGYTG. The library was created from a soil sample collected at the Main Powerhouse on Macquarie Island and is Human Impacts Sample Tracking Database barcode number:52774. These data were collected as part of AAS project 2672 - Pathways of alkane biodegradation in antarctic and subantarctic soils and sediments. proprietary
MI_alk_clones_1 Alkane mono-oxygenase clone library from Macquarie Island soil AU_AADC STAC Catalog 2008-01-01 2008-03-30 158.93, -54.491, 158.931, -54.49 https://cmr.earthdata.nasa.gov/search/concepts/C1214311195-AU_AADC.umm_json This dataset consists of 81 DNA sequences of the alkane mono-oxygenase gene. The sequence data are in FASTA format which can be opened with any word-processing or sequence analysis software. The clone library was created using the primers described by Kloos et al. (2006, Journal Microbiological Methods 66:486-496) F: AAYACNGCNCAYGARCTNGGNCAYAA and R:GCRTGRTGRTCNGARTGNCGYTG. The library was created from a soil sample collected at the Main Powerhouse on Macquarie Island and is Human Impacts Sample Tracking Database barcode number:52774. These data were collected as part of AAS project 2672 - Pathways of alkane biodegradation in antarctic and subantarctic soils and sediments. proprietary
+MI_alk_clones_1 Alkane mono-oxygenase clone library from Macquarie Island soil ALL STAC Catalog 2008-01-01 2008-03-30 158.93, -54.491, 158.931, -54.49 https://cmr.earthdata.nasa.gov/search/concepts/C1214311195-AU_AADC.umm_json This dataset consists of 81 DNA sequences of the alkane mono-oxygenase gene. The sequence data are in FASTA format which can be opened with any word-processing or sequence analysis software. The clone library was created using the primers described by Kloos et al. (2006, Journal Microbiological Methods 66:486-496) F: AAYACNGCNCAYGARCTNGGNCAYAA and R:GCRTGRTGRTCNGARTGNCGYTG. The library was created from a soil sample collected at the Main Powerhouse on Macquarie Island and is Human Impacts Sample Tracking Database barcode number:52774. These data were collected as part of AAS project 2672 - Pathways of alkane biodegradation in antarctic and subantarctic soils and sediments. proprietary
MI_microcosm2006_microbial_data_1 Microbial data from the Macquarie Island Respirometry Experiment 2006 AU_AADC STAC Catalog 2006-09-10 2006-12-24 158.85, -54.64, 158.87, -54.6 https://cmr.earthdata.nasa.gov/search/concepts/C1214311213-AU_AADC.umm_json A microcosm experiment utilising a respirometry system and 14C-labelled hexadecane was initiated to investigate the effects of differing oxygen regimes on hydrocarbon degradation in soil from sub-Antarctic Macquarie Island. Measurements of oxygen consumed, carbon dioxide produced, total petroleum hydrocarbon degradation and nitrate and ammonium concentrations were made. The microbial community structure at the start of the experiment and after 4, 8 and 12 weeks incubation was explored using terminal restriction fragment length polymorphism and real-time PCR quantification of alkane mono-oxygenase, napthalene dioxygenase, nitrous oxide reductase and ribosomal polymerase sub-unitB. The data described here are the microbial community data only. The download file contains an excel spreadsheet. The first sheet provides further information about the dataset. This work was part of AAS projects 2672 and 1163. proprietary
MIvegmap_1 Macquarie Island Vegetation and Drainage Structure Data Set AU_AADC STAC Catalog 1979-01-01 1997-09-01 158.7761, -54.7772, 158.9508, -54.4853 https://cmr.earthdata.nasa.gov/search/concepts/C1214313649-AU_AADC.umm_json The data for this map were collected as part of two ASAC projects - 488 and 956, of which Patricia Selkirk was the chief investigator. Macquarie Island (54 degrees S 159 degrees E) is a subantarctic island (c. 35km by 3 to 5km) approximately equidistant between Tasmania, New Zealand and Antarctica in the Southern Ocean. The vegetation is herbaceous, lacking shrubs and trees. Vegetation and drainage are mapped at a scale of 1:25 000 from field observations, satellite imagery and limited oblique and aerial photography. The categories adopted for mapping vegetation are based on attributes of foliage height and percentage foliage cover of the ground surface (vegetation structure), not on species distribution (floristics). The distribution of vegetation categories is strongly correlated with aspect, topography and rock type. Mires, streams and lakes form an intricate drainage pattern that is strongly influenced by the geology of this tectonically active emergent crest of the submarine Macquarie Ridge at the boundary of the Pacific and Australian plates. The drainage pattern of the whole island is represented in a map with substantially greater accuracy than in any previous map. proprietary
ML1OA_004 MLS/Aura L1 Orbit/Attitude and Tangent Point Geolocation Data V004 (ML1OA) at GES DISC GES_DISC STAC Catalog 2004-08-01 -180, -82, 180, 82 https://cmr.earthdata.nasa.gov/search/concepts/C1265737437-GES_DISC.umm_json ML1OA is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 orbit attitude and tangent point geolocation data. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1OA data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains orbital and attitude information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata. proprietary
@@ -11556,8 +11557,8 @@ MODIS_CCaN_NDVI_Trends_Alaska_1666_1 ABoVE: MODIS- and CCAN-Derived NDVI and Tre
MODIS_CR_Equal_Angle_3h_1.0 MODIS_CR_Equal_Angle_3h GES_DISC STAC Catalog 2002-12-31 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089272156-GES_DISC.umm_json The MODIS Collection 6.1 Equal-Angle Three-Hourly Cloud Regime product. This product is a discrete classification of cloud fields at the mesoscale as observed by the MODIS sensors aboard the Terra and Aqua satellites. Derived by applying the k-means clustering algorithm to joint-histograms of cloud top pressure and cloud optical thickness, the cloud regimes represent different atmospheric systems based on their cloud signatures. proprietary
MODIS_CR_Equal_Angle_Daily_1.0 MODIS_CR_Equal_Angle_Daily GES_DISC STAC Catalog 2002-12-31 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089272480-GES_DISC.umm_json The MODIS Collection 6.1 Equal-Angle Three-Hourly Cloud Regime product. This product is a discrete classification of cloud fields at the mesoscale as observed by the MODIS sensors aboard the Terra and Aqua satellites. Derived by applying the k-means clustering algorithm to joint-histograms of cloud top pressure and cloud optical thickness, the cloud regimes represent different atmospheric systems based on their cloud signatures. proprietary
MODIS_CR_Equal_Area_3h_1.0 MODIS_CR_Equal_Area_3h GES_DISC STAC Catalog 2002-12-31 2020-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2084194432-GES_DISC.umm_json The MODIS Collection 6.1 Equal-Area Three-Hourly Cloud Regime product. This product is a discrete classification of cloud fields at the mesoscale as observed by the MODIS sensors aboard the Terra and Aqua satellites. Derived by applying the k-means clustering algorithm to joint-histograms of cloud top pressure and cloud optical thickness, the cloud regimes represent different atmospheric systems based on their cloud signatures. proprietary
-MODIS_MAIAC_Reflectance_1700_1 ABoVE: Corrected MODIS MAIAC Reflectance at Tower Sites, Alaska and Canada, 2000-2016 ALL STAC Catalog 2000-02-24 2016-07-31 -157.41, 42.64, -74.04, 71.32 https://cmr.earthdata.nasa.gov/search/concepts/C2143403511-ORNL_CLOUD.umm_json This dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances by two methods at each of 62 flux tower sites (1 km x 1 km area) across the ABoVE domain in Alaska and western Canada from 2000 to 2015/2016. The original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) using two independent algorithms: the first based on the original BRDF (Bidirectional Reflectance Distribution Function) parameters provided by the MAIAC team, and the second based on a machine learning approach (random forests). The corrected data preserve the original MAIAC data's sub-daily temporal resolution and 1 km spatial resolution with seven land bands (bands 1-7) and five ocean bands (bands 8-12). The resulting tower site sub-daily timeseries of angular corrected surface reflectances are suitable for long-term studies on patterns, processes, and dynamics of surface phenomena. proprietary
MODIS_MAIAC_Reflectance_1700_1 ABoVE: Corrected MODIS MAIAC Reflectance at Tower Sites, Alaska and Canada, 2000-2016 ORNL_CLOUD STAC Catalog 2000-02-24 2016-07-31 -157.41, 42.64, -74.04, 71.32 https://cmr.earthdata.nasa.gov/search/concepts/C2143403511-ORNL_CLOUD.umm_json This dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances by two methods at each of 62 flux tower sites (1 km x 1 km area) across the ABoVE domain in Alaska and western Canada from 2000 to 2015/2016. The original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) using two independent algorithms: the first based on the original BRDF (Bidirectional Reflectance Distribution Function) parameters provided by the MAIAC team, and the second based on a machine learning approach (random forests). The corrected data preserve the original MAIAC data's sub-daily temporal resolution and 1 km spatial resolution with seven land bands (bands 1-7) and five ocean bands (bands 8-12). The resulting tower site sub-daily timeseries of angular corrected surface reflectances are suitable for long-term studies on patterns, processes, and dynamics of surface phenomena. proprietary
+MODIS_MAIAC_Reflectance_1700_1 ABoVE: Corrected MODIS MAIAC Reflectance at Tower Sites, Alaska and Canada, 2000-2016 ALL STAC Catalog 2000-02-24 2016-07-31 -157.41, 42.64, -74.04, 71.32 https://cmr.earthdata.nasa.gov/search/concepts/C2143403511-ORNL_CLOUD.umm_json This dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances by two methods at each of 62 flux tower sites (1 km x 1 km area) across the ABoVE domain in Alaska and western Canada from 2000 to 2015/2016. The original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) using two independent algorithms: the first based on the original BRDF (Bidirectional Reflectance Distribution Function) parameters provided by the MAIAC team, and the second based on a machine learning approach (random forests). The corrected data preserve the original MAIAC data's sub-daily temporal resolution and 1 km spatial resolution with seven land bands (bands 1-7) and five ocean bands (bands 8-12). The resulting tower site sub-daily timeseries of angular corrected surface reflectances are suitable for long-term studies on patterns, processes, and dynamics of surface phenomena. proprietary
MODIS_PAR_1140_1 NACP: MODIS Daily Land Incident 4-km PAR Images For North America, 2003-2005 ORNL_CLOUD STAC Catalog 2003-01-01 2005-12-31 -180, 0, 0, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2631225371-ORNL_CLOUD.umm_json This data set contains daily Moderate Resolution Imaging Spectroradiometer (MODIS) land incident photosynthetically active radiation (PAR) images over North America for the years 2003 - 2005 and was created to fill the need for daily PAR estimates. Incident PAR is the solar radiation in the range of 400 to 700 nm reaching the earth's surface and plays an important role in modeling terrestrial ecosystem productivity. The daily images were derived by integrating MODIS/Terra and MODIS/Aqua instantaneous PAR data where the instantaneous PAR data is estimated directly from Terra or Aqua MODIS 5-min L1b swath data (Liang et al., 2006 and Wang et al., 2010). The spatial distribution of this data set includes the MODIS tile subsets covering North America, Central America, portions of South America, and Greenland, available for the years 2003 - 2005. There are 45,376 *.hdf files with a spatial resolution of 4 km x 4 km in sinusoidal projection distributed by year in three compressed data files: 2003.zip, 2004.zip, and 2005.zip. Contained within each daily file are 4 separate image files: DirectPar, DiffusePAR, TotalPAR, and Observation Count. There are 46 MODIS tiles that cover the study area extent. proprietary
MODIS_T-JPL-L2P-v2019.0_2019.0 GHRSST Level 2P Global Sea Surface Skin Temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Terra satellite (GDS2) POCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1940475563-POCLOUD.umm_json NASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. MODIS L2P SST data have a 1 km spatial resolution at nadir and are stored in 288 five minute granules per day. Full global coverage is obtained every two days, with coverage poleward of 32.3 degree being complete each day. The production of MODIS L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project, and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS are responsible for SST algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of daily MODIS ocean products. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and ancillary variables, and distributes the data as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous R2014.0 datasets which can be found at https://doi.org/10.5067/GHMDT-2PJ02 proprietary
MODIS_TERRA_L3_SST_MID-IR_8DAY_4KM_NIGHTTIME_V2019.0_2019.0 MODIS Terra Level 3 SST MID-IR 8 day 4km Nighttime V2019.0 POCLOUD STAC Catalog 2000-02-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2036882246-POCLOUD.umm_json Day and night spatially gridded (L3) global NASA skin sea surface temperature (SST) products from the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Average daily, weekly (8 day), monthly and annual skin SST products are available at both 4.63 and 9.26 km spatial resolution. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. To generate the L3 products the L2 pixels are binned into an integerized sinusoidal area grid (ISEAG) and mapped into an equidistant cylindrical (also known as Platte Carre) projection. Additional projection detailed can be found at https://oceancolor.gsfc.nasa.gov/docs/format/ The NASA MODIS L3 SST data products are generated by the NASA Ocean Biology Processing Group (OBPG) Peter Minnett and his team at the Rosenstiel School of Marine and Atmospheric Science (RSMAS) are responsible for sea surface temperature algorithm development, error statistics and quality flagging. JPL acquires and distributes MODIS ocean L3 SST data from the OBPG as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019 superseded the previous v2014.1 datasets which can be found at https://doi.org/10.5067/MODTM-8D4N4 proprietary
@@ -11649,8 +11650,8 @@ MS_Sound_0 Mississippi (MS) Sound optical measurements OB_DAAC STAC Catalog 2005
MTSAT2-OSPO-L2P-v1.0_1.0 GHRSST Level 2P Western Pacific Regional Skin Sea Surface Temperature from the Multifunctional Transport Satellite 2 (MTSAT-2) (GDS version 2) POCLOUD STAC Catalog 2013-08-01 2015-12-04 64, -80, -134, 79 https://cmr.earthdata.nasa.gov/search/concepts/C2499940520-POCLOUD.umm_json Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an aeronautical mission to assist air navigation, plus a meteorological mission to provide imagery over the Asia-Pacific region for the hemisphere centered on 140 East. The meteorological mission includes an imager giving nominal hourly full Earth disk images in five spectral bands (one visible, four infrared). MTSAT are spin stabilized satellites. With this system images are built up by scanning with a mirror that is tilted in small successive steps from the north pole to south pole at a rate such that on each rotation of the satellite an adjacent strip of the Earth is scanned. It takes about 25 minutes to scan the full Earth's disk. This builds a picture 10,000 pixels for the visible images (1.25 km resolution) and 2,500 pixels (4 km resolution) for the infrared images. The MTSAT-2 (also known as Himawari 7) and its radiometer (MTSAT-2 Imager) was successfully launched on 18 February 2006. For this Group for High Resolution Sea Surface Temperature (GHRSST) dataset, skin sea surface temperature (SST) measurements are calculated from the IR channels of the MTSAT-2 Imager full resolution data in satellite projection on a hourly basis by using Bayesian Cloud Mask algorithm at the Office of Satellite and Product Operations (OSPO). L2P datasets including Single Sensor Error Statistics (SSES) are then derived following the GHRSST Data Processing Specification (GDS) version 2.0. proprietary
MUR-JPL-L4-GLOB-v4.1_4.1 GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1) POCLOUD STAC Catalog 2002-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1996881146-POCLOUD.umm_json "A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset (four day latency) and near-real-time dataset (one day latency) at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.01 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains additional variables for some granules including a SST anomaly derived from a MUR climatology and the temporal distance to the nearest IR measurement for each pixel.This dataset is funded by the NASA MEaSUREs program ( http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects ), and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications. Use the file global metadata ""history:"" attribute to determine if a granule is near-realtime or retrospective." proprietary
MUR25-JPL-L4-GLOB-v04.2_4.2 GHRSST Level 4 MUR 0.25deg Global Foundation Sea Surface Temperature Analysis (v4.2) POCLOUD STAC Catalog 2002-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2036880657-POCLOUD.umm_json A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.25 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains an additional SST anomaly variable derived from a MUR climatology (average between 2003 and 2014). This dataset was originally funded by the NASA MEaSUREs program (http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects ) and the NASA CEOS COVERAGE project and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications. proprietary
-MURI_Camouflage_0 A Multi University Research Initiative (MURI) Camouflage Project ALL STAC Catalog 2010-06-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360494-OB_DAAC.umm_json A Multi University Research Initiative was funded to study the biological response to the dynamic, polarized light field in distinct water types. During June 2010, a campaign was undertaken in the coastal waters off Port Aransas, Texas to study the angular/temporal distribution of polarization in multiple environment types (eutrophic sediment laden coastal waters, oligotrophic off-shore), as well as the polarization-reflectance responses of several organisms. In addition to radiometric polarization measurements, water column IOPs, Rrs, benthic reflectance, and pigment concentration measurements were collected. Later campaigns expanded this research in the coastal waters off the Florida Keys. proprietary
MURI_Camouflage_0 A Multi University Research Initiative (MURI) Camouflage Project OB_DAAC STAC Catalog 2010-06-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360494-OB_DAAC.umm_json A Multi University Research Initiative was funded to study the biological response to the dynamic, polarized light field in distinct water types. During June 2010, a campaign was undertaken in the coastal waters off Port Aransas, Texas to study the angular/temporal distribution of polarization in multiple environment types (eutrophic sediment laden coastal waters, oligotrophic off-shore), as well as the polarization-reflectance responses of several organisms. In addition to radiometric polarization measurements, water column IOPs, Rrs, benthic reflectance, and pigment concentration measurements were collected. Later campaigns expanded this research in the coastal waters off the Florida Keys. proprietary
+MURI_Camouflage_0 A Multi University Research Initiative (MURI) Camouflage Project ALL STAC Catalog 2010-06-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360494-OB_DAAC.umm_json A Multi University Research Initiative was funded to study the biological response to the dynamic, polarized light field in distinct water types. During June 2010, a campaign was undertaken in the coastal waters off Port Aransas, Texas to study the angular/temporal distribution of polarization in multiple environment types (eutrophic sediment laden coastal waters, oligotrophic off-shore), as well as the polarization-reflectance responses of several organisms. In addition to radiometric polarization measurements, water column IOPs, Rrs, benthic reflectance, and pigment concentration measurements were collected. Later campaigns expanded this research in the coastal waters off the Florida Keys. proprietary
MURI_HI_0 A Multi University Research Initiative (MURI) near the Hawaiian Islands OB_DAAC STAC Catalog 2012-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360508-OB_DAAC.umm_json Measurements taken by the RV Kilo Moana in 2012 near the Hawaiian Islands. proprietary
MURI_HI_0 A Multi University Research Initiative (MURI) near the Hawaiian Islands ALL STAC Catalog 2012-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360508-OB_DAAC.umm_json Measurements taken by the RV Kilo Moana in 2012 near the Hawaiian Islands. proprietary
MUSE_0 Monterey Ocean Observing System (MOOS) Upper-water-column Science Experiment (MUSE) OB_DAAC STAC Catalog 2002-07-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360509-OB_DAAC.umm_json Measurements made near Monterey Bay under the MOOS Upper-water-column Science Experiment (MUSE). proprietary
@@ -11779,38 +11780,38 @@ Macquarie_Quickbird_2Nov2010_1 Macquarie Island Quickbird Image (2 November 2010
Macquarie_Royals_1962-1968_1 Macquarie Island Royal Penguin studies. Also includes Skua predation study and band resights. 1962 - 1968 AU_AADC STAC Catalog 1962-01-01 1968-12-31 158.76892, -54.78247, 158.95706, -54.48041 https://cmr.earthdata.nasa.gov/search/concepts/C1214311177-AU_AADC.umm_json Scans from one or more field books from observations made at Macquarie Island between 1962 and 1968. The observations were of Royal Penguins, and also of Skua predation and band resights. The following names have been mentioned in the scans: Susan Ingham John Warham John Ling David Nicolls I.T. Simpson Duncan Mackenzie Peter Shaughnessy D. Edwards R.Carrick Merilees Kerry Peter Ormay Schmidt Major S. Harris proprietary
Macquarie_Tide_Gauges_2 Macquarie Island Tide Gauge Data 1993-2007 AU_AADC STAC Catalog 1993-11-01 2007-04-30 158.76068, -54.78802, 158.95844, -54.47323 https://cmr.earthdata.nasa.gov/search/concepts/C1667370487-AU_AADC.umm_json Over time there have been a number of tide gauges deployed at Macquarie Island Station. The data download files contain further information about the gauges, but some of the information has been summarised here. Note that this metadata record only describes tide gauge data from 1993 to 2007. More recent data are described elsewhere. Macquarie Island used Aquatrak and Druck tide gauges during this period. Documentation from the older metadata record: Documentation dated 2001-06-12 The Macquarie Island Tide Gauge System The Macquarie Island Tide Gauge was first commissioned in November 1993. Since then every year attempts have been made to improve the performance of the system. The next improvement involves the installation of radio modems to effect a network link to the tide gauge dataloggers. Other improvements planned are include using the wave guide temperatures to correct the water heights for variations in the velocity of sound in air due to temperature gradients in the waveguide. The system consists of two separate sensors contained in separate housings on a rock shelf on the northern side of Garden Cove. One of the sensors is an Aquatrack acoustic type and the other is a Druck pressure transducer. Both housings contain a Platypus Engineering data logger and a battery. The housings consist each of an Admiralty Bronze ring bolted down to a concrete plinth and a glass fibre reinforced cover held down by a single central bolt and nut. Primary power for both installations comes from a solar panel array mounted on the northern side of the rock ridge behind the rock shelf. The solar panels are attached to an aluminium frame which is bolted to a galvanized steel frame cemented into holes in the rock face. The bolts are made of nylon with nylon washers so that the aluminium frame is not in contact with the galvanized frame. Mounted below the panels is a sealed plastic box with a hinged door. A multicore data cable runs from this box to the tide gauge housings. This cable is run inside a length of plastic conduit along with the power cable. The conduit is concealed in the vegetation and at the lower level is cemented into slots cut into the rock The batteries in the housing are kept charged by the solar panels but are isolated via power diodes, one in each housing. Either or both of the housing batteries or only the solar panel battery may be removed without interruption to data logging. The voltage of either housing battery may be found by interrogation of the appropriate data logger. Tide Gauge Bore Holes. Both gauges obtain access to the ocean via an inclined hole about 12 metres long inclined at approximately 34 and 39 degrees to the horizontal. Both holes are lined with a plastic pipe which is normally not removable. In the Aquatrack sensor hole a 50mm ABS pressure pipe runs down inside the liner and is fitted with a brass strainer and orifice at the lower end. This strainer protrudes into the ocean somewhat clear of the sea floor (see figure). Inside the 50mm pipe runs a 15mm diameter plastic pipe. The bottom end of this is fitted with a 600mm length of red brass tubing and stops about 100mm from the orifice at the bottom of the pipe. The 15mm pipe is held central in the 50mm pipe by three armed spiders placed about every metre down the pipe. The top end of both pipes is secured by a flange with two O rings and stainless steel screws. On top of the 15mm pipe is mounted the Aquatrack acoustic sensor the 15mm pipe acting as a waveguide for sound pulses from the sensor (see figure ). The Aquatrack sensor measures the distance of the water surface from a reference point on the sensor. About one metre down the wave guide is a small hole. This has two functions. One is to act as vent to allow water to rise and fall in the wave guide and the other is to provide an acoustic reflection at a known distance down the wave guide. This allows compensation for velocity of sound changes due to temperature changes. The Aquatrak wave guide has a series of thermistors placed along its length. The bottom one is always submerged and is used to measure the seawater temperature..The top one is placed just below the Sensor and the others evenly spaced along the length of the waveguide. The temperature readings from these can be used to compensate for the change in the velocity of sound due to density changes. This feature has not yet been used. The Druck Sensor has a single thermistor placed beside it which measures seawater temperature. System Components. The Aquatrak Installation houses four main components. 1. The Aquatrack Sensor and Waveguide Assembly. The sensor itself is in a waterproof plastic tube with a cable with a waterproof connector which plugs into the Bartek controller. 2. The Bartek Controller, housed in a waterproof diecast box with waterproof connectors. This lies in the centre of the installation housing. 3. The Platypus Engineering Datalogger 4. The Battery, a 15 Ah, 12 volt sealed gel cell lead acid battery. It is charged from the solar a diode. The battery lies in the main housing opposite the Datalogger . The Druck Installation houses four main components 1. The Druck Pressure Sensor, fitted to the end of a 13 metre cable, submerged in seawater about 10 metres down the borehole. The cable has five conductors and an air vent enclosed within it. 2. The Pressure Sensor Amplifier housed in a waterproof diecast box. This box has a vent leading to a vented bottle filled with silica gel to keep the transducer air vent dry. 3. A Datalogger As above. 4. A battery as above The Solar Panel Installation has three main parts. 1. Three Photo Voltaic Solar Panels, two 60 Watt and one 30 Watt. These are mounted on an aluminium frame attached to a hotdip galvanised steel frame with insulating bolts. 2. A sealed plastic box mounted below the panels containing a12V 24 Ah Battery and a regulator and the radio modem equipment. (The modems are not currently fitted.) 3. Antennae and cables protected with flexible conduit. Data Retrieval Data have been retrieved at approximately 30 day intervals from the Garden Cove gauges by using a portable computer to download the data loggers. The connector for this is in the enclosure by the solar panels allowing the loggers to be accessed during bad weather. Documentation dated 2008-10-17 1. In April 2007, the dataloggers and radio modems at Macquarie Island Tide Gauge site were replaced with Campbell Scientific CR1000 dataloggers. 2. This change enabled data to be streamed from the pressure sensor datalogger every 30 seconds. 3. There has been no change to scaling of records from the Aquatrak sensor as generation of ranges is done by the Aquatrak controller, the datalogger only saving and transmitting the records. Records from the pressure sensor however are now not converted to heights but saved and streamed as raw A/D conversion values. It is intended that appropriate scales and offsets for this sensor be derived after a Floating GPS Buoy exercise. 4. Data is streamed from the pressure sensor logger as this is the only sensor that can be supply 30 seconds average values. This logger also streams 3 minute average values. 5. The aquatrak sensor logger streams 3 minute average value ranges. 6. Data is streamed in NVP (name/Value Pair) format as defined by BoM. 7. Embedded in the streams are battery voltage and aquatrak waveguide temperature values. proprietary
MagMix_0 MagMix project OB_DAAC STAC Catalog 2008-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360470-OB_DAAC.umm_json Estuarine and coastal systems play important roles in society, serving as port facilities, productive fisheries and rookeries, and scenic recreational areas. However, these same values to society mean that these areas can be significantly affected by human activities. Inputs of nutrients, organic matter, and trace metals are among these impacts. The MagMix project seeks to understand the transport and cycling of nutrients and trace elements and relate that to biogeochemical and optical properties in river-dominated coastal systems. The area of study is the outflow region of the Mississippi and Atchafalaya rivers in the northern Gulf of Mexico. The Mississippi River carries high concentrations of plant nutrients derived from fertilizer use on farms in the heartland of the US. These excess nutrients stimulate plant growth in the surface waters of the Louisiana Shelf. These plants, in turn, sink to the bottom waters of the shelf where they serve as food for respiring organisms. The input of this excess food then stimulates an excess of respiration thereby depleting the shelf bottom waters of oxygen during the summer. These oxygen-depleted (or hypoxic) waters then become a dead zone avoided by animals. The overall goal of this research project is to better understand the mixing processes and their relationship to optical and biogeochemical properties as the waters of the Mississippi River and the Atchafalaya River enter the Gulf of Mexico. proprietary
-Main_Melt_Onset_Dates_1841_1.1 ABoVE: Passive Microwave-derived Annual Snowpack Main Melt Onset Date Maps, 1988-2018 ALL STAC Catalog 1988-02-09 2018-02-10 -180, 51.61, -107.83, 72.41 https://cmr.earthdata.nasa.gov/search/concepts/C2143401742-ORNL_CLOUD.umm_json This dataset provides the annual date of snowpack seasonal beginning melt (i.e., main melt onset date, MMOD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. MMOD was derived from the daily 19V (K-band) and 37V (Ka-band) GHz bands from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). The PMW MMOD dataset was validated using the transition date from Freeze Degree Days (FDD) to Thaw Degree Days (TDD) from in situ air temperature observations from 31 SNOw TELemetry network (SNOTEL) observations, and compared to the established Freeze-Thaw ESDR (FT-ESDR) spring onset date. The resulting MMOD data record is suitable for documenting the spatial-temporal impacts of MMOD variability in ecosystem services, wildlife movements, and hydrologic processes across the ABoVE domain. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary
Main_Melt_Onset_Dates_1841_1.1 ABoVE: Passive Microwave-derived Annual Snowpack Main Melt Onset Date Maps, 1988-2018 ORNL_CLOUD STAC Catalog 1988-02-09 2018-02-10 -180, 51.61, -107.83, 72.41 https://cmr.earthdata.nasa.gov/search/concepts/C2143401742-ORNL_CLOUD.umm_json This dataset provides the annual date of snowpack seasonal beginning melt (i.e., main melt onset date, MMOD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. MMOD was derived from the daily 19V (K-band) and 37V (Ka-band) GHz bands from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). The PMW MMOD dataset was validated using the transition date from Freeze Degree Days (FDD) to Thaw Degree Days (TDD) from in situ air temperature observations from 31 SNOw TELemetry network (SNOTEL) observations, and compared to the established Freeze-Thaw ESDR (FT-ESDR) spring onset date. The resulting MMOD data record is suitable for documenting the spatial-temporal impacts of MMOD variability in ecosystem services, wildlife movements, and hydrologic processes across the ABoVE domain. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary
-MaineInvasives A Historical Record of Sponges, Bryozoa and Ascidians on the Coast of Maine: 1843-1980 (Bigelow Laboratory for Ocean Sciences) ALL STAC Catalog 1843-01-01 1980-12-31 -70.7, 42.6, -66.9, 45.2 https://cmr.earthdata.nasa.gov/search/concepts/C1214593917-SCIOPS.umm_json Records of the occurrences of marine and estuarine sponges, bryozoans and ascideans on the coast of Maine have been compiled from the historic literature spanning the time frame of 1843 to 1980. These records variously include information on location, abundance, depth and habitat notes. Also available in many cases are common synonymies and scientific author. Sources include the primary literature, scientific and technical reports and unpublished records and field notes of marine researchers. The taxonomy of the species has been verified on the website WoRMS and by taxonomic experts. A few records need further investigation. These data have been georeferenced and entered into the OBIS database providing world-wide access and various search capabilities. proprietary
+Main_Melt_Onset_Dates_1841_1.1 ABoVE: Passive Microwave-derived Annual Snowpack Main Melt Onset Date Maps, 1988-2018 ALL STAC Catalog 1988-02-09 2018-02-10 -180, 51.61, -107.83, 72.41 https://cmr.earthdata.nasa.gov/search/concepts/C2143401742-ORNL_CLOUD.umm_json This dataset provides the annual date of snowpack seasonal beginning melt (i.e., main melt onset date, MMOD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. MMOD was derived from the daily 19V (K-band) and 37V (Ka-band) GHz bands from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). The PMW MMOD dataset was validated using the transition date from Freeze Degree Days (FDD) to Thaw Degree Days (TDD) from in situ air temperature observations from 31 SNOw TELemetry network (SNOTEL) observations, and compared to the established Freeze-Thaw ESDR (FT-ESDR) spring onset date. The resulting MMOD data record is suitable for documenting the spatial-temporal impacts of MMOD variability in ecosystem services, wildlife movements, and hydrologic processes across the ABoVE domain. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary
MaineInvasives A Historical Record of Sponges, Bryozoa and Ascidians on the Coast of Maine: 1843-1980 (Bigelow Laboratory for Ocean Sciences) SCIOPS STAC Catalog 1843-01-01 1980-12-31 -70.7, 42.6, -66.9, 45.2 https://cmr.earthdata.nasa.gov/search/concepts/C1214593917-SCIOPS.umm_json Records of the occurrences of marine and estuarine sponges, bryozoans and ascideans on the coast of Maine have been compiled from the historic literature spanning the time frame of 1843 to 1980. These records variously include information on location, abundance, depth and habitat notes. Also available in many cases are common synonymies and scientific author. Sources include the primary literature, scientific and technical reports and unpublished records and field notes of marine researchers. The taxonomy of the species has been verified on the website WoRMS and by taxonomic experts. A few records need further investigation. These data have been georeferenced and entered into the OBIS database providing world-wide access and various search capabilities. proprietary
+MaineInvasives A Historical Record of Sponges, Bryozoa and Ascidians on the Coast of Maine: 1843-1980 (Bigelow Laboratory for Ocean Sciences) ALL STAC Catalog 1843-01-01 1980-12-31 -70.7, 42.6, -66.9, 45.2 https://cmr.earthdata.nasa.gov/search/concepts/C1214593917-SCIOPS.umm_json Records of the occurrences of marine and estuarine sponges, bryozoans and ascideans on the coast of Maine have been compiled from the historic literature spanning the time frame of 1843 to 1980. These records variously include information on location, abundance, depth and habitat notes. Also available in many cases are common synonymies and scientific author. Sources include the primary literature, scientific and technical reports and unpublished records and field notes of marine researchers. The taxonomy of the species has been verified on the website WoRMS and by taxonomic experts. A few records need further investigation. These data have been georeferenced and entered into the OBIS database providing world-wide access and various search capabilities. proprietary
Maps_AGB_North_Slope_AK_1565_1 ABoVE: Gridded 30-m Aboveground Biomass, Shrub Dominance, North Slope, AK, 2007-2016 ORNL_CLOUD STAC Catalog 2007-06-01 2016-08-31 -168.58, 64.73, -111.55, 76.23 https://cmr.earthdata.nasa.gov/search/concepts/C2170971358-ORNL_CLOUD.umm_json This dataset includes 30-m gridded estimates of total plant aboveground biomass (AGB), the shrub AGB, and the shrub dominance (shrub/plant AGB) for non-water portions of the Beaufort Coastal Plain and Brooks Foothills ecoregions of the North Slope of Alaska. The estimates were derived by linking biomass harvests from 28 published field site datasets with NDVI from a regional Landsat mosaic derived from Landsat 5 and 7 satellite imagery. The data cover the period 2007-06-01 to 2016-08-31. proprietary
Maps_AGB_North_Slope_AK_1565_1 ABoVE: Gridded 30-m Aboveground Biomass, Shrub Dominance, North Slope, AK, 2007-2016 ALL STAC Catalog 2007-06-01 2016-08-31 -168.58, 64.73, -111.55, 76.23 https://cmr.earthdata.nasa.gov/search/concepts/C2170971358-ORNL_CLOUD.umm_json This dataset includes 30-m gridded estimates of total plant aboveground biomass (AGB), the shrub AGB, and the shrub dominance (shrub/plant AGB) for non-water portions of the Beaufort Coastal Plain and Brooks Foothills ecoregions of the North Slope of Alaska. The estimates were derived by linking biomass harvests from 28 published field site datasets with NDVI from a regional Landsat mosaic derived from Landsat 5 and 7 satellite imagery. The data cover the period 2007-06-01 to 2016-08-31. proprietary
Marine Debris Archive (MARIDA)_1 Marine Debris Archive (MARIDA) MLHUB STAC Catalog 2020-01-01 2023-01-01 -88.8557904, -29.8973351, 129.0745722, 56.4061985 https://cmr.earthdata.nasa.gov/search/concepts/C2781412537-MLHUB.umm_json Marine Debris Archive (MARIDA) is a marine debris-oriented dataset on Sentinel-2 satellite images. It also includes various sea features (clear & turbid water, waves, etc.) and floating materials (Sargassum macroalgae, ships, natural organic material, etc) that co-exist. MARIDA is primarily focused on the weakly supervised pixel-level semantic segmentation task. proprietary
Marine Debris Dataset for Object Detection in Planetscope Imagery_1 Marine Debris Dataset for Object Detection in Planetscope Imagery MLHUB STAC Catalog 2020-01-01 2023-01-01 -88.2971191, 5.4683637, 34.5300293, 39.1087514 https://cmr.earthdata.nasa.gov/search/concepts/C2781412735-MLHUB.umm_json Floating marine debris is a global pollution problem which leads to the loss of marine and terrestrial biodiversity. Large swaths of marine debris are also navigational hazards to ocean vessels. The use of Earth observation data and artificial intelligence techniques can revolutionize the detection of floating marine debris on satellite imagery and pave the way to a global monitoring system for controlling and preventing the accumulation of marine debris in oceans. This dataset consists of images of marine debris which are 256 by 256 pixels in size and labels which are bounding boxes with geographical coordinates. The images were obtained from PlanetScope optical imagery which has a spatial resolution of approximately 3 meters. In this dataset, marine debris consists of floating objects on the ocean surface which can belong to one or more classes namely plastics, algae, sargassum, wood, and other artificial items. Several studies were used for data collection and validation. While a small percentage of the dataset represents the coastlines of Ghana and Greece, most of the observations surround the Bay Islands in Honduras. The marine debris detection models created and the relevant code for using this dataset can be found [here](https://github.com/NASA-IMPACT/marine_debris_ML). proprietary
Marine_Debris_Bibliography_1 Marine Debris Bibliography AU_AADC STAC Catalog 1939-01-01 -180, -70, 180, -47 https://cmr.earthdata.nasa.gov/search/concepts/C1214313632-AU_AADC.umm_json Marine Debris Bibliography compiled by Frederique Olivier contains 210 records. The fields in this dataset are: Bibliography index Subset Date of Publication Author/s Title Source Area Keywords Abstract proprietary
Marine_Plastics_Heard_Macquarie_1 Marine plastics found at Heard Island and Macquarie Island AU_AADC STAC Catalog 1986-01-01 1989-12-31 73.23212, -54.78327, 158.97079, -52.95195 https://cmr.earthdata.nasa.gov/search/concepts/C1214313612-AU_AADC.umm_json This project monitored plastics at the four-bays area on Heard Island and at Sandell Bay on Macquarie Island. It characterised plastics by infra-red spectroscopy both from the beach collection and small pieces from fur-seal stomachs and cormorant boluses. The aim was to assess human impact on the ocean by measuring plastic abundance and type. proprietary
-Marine_Virus_Southern_Ocean_Evans_IPY71_NL_1 Abundances of algae, bacteria, viruses, and heterotrophic nanoflagellates in the Southern Ocean and determination of grazing and viral lysis of the algae SCIOPS STAC Catalog 2007-01-16 2007-02-18 140, -54, 155, -43 https://cmr.earthdata.nasa.gov/search/concepts/C1214594314-SCIOPS.umm_json Samples were collected during the SAZ-Sense cruise (January - February 2007) in the Southern Ocean south of Tasmania, Australia on board RV Aurora Australis. Twenty four stations were sampled in an area between 43 oS to 54 oS and 140 oE to 155 oE. At 3 of the stations designated Process Stations 1, 2 and 3 repeated sampling was completed over a number of days to examine temporal variation. Process Stations 1 to 3 were located in the SAZ to the southwest of Tasmania, the PFZ and in the productive SAZ region southeast of Tasmania respectively, the latter being potentially representative of the future SAZ. Abundances of algae, bacteria, viruses and heterotrophic nanoflagellates were measured using flow cytometry and viral production was determined by an incubation based method. A dilution method was also used to determine grazing and viral lysis of the algae. proprietary
Marine_Virus_Southern_Ocean_Evans_IPY71_NL_1 Abundances of algae, bacteria, viruses, and heterotrophic nanoflagellates in the Southern Ocean and determination of grazing and viral lysis of the algae ALL STAC Catalog 2007-01-16 2007-02-18 140, -54, 155, -43 https://cmr.earthdata.nasa.gov/search/concepts/C1214594314-SCIOPS.umm_json Samples were collected during the SAZ-Sense cruise (January - February 2007) in the Southern Ocean south of Tasmania, Australia on board RV Aurora Australis. Twenty four stations were sampled in an area between 43 oS to 54 oS and 140 oE to 155 oE. At 3 of the stations designated Process Stations 1, 2 and 3 repeated sampling was completed over a number of days to examine temporal variation. Process Stations 1 to 3 were located in the SAZ to the southwest of Tasmania, the PFZ and in the productive SAZ region southeast of Tasmania respectively, the latter being potentially representative of the future SAZ. Abundances of algae, bacteria, viruses and heterotrophic nanoflagellates were measured using flow cytometry and viral production was determined by an incubation based method. A dilution method was also used to determine grazing and viral lysis of the algae. proprietary
+Marine_Virus_Southern_Ocean_Evans_IPY71_NL_1 Abundances of algae, bacteria, viruses, and heterotrophic nanoflagellates in the Southern Ocean and determination of grazing and viral lysis of the algae SCIOPS STAC Catalog 2007-01-16 2007-02-18 140, -54, 155, -43 https://cmr.earthdata.nasa.gov/search/concepts/C1214594314-SCIOPS.umm_json Samples were collected during the SAZ-Sense cruise (January - February 2007) in the Southern Ocean south of Tasmania, Australia on board RV Aurora Australis. Twenty four stations were sampled in an area between 43 oS to 54 oS and 140 oE to 155 oE. At 3 of the stations designated Process Stations 1, 2 and 3 repeated sampling was completed over a number of days to examine temporal variation. Process Stations 1 to 3 were located in the SAZ to the southwest of Tasmania, the PFZ and in the productive SAZ region southeast of Tasmania respectively, the latter being potentially representative of the future SAZ. Abundances of algae, bacteria, viruses and heterotrophic nanoflagellates were measured using flow cytometry and viral production was determined by an incubation based method. A dilution method was also used to determine grazing and viral lysis of the algae. proprietary
Marlon_Lewis_92_0 Marlon Lewis drifting buoys 1992 OB_DAAC STAC Catalog 1992-08-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360473-OB_DAAC.umm_json Data from 3 drifting buoys deployed in fall, 1992. Two of the buoys were air launched near 140W, -999 degrees in the Pacific Ocean, and one was deployed in Monterey Bay attached to a fixed mooring. The fixed mooring was recovered and subjected to post-calibration. proprietary
Marn10k_1 Marine Plain 1:10000 Topographic GIS Dataset AU_AADC STAC Catalog 1958-01-06 1979-01-26 78.0007, -68.666, 78.216, -68.597 https://cmr.earthdata.nasa.gov/search/concepts/C1214313613-AU_AADC.umm_json This dataset details features of Marine Plain in the Vestfold Hills, Antarctica. The dataset includes coastline, 5 metre interval contours and lake shores. These data were captured from aerial photography and are the basis of the Marine Plain Orthophoto Map published for the Australian Antarctic Division in 1993. This map is available from a URL provided in this metadata record. proprietary
Maryland_Temperature_Humidity_1319_1 In-situ Air Temperature and Relative Humidity in Greenbelt, MD, 2013-2015 ORNL_CLOUD STAC Catalog 2013-09-05 2015-12-28 -76.86, 38.99, -76.84, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2736724792-ORNL_CLOUD.umm_json This data set describes the temperature and relative humidity at 12 locations around Goddard Space Flight Center in Greenbelt MD at 15 minute intervals between November 2013 and November 2015. These data were collected to study the impact of surface type on heating in a campus setting and to improve the understanding of urban heating and potential mitigation strategies on the campus scale. Sensors were mounted on posts at 2 m above surface and placed on 7 different surface types around the centre: asphalt parking lot, bright surface roof, grass field, forest, and stormwater mitigation features (bio-retention pond and rain garden). Data were also recorded in an office setting and a garage, both pre- and post-deployment, for calibration purposes. This dataset could be used to validate satellite-based study or could be used as a stand-alone study of the impact of surface type on heating in a campus setting. proprietary
MassBay_LongTerm Long-Term Oceanographic Observations in Massachusetts Bay, 1989-2006 CEOS_EXTRA STAC Catalog 1989-01-01 2006-12-31 -71, 42, -70.5, 42.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552981-CEOS_EXTRA.umm_json This data report presents long-term oceanographic observations made in western Massachusetts Bay at long-term site LT-A (42° 22.6' N., 70° 47.0' W.; nominal water depth 32 meters) from December 1989 through February 2006 and long-term site B LT-B (42° 9.8' N., 70° 38.4' W.; nominal water depth 22 meters) from October 1997 through February 2004. The observations were collected as part of a U.S. Geological Survey (USGS) study designed to understand the transport and long-term fate of sediments and associated contaminants in Massachusetts Bay. The observations include time-series measurements of current, temperature, salinity, light transmission, pressure, oxygen, fluorescence, and sediment-trapping rate. About 160 separate mooring or tripod deployments were made on about 90 research cruises to collect these long-term observations. This report presents a description of the 16-year field program and the instrumentation used to make the measurements, an overview of the data set, more than 2,500 pages of statistics and plots that summarize the data, and the digital data in Network Common Data Form (NetCDF) format. This research was conducted by the USGS in cooperation with the Massachusetts Water Resources Authority and the U.S. Coast Guard. proprietary
MassGIS_GISDATA.COQHMOSAICSCDS_POLY 2001 MrSID Mosaics CD-ROM Index ALL STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592880-SCIOPS.umm_json CD-ROM index scheme for the 2001 color ortho image MrSID mosaics. proprietary
MassGIS_GISDATA.COQHMOSAICSCDS_POLY 2001 MrSID Mosaics CD-ROM Index SCIOPS STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592880-SCIOPS.umm_json CD-ROM index scheme for the 2001 color ortho image MrSID mosaics. proprietary
-MassGIS_GISDATA.COQHMOSAICSDVDS_POLY.xm 2001 MrSID Mosaics DVD Index SCIOPS STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592858-SCIOPS.umm_json DVD index scheme for the 2001 color ortho image MrSID mosaics. proprietary
MassGIS_GISDATA.COQHMOSAICSDVDS_POLY.xm 2001 MrSID Mosaics DVD Index ALL STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592858-SCIOPS.umm_json DVD index scheme for the 2001 color ortho image MrSID mosaics. proprietary
-MassGIS_GISDATA.COQHMOSAICS_POLY 2001 MrSID Mosaics Index ALL STAC Catalog 2002-08-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592815-SCIOPS.umm_json This data layer is used to index the half-meter MrSID mosaics for the 2001/03 1:5,000 Color Ortho Imagery. proprietary
+MassGIS_GISDATA.COQHMOSAICSDVDS_POLY.xm 2001 MrSID Mosaics DVD Index SCIOPS STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592858-SCIOPS.umm_json DVD index scheme for the 2001 color ortho image MrSID mosaics. proprietary
MassGIS_GISDATA.COQHMOSAICS_POLY 2001 MrSID Mosaics Index SCIOPS STAC Catalog 2002-08-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592815-SCIOPS.umm_json This data layer is used to index the half-meter MrSID mosaics for the 2001/03 1:5,000 Color Ortho Imagery. proprietary
+MassGIS_GISDATA.COQHMOSAICS_POLY 2001 MrSID Mosaics Index ALL STAC Catalog 2002-08-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592815-SCIOPS.umm_json This data layer is used to index the half-meter MrSID mosaics for the 2001/03 1:5,000 Color Ortho Imagery. proprietary
MassGIS_GISDATA.COQMOSAICS2005_POLY 2005 MrSID Mosaics Index ALL STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592900-SCIOPS.umm_json Index scheme for the 2005 color ortho image MrSID mosaics. proprietary
MassGIS_GISDATA.COQMOSAICS2005_POLY 2005 MrSID Mosaics Index SCIOPS STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592900-SCIOPS.umm_json Index scheme for the 2005 color ortho image MrSID mosaics. proprietary
MassGIS_GISDATA.COQMOSAICSCDS2005_POLY. 2005 MrSID Mosaics CD-ROM Index SCIOPS STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592882-SCIOPS.umm_json CD-ROM index scheme for the 2005 color ortho image MrSID mosaics. proprietary
MassGIS_GISDATA.COQMOSAICSCDS2005_POLY. 2005 MrSID Mosaics CD-ROM Index ALL STAC Catalog 2006-08-03 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592882-SCIOPS.umm_json CD-ROM index scheme for the 2005 color ortho image MrSID mosaics. proprietary
-MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY 2005 MrSID Mosaics DVD Index SCIOPS STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592901-SCIOPS.umm_json DVD index scheme for the 2005 color ortho image MrSID mosaics. proprietary
MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY 2005 MrSID Mosaics DVD Index ALL STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592901-SCIOPS.umm_json DVD index scheme for the 2005 color ortho image MrSID mosaics. proprietary
-MassGIS_GISDATA.IMG_BWORTHOS 1:5,000 Black and White Digital Orthophoto Images SCIOPS STAC Catalog 1992-01-01 1999-12-31 -73.54455, 41.198524, -69.87159, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592889-SCIOPS.umm_json "These medium resolution images provide a high-quality ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). As of March 31, 2000, the entire state is available. The imagery was captured during the spring from 1992 through 1999. Pixel resolution is 0.5 meters. In ArcSDE the layer is named IMG_BWORTHOS." proprietary
+MassGIS_GISDATA.COQMOSAICSDVDS2005_POLY 2005 MrSID Mosaics DVD Index SCIOPS STAC Catalog 2007-02-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592901-SCIOPS.umm_json DVD index scheme for the 2005 color ortho image MrSID mosaics. proprietary
MassGIS_GISDATA.IMG_BWORTHOS 1:5,000 Black and White Digital Orthophoto Images ALL STAC Catalog 1992-01-01 1999-12-31 -73.54455, 41.198524, -69.87159, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592889-SCIOPS.umm_json "These medium resolution images provide a high-quality ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). As of March 31, 2000, the entire state is available. The imagery was captured during the spring from 1992 through 1999. Pixel resolution is 0.5 meters. In ArcSDE the layer is named IMG_BWORTHOS." proprietary
-MassGIS_GISDATA.IMG_COQ2001 1:5,000 Color Ortho Imagery ALL STAC Catalog 2001-04-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592921-SCIOPS.umm_json "These medium resolution true color images are considered the new ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). MassGIS/EOEA and the Massachusetts Highway Department jointly funded the project. The photography for the mainland was captured in April 2001 when deciduous trees were mostly bare and the ground was generally free of snow." proprietary
+MassGIS_GISDATA.IMG_BWORTHOS 1:5,000 Black and White Digital Orthophoto Images SCIOPS STAC Catalog 1992-01-01 1999-12-31 -73.54455, 41.198524, -69.87159, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592889-SCIOPS.umm_json "These medium resolution images provide a high-quality ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). As of March 31, 2000, the entire state is available. The imagery was captured during the spring from 1992 through 1999. Pixel resolution is 0.5 meters. In ArcSDE the layer is named IMG_BWORTHOS." proprietary
MassGIS_GISDATA.IMG_COQ2001 1:5,000 Color Ortho Imagery SCIOPS STAC Catalog 2001-04-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592921-SCIOPS.umm_json "These medium resolution true color images are considered the new ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). MassGIS/EOEA and the Massachusetts Highway Department jointly funded the project. The photography for the mainland was captured in April 2001 when deciduous trees were mostly bare and the ground was generally free of snow." proprietary
+MassGIS_GISDATA.IMG_COQ2001 1:5,000 Color Ortho Imagery ALL STAC Catalog 2001-04-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592921-SCIOPS.umm_json "These medium resolution true color images are considered the new ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). MassGIS/EOEA and the Massachusetts Highway Department jointly funded the project. The photography for the mainland was captured in April 2001 when deciduous trees were mostly bare and the ground was generally free of snow." proprietary
MassGIS_GISDATA.IMG_COQ2005 1:5,000 Color Ortho Imagery (2005) ALL STAC Catalog 2005-04-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592911-SCIOPS.umm_json "These medium resolution true color images are considered the new ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). The photography for the entire commonwealth was captured in April 2005 when deciduous trees were mostly bare and the ground was generally free of snow. Image type is 4-band (RGBN) natural color (Red, Green, Blue) and Near infrared in 8 bits (values ranging 0-255) per band format. Image horizontal accuracy is +/-3 meters at the 95% confidence level at the nominal scale of 1:5,000. This digital orthoimagery can serve a variety of purposes, from general planning, to field reference for spatial analysis, to a tool for development and revision of vector maps. It can also serve as a reference layer or basemap for myriad applications inside geographic information system (GIS) software. The project was funded by the Executive Office of Environmental Affairs, the Department of Environmental Protection, the Massachusetts Highway Department, and the Department of Public Health." proprietary
MassGIS_GISDATA.IMG_COQ2005 1:5,000 Color Ortho Imagery (2005) SCIOPS STAC Catalog 2005-04-01 -73.54455, 41.19853, -69.8716, 42.908627 https://cmr.earthdata.nasa.gov/search/concepts/C1214592911-SCIOPS.umm_json "These medium resolution true color images are considered the new ""basemap"" for the Commonwealth by MassGIS and the Executive Office of Environmental Affairs (EOEA). The photography for the entire commonwealth was captured in April 2005 when deciduous trees were mostly bare and the ground was generally free of snow. Image type is 4-band (RGBN) natural color (Red, Green, Blue) and Near infrared in 8 bits (values ranging 0-255) per band format. Image horizontal accuracy is +/-3 meters at the 95% confidence level at the nominal scale of 1:5,000. This digital orthoimagery can serve a variety of purposes, from general planning, to field reference for spatial analysis, to a tool for development and revision of vector maps. It can also serve as a reference layer or basemap for myriad applications inside geographic information system (GIS) software. The project was funded by the Executive Office of Environmental Affairs, the Department of Environmental Protection, the Massachusetts Highway Department, and the Department of Public Health." proprietary
MassGIS_GISDATA.VCPEATLAND_POLY Acidic Peatland Community Systems SCIOPS STAC Catalog 2003-04-01 -71.36416, 41.53563, -70.51623, 42.859413 https://cmr.earthdata.nasa.gov/search/concepts/C1214592150-SCIOPS.umm_json Acidic Peatland Community Systems include evergreen forest and shrub bogs, Atlantic White Cedar (AWC) swamps and bogs, and shrub and graminoid fens. This data was created by starting with the DEP Wetlands, creating a new set of just the bog, coniferous and mixed forested wetland types, and then adding, deleting and changing polygon shapes and labels based on aerial photo interpretation of the 1999/2000 photos and field information. In some areas where this wetland layer did not exist, the wetlands were interpreted and digitized from the aerial photos. The Acidic Peatland datalayer is named VCPEATLAND_POLY in ArcSDE. This layer is part of the MassGIS Priority Natural Vegetation Communities dataset, which depicts the distribution of the eight natural community systems identified by the Massachusetts Natural Heritage and Endangered Species Program (NHESP) as most critical to the conservation of the Commonwealth’s biological diversity (Barbour et al., 1998). proprietary
@@ -11821,20 +11822,20 @@ Mawson_SAM_1 Mawson Station GIS Dataset AU_AADC STAC Catalog 1996-03-18 1996-03-
Mawson_Tide_Gauges_2 Mawson Tide Gauge Data 1992-2016 AU_AADC STAC Catalog 1992-03-05 2016-11-04 62.83356, -67.61863, 62.90771, -67.58619 https://cmr.earthdata.nasa.gov/search/concepts/C1667370710-AU_AADC.umm_json "Over time there have been a number of tide gauges deployed at Mawson Station, Antarctica. The data download files contain further information about the gauges, but some of the information has been summarised here. Note that this metadata record only describes tide gauge data from 1992 to 2016. More recent data are described elsewhere. Tide Gauge 1 (TG001) 1992-03-05 - 1992-05-13 This folder contains monthly download files from the first deployment of a submerged tide gauge at Mawson in March 1992. These files are ASCII hexadecimal files. They need to be converted to decimal. The resultant values are absolute seawater pressures in mbar. Tide Gauge 4 (TG004) 1993-03-22 - 1999-12-29 This folder contains the following folders:- old_tidedata monthly download files from the second deployment of a submerged tide gauge at Mawson in March 1993. These files are ASCII hexadecimal files. They need to be converted to decimal. The resultant values are absolute seawater pressures in mbar. raw memory images from submerged tide gauge. file extension is memory bank number. These files are processed by a utility called tgxtract.exe which creates files in same format as those in old_tidedata folder. These file have extension .srt. They are then converted to decimal pressure values. interim files produced during processing of .raw files. output output file (.srt) which have been sent to BoM. Tide Gauge 13 (TG013) 2014-06-04 - 2016-11-04 Tide Gauge 20 (TG020) 1999-11-05 - 2009-12-21 This folder contains the following folders:- raw memory images from submerged tide gauge. file extension is memory bank number. These files are processed by a utility called tgxtract.exe which creates files in same format as original download format. These file have extension .srt. These files are ASCII hexadecimal files. They need to be converted to decimal. The resultant values are absolute seawater pressures in mbar. interim files produced during processing of .raw files. output output file (.srt) which have been sent to BoM. Tide Gauge 41 (TG041) 2008-03-02 - 2010-11-16 This folder contains the following folders:- raw memory images from submerged tide gauge. file extension is memory bank number. These files are processed by a utility called tgxtract.exe which creates files in same format as original download format. These file have extension .srt. These files are ASCII hexadecimal files. They need to be converted to decimal. The resultant values are absolute seawater pressures in mbar. interim files produced during processing of .raw files. output output file (.srt) which have been sent to BoM. Documentation from older metadata record: Documentation dated 2001-03-26 Mawson Submerged Tide Gauge The gauge used at Mawson was designed in 1991/2 by Platypus Engineering, Hobart, Tasmania. It was intended to be submerged in about 7 metres of water in a purpose made concrete mooring in the shape of a truncated pyramid. The gauge measures pressure using a Paroscientific Digiquartz Pressure Transducer with a full scale pressure of 30 psi absolute. The accuracy of the transducer is 1 in 10,000 of full scale over the calibrated temperature range. The overall accuracy of the system is better than +/- 3 mm for a known water density. Data is retrieved from the gauges by lowering a coil assembly on the end of a cable over a projecting knob on the top of the gauge and by use of an interface unit ,a serial connection can be established to the gauge. Time setting and data retrieval can be then achieved. The first of these gauges were first deployed Mawson in early 1992 in a a mooring in Horseshoe Harbour. The gauge was found to have some communications problems and was removed in May 1992. Tidal records from 6/3/92 to present have been retrieved from it. A new gauge was deployed at Mawson in March 1993. Data has been retrieved from these gauges irregularly since then. The records are complete since deployment except for a few days in late 1995. The loss was caused by a fault in the software which allows directory entries to overwrites data when the directory memory has been filled. The first gauge used at Mawson in 1992 was refitted with a higher pressure transducer and was later deployed at Heard Island in Atlas Cove. Conversion of raw data to tidal records is done as detailed in document DATAFORMAT1.DOC . As the current gauge is expected to require a new battery soon, a new mooring has been placed close to the original and a new gauge has been deployed. Levelling Several attempts have been made at precise levelling of the gauge. The first was in the Summer of 1995/6. Roger Handsworth, Tom Gordon and Natasha Adams physically measured the level of the top of the gauge in its mooring and derived a reading when a known column of water was over the gauge. The next attempt was in the Summer of 1996/7 when Roger Handsworth and Paul Delaney made timed water level measurements close to the gauge and the tide gauge benchmark. From this work, and from tidal records, a value for MSL for Mawson was derived. Permanent Gauge In the summer of 1995/6 two possible sites for a permanent Aquatrak type tide gauge were identified. As neither of these sites were approved, a survey in the Summer of 1996/7 identified two more suitable sites. One of these, the site at the base of East arm, near the Variometer Building, was approved and a bore hole was drilled to exit about 6 metres below MSL. A power cable was run from the variometer building to provide two phase 240V power to the site. A heated borehole liner containing an Aquatrak wave guide and a Druck pressure transducer was inserted into the bore hole. Two datalogger will be added to the installation in 2001 to complete the installation. A radio modem will be used to link the dataloggers to the AAD network. Documentation dated 2008-10-17 Mawson A new submerged gauge ,TG41, was deployed at Mawson on 2008-03-03. Submerged Tide gauge TG20 was removed on 2008-08-26. There is a useful overlap of data between the gauges of about 104 days. The dataloggers used in the shored based tide gauge installation have been replaced with Campbell Scientific CR1000 dataloggers. The aquatrak shore based gauge at Mawson has not been operating since march 2008. The shore base pressure gauge is still operating." proprietary
MawsonsHuts2008_2009_1 Mawson's Huts Preservation Program 2007/2008, 2008/2009 and 2009/2010 Data Entry AU_AADC STAC Catalog 2008-10-01 2010-03-31 142.65, -67.1, 142.67, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214313539-AU_AADC.umm_json "723 images where loaded into the AAD image library, ""Image Antarctica"" and attached to records in the Antarctic Heritage Register database. The images documented the condition of the interior and exterior of Mawsons Huts located at Cape Denison including the main hut, the absolute hut, the magnetograph hut and the transit hut during the 2007/2008 season and the 2008/2009 season. The images were taken in both high resolution jpgs as well as raw files. The camera used was a Nikon D80. Also included were images of conserved artefacts as well as details of the conservation treatments uploaded to the Antarctic Heritage Register Database and linked to specific catalogue records. 2011-04-21 - the record was updated to include a file of data from the 2009/2010 season. Raw data from 2008/2009 and 2009/2010 have also been archived in the AADC servers, and are available to AAD personnel upon request." proprietary
Mawsons_Huts_Dataloggers_2 Dataloggers at Mawson's Hut, Cape Denison - microclimate measurements AU_AADC STAC Catalog 1998-01-26 2008-01-30 142.66, -67.009, 142.662, -67.007 https://cmr.earthdata.nasa.gov/search/concepts/C1214313538-AU_AADC.umm_json Dataloggers were installed in a number of locations inside and outside Mawson's Huts at Cape Denison. The dataloggers measure temperature and relative humidity for the purpose of helping gauge corrosivity in the huts. The data are used to assess whether the removal of ice and snow from inside the Hut is affecting the internal microclimate and, therefore, the condition of the building fabric and other artefacts. Currently the data are downloaded by the Research Centre for Materials Conservation and the Built Environment at the Australian Museum, Sydney. Copies of the data are stored in the Australian Antarctic Data Centre. The fields in this dataset are: Date Time Temperature Relative Humidity Thermocouple Site proprietary
-Maxwell_Bay_Beaches_data Ages and Elevations of Raised Beaches around Maxwell Bay, South Shetland Islands ALL STAC Catalog 0500-01-01 2007-04-30 -59, -62.3, -58.833, -62.1 https://cmr.earthdata.nasa.gov/search/concepts/C1214590771-SCIOPS.umm_json This data set includes elevations, OSL ages, and one suspect radiocarbon date from several raised beaches around Maxwell Bay in the South Shetland Islands. It also includes some basic textural parameters (grain size, sorting, and roundness) from modern beaches, talus slopes, and moraines in the area. We also compiled a map of recent moraines in the Gerlache Straight. proprietary
Maxwell_Bay_Beaches_data Ages and Elevations of Raised Beaches around Maxwell Bay, South Shetland Islands SCIOPS STAC Catalog 0500-01-01 2007-04-30 -59, -62.3, -58.833, -62.1 https://cmr.earthdata.nasa.gov/search/concepts/C1214590771-SCIOPS.umm_json This data set includes elevations, OSL ages, and one suspect radiocarbon date from several raised beaches around Maxwell Bay in the South Shetland Islands. It also includes some basic textural parameters (grain size, sorting, and roundness) from modern beaches, talus slopes, and moraines in the area. We also compiled a map of recent moraines in the Gerlache Straight. proprietary
-McMurdo_Predator_Prey_Acoustics Acoustic records near McMurdo Station, Antarctica, 2012 - 2015. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1351106925-SCIOPS.umm_json Sonar data were collected to determine prey fields (krill, fishes) in McMurdo Sound, Antarctica proprietary
+Maxwell_Bay_Beaches_data Ages and Elevations of Raised Beaches around Maxwell Bay, South Shetland Islands ALL STAC Catalog 0500-01-01 2007-04-30 -59, -62.3, -58.833, -62.1 https://cmr.earthdata.nasa.gov/search/concepts/C1214590771-SCIOPS.umm_json This data set includes elevations, OSL ages, and one suspect radiocarbon date from several raised beaches around Maxwell Bay in the South Shetland Islands. It also includes some basic textural parameters (grain size, sorting, and roundness) from modern beaches, talus slopes, and moraines in the area. We also compiled a map of recent moraines in the Gerlache Straight. proprietary
McMurdo_Predator_Prey_Acoustics Acoustic records near McMurdo Station, Antarctica, 2012 - 2015. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1351106925-SCIOPS.umm_json Sonar data were collected to determine prey fields (krill, fishes) in McMurdo Sound, Antarctica proprietary
-McMurdo_Predator_Prey_Adelie_Penguins Adelie Penguins at Cape Royds, Antarctica, 2012 - 2015. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1351106459-SCIOPS.umm_json Adelie penguin data will be deposited in the California Avian Data Center (CADC) hosted by Point Blue Conservation Science (http://data.prbo.org/apps/penguinscience/). proprietary
+McMurdo_Predator_Prey_Acoustics Acoustic records near McMurdo Station, Antarctica, 2012 - 2015. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1351106925-SCIOPS.umm_json Sonar data were collected to determine prey fields (krill, fishes) in McMurdo Sound, Antarctica proprietary
McMurdo_Predator_Prey_Adelie_Penguins Adelie Penguins at Cape Royds, Antarctica, 2012 - 2015. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1351106459-SCIOPS.umm_json Adelie penguin data will be deposited in the California Avian Data Center (CADC) hosted by Point Blue Conservation Science (http://data.prbo.org/apps/penguinscience/). proprietary
+McMurdo_Predator_Prey_Adelie_Penguins Adelie Penguins at Cape Royds, Antarctica, 2012 - 2015. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1351106459-SCIOPS.umm_json Adelie penguin data will be deposited in the California Avian Data Center (CADC) hosted by Point Blue Conservation Science (http://data.prbo.org/apps/penguinscience/). proprietary
Mean_Seasonal_LAI_1653_1 Global Monthly Mean Leaf Area Index Climatology, 1981-2015 ORNL_CLOUD STAC Catalog 1981-08-01 2015-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2764692443-ORNL_CLOUD.umm_json This dataset provides a global 0.25 degree x 0.25 degree gridded monthly mean leaf area index (LAI) climatology as averaged over the period from August 1981 to August 2015. The data were derived from the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) LAI3g version 2, a bi-weekly data product from 1981 to 2015 (GIMMS-LAI3g version 2). The LAI3g version 2 (raw) data were first regridded from 1/12 x 1/12 degree to 0.25 x 0.25 degree resolution, then processed to remove missing and unreasonable values, scaled to obtain LAI values, and the bi-weekly LAI values were averaged for every month. Finally, the monthly long-term mean LAI (1981-2015) was calculated. proprietary
Medit_Ligurian_0 Measurements from the Ligurian Sea OB_DAAC STAC Catalog 1999-09-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360477-OB_DAAC.umm_json Measurements taken in the Mediterranean Sea, the Ligurian Sea near Northern Italy and Southern France, and off the western coast of South Africa. proprietary
Menz50k_1 Mount Menzies 1:50000 Topographic GIS Dataset AU_AADC STAC Catalog 1973-01-15 1989-02-17 60.8667, -73.85, 63.1, -73.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214313643-AU_AADC.umm_json The Mount Menzies dataset is a topographic database. Mount Menzies is situated within the Southern Prince Charles Mountains, surrounded by the Fisher Glacier. The database contains natural features captured at a density appropriate to 1:50,000 scale. Features are represented as lines, points and polygons. The dataset includes a 20 metre interval contour coverage. The data is available for download as shapefiles from a Related URL below. The data conforms to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. Each feature has a Qinfo number which, when entered at the 'Search datasets & quality' tab, provides data quality information for the feature. proprietary
MetOpA_GOME2_SIF_V2_2292_2 L2 Daily Solar-Induced Fluorescence (SIF) from MetOp-A GOME-2, 2007-2018, V2 ORNL_CLOUD STAC Catalog 2007-02-01 2018-02-01 -180, -89.78, 180, 89.6 https://cmr.earthdata.nasa.gov/search/concepts/C2847115945-ORNL_CLOUD.umm_json This dataset provides Level 2 (L2) Solar-Induced Fluorescence (SIF) of chlorophyll estimates derived from the Global Ozone Monitoring Experiment 2 (GOME-2) instrument on the European Meteorological Satellite (EUMETSAT) MetOp-A with ~0.5 nm spectral resolution and wavelengths between 734 and 758 nm. GOME-2 covers global land on an orbital basis at a resolution of approximately 40 km x 80 km (before 15 July 2013) or 40 km x 40 km (since 15 July 2013). Data are provided for the period from 2007-02-01 to 2018-02-01. Each file contains daily raw and bias-adjusted solar-induced fluorescence, quality control information, and ancillary data. SIF measurements can provide information on vegetation's functional status, including light-use efficiency and global primary productivity, which can be used for global carbon cycle modeling and agricultural applications. The GOME-2 SIF product is inherently noisy due to low signal levels and has undergone only a limited amount of validation. The data are provided in netCDF format. proprietary
MetOpB_GOME2_SIF_2182_1 L2 Daily Solar-Induced Fluorescence (SIF) from MetOp-B GOME-2, 2013-2021 ORNL_CLOUD STAC Catalog 2013-04-01 2021-06-07 -180, -89.77, 180, 89.59 https://cmr.earthdata.nasa.gov/search/concepts/C2840822442-ORNL_CLOUD.umm_json This dataset provides Level 2 (L2) Solar-Induced Fluorescence (SIF) of chlorophyll estimates derived from the Global Ozone Monitoring Experiment 2 (GOME-2) instrument on the European Meteorological Satellite (EUMETSAT) MetOp-B with ~0.5 nm spectral resolution and wavelengths between 734 and 758 nm. GOME-2 covers global land (observations up to 75-degree solar zenith angle) at a resolution of approximately 40 km x 80. Data are provided for the period from 2013-04-01 to 2021-06-07. Each file contains daily raw and bias-adjusted solar-induced fluorescence along with quality control information and ancillary data. SIF measurements can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. The GOME-2 SIF product is inherently noisy owing to low signal levels and has undergone only a limited amount of validation. The data are provided in netCDF (*.nc) format. proprietary
Meteorological_1065_1 BIGFOOT Meteorological Data for North and South American Sites, 1991-2004 ORNL_CLOUD STAC Catalog 1991-01-01 2004-12-31 -156.61, -2.87, -54.96, 71.27 https://cmr.earthdata.nasa.gov/search/concepts/C2751482070-ORNL_CLOUD.umm_json The BigFoot Project has compiled daily meteorological measurements for nine EOS Land Validation Sites located from Alaska to Brazil from 1991 to 2004. Each site is representative of one or two distinct biomes, including the Arctic tundra; boreal evergreen needleleaf forest; temperate cropland, grassland, evergreen needleleaf forest, and deciduous broadleaf forest; desert grassland and shrubland; and tropical evergreen broadleaf forest.The BigFoot Project needed meteorological data to run the ecosystem process models used for scaling GPP and NPP products, for monitoring interannual variability, and for model testing. Meteorological data were obtained from various agencies collecting data in the vicinity of the BigFoot sites and for more recent years, collected on co-located CO2 flux measurement towers. A comparable set of original measurements from all sites were aggregated to a common daily time step for use in the BIOME-BGC model. proprietary
-Meteorology_Log_Commonwealth_Bay_1977_1978_1 A log of meteorological observations made at Commonwealth Bay between 1977 and 1978 ALL STAC Catalog 1977-01-01 1978-12-31 142.5, -67, 142.5, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311178-AU_AADC.umm_json This document contains a report/log on meteorological observations from Commonwealth Bay in 1977-1978. Some references are also made to the Australasian Antarctic Expedition of Sir Douglas Mawson, 1911-1914. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Meteorology_Log_Commonwealth_Bay_1977_1978_1 A log of meteorological observations made at Commonwealth Bay between 1977 and 1978 AU_AADC STAC Catalog 1977-01-01 1978-12-31 142.5, -67, 142.5, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311178-AU_AADC.umm_json This document contains a report/log on meteorological observations from Commonwealth Bay in 1977-1978. Some references are also made to the Australasian Antarctic Expedition of Sir Douglas Mawson, 1911-1914. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
+Meteorology_Log_Commonwealth_Bay_1977_1978_1 A log of meteorological observations made at Commonwealth Bay between 1977 and 1978 ALL STAC Catalog 1977-01-01 1978-12-31 142.5, -67, 142.5, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311178-AU_AADC.umm_json This document contains a report/log on meteorological observations from Commonwealth Bay in 1977-1978. Some references are also made to the Australasian Antarctic Expedition of Sir Douglas Mawson, 1911-1914. The hard copy of the log has been archived by the Australian Antarctic Division library. proprietary
Methane_Ebullition_Lakes_AK_1861_1 ABoVE: Methane Ebullition Hotspots in Frozen Lakes near Fairbanks, Alaska, Oct 2014 ORNL_CLOUD STAC Catalog 2014-10-08 2014-10-08 -147.94, 64.86, -147.77, 64.94 https://cmr.earthdata.nasa.gov/search/concepts/C2143401746-ORNL_CLOUD.umm_json This dataset includes maps of the locations and number of methane ebullition hotspots in 15 frozen lakes in the southern portion of the Goldstream Valley and the surrounding landscape just north of Fairbanks, Alaska, USA. Hotspots were identified from early winter high resolution aerial photographs acquired three days after lake-ice formation in October 2014. Hotspot ebullition seeps are defined as point-sources of high ebullition that release methane from lake sediments year-round. High rates of bubbling impede ice formation. In early winter, bubbling leads to dark, round open holes in lake ice which were visible in the aerial photos. This project investigated the role of theromkarst lakes in thawing of permafrost and mobilization of organic carbon in frozen soils. proprietary
Methane_Ebullition_Lakes_AK_1861_1 ABoVE: Methane Ebullition Hotspots in Frozen Lakes near Fairbanks, Alaska, Oct 2014 ALL STAC Catalog 2014-10-08 2014-10-08 -147.94, 64.86, -147.77, 64.94 https://cmr.earthdata.nasa.gov/search/concepts/C2143401746-ORNL_CLOUD.umm_json This dataset includes maps of the locations and number of methane ebullition hotspots in 15 frozen lakes in the southern portion of the Goldstream Valley and the surrounding landscape just north of Fairbanks, Alaska, USA. Hotspots were identified from early winter high resolution aerial photographs acquired three days after lake-ice formation in October 2014. Hotspot ebullition seeps are defined as point-sources of high ebullition that release methane from lake sediments year-round. High rates of bubbling impede ice formation. In early winter, bubbling leads to dark, round open holes in lake ice which were visible in the aerial photos. This project investigated the role of theromkarst lakes in thawing of permafrost and mobilization of organic carbon in frozen soils. proprietary
Methane_Ethane_MA_NH_1982_1 Methane and Ethane Observations for Boston, MA, 2012-2020 ORNL_CLOUD STAC Catalog 2012-08-01 2020-05-31 -72.4, 41.5, -69.8, 43.71 https://cmr.earthdata.nasa.gov/search/concepts/C2345793484-ORNL_CLOUD.umm_json This dataset provides the hourly average of continuous atmospheric measurements of methane (CH4) from two urban sites and three boundary sites in and around Boston, Massachusetts, U.S., from September 2012-May 2020, measured with Picarro cavity ring down spectrometers (CRDS). Five-minute average atmospheric measurements of ethane (C2H6) and methane at Copley Square in Boston, MA, are also provided, with ethane measured with a laser spectrometer and methane measured with a Picarro CRDS. Background CH4 concentrations for the urban sites were determined using Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model trajectories at the boundary of the study region based on measurements at three boundary sites and wind direction from the North American Mesoscale Forecast System (NAM) 12-kilometer meteorology. proprietary
@@ -11843,8 +11844,8 @@ Microbiome_0 Tara microbiome OB_DAAC STAC Catalog 2020-12-26 2022-12-31 -180, -9
Mid-latitude_soils_705_2 Northern and Mid-Latitude Soil Database, Version 1, R1 ORNL_CLOUD STAC Catalog 2001-01-01 2001-12-31 -180, 50.9, -129.3, 71.4 https://cmr.earthdata.nasa.gov/search/concepts/C2216863233-ORNL_CLOUD.umm_json The U.S. Department of Agriculture, Agriculture and Agri-Food Canada, the Russian Academy of Agricultural Sciences, the University of Copenhagen Institute of Geography, the European Soil Bureau, the University of Manchester Institute of Landscape Ecology, MTT Agrifood Research Finland, and the Agricultural Research Institute Iceland have shared data and expertise in order to develop the Northern and Mid Latitude Soil Database (Cryosol Working Group, 2001). This database was the source of data for the current product. The spatial coverage of the Northern and Mid Latitude Soil Database is the polar and mid-latitude regions of the northern hemisphere: Alaska, Canada, Conterminous United States, Eurasia (except Italy), Greenland, Iceland, Kazakstan, Mexico, Mongolia, Italy, and Svalbard. The Northern and Mid-Latitude Soil Database represents the proportion (percentage) of polygon encompassed by the dominant soil or nonsoil. Soils include turbels, orthels, histels, histosols, mollisols, vertisols, aridisols, andisols, entisols, spodosols, inceptisols (and hapludolls), alfisols (cryalf and udalf), natric great groups, aqu-suborders, glaciers, and rocklands. Also included are data on the circumpolar distribution of gelisols (turbels, orthels, and histels), and the ice content (low, medium, or high) of circumpolar soil materials (from the International Permafrost Association, 1997). The resulting maps show the dominant soil of the spatial polygon unless the polygon is over 90 percent rock or ice. Data are in the U.S. soil classification system and includes the distribution of soil types (%) within a map unit (polygon). Data are available in ESRI shapefile format and include the same attribute values with the exception of Italy, which does not contain distribution values. proprietary
Missouri_Reservoirs_RSWQ_0 Retrospective analysis of anthropogenic change in Midwest reservoirs: Integrating earth observing data with statewide reservoir monitoring programs OB_DAAC STAC Catalog 2023-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2785397264-OB_DAAC.umm_json The dataset comprises in-situ hyperspectral data acquired using the on-water approach (aka skylight-blocked approach), using a combination of a downwelling irradiance sensor and an upwelling radiance sensor. These sensors are specifically TriOS RAMSES hyperspectral radiometers, each associated with two calibration files. The data collection was conducted across different reservoirs in the state of Missouri USA. This NASA-funded project directly addresses how Earth-observing satellite data can better inform critical links between the biogeochemical and optical properties of inland waters. It achieves this by using satellite imagery and in-situ measurements from two long-running water quality monitoring programs in the state of Missouri that annually record more than one thousand measurements of nitrogen, phosphorus, chlorophyll-a, Secchi depth, particulate organic and inorganic matter, and cyanotoxins across 100 reservoirs. proprietary
MonthlyWetland_CH4_WetCHARTsV2_2346_1.3.3 CMS: Global 0.5-deg Wetland Methane Emissions and Uncertainty (WetCHARTs v1.3.3) ORNL_CLOUD STAC Catalog 2001-01-01 2022-08-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3236621594-ORNL_CLOUD.umm_json This dataset provides global monthly wetland methane (CH4) emissions estimates at 0.5 by 0.5-degree resolution for the period 2001-01-01 to 2022-08-31 that were derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies that encompass the main sources of uncertainty in wetland CH4 emissions. There are 18 model configurations. WetCHARTs v1.3.3 is an updated product of WetCHARTs v1.3.1 dataset. The intended use of this product is as a process-informed wetland CH4 emission data set for atmospheric chemistry and transport modeling. Users can compare estimates by model configuration to explore variability and sensitivity with respect to ensemble members. The data are provided in netCDF format. proprietary
-Monthly_Hydrological_Fluxes_1647_1 ABoVE: Monthly Hydrological Fluxes for Canada and Alaska, 1979-2018 ORNL_CLOUD STAC Catalog 1979-01-01 2018-04-01 -172.25, 41.75, -53.43, 83.12 https://cmr.earthdata.nasa.gov/search/concepts/C2170971533-ORNL_CLOUD.umm_json This dataset provides modeled estimates of monthly hydrological fluxes at 0.25-degree resolution over Alaska and Canada for the years 1979-2018. The estimates were derived from the Variable Infiltration Capacity (VIC) macroscale hydrological model version 4.1.2 with water and energy balance schemes at 0.25-degree spatial and daily temporal resolution for this 38-year period. The gridded output data products are monthly average water balance variables including precipitation (P), evapotranspiration (E), 'P minus E', evaporation, soil moisture in three soil layers, base flow and runoff, snow depth, snow water equivalent (SWE), and snow sublimation, and energy balance variables including surface temperature, albedo, latent and sensible heat flux, ground heat flux, short- and long-wave and other radiative fluxes. The daily modeled values for precipitation and evapotranspiration were also aggregated to water years and precipitation was also aggregated to a 30-year climate normal average. proprietary
Monthly_Hydrological_Fluxes_1647_1 ABoVE: Monthly Hydrological Fluxes for Canada and Alaska, 1979-2018 ALL STAC Catalog 1979-01-01 2018-04-01 -172.25, 41.75, -53.43, 83.12 https://cmr.earthdata.nasa.gov/search/concepts/C2170971533-ORNL_CLOUD.umm_json This dataset provides modeled estimates of monthly hydrological fluxes at 0.25-degree resolution over Alaska and Canada for the years 1979-2018. The estimates were derived from the Variable Infiltration Capacity (VIC) macroscale hydrological model version 4.1.2 with water and energy balance schemes at 0.25-degree spatial and daily temporal resolution for this 38-year period. The gridded output data products are monthly average water balance variables including precipitation (P), evapotranspiration (E), 'P minus E', evaporation, soil moisture in three soil layers, base flow and runoff, snow depth, snow water equivalent (SWE), and snow sublimation, and energy balance variables including surface temperature, albedo, latent and sensible heat flux, ground heat flux, short- and long-wave and other radiative fluxes. The daily modeled values for precipitation and evapotranspiration were also aggregated to water years and precipitation was also aggregated to a 30-year climate normal average. proprietary
+Monthly_Hydrological_Fluxes_1647_1 ABoVE: Monthly Hydrological Fluxes for Canada and Alaska, 1979-2018 ORNL_CLOUD STAC Catalog 1979-01-01 2018-04-01 -172.25, 41.75, -53.43, 83.12 https://cmr.earthdata.nasa.gov/search/concepts/C2170971533-ORNL_CLOUD.umm_json This dataset provides modeled estimates of monthly hydrological fluxes at 0.25-degree resolution over Alaska and Canada for the years 1979-2018. The estimates were derived from the Variable Infiltration Capacity (VIC) macroscale hydrological model version 4.1.2 with water and energy balance schemes at 0.25-degree spatial and daily temporal resolution for this 38-year period. The gridded output data products are monthly average water balance variables including precipitation (P), evapotranspiration (E), 'P minus E', evaporation, soil moisture in three soil layers, base flow and runoff, snow depth, snow water equivalent (SWE), and snow sublimation, and energy balance variables including surface temperature, albedo, latent and sensible heat flux, ground heat flux, short- and long-wave and other radiative fluxes. The daily modeled values for precipitation and evapotranspiration were also aggregated to water years and precipitation was also aggregated to a 30-year climate normal average. proprietary
MultiInstrumentFusedXCO2_3 Multi-Instrument Fused bias-corrected XCO2 and other select fields aggregated as Level 4 daily files V3 (MultiInstrumentFusedXCO2) GES_DISC STAC Catalog 2014-09-06 2020-07-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2219373930-GES_DISC.umm_json Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Orbiting Carbon Observatory (OCO-2) and Greenhouse Gases Observing Satellite (GOSAT) bias corrected data. This is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page. proprietary
MultiInstrumentFusedXCO2_4 Multi-Instrument Fused bias-corrected XCO2 and other select fields aggregated as Level 3 daily files V4 (MultiInstrumentFusedXCO2) GES_DISC STAC Catalog 2014-09-06 2021-05-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3278456754-GES_DISC.umm_json Gridded carbon dioxide mole fraction (XCO2) and other select variables created by applying local kriging (also known as optimal interpolation) to daily aggregates of Orbiting Carbon Observatory (OCO-2) and Greenhouse Gases Observing Satellite (GOSAT) bias corrected data. This is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page. proprietary
MumfordCove_0 Measurements from Mumford Cove, Connecticut OB_DAAC STAC Catalog 2015-10-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360493-OB_DAAC.umm_json Measurements made in and around Mumford Cove, Connecticut since 2015. proprietary
@@ -12003,8 +12004,8 @@ NAWQAHIS GIS Coverage for the National Water-Quality Assessment (NAWQA) Program
NA_MODIS_Surface_Biophysics_1210_1 MODIS-derived Biophysical Parameters for 5-km Land Cover, North America, 2000-2012 ORNL_CLOUD STAC Catalog 2000-01-01 2012-12-31 -160, 20, -40, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2784871888-ORNL_CLOUD.umm_json This data set provides MODIS-derived surface biophysical climatologies of bidirectional distribution function (BRDF), BDRF/albedo, land surface temperature (LST), leaf area index (LAI), and evapotranspiration (ET) as separate files for each of the MODIS land cover types, and four radiative forcing data files for four scenarios of potential vegetation shifts in North America. Each biophysical variable has temporal periods that represent the average of all 8-day periods from the years 2000-2012. The data have a spatial resolution of 0.05 degree (~5 km) and a temporal resolution of eight days. Additionally, a file containing diffuse fraction of surface downward solar radiation (DiffuseFraction) at a monthly scale, and a file containing snow water equivalent (SWE) are provided. The extent of the data covers the land area of North America, from 20 to 60 degrees N. The land-cover map used was synthesized from nine yearly 500-m MODIS land-cover layers (MCD12 Q1 Collection 5) for 2001-2008. These high-resolution land data were originally developed for quantifying biophysical forcing from land-use changes associated with forestry activities, such as radiative forcing from altered surface albedo. proprietary
NA_TreeAge_1096_1 NACP Forest Age Maps at 1-km Resolution for Canada (2004) and the U.S.A. (2006) ORNL_CLOUD STAC Catalog 1950-01-01 2006-12-31 179.25, 7.71, -39.87, 67.01 https://cmr.earthdata.nasa.gov/search/concepts/C2556019064-ORNL_CLOUD.umm_json This data set provides forest age map products at 1-km resolution for Canada and the United States (U.S.A.). These continental forest age maps were compiled from forest inventory data, historical fire data, optical satellite data, and the images from the NASA Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) project. These input data products have various sources and creation dates as described in the source paper by Pan et al. (2011). Canadian maps were produced with data available through 2004 and U.S.A. maps with data available through 2006. A supplementary map of the standard deviations for age estimates was developed for quantifying uncertainty.Note that the Pan et al. (2011) paper is included as a companion file with this data set and was the source of descriptions in the guide.Forest age, implicitly reflecting the past disturbance legacy, is a simple and direct surrogate for the time since disturbance and may be used in various forest carbon analyses that concern the impact of disturbances. By combining geographic information about forest age with estimated carbon dynamics by forest type, it is possible to conduct a simple but powerful analysis of the net CO2 uptake by forests, and the potential for increasing (or decreasing) this rate as a result of direct human intervention in the disturbance/age status. proprietary
NBCD2000_V2_1161_2 NACP Aboveground Biomass and Carbon Baseline Data, V.2 (NBCD 2000), U.S.A., 2000 ORNL_CLOUD STAC Catalog 1999-01-01 2002-12-31 -126.46, 26.52, -67.96, 49.79 https://cmr.earthdata.nasa.gov/search/concepts/C2539954386-ORNL_CLOUD.umm_json The NBCD 2000 (National Biomass and Carbon Dataset for the Year 2000) data set provides a high-resolution (30 m) map of year-2000 baseline estimates of basal area-weighted canopy height, aboveground live dry biomass, and standing carbon stock for the conterminous United States. This data set distributes, for each of 66 map zones, a set of six raster files in GeoTIFF format. There is a detailed README companion file for each map zone. There is also an ArcGIS shapefile (mapping_zone_shapefile.shp) with the boundaries of all the map zones. A mosaic image of biomass at 240 m resolution for the whole conterminous U.S. is also included.Please read this important note regarding the differences of Version 2 from Version 1 of the NBCD 2000 data. With Version 1, in some mapping zones, certain land cover types (in particular Shrubs, NLCD Type 52) were missing from and unaccounted for in modeled estimates because of a lack of reference data. In Version 1, when landcover types were missing in the models, the model for the deciduous tree cover type was applied. While more woody vegetation was mapped, the authors think this had little effect on model performance as in most cases NLCD version 1 cover type was not a strong predictor of modeled estimates (See companion Mapping Zone Readme files). In Version 2, after renewed modeling efforts and user feedback, these previously unaccounted for cover types are now included in modeled estimates.All 66 mapping zones were updated with the previously unmapped land cover types now mapped. The authors recommend use of the new version for all analyses and will only support the updated version.Development of the data set used an empirical modeling approach that combined USDA Forest Service Forest Inventory and Analysis (FIA) data with high-resolution InSAR data acquired from the 2000 Shuttle Radar Topography Mission (SRTM) and optical remote sensing data acquired from the Landsat ETM+ sensor. Three-season Landsat ETM+ data were systematically compiled by the Multi-Resolution Land Characteristics Consortium (MRLC) between 1999 and 2002 for the entire U.S. and were the foundation for development of both the USGS National Land Cover Dataset 2001 (NLCD 2001) and the Landscape Fire and Resource Management Planning Tools Project (LANDFIRE). Products from both the NLCD 2001 (landcover and canopy density) and LANDFIRE (existing vegetation type) projects as well as topographic information from the USGS National Elevation Dataset (NED) were used within the NBCD 2000 project as spatial predictor layers for canopy height and biomass estimation. Forest survey data provided by the USDA Forest Service FIA program were made available to the project under a national Memorandum of Understanding. The response variables (canopy height and biomass) used in model development and validation were derived from the FIA database (FIADB). Production of the NLCD 2001 and LANDFIRE projects was based on a mapping zone approach in which the conterminous U.S. was split into 66 ecoregionally distinct mapping zones. This mapping zone approach was also adopted by the NBCD 2000 project. proprietary
-NBId0001_101 Africa Outline, Integrated Terrain Units, Agric. Landuse, Soils, Vegetation ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849282-CEOS_EXTRA.umm_json These datasets (Africa Outline, Agricultural Landuse, Africa Soils, Vegetation, Surface Hydrography, Hydrologic Basins, Desertification Hazard Model) are part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses in this case on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP) as part of a project initiated by UNEP. The base maps used were the FAO/UNESCO Soil Map of the World (1977) in Miller Oblated Stereographic projection, FAO Maps and Statistical Data by Administrative Unit and the Rand-McNally New International Atlas (1982) to clarify unit boundaries. All sources were re-registered to the basemap by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm developed by the US Geological Survey and ESRI to create coverage's for one-degree graticules. For details about each dataset, visit the individual entries. proprietary
NBId0001_101 Africa Outline, Integrated Terrain Units, Agric. Landuse, Soils, Vegetation CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849282-CEOS_EXTRA.umm_json These datasets (Africa Outline, Agricultural Landuse, Africa Soils, Vegetation, Surface Hydrography, Hydrologic Basins, Desertification Hazard Model) are part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses in this case on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP) as part of a project initiated by UNEP. The base maps used were the FAO/UNESCO Soil Map of the World (1977) in Miller Oblated Stereographic projection, FAO Maps and Statistical Data by Administrative Unit and the Rand-McNally New International Atlas (1982) to clarify unit boundaries. All sources were re-registered to the basemap by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm developed by the US Geological Survey and ESRI to create coverage's for one-degree graticules. For details about each dataset, visit the individual entries. proprietary
+NBId0001_101 Africa Outline, Integrated Terrain Units, Agric. Landuse, Soils, Vegetation ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849282-CEOS_EXTRA.umm_json These datasets (Africa Outline, Agricultural Landuse, Africa Soils, Vegetation, Surface Hydrography, Hydrologic Basins, Desertification Hazard Model) are part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses in this case on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP) as part of a project initiated by UNEP. The base maps used were the FAO/UNESCO Soil Map of the World (1977) in Miller Oblated Stereographic projection, FAO Maps and Statistical Data by Administrative Unit and the Rand-McNally New International Atlas (1982) to clarify unit boundaries. All sources were re-registered to the basemap by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm developed by the US Geological Survey and ESRI to create coverage's for one-degree graticules. For details about each dataset, visit the individual entries. proprietary
NBId0006_101 African Meteorology (GIS Coverage of Precipitation and Winds) ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848036-CEOS_EXTRA.umm_json New-ID: NBI06 Dataset covers mean annual rainfall distribution, number of wet days, wind speed and velocity. The Africa Meteorological Dataset documentation The Africa Meteorological dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. proprietary
NBId0006_101 African Meteorology (GIS Coverage of Precipitation and Winds) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848036-CEOS_EXTRA.umm_json New-ID: NBI06 Dataset covers mean annual rainfall distribution, number of wet days, wind speed and velocity. The Africa Meteorological Dataset documentation The Africa Meteorological dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. proprietary
NBId0007_101 Africa Administrative Units (GIS Coverage of Administrative Boundaries) ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847851-CEOS_EXTRA.umm_json "New-ID: NBI07 This dataset shows adminstrative boundries of Africa at continental, national, second and third levels in lat/long. The Administrative units Dataset documentation Files: ADMINLL.E00 Code: 100012-002 Vector Member The files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. The administrative units dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP as part of a project initiated by UNEP. The base maps used were the FAO/UNESCO Soil Map of the World (1977) in Miller Oblated Stereographic projection, FAO Maps and Statistical Data by Administrative Unit (1983), and the Rand-McNally New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverage""'""s for one-degree graticules. Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service 00100, Rome, Italy. ESRI, 380 New York Street, Redlands, CA 92373, USA The ADMINLL file shows adminstrative boundries at continental, national, second and third levels in lat/long References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication ESRI, FAO and UNEP FAO, UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. G.M.Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society Washington DC. Source : FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Geographic Lat/Long Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets TOWNS2 100022-002, Human settlements and airports ROADS2 100021-001, major roads" proprietary
@@ -12016,20 +12017,20 @@ NBId0018_101 Africa FAO Major Infrastructure and Human Settlements (GIS Coverage
NBId0018_101 Africa FAO Major Infrastructure and Human Settlements (GIS Coverage) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849221-CEOS_EXTRA.umm_json New-ID: NBI18 The Africa Major Infrastructure and Human Settlements Dataset Files: TOWNS2.E00 Code: 100022-002 ROADS2.E00 100021-002 Vector Members: The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename The Africa major infrastructure and human settlements dataset form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the basemap those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA. 92373, USA The ROADS2 file shows major roads of the African continent The TOWNS2 file shows human settlements and airports for the African continent References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC. DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago Source: FAO Soil Map of the World. Scale 1:5000000 Publication Date: Dec 1984 Projection: Miller Type: Points Format: Arc/Info export non-compressed Related Datasets: All UNEP/FAO/ESRI Datasets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments: There is no outline of Africa proprietary
NBId0019_101 FAO Major Elevation Zones of Africa (GIS Coverage) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849111-CEOS_EXTRA.umm_json New-ID: NBI19 The Africa Major Elevation Zones Dataset documentation File: ELEVLL Code: 100070-003 Vector Member The above file is in Arc/Info Export format and should be imported using the Arc/Info command Import cover In-Filename Out-Filename The Africa elevation major zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The manuscript derived from the topographic film separates of the UNESCO/FAO Soil Map of the World (1977) in Miller Oblated Stereographic projection was used to provide a generalized coverage of elevation values providing information as both line-related and polygonal form. The map was prepared by overlaying the topography film separate with a matte drafting film and then delineating the selected elevation contours. Some of the line crenulation was removed during the delineation process, because this map was designed to define general elevation zones rather than constitute a true topographic base. Code values were recorded directly on the map and were key-entered during the digitizing process with a spatial resolution of 0.002 inches, as part of the polygon or line sequence indentification number. The map was then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy. ESRI, 380 New York Street, Redlands, CA 92373, USA The ELEVLL2 data shows Major Elevation zones of Africa, in lat/lon References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO/FAO Soil Map of the World(1977). Scale 1:5000000. UNESCO, Paris DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society Washington DC. Source: FAO Soil Map of the World, scale 1:5000000 Publication Date: Dec 1984 Projection: Miller Type: Polygon and line Format: Arc/Info export non compressed Related Datasets: All UNEP/FAO/ESRI Datasets AFELBA elevation and Bathymetry (100048) proprietary
NBId0020_101 Countries, Coasts and Islands of Africa (Global Change Data Base - Digital Boundaries and Coastlines) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848088-CEOS_EXTRA.umm_json New-ID: NBI20 Countries, Coasts and Islands Dataset documentation (Micro World Data Bank II) Files: COASTS.E00 Code: 100051-001 COUNTRY.E00 100052-001 ISLANDS.E00 100054-001 Vector Members Original files were in IDRISI VEC format coverted to Arc/Info. The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename. Micro World Data Bank II (MWDB-II) comprising Coastlines, Country boundries and Islands data sets is part of NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II and is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact: NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The COASTS file shows African Coastlines The COUNTRY file shows African Country Boundaries without coast, no names - only lines The ISLANDS file shows African Islands References: NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds. Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, Vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map: digitized from available sources Publication Date: Jun 1992 Projection: Lat/Lon Type: Polygon and line Format: Arc/Info Export non-compressed proprietary
-NBId0022_101 Africa Olson World Ecosystems CEOS_EXTRA STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232846860-CEOS_EXTRA.umm_json "New-ID: NBI22 OLSON WORLD ECOSYSTEMS DATASET DOCUMENTATION File: AFWE20.IMG Code: 100032-001 Raster Member This IMG file is in IDRISI format Olson World Ecosystems data base is part of Global Change Data Base produced by The World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysycal Data Center (NGDC) and for cooperative project called Global Ecosystems Database Project between NDAA(National Oceanic & Atmospheric Administration, USA)/NGDC and the U.S. Environmental Protection Agency. The software (known as IDRISI) was developed and adopted for this project at Clark University. The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California, has joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/Latitude) projection. Each data set is accompanied by an ASCII documentation file. Which contains information necessary for use of the data set in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWE20 file shows Olson ecosystem classes version 1.4 References: Olson, J.S. Earth""'""s Vegetation and Atmospheric Carbon Dioxide, in Carbon Dioxide Review: 1982. Ed. by W.C. Clark (1983), Exford Univ. Press, New York, pp.388-398. Olson, J.S., J.A. Watts, and L.J. Allison. Carbon in Live Vegetation of Major World Ecosystems (1983). Report ORNL-5862, Oark Ridge Laboratory, Oak Ridge, Tennessee. Olson, J.S. and J.A. Watts. Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation (1982). Oak Ridge National Laboratory, Oak Ridge, Tennesse (map). Source map : from available maps and observations. Publication Date : 1989 Projection : lat/lon. Type : Raster Format : IDRISI" proprietary
NBId0022_101 Africa Olson World Ecosystems ALL STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232846860-CEOS_EXTRA.umm_json "New-ID: NBI22 OLSON WORLD ECOSYSTEMS DATASET DOCUMENTATION File: AFWE20.IMG Code: 100032-001 Raster Member This IMG file is in IDRISI format Olson World Ecosystems data base is part of Global Change Data Base produced by The World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysycal Data Center (NGDC) and for cooperative project called Global Ecosystems Database Project between NDAA(National Oceanic & Atmospheric Administration, USA)/NGDC and the U.S. Environmental Protection Agency. The software (known as IDRISI) was developed and adopted for this project at Clark University. The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California, has joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/Latitude) projection. Each data set is accompanied by an ASCII documentation file. Which contains information necessary for use of the data set in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWE20 file shows Olson ecosystem classes version 1.4 References: Olson, J.S. Earth""'""s Vegetation and Atmospheric Carbon Dioxide, in Carbon Dioxide Review: 1982. Ed. by W.C. Clark (1983), Exford Univ. Press, New York, pp.388-398. Olson, J.S., J.A. Watts, and L.J. Allison. Carbon in Live Vegetation of Major World Ecosystems (1983). Report ORNL-5862, Oark Ridge Laboratory, Oak Ridge, Tennessee. Olson, J.S. and J.A. Watts. Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation (1982). Oak Ridge National Laboratory, Oak Ridge, Tennesse (map). Source map : from available maps and observations. Publication Date : 1989 Projection : lat/lon. Type : Raster Format : IDRISI" proprietary
+NBId0022_101 Africa Olson World Ecosystems CEOS_EXTRA STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232846860-CEOS_EXTRA.umm_json "New-ID: NBI22 OLSON WORLD ECOSYSTEMS DATASET DOCUMENTATION File: AFWE20.IMG Code: 100032-001 Raster Member This IMG file is in IDRISI format Olson World Ecosystems data base is part of Global Change Data Base produced by The World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysycal Data Center (NGDC) and for cooperative project called Global Ecosystems Database Project between NDAA(National Oceanic & Atmospheric Administration, USA)/NGDC and the U.S. Environmental Protection Agency. The software (known as IDRISI) was developed and adopted for this project at Clark University. The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California, has joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/Latitude) projection. Each data set is accompanied by an ASCII documentation file. Which contains information necessary for use of the data set in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWE20 file shows Olson ecosystem classes version 1.4 References: Olson, J.S. Earth""'""s Vegetation and Atmospheric Carbon Dioxide, in Carbon Dioxide Review: 1982. Ed. by W.C. Clark (1983), Exford Univ. Press, New York, pp.388-398. Olson, J.S., J.A. Watts, and L.J. Allison. Carbon in Live Vegetation of Major World Ecosystems (1983). Report ORNL-5862, Oark Ridge Laboratory, Oak Ridge, Tennessee. Olson, J.S. and J.A. Watts. Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation (1982). Oak Ridge National Laboratory, Oak Ridge, Tennesse (map). Source map : from available maps and observations. Publication Date : 1989 Projection : lat/lon. Type : Raster Format : IDRISI" proprietary
NBId0023_101 Africa Holdridge Life Zone Classification (Vegetation and Climate) ALL STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847334-CEOS_EXTRA.umm_json New-ID: NBI23 Holdridge Life Zone is a coverage showing zone classification, vegetation relation to climate and vice versa. proprietary
NBId0023_101 Africa Holdridge Life Zone Classification (Vegetation and Climate) CEOS_EXTRA STAC Catalog 1970-01-01 16, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847334-CEOS_EXTRA.umm_json New-ID: NBI23 Holdridge Life Zone is a coverage showing zone classification, vegetation relation to climate and vice versa. proprietary
NBId0024_101 Africa Soil Classification by Wilson and Henderson-Sellers ALL STAC Catalog 1970-01-01 12.88, 6.67, 24.97, 24.19 https://cmr.earthdata.nasa.gov/search/concepts/C2232848824-CEOS_EXTRA.umm_json New-ID: NBI24 Wilson and Henderson-Sellers soil classes and soil class reliability. The Wilson and Henderson-Sellers Soil Classes Dataset Files: AFWSOILS.IMG Code: 100043-001 AFWSOILR.IMG 100043-002 Raster Members The IMG files are in IDRISI format. The Wilson and Henderson-Sellers soils data set is part of Wilson Henderson-Sellers land cover and soils for global circulation modeling project was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II. This data Bank is provided on a Database on diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : Roy Jenne, NCAR, P.O. Box 3000, Boulder, CO 80307-3000 The AFWSOILS file shows Wilson/Henderson-Sellers Soil Classes The ASWSOILR file shows Wilson/Henderson-Sellers Soil Class Reliability References: Wilson, M.F/ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general ciruclation climate models. Journal of Climatology, vol.5, pp.119-143. Source : Digitized from available sources: FAO/UNESCO Soil Map of the World. Oxford Regional Economic Atlas of USSR and Eastern Europe Publication Date : 1985 Projection : Lat/Lon Type : Raster Format : IDRISI proprietary
NBId0024_101 Africa Soil Classification by Wilson and Henderson-Sellers CEOS_EXTRA STAC Catalog 1970-01-01 12.88, 6.67, 24.97, 24.19 https://cmr.earthdata.nasa.gov/search/concepts/C2232848824-CEOS_EXTRA.umm_json New-ID: NBI24 Wilson and Henderson-Sellers soil classes and soil class reliability. The Wilson and Henderson-Sellers Soil Classes Dataset Files: AFWSOILS.IMG Code: 100043-001 AFWSOILR.IMG 100043-002 Raster Members The IMG files are in IDRISI format. The Wilson and Henderson-Sellers soils data set is part of Wilson Henderson-Sellers land cover and soils for global circulation modeling project was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II. This data Bank is provided on a Database on diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : Roy Jenne, NCAR, P.O. Box 3000, Boulder, CO 80307-3000 The AFWSOILS file shows Wilson/Henderson-Sellers Soil Classes The ASWSOILR file shows Wilson/Henderson-Sellers Soil Class Reliability References: Wilson, M.F/ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general ciruclation climate models. Journal of Climatology, vol.5, pp.119-143. Source : Digitized from available sources: FAO/UNESCO Soil Map of the World. Oxford Regional Economic Atlas of USSR and Eastern Europe Publication Date : 1985 Projection : Lat/Lon Type : Raster Format : IDRISI proprietary
NBId0025_101 Africa Soil Classification by Zobler CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848306-CEOS_EXTRA.umm_json New-ID: NBI25 Africa ZOBLER Soil Type, Soil Texture, Surface Slope Classes Dataset Documentation Files: AFZSOILS.IMG Code: 100090-001 AFZTEX.IMG 100090-002 AFZSUBSD.IMG 100090-003 AFZSP3.IMG 100090-004 AFZPHS.IMG 100090-005 AFZSLOPE.IMG 100092-001 Raster Members The IMG files are in IDRISI format The Zobler soil type, soil texture and surface slope dataset was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of a larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFZSOILS file shows Zobler soil types The AFZTEX file shows Zobler soil texture The AFZSUBSD file shows subsidiary soil units The AFZSP3 file shows Zobler special codes The AFZPHS file shows Zobler phase codes The AFZSLOPE file shows Zobler surface slope References: FAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO, Paris. Staub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil Type, Soil Texture, Surface Slope, and other properties: Documentation of Archived Tape Data. NASA Technical Memorandum No.100685. Henderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current Global Land Surface Data Sets for Use in Climate-Related Studies. (1986). Matthews, E. Global vegetation and land use: New high resolution data bases for climate studies (1983). J. Clim. Appl. Meteor., vol.22, pp.474-487. -----. Vegetation, Land-use and Seasonal Albedo Data Sets: Documentation of Archived Data Tape (1984). NASA Technical Memorandum. No.86107. Wilson. M.F. and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models (1985). Journal of Climatology, vol.5, pp.119-143. Source map : various Publication Date : 1987 Projection : Lat/lon Type : Raster Format : IDRISI proprietary
NBId0025_101 Africa Soil Classification by Zobler ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848306-CEOS_EXTRA.umm_json New-ID: NBI25 Africa ZOBLER Soil Type, Soil Texture, Surface Slope Classes Dataset Documentation Files: AFZSOILS.IMG Code: 100090-001 AFZTEX.IMG 100090-002 AFZSUBSD.IMG 100090-003 AFZSP3.IMG 100090-004 AFZPHS.IMG 100090-005 AFZSLOPE.IMG 100092-001 Raster Members The IMG files are in IDRISI format The Zobler soil type, soil texture and surface slope dataset was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of a larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFZSOILS file shows Zobler soil types The AFZTEX file shows Zobler soil texture The AFZSUBSD file shows subsidiary soil units The AFZSP3 file shows Zobler special codes The AFZPHS file shows Zobler phase codes The AFZSLOPE file shows Zobler surface slope References: FAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO, Paris. Staub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil Type, Soil Texture, Surface Slope, and other properties: Documentation of Archived Tape Data. NASA Technical Memorandum No.100685. Henderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current Global Land Surface Data Sets for Use in Climate-Related Studies. (1986). Matthews, E. Global vegetation and land use: New high resolution data bases for climate studies (1983). J. Clim. Appl. Meteor., vol.22, pp.474-487. -----. Vegetation, Land-use and Seasonal Albedo Data Sets: Documentation of Archived Data Tape (1984). NASA Technical Memorandum. No.86107. Wilson. M.F. and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models (1985). Journal of Climatology, vol.5, pp.119-143. Source map : various Publication Date : 1987 Projection : Lat/lon Type : Raster Format : IDRISI proprietary
-NBId0036_101 Africa Lakes and Rivers (World Data Bank II) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849206-CEOS_EXTRA.umm_json New-ID: NBI36 Africa Lakes and Rivers. Lakes and Rivers Dataset documentation (Micro World Data Bank II) Files: LAKES.VEC Code: 100055-001 RIVERS.VEC 100061-001 AFRIVER.IMG 100002-001 Raster Members The VEC and IMG files are in IDRISI format Africa lakes and rivers datasets are part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The LAKES file shows African lakes The RIVERS file shows African rivers The AFRIVER file shows African rivers References: NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds. Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. vol. 2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : digitized from available sources Publication Date : 1988 Projection : Lat/lon Type : Raster Format : IDRISI proprietary
NBId0036_101 Africa Lakes and Rivers (World Data Bank II) ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849206-CEOS_EXTRA.umm_json New-ID: NBI36 Africa Lakes and Rivers. Lakes and Rivers Dataset documentation (Micro World Data Bank II) Files: LAKES.VEC Code: 100055-001 RIVERS.VEC 100061-001 AFRIVER.IMG 100002-001 Raster Members The VEC and IMG files are in IDRISI format Africa lakes and rivers datasets are part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The LAKES file shows African lakes The RIVERS file shows African rivers The AFRIVER file shows African rivers References: NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds. Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. vol. 2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : digitized from available sources Publication Date : 1988 Projection : Lat/lon Type : Raster Format : IDRISI proprietary
+NBId0036_101 Africa Lakes and Rivers (World Data Bank II) CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849206-CEOS_EXTRA.umm_json New-ID: NBI36 Africa Lakes and Rivers. Lakes and Rivers Dataset documentation (Micro World Data Bank II) Files: LAKES.VEC Code: 100055-001 RIVERS.VEC 100061-001 AFRIVER.IMG 100002-001 Raster Members The VEC and IMG files are in IDRISI format Africa lakes and rivers datasets are part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The LAKES file shows African lakes The RIVERS file shows African rivers The AFRIVER file shows African rivers References: NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds. Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. vol. 2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : digitized from available sources Publication Date : 1988 Projection : Lat/lon Type : Raster Format : IDRISI proprietary
NBId0041_101 FNOC Elevation (meters), Terrain and Surface Characteristics for Africa CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847281-CEOS_EXTRA.umm_json New-ID: NBI41 Africa FNOC Elevation (meters), Terrain and Surface characteristics. Africa Elevation (meters), Terrain, and Surface Characteristics Dataset Documentation Files: AFMAX.IMG Code: 100082-001 AFMIN.IMG 100082-002 AFMOD.IMG 100082-003 Raster Members The IMG files are in IDRISI format Africa elevation dataset is part of the revised FNOC elevation, terrain and surface characteritics. It formed part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFMAX file shows maximum elevation (meters) The AFMIN file shows minimum elevation (meters) The AFMOD shows modal elevation (meters) Reference: Cuming, Michael J. and Barbara A. Hawkins. TERDAT: The FNOC System for Terrain Data Extraction and Processing (1981). Techn. Report MII Project M-254 (2nd Ed.) Prepared for Fleet Numerical Oceanography Center (Monterey, CA). Published by Meteorology International Incorporated. Source map : Digitized from available maps and reprocessed: US Defense Operational Navigation Charts (ONC), scale 1:1000000; some World Aeronautical Charts and charts from Jet Navigation. Publication Date : 1985 Projection : Lat/Lon Type : Raster Format : IDRISI proprietary
NBId0042_101 NOAA Monthly 10-Minute Normalized Vegetation Index (April 1985-December 1988) for Africa CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848152-CEOS_EXTRA.umm_json "New-ID: NBI42 NOAA monthly Normalized Vegetation Index (NDVI) for Africa. NOAA Monthly 10-Min Normalized Vegetation Index Dataset (APRIL 1985 - DECEMBER 1988) Files: AFAPR85.IMG-AFDEC85.IMG Code: 100041-001 AFJAN86.IMG-AFDEC86.IMG 100041-001 AFJAN87.IMG-AFDEC87.IMG 100041-001 AFJAN88.IMG-AFDEC88.IMG 100041-001 Raster Members The IMG files are in IDRISI format Africa monthly 10-min normalized difference vegetation index dataset is part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysycal Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA AFAPR85-AFDEC88 (45 months) show monthly Normalized Vegetation Index (NDVI) References: Kidwell, Katherin B. (ed.). Global Vegetion Index User""'""s Guide (1990). NOAA/NHESDIS/SDSD. for additional references see Appendix A-26-A32 of the Global Change Data Base documentation Source map : digitized from available maps and reprocessed Publication Date : Jun 1992 Projection : Lat/lon Type : Raster Format : IDRISI" proprietary
-NBId0043_101 Africa Integrated Elevation and Bathymetry CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847599-CEOS_EXTRA.umm_json "New-ID: NBI43 Africa Integrated Elevation and Bathymetry (feet). Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1992 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary
NBId0043_101 Africa Integrated Elevation and Bathymetry ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847599-CEOS_EXTRA.umm_json "New-ID: NBI43 Africa Integrated Elevation and Bathymetry (feet). Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1992 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary
+NBId0043_101 Africa Integrated Elevation and Bathymetry CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847599-CEOS_EXTRA.umm_json "New-ID: NBI43 Africa Integrated Elevation and Bathymetry (feet). Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1992 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary
NBId0044_101 Africa Ocean Mask CEOS_EXTRA STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849137-CEOS_EXTRA.umm_json "New-ID: NBI44 Ocean mask for Africa. Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1985 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary
NBId0044_101 Africa Ocean Mask ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849137-CEOS_EXTRA.umm_json "New-ID: NBI44 Ocean mask for Africa. Integrated Elevation and Bathymetry Dataset Documentation File: AFELBA.IMG Code: 100048-001 Raster Member This IMG file is in IDRISI format Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet) References: Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master""'""s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106. Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004. NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado. Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review. Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231. Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois. Source map : various sources Publication Date : Jun1985 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI" proprietary
NBId0053_101 Africa Revised FNOC Percent Water Cover ALL STAC Catalog 1970-01-01 -30, -45, 60, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847596-CEOS_EXTRA.umm_json New-ID: NBI53 Africa Revised FNOC Percent Water Cover Dataset Documentation File: AFWATER.IMG Code: 100082-005 Raster Member The IMG file is in IDRISI format The percent water cover dataset is part of the revised FNOC elevation, terrain and surface characteritics. It formed part of the NOAA project that was developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is part of the World Data Bank II provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFWATER file shows the revised FNOC percent water cover for Africa. Reference: Cuming, Michael J. and Barbara A. Hwakins. TERDAT: The FNOC System for Terrain Data Extraction and Processing (1981). Techn. Report MII Project M-254 (2nd Ed.) Prepared for Fleet Numerical Oceanography Center (Monterey, CA). Published by Meteorology International Incorporated. Source map : Digitized from available maps and reprocessed: US Defense Operational Navigation Charts (ONC), scale 1:1000000; some World Aeronautical Charts and charts from Jet Navigation. Publication Date : 1985 Projection : Lon/lat Type : Raster Format : IDRISI proprietary
@@ -12051,43 +12052,43 @@ NBId0153_101 Benito River dataset of Equatorial Guinea CEOS_EXTRA STAC Catalog 1
NBId0161_101 Climate Dataset of Senegal CEOS_EXTRA STAC Catalog 1970-01-01 -17.53, 12.02, -10.89, 17.14 https://cmr.earthdata.nasa.gov/search/concepts/C2232849116-CEOS_EXTRA.umm_json New-ID: NBI161 The Climate Dataset of Senegal documentation Files: SENEGAL4.IMG Code: 146005-001 SENEGAL5.IMG 146006-001 SENEGAL6.IMG 146007-001 Raster Members IMG files are in IDRISI format The Senegal Climate Dataset was originally digitized for the UNEP/FAO/ESRI Database for Africa from hand-drawn maps provided by FAO for the Desertification Hazard Mapping project. GRID-Geneva rasterized the coverages for UNEP/GRID/WHO/CISFAM Senegal Database with a cell size of 30 seconds and two minutes lat/lon (approximately one- and four kilometeter-square pixels, respectively). Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy The SENEGAL4 file shows mean annual wind velocity meters per second (8 classes). The SENEGAL5 file shows number of wet days per year (6 classes). The SENEGAL6 file shows mean annual rainfall in millimeters (10 classes). REMARK: file may have limited applicability at national scale as was extracted from continental. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP. CISFAM. Consolidated Information System for Famine Management in Africa, Phase I Report (Apr. 1987), Univ. of Louvain, Brussels, Belgium. Source and scale : unknown Report Publication Date : Dec 1988 Projection : lat/lon Type : Raster Format : IDRISI Related Datasets : All UNEP/FAO/ESRI climate Datasets proprietary
NBId0169_101 Baringo (Kenya) Pilot Study for Desertification Assessment and Mapping CEOS_EXTRA STAC Catalog 1984-01-01 1992-12-30 35, -1, 36, 0 https://cmr.earthdata.nasa.gov/search/concepts/C2232849286-CEOS_EXTRA.umm_json The purpose of the Kenya Pilot Study was to evaluate the FAO/UNEP Provisional Methodology for Assessment and Mapping of Desertification, and to recommend an effective, simple methodology for desertification assessment within Kenya. The FAO/UNEP Provisional Methodology (1984) proposes seven processes for consideration in desertification assessment: degradation of vegetation, water erosion, wind erosion, salinization, reduction of organic content, soil crusting and compaction. In late 1985, a pilot project for the assessment of the FAO/UNEP Methodology within Kenya was proposed, and in 1987 a memorandum of understanding between the Government of Kenya and UNEP for the implementation of that study was signed. The study areas were: 1) Models can be useful to assist in desertification assessment. Models can be developed from FAO/UNEP Methodology. 2) Any modeling output requires verification. 3) Ground survey and remote sensing can be important sources of data. 4) An evaluation of data and methodologies necessary to allow verification of desertification assessment modeling is required. 5) A human use component should be incorporated into desertification assessment that considers management implications and social, as well as, economic context. 6) Computer implementation of desertificaiton assessment can be effective, however, procedures should be well defined. This study within the Baringo Study Area was designed to address these concerns. The Baringo Study Area identified in this study would be typical of such a training area. The models developed during this study could be applied to the general region. The study area lies between 0 15'-1 N and 35 30' -36 30' E. It is located between the Laikipia escarpment to the East and the Tugen Hills to the West. Topographic elevations vary from 900m on the Njemps flats to 2000m in the Puka, Tangulbei and Pokot highlands. The size of the study area is approximately 15ookm2. 4.0 DATA COLLECTION A wide variety of data was collected. Detailed data was required to provide a basis for evaluating more general cost effective data gathering techniques and to provide a basis for model verification, particularly the socio/economic data. Physical Environment Topographic Data Topographic contours were digitized directly from 1:250,000 Survey of Kenya topographic maps. The contour interval was 200 feet. A digital elevation model was constructed using triangular irregular networks (TIN). Soil Data Soil types were mapped at 1:100,000 scale using existing soil maps, manual interpretation of SPOT imagery, and field investigations (Figure 3). During field trips, soil samples were taken from each soil unit and analyzed by the Kenya National Agricultural Center. 4.2 Climate Data 4.2.1 Rainfall Data Rainfall data from the Kenya Meteorological Department was analyzed for 33 stations within and surrounding the study area. A rainfall erosivity index was calculated based on the Fourier Index (R). 12 RE (p /P) 12 where P = annual rainfall p = monthly rainfall A relationship between this erosivity index and the annual rainfall for each station was calculated using linear regression (Bake, 1988). A map of rainfall erosivity was generated for the study area by relating annual rainfall isoheyts to the following: y = 0.108x - 0.68 This data was coded and digitized. Wind Erosion Potential The following required conditions were determined to create high wind erosion potential (Kinuthia, 1989): 1) Annual rainfall less than 300mm. 2) P/E greater than zero and less than 1, where: P=mean monthly rainfall (cm). E=mean monthly PET (cm). 3) Wind velocity greater than 4 m/s at 10m height. Vegetation Data A vegetation map for the study area was produced at a scale of 1:100,000 through manual interpretation of a SPOT image and field investigations (Figure 6). A structural classification system as adopted by DRSRS was used for naming vegetation types (Grunb). Systematic Reconnaissance Flight Data Since 1977, DRSRS has been conducting aerial surveys of Kenyan rangelands. In addition to data on the number of wildlife and livestock, observations of land use and environmental condition are also made. Socio/economic Data Social Factors A wide variety of data was collected through literature review and a field administered questionnaire. Nutritional status was estimated by measurement of childrens' mid upper arm. Such data is useful for a Level 1 type assessment. Permanent Structures Data For the Level 2 assessment, data on permanent structures was extracted from DRSRS SRF data. This data was used to indicate presence and concentration of sedentary populations. Example Files: VDS.E00 (Vegetation degradation) DES.E00 (Plant Species) Others available on request. proprietary
NBId0177_101 Laikipia (Kenya) Research Programme GIS Datasets CEOS_EXTRA STAC Catalog 1990-01-01 1994-12-30 36, 0, 37, 1 https://cmr.earthdata.nasa.gov/search/concepts/C2232848187-CEOS_EXTRA.umm_json Laikipia Research Programme GIS Datasets are divided into two main different study area scales: the Regional level [Laikipia district, the Ewaso Ng'iro Basin] and the Local level [Land parcels-farm(s), catchments of a few kilometer square]. Coordinate Reference System Coverage data is organized thematically as a series of layers. The coordinate reference systems used in LRP dataset are:- (a) global coordinate system - Universal Transverse Mercator (UTM), (b) Local coordinate system. Digitizing Scale and Fuzzy Tolerance The initial digitizing scale for the LRP GIS Dataset is dependent on the scale of the study areas. There are two major research levels carried by LRP namely Regional and Local. The scales used for regional level are 1:250,000 and 1:50,000. FUZZY TOLERANCE is the minimum distance between coordinates in a coverage. The resolution of a coverage is defined by the minimum distance separating the coordinates used to store coverage features. Resolution is limited by the map scale in initial digitizing. The fuzzy tolerance can be calculated as follows for digitizing table: Initial Scale for Coverage of Fuzzy Tolerance Digitizing Units Value 1;250,000 Meters 6.35 1:50,000 Meters 1.25 1:10,000 Meters 0.25 1:5,000 Meters 0.125 1:2,500 Meters 0.0625 Files: Roads.E00 (Roads) Settle.E00 (Settlement Pattern) Centres.E00 (Urban Centres) (other files exist also) proprietary
-NBId0203_101 Africa Water Balance high/lowland crops, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847252-CEOS_EXTRA.umm_json "The Africa Water Balance data set which is prepared by watershed and by country, belongs to the group of ""Irrigation and Water Resources Potential"" study. It covers 55 countries and 25 major basins which contain 335 watersheds. The digitized data base for Africa and the World was originally prepared for an FAO/UNEP project on Desertification in 1982-1984. UNEP financed preparation and analysis of the digitized map data and FAO prepared the data and methodology. The main input maps (all in Miller Oblated Stereographic projection) are the 1975 UNESCO Geological Map of Africa (originally at a scale of 1:10 million); the FAO/UNESCO Soil Map of Africa; Mean Annual Rainfall Map from hand drawn FAO/AGS climate maps; Template; Watersheds; and Administrative Units map - all at a scale 1:5 m. The methodology was based on water balance approach. This determines the suitability of the soil for irrigation and estimates the amount of water the soil requires. Estimates of the surface and groundwater are then compared to the potential irrigation use. If use exceeds available water resources, the irrigable area is correspondingly reduced; in the event of water surplus, some of the water is routed to the downstream basin. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except lowland crops). For further details refer to FAO contact for the 1987 FAO Irrigation and Water Resources Potential for Africa AGL/MISC/11/87. FAO, Land and Water Development Division via Delle Terme di Caracalla, 00100, Rome, Italy Vector Member The file is in Arc/Info Export format. Reference: FAO. Irrigation and Water Resources Potential for Africa. (1987) FAO. Final Report UNEP/FAO world and Africa GIS data base (1984), unpublished publication of ESRI, FAO and UNEP. UNESCO. Geological Map of Africa (1975). Scale 1:5 000 000." proprietary
NBId0203_101 Africa Water Balance high/lowland crops, 1987 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847252-CEOS_EXTRA.umm_json "The Africa Water Balance data set which is prepared by watershed and by country, belongs to the group of ""Irrigation and Water Resources Potential"" study. It covers 55 countries and 25 major basins which contain 335 watersheds. The digitized data base for Africa and the World was originally prepared for an FAO/UNEP project on Desertification in 1982-1984. UNEP financed preparation and analysis of the digitized map data and FAO prepared the data and methodology. The main input maps (all in Miller Oblated Stereographic projection) are the 1975 UNESCO Geological Map of Africa (originally at a scale of 1:10 million); the FAO/UNESCO Soil Map of Africa; Mean Annual Rainfall Map from hand drawn FAO/AGS climate maps; Template; Watersheds; and Administrative Units map - all at a scale 1:5 m. The methodology was based on water balance approach. This determines the suitability of the soil for irrigation and estimates the amount of water the soil requires. Estimates of the surface and groundwater are then compared to the potential irrigation use. If use exceeds available water resources, the irrigable area is correspondingly reduced; in the event of water surplus, some of the water is routed to the downstream basin. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except lowland crops). For further details refer to FAO contact for the 1987 FAO Irrigation and Water Resources Potential for Africa AGL/MISC/11/87. FAO, Land and Water Development Division via Delle Terme di Caracalla, 00100, Rome, Italy Vector Member The file is in Arc/Info Export format. Reference: FAO. Irrigation and Water Resources Potential for Africa. (1987) FAO. Final Report UNEP/FAO world and Africa GIS data base (1984), unpublished publication of ESRI, FAO and UNEP. UNESCO. Geological Map of Africa (1975). Scale 1:5 000 000." proprietary
+NBId0203_101 Africa Water Balance high/lowland crops, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847252-CEOS_EXTRA.umm_json "The Africa Water Balance data set which is prepared by watershed and by country, belongs to the group of ""Irrigation and Water Resources Potential"" study. It covers 55 countries and 25 major basins which contain 335 watersheds. The digitized data base for Africa and the World was originally prepared for an FAO/UNEP project on Desertification in 1982-1984. UNEP financed preparation and analysis of the digitized map data and FAO prepared the data and methodology. The main input maps (all in Miller Oblated Stereographic projection) are the 1975 UNESCO Geological Map of Africa (originally at a scale of 1:10 million); the FAO/UNESCO Soil Map of Africa; Mean Annual Rainfall Map from hand drawn FAO/AGS climate maps; Template; Watersheds; and Administrative Units map - all at a scale 1:5 m. The methodology was based on water balance approach. This determines the suitability of the soil for irrigation and estimates the amount of water the soil requires. Estimates of the surface and groundwater are then compared to the potential irrigation use. If use exceeds available water resources, the irrigable area is correspondingly reduced; in the event of water surplus, some of the water is routed to the downstream basin. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except lowland crops). For further details refer to FAO contact for the 1987 FAO Irrigation and Water Resources Potential for Africa AGL/MISC/11/87. FAO, Land and Water Development Division via Delle Terme di Caracalla, 00100, Rome, Italy Vector Member The file is in Arc/Info Export format. Reference: FAO. Irrigation and Water Resources Potential for Africa. (1987) FAO. Final Report UNEP/FAO world and Africa GIS data base (1984), unpublished publication of ESRI, FAO and UNEP. UNESCO. Geological Map of Africa (1975). Scale 1:5 000 000." proprietary
NBId0207_101 IGADD Member Countries Crop types and distribution by administrative units, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 22, -12, 51, 23 https://cmr.earthdata.nasa.gov/search/concepts/C2232849119-CEOS_EXTRA.umm_json "The IGADD (Inter-Governmental Authority on Drought and Development) crop zones dataset is part of the Africa UNEP/FAO/ESRI Crops Data. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. The data was provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service, Land and Water Development Division, Italy. The datasets were then developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Administrative Units map and the World Atlas of Agriculture (1969). All sources were re-registered to the base map by comparing known features on the base map and the source maps. In the original Database (Africa), a considerable study was made of crop water requirements for a range of crops in the various African climates during the time of the year when irrigation would be required. It was found that a relatively simple relationship exists between annual rainfall and the crop irrigation water requirements for the African food grain crops. It was also observed that water requirements for food grains vary between fruit and vegetable crops on the one side and fiber crops and fodder on the other. No attempt was made to produce complex crop patterns. There is a maximum of 13 crop types in one country. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO/UNESCO Soil Map of the Africa (1977). Scale 1:5000000. UNESCO, Paris. FAO. Administration units map. Scale 1:5 000 000. Rome. FAO. Irrigation and Water Resources Potential for Africa. (1987) Source :UNESCO/FAO Soil Map of the World. Scale 1:5000000 Publication Date :Nov 1987 Projection :Miller Type :Polygon Format :Arc/Info Export non-compressed Related Data sets :All UNEP/FAO/ESRI Data sets FAO Irrigable Data sets 100050: "" IRRIGLB lowland crops, best soils "" IRRIGLT lowland crops, best plus suitable soils "" IRRIGUB upland crops, best soils "" IRRIGUT upland crops, best plus suitable soils FAO Soil water balance 100053: "" WATBALLB lowland crops, best soils "" WATBALLT lowland crops, best plus suitable soils "" WATBALUB upland crops, best soils "" WATBALUT upland crops, best plus suitable soils FAO Agro-ecological zones AEZBLL08 North-west of continent AEZBLL09 North-east of continent AEZBLL10 South of continent" proprietary
NBId0208_101 Africa Major Human Settlements and Landuse, 1984 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848068-CEOS_EXTRA.umm_json The Africa Human Settlements and Landuse data sets form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the base map those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC. DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Miller Type :Points Format :Arc/Info export non-compressed Related Data sets :All UNEP/FAO/ESRI Data sets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments : no outline of Africa proprietary
NBId0208_101 Africa Major Human Settlements and Landuse, 1984 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848068-CEOS_EXTRA.umm_json The Africa Human Settlements and Landuse data sets form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the base map those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC. DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC. Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Miller Type :Points Format :Arc/Info export non-compressed Related Data sets :All UNEP/FAO/ESRI Data sets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments : no outline of Africa proprietary
-NBId0211_101 Africa Irrigation Potential, Best soils, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848204-CEOS_EXTRA.umm_json The Africa Irrigation Potential data set, which represents the best soils suitable for upland, is part of the FAO Irrigation and Water Resources Potential Database. The main input maps were the 1977 FAO/UNESCO Soil Map of the Africa, UNESCO Geological World Atlas (scale 1:10 m), Mean Annual Rainfall map from hand drawn FAO/AGS climate maps, Template with water related features, Administrative Units map, and Watersheds map. All maps, apart from where specified were at a scale of 1:5 million, and all in Miller Oblated Stereographic projection. The soil suitability for irrigation was determined by evaluating the properties of all soil components: dominant soil, associations and inclusions, phases, slope, drainage, and texture. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops). The soils source includes a list of attributes for each soil unit including: slope, drainage, texture and phase (re: UNEP/FAO/ESRI ITU 100004). Then for both cases (lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops)), two maps were generated. One with all soils which are suitable, and one where slope, texture, drainage and phase were considered. Each different soil type is classed according to suitability, S1 irrigation with no constraints, S2 irrigation with some constraints, N1 not suitable without major improvements, N2 permanently not suitable. Because one soil unit can consist of more soil components (unit Af26-a can mean 30%Bf and 70% Af) the suitability is expressed in percentage of the unit that is suitable (1 >50% suitable, 2 = 25-50% etc.). Then the soil characteristics are used to refine the ranking. This refining is done were the original soil rank is increased decreased or changed from their original suitability to a new suitability (so or soil gets new class S1, N1 etc. or ranking changes like, -1 lower soil rank by one, +1 raise soil rank with one). The Ranking of Soils is as follows The soils considered not suitable are: Lithosols, Arenosols, Rendzinas, Yermosols, Podzols, Thionic Fluvisols, Miscellaneous land units such as rock debris, desert debris, Gypsum units, Soils with stonic, lythic or petrogypsic phase. proprietary
NBId0211_101 Africa Irrigation Potential, Best soils, 1987 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848204-CEOS_EXTRA.umm_json The Africa Irrigation Potential data set, which represents the best soils suitable for upland, is part of the FAO Irrigation and Water Resources Potential Database. The main input maps were the 1977 FAO/UNESCO Soil Map of the Africa, UNESCO Geological World Atlas (scale 1:10 m), Mean Annual Rainfall map from hand drawn FAO/AGS climate maps, Template with water related features, Administrative Units map, and Watersheds map. All maps, apart from where specified were at a scale of 1:5 million, and all in Miller Oblated Stereographic projection. The soil suitability for irrigation was determined by evaluating the properties of all soil components: dominant soil, associations and inclusions, phases, slope, drainage, and texture. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops). The soils source includes a list of attributes for each soil unit including: slope, drainage, texture and phase (re: UNEP/FAO/ESRI ITU 100004). Then for both cases (lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops)), two maps were generated. One with all soils which are suitable, and one where slope, texture, drainage and phase were considered. Each different soil type is classed according to suitability, S1 irrigation with no constraints, S2 irrigation with some constraints, N1 not suitable without major improvements, N2 permanently not suitable. Because one soil unit can consist of more soil components (unit Af26-a can mean 30%Bf and 70% Af) the suitability is expressed in percentage of the unit that is suitable (1 >50% suitable, 2 = 25-50% etc.). Then the soil characteristics are used to refine the ranking. This refining is done were the original soil rank is increased decreased or changed from their original suitability to a new suitability (so or soil gets new class S1, N1 etc. or ranking changes like, -1 lower soil rank by one, +1 raise soil rank with one). The Ranking of Soils is as follows The soils considered not suitable are: Lithosols, Arenosols, Rendzinas, Yermosols, Podzols, Thionic Fluvisols, Miscellaneous land units such as rock debris, desert debris, Gypsum units, Soils with stonic, lythic or petrogypsic phase. proprietary
-NBId0216_101 Africa Number of Wet Days per Year and Wind Velocity, 1984 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849224-CEOS_EXTRA.umm_json "The Africa Number of Wet Days per year and Wind Velocity data sets are part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa in this case. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). ""Internal Publication from ESRI, FAO and UNEP ""FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris ""FAO. Map of Mean Annual Rainfall and general Climate zones for P/Pet for Africa. (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resources, Management and Conservation Service. FAO, Rome ""FAO. Maps of Mean annual Wind Velocity for Africa (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome"" ""FAO. Maps of Number of Wet Days per Year (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Type :Polygon and line Format :Arc/Info Export non-compressed Related Datasets :All UNEP/FAO/ESRI Data sets" proprietary
+NBId0211_101 Africa Irrigation Potential, Best soils, 1987 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848204-CEOS_EXTRA.umm_json The Africa Irrigation Potential data set, which represents the best soils suitable for upland, is part of the FAO Irrigation and Water Resources Potential Database. The main input maps were the 1977 FAO/UNESCO Soil Map of the Africa, UNESCO Geological World Atlas (scale 1:10 m), Mean Annual Rainfall map from hand drawn FAO/AGS climate maps, Template with water related features, Administrative Units map, and Watersheds map. All maps, apart from where specified were at a scale of 1:5 million, and all in Miller Oblated Stereographic projection. The soil suitability for irrigation was determined by evaluating the properties of all soil components: dominant soil, associations and inclusions, phases, slope, drainage, and texture. The classification was done for two major crop types: lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops). The soils source includes a list of attributes for each soil unit including: slope, drainage, texture and phase (re: UNEP/FAO/ESRI ITU 100004). Then for both cases (lowland crops (flooded rice), and upland crops (for all other irrigated crops except the lowland crops)), two maps were generated. One with all soils which are suitable, and one where slope, texture, drainage and phase were considered. Each different soil type is classed according to suitability, S1 irrigation with no constraints, S2 irrigation with some constraints, N1 not suitable without major improvements, N2 permanently not suitable. Because one soil unit can consist of more soil components (unit Af26-a can mean 30%Bf and 70% Af) the suitability is expressed in percentage of the unit that is suitable (1 >50% suitable, 2 = 25-50% etc.). Then the soil characteristics are used to refine the ranking. This refining is done were the original soil rank is increased decreased or changed from their original suitability to a new suitability (so or soil gets new class S1, N1 etc. or ranking changes like, -1 lower soil rank by one, +1 raise soil rank with one). The Ranking of Soils is as follows The soils considered not suitable are: Lithosols, Arenosols, Rendzinas, Yermosols, Podzols, Thionic Fluvisols, Miscellaneous land units such as rock debris, desert debris, Gypsum units, Soils with stonic, lythic or petrogypsic phase. proprietary
NBId0216_101 Africa Number of Wet Days per Year and Wind Velocity, 1984 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849224-CEOS_EXTRA.umm_json "The Africa Number of Wet Days per year and Wind Velocity data sets are part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa in this case. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). ""Internal Publication from ESRI, FAO and UNEP ""FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris ""FAO. Map of Mean Annual Rainfall and general Climate zones for P/Pet for Africa. (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resources, Management and Conservation Service. FAO, Rome ""FAO. Maps of Mean annual Wind Velocity for Africa (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome"" ""FAO. Maps of Number of Wet Days per Year (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Type :Polygon and line Format :Arc/Info Export non-compressed Related Datasets :All UNEP/FAO/ESRI Data sets" proprietary
+NBId0216_101 Africa Number of Wet Days per Year and Wind Velocity, 1984 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849224-CEOS_EXTRA.umm_json "The Africa Number of Wet Days per year and Wind Velocity data sets are part of the UNEP/FAO/ESRI Database project that covers the entire world but focused on Africa in this case. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. This data set was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were hand drawn climate maps from FAO. All sources were re-registered to the FAO Soil Map of the world (1984) in Miller Oblated Stereographic projection by comparing known features on the basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/ longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. References: ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). ""Internal Publication from ESRI, FAO and UNEP ""FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris ""FAO. Map of Mean Annual Rainfall and general Climate zones for P/Pet for Africa. (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resources, Management and Conservation Service. FAO, Rome ""FAO. Maps of Mean annual Wind Velocity for Africa (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome"" ""FAO. Maps of Number of Wet Days per Year (1983). Scale 1:5000000. Todor Boyadgiev, Soil Resourcs, Management and Conservation Service. FAO, Rome Source :FAO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Type :Polygon and line Format :Arc/Info Export non-compressed Related Datasets :All UNEP/FAO/ESRI Data sets" proprietary
NBId0218_101 Africa Surface Hydrography, 1984 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848062-CEOS_EXTRA.umm_json The First-Third Order Stream Network member of the African Surface Hydrography data set is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP), as part of a project initiated by the same. The base map used was the FAO/UNESCO Soil Map of the World, scale 1:5000000 (1977) in Miller Oblated Stereographic projection. All sources were re-registered to the base map by comparing known features on the base map and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm by US Geological Survey and ESRI) to create coverage for one-degree graticules. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1977). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World.(1977). Scale 1:5000000. UNESCO, Paris Source :FAO/UNESCO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Feature type :line Related Data sets :All UNEP/FAO/ESRI Data sets, Outline of Africa OUTLINE3.E00, HYDRMAJLL, HYDRMINLL (Surface Hydrography), Hydrologic Basins Comment : No boundary (outline) for Africa. proprietary
NBId0218_101 Africa Surface Hydrography, 1984 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848062-CEOS_EXTRA.umm_json The First-Third Order Stream Network member of the African Surface Hydrography data set is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. The database was developed by the United Nations Environment Program (UNEP), as part of a project initiated by the same. The base map used was the FAO/UNESCO Soil Map of the World, scale 1:5000000 (1977) in Miller Oblated Stereographic projection. All sources were re-registered to the base map by comparing known features on the base map and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm by US Geological Survey and ESRI) to create coverage for one-degree graticules. References: ESRI. Final Report UNEP/FAO World and Africa GIS data base (1977). Internal Publication by ESRI, FAO and UNEP FAO. UNESCO Soil Map of the World.(1977). Scale 1:5000000. UNESCO, Paris Source :FAO/UNESCO Soil Map of the World. Scale 1:5000000 Publication Date :Dec 1984 Projection :Geographic (lat/lon) Feature type :line Related Data sets :All UNEP/FAO/ESRI Data sets, Outline of Africa OUTLINE3.E00, HYDRMAJLL, HYDRMINLL (Surface Hydrography), Hydrologic Basins Comment : No boundary (outline) for Africa. proprietary
NBId0220_101 Africa Rainfall and Maximum Temperature Measuring Stations (12 average monthly), 1989 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849335-CEOS_EXTRA.umm_json "The Africa Rain Measuring Stations data set, for monthly rainfall is part of the UNEP/ILRAD, now ILRI East Coast Fever (ECF) Database project. The point data was reformatted (Miller, scale 1:5 000 000) from CIAT tabular data based on 12 average monthly rainfall, evaporation, and minimum/maximum temperature. The data was used in the calculation of interpolated surfaces for rainfall and temperature distribution as the basis for modeling of climatic stress factors that constrain the distribution of ticks that transfer ECF. Vector Member The file is in Arc/Info Export format. The RAINSTNS point data represents rainfall measuring stations (12 average monthly) should go with file DATREAD.ME References: P. Lessard, R. L'Eppattenier, R.A. Norval, B.D. Perry, T.T. Dolan, K. Kundert, H. Croze, J.B. Walker, A.D. Irvin. Geographic Information System for studying the Epidemiology of East Coast Fever (Theileria parva) (1989). K. Kundert. Isolating East Coast Fever High risk Areas (1989). Arc/Info European User Conference, Rome, October 1989. CSIRO. Users guide to CLIMEX, A computer program for comparing climates in ecology. CSIRO Aust. Div Rep No.35, pp.-29 Source : CIAT tabular data Publication Date :Jan 1989 Projection :Miller Type :Point Format :Arc/Info Export non-compressed ""Related Data sets :East Coast Fever (100057-002-/66-002): ECFMAP, TICKSUIT, BUFFALO2, CATTLE, CATTYP, BUFCAT2, RAPOLY, RAPNTS, RDPNTS, RNPNTS and RZPNTS. Comment : No boundary (outline) for Africa" proprietary
NBId0220_101 Africa Rainfall and Maximum Temperature Measuring Stations (12 average monthly), 1989 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232849335-CEOS_EXTRA.umm_json "The Africa Rain Measuring Stations data set, for monthly rainfall is part of the UNEP/ILRAD, now ILRI East Coast Fever (ECF) Database project. The point data was reformatted (Miller, scale 1:5 000 000) from CIAT tabular data based on 12 average monthly rainfall, evaporation, and minimum/maximum temperature. The data was used in the calculation of interpolated surfaces for rainfall and temperature distribution as the basis for modeling of climatic stress factors that constrain the distribution of ticks that transfer ECF. Vector Member The file is in Arc/Info Export format. The RAINSTNS point data represents rainfall measuring stations (12 average monthly) should go with file DATREAD.ME References: P. Lessard, R. L'Eppattenier, R.A. Norval, B.D. Perry, T.T. Dolan, K. Kundert, H. Croze, J.B. Walker, A.D. Irvin. Geographic Information System for studying the Epidemiology of East Coast Fever (Theileria parva) (1989). K. Kundert. Isolating East Coast Fever High risk Areas (1989). Arc/Info European User Conference, Rome, October 1989. CSIRO. Users guide to CLIMEX, A computer program for comparing climates in ecology. CSIRO Aust. Div Rep No.35, pp.-29 Source : CIAT tabular data Publication Date :Jan 1989 Projection :Miller Type :Point Format :Arc/Info Export non-compressed ""Related Data sets :East Coast Fever (100057-002-/66-002): ECFMAP, TICKSUIT, BUFFALO2, CATTLE, CATTYP, BUFCAT2, RAPOLY, RAPNTS, RDPNTS, RNPNTS and RZPNTS. Comment : No boundary (outline) for Africa" proprietary
-NBId0223_101 Africa Zobler Soils (Texture Classes, Slope, Phases), 1987 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848713-CEOS_EXTRA.umm_json "The Zobler soil datasets were developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The data set is part of the World Data Bank II and is part of ""The Global Change Data Base"". The World Data Bank II is part of a larger project called ""Global Ecosystems Database Project"". The project was a joint effort between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. The texture data is based on the FAO Soil Map of the World, and compiled into digital form by Zobler. Each matrix element represents the near-surface texture (upper 30 cm) of the dominant soil unit in a one-degree square cell of the earth's surface. The data conforms in location, and nominal classification (land, land-ice, water) to Matthew's vegetation data set. References: FAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO, Paris. Staub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil Type, Soil Texture, Surface Slope, and other properties: Documentation of Archived Tape Data. NASA Technical Memorandum No.100685. Henderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current Global Land Surface Data Sets for Use in Climate-Related Studies. (1986). Matthews, E. Global vegetation and land use: New high resolution data bases for climate studies (1983). J. Clim. Appl. Meteor., vol.22, pp.474-487. Vegetation, Land-use and Seasonal Albedo Data Sets: Documentation of Archived Data Tape (1984). NASA Technical Memorandum. No.86107. Wilson. M.F. and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models (1985). Journal of Climatology, vol.5, pp.119-143. Source map :FAO/UNESCO Soil Map of the World Publication Date :1987 Projection :lat/lon Type :Raster Format :IDRISI" proprietary
NBId0223_101 Africa Zobler Soils (Texture Classes, Slope, Phases), 1987 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848713-CEOS_EXTRA.umm_json "The Zobler soil datasets were developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The data set is part of the World Data Bank II and is part of ""The Global Change Data Base"". The World Data Bank II is part of a larger project called ""Global Ecosystems Database Project"". The project was a joint effort between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. The texture data is based on the FAO Soil Map of the World, and compiled into digital form by Zobler. Each matrix element represents the near-surface texture (upper 30 cm) of the dominant soil unit in a one-degree square cell of the earth's surface. The data conforms in location, and nominal classification (land, land-ice, water) to Matthew's vegetation data set. References: FAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO, Paris. Staub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil Type, Soil Texture, Surface Slope, and other properties: Documentation of Archived Tape Data. NASA Technical Memorandum No.100685. Henderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current Global Land Surface Data Sets for Use in Climate-Related Studies. (1986). Matthews, E. Global vegetation and land use: New high resolution data bases for climate studies (1983). J. Clim. Appl. Meteor., vol.22, pp.474-487. Vegetation, Land-use and Seasonal Albedo Data Sets: Documentation of Archived Data Tape (1984). NASA Technical Memorandum. No.86107. Wilson. M.F. and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models (1985). Journal of Climatology, vol.5, pp.119-143. Source map :FAO/UNESCO Soil Map of the World Publication Date :1987 Projection :lat/lon Type :Raster Format :IDRISI" proprietary
+NBId0223_101 Africa Zobler Soils (Texture Classes, Slope, Phases), 1987 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848713-CEOS_EXTRA.umm_json "The Zobler soil datasets were developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The data set is part of the World Data Bank II and is part of ""The Global Change Data Base"". The World Data Bank II is part of a larger project called ""Global Ecosystems Database Project"". The project was a joint effort between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. The texture data is based on the FAO Soil Map of the World, and compiled into digital form by Zobler. Each matrix element represents the near-surface texture (upper 30 cm) of the dominant soil unit in a one-degree square cell of the earth's surface. The data conforms in location, and nominal classification (land, land-ice, water) to Matthew's vegetation data set. References: FAO. FAO-UNESCO Soil Map of the World (1974). Scale 1:5000000. UNESCO, Paris. Staub, Brad and Cynthia Rosenzweig. Global Digital Data Sets of Soil Type, Soil Texture, Surface Slope, and other properties: Documentation of Archived Tape Data. NASA Technical Memorandum No.100685. Henderson-Sellers, A., M.F. Wilson, G. Thomas, R.E. Dickinson. Current Global Land Surface Data Sets for Use in Climate-Related Studies. (1986). Matthews, E. Global vegetation and land use: New high resolution data bases for climate studies (1983). J. Clim. Appl. Meteor., vol.22, pp.474-487. Vegetation, Land-use and Seasonal Albedo Data Sets: Documentation of Archived Data Tape (1984). NASA Technical Memorandum. No.86107. Wilson. M.F. and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models (1985). Journal of Climatology, vol.5, pp.119-143. Source map :FAO/UNESCO Soil Map of the World Publication Date :1987 Projection :lat/lon Type :Raster Format :IDRISI" proprietary
NBId0233_101 Africa Population Density Model (Land Degradation Project), 1992 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848719-CEOS_EXTRA.umm_json The Africa Population density model represents ranges of population density of inhabitants per square kilometer. The estimated population densities are expressed on a regularly spaced latitude/longitude raster grid covering Africa with an approximate resolution of 10 km x 10 km at the Equator. The data set which is an assessment of one of the factors causing soil degradation, namely the spatial distribution and density of population. It was developed for the GEMS/UNITAR Africa Database and later used for GLASOD. The data sources include: 600 African towns and cities with figures standardized to 1988 values ( a combination of 479 cities from Birkbeck College and 363 cities in 51 African countries from PC Globe 3.0); UNEP/FAO population data from the 1984 Africa database; the Sierra Club Wilderness Area IUCN Protected Areas, used to delimit areas with extremely sparse populations and treated as having a density of less than one person per square kilometer. For methodology and further detail refer to references listed: UN Institute for Training & Research (UNITAR). GEMS/UNITAR Africa Database. Deichmann, U. and Lars Eklundh. Global Digital Datasets for Land Degradation Studies (1991), GRID Case Studies No.4. UNEP/GRID, Nairobi. UNEP. World Atlas of Desertification (1992). Edward Arnold: A division of Hodder and Stoughton, London. Projection :Geographic Type :Raster Format :IDRISI Related files :POPDENSL.E00, POPDENGR.E00 Associated files :POPDENS.DOC and POPDENS.PAL proprietary
NBId0233_101 Africa Population Density Model (Land Degradation Project), 1992 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848719-CEOS_EXTRA.umm_json The Africa Population density model represents ranges of population density of inhabitants per square kilometer. The estimated population densities are expressed on a regularly spaced latitude/longitude raster grid covering Africa with an approximate resolution of 10 km x 10 km at the Equator. The data set which is an assessment of one of the factors causing soil degradation, namely the spatial distribution and density of population. It was developed for the GEMS/UNITAR Africa Database and later used for GLASOD. The data sources include: 600 African towns and cities with figures standardized to 1988 values ( a combination of 479 cities from Birkbeck College and 363 cities in 51 African countries from PC Globe 3.0); UNEP/FAO population data from the 1984 Africa database; the Sierra Club Wilderness Area IUCN Protected Areas, used to delimit areas with extremely sparse populations and treated as having a density of less than one person per square kilometer. For methodology and further detail refer to references listed: UN Institute for Training & Research (UNITAR). GEMS/UNITAR Africa Database. Deichmann, U. and Lars Eklundh. Global Digital Datasets for Land Degradation Studies (1991), GRID Case Studies No.4. UNEP/GRID, Nairobi. UNEP. World Atlas of Desertification (1992). Edward Arnold: A division of Hodder and Stoughton, London. Projection :Geographic Type :Raster Format :IDRISI Related files :POPDENSL.E00, POPDENGR.E00 Associated files :POPDENS.DOC and POPDENS.PAL proprietary
-NBId0236_101 Africa Cattle Type (East Coast Fever Project), 1989 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847818-CEOS_EXTRA.umm_json The Cattle Type data set is part of the East Coast Fever (ECF) database covering sub-Saharan, East, and Central Africa. The ECF study determined both areas at risk and potential migration of the disease by cattle and a potential pool of infection for transmitting the disease to domestic cattle by buffalo which is the main wildlife host of the ECF. The study was carried out in Nairobi by United Nations Environment Program, Global Resource Information Database (UNEP/GRID) in collaboration with the International Laboratory for Research on Animal Diseases (ILRAD), now called International Livestock Research Institute (ILRI). proprietary
NBId0236_101 Africa Cattle Type (East Coast Fever Project), 1989 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847818-CEOS_EXTRA.umm_json The Cattle Type data set is part of the East Coast Fever (ECF) database covering sub-Saharan, East, and Central Africa. The ECF study determined both areas at risk and potential migration of the disease by cattle and a potential pool of infection for transmitting the disease to domestic cattle by buffalo which is the main wildlife host of the ECF. The study was carried out in Nairobi by United Nations Environment Program, Global Resource Information Database (UNEP/GRID) in collaboration with the International Laboratory for Research on Animal Diseases (ILRAD), now called International Livestock Research Institute (ILRI). proprietary
-NBId0248_101 Africa Wilson & Henderson-Sellers Secondary Vegetation Classes and Class Reliability, 1985 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848868-CEOS_EXTRA.umm_json "The Wilson and Henderson-Sellers Secondary Vegetation Classes and Class Reliability data sets are part of the ""Wilson Henderson-Sellers land cover and soils for global circulation modeling project "" and were developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the US National Geophysical Data Center (NGDC). The data sets are part of the World Data Bank II. This data Bank is provided in a Database on diskette called """"The Global Change Data Base"""". The Data Bank II is part of larger project called ""Global Ecosystems Database Project"". This is a cooperative effort between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the US Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The data sets are accompanied by an ASCII documentation file which contains information necessary for the use of the dataset in GIS or other software. References: Wilson, M.F./ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models. Journal of Climatology, vol.5, pp.119-143. Source : Digitized from available sources: FAO/UNESCO Soil Map of the World Publication Date : 1985 Projection : lat/lon Type : Raster Format : IDRISI" proprietary
+NBId0236_101 Africa Cattle Type (East Coast Fever Project), 1989 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847818-CEOS_EXTRA.umm_json The Cattle Type data set is part of the East Coast Fever (ECF) database covering sub-Saharan, East, and Central Africa. The ECF study determined both areas at risk and potential migration of the disease by cattle and a potential pool of infection for transmitting the disease to domestic cattle by buffalo which is the main wildlife host of the ECF. The study was carried out in Nairobi by United Nations Environment Program, Global Resource Information Database (UNEP/GRID) in collaboration with the International Laboratory for Research on Animal Diseases (ILRAD), now called International Livestock Research Institute (ILRI). proprietary
NBId0248_101 Africa Wilson & Henderson-Sellers Secondary Vegetation Classes and Class Reliability, 1985 CEOS_EXTRA STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848868-CEOS_EXTRA.umm_json "The Wilson and Henderson-Sellers Secondary Vegetation Classes and Class Reliability data sets are part of the ""Wilson Henderson-Sellers land cover and soils for global circulation modeling project "" and were developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the US National Geophysical Data Center (NGDC). The data sets are part of the World Data Bank II. This data Bank is provided in a Database on diskette called """"The Global Change Data Base"""". The Data Bank II is part of larger project called ""Global Ecosystems Database Project"". This is a cooperative effort between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the US Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The data sets are accompanied by an ASCII documentation file which contains information necessary for the use of the dataset in GIS or other software. References: Wilson, M.F./ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models. Journal of Climatology, vol.5, pp.119-143. Source : Digitized from available sources: FAO/UNESCO Soil Map of the World Publication Date : 1985 Projection : lat/lon Type : Raster Format : IDRISI" proprietary
+NBId0248_101 Africa Wilson & Henderson-Sellers Secondary Vegetation Classes and Class Reliability, 1985 ALL STAC Catalog 1970-01-01 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232848868-CEOS_EXTRA.umm_json "The Wilson and Henderson-Sellers Secondary Vegetation Classes and Class Reliability data sets are part of the ""Wilson Henderson-Sellers land cover and soils for global circulation modeling project "" and were developed by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the US National Geophysical Data Center (NGDC). The data sets are part of the World Data Bank II. This data Bank is provided in a Database on diskette called """"The Global Change Data Base"""". The Data Bank II is part of larger project called ""Global Ecosystems Database Project"". This is a cooperative effort between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the US Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. A nominal 10 arc-minute scale was chosen to provide compatibility with other scales and because this corresponds closely with the resolution of global AVHRR coverage. All data are provided in geographic (longitude/latitude) projection. The data sets are accompanied by an ASCII documentation file which contains information necessary for the use of the dataset in GIS or other software. References: Wilson, M.F./ and A. Henderson-Sellers. A global archive of land cover and soils data for use in general circulation climate models. Journal of Climatology, vol.5, pp.119-143. Source : Digitized from available sources: FAO/UNESCO Soil Map of the World Publication Date : 1985 Projection : lat/lon Type : Raster Format : IDRISI" proprietary
NBId0270_101 Desertification Atlas (Africa) Maps 1-17 CEOS_EXTRA STAC Catalog 1990-01-01 1992-12-30 -20, -35, 55, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2232847403-CEOS_EXTRA.umm_json INTRODUCTION Desertification/Land Degradation - The Background More than 6.1 billion hectares, over one third of the Earth's land area, is dryland. Nearly one billion hectares of this area are naturally hvperarid deserts, with very low biological productivity. The remaining 5.1 billion hectares are made up of arid, semiarid and dry subhumid areas, part of which have become desert since the dawn of civilization while other parts of these areas are still being degraded by human action today. These lands are the habitat and the source of livelihood for one quarter of the world's population. They are areas characterized by the persistent natural menace of recurrent drought, a natural hazard accentuated by imbalanced management of natural resources. Particularly acute drought years in the Sahelian region of Africa from 1968 to 1973, and their tragic effects on the peoples of the region, drew worldwide attention to the problems of human survival and development in drylands, particularly on desert margins. These problems have been addressed by the United Nations (UN) General Assembly, in conformity with the Charter of the United Nations. The UN General Assembly's Resolution 3202 (vi) of 1 May 1974 recommended that the international community undertake concrete and speedy measures to arrest desertification and assist the economic development of affected areas. The Economic and Social Council's Resolution 1878 (LVII) of 16 July 1974 requested all the concerned organizations of the UN system to pursue a broad attack on the drought problem. Decisions of the Governing Councils of the UN Development Programme (UNDP) and the UN Environment Programme (UNEP) emphasized the need for undertaking measures to check the spread of desert conditions. The General Assembly then decided, by Resolution 3337 (xxix) of 17 December 1974, to initiate concerted international action to combat desertification and, in order to provide an impetus to this action, to convene a UN Conference on Desertification (UNCOD), between 29 August and 9 September 1977 in Nairobi, Kenya, which would produce an effective, comprehensive and coordinated programme for solving the problem. For the purposes of this atlas, desertification/land degradation is defined as: Land degradation in arid, semiarid and dry subhumid areas resulting mainly from adverse human impact. proprietary
NBId0288_101 Desertification Atlas (Global) Maps 1-20 CEOS_EXTRA STAC Catalog 1990-01-01 1992-12-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232848998-CEOS_EXTRA.umm_json INTRODUCTION Desertification/Land Degradation - The Background More than 6.1 billion hectares, over one third of the Earth's land area, is dryland. Nearly one billion hectares of this area are naturally hvperarid deserts, with very low biological productivity. The remaining 5.1 billion hectares are made up of arid, semiarid and dry subhumid areas, part of which have become desert since the dawn of civilization while other parts of these areas are still being degraded by human action today. These lands are the habitat and the source of livelihood for one quarter of the world's population. They are areas characterized by the persistent natural menace of recurrent drought, a natural hazard accentuated by imbalanced management of natural resources. Particularly acute drought years in the Sahelian region of Africa from 1968 to 1973, and their tragic effects on the peoples of the region, drew worldwide attention to the problems of human survival and development in drylands, particularly on desert margins. These problems have been addressed by the United Nations (UN) General Assembly, in conformity with the Charter of the United Nations. The UN General Assembly's Resolution 3202 (vi) of 1 May 1974 recommended that the international community undertake concrete and speedy measures to arrest desertification and assist the economic development of affected areas. The Economic and Social Council's Resolution 1878 (LVII) of 16 July 1974 requested all the concerned organizations of the UN system to pursue a broad attack on the drought problem. Decisions of the Governing Councils of the UN Development Programme (UNDP) and the UN Environment Programme (UNEP) emphasized the need for undertaking measures to check the spread of desert conditions. The General Assembly then decided, by Resolution 3337 (xxix) of 17 December 1974, to initiate concerted international action to combat desertification and, in order to provide an impetus to this action, to convene a UN Conference on Desertification (UNCOD), between 29 August and 9 September 1977 in Nairobi, Kenya, which would produce an effective, comprehensive and coordinated programme for solving the problem. For the purposes of this atlas, desertification/land degradation is defined as: Land degradation in arid, semiarid and dry subhumid areas resulting mainly from adverse human impact. proprietary
NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1 2005 NBPalmer sulfur data. Surface transect (NZ to Ross Sea) and Ross Sea depth profiles and rates SCIOPS STAC Catalog 2004-12-17 2005-11-30 -179.488, -77.642, -166.989, -49.014 https://cmr.earthdata.nasa.gov/search/concepts/C1214590838-SCIOPS.umm_json This data set contains concentration and rate data for the following sulfur compounds: dimethylsulfide (DMS), dimethylsulfoxide (DMSO) and dimethylsulfoniopropionate (DMSP). Data were obtained in a transect from New Zealand to the Ross Sea, Antarctica, and in the Ross Sea Polynya. Data were obtained during two research cruises to the Ross Sea aboard the RIV Nathaniel B. Palmer in December 2004 to January 2005 (NBP04-09) and in October to November 2005 (NBP05-08). A data set is also provide for biological data (bacterial biomass, bacterial productivity), CTD data and GUV irradiance data obtained during our Nathanial B. Palmer (NBP) cruises to the Ross Sea in 2004 and 2005 (NBP04-09 and NBP05-08). proprietary
NBPalmer_Transect_and_Ross_Sea_Sulfur_Data_1 2005 NBPalmer sulfur data. Surface transect (NZ to Ross Sea) and Ross Sea depth profiles and rates ALL STAC Catalog 2004-12-17 2005-11-30 -179.488, -77.642, -166.989, -49.014 https://cmr.earthdata.nasa.gov/search/concepts/C1214590838-SCIOPS.umm_json This data set contains concentration and rate data for the following sulfur compounds: dimethylsulfide (DMS), dimethylsulfoxide (DMSO) and dimethylsulfoniopropionate (DMSP). Data were obtained in a transect from New Zealand to the Ross Sea, Antarctica, and in the Ross Sea Polynya. Data were obtained during two research cruises to the Ross Sea aboard the RIV Nathaniel B. Palmer in December 2004 to January 2005 (NBP04-09) and in October to November 2005 (NBP05-08). A data set is also provide for biological data (bacterial biomass, bacterial productivity), CTD data and GUV irradiance data obtained during our Nathanial B. Palmer (NBP) cruises to the Ross Sea in 2004 and 2005 (NBP04-09 and NBP05-08). proprietary
NCALDAS_NOAH0125_D_2.0 NCA-LDAS Noah-3.3 Land Surface Model L4 Daily 0.125 x 0.125 degree V2.0 (NCALDAS_NOAH0125_D) at GES DISC GES_DISC STAC Catalog 1979-01-02 2016-12-31 -125, 25, -67, 53 https://cmr.earthdata.nasa.gov/search/concepts/C1454297282-GES_DISC.umm_json The National Climate Assessment - Land Data Assimilation System, or NCA-LDAS, is a terrestrial water reanalysis in support of the United States Global Change Research Program's NCA activities. NCA-LDAS features high resolution, gridded, daily time series data products of terrestrial water and energy balance stores, states, and fluxes over the continental U.S., derived from land surface hydrologic modeling with multivariate assimilation of satellite Environmental Data Records (EDRs). The overall goal is to provide the highest quality terrestrial hydrology products that enable improved scientific understanding, adaptation, and management of water and related energy resources during a changing climate. An overview of NCA-LDAS and its capability for developing climate change indicators are provided in Jasinski et al. (2019). Details on the data assimilation used in NCA-LDAS are described in Kumar et al. (2019). Sample mean annual trends are provided in the NCA-LDAS V2.0 README document. This NCA-LDAS version 2.0 data product was simulated for the continental United States for the satellite era from January 1979 to December 2016. The core of NCA-LDAS is the multivariate assimilation of past and current satellite based data records within the Noah Version 3.3 land-surface model (LSM) at 1/8th degree resolution using NASA's Land Information System (LIS; Kumar et al. 2006) software framework during the Earth observing satellite era. The temporal resolution is daily. NCA-LDAS V001 data will no longer be available and have been superseded by V2.0. NCA-LDAS includes 42 variables including land-surface fluxes (e.g. precipitation, radiation and latent and sensible heat, etc.), stores (e.g. soil moisture and snow), states (e.g., surface temperature), and routing variables (e.g., runoff, streamflow, flooded area, etc.), driven by the atmospheric forcing data from North American Land Data Assimilation System Phase 2 (NLDAS-2; Xia et al., 2012). NCA-LDAS builds upon NLDAS through the addition of multivariate assimilation of earth observations such as soil moisture (Kumar et al, 2014), snow (Liu et al, 2015; Kumar et al, 2015a) and irrigation (Ozdagon et al, 2010; Kumar et al, 2015b). The EDRs that have been assimilated into the NCA-LDAS include soil moisture and snow depth from principally microwave sensors including SMMR, SSM/I, AMSR-E, ASCAT, AMSR-2, SMOS, and SMAP, irrigation intensity estimates from MODIS, and snow covered area from MODIS and from the multisensor IMS snow product. proprietary
NCALDAS_NOAH0125_Trends_2.0 NCA-LDAS Noah-3.3 Land Surface Model L4 Trends 0.125 x 0.125 degree V2.0 (NCALDAS_NOAH0125_Trends) at GES DISC GES_DISC STAC Catalog 1979-10-01 2015-09-30 -125, 25, -67, 53 https://cmr.earthdata.nasa.gov/search/concepts/C1646132439-GES_DISC.umm_json The National Climate Assessment - Land Data Assimilation System, or NCA-LDAS, is a terrestrial water reanalysis in support of the United States Global Change Research Program's NCA activities. NCA-LDAS features high resolution, gridded, daily time series data products of terrestrial water and energy balance stores, states, and fluxes over the continental U.S., derived from land surface hydrologic modeling with multivariate assimilation of satellite Environmental Data Records (EDRs). The overall goal is to provide the highest quality terrestrial hydrology products that enable improved scientific understanding, adaptation, and management of water and related energy resources during a changing climate. This dataset consists of a suite of historical trends in terrestrial hydrology over the conterminous United States estimated for the water years of 1980-2015 using the NCA-LDAS daily reanalysis. NCA-LDAS provides gridded daily outputs from the uncoupled Noah version 3.3 land surface model (LSM) at 1/8th degree resolution forced with NLDAS-2 meteorology (Xia et al., 2012), rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth, and irrigation products (Jasinski et al., 2019; Kumar et al., 2019). Trends in annual hydrologic indicators are reported using the nonparametric Mann-Kendall test at p < 0.1 significance. An additional precipitation trend field (annual total), with no significance test applied, is included for comparison purposes. Collectively, these fields represent the bulk of the results presented in Jasinski et al. (2019). proprietary
-NCAR_DS474.0 AARI Russian North Polar Drifting Station Data, from NSIDC SCIOPS STAC Catalog 1937-05-01 1991-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214056415-SCIOPS.umm_json This dataset consists of 31 Russian north polar drifting stations which took observations of surface variables for the periods 1937-1938 and 1950-1991. We received the latest version of this data from the Arctic and Antarctic Research Institute (AARI) via the National Snow and Ice Data Center (NSIDC). proprietary
NCAR_DS474.0 AARI Russian North Polar Drifting Station Data, from NSIDC ALL STAC Catalog 1937-05-01 1991-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214056415-SCIOPS.umm_json This dataset consists of 31 Russian north polar drifting stations which took observations of surface variables for the periods 1937-1938 and 1950-1991. We received the latest version of this data from the Arctic and Antarctic Research Institute (AARI) via the National Snow and Ice Data Center (NSIDC). proprietary
+NCAR_DS474.0 AARI Russian North Polar Drifting Station Data, from NSIDC SCIOPS STAC Catalog 1937-05-01 1991-03-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214056415-SCIOPS.umm_json This dataset consists of 31 Russian north polar drifting stations which took observations of surface variables for the periods 1937-1938 and 1950-1991. We received the latest version of this data from the Arctic and Antarctic Research Institute (AARI) via the National Snow and Ice Data Center (NSIDC). proprietary
NCAR_DS510.5 A Quality-Controlled Dataset for Long-Term U.S. Snowfall Trends ALL STAC Catalog 1890-01-01 2007-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214110939-SCIOPS.umm_json NCDC's U.S. Cooperative Summary of Data (DSI3200) dataset was screened for stations with long continuous observations for use in assessing 20th-century U.S. snowfall trends. The result is a subset of 424 stations with quality-controlled snowfall, precipitation, and temperature data for snow-season months (October through May). Most of the stations have observations that begin prior to the winter of 1930-31, making for station periods of longer than 77 winters. Several stations have data as far back as the 1890s. proprietary
NCAR_DS510.5 A Quality-Controlled Dataset for Long-Term U.S. Snowfall Trends SCIOPS STAC Catalog 1890-01-01 2007-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214110939-SCIOPS.umm_json NCDC's U.S. Cooperative Summary of Data (DSI3200) dataset was screened for stations with long continuous observations for use in assessing 20th-century U.S. snowfall trends. The result is a subset of 424 stations with quality-controlled snowfall, precipitation, and temperature data for snow-season months (October through May). Most of the stations have observations that begin prior to the winter of 1930-31, making for station periods of longer than 77 winters. Several stations have data as far back as the 1890s. proprietary
NCAR_DS744.7 ADEOS Scatterometer Winds, Level 2B ALL STAC Catalog 2002-06-04 2002-06-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214055329-SCIOPS.umm_json Sea surface wind estimated by scatterometer instruments on the ADEOS satellite. JPL PO.DAAC [http://podaac.jpl.nasa.gov/] has initiated reprocessing of all ADEOS and QuikSCAT data with superior algorithms for retrievals in high wind speed and light rain areas. This reprocessing could affect this dataset. proprietary
NCAR_DS744.7 ADEOS Scatterometer Winds, Level 2B SCIOPS STAC Catalog 2002-06-04 2002-06-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214055329-SCIOPS.umm_json Sea surface wind estimated by scatterometer instruments on the ADEOS satellite. JPL PO.DAAC [http://podaac.jpl.nasa.gov/] has initiated reprocessing of all ADEOS and QuikSCAT data with superior algorithms for retrievals in high wind speed and light rain areas. This reprocessing could affect this dataset. proprietary
-NCAR_DS871.0 ADAPTE: Minimum and Maximum Temperature and Relative Humidity for Latin American Cities Data ALL STAC Catalog 2000-01-01 2006-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214110979-SCIOPS.umm_json Temperature data classified as maximum, mean, and minimum temperature and relative humidity measures from the meteorological station located at the regional airport in Bogota and Buenos Aries, called the National Service of Hydrology and Meteorology. Mexico data was collected from the National Polytechnic Institute of Mexico and National Meteorological System. In Santiago, Chile weather data was provided by the air pollution monitoring network with stations across the city, the REDCAM2 (Red de Monitoreo Automatica de la Calidad del Aire Metropolitana) Automatic Monitoring Network of Metropolitan Air Quality. The data from these stations were averaged to obtain temperature values for the Gran Santiago region. Daily temperature and relative humidity readings were made by automatic-recording instruments. proprietary
NCAR_DS871.0 ADAPTE: Minimum and Maximum Temperature and Relative Humidity for Latin American Cities Data SCIOPS STAC Catalog 2000-01-01 2006-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214110979-SCIOPS.umm_json Temperature data classified as maximum, mean, and minimum temperature and relative humidity measures from the meteorological station located at the regional airport in Bogota and Buenos Aries, called the National Service of Hydrology and Meteorology. Mexico data was collected from the National Polytechnic Institute of Mexico and National Meteorological System. In Santiago, Chile weather data was provided by the air pollution monitoring network with stations across the city, the REDCAM2 (Red de Monitoreo Automatica de la Calidad del Aire Metropolitana) Automatic Monitoring Network of Metropolitan Air Quality. The data from these stations were averaged to obtain temperature values for the Gran Santiago region. Daily temperature and relative humidity readings were made by automatic-recording instruments. proprietary
-NCEI DSI 1167_01_Not Applicable Active Marine Station Metadata NOAA_NCEI STAC Catalog 2012-05-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093639-NOAA_NCEI.umm_json The Active Marine Station Metadata is a daily metadata report for active marine bouy and C-MAN (Coastal Marine Automated Network) platforms from the National Data Buoy Center (NDBC). Metadata includes the station id, latitude/longitude (resolution to thousandths of a degree), the station name, the station owner, the program the station is associated with (e.g., TAO, NDBC, tsunami, NOS, etc.), station type (e.g., buoy, fixed, oil rig, etc.), notification if the station observes meteorology, currents, and water quality (signified by 'y' for yes and 'n' for no). If there is a 'y' associated with one of these tags, then the station has reported data in that category within the last 8 hours (or 24 hours for DART stations--Deep-Ocean Assessment Reporting of Tsunamis). If there is an 'n', data has not been received within those times. Stations are removed from the list when they are dismantled. The metadata information is written to a daily XML-formatted file. proprietary
+NCAR_DS871.0 ADAPTE: Minimum and Maximum Temperature and Relative Humidity for Latin American Cities Data ALL STAC Catalog 2000-01-01 2006-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214110979-SCIOPS.umm_json Temperature data classified as maximum, mean, and minimum temperature and relative humidity measures from the meteorological station located at the regional airport in Bogota and Buenos Aries, called the National Service of Hydrology and Meteorology. Mexico data was collected from the National Polytechnic Institute of Mexico and National Meteorological System. In Santiago, Chile weather data was provided by the air pollution monitoring network with stations across the city, the REDCAM2 (Red de Monitoreo Automatica de la Calidad del Aire Metropolitana) Automatic Monitoring Network of Metropolitan Air Quality. The data from these stations were averaged to obtain temperature values for the Gran Santiago region. Daily temperature and relative humidity readings were made by automatic-recording instruments. proprietary
NCEI DSI 1167_01_Not Applicable Active Marine Station Metadata ALL STAC Catalog 2012-05-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093639-NOAA_NCEI.umm_json The Active Marine Station Metadata is a daily metadata report for active marine bouy and C-MAN (Coastal Marine Automated Network) platforms from the National Data Buoy Center (NDBC). Metadata includes the station id, latitude/longitude (resolution to thousandths of a degree), the station name, the station owner, the program the station is associated with (e.g., TAO, NDBC, tsunami, NOS, etc.), station type (e.g., buoy, fixed, oil rig, etc.), notification if the station observes meteorology, currents, and water quality (signified by 'y' for yes and 'n' for no). If there is a 'y' associated with one of these tags, then the station has reported data in that category within the last 8 hours (or 24 hours for DART stations--Deep-Ocean Assessment Reporting of Tsunamis). If there is an 'n', data has not been received within those times. Stations are removed from the list when they are dismantled. The metadata information is written to a daily XML-formatted file. proprietary
+NCEI DSI 1167_01_Not Applicable Active Marine Station Metadata NOAA_NCEI STAC Catalog 2012-05-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093639-NOAA_NCEI.umm_json The Active Marine Station Metadata is a daily metadata report for active marine bouy and C-MAN (Coastal Marine Automated Network) platforms from the National Data Buoy Center (NDBC). Metadata includes the station id, latitude/longitude (resolution to thousandths of a degree), the station name, the station owner, the program the station is associated with (e.g., TAO, NDBC, tsunami, NOS, etc.), station type (e.g., buoy, fixed, oil rig, etc.), notification if the station observes meteorology, currents, and water quality (signified by 'y' for yes and 'n' for no). If there is a 'y' associated with one of these tags, then the station has reported data in that category within the last 8 hours (or 24 hours for DART stations--Deep-Ocean Assessment Reporting of Tsunamis). If there is an 'n', data has not been received within those times. Stations are removed from the list when they are dismantled. The metadata information is written to a daily XML-formatted file. proprietary
NCEI DSI 2001_01_Not Applicable Climate Forecast System Version 2 (CFSv2) Operational Forecasts NOAA_NCEI STAC Catalog 2011-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093673-NOAA_NCEI.umm_json The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the interaction between the Earth's oceans, land and atmosphere. The four-times-daily, 9-month control runs, consist of all 6-hourly forecasts, and the monthly means and variable time-series (all variables). The CFSv2 outputs include: 2-D Energetics (EGY); 2-D Surface and Radiative Fluxes (FLX); 3-D Pressure Level Data (PGB); 3-D Isentropic Level Data (IPV); 3-D Ocean Data (OCN); Low-resolution output (GRBLOW); Dumps (DMP); and High- and Low-resolution Initial Conditions (HIC and LIC). The monthly CDAS variable timeseries includes all variables. The CFSv2 period of record begins on April 1, 2011 and continues onward. CFS output is in GRIB-2 file format. proprietary
NCEI DSI 2002_01_Not Applicable Climate Forecast System Version 2 (CFSv2) Operational Analysis NOAA_NCEI STAC Catalog 2011-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093682-NOAA_NCEI.umm_json The Climate Forecast System Version 2 (CFSv2) produced by the NOAA National Centers for Environmental Prediction (NCEP) is a fully coupled model representing the interaction between the Earth's oceans, land and atmosphere. The CFSv2 Operational Analysis or Climate Data Assimilation System (CDAS), consist of all 6-Hourly CDAS, and the monthly CDAS monthly means and variable time-series (all variables). The CFSv2 outputs include: 2-D Energetics (EGY); 2-D Surface and Radiative Fluxes (FLX); 3-D Pressure Level Data (PGB); 3-D Isentropic Level Data (IPV); 3-D Ocean Data (OCN); Low-resolution output (GRBLOW); Dumps (DMP); and High- and Low-resolution Initial Conditions (HIC and LIC). The monthly CDAS variable timeseries includes all variables. The CFSv2 period of record begins on April 1, 2011 and continues onward. CFS output is in GRIB-2 file format. proprietary
NCEI DSI 3298_01 (original)_Not Applicable Climate Record Books Keyed Data NOAA_NCEI STAC Catalog 1850-01-01 1990-12-31 134, -15, -64, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2102893128-NOAA_NCEI.umm_json Climate Record Books (CRB) Data were keyed as part of the Climate Database Modernization Program (CDMP). These original keyed files as well as documentation relating to the format and keying process is available within the 3298_01 archive. The Northeast Regional Climate Center (NRCC) reformatted and performed quality control checks on the data, ensuring that the data could be used in high quality datasets and applications. Data and documentation for this data is available within the 3298_02 archive. The dataset consists of 171 stations that are located throughout the US. Variables include: maximum temperature, minimum temperature, average temperature, precipitation, and snowfall. Temporal resolution is daily, but observation times are not available for this dataset. However, data coverage varies by station. The records for individual stations range in length from 9 months to 121 years. Parts of the records may be duplicated in other, higher-priority ACIS data sources. proprietary
@@ -12112,8 +12113,8 @@ NCEI DSI 9694_01_Not Applicable Cedar Hill Tower Data NOAA_NCEI STAC Catalog 196
NCEI DSI 9715_01_Not Applicable Climatological Data National Summary (CDNS) Monthly Surface NOAA_NCEI STAC Catalog 1961-01-01 1964-12-31 134, -15, -64, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2102893102-NOAA_NCEI.umm_json These data are keyed (digitized) data from the images of the Climatological Data National Summary containing monthly summaries for cities in the United States (and territories). Variables include temperature, precipitation, station and sea level pressure, average dew point, average relative humidity, weather occurrence, wind, cloudiness/sunshine and degree days. Period of record is 1961-1964. proprietary
NCEI DSI 9795_01_Not Applicable Climate Diagnostics Data Base NOAA_NCEI STAC Catalog 1978-10-01 1983-09-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102892556-NOAA_NCEI.umm_json The Climatic Diagnostics Database, DSI-9795, is a historical data set created by the Climate Analysis Center using global climatic data from the period October 1, 1978 through September 30, 1983. The Climate Diagnostics Database contains monthly averages of selected fields from the National Meteorological Center's (NMC; now National Centers for Environmental Prediction, NCEP) Global Data Assimilation System (GDAS). The major parameters are monthly averages of the following elements for constant pressure levels of 1000-, 850-, 700-, 500-, 300-, 250-, 200-, 100-, and 50-millibars: 1. U (West/East) component of wind (meters/second), 2. V (South/North) component of wind (meters/second), 3. Temperature (Deg. K), 4. Geopotential height (geopotential meters), 5. Vertical velocity (millibars/second), 6. Specific humidity (grams/kilogram) 7. Vorticity (seconds-1), 8. Pressure (millibars), 9. Sums squared of U (West/East) component of wind (meters/second), 10. Sums squared of V (South/North) component of wind (meters/second), 11. Sums squared of temperature (K), 12. Sums squared of geopotential height (geopotential meters). 13. Sums squared of vertical velocity (millibars/second), 14. Sums squared of specific humidity (grams/kilogram), 15. Sums squared of vertical velocity (seconds-1), 16. Sum of cross product UV wind components (m2s-2), East-West transport of poleward momentum, 17. Sum of cross product U and temperature (ms-1K), East-West transport of heat, 18. Sum of cross product U and geopotential height (ms-1gpm), East-West transport of mass, 19. Sum of cross product U and vertical velocity (mmbs-2), East-West transport of vertical momentum, 20. Sum of cross product U and specific humidity (mgs-1Kg-1), East-West transport of moisture, 21. Sum of cross product U and vorticity (ms-2), East-West transport of relative vorticity, 22. Sum of cross product V and temperature, North-South transport of heat, 23. Sum of cross product V and geopotential height (ms-1gpm), North-South transport of mass, 24. Sum of cross product V and vertical velocity (mmbs-2), North-South transport of vertical momentum, 25. Sum of cross products V and specific humidity (mgs-1Kg-1), North-South transport of moisture, 26. Sum of cross products V and vorticity (ms-2), North-South transport of relative vorticity, 27. Stretching of vortex tubes (s-2). proprietary
NCEI DSI 9796_01_Not Applicable Atmospheric Handbook Data Tables NOAA_NCEI STAC Catalog 1896-01-01 1982-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102892524-NOAA_NCEI.umm_json Atmospheric Handbook Data Tables consists of one combined file containing 226 data files. The files contains information, programs, and data largely taken from results published in scientific journals. In general, sections of files are grouped according to the atmospheric area. Atmospheric data tables in this data set are described in World Data Center A for Meteorology and World Data Center A for Solar Terrestrial Physics Report UAG-89. Data areas cover attenuation coefficients for the atmosphere and H2O; 1962 standard atmospheres; cloud drop size distributions for water and ice spheres; solar spectral irradiance (NIMBUS and SMM satellite solar irradiance data); sky spectral radiance; Rayleigh coefficients for air; refractive indices for air, ice, liquid H2O, and various atmospheric aerosols; and relative reflectance for ice and H2O. proprietary
-NCEI DSI 9799_Not Applicable African Historical Precipitation Data ALL STAC Catalog 1850-01-01 1984-12-31 -25, -31, 52, 28 https://cmr.earthdata.nasa.gov/search/concepts/C2102892476-NOAA_NCEI.umm_json African Historical Precipitation Data is digital data set DSI-9799, archived at the National Climatic Data Center (NCDC). This data is a collection from various sources of data from Africa, including publications, hand-written data secured from visiting scientists, and visits to African nations. The activity was supported by funds provided by the Agency for International Development (AID). The geographic coverage is selected stations from Africa in the following regions: Subequatorial, Tropical West, Sahel, Horn. Not included are most of northern and southern Africa. The time period covered is variable; earliest is 1850 and latest is 1984. The major parameter is sequential monthly total precipitation (mm). proprietary
NCEI DSI 9799_Not Applicable African Historical Precipitation Data NOAA_NCEI STAC Catalog 1850-01-01 1984-12-31 -25, -31, 52, 28 https://cmr.earthdata.nasa.gov/search/concepts/C2102892476-NOAA_NCEI.umm_json African Historical Precipitation Data is digital data set DSI-9799, archived at the National Climatic Data Center (NCDC). This data is a collection from various sources of data from Africa, including publications, hand-written data secured from visiting scientists, and visits to African nations. The activity was supported by funds provided by the Agency for International Development (AID). The geographic coverage is selected stations from Africa in the following regions: Subequatorial, Tropical West, Sahel, Horn. Not included are most of northern and southern Africa. The time period covered is variable; earliest is 1850 and latest is 1984. The major parameter is sequential monthly total precipitation (mm). proprietary
+NCEI DSI 9799_Not Applicable African Historical Precipitation Data ALL STAC Catalog 1850-01-01 1984-12-31 -25, -31, 52, 28 https://cmr.earthdata.nasa.gov/search/concepts/C2102892476-NOAA_NCEI.umm_json African Historical Precipitation Data is digital data set DSI-9799, archived at the National Climatic Data Center (NCDC). This data is a collection from various sources of data from Africa, including publications, hand-written data secured from visiting scientists, and visits to African nations. The activity was supported by funds provided by the Agency for International Development (AID). The geographic coverage is selected stations from Africa in the following regions: Subequatorial, Tropical West, Sahel, Horn. Not included are most of northern and southern Africa. The time period covered is variable; earliest is 1850 and latest is 1984. The major parameter is sequential monthly total precipitation (mm). proprietary
NCEI DSI 9873_01_Not Applicable Baseline Surface Radiation Network (BSRN) Solar Radiation Data (Disposition Review) NOAA_NCEI STAC Catalog 1993-01-01 2008-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2102893059-NOAA_NCEI.umm_json "The dataset DSI 9873 is a subset of the Baseline Surface Radiation Network data monitored by NOAA ESRL Global Radiation (G-Rad) group in Boulder, Colorado. The ""STAR"" network is a name that Ells (Ellsworth Dutton, deceased) came up with for the NOAA Global Monitoring Division (formerly CMDL) radiation measurements at GMD's baseline sites at Barrow, Mauna Loa, American Samoa, Boulder Atmospheric Observatory (BAO tower), South Pole, and other sites at Kwajalein, Bermuda, and Trinidad Head (CA). Before STAR, they were just referred to as ""Baseline sites"". As the NCEI archive only contains a subset (The ""STAR"" stations continue to operate, so their data set does extend beyond 2008), users are encouraged to contact the ESRL Global Monitoring Division for the most up-to-date information. Per MACI team: The dataset DSI 9873 is a subset of the Baseline Surface Radiation Network data monitored by NOAA ESRL Global Radiation (G-Rad) group in Boulder, Colorado. Dave Longenecker is the data manager in Boulder and he provides the data to the global network (see online resource URL). In a phone conversation with Mara Sprain, 22 Aug 2016, Dave related that he didn't know we had this small subset. He had no direction to provide us with additional data. This dataset needs a submission agreement (if it's to be maintained) or it should be a candidate for removal. It's duplicated both in Boulder (FTP) and Germany (FTP and PANGAEA). From John Augustine email, 19 Aug 2016: The ""STAR"" network is a name that Ells (Ellsworth Dutton, deceased) came up with for the NOAA Global Monitoring Division (formerly CMDL) radiation measurements at GMD's baseline sites at Barrow, Mauna Loa, American Samoa, Boulder Atmospheric Observatory (BAO tower), South Pole, and other sites at Kwajalein, Bermuda, and Trinidad Head (CA). Before STAR, they were just referred to as ""Baseline sites"". When NCDC found out about these measurements (circa 2008), they requested that their data be submitted there. I wrote a program for Ells to do that and several years of data were submitted. I am not sure how up-to-date those submissions are because I don't do them. If you want metadata on the Baseline sites, you will have to contact Dave Longenecker (david.u.longenecker@noaa.gov). He has been the data manager for them for many years. Bermuda and Kwajalein have been supported by NASA, but they cut those funds this year. I am not sure whether they will continue. Bermuda has not operated for about three years because of communication problems and other issues. It will be brought back up soon. The ""STAR"" stations continue to operate, so their data set does extend beyond 2008. Data are also (?) held in Colorado archive." proprietary
NCEI DSI 9926_01_Not Applicable Bulletin W Monthly Summary Data NOAA_NCEI STAC Catalog 1891-01-01 1960-01-01 134, -15, -64, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2102893120-NOAA_NCEI.umm_json Monthly station summaries of precipitation (including snowfall), maximum temperature and minimum temperature are provided. Also included are number of days with temperature and precipitation meeting defined threshold values. Also included are extreme highest and lowest temperature, and years of record. Period of record is generally 1891-1960, with coverage in the United States, Puerto Rico, the U.S. Virgin Islands and the Pacific islands. proprietary
NCEI DSI 9949_01_Not Applicable Automation of Field Operations and Services (AFOS) National Weather Service (NWS) Service Records and Retention System (SRRS) Data NOAA_NCEI STAC Catalog 1983-05-31 2001-08-05 134, -15, -64, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2107093299-NOAA_NCEI.umm_json Service Records and Retention System (SRRS) is historical digital data set DSI-9949, a collection of products created by the U.S. National Weather Service (NWS) and archived at the National Centers for Environmental Information (NCEI) [formerly National Climatic Data Center (NCDC)]. SRRS was a network of computers and associated hardware whose purpose was to transmit and store a large number of NWS products and make them available as needed. Basic meteorological and hydrological data, analyses, forecasts, and warnings are distributed among NWS offices over the AFOS (Automation of Field Operations and Services) communications system since 1978. These include PIREP (aircraft reports from pilots), AIRMET (aeronautical meteorological bulletins), SIGMET (significant meteorological information), surface and upper air plotted unanalyzed maps, air stagnation, precipitable water, Forecasts such as wind and temperature aloft, thickness and analysis, fire weather, area, local, zone, state, agricultural advisory, and terminal; and Warnings such as marine, severe weather, hurricane and tornado. The AFOS system was developed to increase the productivity and effectiveness of NWS personnel and to increase the timeliness and quality of their warning and forecasting services. This format version of the SRRS data was archived at NCEI from 1983 to 2001 (when a new format was created). The NCEI can service requests for products from the SRRS; two types of products are available to the user: 1) graphic displays of meteorological analyses and forecast charts (limited), and 2) alphanumeric displays of narrative summaries and meteorological/hydrological data. The following is a partial list of historical SRRS products available through the NCDC: rawinsonde data above 100 MB; AIREPS buoy reports; coastal flood warning; Coast Guard surface report; climatological report (daily and misc, incl monthly reports); weather advisory Coastal Waters Forecast Center (CWSU); weather statement; 3- to 5-day extended forecast; average 6- to 10-day weather outlook (local and national); aviation area forecast winds aloft forecast; flash flood statements, watches and warnings; flood statement; flood warning forecast; medium range guidance; FOUS relative humidity/temperature guidance; FOUS prog max/min temp/POP guidance; FOUS wind/cloud guidance; Great Lakes forecast; hurricane local statement; high seas forecast; international aviation observations; local forecast; local storm report; rawinsonde observation - mandatory levels;, METAR formatted surface weather observation; marine weather statement; short term rorecast; non-precipitation warnings/watches/advisories; nearshore marine forecast (Great Lakes only), offshore aviation area forecast; offshore forecast; other marine products, other surface weather observations, pilot report plain language, ship report, state pilot report, collective recreational report; narrative radar summary radar observation; hydrology-meteorology data report; river summary; river forecast; miscellaneous river product; river recreation statement; ; regional weather summary; surface aviation observation; preliminary notice of watch and canc msg SVR; local storm watch and warning; cancelation msg SELS watch; point information message; state forecast discussion ; state forecast rawinsonde observation - significant levels; surface ship report at intermediate synoptic time; surface ship report at non-synoptic time; surface ship report at synoptic time; special weather statement international; SIGMET severe local storm watch and area outline; special marine warning; intermediate surface synoptic observation; main surface synoptic observation; severe thunderstorm warning; severe weather statement; severe storm outlook; narrative state weather summary; terminal forecast; tropical cyclone discussion; marine/aviation tropical cyclone advisory; public tropical cyclone advisory; tornado warning; transcribed weather broadcast; tropical weather discussion; tropical weather outlook and summary; AIRMET SIGMET zone forecast; terminal forecast (prior to 7/1/96); winter weather warnings, watches, advisories; marine advisory/warning; special marine warning; miscellaneous product convective SIGMET ; local ice forecast; area forecast discussion; public information statement. SRRS (DSI-9949) by the Gateway SRRS (DSI-9957; C00583). NWS products after 2001 can be obtained from those systems, from NCEI. proprietary
@@ -12175,36 +12176,36 @@ ND30_REE_Water_Chemistry_1131_1 LBA-ECO ND-30 Water Chemistry, Rainfall Exclusio
NDVI_Forest_Structure_1797_1 NDVI, Species Cover, and LAI, Burned and Unburned sites, Interior Alaska, 2017-2018 ORNL_CLOUD STAC Catalog 2017-08-29 2018-08-20 -149.96, 63.82, -144.96, 65.96 https://cmr.earthdata.nasa.gov/search/concepts/C2162189202-ORNL_CLOUD.umm_json This dataset provides leaf area index (LAI), tree species and canopy cover, normalized difference vegetation index (NDVI), and NDVI trends for boreal forests in interior Alaska, U.S. These data were collected to investigate NDVI trends with forest structure and composition as influenced by disturbance and succession. The data are from 102 sites surveyed in 2017 and 2018 and include locations with and without a fire since 1940. A time series of NDVI was developed from Landsat (1999-2018) to measure NDVI trends. The field data cover the period 2017-08-29 to 2018-08-20. The surveyed forest stands spanned a distance of over 425 km across interior Alaska. The sites were selected before visiting the field to include locations with and without a fire since 1940. Recently burned sites were selected to span a range of years since fire, while sites without a recent fire were selected to include a range of Landsat NDVI trends. For each year, the median NDVI during the growing season was calculated. Then, a simple linear regression trend was calculated for years 1999-2018. proprietary
NEMSN5L2_001 NEMS/Nimbus-5 Level 2 Output Data V001 (NEMSN5L2) at GES DISC GES_DISC STAC Catalog 1972-12-17 1973-10-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1990675367-GES_DISC.umm_json NEMSN5L2 is the Nimbus-5 or Nimbus-E Microwave Spectrometer (NEMS) Level-2 Output Data product and contains surface reflectivity, water vapor, liquid water, layer thickness, temperature at standard pressure levels, surface brightness temperature, and surface type information, as well as the input antenna and brightness temperatures at 5 microwave channels (H2O channels 22.235 and 31.4 GHz, and O2 channels 53.65, 54.9 and 58.8 GHz). The NEMS instrument views the nadir with a footprint is a 180-km diameter circle on the earth's surface. Data are available for the time period from 1972-12-17 to 1973-10-31 with data for about five days stored in a single binary data file. The principal investigator for the NEMS experiment was David H. Staelin from MIT. An advanced version of this instrument, the Scanning Microwave Spectrometer (SCAMS) was flown on the subsequent Nimbus-6 satellite. proprietary
NES-LTER_0 Northeast U.S. Shelf (NES), Long-Term Ecological Research (LTER) OB_DAAC STAC Catalog 2018-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2208430341-OB_DAAC.umm_json The Northeast U.S. Shelf (NES) Long-Term Ecological Research (LTER) project integrates observations, experiments, and models to understand and predict how planktonic food webs are changing, and how those changes impact the productivity of higher trophic levels. The NES-LTER is co-located with the Northeast U.S. Continental Shelf Large Marine Ecosystem, spanning the Middle Atlantic Bight and Gulf of Maine. Our focal cross-shelf transect extends about 150 km southward from Martha's Vineyard, MA, to just beyond the shelf break. proprietary
-NESP_2015_SRW 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1381760732-SCIOPS.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the ?western? Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the ?eastern? subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected ?western? count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary
NESP_2015_SRW 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia SCIOPS STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1381760732-SCIOPS.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the ?western? Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the ?eastern? subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected ?western? count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary
+NESP_2015_SRW 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1381760732-SCIOPS.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the ?western? Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the ?eastern? subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected ?western? count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary
NESP_2015_SRW_3 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1333031622-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary
NESP_2015_SRW_3 2015 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2015-02-09 2015-07-09 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1333031622-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in September 2015. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2015. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary
-NESP_2016_SRW_3 2016 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2016-08-24 2016-08-29 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1412710076-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2016. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2016. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary
NESP_2016_SRW_3 2016 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2016-08-24 2016-08-29 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1412710076-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2016. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2016. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary
-NESP_2017_SRW_1 2017 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2017-08-23 2017-08-27 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847804-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2017. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 25-year period 1993-2017. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future proprietary
+NESP_2016_SRW_3 2016 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2016-08-24 2016-08-29 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1412710076-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2016. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 23-year period 1993-2016. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. A data update was provided in August, 2020 to correct some incorrectly given longitude values. proprietary
NESP_2017_SRW_1 2017 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2017-08-23 2017-08-27 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847804-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2017. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 25-year period 1993-2017. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future proprietary
+NESP_2017_SRW_1 2017 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2017-08-23 2017-08-27 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847804-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2017. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 25-year period 1993-2017. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future proprietary
NESP_2018_SRW_1 2018 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2018-08-18 2018-08-23 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847807-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2018. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 26-year period 1993-2018. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary
NESP_2018_SRW_1 2018 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2018-08-18 2018-08-23 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847807-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2018. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 26-year period 1993-2018. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary
-NESP_2019_SRW_1 2019 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2019-08-18 2019-08-24 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847810-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2019. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 27-year period 1993-2019. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary
NESP_2019_SRW_1 2019 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia ALL STAC Catalog 2019-08-18 2019-08-24 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847810-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2019. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 27-year period 1993-2019. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary
+NESP_2019_SRW_1 2019 Aerial survey data of southern right whales (Eubalaena australis) off southern Australia AU_AADC STAC Catalog 2019-08-18 2019-08-24 113.02734, -36.59789, 138.69141, -29.993 https://cmr.earthdata.nasa.gov/search/concepts/C1968847810-AU_AADC.umm_json These aerial survey data of southern right whales (Eubalaena australis) off southern Australia were collected in August 2019. Such annual flights in winter/spring between Cape Leeuwin (Western Australia) and Ceduna (South Australia) have now been conducted over a 27-year period 1993-2019. These surveys have provided evidence of a population trend of around 6% per year, and a current (at 2014) population size of approximately 2300 of what has been regarded as the 'western' Australian right whale subpopulation. With estimated population size in the low thousands, it is presumed to be still well below carrying capacity. No trend information is available for the 'eastern' subpopulation of animals occurring around the remainder of the southern Australian Coast, to at least as far as Sydney, New South Wales and the populations size is relatively small, probably in the low hundreds. A lower than expected 'western' count in 2015 gives weak evidence that the growth rate may be starting to show signs of slowing, though an exponential increase remains the best description of the data. If the low 2015 count is anomalous, future counts may be expected to show an exponential increase, but if it is not, modelling growth as other than simple exponential may be useful to explore in future. proprietary
NEUROST_SSH-SST_L4_V2024.0_2024.0 Daily NeurOST L4 Sea Surface Height and Surface Geostrophic Currents POCLOUD STAC Catalog 2010-01-01 2024-06-15 -180, -70, 180, 79.9 https://cmr.earthdata.nasa.gov/search/concepts/C3085229833-POCLOUD.umm_json This Daily NeurOST Level 4 Sea Surface Height and Surface Geostrophic Currents analysis product from the University of Washington and JPL was mapped by a neural network trained with sparse Level 3 nadir altimetry observations (CMEMS, E.U. Copernicus Marine Service Information) and the MUR Level 4 gridded sea surface temperature product (PO.DAAC). proprietary
NEWS_WEB_ACLIM_1.0 NASA Energy and Water cycle Study (NEWS) Annual Climatology of the 1st decade of the 21st Century V1.0 (NEWS_WEB_ACLIM) at GES DISC GES_DISC STAC Catalog 1998-01-01 2010-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233781718-GES_DISC.umm_json NASA Energy and Water cycle Study (NEWS) Climatology of the 1st decade of the 21st Century Dataset summarizes the original observationally-based mean fluxes of water and energy budget components during the first decade of the 21st Century, for each continent and ocean basin on monthly and annual scales as well as means over all oceans, all continents, and the globe. A careful accounting of uncertainty in the estimates is included. Also, it includes optimized versions of all component fluxes that simultaneously satisfy energy and water cycle balance constraints. The NEWS Climatology contains two data products: an annual climatology data product and a monthly climatology data product. This data product is the annual climatology product. The climatology base period is roughly 1998-2010, where individual datasets cover various periods starting as early as 1998 and as late as 2002, not all extending to 2010. The continents and ocean basins boundaries map is used in this study to compute regional means. The ocean basin data was provided by Kyle Hilburn and Chelle Gentemann at Remote Sensing Systems. The land portion and some inland water bodies of the data are delineated into continents according to general definitions found in Wikipedia and relevant past studies. The data are distributed with four different units (1000 km^3/year, W/m^2, cm/year, and mm/day), in three formats (NetCDF, xlsx, and csv). proprietary
NEWS_WEB_MCLIM_1.0 NASA Energy and Water cycle Study (NEWS) Monthly Climatology of the 1st decade of the 21st Century V1.0 (NEWS_WEB_MCLIM) at GES DISC GES_DISC STAC Catalog 1998-01-01 2010-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1233781717-GES_DISC.umm_json NASA Energy and Water cycle Study (NEWS) Climatology of the 1st decade of the 21st Century Dataset summarizes the original observationally-based mean fluxes of water and energy budget components during the first decade of the 21st Century, for each continent and ocean basin on monthly and annual scales as well as means over all oceans, all continents, and the globe. A careful accounting of uncertainty in the estimates is included. Also, it includes optimized versions of all component fluxes that simultaneously satisfy energy and water cycle balance constraints. The NEWS Climatology contains two data products: an annual climatology data product and a monthly climatology data product. This data product is the monthly climatology product. The climatology base period is roughly 1998-2010, where individual datasets cover various periods starting as early as 1998 and as late as 2002, not all extending to 2010. The continents and ocean basins boundaries map is used in this study to compute regional means. The ocean basin data was provided by Kyle Hilburn and Chelle Gentemann at Remote Sensing Systems. The land portion and some inland water bodies of the data are delineated into continents according to general definitions found in Wikipedia and relevant past studies. The data are distributed with four different units (1000 km^3/month, W/m^2, cm/month, and mm/day), in three formats (NetCDF, xlsx, and csv). proprietary
NEX-DCP30_1 Downscaled 30 Arc-Second CMIP5 Climate Projections for Studies of Climate Change Impacts in the United States NCCS STAC Catalog 1950-01-01 2099-12-31 -125.0208333, 24.0625, -66.4791667, 49.9375 https://cmr.earthdata.nasa.gov/search/concepts/C1542175061-NCCS.umm_json This NASA dataset is provided to assist the science community in conducting studies of climate change impacts at local to regional scales, and to enhance public understanding of possible future climate patterns and climate impacts at the scale of individual neighborhoods and communities. This dataset is intended for use in scientific research only, and use of this dataset for other purposes, such as commercial applications, and engineering or design studies is not recommended without consultation with a qualified expert. Community feedback to improve and validate the dataset for modeling usage is appreciated. Email comments to bridget@climateanalyticsgroup.org. Dataset File Name: NASA Earth Exchange (NEX) Downscaled Climate Projections (NEXDCP30), https://portal.nccs.nasa.gov/portal_home/published/NEX.html proprietary
NEX-GDDP_1 NASA Earth Exchange Global Daily Downscaled Projections NCCS STAC Catalog 1950-01-01 2100-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1374483929-NCCS.umm_json The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate scenarios for the globe that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs). The CMIP5 GCM runs were developed in support of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The NEX-GDDP dataset includes downscaled projections for RCP 4.5 and RCP 8.5 from the 21 models and scenarios for which daily scenarios were produced and distributed under CMIP5. Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100. The spatial resolution of the dataset is 0.25 degrees (~25 km x 25 km). The NEX-GDDP dataset is provided to assist the science community in conducting studies of climate change impacts at local to regional scales, and to enhance public understanding of possible future global climate patterns at the spatial scale of individual towns, cities, and watersheds. Each of the climate projections includes monthly averaged maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run). proprietary
NFRDI_0 National Fisheries Research and Development Institute (NFRDI) OB_DAAC STAC Catalog 2000-02-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360518-OB_DAAC.umm_json Measurements made by the National Fisheries Research and Development Institute (NFRDI), Ministry of Oceans and Fisheries for Korea, in the East China Sea in 2000. proprietary
-"NGA178
- _1.0" Advanced Terrestrial Simulator ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388528-SCIOPS.umm_json The Advanced Terrestrial Simulator (formerly sometimes known as the Arctic Terrestrial Simulator) is a code for solving ecosystem-based, integrated, distributed hydrology. Capabilities are largely based on solving various forms of Richards equation coupled to a surface flow equation, along with the needed sources and sinks for ecosystem and climate models. This can (but need not) include thermal processes (especially ice for frozen soils), evapo-transpiration, albedo-driven surface energy balances, snow, biogeochemistry, plant dynamics, deformation, transport, and much more. proprietary
"NGA178
_1.0" Advanced Terrestrial Simulator SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388528-SCIOPS.umm_json The Advanced Terrestrial Simulator (formerly sometimes known as the Arctic Terrestrial Simulator) is a code for solving ecosystem-based, integrated, distributed hydrology. Capabilities are largely based on solving various forms of Richards equation coupled to a surface flow equation, along with the needed sources and sinks for ecosystem and climate models. This can (but need not) include thermal processes (especially ice for frozen soils), evapo-transpiration, albedo-driven surface energy balances, snow, biogeochemistry, plant dynamics, deformation, transport, and much more. proprietary
+"NGA178
+ _1.0" Advanced Terrestrial Simulator ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388528-SCIOPS.umm_json The Advanced Terrestrial Simulator (formerly sometimes known as the Arctic Terrestrial Simulator) is a code for solving ecosystem-based, integrated, distributed hydrology. Capabilities are largely based on solving various forms of Richards equation coupled to a surface flow equation, along with the needed sources and sinks for ecosystem and climate models. This can (but need not) include thermal processes (especially ice for frozen soils), evapo-transpiration, albedo-driven surface energy balances, snow, biogeochemistry, plant dynamics, deformation, transport, and much more. proprietary
"NGA183
_1.0" Active Layer Hydrology in an Arctic Tundra Ecosystem: Quantifying Water Sources and Cycling Using Water Stable Isotopes: Supporting Data SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388529-SCIOPS.umm_json Data include results from water isotope analyses (one *.csv file) for samples collected in Utqiagvik (Barrow), Alaska during August and September 2012. Samples were from surface and soil pore waters from 17 drainages that could be interlake (basins with polygonal terrain), different-aged drain thaw lake basins (young, medium, old, or ancient), or a combination of different aged basins. Samples taken in different drainage flow types at three different depths at each location in and around the Barrow Environmental Observatory. Precipitation stable isotope data are also included (added in October 2019 with no changes to previously released data). This dataset used in Throckmorton, et.al. 2016.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM). proprietary
"NGA183
_1.0" Active Layer Hydrology in an Arctic Tundra Ecosystem: Quantifying Water Sources and Cycling Using Water Stable Isotopes: Supporting Data ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388529-SCIOPS.umm_json Data include results from water isotope analyses (one *.csv file) for samples collected in Utqiagvik (Barrow), Alaska during August and September 2012. Samples were from surface and soil pore waters from 17 drainages that could be interlake (basins with polygonal terrain), different-aged drain thaw lake basins (young, medium, old, or ancient), or a combination of different aged basins. Samples taken in different drainage flow types at three different depths at each location in and around the Barrow Environmental Observatory. Precipitation stable isotope data are also included (added in October 2019 with no changes to previously released data). This dataset used in Throckmorton, et.al. 2016.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM). proprietary
-"NGA232
- _1.0" A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra: Supporting Data SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388919-SCIOPS.umm_json Remote sensing data collected from Brookhaven National Laboratory’s (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform – the Osprey – operated by the Terrestrial Ecosystem Science and Technology (TEST) group. Data was collected from a single flight over the Kougarok hillslope site on 26 July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface “skin” temperature imagery, as well as surface reflectance at 1nm intervals in the visible to near-infrared spectral range from ~350-1000 nm measured at regular intervals along each flight path. Derived image products include ortho-mosaiced RGB and TIR images, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, digital terrain model (DTM), and a canopy height model. Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM). proprietary
"NGA232
_1.0" A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra: Supporting Data ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388919-SCIOPS.umm_json Remote sensing data collected from Brookhaven National Laboratory’s (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform – the Osprey – operated by the Terrestrial Ecosystem Science and Technology (TEST) group. Data was collected from a single flight over the Kougarok hillslope site on 26 July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface “skin” temperature imagery, as well as surface reflectance at 1nm intervals in the visible to near-infrared spectral range from ~350-1000 nm measured at regular intervals along each flight path. Derived image products include ortho-mosaiced RGB and TIR images, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, digital terrain model (DTM), and a canopy height model. Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM). proprietary
+"NGA232
+ _1.0" A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra: Supporting Data SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2108388919-SCIOPS.umm_json Remote sensing data collected from Brookhaven National Laboratory’s (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform – the Osprey – operated by the Terrestrial Ecosystem Science and Technology (TEST) group. Data was collected from a single flight over the Kougarok hillslope site on 26 July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface “skin” temperature imagery, as well as surface reflectance at 1nm intervals in the visible to near-infrared spectral range from ~350-1000 nm measured at regular intervals along each flight path. Derived image products include ortho-mosaiced RGB and TIR images, an RGB-based digital surface model (DSM) using the structure from motion (SfM) technique, digital terrain model (DTM), and a canopy height model. Ancillary aircraft data, flight mission parameters, and general flight conditions are also included. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM). proprietary
NGLI_Lake_Bourne_0 Northern Gulf Littoral Initiative (NGLI) measurements in Lake Bourne, Louisiana OB_DAAC STAC Catalog 2001-04-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360520-OB_DAAC.umm_json Measurements made under the Northern Gulf Littoral Initiative (NGLI) in the Gulf of Mexico near the Mississippi River outflow region in 2001. proprietary
NHAP National High Altitude Photography USGS_LTA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220566467-USGS_LTA.umm_json The National High Altitude Photography (NHAP) program, which was operated from 1980 - 1989, was coordinated by the U.S. Geological Survey as an interagency project to eliminate duplicate photography in various Government programs. The aim of the program was to cover the 48 conterminous states of the USA over a 5-year span. In the NHAP program, black-and-white and color-infrared aerial photographs were obtained on 9-inch film from an altitude of 40,000 feet above mean terrain elevation and are centered over USGS 7.5-minute quadrangles. The color-infrared photographs are at a scale of 1:58,000 (1 inch equals about .9 miles) and the black-and-white photographs are at a scale of 1:80,000 (1 inch equals about 1.26 miles). proprietary
NHICEM_001 Northern Hemisphere Ice Cover Monthly Statistics at 1 Degree Resolution V001 (NHICEM) at GES DISC GES_DISC STAC Catalog 2000-01-01 2014-11-30 -180, 0, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239898024-GES_DISC.umm_json This product is monthly Ice Cover Statistics. The dataset was prepared by Dr. Peter Romanov at Cooperative Institute for Climate Studies(CICS) of the University of Maryland for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program. The product includes the monthly ice statistics (frequency of occurrence) for Northern Hemisphere at 1x1 degree spatial resolution. The dataset covers the time period starting January 2000 to November 2014. The data was derived from daily ice cover charts produced at NOAA/NESDIS within Interactive Multisensor Ice Mapping System (IMS). proprietary
@@ -12393,8 +12394,8 @@ NSCAT_LEVEL_2_V2_2 NSCAT Level 2 Ocean Wind Vector Geophysical Data Record POCLO
NSCAT_LEVEL_3_BROWSE_IMAGES_2 NSCAT Level 3 Daily Gridded Ocean Surface Wind Vector Browse Images (JPL) POCLOUD STAC Catalog 1996-09-15 1997-06-29 -180, -75, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2617226745-POCLOUD.umm_json This dataset provides browse images of the NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors, which are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function. Information and access to the Level 3 source data used to generate these browse images may be accessed at: http://podaac.jpl.nasa.gov/dataset/NSCAT%20LEVEL%203. proprietary
NSCAT_LEVEL_3_V2_2 NSCAT Level 3 Daily Gridded Ocean Surface Wind Vectors (JPL) POCLOUD STAC Catalog 1996-09-15 1997-06-30 -180, -75, 180, 75 https://cmr.earthdata.nasa.gov/search/concepts/C2617226815-POCLOUD.umm_json The NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing, ambiguous, or contaminated by land/sea-ice. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function. proprietary
NSCAT_W25_RMGDR_V2_2 NSCAT High Resolution R-MGDR, Selected Ocean Wind Vectors (JPL) POCLOUD STAC Catalog 1996-09-15 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2617226887-POCLOUD.umm_json The NASA Scatterometer (NSCAT) Level 2.5 high-resolution reduced MGDR contains only wind vector data (sigma-0 is excluded) in 25 km wind vector cell (WVC) swaths which contain daily data from ascending and descending passes. Wind vectors are accurate to within 2 m/s (vector speed) and 20 degrees (vector direction). Wind vectors are not considered valid in rain contaminated regions; rain flags and precipitation information are not provided. Data is flagged where measurements are either missing or ambiguous. In the presence of land or sea ice winds values are set to 0. Wind vectors are processed using the NSCAT-2 geophysical model function. proprietary
-NSF-ANT-1142074-penguins_1.0 Adelie penguin satellite position and dive data for NSF-ANT-1142074 from the California Avian Data Center hosted by Point Blue Conservation Science ALL STAC Catalog 2012-12-15 2013-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1219899602-SCIOPS.umm_json Satellite positions and dive data collected on Adelie penguins in the 2012-13 season for purposes of evaluating food-web dynamics.. proprietary
NSF-ANT-1142074-penguins_1.0 Adelie penguin satellite position and dive data for NSF-ANT-1142074 from the California Avian Data Center hosted by Point Blue Conservation Science SCIOPS STAC Catalog 2012-12-15 2013-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1219899602-SCIOPS.umm_json Satellite positions and dive data collected on Adelie penguins in the 2012-13 season for purposes of evaluating food-web dynamics.. proprietary
+NSF-ANT-1142074-penguins_1.0 Adelie penguin satellite position and dive data for NSF-ANT-1142074 from the California Avian Data Center hosted by Point Blue Conservation Science ALL STAC Catalog 2012-12-15 2013-01-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C1219899602-SCIOPS.umm_json Satellite positions and dive data collected on Adelie penguins in the 2012-13 season for purposes of evaluating food-web dynamics.. proprietary
NSF-ANT02-28842 Boron in Antarctic granulite-facies rocks: under what conditions is boron retained in the middle crust? AMD_USAPDC STAC Catalog 2003-06-01 2009-11-30 76, -69.5, 76.5, -69.3 https://cmr.earthdata.nasa.gov/search/concepts/C2534797156-AMD_USAPDC.umm_json This award, provided by the Antarctic Geology and Geophysics Program of the Office of Polar Programs, supports a project to investigate the role and fate of Boron in high-grade metamorphic rocks of the Larsemann Hills region of Antarctica. Trace elements provide valuable information on the changes sedimentary rocks undergo as temperature and pressure increase during burial. One such element, boron, is particularly sensitive to increasing temperature because of its affinity for aqueous fluids, which are lost as rocks are buried. Boron contents of unmetamorphosed pelitic sediments range from 20 to over 200 parts per million, but rarely exceed 5 parts per million in rocks subjected to conditions of the middle and lower crust, that is, temperatures of 700 degrees C or more in the granulite-facies, which is characterized by very low water activities at pressures of 5 to 10 kbar (18-35 km burial). Devolatization reactions with loss of aqueous fluid and partial melting with removal of melt have been cited as primary causes for boron depletion under granulite-facies conditions. Despite the pervasiveness of both these processes, rocks rich in boron are locally found in the granulite-facies, that is, there are mechanisms for retaining boron during the metamorphic process. The Larsemann Hills, Prydz Bay, Antarctica, are a prime example. More than 20 lenses and layered bodies containing four borosilicate mineral species crop out over a 50 square kilometer area, which thus would be well suited for research on boron-rich granulite-facies metamorphic rocks. While most investigators have focused on the causes for loss of boron, this work will investigate how boron is retained during high-grade metamorphism. Field observations and mapping in the Larsemann Hills, chemical analyses of minerals and their host rocks, and microprobe age dating will be used to identify possible precursors and deduce how the precursor materials recrystallized into borosilicate rocks under granulite-facies conditions. The working hypothesis is that high initial boron content facilitates retention of boron during metamorphism because above a certain threshold boron content, a mechanism 'kicks in' that facilitates retention of boron in metamorphosed rocks. For example, in a rock with large amounts of the borosilicate tourmaline, such as stratabound tourmalinite, the breakdown of tourmaline to melt could result in the formation of prismatine and grandidierite, two borosilicates found in the Larsemann Hills. This situation is rarely observed in rocks with modest boron content, in which breakdown of tourmaline releases boron into partial melts, which in turn remove boron when they leave the system. Stratabound tourmalinite is associated with manganese-rich quartzite, phosphorus-rich rocks and sulfide concentrations that could be diagnostic for recognizing a tourmalinite protolith in a highly metamorphosed complex where sedimentary features have been destroyed by deformation. Because partial melting plays an important role in the fate of boron during metamorphism, our field and laboratory research will focus on the relationship between the borosilicate units, granite pegmatites and other granitic intrusives. The results of our study will provide information on cycling of boron at deeper levels in the Earth's crust and on possible sources of boron for granites originating from deep-seated rocks. An undergraduate student will participate in the electron microprobe age-dating of monazite and xenotime as part of a senior project, thereby integrating the proposed research into the educational mission of the University of Maine. In response to a proposal for fieldwork, the Australian Antarctic Division, which maintains Davis station near the Larsemann Hills, has indicated that they will support the Antarctic fieldwork. proprietary
NSF-ANT04-36190_1 Biodiversity, Buoyancy and Morphological Studies of Non-Antarctic Notothenioid Fishes AMD_USAPDC STAC Catalog 2005-04-01 2009-03-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532069293-AMD_USAPDC.umm_json Patterns of biodiversity, as revealed by basic research in organismal biology, may be derived from ecological and evolutionary processes expressed in unique settings, such as Antarctica. The polar regions and their faunas are commanding increased attention as declining species diversity, environmental change, commercial fisheries, and resource management are now being viewed in a global context. Commercial fishing is known to have a direct and pervasive effect on marine biodiversity, and occurs in the Southern Ocean as far south as the Ross Sea. The nature of fish biodiversity in the Antarctic is different than in all other ocean shelf areas. Waters of the Antarctic continental shelf are ice covered for most of the year and water temperatures are nearly constant at -1.5 C. In these waters components of the phyletically derived Antarctic clade of Notothenioids dominate fish diversity. In some regions, including the southwestern Ross Sea, Notothenioids are overwhelmingly dominant in terms of number of species, abundance, and biomass. Such dominance by a single taxonomic group is unique among shelf faunas of the world. In the absence of competition from a taxonomically diverse fauna, Notothenioids underwent a habitat or depth related diversification keyed to the utilization of unfilled niches in the water column, especially pelagic or partially pelagic zooplanktivory and piscivory. This has been accomplished in the absence of a swim bladder for buoyancy control. They also may form a special type of adaptive radiation known as a species flock, which is an assemblage of a disproportionately high number of related species that have evolved rapidly within a defined area where most species are endemic. Diversification in buoyancy is the hallmark of the notothenioid radiation. Buoyancy is the feature of notothenioid biology that determines whether a species lives on the substrate, in the water column or both. Buoyancy also influences other key aspects of life history including swimming, feeding and reproduction and thus has implications for the role of the species in the ecosystem. With similarities to classic evolutionary hot spots, the Antarctic shelf and its Notothenioid radiation merit further exploration. The 2004 'International Collaborative Expedition to collect and study Fish Indigenous to Sub-Antarctic Habitats,' or, 'ICEFISH,' provided a platform for collection of notothenioid fishes from sub-Antarctic waters between South America and Africa, which will be examined in this project. This study will determine buoyancy for samples of all notothenioid species captured during the ICEFISH cruise. This essential aspect of the biology is known for only 19% of the notothenioid fauna. Also, the gross and microscopic anatomy of brains and sense organs of the phyletically basal families Bovichtidae, Eleginopidae, and of the non-Antarctic species of the primarily Antarctic family Nototheniidae will be examined. The fish biodiversity and endemicity in poorly known localities along the ICEFISH cruise track, seamounts and deep trenches will be quantified. Broader impacts include improved information for comprehending and conserving biodiversity, a scientific and societal priority. proprietary
NSF-ANT04-39906_1 Abandoned Elephant Seal Colonies in Antarctica: Integration of Genetic, Isotopic, and Geologic Approaches toward Understanding Holocene Environmental Change ALL STAC Catalog 2005-09-15 2009-08-31 162, -78, 168, -72 https://cmr.earthdata.nasa.gov/search/concepts/C2532069615-AMD_USAPDC.umm_json During previous NSF-sponsored research, the PI's discovered that southern elephant seal colonies once existed along the Victoria Land coast (VLC) of Antarctica, a region where they are no longer observed. Molted seal skin and hair occur along 300 km of coastline, more than 1000 km from any extant colony. The last record of a seal at a former colony site is at ~A.D. 1600. Because abandonment occurred prior to subantarctic sealing, disappearance of the VLC colony probably was due to environmental factors, possibly cooling and encroachment of land-fast, perennial sea ice that made access to haul-out sites difficult. The record of seal inhabitation along the VLC, therefore, has potential as a proxy for climate change. Elephant seals are a predominantly subantarctic species with circumpolar distribution. Genetic studies have revealed significant differentiation among populations, particularly with regard to that at Macquarie I., which is the extant population nearest to the abandoned VLC colony. Not only is the Macquarie population unique genetically, but it is has undergone unexplained decline of 2%/yr over the last 50 years3. In a pilot study, genetic analyses showed a close relationship between the VLC seals and those at Macquarie I. An understanding of the relationship between the two populations, as well as of the environmental pressures that led to the demise of the VLC colonies, will provide a better understanding of present-day population genetic structure, the effect of environmental change on seal populations, and possibly the reasons underlying the modern decline at Macquarie Island. This project addresses several key research problems: (1) Why did elephant seals colonize and then abandon the VLC? (2) What does the elephant seal record reveal about Holocene climate change and sea-ice conditions? (3) What were the foraging strategies of the seals and did these strategies change over time as climate varied? (4) How does the genetic structure of the VLC seals relate to extant populations? (5) How did genetic diversity change over time and with colony decline? (6) Using ancient samples to estimate mtDNA mutation rates, what can be learned about VLC population dynamics over time? (7) What was the ecological relationship between elephant seals and Adelie penguins that occupied the same sites, but apparently at different times? The proposed work includes the professional training of young researchers and incorporation of data into graduate and undergraduate courses. Because of extreme isolation of the Antarctic continent since the Early Oligocene, one expects a unique invertebrate benthic fauna with a high degree of endemism. Yet some invertebrate taxa that constitute important ecological components of sedimentary benthic communities include more than 40 percent non-endemic species (e.g., benthic polychaetes). To account for non-endemic species, intermittent genetic exchange must occur between Antarctic and other (e.g. South American) populations. The most likely mechanism for such gene flow, at least for in-faunal and mobile macrobenthos, is dispersal of planktonic larvae across the sub- Antarctic and Antarctic polar fronts. To test for larval dispersal as a mechanism of maintaining genetic continuity across polar fronts, the scientists propose to (1) take plankton samples along transects across Drake passage during both the austral summer and winter seasons while concurrently collecting the appropriate hydrographic data. Such data will help elucidate the hydrographic mechanisms that allow dispersal across Drake Passage. Using a molecular phylogenetic approach, they will (2) compare seemingly identical adult forms from Antarctic and South America continents to identify genetic breaks, historical gene flow, and control for the presence of cryptic species. (3) Similar molecular tools will be used to relate planktonic larvae to their adult forms. Through this procedure, they propose to link the larval forms respectively to their Antarctic or South America origins. The proposed work builds on previous research that provides the basis for this effort to develop a synthetic understanding of historical gene flow and present day dispersal mechanism in South American/Drake Passage/ Antarctic Peninsular region. Furthermore, this work represents one of the first attempts to examine recent gene flow in Antarctic benthic invertebrates. Graduate students and a postdoctoral fellow will be trained during this research proprietary
@@ -12417,32 +12418,32 @@ NSF-ANT07-39464_1 Atmosphere-Ocean-Ice Interaction in a Coastal Polynya AMD_USAP
NSF-ANT08-37988 Antarctic Climate Reconstruction Utilizing the US ITASE Ice Core Array (2009-2012) AMD_USAPDC STAC Catalog 2009-06-01 2013-05-31 -180, -90, 180, -65 https://cmr.earthdata.nasa.gov/search/concepts/C2532069463-AMD_USAPDC.umm_json This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). This award supports a project to reconstruct the past physical and chemical climate of Antarctica, with an emphasis on the region surrounding the Ross Sea Embayment, using >60 ice cores collected in this region by US ITASE and by Australian, Brazilian, Chilean, and New Zealand ITASE teams. The ice core records are annually resolved and exceptionally well dated, and will provide, through the analyses of stable isotopes, major soluble ions and for some trace elements, instrumentally calibrated proxies for past temperature, precipitation, atmospheric circulation, chemistry of the atmosphere, sea ice extent, and volcanic activity. These records will be used to understand the role of solar, volcanic, and human forcing on Antarctic climate and to investigate the character of recent abrupt climate change over Antarctica in the context of broader Southern Hemisphere and global climate variability. The intellectual merit of the project is that ITASE has resulted in an array of ice core records, increasing the spatial resolution of observations of recent Antarctic climate variability by more than an order of magnitude and provides the basis for assessment of past and current change and establishes a framework for monitoring of future climate change in the Southern Hemisphere. This comes at a critical time as global record warming and other impacts are noted in the Southern Ocean, the Antarctic Peninsula, and on the Antarctic ice sheet. The broader impacts of the project are that Post-doctoral and graduate students involved in the project will benefit from exposure to observational and modeling approaches to climate change research and working meetings to be held at the two collaborating institutions plus other prominent climate change institutions. The results are of prime interest to the public and the media Websites hosted by the two collaborating institutions contain climate change position papers, scientific exchanges concerning current climate change issues, and scientific contribution series. proprietary
NSF-ANT08-38955_1 Alternative Nutritional Strategies in Antarctic Protists AMD_USAPDC STAC Catalog 2009-08-01 2013-07-31 71.504166, -76.585556, 71.60472, -76.159164 https://cmr.earthdata.nasa.gov/search/concepts/C2532069762-AMD_USAPDC.umm_json This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Most organisms meet their carbon and energy needs using photosynthesis (phototrophy) or ingestion/assimilation of organic substances (heterotrophy). However, a nutritional strategy that combines phototrophy and heterotrophy - mixotrophy - is geographically and taxonomically widespread in aquatic systems. While the presence of mixotrophs in the Southern Ocean is known only recently, preliminary evidence indicates a significant role in Southern Ocean food webs. Recent work on Southern Ocean dinoflagellate, Kleptodinium, suggests that it sequesters functional chloroplasts of the bloom-forming haptophyte, Phaeocystis antarctica. This dinoflagellate is abundant in the Ross Sea, has been reported elsewhere in the Southern Ocean, and may have a circumpolar distribution. By combining nutritional modes. mixotrophy may offer competitive advantages over pure autotrophs and heterotrophs. The goals of this project are to understand the importance of alternative nutritional strategies for Antarctic species that combine phototrophic and phagotrophic processes in the same organism. The research will combine field investigations of plankton and ice communities in the Southern Ocean with laboratory experiments on Kleptodinium and recently identified mixotrophs from our Antarctic culture collections. The research will address: 1) the relative contributions of phototrophy and phagotrophy in Antarctic mixotrophs; 2) the nature of the relationship between Kleptodinium and its kleptoplastids; 3) the distributions and abundances of mixotrophs and Kleptodinium in the Southern Ocean during austral spring/summer; and 4) the impacts of mixotrophs and Kleptodinium on prey populations, the factors influencing these behaviors and the physiological conditions of these groups in their natural environment. The project will contribute to the maintenance of a culture collection of heterotrophic, phototrophic and mixotrophic Antarctic protists that are available to the scientific community, and it will train graduate and undergraduate students at Temple University. Research findings and activities will be summarized for non-scientific audiences through the PIs' websites and through other public forums, and will involve middle school teachers via collaboration with COSEE-New England. proprietary
NSF-ANT08-38996_1 Ammonia Oxidation Versus Heterotrophy in Crenarchaeota Populations from Marine Environments West of the Antarctic Peninsula AMD_USAPDC STAC Catalog 2009-08-15 2013-12-31 -79, -71, -64, -63 https://cmr.earthdata.nasa.gov/search/concepts/C2532069861-AMD_USAPDC.umm_json Ammonia oxidation is the first step in the conversion of regenerated nitrogen to dinitrogen gas, a 3-step pathway mediated by 3 distinct guilds of bacteria and archaea. Ammonia oxidation and the overall process of nitrification-denitrification have received relatively little attention in polar oceans where the effects of climate change on biogeochemical rates are likely to be pronounced. Previous work on Ammonia Oxidizing Archaea (AOA) in the Palmer LTER study area West of the Antarctic Peninsula (WAP), has suggested strong vertical segregation of crenarchaeote metabolism, with the 'winter water' (WW, ~50-100 m depth range) dominated by non-AOA crenarchaeotes, while Crenarchaeota populations in the 'circumpolar deep water' (CDW), which lies immediately below the winter water (150-3500 m), are dominated by AOA. Analysis of a limited number of samples from the Arctic Ocean did not reveal a comparable vertical segregation of AOA, and suggested that AOA and Crenarchaeota abundance is much lower there than in the Antarctic. These findings led to 3 hypotheses that will be tested in this project: 1) the apparent low abundance of Crenarchaeota and AOA in Arctic Ocean samples may be due to spatial or temporal variability in populations; 2) the WW population of Crenarchaeota in the WAP is dominated by a heterotroph; 3) the WW population of Crenarchaeota in the WAP 'grows in' during spring and summer after this water mass forms. The study will contribute substantially to understanding an important aspect of the nitrogen cycle in the Palmer LTER (Long Term Ecological Research) study area by providing insights into the ecology and physiology of AOA. The natural segregation of crenarchaeote phenotypes in waters of the WAP, coupled with metagenomic studies in progress in the same area by others (A. Murray, H. Ducklow), offers the possibility of major breakthroughs in understanding of the metabolic capabilities of these organisms. This knowledge is needed to model how water column nitrification will respond to changes in polar ecosystems accompanying global climate change. The Principal Investigator will participate fully in the education and outreach efforts of the Palmer LTER, including making highlights of our findings available for posting to their project web site and participating in outreach (for example, Schoolyard LTER). The research also will involve undergraduates (including the field work if possible) and will support high school interns in the P.I.'s laboratory over the summer. proprietary
-NSF-ANT09-44042 Acoustic Assessment of Southern Ocean Salps and Their Ecosystem Impact ALL STAC Catalog 2010-09-01 2013-08-31 -70, -66, -50, -59 https://cmr.earthdata.nasa.gov/search/concepts/C2532069797-AMD_USAPDC.umm_json The importance of gelatinous zooplankton in marine systems worldwide is increasing. In Southern Ocean, increasing salp densities could have a detrimental effect on higher predators, including penguins, fur seals, and baleen whales. The proposed research is a methods-develoment project that will improve the capability to indirectly assess abundances and distributions of salps in the Southern Ocean through acoustic surveys. Hydrographic, net tow, and acoustic backscatter data will be collected in the waters surrounding the South Shetland Islands and the Antarctic peninsula, where both krill and salps are found and compete for food. Shipboard experimental manipulations and measurements will lead to improved techniques for assessment of salp biomass acoustically. Experiments will focus on material properties (density and sound speed), size and shape of salps, as well as how these physical properties will vary with the salp\'s environment, feeding rate, and reproductive status. In the field, volume backscattering data from an acoustic echosounder will be collected at the same locations as the net tows to enable comparison of net and acoustic estimates of salp abundance. A physics-based scattering model for salps will be developed and validated, to determine if multiple acoustic frequencies can be used to discriminate between scattering associated with krill swarms and that from salp blooms. During the same period as the Antarctic field work, a parallel outreach and education study will be undertaken in Long Island, New York examining local gelatinous zooplankton. This study will enable project participants to learn and practice research procedures and methods before traveling to Antarctica; provide a comparison time-series that will be used for educational purposes; and include many more students and teachers in the research project than would be able to participate in the Antarctic field component. proprietary
NSF-ANT09-44042 Acoustic Assessment of Southern Ocean Salps and Their Ecosystem Impact AMD_USAPDC STAC Catalog 2010-09-01 2013-08-31 -70, -66, -50, -59 https://cmr.earthdata.nasa.gov/search/concepts/C2532069797-AMD_USAPDC.umm_json The importance of gelatinous zooplankton in marine systems worldwide is increasing. In Southern Ocean, increasing salp densities could have a detrimental effect on higher predators, including penguins, fur seals, and baleen whales. The proposed research is a methods-develoment project that will improve the capability to indirectly assess abundances and distributions of salps in the Southern Ocean through acoustic surveys. Hydrographic, net tow, and acoustic backscatter data will be collected in the waters surrounding the South Shetland Islands and the Antarctic peninsula, where both krill and salps are found and compete for food. Shipboard experimental manipulations and measurements will lead to improved techniques for assessment of salp biomass acoustically. Experiments will focus on material properties (density and sound speed), size and shape of salps, as well as how these physical properties will vary with the salp\'s environment, feeding rate, and reproductive status. In the field, volume backscattering data from an acoustic echosounder will be collected at the same locations as the net tows to enable comparison of net and acoustic estimates of salp abundance. A physics-based scattering model for salps will be developed and validated, to determine if multiple acoustic frequencies can be used to discriminate between scattering associated with krill swarms and that from salp blooms. During the same period as the Antarctic field work, a parallel outreach and education study will be undertaken in Long Island, New York examining local gelatinous zooplankton. This study will enable project participants to learn and practice research procedures and methods before traveling to Antarctica; provide a comparison time-series that will be used for educational purposes; and include many more students and teachers in the research project than would be able to participate in the Antarctic field component. proprietary
-NSF-ANT09-44358 Adelie Penguin Response to Climate Change at the Individual, Colony and Metapopulation Levels - NSF-ANT09-44358 AMD_USAPDC STAC Catalog 2010-09-15 2015-08-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C2532070119-AMD_USAPDC.umm_json While changes in populations typically are tracked to gauge the impact of climate or habitat change, the process involves the response of individuals as each copes with an altered environment. In a study of Adelie penguins that spans 13 breeding seasons, results indicate that only 20% of individuals within a colony successfully raise offspring, and that they do so because of their exemplary foraging proficiency. Moreover, foraging appears to require more effort at the largest colony, where intraspecific competition is higher than at small colonies, and also requires more proficiency during periods of environmental stress. When conditions are particularly daunting, emigration dramatically increases, countering the long-standing assumption that Ad?lie penguins are highly philopatric. The research project will 1) determine the effect of age, experience and physiology on individual foraging efficiency; 2) determine the effect of age, experience, and individual quality on breeding success and survival in varying environmental and competitive conditions at the colony level; and 3) develop a comprehensive model for the Ross-Beaufort Island metapopulation dynamics. Broader impacts include training of interns, continuation of public outreach through the highly successful project website penguinscience.com, development of classroom materials and other standards-based instructional resources. proprietary
+NSF-ANT09-44042 Acoustic Assessment of Southern Ocean Salps and Their Ecosystem Impact ALL STAC Catalog 2010-09-01 2013-08-31 -70, -66, -50, -59 https://cmr.earthdata.nasa.gov/search/concepts/C2532069797-AMD_USAPDC.umm_json The importance of gelatinous zooplankton in marine systems worldwide is increasing. In Southern Ocean, increasing salp densities could have a detrimental effect on higher predators, including penguins, fur seals, and baleen whales. The proposed research is a methods-develoment project that will improve the capability to indirectly assess abundances and distributions of salps in the Southern Ocean through acoustic surveys. Hydrographic, net tow, and acoustic backscatter data will be collected in the waters surrounding the South Shetland Islands and the Antarctic peninsula, where both krill and salps are found and compete for food. Shipboard experimental manipulations and measurements will lead to improved techniques for assessment of salp biomass acoustically. Experiments will focus on material properties (density and sound speed), size and shape of salps, as well as how these physical properties will vary with the salp\'s environment, feeding rate, and reproductive status. In the field, volume backscattering data from an acoustic echosounder will be collected at the same locations as the net tows to enable comparison of net and acoustic estimates of salp abundance. A physics-based scattering model for salps will be developed and validated, to determine if multiple acoustic frequencies can be used to discriminate between scattering associated with krill swarms and that from salp blooms. During the same period as the Antarctic field work, a parallel outreach and education study will be undertaken in Long Island, New York examining local gelatinous zooplankton. This study will enable project participants to learn and practice research procedures and methods before traveling to Antarctica; provide a comparison time-series that will be used for educational purposes; and include many more students and teachers in the research project than would be able to participate in the Antarctic field component. proprietary
NSF-ANT09-44358 Adelie Penguin Response to Climate Change at the Individual, Colony and Metapopulation Levels - NSF-ANT09-44358 ALL STAC Catalog 2010-09-15 2015-08-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C2532070119-AMD_USAPDC.umm_json While changes in populations typically are tracked to gauge the impact of climate or habitat change, the process involves the response of individuals as each copes with an altered environment. In a study of Adelie penguins that spans 13 breeding seasons, results indicate that only 20% of individuals within a colony successfully raise offspring, and that they do so because of their exemplary foraging proficiency. Moreover, foraging appears to require more effort at the largest colony, where intraspecific competition is higher than at small colonies, and also requires more proficiency during periods of environmental stress. When conditions are particularly daunting, emigration dramatically increases, countering the long-standing assumption that Ad?lie penguins are highly philopatric. The research project will 1) determine the effect of age, experience and physiology on individual foraging efficiency; 2) determine the effect of age, experience, and individual quality on breeding success and survival in varying environmental and competitive conditions at the colony level; and 3) develop a comprehensive model for the Ross-Beaufort Island metapopulation dynamics. Broader impacts include training of interns, continuation of public outreach through the highly successful project website penguinscience.com, development of classroom materials and other standards-based instructional resources. proprietary
+NSF-ANT09-44358 Adelie Penguin Response to Climate Change at the Individual, Colony and Metapopulation Levels - NSF-ANT09-44358 AMD_USAPDC STAC Catalog 2010-09-15 2015-08-31 165.9, -77.6, 169.4, -76.9 https://cmr.earthdata.nasa.gov/search/concepts/C2532070119-AMD_USAPDC.umm_json While changes in populations typically are tracked to gauge the impact of climate or habitat change, the process involves the response of individuals as each copes with an altered environment. In a study of Adelie penguins that spans 13 breeding seasons, results indicate that only 20% of individuals within a colony successfully raise offspring, and that they do so because of their exemplary foraging proficiency. Moreover, foraging appears to require more effort at the largest colony, where intraspecific competition is higher than at small colonies, and also requires more proficiency during periods of environmental stress. When conditions are particularly daunting, emigration dramatically increases, countering the long-standing assumption that Ad?lie penguins are highly philopatric. The research project will 1) determine the effect of age, experience and physiology on individual foraging efficiency; 2) determine the effect of age, experience, and individual quality on breeding success and survival in varying environmental and competitive conditions at the colony level; and 3) develop a comprehensive model for the Ross-Beaufort Island metapopulation dynamics. Broader impacts include training of interns, continuation of public outreach through the highly successful project website penguinscience.com, development of classroom materials and other standards-based instructional resources. proprietary
NSF-ANT09-44411 Adelie Penguin Response to Climate Change at the Individual, Colony and Metapopulation Levels AMD_USAPDC STAC Catalog 2010-09-15 2015-08-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532069734-AMD_USAPDC.umm_json While changes in populations typically are tracked to gauge the impact of climate or habitat change, the process involves the response of individuals as each copes with an altered environment. In a study of Adelie penguins that spans 13 breeding seasons, results indicate that only 20% of individuals within a colony successfully raise offspring, and that they do so because of their exemplary foraging proficiency. Moreover, foraging appears to require more effort at the largest colony, where intraspecific competition is higher than at small colonies, and also requires more proficiency during periods of environmental stress. When conditions are particularly daunting, emigration dramatically increases, countering the long-standing assumption that Adélie penguins are highly philopatric. The research project will 1) determine the effect of age, experience and physiology on individual foraging efficiency; 2) determine the effect of age, experience, and individual quality on breeding success and survival in varying environmental and competitive conditions at the colony level; and 3) develop a comprehensive model for the Ross-Beaufort Island metapopulation dynamics. Broader impacts include training of interns, continuation of public outreach through the highly successful project website penguinscience.com, development of classroom materials and other standards-based instructional resources. proprietary
NSF-ANT09-44411 Adelie Penguin Response to Climate Change at the Individual, Colony and Metapopulation Levels ALL STAC Catalog 2010-09-15 2015-08-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532069734-AMD_USAPDC.umm_json While changes in populations typically are tracked to gauge the impact of climate or habitat change, the process involves the response of individuals as each copes with an altered environment. In a study of Adelie penguins that spans 13 breeding seasons, results indicate that only 20% of individuals within a colony successfully raise offspring, and that they do so because of their exemplary foraging proficiency. Moreover, foraging appears to require more effort at the largest colony, where intraspecific competition is higher than at small colonies, and also requires more proficiency during periods of environmental stress. When conditions are particularly daunting, emigration dramatically increases, countering the long-standing assumption that Adélie penguins are highly philopatric. The research project will 1) determine the effect of age, experience and physiology on individual foraging efficiency; 2) determine the effect of age, experience, and individual quality on breeding success and survival in varying environmental and competitive conditions at the colony level; and 3) develop a comprehensive model for the Ross-Beaufort Island metapopulation dynamics. Broader impacts include training of interns, continuation of public outreach through the highly successful project website penguinscience.com, development of classroom materials and other standards-based instructional resources. proprietary
NSF-ANT09-44532 Application of Detrital Zircon Isotope Characteristics and Sandstone Analysis of Beacon Strata to the Tectonic Evolution of the Antarctic Sector of Gondwana AMD_USAPDC STAC Catalog 2010-07-01 2013-06-30 158.9, -85.1, 165.73, -83 https://cmr.earthdata.nasa.gov/search/concepts/C2532069801-AMD_USAPDC.umm_json Intellectual Merit: The goal of this project is to address relationships between foreland basins and their tectonic settings by combining detrital zircon isotope characteristics and sedimentological data. To accomplish this goal the PIs will develop a detailed geochronology and analyze Hf- and O-isotopes of detrital zircons in sandstones of the Devonian Taylor Group and the Permian-Triassic Victoria Group. These data will allow them to better determine provenance and basin fill, and to understand the nature of the now ice covered source regions in East and West Antarctica. The PIs will document possible unexposed/unknown crustal terrains in West Antarctica, investigate sub-glacial terrains of East Antarctica that were exposed to erosion during Devonian to Triassic time, and determine the evolving provenance and tectonic history of the Devonian to Triassic Gondwana basins in the central Transantarctic Mountains. Detrital zircon data will be interpreted in the context of fluvial dispersal/drainage patterns, sandstone petrology, and sequence stratigraphy. This interpretation will identify source terrains and evolving sediment provenances. Paleocurrent analysis and sequence stratigraphy will determine the timing and nature of changing tectonic conditions associated with development of the depositional basins and document the tectonic history of the Antarctic sector of Gondwana. Results from this study will answer questions about the Panthalassan margin of Gondwana, the Antarctic craton, and the Beacon depositional basin and their respective roles in global tectonics and the geologic and biotic history of Antarctica. The Beacon basin and adjacent uplands played an important role in the development and demise of Gondwanan glaciation through modification of polar climates, development of peat-forming mires, colonization of the landscape by plants, and were a migration route for Mesozoic vertebrates into Antarctica. Broader impacts: This proposal includes support for two graduate students who will participate in the fieldwork, and also support for other students to participate in laboratory studies. Results of the research will be incorporated in classroom teaching at the undergraduate and graduate levels and will help train the next generation of field geologists. Interactions with K-12 science classes will be achieved by video/computer conferencing and satellite phone connections from Antarctica. Another outreach effort is the developing cooperation between the Byrd Polar Research Center and the Center of Science and Industry in Columbus. proprietary
NSF-ANT09-44653_1 Annual Satellite Era Accumulation Patterns Over WAIS Divide: A Study Using Shallow Ice Cores, Near-Surface Radars and Satellites AMD_USAPDC STAC Catalog 2010-08-01 2015-07-31 -110, -80, -119.4, -78.1 https://cmr.earthdata.nasa.gov/search/concepts/C2532069942-AMD_USAPDC.umm_json This award supports a project to broaden the knowledge of annual accumulation patterns over the West Antarctic Ice Sheet by processing existing near-surface radar data taken on the US ITASE traverse in 2000 and by gathering and validating new ultra/super-high-frequency (UHF) radar images of near surface layers (to depths of ~15 m), expanding abilities to monitor recent annual accumulation patterns from point source ice cores to radar lines. Shallow (15 m) ice cores will be collected in conjunction with UHF radar images to confirm that radar echoed returns correspond with annual layers, and/or sub-annual density changes in the near-surface snow, as determined from ice core stable isotopes. This project will additionally improve accumulation monitoring from space-borne instruments by comparing the spatial-radar-derived-annual accumulation time series to the passive microwave time series dating back over 3 decades and covering most of Antarctica. The intellectual merit of this project is that mapping the spatial and temporal variations in accumulation rates over the Antarctic ice sheet is essential for understanding ice sheet responses to climate forcing. Antarctic precipitation rate is projected to increase up to 20% in the coming century from the predicted warming. Accumulation is a key component for determining ice sheet mass balance and, hence, sea level rise, yet our ability to measure annual accumulation variability over the past 5 decades (satellite era) is mostly limited to point-source ice cores. Developing a radar and ice core derived annual accumulation dataset will provide validation data for space-born remote sensing algorithms, climate models and, additionally, establish accumulation trends. The broader impacts of the project are that it will advance discovery and understanding within the climatology, glaciology and remote sensing communities by verifying the use of UHF radars to monitor annual layers as determined by visual, chemical and isotopic analysis from corresponding shallow ice cores and will provide a dataset of annual to near-annual accumulation measurements over the past ~5 decades across WAIS divide from existing radar data and proposed radar data. By determining if temporal changes in the passive microwave signal are correlated with temporal changes in accumulation will help assess the utility of passive microwave remote sensing to monitor accumulation rates over ice sheets for future decades. The project will promote teaching, training and learning, and increase representation of underrepresented groups by becoming involved in the NASA History of Winter project and Thermochron Mission and by providing K-12 teachers with training to monitor snow accumulation and temperature here in the US, linking polar research to the student's backyard. The project will train both undergraduate and graduate students in polar research and will encouraging young investigators to become involved in careers in science. In particular, two REU students will participate in original research projects as part of this larger project, from development of a hypothesis to presentation and publication of the results. The support of a new, young woman scientist will help to increase gender diversity in polar research. proprietary
NSF-ANT09-44727 ASPIRE: Amundsen Sea Polynya International Research Expedition AMD_USAPDC STAC Catalog 2010-10-01 2014-09-30 -118.3, -74.2, -111, -71.6 https://cmr.earthdata.nasa.gov/search/concepts/C2532069918-AMD_USAPDC.umm_json ASPIRE is an NSF-funded project that will examine the ecology of the Amundsen Sea during the Austral summer of 2010. ASPIRE includes an international team of trace metal and carbon chemists, phytoplankton physiologists, microbial and zooplankton ecologists, and physical oceanographers, that will investigate why and how the Amundsen Sea Polynya is so much more productive than other polynyas and whether interannual variability can provide insight to climate-sensitive mechanisms driving carbon fluxes. This project will compliment the existing ASPIRE effort by using 1) experimental manipulations to understand photoacclimation of the dominant phytoplankton taxa under conditions of varying light and trace metal abundance, 2) nutrient addition bioassays to determine the importance of trace metal versus nitrogen limitation of phytoplankton growth, and 3) a numerical ecosystem model to understand the importance of differences in mixing regime, flow field, and Fe sources in controlling phytoplankton bloom dynamics and community composition in this unusually productive polynya system. The research strategy will integrate satellite remote sensing, field-based experimental manipulations, and numerical modeling. Outreach and education include participation in Stanford's Summer Program for Professional Development for Science Teachers, Stanford's School of Earth Sciences high school internship program, and development of curriculum for local science training centers, including the Chabot Space and Science Center. Undergraduate participation and training will include support for both graduate students and undergraduate assistants. proprietary
NSF-ANT10-43145_1 Bromide in Snow in the Sea Ice Zone AMD_USAPDC STAC Catalog 2011-08-15 2015-07-31 164.1005, -77.8645, 166.7398, -77.1188 https://cmr.earthdata.nasa.gov/search/concepts/C2532070132-AMD_USAPDC.umm_json A range of chemical and microphysical pathways in polar latitudes, including spring time (tropospheric) ozone depletion, oxidative pathways for mercury, and cloud condensation nuclei (CCN) production leading to changes in the cloud cover and attendant surface energy budgets, have been invoked as being dependent upon the emission of halogen gases formed in sea-ice. The prospects for climate warming induced reductions in sea ice extent causing alteration of these incompletely known surface-atmospheric feedbacks and interactions requires confirmation of mechanistic details in both laboratory studies and field campaigns. One such mechanistic question is how bromine (BrO and Br) enriched snow migrates or is formed through processes in sea-ice, prior to its subsequent mobilization as an aerosol fraction into the atmosphere by strong winds. Once aloft, it may react with ozone and other atmospheric species. Dartmouth researchers will collect snow from the surface of sea ice, from freely blowing snow and in sea-ice cores from Cape Byrd, Ross Sea. A range of spectroscopic, microanalytic and and microstructural approaches will be subsequently used to determine the Br distribution gradients through sea-ice, in order to shed light on how sea-ice first forms and then releases bromine species into the polar atmospheric boundary layer. proprietary
-NSF-ANT10-43485_1 A New Reconstruction of the Last West Antarctic Ice Sheet Deglaciation in the Ross Sea ALL STAC Catalog 2011-07-01 2015-06-30 -160, -78, -150, -68 https://cmr.earthdata.nasa.gov/search/concepts/C2532069944-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary
NSF-ANT10-43485_1 A New Reconstruction of the Last West Antarctic Ice Sheet Deglaciation in the Ross Sea AMD_USAPDC STAC Catalog 2011-07-01 2015-06-30 -160, -78, -150, -68 https://cmr.earthdata.nasa.gov/search/concepts/C2532069944-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary
-NSF-ANT10-43517 A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea AMD_USAPDC STAC Catalog 2011-07-01 2015-06-30 163.5, -78.32, 165.35, -77.57 https://cmr.earthdata.nasa.gov/search/concepts/C2532070432-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary
+NSF-ANT10-43485_1 A New Reconstruction of the Last West Antarctic Ice Sheet Deglaciation in the Ross Sea ALL STAC Catalog 2011-07-01 2015-06-30 -160, -78, -150, -68 https://cmr.earthdata.nasa.gov/search/concepts/C2532069944-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary
NSF-ANT10-43517 A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea ALL STAC Catalog 2011-07-01 2015-06-30 163.5, -78.32, 165.35, -77.57 https://cmr.earthdata.nasa.gov/search/concepts/C2532070432-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary
-NSF-ANT10-43554_1 Activation of high-elevation alluvial fans in the Transantarctic Mountains - a proxy for Plio-Pleistocene warmth along East Antarctic ice margins ALL STAC Catalog 2011-07-01 2015-06-30 161.5, -77.5, 161.5, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532070458-AMD_USAPDC.umm_json The PIs propose to address the question of whether ice surface melting zones developed at high elevations during warm climatic phases in the Transantarctic Mountains. Evidence from sediment cores drilled by the ANDRILL program indicates that open water in the Ross Sea could have been a source of warmth during Pliocene and Pleistocene. The question is whether marine warmth penetrated inland to the ice sheet margins. The glacial record may be ill suited to answer this question, as cold-based glaciers may respond too slowly to register brief warmth. Questions also surround possible orbital controls on regional climate and ice sheet margins. Northern Hemisphere insolation at obliquity and precession timescales is thought to control Antarctic climate through oceanic or atmospheric connections, but new thinking suggests that the duration of Southern Hemisphere summer may be more important. The PIs propose to use high elevation alluvial deposits in the Transantarctic Mountains as a proxy for inland warmth. These relatively young fans, channels, and debris flow levees stand out as visible evidence for the presence of melt water in an otherwise ancient, frozen landscape. Based on initial analyses of an alluvial fan in the Olympus Range, these deposits are sensitive recorders of rare melt events that occur at orbital timescales. For their study they will 1) map alluvial deposits using aerial photography, satellite imagery and GPS assisted field surveys to establish water sources and to quantify parameters effecting melt water production, 2) date stratigraphic sequences within these deposits using OSL, cosmogenic nuclide, and interbedded volcanic ash chronologies, 3) use paired nuclide analyses to estimate exposure and burial times, and rates of deposition and erosion, and 4) use micro and regional scale climate modeling to estimate paleoenvironmental conditions associated with melt events. This study will produce a record of inland melting from sites adjacent to ice sheet margins to help determine controls on regional climate along margins of the East Antarctic Ice Sheet to aid ice sheet and sea level modeling studies. The proposal will support several graduate and undergraduates. A PhD student will be supported on existing funding. The PIs will work with multiple K-12 schools to conduct interviews and webcasts from Antarctica and they will make follow up visits to classrooms after the field season is complete. proprietary
+NSF-ANT10-43517 A new reconstruction of the last West Antarctic Ice Sheet deglaciation in the Ross Sea AMD_USAPDC STAC Catalog 2011-07-01 2015-06-30 163.5, -78.32, 165.35, -77.57 https://cmr.earthdata.nasa.gov/search/concepts/C2532070432-AMD_USAPDC.umm_json This award supports a project to develop a better understanding of the response of the WAIS to climate change. The timing of the last deglaciation of the western Ross Sea will be improved using in situ terrestrial cosmogenic nuclides (3He, 10Be, 14C, 26Al, 36Cl) to date glacial erratics at key areas and elevations along the western Ross Sea coast. A state-of-the art ice sheet-shelf model will be used to identify mechanisms of deglaciation of the Ross Sea sector of WAIS. The model results and forcing will be compared with observations including the new cosmogenic data proposed here, with the aim of better determining and understanding the history and causes of WAIS deglaciation in the Ross Sea. There is considerable uncertainty, however, in the history of grounding line retreat from its last glacial maximum position, and virtually nothing is known about the timing of ice- surface lowering prior to ~10,000 years ago. Given these uncertainties, we are currently unable to assess one of the most important questions regarding the last deglaciation of the global ice sheets, namely as to whether the Ross Sea sector of WAIS contributed significantly to meltwater pulse 1A (MWP-1A), an extraordinarily rapid (~500-year duration) episode of ~20 m sea-level rise that occurred ~14,500 years ago. The intellectual merit of this project is that recent observations of startling changes at the margins of the Greenland and Antarctic ice sheets indicate that dynamic responses to warming may play a much greater role in the future mass balance of ice sheets than considered in current numerical projections of sea level rise. The broader impacts of this work are that it has direct societal relevance to developing an improved understanding of the response of the West Antarctic ice sheet to current and possible future environmental changes including the sea-level response to glacier and ice sheet melting due to global warming. The PI will communicate results from this project to a variety of audiences through the publication of peer-reviewed papers and by giving talks to public audiences. Finally the project will support a graduate student and undergraduate students in all phases of field-work, laboratory work and data interpretation. proprietary
NSF-ANT10-43554_1 Activation of high-elevation alluvial fans in the Transantarctic Mountains - a proxy for Plio-Pleistocene warmth along East Antarctic ice margins AMD_USAPDC STAC Catalog 2011-07-01 2015-06-30 161.5, -77.5, 161.5, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532070458-AMD_USAPDC.umm_json The PIs propose to address the question of whether ice surface melting zones developed at high elevations during warm climatic phases in the Transantarctic Mountains. Evidence from sediment cores drilled by the ANDRILL program indicates that open water in the Ross Sea could have been a source of warmth during Pliocene and Pleistocene. The question is whether marine warmth penetrated inland to the ice sheet margins. The glacial record may be ill suited to answer this question, as cold-based glaciers may respond too slowly to register brief warmth. Questions also surround possible orbital controls on regional climate and ice sheet margins. Northern Hemisphere insolation at obliquity and precession timescales is thought to control Antarctic climate through oceanic or atmospheric connections, but new thinking suggests that the duration of Southern Hemisphere summer may be more important. The PIs propose to use high elevation alluvial deposits in the Transantarctic Mountains as a proxy for inland warmth. These relatively young fans, channels, and debris flow levees stand out as visible evidence for the presence of melt water in an otherwise ancient, frozen landscape. Based on initial analyses of an alluvial fan in the Olympus Range, these deposits are sensitive recorders of rare melt events that occur at orbital timescales. For their study they will 1) map alluvial deposits using aerial photography, satellite imagery and GPS assisted field surveys to establish water sources and to quantify parameters effecting melt water production, 2) date stratigraphic sequences within these deposits using OSL, cosmogenic nuclide, and interbedded volcanic ash chronologies, 3) use paired nuclide analyses to estimate exposure and burial times, and rates of deposition and erosion, and 4) use micro and regional scale climate modeling to estimate paleoenvironmental conditions associated with melt events. This study will produce a record of inland melting from sites adjacent to ice sheet margins to help determine controls on regional climate along margins of the East Antarctic Ice Sheet to aid ice sheet and sea level modeling studies. The proposal will support several graduate and undergraduates. A PhD student will be supported on existing funding. The PIs will work with multiple K-12 schools to conduct interviews and webcasts from Antarctica and they will make follow up visits to classrooms after the field season is complete. proprietary
-NSF-ANT10-43621 A Comparison of Conjugate Auroral Electojet Indices AMD_USAPDC STAC Catalog 2011-06-01 2013-05-31 -180, -79.5, 180, -54.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069751-AMD_USAPDC.umm_json The auroral electrojet index (AE) is used as an indicator of geomagnetic activity at high latitudes representing the strength of auroral electrojet currents in the Northern polar ionosphere. A similar AE index for the Southern hemisphere is not available due to lack of complete coverage the Southern auroral zone (half of which extends over the ocean) with continuous magnetometer observations. While in general global auroral phenomena are expected to be conjugate, differences have been observed in the conjugate observations from the ground and from the Earth's satellites. These differences indicate a need for an equivalent Southern auroral geomagnetic activity index. The goal of this award is to create the Southern AE (SAE) index that would accurately reflect auroral activity in that hemisphere. With this index, it would be possible to investigate the similarities and the cause of differences between the SAE and 'standard' AE index from the Northern hemisphere. It would also make it possible to identify when the SAE does not provide a reliable calculation of the Southern hemisphere activity, and to determine when it is statistically beneficial to consider the SAE index in addition to the standard AE while analyzing geospace data from the Northern and Southern polar regions. The study will address these questions by creating the SAE index and its 'near-conjugate' NAE index from collected Antarctic magnetometer data, and will analyze variations in the cross-correlation of these indices and their differences as a function of geomagnetic activity, season, Universal Time, Magnetic Local Time, and interplanetary magnetic field and solar wind plasma parameters. The broader impact resulting from the proposed effort is in its importance to the worldwide geospace scientific community that currently uses only the standard AE index in a variety of geospace models as necessary input. proprietary
+NSF-ANT10-43554_1 Activation of high-elevation alluvial fans in the Transantarctic Mountains - a proxy for Plio-Pleistocene warmth along East Antarctic ice margins ALL STAC Catalog 2011-07-01 2015-06-30 161.5, -77.5, 161.5, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532070458-AMD_USAPDC.umm_json The PIs propose to address the question of whether ice surface melting zones developed at high elevations during warm climatic phases in the Transantarctic Mountains. Evidence from sediment cores drilled by the ANDRILL program indicates that open water in the Ross Sea could have been a source of warmth during Pliocene and Pleistocene. The question is whether marine warmth penetrated inland to the ice sheet margins. The glacial record may be ill suited to answer this question, as cold-based glaciers may respond too slowly to register brief warmth. Questions also surround possible orbital controls on regional climate and ice sheet margins. Northern Hemisphere insolation at obliquity and precession timescales is thought to control Antarctic climate through oceanic or atmospheric connections, but new thinking suggests that the duration of Southern Hemisphere summer may be more important. The PIs propose to use high elevation alluvial deposits in the Transantarctic Mountains as a proxy for inland warmth. These relatively young fans, channels, and debris flow levees stand out as visible evidence for the presence of melt water in an otherwise ancient, frozen landscape. Based on initial analyses of an alluvial fan in the Olympus Range, these deposits are sensitive recorders of rare melt events that occur at orbital timescales. For their study they will 1) map alluvial deposits using aerial photography, satellite imagery and GPS assisted field surveys to establish water sources and to quantify parameters effecting melt water production, 2) date stratigraphic sequences within these deposits using OSL, cosmogenic nuclide, and interbedded volcanic ash chronologies, 3) use paired nuclide analyses to estimate exposure and burial times, and rates of deposition and erosion, and 4) use micro and regional scale climate modeling to estimate paleoenvironmental conditions associated with melt events. This study will produce a record of inland melting from sites adjacent to ice sheet margins to help determine controls on regional climate along margins of the East Antarctic Ice Sheet to aid ice sheet and sea level modeling studies. The proposal will support several graduate and undergraduates. A PhD student will be supported on existing funding. The PIs will work with multiple K-12 schools to conduct interviews and webcasts from Antarctica and they will make follow up visits to classrooms after the field season is complete. proprietary
NSF-ANT10-43621 A Comparison of Conjugate Auroral Electojet Indices ALL STAC Catalog 2011-06-01 2013-05-31 -180, -79.5, 180, -54.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069751-AMD_USAPDC.umm_json The auroral electrojet index (AE) is used as an indicator of geomagnetic activity at high latitudes representing the strength of auroral electrojet currents in the Northern polar ionosphere. A similar AE index for the Southern hemisphere is not available due to lack of complete coverage the Southern auroral zone (half of which extends over the ocean) with continuous magnetometer observations. While in general global auroral phenomena are expected to be conjugate, differences have been observed in the conjugate observations from the ground and from the Earth's satellites. These differences indicate a need for an equivalent Southern auroral geomagnetic activity index. The goal of this award is to create the Southern AE (SAE) index that would accurately reflect auroral activity in that hemisphere. With this index, it would be possible to investigate the similarities and the cause of differences between the SAE and 'standard' AE index from the Northern hemisphere. It would also make it possible to identify when the SAE does not provide a reliable calculation of the Southern hemisphere activity, and to determine when it is statistically beneficial to consider the SAE index in addition to the standard AE while analyzing geospace data from the Northern and Southern polar regions. The study will address these questions by creating the SAE index and its 'near-conjugate' NAE index from collected Antarctic magnetometer data, and will analyze variations in the cross-correlation of these indices and their differences as a function of geomagnetic activity, season, Universal Time, Magnetic Local Time, and interplanetary magnetic field and solar wind plasma parameters. The broader impact resulting from the proposed effort is in its importance to the worldwide geospace scientific community that currently uses only the standard AE index in a variety of geospace models as necessary input. proprietary
+NSF-ANT10-43621 A Comparison of Conjugate Auroral Electojet Indices AMD_USAPDC STAC Catalog 2011-06-01 2013-05-31 -180, -79.5, 180, -54.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069751-AMD_USAPDC.umm_json The auroral electrojet index (AE) is used as an indicator of geomagnetic activity at high latitudes representing the strength of auroral electrojet currents in the Northern polar ionosphere. A similar AE index for the Southern hemisphere is not available due to lack of complete coverage the Southern auroral zone (half of which extends over the ocean) with continuous magnetometer observations. While in general global auroral phenomena are expected to be conjugate, differences have been observed in the conjugate observations from the ground and from the Earth's satellites. These differences indicate a need for an equivalent Southern auroral geomagnetic activity index. The goal of this award is to create the Southern AE (SAE) index that would accurately reflect auroral activity in that hemisphere. With this index, it would be possible to investigate the similarities and the cause of differences between the SAE and 'standard' AE index from the Northern hemisphere. It would also make it possible to identify when the SAE does not provide a reliable calculation of the Southern hemisphere activity, and to determine when it is statistically beneficial to consider the SAE index in addition to the standard AE while analyzing geospace data from the Northern and Southern polar regions. The study will address these questions by creating the SAE index and its 'near-conjugate' NAE index from collected Antarctic magnetometer data, and will analyze variations in the cross-correlation of these indices and their differences as a function of geomagnetic activity, season, Universal Time, Magnetic Local Time, and interplanetary magnetic field and solar wind plasma parameters. The broader impact resulting from the proposed effort is in its importance to the worldwide geospace scientific community that currently uses only the standard AE index in a variety of geospace models as necessary input. proprietary
NSF-ANT10-44978 BICEP2 and SPUD - A Search for Inflation with Degree-Scale Polarimetry from the South Pole AMD_USAPDC STAC Catalog 2008-05-15 2017-09-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532070162-AMD_USAPDC.umm_json BICEP2 and SPUD - A Search for Inflation with Degree-Scale Polarimetry from the South Pole. The proposed work is a four-year program of research activities directed toward upgrading the BICEP (Background Imaging of Cosmic Extragalactic Polarization) telescope operating at South Pole since early 2006 to reach far =stretching goals of detection of the Cosmic Gravitational-wave Background (CGB). This telescope is a first Cosmic Microwave Background (CMB) B-mode polarimeter, specifically designed to search for CGB signatures while mapping ~2% of the southern sky that is free of the Milky Way foreground galactic radiation at 100 GH and 150 GHz. The BICEP1 telescope will reach its designed sensitivity by the end of 2008. A coordinated series of upgrades to BICEP1 will provide the increased sensitivity and more exacting control of instrumental effects and potential confusion from galactic foregrounds necessary to search for the B-mode signal more deeply through space. A powerful new 150 GHz receiver, BICEP2, will replace the current detector at the beginning of 2009, increasing the mapping speed almost ten-fold. In 2010, the first of a series of compact, mechanically-cooled receivers (called SPUD - Small Polarimeter Upgrade for DASI) will be deployed on the existing DASI mount and tower, providing similar mapping speed at 100 GHz in parallel with BICEP2. The latter instrument will reach (and exceed with the addition of a SPUD polarimeter) the target sensitivity r = 0.15 set forth by the Interagency (NSF/NASA/DoE) Task Force on CMB Research for a future space mission dedicated to the detection and characterization of primordial gravitational waves. This Task Force has identified detection of the Inflation's gravitational waves as the number one priority for the modern cosmology. More broadly, as the cosmology captures a lot of the public imagination, it is a remarkably effective vehicle for stimulating interest in basic science. The CGB detection would be to Inflation what the discovery of the CMB radiation was to the Big Bang. The project will contribute to the training of the next generation of cosmologists by integrating graduate and undergraduate education with the technology and instrumentation development, astronomical observations and scientific analysis. Sharing of the forefront research results with public extends the new knowledge beyond the universities. This project will be undertaken in collaboration between the California Institute of Technology and the University of Chicago. proprietary
NSF-ANT10-48343_1 CAREER: Deciphering Antarctic Climate Variability during the Temperate/Polar Transition and Improving Climate Change Literacy in Louisiana through a Companion Outreach Program AMD_USAPDC STAC Catalog 2011-03-01 2016-02-29 57.217, -70.373, 153.359, -63.664 https://cmr.earthdata.nasa.gov/search/concepts/C2532069731-AMD_USAPDC.umm_json Intellectual Merit: The PI proposes a high-resolution paleoenvironmental study of pollen, spore, fresh-water algae, and dinoflagellate cyst assemblages to investigate the palynological record of sudden warming events in the Antarctic as recorded by the ANDRILL SMS drill core and terrestrial sections. These data will be used to derive causal mechanisms for these rapid climate events. Terrestrial samples will be obtained at various altitudes in the Dry Valleys region. The pollen and spores will provide data on atmospheric conditions, while the algae will provide data on sea-surface conditions. These data will help identify the triggers for sudden climatic shifts. If they are caused by changes in oceanic currents, a signal will be visible in the dinocyst assemblages first as currents influence their distribution. Conversely, if these shifts are triggered by atmospheric factors, then the shifts will first affect plants and be visible in the pollen record. Broader impacts: The PI proposes a suite of activities to bring field-based climate change research to a broader audience. The PI will advise a diverse group of students and educators. The palynological data collected as part of this research will be utilized, in part, to develop new lectures on Antarctic palynology and these new lectures will be made available via a collaboration with the LSU HHMI program. In addition, the PI will direct three Louisiana middle-school teachers as they pursue a Masters of Natural Science for science educators. These teachers will help the PI develop a professional development program for science teachers. Community-based activities will be organized to raise science awareness and alert students and the public of opportunities in science. proprietary
NSF-ANT10-63592_1 Application for an Early-concept Grant for Exploratory Reasearch (EAGER) to develop a Pathway/Genome Database (PGDB) for the Southern Ocean Haptophyte Phaeocystis Antarctica. AMD_USAPDC STAC Catalog 2011-05-15 2015-04-30 -75.8, -67.12, -62.37, -61.08 https://cmr.earthdata.nasa.gov/search/concepts/C2532069964-AMD_USAPDC.umm_json Phaeocystis antarctica is capable of forming blooms that are denser and more extensive than any other member of the Southern Ocean phytoplankton community. The factors that enable P Antarctica to dominate its competitors are not clear but are likely related to its colonial lifestyle. The goal of the project is to map all the reactions in metabolic pathways that are key to defining the ecological niche of Phaeocystis antarctica by developing a Pathway/Genome Database (PGDB) using Pathway Tools software. The investigators will assign proteins and enzymes to key pathways in P. Antarctica, continually improve and edit the database as the full Phaeocystis genome comes online, and host the database on the BioCyc webpage. The end product will be the first database for a eukaryotic phytoplankton genome where researchers can query extant metabolic pathways and place new proteins and enzymes of interest within metabolic networks. The risk is that a substantial percentage of catalytic enzymes may belong to pathways that are poorly characterized. The science impact is to link genomes to metabolic potential in the context of Phaeocystis life history but also in comparison to other organisms across the tree of life. The education and outreach includes work with a high school teacher and intern and curriculum development. proprietary
NSF-ANT11-42018_1 Adaptive Responses of Phaeocystis Populations in Antarctic Ecosystems AMD_USAPDC STAC Catalog 2011-05-15 2015-04-30 -75.8, -67.12, -62.37, -61.08 https://cmr.earthdata.nasa.gov/search/concepts/C2532070261-AMD_USAPDC.umm_json Global climate change is having significant effects on areas of the Southern Ocean, and a better understanding of this ecosystem will permit predictions about the large-scale implications of these shifts. The haptophyte Phaeocystis antarctica is an important component of the phytoplankton communities in this region, but little is known about the factors controlling its distribution. Preliminary data suggest that P. antarctica posses unique adaptations that allow it to thrive in regions with dynamic light regimes. This research will extend these results to identify the physiological and genetic mechanisms that affect the growth and distribution of P. antarctica. This work will use field and laboratory-based studies and a suite of modern molecular techniques to better understand the biogeography and physiology of this key organism. Results will be widely disseminated through publications as well as through presentations at national and international meetings. In addition, raw data will be made available through open-access databases. This project will support the research and training of two graduate students and will foster an established international collaboration with Dutch scientists. Researchers on this project will participate in outreach programs targeting K12 teachers as well as high school students. proprietary
NSF-ANT11-42018_1 Adaptive Responses of Phaeocystis Populations in Antarctic Ecosystems ALL STAC Catalog 2011-05-15 2015-04-30 -75.8, -67.12, -62.37, -61.08 https://cmr.earthdata.nasa.gov/search/concepts/C2532070261-AMD_USAPDC.umm_json Global climate change is having significant effects on areas of the Southern Ocean, and a better understanding of this ecosystem will permit predictions about the large-scale implications of these shifts. The haptophyte Phaeocystis antarctica is an important component of the phytoplankton communities in this region, but little is known about the factors controlling its distribution. Preliminary data suggest that P. antarctica posses unique adaptations that allow it to thrive in regions with dynamic light regimes. This research will extend these results to identify the physiological and genetic mechanisms that affect the growth and distribution of P. antarctica. This work will use field and laboratory-based studies and a suite of modern molecular techniques to better understand the biogeography and physiology of this key organism. Results will be widely disseminated through publications as well as through presentations at national and international meetings. In addition, raw data will be made available through open-access databases. This project will support the research and training of two graduate students and will foster an established international collaboration with Dutch scientists. Researchers on this project will participate in outreach programs targeting K12 teachers as well as high school students. proprietary
NSF-ANT11-42102 An Integrated Ecological Investigation of McMurdo Dry Valley's Active Soil Microbial Communities AMD_USAPDC STAC Catalog 2012-07-01 2015-06-30 161, -77.5, 164, -77 https://cmr.earthdata.nasa.gov/search/concepts/C2532070421-AMD_USAPDC.umm_json The McMurdo Dry Valleys in Antarctica are among the coldest, driest habitats on the planet. Previous research has documented the presence of surprisingly diverse microbial communities in the soils of the Dry Valleys despite these extreme conditions. However, the degree to which these organisms are active is unknown; it is possible that much of this diversity reflects microbes that have blown into this environment that are subsequently preserved in these cold, dry conditions. This research will use modern molecular techniques to answer a fundamental question regarding these communities: which organisms are active and how do they live in such extreme conditions? The research will include manipulations to explore how changes in water, salt and carbon affect the microbial community, to address the role that these organisms play in nutrient cycling in this environment. The results of this work will provide a broader understanding of how life adapts to such extreme conditions as well as the role of dormancy in the life history of microorganisms. Results will be widely disseminated through publications as well as through presentations at national and international meetings; raw data will be made available through a high-profile web-based portal. The research will support two graduate students, two undergraduate research assistants and a postdoctoral fellow. The results will be incorporated into a webinar targeted to secondary and post-secondary educators and a complimentary hands-on class activity kit will be developed and made available to various teacher and outreach organizations. proprietary
-NSF-ANT12-41487 A Planning Workshop for a McMurdo Dry Valleys Terrestrial Observation Network AMD_USAPDC STAC Catalog 2012-06-01 2013-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2532069735-AMD_USAPDC.umm_json This award will support the participation of US scientists in an international planning workshop devoted to discussions of how to best facilitate and coordinate international efforts for terrestrial system studies at the McMurdo Dry Valleys of Antarctica. To date, various aspects of the different Dry Valley landscape features (lakes, soils, glaciers, streams) and their biota have been studied most intensively by US and New Zealand scientists, but these efforts could significantly improve their explanatory power if they were coordinated so as to reduce redundancy, decrease environmental degradation and, most importantly, produce comparable datasets. Additionally, many of the present environmental management programs are based on the past baseline composition and location of biotic communities. As these communities become rearranged across the valleys in the future there is interest in assessing whether today's management plans are adequate. To efficiently move these research programs forward for the McMurdo Dry Valleys requires a coordinated, interdisciplinary, long-term data monitoring and observation network. The ultimate objectives of the workshop are to: i) identify the optimal, complementary suites of measurements required to assess and address key processes associated with environmental change in Dry Valley ecosystems; ii) develop standards and protocols for gathering the most critical biotic and abiotic measurements associated with the key processes driving environmental change; iii) generate a draft data coordination and development plan that will maximize the utility of these data; iv) assess the effectiveness of current McMurdo Dry Valley ASMA (Antarctic Special Management Area) environmental protection guidelines. proprietary
NSF-ANT12-41487 A Planning Workshop for a McMurdo Dry Valleys Terrestrial Observation Network ALL STAC Catalog 2012-06-01 2013-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2532069735-AMD_USAPDC.umm_json This award will support the participation of US scientists in an international planning workshop devoted to discussions of how to best facilitate and coordinate international efforts for terrestrial system studies at the McMurdo Dry Valleys of Antarctica. To date, various aspects of the different Dry Valley landscape features (lakes, soils, glaciers, streams) and their biota have been studied most intensively by US and New Zealand scientists, but these efforts could significantly improve their explanatory power if they were coordinated so as to reduce redundancy, decrease environmental degradation and, most importantly, produce comparable datasets. Additionally, many of the present environmental management programs are based on the past baseline composition and location of biotic communities. As these communities become rearranged across the valleys in the future there is interest in assessing whether today's management plans are adequate. To efficiently move these research programs forward for the McMurdo Dry Valleys requires a coordinated, interdisciplinary, long-term data monitoring and observation network. The ultimate objectives of the workshop are to: i) identify the optimal, complementary suites of measurements required to assess and address key processes associated with environmental change in Dry Valley ecosystems; ii) develop standards and protocols for gathering the most critical biotic and abiotic measurements associated with the key processes driving environmental change; iii) generate a draft data coordination and development plan that will maximize the utility of these data; iv) assess the effectiveness of current McMurdo Dry Valley ASMA (Antarctic Special Management Area) environmental protection guidelines. proprietary
+NSF-ANT12-41487 A Planning Workshop for a McMurdo Dry Valleys Terrestrial Observation Network AMD_USAPDC STAC Catalog 2012-06-01 2013-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2532069735-AMD_USAPDC.umm_json This award will support the participation of US scientists in an international planning workshop devoted to discussions of how to best facilitate and coordinate international efforts for terrestrial system studies at the McMurdo Dry Valleys of Antarctica. To date, various aspects of the different Dry Valley landscape features (lakes, soils, glaciers, streams) and their biota have been studied most intensively by US and New Zealand scientists, but these efforts could significantly improve their explanatory power if they were coordinated so as to reduce redundancy, decrease environmental degradation and, most importantly, produce comparable datasets. Additionally, many of the present environmental management programs are based on the past baseline composition and location of biotic communities. As these communities become rearranged across the valleys in the future there is interest in assessing whether today's management plans are adequate. To efficiently move these research programs forward for the McMurdo Dry Valleys requires a coordinated, interdisciplinary, long-term data monitoring and observation network. The ultimate objectives of the workshop are to: i) identify the optimal, complementary suites of measurements required to assess and address key processes associated with environmental change in Dry Valley ecosystems; ii) develop standards and protocols for gathering the most critical biotic and abiotic measurements associated with the key processes driving environmental change; iii) generate a draft data coordination and development plan that will maximize the utility of these data; iv) assess the effectiveness of current McMurdo Dry Valley ASMA (Antarctic Special Management Area) environmental protection guidelines. proprietary
NSF-ANT13-55533_1 A Multi-decadal Record of Antarctic Benthos: Image Analysis to Maximize Data Utilization AMD_USAPDC STAC Catalog 2013-10-01 2015-09-30 163, -78.5, 167, -78 https://cmr.earthdata.nasa.gov/search/concepts/C2532070231-AMD_USAPDC.umm_json Antarctic benthic communities are characterized by many species of sponges (Phylum Porifera), long thought to exhibit extremely slow demographic patterns of settlement, growth and reproduction. This project will analyze many hundreds of diver and remotely operated underwater vehicle photographs documenting a unique, episodic settlement event that occurred between 2000 and 2010 in McMurdo Sound that challenges this paradigm of slow growth. Artificial structures were placed on the seafloor between 1967 and 1974 at several sites, but no sponges were observed to settle on these structures until 2004. By 2010 some 40 species of sponges had settled and grown to be surprisingly large. Given the paradigm of slow settlement and growth supported by the long observation period (37 years, 1967-2004), this extraordinary large-scale settlement and rapid growth over just a 6-year time span is astonishing. This project utilizes image processing software (ImageJ) to obtain metrics (linear dimensions to estimate size, frequency, percent cover) for sponges and other fauna visible in the photographs. It uses R to conduct multidimensional scaling to ordinate community data and ANOSIM to test for differences of community data among sites and times and structures. It will also use SIMPER and ranked species abundances to discriminate species responsible for any differences. This work focuses on Antarctic sponges, but the observations of massive episodic recruitment and growth are important to understanding seafloor communities worldwide. Ecosystems are composed of populations, and populations are ecologically described by their distribution and abundance. A little appreciated fact is that sponges often dominate marine communities, but because sponges are so hard to study, most workers focus on other groups such as corals, kelps, or bivalves. Because most sponges settle and grow slowly their life history is virtually unstudied. The assumption of relative stasis of the Antarctic seafloor community is common, and this project will shatter this paradigm by documenting a dramatic episodic event. Finally, the project takes advantage of old transects from the 1960s and 1970s and compares them with extensive 2010 surveys of the same habitats and sometimes the same intact transect lines, offering a long-term perspective of community change. The investigators will publish these results in peer-reviewed journals, give presentations to the general public and will involve students from local outreach programs, high schools, and undergraduates at UCSD to help with the analysis. proprietary
NSF-ANT13-55533_1 A Multi-decadal Record of Antarctic Benthos: Image Analysis to Maximize Data Utilization ALL STAC Catalog 2013-10-01 2015-09-30 163, -78.5, 167, -78 https://cmr.earthdata.nasa.gov/search/concepts/C2532070231-AMD_USAPDC.umm_json Antarctic benthic communities are characterized by many species of sponges (Phylum Porifera), long thought to exhibit extremely slow demographic patterns of settlement, growth and reproduction. This project will analyze many hundreds of diver and remotely operated underwater vehicle photographs documenting a unique, episodic settlement event that occurred between 2000 and 2010 in McMurdo Sound that challenges this paradigm of slow growth. Artificial structures were placed on the seafloor between 1967 and 1974 at several sites, but no sponges were observed to settle on these structures until 2004. By 2010 some 40 species of sponges had settled and grown to be surprisingly large. Given the paradigm of slow settlement and growth supported by the long observation period (37 years, 1967-2004), this extraordinary large-scale settlement and rapid growth over just a 6-year time span is astonishing. This project utilizes image processing software (ImageJ) to obtain metrics (linear dimensions to estimate size, frequency, percent cover) for sponges and other fauna visible in the photographs. It uses R to conduct multidimensional scaling to ordinate community data and ANOSIM to test for differences of community data among sites and times and structures. It will also use SIMPER and ranked species abundances to discriminate species responsible for any differences. This work focuses on Antarctic sponges, but the observations of massive episodic recruitment and growth are important to understanding seafloor communities worldwide. Ecosystems are composed of populations, and populations are ecologically described by their distribution and abundance. A little appreciated fact is that sponges often dominate marine communities, but because sponges are so hard to study, most workers focus on other groups such as corals, kelps, or bivalves. Because most sponges settle and grow slowly their life history is virtually unstudied. The assumption of relative stasis of the Antarctic seafloor community is common, and this project will shatter this paradigm by documenting a dramatic episodic event. Finally, the project takes advantage of old transects from the 1960s and 1970s and compares them with extensive 2010 surveys of the same habitats and sometimes the same intact transect lines, offering a long-term perspective of community change. The investigators will publish these results in peer-reviewed journals, give presentations to the general public and will involve students from local outreach programs, high schools, and undergraduates at UCSD to help with the analysis. proprietary
NSF-ANT90-24544 Atmospheric Boundary Layer Measurements on the Weddell Sea Drifting Station AMD_USAPDC STAC Catalog 1992-02-21 1992-06-05 -53.8, -71.4, -43.2, -61.2 https://cmr.earthdata.nasa.gov/search/concepts/C2534797194-AMD_USAPDC.umm_json Location: Ice camp on perennial sea ice in the southwestern corner of the Weddell Sea, Antarctic The first direct radiative and turbulent surface flux measurements ever made over floating Antarctic sea ice. The data are from Ice Station Weddell as it drifted in the western Weddell Sea from February to late May 1992. Data Types: Hourly measurements of the turbulent surface fluxes of momentum and sensible and latent heat by eddy covariance at a height of 4.65 m above snow-covered sea ice. Instruments were a 3-axis sonic anemometer/thermometer and a Lyman-alpha hygrometer. Hourly, surface-level measurements of the four radiation components: in-coming and out-going longwave and shortwave radiation. Instruments were hemispherical pyranometers and pyrgeometers. Hourly mean values of standard meteorological variables: air temperature, dew point temperature, wind speed and direction, barometric pressure, surface temperature. Instruments were a propeller-vane for wind speed and direction and cooled-mirror dew-point hygrometers and platinum resistance thermometers for dew-points and temperatures. Surface temperature came from a Barnes PRT-5 infrared thermometer. Flux Data The entire data kit is bundled as a zip file named ISW_Flux_Data.zip The main data file is comma delimited. The README file is ASCII. The associated reprints of publications are in pdf. Radiosounding data: On Ice Station Weddell, typically twice a day from 21 February through 4 June 1992 made with both tethered (i.e., only boundary-layer profiles) and (more rarely) free-flying sondes that did not measure wind speed. (168 soundings). ISW Radiosoundings The entire data kit is bundled as a zip file named ISW_Radiosounding.zip. The README file is in ASCII. Two summary files that include the list of sounding and the declinations are in ASCII. The 168 individual sounding files are in ASCII. Two supporting publications that describe the data and some analyses are in pdf. Radiosounding data collected from the Russian ship Akademic Fedorov from 26 May through 5 June 1992 at 6-hourly intervals as it approached Ice Station Weddell from the north. These soundings include wind vector, temperature, humidity, and pressure. (40 soundings) Akademic Federov Radiosoundings The entire data kit is bundled as a zip file named Akad_Federov_Radiosounding.zip. The README file is in ASCII. A summary file that lists the soundings is in ASCII. The 40 individual sounding files are in ASCII. Two supporting publications that describe the data and some analyses are in pdf. Documentation: Andreas, E. L, and K. J. Claffey, 1995: Air-ice drag coefficients in the western Weddell Sea: 1. Values deduced from profile measurements. Journal of Geophysical Research, 100, 4821–4831. Andreas, E. L, K. J. Claffey, and A. P. Makshtas, 2000: Low-level atmospheric jets and inversions over the western Weddell Sea. Boundary-Layer Meteorology, 97, 459–486. Andreas, E. L, R. E. Jordan, and A. P. Makshtas, 2004: Simulations of snow, ice, and near-surface atmospheric processes on Ice Station Weddell. Journal of Hydrometeorology, 5, 611–624. Andreas, E. L, R. E. Jordan, and A. P. Makshtas, 2005: Parameterizing turbulent exchange over sea ice: The Ice Station Weddell results. Boundary-Layer Meteorology, 114, 439–460. Andreas, E. L, P. O. G. Persson, R. E. Jordan, T. W. Horst, P. S. Guest, A. A. Grachev, and C. W. Fairall, 2010: Parameterizing turbulent exchange over sea ice in winter. Journal of Hydrometeorology, 11, 87–104. Claffey, K. J., E. L Andreas, and A. P. Makshtas, 1994: Upper-air data collected on Ice Station Weddell. Special Report 94-25, U.S. Army Cold Regions Research and Engineering Laboratory, Hanover, NH, 62 pp. ISW Group, 1993: Weddell Sea exploration from ice station. Eos, Transactions, American Geophysical Union, 74, 121–126. Makshtas, A. P., E. L Andreas, P. N. Svyaschennikov, and V. F. Timachev, 1999: Accounting for clouds in sea ice models. Atmospheric Research, 52, 77–113. proprietary
@@ -12531,8 +12532,8 @@ NSIDC-0314_1 Atmospheric CO2 and Climate: Byrd Ice Core, Antarctica AMD_USAPDC S
NSIDC-0315_1 Atmospheric CO2 and Climate: Taylor Dome Ice Core, Antarctica AMD_USAPDC STAC Catalog 1970-01-01 158, -77.666667, 158, -77.666667 https://cmr.earthdata.nasa.gov/search/concepts/C2532070838-AMD_USAPDC.umm_json Using new and existing ice core CO2 data from 65 - 30 ka BP a new chronology for Taylor Dome ice core CO2 is established and synchronized with Greenland ice core records to study how high latitude climate change and the carbon cycle were linked during the last glacial period. The new data and chronology should provide a better target for models attempting to explain CO2 variability and abrupt climate change. proprietary
NSIDC-0318_1 Antarctic Mean Annual Temperature Map AMD_USAPDC STAC Catalog 1957-01-01 2003-12-31 -180, -90, 180, -65 https://cmr.earthdata.nasa.gov/search/concepts/C2532070844-AMD_USAPDC.umm_json The Mean Annual Temperature map was calculated by creating a contour map using compiled 10 meter firn temperature data from NSIDC and other mean annual temperature data from both cores and stations. The 10 meter data contains temperature measurements dating back to 1957 and the International Geophysical Year, including measurements from several major recent surveys. Data cover the entire continental ice sheet and several ice shelves, but coverage density is generally low. Data are stored in Microsoft Excel and Tagged Image File Format (TIFF), and are available sporadically from 1957 to 2003 via FTP. proprietary
NSIDC-0321_1 Global EASE-Grid 8-day Blended SSM/I and MODIS Snow Cover, Version 1 NSIDCV0 STAC Catalog 2000-03-05 2008-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1386250333-NSIDCV0.umm_json This data set comprises global, 8-day Snow-Covered Area (SCA) and Snow Water Equivalent (SWE) data from 2000 through 2008. Global SWE data are derived from the Special Sensor Microwave Imager (SSM/I) and are enhanced with MODIS/Terra Snow Cover 8-Day Level 3 Global 0.05 degree Climate Modeling Grid (CMG) data. Global data are gridded to the Northern and Southern 25 km Equal-Area Scalable Earth Grids (EASE-Grids). These data are suitable for continental-scale time-series studies of snow cover and snow water equivalent. proprietary
-NSIDC-0326_1 Ablation Rates of Taylor Glacier, Antarctica AMD_USAPDC STAC Catalog 2002-11-19 2011-01-12 160.1, -77.9, 162.2, -77.6 https://cmr.earthdata.nasa.gov/search/concepts/C2532070867-AMD_USAPDC.umm_json This data set provides glacier surface ablation rates for a network of approximately 250 sites on Taylor Glacier, spanning a period from 2003 to 2011. Here sublimation is the dominant ablation mechanism, though a few sites have accumulation. Ablation data are provided in meters water equivalent per year. Data are available via FTP in space-delimited ASCII format. proprietary
NSIDC-0326_1 Ablation Rates of Taylor Glacier, Antarctica ALL STAC Catalog 2002-11-19 2011-01-12 160.1, -77.9, 162.2, -77.6 https://cmr.earthdata.nasa.gov/search/concepts/C2532070867-AMD_USAPDC.umm_json This data set provides glacier surface ablation rates for a network of approximately 250 sites on Taylor Glacier, spanning a period from 2003 to 2011. Here sublimation is the dominant ablation mechanism, though a few sites have accumulation. Ablation data are provided in meters water equivalent per year. Data are available via FTP in space-delimited ASCII format. proprietary
+NSIDC-0326_1 Ablation Rates of Taylor Glacier, Antarctica AMD_USAPDC STAC Catalog 2002-11-19 2011-01-12 160.1, -77.9, 162.2, -77.6 https://cmr.earthdata.nasa.gov/search/concepts/C2532070867-AMD_USAPDC.umm_json This data set provides glacier surface ablation rates for a network of approximately 250 sites on Taylor Glacier, spanning a period from 2003 to 2011. Here sublimation is the dominant ablation mechanism, though a few sites have accumulation. Ablation data are provided in meters water equivalent per year. Data are available via FTP in space-delimited ASCII format. proprietary
NSIDC-0334_1 Airborne Laser Altimetry of the Thwaites Glacier Catchment, West Antarctica AMD_USAPDC STAC Catalog 2004-12-10 2005-01-29 -130, -80, -95, -75 https://cmr.earthdata.nasa.gov/search/concepts/C2532070878-AMD_USAPDC.umm_json This data set includes airborne altimetry collected over the catchment and main trunk of Thwaites Glacier, one of Antarctica's most active ice streams. The airborne altimetry comprises 35,000 line-kilometers sampled at 20 meters along track. The full dataset has an internal error of �20 cm; a primary subset has an error of �8 cm. We find a +20 cm bias with Geoscience Laser Altimeter System data over a flat interior region. These data will serve as an additional temporal reference for the evolution of Thwaites Glacier surface, as well as aid the construction of future high resolution Digital Elevation Models (DEM). Line data are available in space-delimited ASCII format and are available via FTP. proprietary
NSIDC-0334_1 Airborne Laser Altimetry of the Thwaites Glacier Catchment, West Antarctica ALL STAC Catalog 2004-12-10 2005-01-29 -130, -80, -95, -75 https://cmr.earthdata.nasa.gov/search/concepts/C2532070878-AMD_USAPDC.umm_json This data set includes airborne altimetry collected over the catchment and main trunk of Thwaites Glacier, one of Antarctica's most active ice streams. The airborne altimetry comprises 35,000 line-kilometers sampled at 20 meters along track. The full dataset has an internal error of �20 cm; a primary subset has an error of �8 cm. We find a +20 cm bias with Geoscience Laser Altimeter System data over a flat interior region. These data will serve as an additional temporal reference for the evolution of Thwaites Glacier surface, as well as aid the construction of future high resolution Digital Elevation Models (DEM). Line data are available in space-delimited ASCII format and are available via FTP. proprietary
NSIDC-0336_1 Antarctic Subglacial Lake Classification Inventory AMD_USAPDC STAC Catalog 1998-12-01 2001-02-28 -160, -90, 15, -70 https://cmr.earthdata.nasa.gov/search/concepts/C2532070882-AMD_USAPDC.umm_json This data set is an Antarctic radar-based subglacial lake classification collection, which focuses on the radar reflection properties of each given lake. The Subglacial lakes are separated into four categories specified by radar reflection properties. Additional information includes: latitude, longitude, length (in kilometers), hydro-potential (in meters), bed elevation (in meters above WGS84), and ice thickness (in meters). Source data used to compile this data set were collected between 1998 and 2001. Data are available via FTP as a Microsoft Excel Spreadsheet (XLS), and Tagged Image File Format (TIF). proprietary
@@ -12561,8 +12562,8 @@ NSIDC-0478_2 MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data V002 NSID
NSIDC-0481_4 MEaSUREs Greenland Ice Velocity: Selected Glacier Site Velocity Maps from InSAR V004 NSIDC_ECS STAC Catalog 2008-06-12 2023-09-20 -70, 60, -20, 82 https://cmr.earthdata.nasa.gov/search/concepts/C2076118670-NSIDC_ECS.umm_json "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides velocity estimates determined from Interferometric Synthetic Aperture Radar (InSAR) data for major glacier outlet areas in Greenland, some of which have shown profound velocity changes over the MEaSUREs observation period. The InSAR Selected Glacier Site Velocity Maps are produced from image pairs measured by the German Aerospace Center's (DLR) twin satellites TerraSAR-X / TanDEM-X (TSX / TDX). The measurements in this data set are provided in addition to the ice sheet-wide data from the related data set, MEaSUREs Greenland Ice Sheet Velocity Map from InSAR Data. See Greenland Ice Mapping Project (GrIMP) for more related data." proprietary
NSIDC-0484_2 MEaSUREs InSAR-Based Antarctica Ice Velocity Map V002 NSIDC_ECS STAC Catalog 1996-01-01 2016-12-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1414573008-NSIDC_ECS.umm_json "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program, provides the first comprehensive, high-resolution, digital mosaics of ice motion in Antarctica assembled from multiple satellite interferometric, synthetic-aperture radar systems. Data were largely acquired during the International Polar Years 2007 to 2009, as well as between 2013 and 2016. Additional data acquired between 1996 and 2016 were used as needed to maximize coverage. See Antarctic Ice Sheet Velocity and Mapping Data for related data." proprietary
NSIDC-0498_2 MEaSUREs Antarctic Grounding Line from Differential Satellite Radar Interferometry V002 NSIDC_ECS STAC Catalog 1992-02-07 2014-12-17 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1573480652-NSIDC_ECS.umm_json "This data set, part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, provides 22 years of comprehensive high-resolution mapping of grounding lines in Antarctica from 1992 to 2014. The data were derived using differential satellite synthetic aperture radar interferometry (DInSAR) measurements from the following platforms: Earth Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2), RADARSAT-1, RADARSAT-2, the Advanced Land Observing System Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR), Cosmo Skymed, and Copernicus Sentinel-1. See Antarctic Ice Sheet Velocity and Mapping Data for related data." proprietary
-NSIDC-0504_1 Alkanes in Firn Air Samples, Antarctica and Greenland AMD_USAPDC STAC Catalog 2005-12-01 2009-01-31 -38.3833, -79.47, 112.09, 72.5833 https://cmr.earthdata.nasa.gov/search/concepts/C2532070980-AMD_USAPDC.umm_json This data set contains ethane, propane, and n-butane measurements in firn air from the South Pole and the West Antarctic Ice Sheet (WAIS) Divide in Antarctica, and from Summit, Greenland. The WAIS Divide and South Pole samples were collected in December to January of of 2005/06 and 2008/09, respectively. The Summit firn was sampled in the summer of 2006. Analyses were conducted on a gas chromatography - mass spectrometry (GC-MS) system at the University of California, Irvine. Measurements and the associated uncertainties are reported as dry air molar mixing ratios in part per trillion (ppt). The reported measurements for each sampling depth represent a mean of multiple measurements on more than one flask in most cases. Data are available via FTP in Microsoft Excel (.xls) format. proprietary
NSIDC-0504_1 Alkanes in Firn Air Samples, Antarctica and Greenland ALL STAC Catalog 2005-12-01 2009-01-31 -38.3833, -79.47, 112.09, 72.5833 https://cmr.earthdata.nasa.gov/search/concepts/C2532070980-AMD_USAPDC.umm_json This data set contains ethane, propane, and n-butane measurements in firn air from the South Pole and the West Antarctic Ice Sheet (WAIS) Divide in Antarctica, and from Summit, Greenland. The WAIS Divide and South Pole samples were collected in December to January of of 2005/06 and 2008/09, respectively. The Summit firn was sampled in the summer of 2006. Analyses were conducted on a gas chromatography - mass spectrometry (GC-MS) system at the University of California, Irvine. Measurements and the associated uncertainties are reported as dry air molar mixing ratios in part per trillion (ppt). The reported measurements for each sampling depth represent a mean of multiple measurements on more than one flask in most cases. Data are available via FTP in Microsoft Excel (.xls) format. proprietary
+NSIDC-0504_1 Alkanes in Firn Air Samples, Antarctica and Greenland AMD_USAPDC STAC Catalog 2005-12-01 2009-01-31 -38.3833, -79.47, 112.09, 72.5833 https://cmr.earthdata.nasa.gov/search/concepts/C2532070980-AMD_USAPDC.umm_json This data set contains ethane, propane, and n-butane measurements in firn air from the South Pole and the West Antarctic Ice Sheet (WAIS) Divide in Antarctica, and from Summit, Greenland. The WAIS Divide and South Pole samples were collected in December to January of of 2005/06 and 2008/09, respectively. The Summit firn was sampled in the summer of 2006. Analyses were conducted on a gas chromatography - mass spectrometry (GC-MS) system at the University of California, Irvine. Measurements and the associated uncertainties are reported as dry air molar mixing ratios in part per trillion (ppt). The reported measurements for each sampling depth represent a mean of multiple measurements on more than one flask in most cases. Data are available via FTP in Microsoft Excel (.xls) format. proprietary
NSIDC-0515_1 Annual Layers at Siple Dome, Antarctica, from Borehole Optical Stratigraphy AMD_USAPDC STAC Catalog 2000-12-15 2001-11-15 -148.82, -81.66, -148.82, -81.66 https://cmr.earthdata.nasa.gov/search/concepts/C2532070824-AMD_USAPDC.umm_json Researchers gathered data on annual snow layers at Siple Dome, Antarctica, using borehole optical stratigraphy. This data set contains annual layer depths and firn optical brightness. The brightness log is a record of reflectivity of the firn, and peaks in brightness are interpreted to be fine-grained high-density winter snow, as part of the wind slab depth-hoar couplet. Data are available via FTP in ASCII text (.txt) format proprietary
NSIDC-0516_1 Antarctic Peninsula 100 m Digital Elevation Model Derived from ASTER GDEM AMD_USAPDC STAC Catalog 2000-01-01 2009-12-31 -70, -70, -55, -63 https://cmr.earthdata.nasa.gov/search/concepts/C2532070816-AMD_USAPDC.umm_json This data set provides a 100 meter resolution surface topography Digital Elevation Model (DEM) of the Antarctic Peninsula. The DEM is based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) data. proprietary
NSIDC-0517_1 AGASEA Ice Thickness Profile Data from the Amundsen Sea Embayment, Antarctica ALL STAC Catalog 2004-12-10 2005-01-29 -125, -83, -90, -73 https://cmr.earthdata.nasa.gov/search/concepts/C2532070806-AMD_USAPDC.umm_json This data set contains line-based radar-derived ice thickness and bed elevation data, collected as part of the Airborne Geophysical Survey of the Amundsen Embayment (AGASEA) expedition, which took place over Thwaites Glacier in West Antarctica from 2004 to 2005. The data set includes ice thickness, ice sheet bed elevation, and ice sheet surface elevation, derived from ice-penetrating radar and aircraft GPS positions. The data are spaced on a 15 km by 15 km grid over the entire catchment of the glacier, and sampled at approximately 15 meters along track. Most of the radar data used for this dataset has been processed using a 1-D focusing algorithm, to reduce the along track resolution to tens of meters, to improve boundary conditions for ice sheet models. Data are available via FTP in space-delimited ASCII format. proprietary
@@ -12691,8 +12692,8 @@ NmTHIRmtg-1T_1 Nimbus Temperature-Humidity Infrared Radiometer Global Montage Gr
Nome_Veg_Plots_1372_1 Arctic Vegetation Plots at Nome, Alaska, 1951 ORNL_CLOUD STAC Catalog 1951-07-30 1951-08-02 -165.26, 64.63, -165.26, 64.63 https://cmr.earthdata.nasa.gov/search/concepts/C2170969899-ORNL_CLOUD.umm_json This data set provides environmental, soil, and vegetation data collected in July and August 1951 from 80 study plots in the Nome River Valley about 10 miles northeast of Nome, Alaska on the Seward Peninsula. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in plant communities that were found to occur in 5 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species and cover, and soil characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping and analysis of geo-botanical factors in the Nome River Valley and across Alaska. proprietary
Non-Forest_Trees_Sahara_Sahel_1832_1 An Unexpectedly Large Count of Trees in the West African Sahara and Sahel ORNL_CLOUD STAC Catalog 2005-11-01 2018-03-31 -18, 11.35, -5.49, 24.03 https://cmr.earthdata.nasa.gov/search/concepts/C2761798565-ORNL_CLOUD.umm_json This dataset provides georeferenced polygon vectors of individual tree canopy geometries for dryland areas in West African Sahara and Sahel that were derived using deep learning applied to 50-cm resolution satellite imagery. More than 1.8 billion non-forest trees (i.e., woody plants with a crown size over 3 m2) over about 1.3 million km2 were identified from panchromatic and pansharpened normalized difference vegetation index (NDVI) images at 0.5-m spatial resolution using an automatic tree detection framework based on supervised deep-learning techniques. Combined with existing and future fieldwork, these data lay the foundation for a comprehensive database that contains information on all individual trees outside of forests and could provide accurate estimates of woody carbon in arid and semi-arid areas throughout the Earth for the first time. proprietary
Nongrowing_Season_CO2_Flux_1692_1 Synthesis of Winter In Situ Soil CO2 Flux in pan-Arctic and Boreal Regions, 1989-2017 ORNL_CLOUD STAC Catalog 1989-09-01 2017-04-30 -163.71, 53.88, 161.99, 78.92 https://cmr.earthdata.nasa.gov/search/concepts/C2143403370-ORNL_CLOUD.umm_json This dataset provides a synthesis of winter ( September-April) in situ soil CO2 flux measurement data from locations across pan-Arctic and Boreal permafrost regions. The in situ data were compiled from 66 published and 21 unpublished studies conducted from 1989-2017. The data sources (publication references) are provided. Sampling sites spanned pan-Arctic Boreal and tundra regions (>53 Deg N) in continuous, discontinuous, and isolated/sporadic permafrost zones. The CO2 flux measurements were aggregated at the monthly level, or seasonally when monthly data were not available, and are reported as the daily average (g C m-2 day-1) over the interval. Soil moisture and temperature data plus environmental and ecological model driver data (e.g., vegetation type and productivity, soil substrate availability) are also included based on gridded satellite remote sensing and reanalysis sources. proprietary
-NorthSlope_NEE_TVPRM_1920_1 ABoVE: TVPRM Simulated Net Ecosystem Exchange, Alaskan North Slope, 2008-2017 ALL STAC Catalog 2008-01-01 2017-12-31 -177.47, 56.09, -128.59, 77.26 https://cmr.earthdata.nasa.gov/search/concepts/C2240727916-ORNL_CLOUD.umm_json This dataset includes hourly net ecosystem exchange (NEE) simulated by the Tundra Vegetation Photosynthesis and Respiration Model (TVPRM) at 30 km horizontal resolution for the Alaskan North Slope for 2008-2017. TVPRM calculates tundra NEE from air temperature, soil temperature, photosynthetically active radiation (PAR), and solar-induced chlorophyll fluorescence (SIF) using functional relationships derived from eddy covariance tower measurements. These relationships were then scaled over the region using gridded meteorology and a vegetation map. The site-level CO2 fluxes fell into two distinct ecosystem groups: inland tundra (ICS, ICT, ICH, IVO) and coastal tundra (ATQ, BES, BEO, CMDL). The expanded modeling framework allowed for the easy substitution of ecological behaviors and environmental drivers, including the choice of representative inland tundra site, coastal tundra site, vegetation map (CAVM, RasterCAVM, or ABoVE-LC), meteorological reanalysis product (NARR or ERA5), and SIF product (GOME2, GOSIF, or CSIF). Using all of these variations generated an ensemble of 288 different TVPRM simulations of regional CO2 flux and one additional simulation option with added aquatic and zero curtain fluxes (AqZC). proprietary
NorthSlope_NEE_TVPRM_1920_1 ABoVE: TVPRM Simulated Net Ecosystem Exchange, Alaskan North Slope, 2008-2017 ORNL_CLOUD STAC Catalog 2008-01-01 2017-12-31 -177.47, 56.09, -128.59, 77.26 https://cmr.earthdata.nasa.gov/search/concepts/C2240727916-ORNL_CLOUD.umm_json This dataset includes hourly net ecosystem exchange (NEE) simulated by the Tundra Vegetation Photosynthesis and Respiration Model (TVPRM) at 30 km horizontal resolution for the Alaskan North Slope for 2008-2017. TVPRM calculates tundra NEE from air temperature, soil temperature, photosynthetically active radiation (PAR), and solar-induced chlorophyll fluorescence (SIF) using functional relationships derived from eddy covariance tower measurements. These relationships were then scaled over the region using gridded meteorology and a vegetation map. The site-level CO2 fluxes fell into two distinct ecosystem groups: inland tundra (ICS, ICT, ICH, IVO) and coastal tundra (ATQ, BES, BEO, CMDL). The expanded modeling framework allowed for the easy substitution of ecological behaviors and environmental drivers, including the choice of representative inland tundra site, coastal tundra site, vegetation map (CAVM, RasterCAVM, or ABoVE-LC), meteorological reanalysis product (NARR or ERA5), and SIF product (GOME2, GOSIF, or CSIF). Using all of these variations generated an ensemble of 288 different TVPRM simulations of regional CO2 flux and one additional simulation option with added aquatic and zero curtain fluxes (AqZC). proprietary
+NorthSlope_NEE_TVPRM_1920_1 ABoVE: TVPRM Simulated Net Ecosystem Exchange, Alaskan North Slope, 2008-2017 ALL STAC Catalog 2008-01-01 2017-12-31 -177.47, 56.09, -128.59, 77.26 https://cmr.earthdata.nasa.gov/search/concepts/C2240727916-ORNL_CLOUD.umm_json This dataset includes hourly net ecosystem exchange (NEE) simulated by the Tundra Vegetation Photosynthesis and Respiration Model (TVPRM) at 30 km horizontal resolution for the Alaskan North Slope for 2008-2017. TVPRM calculates tundra NEE from air temperature, soil temperature, photosynthetically active radiation (PAR), and solar-induced chlorophyll fluorescence (SIF) using functional relationships derived from eddy covariance tower measurements. These relationships were then scaled over the region using gridded meteorology and a vegetation map. The site-level CO2 fluxes fell into two distinct ecosystem groups: inland tundra (ICS, ICT, ICH, IVO) and coastal tundra (ATQ, BES, BEO, CMDL). The expanded modeling framework allowed for the easy substitution of ecological behaviors and environmental drivers, including the choice of representative inland tundra site, coastal tundra site, vegetation map (CAVM, RasterCAVM, or ABoVE-LC), meteorological reanalysis product (NARR or ERA5), and SIF product (GOME2, GOSIF, or CSIF). Using all of these variations generated an ensemble of 288 different TVPRM simulations of regional CO2 flux and one additional simulation option with added aquatic and zero curtain fluxes (AqZC). proprietary
North_Carolina_Coast_0 Measurements made off the North Carolina coast OB_DAAC STAC Catalog 2001-04-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360528-OB_DAAC.umm_json Measurements made off the North Carolina coast. proprietary
North_Carolina_Sabrina_0 Measurements from the Outer Banks and coastal regions of North Carolina onboard the R/V Sabrina OB_DAAC STAC Catalog 2002-09-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360529-OB_DAAC.umm_json Measurements taken by the research vessel Sabrina in the Outer Banks and coastal regions of North Carolina in 2002 and 2003. proprietary
North_Sea_0 Measurements taken in the North Sea in 1994 OB_DAAC STAC Catalog 1994-07-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360530-OB_DAAC.umm_json Measurements taken in the North Sea in 1994. proprietary
@@ -12812,30 +12813,30 @@ OCO3_L2_Standard_10 OCO-3 Level 2 geolocated XCO2 retrievals results, physical m
OCO3_L2_Standard_10r OCO-3 Level 2 geolocated XCO2 retrievals results, physical model, Retrospective Processing V10r (OCO3_L2_Standard) at GES DISC GES_DISC STAC Catalog 2019-08-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2082387252-GES_DISC.umm_json Version 10r is the current version of the data set. Older versions will no longer be available and are superseded by Version 10r. The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations. The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. proprietary
OCO3_L2_Standard_11 OCO-3 Level 2 geolocated XCO2 retrievals results, physical model, Forward Processing V11 (OCO3_L2_Standard) at GES DISC GES_DISC STAC Catalog 2019-08-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3272764617-GES_DISC.umm_json Version 11 is the current version of the data set. Older versions will no longer be available and are superseded by Version 11. The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations. The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. proprietary
OCO3_L2_Standard_11r OCO-3 Level 2 geolocated XCO2 retrievals results, physical model, Retrospective Processing V11r (OCO3_L2_Standard) at GES DISC GES_DISC STAC Catalog 2019-08-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2910086890-GES_DISC.umm_json Version 11r is the current version of the data set. Older versions will no longer be available and are superseded by Version 11r. The Orbiting Carbon Observatory -3 (OCO-3) was deployed to the International Space Station in May, 2019. It is technically a single instrument, almost identical to OCO-2. The Orbiting Carbon Observatory is the first NASA mission designed to collect space-based measurements of atmospheric carbon dioxide with the precision, resolution, and coverage needed to characterize the processes controlling its buildup in the atmosphere. OCO-3 incorporates three high-resolution spectrometers that make coincident measurements of reflected sunlight in the near-infrared CO2 near 1.61 and 2.06 micrometers and in molecular oxygen (O2) A-Band at 0.76 micrometers. The three spectrometers have different characteristics and are calibrated independently. Oxygen-A Band cloud screening algorithm is one of the primary cloud screening tools implemented in the operational OCO processing pipeline. The algorithm was introduced and applied to early GOSAT data with further analysis performed on OCO-2 simulations. The OCO ABO2 algorithm employs a fast Bayesian retrieval to estimate surface pressure and surface albedo from high resolution spectra of the molecular oxygen (O2) A-band, near 0.765 µm. The radiative transfer forward model (FM) assumes a clear-sky condition, i.e. Rayleigh scattering only, such that differences between the modeled and measured radiances are apparent when the measurement scene contains cloud or aerosol. proprietary
-OCTS_L1_1 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Data Regional Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034340-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L1_1 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Data Regional Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034340-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L1_1 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Data Regional Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034340-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L1_2 ADEOS-I OCTS Level-1A Data, version 2 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834679-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L1_2 ADEOS-I OCTS Level-1A Data, version 2 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834679-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
-OCTS_L2_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Regional Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034360-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L2_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Regional Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034360-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L2_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Regional Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034360-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L2_IOP_2022.0 ADEOS-I OCTS Level-2 Regional Inherent Optical Properties (IOP) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834690-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L2_IOP_2022.0 ADEOS-I OCTS Level-2 Regional Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834690-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L2_OC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Ocean Color (OC) Regional Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034380-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L2_OC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Ocean Color (OC) Regional Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034380-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
-OCTS_L2_OC_2022.0 ADEOS-I OCTS Level-2 Regional Ocean Color (OC) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834711-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L2_OC_2022.0 ADEOS-I OCTS Level-2 Regional Ocean Color (OC) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834711-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
-OCTS_L3b_CHL_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll (CHL) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034361-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L2_OC_2022.0 ADEOS-I OCTS Level-2 Regional Ocean Color (OC) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834711-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3b_CHL_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll (CHL) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034361-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L3b_CHL_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Chlorophyll (CHL) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034361-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3b_CHL_2022.0 ADEOS-I OCTS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834719-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3b_CHL_2022.0 ADEOS-I OCTS Level-3 Global Binned Chlorophyll (CHL) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834719-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
-OCTS_L3b_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034381-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3b_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034381-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
-OCTS_L3b_IOP_2022.0 ADEOS-I OCTS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834731-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L3b_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034381-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3b_IOP_2022.0 ADEOS-I OCTS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834731-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L3b_IOP_2022.0 ADEOS-I OCTS Level-3 Global Binned Inherent Optical Properties (IOP) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834731-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3b_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034362-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3b_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034362-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
-OCTS_L3b_KD_2022.0 ADEOS-I OCTS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834737-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3b_KD_2022.0 ADEOS-I OCTS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834737-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L3b_KD_2022.0 ADEOS-I OCTS Level-3 Global Binned Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834737-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3b_PAR_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Binned Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034341-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3b_PAR_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Binned Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034341-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3b_PAR_2022.0 ADEOS-I OCTS Level-3 Global Binned Photosynthetically Active Radiation (PAR) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834749-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
@@ -12858,34 +12859,34 @@ OCTS_L3m_CHL_2022.0 ADEOS-I OCTS Level-3 Global Mapped Chlorophyll (CHL) Data, v
OCTS_L3m_CHL_2022.0 ADEOS-I OCTS Level-3 Global Mapped Chlorophyll (CHL) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834809-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3m_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034365-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3m_IOP_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Inherent Optical Properties (IOP) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034365-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
-OCTS_L3m_IOP_2022.0 ADEOS-I OCTS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834819-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3m_IOP_2022.0 ADEOS-I OCTS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834819-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
-OCTS_L3m_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034383-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L3m_IOP_2022.0 ADEOS-I OCTS Level-3 Global Mapped Inherent Optical Properties (IOP) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834819-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3m_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034383-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
-OCTS_L3m_KD_2022.0 ADEOS-I OCTS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834825-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L3m_KD_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034383-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3m_KD_2022.0 ADEOS-I OCTS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834825-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
-OCTS_L3m_PAR_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034366-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L3m_KD_2022.0 ADEOS-I OCTS Level-3 Global Mapped Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834825-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3m_PAR_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034366-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
-OCTS_L3m_PAR_2022.0 ADEOS-I OCTS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834829-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L3m_PAR_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Photosynthetically Available Radiation (PAR) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034366-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3m_PAR_2022.0 ADEOS-I OCTS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834829-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
-OCTS_L3m_PIC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034384-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L3m_PAR_2022.0 ADEOS-I OCTS Level-3 Global Mapped Photosynthetically Active Radiation (PAR) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834829-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3m_PIC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034384-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L3m_PIC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Inorganic Carbon (PIC) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034384-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3m_PIC_2022.0 ADEOS-I OCTS Level-3 Global Mapped Particulate Inorganic Carbon (PIC) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834831-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3m_PIC_2022.0 ADEOS-I OCTS Level-3 Global Mapped Particulate Inorganic Carbon (PIC) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834831-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
-OCTS_L3m_POC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034367-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3m_POC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034367-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L3m_POC_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Particulate Organic Carbon (POC) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034367-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3m_POC_2022.0 ADEOS-I OCTS Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834842-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3m_POC_2022.0 ADEOS-I OCTS Level-3 Global Mapped Particulate Organic Carbon (POC) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834842-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
-OCTS_L3m_RRS_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034385-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3m_RRS_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Mapped Data ALL STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034385-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
-OCTS_L3m_RRS_2022.0 ADEOS-I OCTS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834849-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L3m_RRS_2014 ADEOS-I Ocean Color and Temperature Scanner (OCTS) Remote-Sensing Reflectance (RRS) Global Mapped Data OB_DAAC STAC Catalog 1996-11-01 1997-06-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1200034385-OB_DAAC.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
OCTS_L3m_RRS_2022.0 ADEOS-I OCTS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0 ALL STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834849-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
+OCTS_L3m_RRS_2022.0 ADEOS-I OCTS Level-3 Global Mapped Remote-Sensing Reflectance (RRS) Data, version 2022.0 OB_CLOUD STAC Catalog 1996-10-31 1997-06-29 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3300834849-OB_CLOUD.umm_json On August 17, 1996, the Japanese Space Agency (NASDA - National Space Development Agency) launched the Advanced Earth Observing Satellite (ADEOS). ADEOS was in a descending, Sun synchronous orbit with a nominal equatorial crossing time of 10:30 a.m. Amoung the instruments carried aboard the ADEOS spacecraft was the Ocean Color and Temperature Scanner (OCTS). OCTS is an optical radiometer with 12 bands covering the visible, near infrared and thermal infrared regions. (Eight of the bands are in the VIS/NIR. These are the only bands calibrated and processed by the OBPG) OCTS has a swath width of approximately 1400 km, and a nominal nadir resolution of 700 m. The instrument operated at three tilt states (20 degrees aft, nadir and 20 degrees fore), similar to SeaWiFS. proprietary
ODIN.SMR_5.0 ODIN SMR data products ESA STAC Catalog 2001-02-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689700-ESA.umm_json The latest Odin Sub-Millimetre Radiometer (SMR) datasets have been generated by Chalmers University of Technology and Molflow within the Odin-SMR Recalibration and Harmonisation project (http://odin.rss.chalmers.se/), funded by the European Space Agency (ESA) to create a fully consistent and homogeneous dataset from the 20 years of satellite operations. The Odin satellite was launched in February 2001 as a joint undertaking between Sweden, Canada, France and Finland, and is part of the ESA Third Party Missions (TPM) programme since 2007. The complete Odin-SMR data archive was reprocessed applying a revised calibration scheme and upgraded algorithms. The Level 1b dataset is entirely reconsolidated, while Level 2 products are regenerated for the main mesospheric and stratospheric frequency modes (i.e., FM 01, 02, 08, 13, 14, 19, 21, 22, 24). The resulting dataset represents the first full-mission reprocessing campaign of the mission, which is still in operation. proprietary
ODU_CBM_0 Old Dominion University (ODU) - Chesapeake Bay Mouth (CBM) measurements OB_DAAC STAC Catalog 2004-05-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360566-OB_DAAC.umm_json Measurements made of the Chesapeake Bay Mouth (CBM) by Old Dominion University (ODU) between 2004 and 2006. proprietary
-OFR_94-212 A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212 ALL STAC Catalog 1980-05-01 1988-09-06 -122, 46, -122, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2232411623-CEOS_EXTRA.umm_json Airborne monitoring of Mount St. Helens by the USGS began in May 1980 for sulfur dioxide emissions and in July 1980 for carbon dioxide emissions. A correlation spectrometer, or COSPEC, was used to measure sulfur dioxide in Mount St. Helens' plume. The upward-looking COSPEC was mounted in a fixed-wing aircraft and flown below and at right angles to the plume. Typically, three to six traverses were made underneath the plume to determine the SO2 burden (concentration x pathlength) within a cross-section of the plume. Knowing the burden along with the plume width and plume velocity (assumed to be the same as ambient wind speed), we could then calculate the emission rate of SO2. The use of correlation spectroscopy for determining the sulfur dioxide output of volcanoes is well established and the technique has been discussed in detail by a number of investigators (Malinconico, 1979; Casadevall and others, 1981; Stoiber and others, 1983). Carbon dioxide in the Mount St. Helens plume was measured by an infrared spectrometer tuned to the 4.26 um CO2 absorption band. An external sample tube was attached to the fuselage of a twin-engine aircraft to deliver outside air to the gas cell of the spectrometer. The aircraft was then flown at several different elevations through the plume at right angles to plume trajectory to define plume area and carbon dioxide concentration in a vertical cross-section of the plume. These two parameters along with the density of CO2 for the altitude of the plume and the plume velocity (assumed as above to be equal to ambient wind speed) were then used to calculate the CO2 emission rate (Harris and others, 1981). proprietary
OFR_94-212 A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212 CEOS_EXTRA STAC Catalog 1980-05-01 1988-09-06 -122, 46, -122, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2232411623-CEOS_EXTRA.umm_json Airborne monitoring of Mount St. Helens by the USGS began in May 1980 for sulfur dioxide emissions and in July 1980 for carbon dioxide emissions. A correlation spectrometer, or COSPEC, was used to measure sulfur dioxide in Mount St. Helens' plume. The upward-looking COSPEC was mounted in a fixed-wing aircraft and flown below and at right angles to the plume. Typically, three to six traverses were made underneath the plume to determine the SO2 burden (concentration x pathlength) within a cross-section of the plume. Knowing the burden along with the plume width and plume velocity (assumed to be the same as ambient wind speed), we could then calculate the emission rate of SO2. The use of correlation spectroscopy for determining the sulfur dioxide output of volcanoes is well established and the technique has been discussed in detail by a number of investigators (Malinconico, 1979; Casadevall and others, 1981; Stoiber and others, 1983). Carbon dioxide in the Mount St. Helens plume was measured by an infrared spectrometer tuned to the 4.26 um CO2 absorption band. An external sample tube was attached to the fuselage of a twin-engine aircraft to deliver outside air to the gas cell of the spectrometer. The aircraft was then flown at several different elevations through the plume at right angles to plume trajectory to define plume area and carbon dioxide concentration in a vertical cross-section of the plume. These two parameters along with the density of CO2 for the altitude of the plume and the plume velocity (assumed as above to be equal to ambient wind speed) were then used to calculate the CO2 emission rate (Harris and others, 1981). proprietary
-OFR_95-55 A Compilation of Sulphur Dioxide and Carbon Dioxide Emission-Rate Data from Cook Inlet Volcanoes, Alaska During the Period from 1990 to 1994 ALL STAC Catalog 1990-03-20 1994-07-07 -154, 56, -152, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2232411611-CEOS_EXTRA.umm_json This report contains all of the available daily sulfur dioxide and carbon dioxide emission rates from Cook Inlet volcanoes as determined by the U.S. Geological Survey (USGS) from March 1990 through July 1994. Airborne sulfur dioxide gas sampling of the Cook Inlet volcanoes (Redoubt, Spurr, Iliamna, and Augustine) began in 1986 when several measurements were carried out at Augustine volcano during the eruption of 1986. Systematic monitoring for sulfur dioxide and carbon dioxide began in March 1990 at Redoubt volcano and continues to the present. Intermittent measurements at Augustine and Iliamna volcanoes began in 1990 and continues to the present. Intermittent measurements began at Spurr volcano in 1991, and were continued at more regular intervals from June, 1992 through the 1992 eruption at the Crater Peak vent to the present. proprietary
+OFR_94-212 A Compilation of Sulfur Dioxide and Carbon Dioxide Emission-Rate Data from Mount St. Helens during 1980-88 USGS Open File Report 94-212 ALL STAC Catalog 1980-05-01 1988-09-06 -122, 46, -122, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2232411623-CEOS_EXTRA.umm_json Airborne monitoring of Mount St. Helens by the USGS began in May 1980 for sulfur dioxide emissions and in July 1980 for carbon dioxide emissions. A correlation spectrometer, or COSPEC, was used to measure sulfur dioxide in Mount St. Helens' plume. The upward-looking COSPEC was mounted in a fixed-wing aircraft and flown below and at right angles to the plume. Typically, three to six traverses were made underneath the plume to determine the SO2 burden (concentration x pathlength) within a cross-section of the plume. Knowing the burden along with the plume width and plume velocity (assumed to be the same as ambient wind speed), we could then calculate the emission rate of SO2. The use of correlation spectroscopy for determining the sulfur dioxide output of volcanoes is well established and the technique has been discussed in detail by a number of investigators (Malinconico, 1979; Casadevall and others, 1981; Stoiber and others, 1983). Carbon dioxide in the Mount St. Helens plume was measured by an infrared spectrometer tuned to the 4.26 um CO2 absorption band. An external sample tube was attached to the fuselage of a twin-engine aircraft to deliver outside air to the gas cell of the spectrometer. The aircraft was then flown at several different elevations through the plume at right angles to plume trajectory to define plume area and carbon dioxide concentration in a vertical cross-section of the plume. These two parameters along with the density of CO2 for the altitude of the plume and the plume velocity (assumed as above to be equal to ambient wind speed) were then used to calculate the CO2 emission rate (Harris and others, 1981). proprietary
OFR_95-55 A Compilation of Sulphur Dioxide and Carbon Dioxide Emission-Rate Data from Cook Inlet Volcanoes, Alaska During the Period from 1990 to 1994 CEOS_EXTRA STAC Catalog 1990-03-20 1994-07-07 -154, 56, -152, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2232411611-CEOS_EXTRA.umm_json This report contains all of the available daily sulfur dioxide and carbon dioxide emission rates from Cook Inlet volcanoes as determined by the U.S. Geological Survey (USGS) from March 1990 through July 1994. Airborne sulfur dioxide gas sampling of the Cook Inlet volcanoes (Redoubt, Spurr, Iliamna, and Augustine) began in 1986 when several measurements were carried out at Augustine volcano during the eruption of 1986. Systematic monitoring for sulfur dioxide and carbon dioxide began in March 1990 at Redoubt volcano and continues to the present. Intermittent measurements at Augustine and Iliamna volcanoes began in 1990 and continues to the present. Intermittent measurements began at Spurr volcano in 1991, and were continued at more regular intervals from June, 1992 through the 1992 eruption at the Crater Peak vent to the present. proprietary
+OFR_95-55 A Compilation of Sulphur Dioxide and Carbon Dioxide Emission-Rate Data from Cook Inlet Volcanoes, Alaska During the Period from 1990 to 1994 ALL STAC Catalog 1990-03-20 1994-07-07 -154, 56, -152, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2232411611-CEOS_EXTRA.umm_json This report contains all of the available daily sulfur dioxide and carbon dioxide emission rates from Cook Inlet volcanoes as determined by the U.S. Geological Survey (USGS) from March 1990 through July 1994. Airborne sulfur dioxide gas sampling of the Cook Inlet volcanoes (Redoubt, Spurr, Iliamna, and Augustine) began in 1986 when several measurements were carried out at Augustine volcano during the eruption of 1986. Systematic monitoring for sulfur dioxide and carbon dioxide began in March 1990 at Redoubt volcano and continues to the present. Intermittent measurements at Augustine and Iliamna volcanoes began in 1990 and continues to the present. Intermittent measurements began at Spurr volcano in 1991, and were continued at more regular intervals from June, 1992 through the 1992 eruption at the Crater Peak vent to the present. proprietary
OFR_95-78_1 Geometeorological data collected by the USGS Desert Winds Project at Gold Spring, Great Basin Desert, northeastern Arizona, 1979-1992 CEOS_EXTRA STAC Catalog 1979-01-27 1992-12-31 -111, 35, -111, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231550505-CEOS_EXTRA.umm_json This data set contains meteorological data files pertaining to the Gold Spring Geomet research site. Documentation files and data-accessing display software are also included. The meteorological data are wind speed, peak gust, wind direction, precipitation, air temperature, soil temperature, barometric pressure, and humidity. Data from the monitoring station are voluminous; 14 observations from each station are made as often as ten times per hour, totaling more than a million observations per station per year. proprietary
OISSS_L4_multimission_7day_v1_1.0 Multi-Mission Optimally Interpolated Sea Surface Salinity Global Dataset V1 POCLOUD STAC Catalog 2011-08-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2095055342-POCLOUD.umm_json This is a level 4 product on a 0.25-degree spatial and 4-day temporal grid. The product is derived from the level 2 swath data of three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. The product offers a continuous record from August 28, 2011 to present by concatenating the measurements from Aquarius (September 2011 - June 2015) and SMAP (April 2015 present). ESAs SMOS data was used to fill the gap in SMAP data between June and July 2019, when the SMAP satellite was in a safe mode. The two-month overlap (April - June 2015) between Aquarius and SMAP was used to ensure consistency and continuity in data record. The product covers the global ocean, including the Arctic and Antarctic in the areas free of sea ice, but does not cover internal seas such as Mediterranean and Baltic Sea. In-situ salinity from Argo floats and moored buoys are used to derive a large-scale bias correction and to ensure consistency and accuracy of the OISSS dataset. This dataset is produced by the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide. proprietary
OISSS_L4_multimission_7day_v2_2.0 Multi-Mission Optimally Interpolated Sea Surface Salinity Global Dataset V2 POCLOUD STAC Catalog 2011-08-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2589160971-POCLOUD.umm_json This is a level 4 product on a 0.25-degree spatial and 4-day temporal grid. The product is derived from the level 2 swath data of three satellite missions: the Aquarius/SAC-D, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) using Optimal Interpolation (OI) with a 7-day decorrelation time scale. The product offers a continuous record from August 28, 2011 to present by concatenating the measurements from Aquarius (September 2011 - June 2015) and SMAP (April 2015 present). ESAs SMOS data was used to fill the gap in SMAP data between June and July 2019, when the SMAP satellite was in a safe mode. The two-month overlap (April - June 2015) between Aquarius and SMAP was used to ensure consistency and continuity in data record. The product covers the global ocean, including the Arctic and Antarctic in the areas free of sea ice, but does not cover internal seas such as Mediterranean and Baltic Sea. In-situ salinity from Argo floats and moored buoys are used to derive a large-scale bias correction and to ensure consistency and accuracy of the OISSS dataset. This dataset is produced by the Earth and Space Research (ESR), Seattle, WA and the International Pacific Research Center (IPRC) of the University of Hawaii at Manoa in collaboration with the Remote Sensing Systems (RSS), Santa Rosa, California. More details can be found in the users guide. proprietary
@@ -12987,8 +12988,8 @@ OMCLDO2Z_003 OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) Zoomed 1-Or
OMCLDO2_003 OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) 1-Orbit L2 Swath 13x24km V003 (OMCLDO2) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966787-GES_DISC.umm_json The reprocessed OMI/Aura Level-2 cloud data product OMCLDO2 is now available from the NASA GoddardEarth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed in late 2011. OMI provides two cloud products based on two different algorithms, the Rotational Raman Scattering method, and O2-O2 absorption method using the DOAS technique. This level-2 global cloud product, with a pixel resolution of 13x24 km2at nadir, is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. This product contains cloud pressure, cloud fraction, slant column O2-O2, ozone, ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The lead scientist for this product is Dr. Pepijn Veefkind. The OMCLDO2 product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) and is roughly 15.096 MB in size. There are approximately 14 orbits per day thus the total data volume is approximately 200 GB/day. proprietary
OMCLDO2_CPR_003 OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) 200-km swath subset along CloudSat track V003 (OMCLDO2_CPR) at GES DISC GES_DISC STAC Catalog 2006-06-01 2018-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350939-GES_DISC.umm_json This the OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) subset along CloudSat track, for the purposes of the A-Train mission. The original product uses the DOAS technique method. This level-2 global cloud product at the pixel resolution (13x24 km2 at nadir) is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track. This product contains cloud pressure, cloud fraction, slant column O2-O2 and O3, ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this Level-2 OMI cloud pressure and fraction (O2-O2 absorption) subset along CloudSat track product is OMCLDO2_CPR) proprietary
OMCLDRRG_003 OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMCLDRRG) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136100-GES_DISC.umm_json This Level-2G daily global gridded product OMCLDRRG is based on the pixel level OMI Level-2 CLDRR product OMCLDRR. This level-2G global cloud product (OMCLDRRG) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The algorithm lead for the products OMCLDRR and OMCLDRRG is NASA OMI scientist Dr. Joanna Joinner. OMCLDRRG data product is a special Level-2G Gridded Global Product where pixel level data (OMCLDRR)are binned into 0.25x0.25 degree global grids. It contains the OMCLDRR data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMCLDRRG data products are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (~14 orbits). The average file size for the OMCLDRRG data product is about 75 Mbytes. proprietary
-OMCLDRR_003 OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 (OMCLDRR) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.umm_json The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. proprietary
OMCLDRR_003 OMI/Aura Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000100-OMINRT.umm_json The reprocessed Aura OMI Version 003 Level 2 Cloud Data Product OMCLDRR is made available (in April 2012) to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). http://disc.gsfc.nasa.gov/Aura/OMI/omcldrr_v003.shtml ) Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product (OMCLDRR) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. OMCLDRR files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml . A short OMCLDRR Readme Document that includes brief algorithm description and data quality is also provided by the OMCLDRR Algorithm lead. The Ozone Monitoring Instrument (OMI) was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products are archived at the NASA GES DISC. For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/instruments/omi/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMCLDRR data file contains one swath which consists of two groups: Data fields: Two Effective Cloud Fraction and two Cloud Top Pressures that are based on two different clear and cloudy scene reflectivity criteria, Chlorophyll Amount, Effective Reflectivity (394.1 micron), UV Aerosol Index (based on 360 and 388 nm), and many Auxiliary Algorithm Parameter and Quality Flags. Geolocation Fields: Latitude, Longitude, Time, Solar Zenith Angle, Viewing Zenith Angle, Relative Azimuth Angle, Terrain Height, and Ground Pixel Quality Flags. OMI Atmospheric data and documents are available from the following sites: http://disc.gsfc.nasa.gov/Aura/OMI/ http://mirador.gsfc.nasa.gov/ proprietary
+OMCLDRR_003 OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V003 (OMCLDRR) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966791-GES_DISC.umm_json The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. proprietary
OMCLDRR_004 OMI/Aura Effective Cloud Pressure and Fraction (Raman Scattering) 1-Orbit L2 Swath 13x24 km V004 (OMCLDRR) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3159637081-GES_DISC.umm_json This is the Aura Ozone Monitoring Instrument (OMI) Version 004 Level 2 Cloud Data Product OMCLDRR. OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes. proprietary
OMCLDRR_CPR_003 OMI/Aura Cloud Pressure and Fraction (Raman Scattering) 200-km swath subset along CloudSat track V003 (OMCLDRR_CPR) at GES DISC GES_DISC STAC Catalog 2006-06-01 2018-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350980-GES_DISC.umm_json This is the OMI/Aura Cloud Pressure and Fraction (Raman Scattering) subset along CloudSat tracks, for the purposes of the A-Train mission. The original data product uses the Rotational Raman Scattering method. This level-2 global cloud product provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). The goal of this subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this Level-2 OMI cloud pressure and fraction subset along CloudSat tracks product is OMCLDRR_CPR) proprietary
OMDOAO3G_003 OMI/Aura Ozone (O3) DOAS Total Column Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMDOAO3G) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136103-GES_DISC.umm_json This Level-2G daily global gridded product OMDOAO3G is based on the pixel level OMI Level-2 DOAO3 product OMDOAO3. This Level-2G global total column ozone product is derived from OMDOAO3 which is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI visible radiance values between 331.1 and 336.1 nm. In addition to the total ozone column this product also contains some auxiliary derived and ancillary input parameters, e.g. ozone slant column density, ozone ghost column density, etc. The short name for this Level-2 OMI ozone product is OMDOAO3G and the lead algorithm scientist for this product and for OMDOAO3 (the data source of OMDOAO3G) is Dr. Pepijn Veefkind from KNMI. The OMDOAO3G product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (approximately 14 orbits) and is roughly 80 MB in size. proprietary
@@ -13076,8 +13077,8 @@ OMSO2_003 OMI/Aura Sulphur Dioxide (SO2) Total Column 1-orbit L2 Swath 13x24 km
OMSO2_CPR_003 OMI/Aura Level 2 Sulphur Dioxide (SO2) Trace Gas Column Data 1-Orbit Subset and Collocated Swath along CloudSat V003 (OMSO2_CPR) at GES DISC GES_DISC STAC Catalog 2006-06-01 2018-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350970-GES_DISC.umm_json "This is a CloudSat-collocated subset of the original product OMSO2, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated subset of the original product OMSO2 Product is OMSO2_CPR_V003) This document describes the original OMI SO2 product (OMSO2) produced from global mode UV measurements of the Ozone Monitoring Instrument (OMI). OMI was launched on July 15, 2004 on the EOS Aura satellite, which is in a sun-synchronous ascending polar orbit with 1:45pm local equator crossing time. The data collection started on August 17, 2004 (orbit 482) and continues to this day with only minor data gaps. The minimum SO2 mass detectable by OMI is about two orders of magnitude smaller than the detection threshold of the legacy Total Ozone Mapping Spectrometer (TOMS) SO2 data (1978-2005) [Krueger et al 1995]. This is due to smaller OMI footprint and the use of wavelengths better optimized for separating O3 from SO2. The product file, called a data granule, covers the sunlit portion of the orbit with an approximately 2600 km wide swath containing 60 pixels per viewing line. During normal operations, 14 or 15 granules are produced daily, providing fully contiguous coverage of the globe. Currently, OMSO2 products are not produced when OMI goes into the ""zoom mode"" for one day every 452 orbits (~32 days). For each OMI pixel we provide 4 different estimates of the column density of SO2 in Dobson Units (1DU=2.69x10^16 molecules/cm2) obtained by making different assumptions about the vertical distribution of the SO2. However, it is important to note that in most cases the precise vertical distribution of SO2 is unimportant. The users can use either the SO2 plume height, or the center of mass altitude (CMA) derived from SO2 vertical distribution, to interpolate between the 4 values: 1)Planetary Boundary Layer (PBL) SO2 column (ColumnAmountSO2_PBL), corresponding to CMA of 0.9 km. 2)Lower tropospheric SO2 column (ColumnAmountSO2_TRL), corresponding to CMA of 2.5 km. 3)Middle tropospheric SO2 column, (ColumnAmountSO2_TRM), usually produced by volcanic degassing, corresponding to CMA of 7.5 km, 4)Upper tropospheric and Stratospheric SO2 column (ColumnAmountSO2_STL), usually produced by explosive volcanic eruption, corresponding to CMA of 17 km. The accuracy and precision of the derived SO2 columns vary significantly with the SO2 CMA and column amount, observational geometry, and slant column ozone. OMI becomes more sensitive to SO2 above clouds and snow/ice, and less sensitive to SO2 below clouds. Preliminary error estimates are discussed below (see Data Quality Assessment). OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 9 Mbytes." proprietary
OMSO2e_003 OMI/Aura Sulfur Dioxide (SO2) Total Column Daily L3 1 day Best Pixel in 0.25 degree x 0.25 degree V3 (OMSO2e) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136112-GES_DISC.umm_json "The OMI science team produces this Level-3 Aura/OMI Global OMSO2e Data Products (0.25 degree Latitude/Longitude grids). In this Level-3 daily global SO2 data product, each grid contains only one observation of Total Column Density of SO2 in the Planetary Boundary Layer (PBL), based on an improved Principal Component Analysis (PCA) Algorithm. This single observation is the ""best pixel"", selected from all ""good"" L2 pixels of OMSO2 that overlap this grid and have UTC time between UTC times of 00:00:00 and 23:59:59.999. In addition to the SO2 Vertical column value some ancillary parameters, e.g., cloud fraction, terrain height, scene number, solar and satellite viewing angles, row anomaly flags, and quality flags have been also made available corresponding to the best selected SO2 data pixel in each grid. The OMSO2e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5) using the grid model." proprietary
OMTO3G_003 OMI/Aura Ozone (O3) Total Column Daily L2 Global Gridded 0.25 degree x 0.25 degree V3 (OMTO3G) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136114-GES_DISC.umm_json This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved Without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains UV aerosol index, cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes. proprietary
-OMTO3_003 OMI/Aura Ozone (O3) Total Column 1-Orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000140-OMINRT.umm_json The OMI/Aura Level-2 Total Column Ozone Data Product OMTO3 Near Real Time data is made available from the OMI SIPS NASA for the public access. The Ozone Monitoring Instrument (OMI)was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator's (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. This level-2 global total column ozone product (OMTO3)is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI additional hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrieval (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and extensive quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3 and the algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia ( Pawan.K.Bhartia@nasa.gov). OMTO3 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is about 35 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMTO3 data file contains one swath which consists of two groups: Data fields: OMI Total Ozone,Effective Reflectivity (331 - 360 nm), N-value, Cloud Fraction, Cloud Top Pressure, O3 below Cloud, UV Aerosol Index, SO2 index, Wavelength used in the algorithm, many Auxiliary Algorithm Parameter and Quality Flags Geolocation Fields: Latitude, Longitude, Time, Relative Azimuth, Solar Zenith and Azimuth, Viewing Zenith and Azimuth angles, Spacecraft Altitude, Latitude, Longitude, Terrain Height, Ground Pixel Quality Flags.For the full set of Aura data products available from the GES DISC, please see the link http://disc.sci.gsfc.nasa.gov/Aura/ . proprietary
OMTO3_003 OMI/Aura Ozone(O3) Total Column 1-Orbit L2 Swath 13x24 km V003 (OMTO3) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239966818-GES_DISC.umm_json The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Version 003) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI provides two Level-2 (OMTO3 and OMDOAO3) total column ozone products at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB. proprietary
+OMTO3_003 OMI/Aura Ozone (O3) Total Column 1-Orbit L2 Swath 13x24 km V003 NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1000000140-OMINRT.umm_json The OMI/Aura Level-2 Total Column Ozone Data Product OMTO3 Near Real Time data is made available from the OMI SIPS NASA for the public access. The Ozone Monitoring Instrument (OMI)was launched aboard the EOS-Aura satellite on July 15, 2004(1:38 pm equator crossing time, ascending mode). OMI with its 2600 km viewing swath width provides almost daily global coverage. OMI is a contribution of the Netherlands Agency for Aerospace Programs (NIVR)in collaboration with Finish Meterological Institute (FMI), to the US EOS-Aura Mission. The principal investigator's (Dr. Pieternel Levelt) institute is the KNMI (Royal Netherlands Meteorological Institute). OMI is designed to monitor stratospheric and tropospheric ozone, clouds, aerosols and smoke from biomass burning, SO2 from volcanic eruptions, and key tropospheric pollutants (HCHO, NO2) and ozone depleting gases (OClO and BrO). OMI sensor counts, calibrated and geolocated radiances, and all derived geophysical atmospheric products will be archived at the NASA Goddard DAAC. This level-2 global total column ozone product (OMTO3)is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI additional hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrieval (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and extensive quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3 and the algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia ( Pawan.K.Bhartia@nasa.gov). OMTO3 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is about 35 Mbytes. A list of tools for browsing and extracting data from these files can be found at: http://disc.gsfc.nasa.gov/Aura/tools.shtml For more information on Ozone Monitoring Instrument and atmospheric data products, please visit the OMI-Aura sites: http://aura.gsfc.nasa.gov/ http://www.knmi.nl/omi/research/documents/ . Data Category Parameters: The OMTO3 data file contains one swath which consists of two groups: Data fields: OMI Total Ozone,Effective Reflectivity (331 - 360 nm), N-value, Cloud Fraction, Cloud Top Pressure, O3 below Cloud, UV Aerosol Index, SO2 index, Wavelength used in the algorithm, many Auxiliary Algorithm Parameter and Quality Flags Geolocation Fields: Latitude, Longitude, Time, Relative Azimuth, Solar Zenith and Azimuth, Viewing Zenith and Azimuth angles, Spacecraft Altitude, Latitude, Longitude, Terrain Height, Ground Pixel Quality Flags.For the full set of Aura data products available from the GES DISC, please see the link http://disc.sci.gsfc.nasa.gov/Aura/ . proprietary
OMTO3_CPR_003 OMI/Aura Level 2 Ozone (O3) Total Column 1-Orbit Subset and Collocated Swath along CloudSat track 200-km wide at 13x24 km2 resolution GES_DISC STAC Catalog 2006-06-01 2018-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350982-GES_DISC.umm_json This is a CloudSat-collocated subset of the original product OMTO3, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated OMI Level 2 Total Ozone Column subset is OMTO3_CPR_V003) proprietary
OMTO3d_003 OMI/Aura TOMS-Like Ozone, Aerosol Index, Cloud Radiance Fraction L3 1 day 1 degree x 1 degree V3 (OMTO3d) at GES DISC GES_DISC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1266136070-GES_DISC.umm_json The OMI science team produces this Level-3 daily global TOMS-Like Total Column Ozone gridded product OMTO3d (1 deg Lat/Lon grids). The OMTO3d product is produced by gridding and averaging only good quality level-2 total column ozone orbital swath data (OMTO3, based on the enhanced TOMS version-8 algorithm) on the 1x1 degree global grids. The OMTO3d files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3d data product is about 0.65 Mbytes. proprietary
OMTO3e_003 OMI/Aura Ozone (O3) Total Column Daily L3 Global 0.25deg Lat/Lon Grid NRT OMINRT STAC Catalog 2004-07-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1428966163-OMINRT.umm_json The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles. OMTO3e files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes. (The shortname for this Level-3 TOMS-Like Total Column Ozone gridded product is OMTO3e) . proprietary
@@ -13199,14 +13200,14 @@ PAD_935_1 Surface Water Elevation and Quality, Peace-Athabasca Delta, Canada, 20
PAGESAntTemp2013_1 Antarctica continental-scale temperature variability during the past two millennia AU_AADC STAC Catalog 2013-01-01 2013-12-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214313668-AU_AADC.umm_json As part of a larger reconstruction of global temperatures over the last 2000 years, work was done to bring together all the Antarctic temperature datasets into one combined dataset. Taken from the PAGES website: Antarctica and the Southern Ocean play a key role in the global climate system (e.g. Mayewski et al., 2009; Convey et al., 2009). The processes that occur at these high southern latitudes play a pivotal role in global atmospheric and oceanic circulation, oceanic uptake of heat and carbon, and planetary energy balance, through the ice-albedo feedback. The ability to detect and attribute climate change in the Antarctic and Southern Ocean is dependent upon climate observations; however, this region is the most observation-sparse and record-length-limited part of the globe. There are few systematic observations extending back before the mid-20th century and good coverage is only available since the satellite era (i.e. the last 3-4 decades). In this context, key questions of the PAGES 2k Network underscore an acute need for good high resolution palaeoclimate data extending out to 2000 years before the present, but also with good coverage through the instrumental period so as to permit proxy calibration. Obtaining well-resolved ice cores over large parts of Antarctica is a challenge, but one that is becoming more tractable with the use of new technology. Antarctica2k seeks to integrate such records with other available proxies in order to address the goals of the 2k Network. proprietary
PAL-LTER_0 Palmer Station Antarctica (PAL) Long Term Ecological Research Network (LTER) OB_DAAC STAC Catalog 1991-12-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360585-OB_DAAC.umm_json Measurements made under the Long Term Ecological Research Network (LTER) Palmer Station Antarctica (PAL) program. proprietary
PARASOLRB_CPR_001 POLDER/Parasol L2 Radiation Budget subset along CloudSat track V001 (PARASOLRB_CPR) at GES DISC GES_DISC STAC Catalog 2006-06-01 2010-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1236350976-GES_DISC.umm_json This is the POLDER/Parasol Level-2 Radiation Budget Subset, collocated with the CloudSat track. The subset is processed at the A-Train Data Depot of the GES DISC, NASA. The algorithm first converts the original POLDER binary data, which is Level-2 but nevertheless in a sinusoidal grid, into HDF4 format, and thus stores the full-sized data in HDF4. Then, it calculates the CloudSat ground track coordinates, and proceeds to extract the closest POLDER grid cells. Along with the extraction, the algorithm re-orders the subset grid cells in a line-by-line fashion, so that the output subset is in array format and resembles a swath. This array has a cross-track dimension of 11 columns. That makes about 200-km-wide coverage. All original parameters are preserved in the subset. As it is collocated with CloudSat, the subset is automatically collocated with CALIPSO as well. proprietary
-PASSCAL_ABBA Adirondack Broad Band Array (ABBA) SCIOPS STAC Catalog 1995-01-01 1996-12-31 -74.5, 43.5, -73.8, 44.4 https://cmr.earthdata.nasa.gov/search/concepts/C1214608962-SCIOPS.umm_json Objective: Determination of anistropy and depth/characteristics of discontinuties in the mantle and the Moho beneath the Adirondacks. Preliminary results: Azimuthal Anisotropy is oriented ENE-WSW with a delay time of about 1 s. Discontinuity studies are still in progress. proprietary
PASSCAL_ABBA Adirondack Broad Band Array (ABBA) ALL STAC Catalog 1995-01-01 1996-12-31 -74.5, 43.5, -73.8, 44.4 https://cmr.earthdata.nasa.gov/search/concepts/C1214608962-SCIOPS.umm_json Objective: Determination of anistropy and depth/characteristics of discontinuties in the mantle and the Moho beneath the Adirondacks. Preliminary results: Azimuthal Anisotropy is oriented ENE-WSW with a delay time of about 1 s. Discontinuity studies are still in progress. proprietary
-PASSCAL_ALAR Aleutian Arc Seismic Experiment SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214610603-SCIOPS.umm_json "27 instruments were deployed at 18 different locations in the Aleutian Islands to record the airguns from the Ewing as it shot offshore. The full data report is available in PDF at the following URL: ""http://www.iris.edu/data/reports/1996/96-016.pdf""" proprietary
+PASSCAL_ABBA Adirondack Broad Band Array (ABBA) SCIOPS STAC Catalog 1995-01-01 1996-12-31 -74.5, 43.5, -73.8, 44.4 https://cmr.earthdata.nasa.gov/search/concepts/C1214608962-SCIOPS.umm_json Objective: Determination of anistropy and depth/characteristics of discontinuties in the mantle and the Moho beneath the Adirondacks. Preliminary results: Azimuthal Anisotropy is oriented ENE-WSW with a delay time of about 1 s. Discontinuity studies are still in progress. proprietary
PASSCAL_ALAR Aleutian Arc Seismic Experiment ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214610603-SCIOPS.umm_json "27 instruments were deployed at 18 different locations in the Aleutian Islands to record the airguns from the Ewing as it shot offshore. The full data report is available in PDF at the following URL: ""http://www.iris.edu/data/reports/1996/96-016.pdf""" proprietary
+PASSCAL_ALAR Aleutian Arc Seismic Experiment SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214610603-SCIOPS.umm_json "27 instruments were deployed at 18 different locations in the Aleutian Islands to record the airguns from the Ewing as it shot offshore. The full data report is available in PDF at the following URL: ""http://www.iris.edu/data/reports/1996/96-016.pdf""" proprietary
PASSCAL_KRAFLA 1994 Krafla Undershooting Experiment SCIOPS STAC Catalog 1970-01-01 -24.55, 62.81, -12.79, 67.01 https://cmr.earthdata.nasa.gov/search/concepts/C1214610676-SCIOPS.umm_json Thirty-eight instruments were used to shoot two perpendicular refraction profiles across the Krafla central volcano. The North/South profile is 20 km long while the East/West profile is 55 km long. Average station spacing was 500 m in the caldera and 1-4 km elswhere. A total of three shots were used in the NS profile and 6 shots were used in the EW profile. proprietary
PASSCAL_KRAFLA 1994 Krafla Undershooting Experiment ALL STAC Catalog 1970-01-01 -24.55, 62.81, -12.79, 67.01 https://cmr.earthdata.nasa.gov/search/concepts/C1214610676-SCIOPS.umm_json Thirty-eight instruments were used to shoot two perpendicular refraction profiles across the Krafla central volcano. The North/South profile is 20 km long while the East/West profile is 55 km long. Average station spacing was 500 m in the caldera and 1-4 km elswhere. A total of three shots were used in the NS profile and 6 shots were used in the EW profile. proprietary
-PASSCAL_WABASH A comprehensive geophysical investigation to assess seismic hazards in the coassesment of seismicity in the Wabash Valley ALL STAC Catalog 1995-11-01 1996-06-30 -88.1706, 38.2057, -88.1706, 38.2057 https://cmr.earthdata.nasa.gov/search/concepts/C1214608969-SCIOPS.umm_json Recent paleoseismic evidence had shown there were 5-8 magnitude greater than 6 earthquakes in this region in the past 20,000 years. The study area has always been at the fringe of previously operated seismic networks. A focused, short-term deployment was designed to lower the detection threshold to determine seismicity rates for the region for comparison with estimates derived from paleoseismicity. The researchers hoped to relate observed seismicity to faults mapped in the subsurface through new seismic reflection data made available to the Illinois Basin Consortium. proprietary
PASSCAL_WABASH A comprehensive geophysical investigation to assess seismic hazards in the coassesment of seismicity in the Wabash Valley SCIOPS STAC Catalog 1995-11-01 1996-06-30 -88.1706, 38.2057, -88.1706, 38.2057 https://cmr.earthdata.nasa.gov/search/concepts/C1214608969-SCIOPS.umm_json Recent paleoseismic evidence had shown there were 5-8 magnitude greater than 6 earthquakes in this region in the past 20,000 years. The study area has always been at the fringe of previously operated seismic networks. A focused, short-term deployment was designed to lower the detection threshold to determine seismicity rates for the region for comparison with estimates derived from paleoseismicity. The researchers hoped to relate observed seismicity to faults mapped in the subsurface through new seismic reflection data made available to the Illinois Basin Consortium. proprietary
+PASSCAL_WABASH A comprehensive geophysical investigation to assess seismic hazards in the coassesment of seismicity in the Wabash Valley ALL STAC Catalog 1995-11-01 1996-06-30 -88.1706, 38.2057, -88.1706, 38.2057 https://cmr.earthdata.nasa.gov/search/concepts/C1214608969-SCIOPS.umm_json Recent paleoseismic evidence had shown there were 5-8 magnitude greater than 6 earthquakes in this region in the past 20,000 years. The study area has always been at the fringe of previously operated seismic networks. A focused, short-term deployment was designed to lower the detection threshold to determine seismicity rates for the region for comparison with estimates derived from paleoseismicity. The researchers hoped to relate observed seismicity to faults mapped in the subsurface through new seismic reflection data made available to the Illinois Basin Consortium. proprietary
PATEX_0 PATagonia EXperiment (PATEX) Project OB_DAAC STAC Catalog 2004-11-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360589-OB_DAAC.umm_json PATagonia EXperiment (PATEX) Project is a Brazilian research project, which has the overall objective of characterizing the environmental constraints, phytoplankton assemblages, primary production rates, bio-optical characteristics, and air-sea CO2 fluxes waters along the Argentinean shelf-break during austral spring and summer. A set of seven PATEX cruises were conducted from 2004 to 2009. Garcia et al., 2011 (doi:10.1029/2010JC006595) proprietary
PAZ.ESA.archive_16.0 PAZ ESA archive ESA STAC Catalog 2018-09-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2547579176-ESA.umm_json "The PAZ ESA archive collection consists of PAZ Level 1 data previously requested by ESA supported projects over their areas of interest around the world and, as a consequence, the products are scattered and dispersed worldwide and in different time windows. The dataset regularly grows as ESA collects new products over the years. Available modes are: • StripMap mode (SM): SSD less than 3m for a scene 30km x 50km in single polarization or 15km x 50km in dual polarisation • ScanSAR mode (SC): the scene is 100 x 150 km2, SSD less than 18m in signle pol only • Wide ScanSAR mode (WS): single polarisation only, with SS less than 40m and scene size of 270 x 200 km2 • Spotlight modes (SL): SSD less than 2m for a scene 10km x 10km, both single and dual polarization are available • High Resolution Spotlight mode (HS): in both single and dual polarisation, the scene is 10x5 km2, SSD less than 1m • Staring Spotlight mode (ST): SSD is 25cm, the scene size is 4 x 4 km2, in single polarisation only. The available geometric projections are: • Single Look Slant Range Complex (SSC): single look product, no geocoding, no radiometric artifact included, the pixel spacing is equidistant in azimuth and in ground range • Multi Look Ground Range Detected (MGD): detected multi look product, simple polynomial slant-to-ground projection is performed in range, no image rotation to a map coordinate system is performed • Geocoded Ellipsoid Corrected (GEC): multi look detected product, projected and re-sampled to the WGS84 reference ellipsoid with no terrain corrections • Enhanced Ellipsoid Corrected (EEC): multi look detected product, projected and re-sampled to the WGS84 reference ellipsoid, the image distortions caused by varying terrain height are corrected using a DEM The following table summarises the offered product types EO-SIP product type Operation Mode Geometric Projection PSP_SM_SSC Stripmap (SM) Single Look Slant Range Complex (SSC) PSP_SM_MGD Stripmap (SM) Multi Look Ground Range Detected (MGD) PSP_SM_GEC Stripmap (SM) Geocoded Ellipsoid Corrected (GEC) PSP_SM_EEC Stripmap (SM) Enhanced Ellipsoid Corrected (EEC) PSP_SC_MGD ScanSAR (SC) Single Look Slant Range Complex (SSC) PSP_SC_GEC ScanSAR (SC) Multi Look Ground Range Detected (MGD) PSP_SC_EEC ScanSAR (SC) Geocoded Ellipsoid Corrected (GEC) PSP_SC_SSC ScanSAR (SC) Enhanced Ellipsoid Corrected (EEC) PSP_SL_SSC Spotlight (SL) Single Look Slant Range Complex (SSC) PSP_SL_MGD Spotlight (SL) Multi Look Ground Range Detected (MGD) PSP_SL_GEC Spotlight (SL) Geocoded Ellipsoid Corrected (GEC) PSP_SL_EEC Spotlight (SL) Enhanced Ellipsoid Corrected (EEC) PSP_HS_SSC High Resolution Spotlight (HS) Single Look Slant Range Complex (SSC) PSP_HS_MGD High Resolution Spotlight (HS) Multi Look Ground Range Detected (MGD) PSP_HS_GEC High Resolution Spotlight (HS) Geocoded Ellipsoid Corrected (GEC) PSP_HS_EEC High Resolution Spotlight (HS) Enhanced Ellipsoid Corrected (EEC) PSP_ST_SSC Staring Spotlight (ST) Single Look Slant Range Complex (SSC) PSP_ST_MGD Staring Spotlight (ST) Multi Look Ground Range Detected (MGD) PSP_ST_GEC Staring Spotlight (ST) Geocoded Ellipsoid Corrected (GEC) PSP_ST_EEC Staring Spotlight (ST) Enhanced Ellipsoid Corrected (EEC) PSP_WS_SSC Wide ScanSAR (WS) Single Look Slant Range Complex (SSC) PSP_WS_MGD Wide ScanSAR (WS) Multi Look Ground Range Detected (MGD) PSP_WS_GEC Wide ScanSAR (WS) Geocoded Ellipsoid Corrected (GEC) PSP_WS_EEC Wide ScanSAR (WS) Enhanced Ellipsoid Corrected (EEC)" proprietary
PAZ.Full.Archive.and.New.Tasking_7.0 PAZ Full Archive and New Tasking ESA STAC Catalog 2018-09-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689657-ESA.umm_json PAZ Image Products can be acquired in 8 image modes with flexible resolutions (from 1 m to 40 m) and scene sizes. Thanks to different polarimetric combinations and processing levels the delivered imagery can be tailored specifically to meet the requirements of the application. Available modes are: • StripMap mode (SM) in single and dual polarisation: The ground swath is illuminated with a continuous train of pulses while the antenna beam is pointed to a fixed angle, both in elevation and in azimuth. • ScanSAR mode (SC) in single polarisation: the swath width is increased respecting to the StripMap mode, it is composed of four different sub-swaths, which are obtained by antenna steering in elevation direction. • Wide ScanSAR mode (WS), in single polarisation: the usage of six sub-swaths allows to obtain a higher swath coverage product. • Spotlight modes: in single and dual polarisation: Spotlight modes take advantage of the beam steering capability in the azimuth plane to illuminate for a longer time the area of interest: a sensible improvement of the azimuth resolution is achieved at the expense of a shorter scene size. Spotlight mode (SL) is designed to maximise the azimuth scene extension at the expense of the spatial resolution, and High Resolution Spotlight mode (HS) is designed to maximize the spatial resolutions at the expense of the scene extension. • Staring Spotlight mode (ST), in single polarisation: The virtual rotation point coincides with the center of the beam: the image length in the flight direction is constrained by the projection on- ground of the azimuth beamwidth and it leads to a target azimuth illumination time increment and to achieve the best azimuth resolution. There are two main classes of products: • Spatially Enhanced products (SE): designed with the target of maximize the spatial resolution in pixels with squared size, so the larger resolution value of azimuth or ground range determines the square pixel size, and the smaller resolution value is adjusted to this size and the corresponding reduction of the bandwidth is used for speckle reduction. • Radiometrically Enhanced products (RE): designed with the target of maximize the radiometry, so the range and azimuth resolutions are intentionally decreased to significantly reduce speckle by averaging several looks. The following geometric projections are offered: • Single Look Slant Range Complex (SSC): single look product of the focused radar signal: the pixels are spaced equidistant in azimuth and in slant range. No geocoding is available, no radiometric artifacts included. Product delivered in the DLR-defined binary COSAR format. The SSC product is intended for applications that require the full bandwidth and phase information, e.g. for SAR interferometry and polarimetry. • Multi Look Ground Range Detected (MGD): detected multi look product in GeoTiff format with reduced speckle and approximately square resolution cells on ground. The image coordinates are oriented along flight direction and along ground range; the pixel spacing is equidistant in azimuth and in ground range. A simple polynomial slant to ground projection is performed in range using a WGS84 ellipsoid and an average, constant terrain height parameter. No image rotation to a map coordinate system is performed and interpolation artifacts are thus avoided. • Geocoded Ellipsoid Corrected (GEC): multi look detected product in GeoTiff format. It is projected and re-sampled to the WGS84 reference ellipsoid assuming one average terrain height. No terrain correction performed. UTM is the standard projection, for polar regions UPS is applied. • Enhanced Ellipsoid Corrected (EEC): multi look detected product in GeoTiff format. It is projected and re-sampled to the WGS84 reference ellipsoid. The image distortions caused by varying terrain height are corrected using an external DEM; therefore the pixel localization in these products is highly accurate. UTM is the standard projection, for polar regions UPS is applied. StripMap Single Mode ID: SM-S Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: 30 x 50 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 2.99 - 3.52 at (45° - 20°) - MGD, GEC, EEC (RE)[Ground range] 6.53 - 7.65 at (45° - 20°) - SSC[Slant range] 1.1 (150 MHz bandwidth) 1.7 (100 MHz bandwidth) Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 3.05 - MGD, GEC, EEC (RE) 6.53 - 7.60 at (45° - 20°) - SSC 3.01 StripMap Dual Mode ID: SM-D Polarizations: HH/VV, HH/HV, VV/VH Scene size (Range x Azimuth) [km]: 15 x 50 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 6 - MGD, GEC, EEC (RE)[Ground range] 7.51 - 10.43 at (45° - 20°) - SSC[Slant range] 1.18 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 6.11 - MGD, GEC, EEC (RE) 7.52 - 10.4 at (45° - 20°) - SSC ScanSAR Mode ID: SC Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: 100 x 150 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] N/A - MGD, GEC, EEC (RE)[Ground range] 16.79 - 18.19 at (45° - 20°) - SSC[Slant range] 1.17 - 3.4 (depending on range bandwidth) Azimuth Resolution [m]: - MGD, GEC, EEC (SE) N/A - MGD, GEC, EEC (RE) 17.66 - 18.18 at (45° - 20°) - SSC 18.5 Wide ScanSAR Mode ID: WS Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: [273-196] x 208 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] N/A - MGD, GEC, EEC (RE)[Ground range] 35 - SSC[Slant range] 1.75 - 3.18 (depending on range bandwidth) Azimuth Resolution [m]: - MGD, GEC, EEC (SE) N/A - MGD, GEC, EEC (RE) 39 - SSC 38.27 Spotlight Single Mode ID: SL-S Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: 10 x 10 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 1.55 - 3.43 at (55° - 20°) - MGD, GEC, EEC (RE)[Ground range] 3.51 - 5.43 at (55° - 20°) - SSC[Slant range] 1.18 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 1.56 - 2.9 at (55° - 20°) - MGD, GEC, EEC (RE) 3.51 - 5.4 at (55° - 20°) - SSC 1.46 Spotlight Dual Mode ID: SL-D Polarizations: HH/VV, HH/HV, VV/VH Scene size (Range x Azimuth) [km]: 10 x 10 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 3.09 - 3.5 at (55° - 20°) - MGD, GEC, EEC (RE)[Ground range] 4.98 - 7.63 at (55° - 20°) - SSC[Slant range] 1.17 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 3.53 - MGD, GEC, EEC (RE) 4.99 - 7.64 at (55° - 20°) - SSC 3.1 HR Spotlight Single Mode ID: HS-S Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: 10-6 x 5 (depending on incident angle) Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 1 - 1.76 at (55° - 20°) - MGD, GEC, EEC (RE)[Ground range] 2.83 - 3.11 at (55° - 20°) - SSC[Slant range] 0.6 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 1 - 1.49 at (55 °- 20°) - MGD, GEC, EEC (RE) 2.83 - 3.13 at (55° - 20°) - SSC 1.05 HR Spotlight Dual Mode ID: HS-D Polarizations: HH/VV, HH/HV, VV/VH Scene size (Range x Azimuth) [km]: 10 x 5 Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 2 - 3.5 at (55° - 20°) - MGD, GEC, EEC (RE)[Ground range] 4 - 6.2 at (55° - 20°) - SSC[Slant range] 1.17 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 2.38 - 2.93 at (55° - 20°) - MGD, GEC, EEC (RE) 4 - 6.25 at (55° - 20°) - SSC 2.16 Staring Spotlight Mode ID: ST Polarizations: HH, VV, HV, VH Scene size (Range x Azimuth) [km]: [9-4.6] x [2.7-3.6] Range Resolution [m]: - MGD, GEC, EEC (SE)[Ground range] 0.96 - 1.78 at (45°- 20°) - MGD, GEC, EEC (RE)[Ground range] 0.97 - 1.78 at (45°-20°) - SSC[Slant range] 0.59 Azimuth Resolution [m]: - MGD, GEC, EEC (SE) 0.38 - 0.7 at (45°-20°) - MGD, GEC, EEC (RE) 0.97 - 1.42 at (45°-20°) - SSC 0.22 All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. For archive data, the user is invited to search PAZ products by using the USP (User Service Provider) web portal (http://www.geos.hisdesat.es/) (self registration required) in order to verify the availability over the Area of Interest in the Time of Interest. proprietary
@@ -13240,12 +13241,12 @@ POLARIS_TraceGas_AircraftInSitu_ER2_Data_1 POLARIS ER-2 Aircraft In-situ Trace G
POLARIS_jValue_AircraftInSitu_ER2_Data_1 POLARIS Photolysis Frequencies (J-Values) LARC_ASDC STAC Catalog 1997-01-06 1997-09-26 180, -3.37, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2712794398-LARC_ASDC.umm_json POLARIS_jValue_AircraftInSitu_ER2_Data is the photolysis frequencies (j-values) collected during the Photochemistry of Ozone Loss in the Arctic Region in Summer (POLARIS) campaign. Data from the the Composition and Photo-Dissociative Flux Measurement (CPFM) is featured in this collection. Data collection for this product is complete. The POLARIS mission was a joint effort of NASA and NOAA that occurred in 1997 and was designed to expand on the photochemical and transport processes that cause the summer polar decreases in the stratospheric ozone. The POLARIS campaign had the overarching goal of better understanding the change of stratospheric ozone levels from very high concentrations in the spring to very low concentrations in the autumn. The NASA ER-2 high-altitude aircraft was the primary platform deployed along with balloons, satellites, and ground-sites. The POLARIS campaign was based in Fairbanks, Alaska with some flights being conducted from California and Hawaii. Flights were conducted between the summer solstice and fall equinox at mid- to high latitudes. The data collected included meteorological variables; long-lived tracers in reference to summertime transport questions; select species with reactive nitrogen (NOy), halogen (Cly), and hydrogen (HOx) reservoirs; and aerosols. More specifically, the ER-2 utilized various techniques/instruments including Laser Absorption, Gas Chromatography, Non-dispersive IR, UV Photometry, Catalysis, and IR Absorption. These techniques/instruments were used to collect data including N2O, CH4, CH3CCl3, CO2, O3, H2O, and NOy. Ground stations were responsible for collecting SO2 and O3, while balloons recorded pressure, temperature, wind speed, and wind directions. Satellites partnered with these platforms collected meteorological data and Lidar imagery. The observations were used to constrain stratospheric computer models to evaluate ozone changes due to chemistry and transport. proprietary
POLYNYA_ship_1 Mertz Polynya Experiment, Aurora Australis science cruises au9807 and au9901, and Tangaroa science cruise ta0051 - ship-based CTD, ADCP, LADCP and mooring data AU_AADC STAC Catalog 1998-04-03 2000-03-20 142, -67.5, 148, -64.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214313670-AU_AADC.umm_json Oceanographic measurements were conducted in the vicinity of the Mertz Polynya, encompassing 2 consecutive seasonal cycles from 1998 to 2000. In the southern winter of 1999, a total of 92 CTD/LADCP vertical profile stations were taken, most to within 20 m of the bottom, with 3 laps completed around the boundary of a box adjacent to the Mertz Glacier. Over 700 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients, oxygen 18, dimethyl sulphide, and biological parameters, using a 12 bottle rosette sampler mounted on a 24 bottle frame. Additional CTD vertical profiles were taken in April 1998, July 1998 and February 2000. Near surface current data were collected on all cruises using ship mounted ADCP. Two mooring arrays comprising thermosalinographs, current meters and upward looking sonars were deployed in the region of the Polynya. The first array of 7 moorings was deployed in April 1998. The second array of 4 moorings was deployed in the winter of 1999. All 11 Polynya moorings were recovered in February 2000. A summary of all data and data quality is presented in the data report. This work was completed as part of ASAC projects 2223 and 189. proprietary
POMME_0 Programme Ocean Multidisciplinaire Meso-Echelle (POMME) OB_DAAC STAC Catalog 2001-02-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360620-OB_DAAC.umm_json Measurements made during the Programme Ocean Multidisciplinaire Meso-Echelle (POMME) or Multidisciplinary middle-level ocean program in 2001. proprietary
-POSTER-03CYCLONE_Not Applicable 2003 Tropical Cyclones of the World ALL STAC Catalog 2003-01-08 2003-12-21 -180, -65, 180, 65 https://cmr.earthdata.nasa.gov/search/concepts/C2107093337-NOAA_NCEI.umm_json "Year 2003 Tropical Cyclones of the World poster. During calendar year 2003, fifty-one tropical cyclones with sustained surface winds of at least 64 knots were observed around the world. NOAA's Polar-Orbiting Operational Environmental Satellites (POES) captured these powerful storms near peak intensity, which are all presented in this colorful poster. Poster size is 36""x 27""." proprietary
POSTER-03CYCLONE_Not Applicable 2003 Tropical Cyclones of the World NOAA_NCEI STAC Catalog 2003-01-08 2003-12-21 -180, -65, 180, 65 https://cmr.earthdata.nasa.gov/search/concepts/C2107093337-NOAA_NCEI.umm_json "Year 2003 Tropical Cyclones of the World poster. During calendar year 2003, fifty-one tropical cyclones with sustained surface winds of at least 64 knots were observed around the world. NOAA's Polar-Orbiting Operational Environmental Satellites (POES) captured these powerful storms near peak intensity, which are all presented in this colorful poster. Poster size is 36""x 27""." proprietary
-POSTER-2004 Hurricanes_Not Applicable 2004 Landfalling Hurricanes Poster ALL STAC Catalog 2004-08-13 2004-09-25 -91, 8, -33, 46.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093388-NOAA_NCEI.umm_json "The 2004 U.S. Landfalling Hurricanes poster is a special edition poster which contains two sets of images of Hurricanes Charley, Frances, Ivan, and Jeanne, created from NOAA's operational satellites. In addtion to the images, the poster has a map depicting the general track of each storm; information on each storm's landfall location, date of landfall, and category level at time of landfall; as well as, a Saffir-Simpson Hurricane Scale chart. Poster size is 34""x27""." proprietary
+POSTER-03CYCLONE_Not Applicable 2003 Tropical Cyclones of the World ALL STAC Catalog 2003-01-08 2003-12-21 -180, -65, 180, 65 https://cmr.earthdata.nasa.gov/search/concepts/C2107093337-NOAA_NCEI.umm_json "Year 2003 Tropical Cyclones of the World poster. During calendar year 2003, fifty-one tropical cyclones with sustained surface winds of at least 64 knots were observed around the world. NOAA's Polar-Orbiting Operational Environmental Satellites (POES) captured these powerful storms near peak intensity, which are all presented in this colorful poster. Poster size is 36""x 27""." proprietary
POSTER-2004 Hurricanes_Not Applicable 2004 Landfalling Hurricanes Poster NOAA_NCEI STAC Catalog 2004-08-13 2004-09-25 -91, 8, -33, 46.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093388-NOAA_NCEI.umm_json "The 2004 U.S. Landfalling Hurricanes poster is a special edition poster which contains two sets of images of Hurricanes Charley, Frances, Ivan, and Jeanne, created from NOAA's operational satellites. In addtion to the images, the poster has a map depicting the general track of each storm; information on each storm's landfall location, date of landfall, and category level at time of landfall; as well as, a Saffir-Simpson Hurricane Scale chart. Poster size is 34""x27""." proprietary
-POSTER-2005 Atl Hurricanes_Not Applicable 2005 Atlantic Hurricanes Poster NOAA_NCEI STAC Catalog 2005-07-03 2005-12-08 -97, 20, -65, 40.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093322-NOAA_NCEI.umm_json "The 2005 Atlantic Hurricanes poster features high quality satellite images of 15 hurricanes which formed in the Atlantic Basin (includes Gulf of Mexico and Caribbean Sea) in the year 2005 which was the busiest season on record. The images show each storm near maximum intensity. Also, under each image there is additional information including, lowest pressure, maximum sustained winds, date range of the storm, highest category level reached on the Saffir-Simpson Hurricane Scale, and approximate position of each storm when the image was taken. Poster size is 35""x30""." proprietary
+POSTER-2004 Hurricanes_Not Applicable 2004 Landfalling Hurricanes Poster ALL STAC Catalog 2004-08-13 2004-09-25 -91, 8, -33, 46.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093388-NOAA_NCEI.umm_json "The 2004 U.S. Landfalling Hurricanes poster is a special edition poster which contains two sets of images of Hurricanes Charley, Frances, Ivan, and Jeanne, created from NOAA's operational satellites. In addtion to the images, the poster has a map depicting the general track of each storm; information on each storm's landfall location, date of landfall, and category level at time of landfall; as well as, a Saffir-Simpson Hurricane Scale chart. Poster size is 34""x27""." proprietary
POSTER-2005 Atl Hurricanes_Not Applicable 2005 Atlantic Hurricanes Poster ALL STAC Catalog 2005-07-03 2005-12-08 -97, 20, -65, 40.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093322-NOAA_NCEI.umm_json "The 2005 Atlantic Hurricanes poster features high quality satellite images of 15 hurricanes which formed in the Atlantic Basin (includes Gulf of Mexico and Caribbean Sea) in the year 2005 which was the busiest season on record. The images show each storm near maximum intensity. Also, under each image there is additional information including, lowest pressure, maximum sustained winds, date range of the storm, highest category level reached on the Saffir-Simpson Hurricane Scale, and approximate position of each storm when the image was taken. Poster size is 35""x30""." proprietary
+POSTER-2005 Atl Hurricanes_Not Applicable 2005 Atlantic Hurricanes Poster NOAA_NCEI STAC Catalog 2005-07-03 2005-12-08 -97, 20, -65, 40.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093322-NOAA_NCEI.umm_json "The 2005 Atlantic Hurricanes poster features high quality satellite images of 15 hurricanes which formed in the Atlantic Basin (includes Gulf of Mexico and Caribbean Sea) in the year 2005 which was the busiest season on record. The images show each storm near maximum intensity. Also, under each image there is additional information including, lowest pressure, maximum sustained winds, date range of the storm, highest category level reached on the Saffir-Simpson Hurricane Scale, and approximate position of each storm when the image was taken. Poster size is 35""x30""." proprietary
POSTER-2005 Sig Hurricanes_Not Applicable 2005 Significant U.S. Hurricane Strikes Poster NOAA_NCEI STAC Catalog 2005-07-10 2005-10-24 -102, 12, -69, 40.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093260-NOAA_NCEI.umm_json "The 2005 Significant U.S. Hurricane Strikes poster is one of two special edition posters for the Atlantic Hurricanes. This beautiful poster contains two sets of images of five hurricanes that impacted the United States in 2005, namely Katrina, Ophelia, Rita and Wilma. The images were created from NOAA's geostationary and polar-orbiting environmental satellites. In addition to the images, the poster has a map depicting the general track of each storm, a color temperature scale to read the hurricane cloud top temperatures, high level information on each storm, the category at time of landfall; as well as, a Saffir-Simpson Hurricane Scale. Poster size is 36""x32""." proprietary
POSTER-2005 Sig Hurricanes_Not Applicable 2005 Significant U.S. Hurricane Strikes Poster ALL STAC Catalog 2005-07-10 2005-10-24 -102, 12, -69, 40.5 https://cmr.earthdata.nasa.gov/search/concepts/C2107093260-NOAA_NCEI.umm_json "The 2005 Significant U.S. Hurricane Strikes poster is one of two special edition posters for the Atlantic Hurricanes. This beautiful poster contains two sets of images of five hurricanes that impacted the United States in 2005, namely Katrina, Ophelia, Rita and Wilma. The images were created from NOAA's geostationary and polar-orbiting environmental satellites. In addition to the images, the poster has a map depicting the general track of each storm, a color temperature scale to read the hurricane cloud top temperatures, high level information on each storm, the category at time of landfall; as well as, a Saffir-Simpson Hurricane Scale. Poster size is 36""x32""." proprietary
PRECIP_AMSR2_GCOMW1_1 NASA MEASURES Precipitation Ensemble based on AMSR2 GCOMW1 NASA PPS L1C V05 TBs 1-orbit L2 Swath 10x10km V1 (PRECIP_AMSR2_GCOMW1) at GES DISC GES_DISC STAC Catalog 2012-07-02 2021-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2368305620-GES_DISC.umm_json The data presented in this level 2 orbital product are rain rate estimates expressed as mm/hour determined from brightness temperatures (Tbs) obtained from the Advanced Microwave Scanning Radiometer-2 (AMSR-2) flown on the Global Climate Observing Mission-Water 1 (GCOM-W1). Most of the products generated in this data set are based upon the algorithms developed for the 3rd Algorithm Intercomparison Project (AIP-3) of the Global Precipitation Climatology Project (GPCP). Details of these 15 algorithms and development of a quality score which is a measure of confidence in the estimate, along with processing and algorithmic flags, can be found in the Algorithm Theoretical Basis Document (ATBD). The data in this product cover the period from 2012 to 2020 with one file per orbit. proprietary
@@ -13294,8 +13295,8 @@ PVST_SMARTS_0 Validating PACE aerosol columnar properties and OCI water-leaving
PVST_VDIUP_0 Validation of Ocean Surface Downwelling Irradiance and Its Underwater Propagation for the PACE Mission OB_DAAC STAC Catalog 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3252791852-OB_DAAC.umm_json This project contributes to the validation of global surface radiation products and diffuse attenuation coefficients (Kd) generated by the PACE mission, essential for quantifying net primary production. The radiation products include instantaneous, daily mean, planar, and scalar fluxes products, in particular daily mean photosynthetically available radiation (PAR). In-situ observations are gathered through a network of automatic stations measuring hyperspectral downward planar irradiance (Ed(0+)) at selected AERONET-OC sites, and BGC-Argo profilers equipped with hyperspectral Ed sensors. BGC-Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org). The Argo Program is part of the Global Ocean Observing System https://doi.org/10.17882/42182. Link to BGC-Argo GDAC for raw float data: https://data-argo.ifremer.fr/aux/coriolis/. proprietary
PanamaCity_0 Panama City, Florida optical measurements in 1993 OB_DAAC STAC Catalog 1993-10-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360586-OB_DAAC.umm_json Measurements taken in the Gulf of Mexico near Panama City, Florida in 1993. proprietary
Panhandle_OWQ_0 Optical Water quality measurements made in the Florida Panhandle estuaries OB_DAAC STAC Catalog 2015-12-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360587-OB_DAAC.umm_json Measurements made in the Florida Panhandle estuaries in partnership with USF and FWC-FWRI. proprietary
-Passive_Microwave_Snowoff_Data_1711_1.1 ABoVE: Passive Microwave-derived Annual Snowoff Date Maps, 1988-2018 ALL STAC Catalog 1988-01-01 2018-12-31 -180, 37.98, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2221862177-ORNL_CLOUD.umm_json This dataset provides annual maps of the snowoff (SO) date from 1988-2018 across Alaska and parts of Far East Russia and northwest Canada at a resolution of 6.25 km. SO date is defined as the last day of persistent snow and was derived from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR) product. The spatial domain was intended to match MODIS Alaska Snow Metrics and extend its temporal fidelity beyond the MODIS era. SO date estimates were compared to snow depth measurements collected at SNOTEL stations across Alaska and to three SO datasets derived from MODIS, Landsat, and the Interactive Multisensor Snow and Ice Mapping System (IMS). The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary
Passive_Microwave_Snowoff_Data_1711_1.1 ABoVE: Passive Microwave-derived Annual Snowoff Date Maps, 1988-2018 ORNL_CLOUD STAC Catalog 1988-01-01 2018-12-31 -180, 37.98, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2221862177-ORNL_CLOUD.umm_json This dataset provides annual maps of the snowoff (SO) date from 1988-2018 across Alaska and parts of Far East Russia and northwest Canada at a resolution of 6.25 km. SO date is defined as the last day of persistent snow and was derived from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR) product. The spatial domain was intended to match MODIS Alaska Snow Metrics and extend its temporal fidelity beyond the MODIS era. SO date estimates were compared to snow depth measurements collected at SNOTEL stations across Alaska and to three SO datasets derived from MODIS, Landsat, and the Interactive Multisensor Snow and Ice Mapping System (IMS). The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary
+Passive_Microwave_Snowoff_Data_1711_1.1 ABoVE: Passive Microwave-derived Annual Snowoff Date Maps, 1988-2018 ALL STAC Catalog 1988-01-01 2018-12-31 -180, 37.98, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2221862177-ORNL_CLOUD.umm_json This dataset provides annual maps of the snowoff (SO) date from 1988-2018 across Alaska and parts of Far East Russia and northwest Canada at a resolution of 6.25 km. SO date is defined as the last day of persistent snow and was derived from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR) product. The spatial domain was intended to match MODIS Alaska Snow Metrics and extend its temporal fidelity beyond the MODIS era. SO date estimates were compared to snow depth measurements collected at SNOTEL stations across Alaska and to three SO datasets derived from MODIS, Landsat, and the Interactive Multisensor Snow and Ice Mapping System (IMS). The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary
Patagonian_Coastal_0 Measurements off the Argentinian coast near Drakes Passage OB_DAAC STAC Catalog 2008-12-16 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360588-OB_DAAC.umm_json Measurements made in the South Atlantic Ocean in 2008 and 2009 off the Argentinian coast near Drakes Passage. proprietary
Peatland_carbon_balance_1382_1 Global Peatland Carbon Balance and Land Use Change CO2 Emissions Through the Holocene ORNL_CLOUD STAC Catalog 1000-01-01 2001-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2216864221-ORNL_CLOUD.umm_json This data set provides a time series of global peatland carbon balance and carbon dioxide emissions from land use change throughout the Holocene (the past 11,000 yrs). Global peatland carbon balance was quantified using a) a continuous net carbon balance history throughout the Holocene derived from a data set of 64 dated peat cores, and b) global model simulations with the LPX-Bern model hindcasting the dynamics of past peatland distribution and carbon balance. CO2 emissions from land-use change are based on published scenarios for anthropogenic land use change (HYDE 3.1, HYDE 3.2, KK10) covering the last 10,000 years. This combination of model estimates with CO2 budget constraints narrows the range of past anthropogenic land use change emissions and their contribution to past carbon cycle changes. proprietary
Pelican_PCO2_0 Partial pressure of carbon dioxide (PCO2) onboard the Pelican research vessel OB_DAAC STAC Catalog 2006-04-27 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360591-OB_DAAC.umm_json Measurements from the Pelican research vessel made off the southern coast of Louisiana in the Gulf of Mexico from 2006. proprietary
@@ -13303,8 +13304,8 @@ PenBaySurvey_0 Penobscot Bay Optical Survey OB_DAAC STAC Catalog 2007-11-15 -18
PermafrostThaw_CarbonEmissions_1872_1 Projections of Permafrost Thaw and Carbon Release for RCP 4.5 and 8.5, 1901-2299 ORNL_CLOUD STAC Catalog 1901-01-01 2300-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2254686682-ORNL_CLOUD.umm_json This dataset consists of an ensemble of model projections from 1901 to 2299 for the northern hemisphere permafrost domain. The model projections include monthly average values for a common set of diagnostic outputs at a spatial resolution of 0.5 x 0.5 degrees latitude and longitude. The model simulations resulted from a synthesis effort organized by the Permafrost Carbon Network to evaluate the impacts of climate change on the carbon cycle in permafrost regions in the high northern latitudes. The model teams used different historical input weather data, but most used driver data developed by the Climate Research Unit - National Centers for Environmental Prediction (CRUNCEP) as modified for the Multiscale Terrestrial Model Intercomparison Project (MsTMIP). The teams scaled the driver data for the projections using output from global climate models from the fifth Coupled Model Intercomparison Project (CMIP5). The synthesis evaluated the terrestrial carbon cycle in the modern era and projected future emissions of carbon under two climate warming scenarios: Representative Concentration Pathways 4.5 and 8.5 (RCP45 and RCP85) from CMIP5. RCP45 represents emissions resulting in a global climate close to the target climate in the Paris Accord. RCP85 represents unconstrained greenhouse gas emissions. proprietary
Permafrost_ActiveLayer_NSlope_1759_1 ABoVE: Active Layer Soil Characterization of Permafrost Sites, Northern Alaska, 2018 ALL STAC Catalog 2018-08-22 2018-08-26 -149.31, 68.61, -148.56, 69.81 https://cmr.earthdata.nasa.gov/search/concepts/C2143402217-ORNL_CLOUD.umm_json This dataset provides in situ soil measurements including soil dielectric properties, temperature, and moisture profiles, active layer thickness (ALT), and measurements of soil organic matter, bulk density, porosity, texture, and coarse root biomass. Samples were collected from the surface to permafrost table in soil pits at selected sites along the Dalton Highway in Northern Alaska. From North to South, the study sites include Franklin Bluffs, Sagwon, Happy Valley, Ice Cut, and Imnavait Creek. Measurements were made from August 22 to August 26, 2018. The purpose of the field campaign was to characterize the dielectric properties of permafrost active layer soils in support of the NASA Arctic and Boreal Vulnerability Experiment (ABoVE) Airborne Campaign. proprietary
Permafrost_ActiveLayer_NSlope_1759_1 ABoVE: Active Layer Soil Characterization of Permafrost Sites, Northern Alaska, 2018 ORNL_CLOUD STAC Catalog 2018-08-22 2018-08-26 -149.31, 68.61, -148.56, 69.81 https://cmr.earthdata.nasa.gov/search/concepts/C2143402217-ORNL_CLOUD.umm_json This dataset provides in situ soil measurements including soil dielectric properties, temperature, and moisture profiles, active layer thickness (ALT), and measurements of soil organic matter, bulk density, porosity, texture, and coarse root biomass. Samples were collected from the surface to permafrost table in soil pits at selected sites along the Dalton Highway in Northern Alaska. From North to South, the study sites include Franklin Bluffs, Sagwon, Happy Valley, Ice Cut, and Imnavait Creek. Measurements were made from August 22 to August 26, 2018. The purpose of the field campaign was to characterize the dielectric properties of permafrost active layer soils in support of the NASA Arctic and Boreal Vulnerability Experiment (ABoVE) Airborne Campaign. proprietary
-Permafrost_Thaw_Depth_YK_1598_1 ABoVE: Permafrost Measurements and Distribution Across the Y-K Delta, Alaska, 2016 ALL STAC Catalog 2009-06-27 2016-07-17 -165.69, 61.17, -165.03, 61.29 https://cmr.earthdata.nasa.gov/search/concepts/C2162142273-ORNL_CLOUD.umm_json This dataset provides field observations of thaw depth and dominant vegetation types, a LiDAR-derived elevation map, and permafrost distribution and probability maps for an area on the coastal plain of the Yukon-Kuskokwim Delta (YKD), in western Alaska, USA. Field data were collected during July 8-17, 2016 to parameterize and to validate the derived permafrost maps. The YKD is in the sporadic to isolated permafrost zone where permafrost forms extensive elevated plateaus on abandoned floodplains. The region is extremely flat and vulnerable to eustatic sea-level rise and inland storm surges. These high-resolution permafrost maps support landscape change analyses and assessments of the impacts of climate change on permafrost in this region of high biological productivity, critical wildlife habitats, and subsistence-based human economy. proprietary
Permafrost_Thaw_Depth_YK_1598_1 ABoVE: Permafrost Measurements and Distribution Across the Y-K Delta, Alaska, 2016 ORNL_CLOUD STAC Catalog 2009-06-27 2016-07-17 -165.69, 61.17, -165.03, 61.29 https://cmr.earthdata.nasa.gov/search/concepts/C2162142273-ORNL_CLOUD.umm_json This dataset provides field observations of thaw depth and dominant vegetation types, a LiDAR-derived elevation map, and permafrost distribution and probability maps for an area on the coastal plain of the Yukon-Kuskokwim Delta (YKD), in western Alaska, USA. Field data were collected during July 8-17, 2016 to parameterize and to validate the derived permafrost maps. The YKD is in the sporadic to isolated permafrost zone where permafrost forms extensive elevated plateaus on abandoned floodplains. The region is extremely flat and vulnerable to eustatic sea-level rise and inland storm surges. These high-resolution permafrost maps support landscape change analyses and assessments of the impacts of climate change on permafrost in this region of high biological productivity, critical wildlife habitats, and subsistence-based human economy. proprietary
+Permafrost_Thaw_Depth_YK_1598_1 ABoVE: Permafrost Measurements and Distribution Across the Y-K Delta, Alaska, 2016 ALL STAC Catalog 2009-06-27 2016-07-17 -165.69, 61.17, -165.03, 61.29 https://cmr.earthdata.nasa.gov/search/concepts/C2162142273-ORNL_CLOUD.umm_json This dataset provides field observations of thaw depth and dominant vegetation types, a LiDAR-derived elevation map, and permafrost distribution and probability maps for an area on the coastal plain of the Yukon-Kuskokwim Delta (YKD), in western Alaska, USA. Field data were collected during July 8-17, 2016 to parameterize and to validate the derived permafrost maps. The YKD is in the sporadic to isolated permafrost zone where permafrost forms extensive elevated plateaus on abandoned floodplains. The region is extremely flat and vulnerable to eustatic sea-level rise and inland storm surges. These high-resolution permafrost maps support landscape change analyses and assessments of the impacts of climate change on permafrost in this region of high biological productivity, critical wildlife habitats, and subsistence-based human economy. proprietary
PhenoCam_V2_1674_2 PhenoCam Dataset v2.0: Vegetation Phenology from Digital Camera Imagery, 2000-2018 ORNL_CLOUD STAC Catalog 1999-11-16 2018-12-31 -158.15, -22.97, 119.22, 71.28 https://cmr.earthdata.nasa.gov/search/concepts/C2764826583-ORNL_CLOUD.umm_json This data set provides a time series of vegetation phenological observations for 393 sites across diverse ecosystems of the world (mostly North America) from 2000-2018. The phenology data were derived from conventional visible-wavelength automated digital camera imagery collected through the PhenoCam Network at each site. From each acquired image, RGB (red, green, blue) color channel information was extracted and means and other statistics calculated for a region-of-interest (ROI) that delineates an area of specific vegetation type. From the high-frequency (typically, 30 minute) imagery collected over several years, time series characterizing vegetation color, including canopy greenness, plus greenness rising and greenness falling transition dates, were summarized over 1- and 3-day intervals. proprietary
Phenocam_Images_V2_1689_2 PhenoCam Dataset v2.0: Digital Camera Imagery from the PhenoCam Network, 2000-2018 ORNL_CLOUD STAC Catalog 1999-11-16 2018-12-31 -158.15, -22.97, 119.22, 71.28 https://cmr.earthdata.nasa.gov/search/concepts/C2764728896-ORNL_CLOUD.umm_json This dataset provides a time series of visible-wavelength digital camera imagery collected through the PhenoCam Network at each of 393 sites predominantly in North America from 2000-2018. The raw imagery was used to derive information on phenology, including time series of vegetation color, canopy greenness, and phenology transition dates for the PhenoCam Dataset v2.0. proprietary
Phenology_AmeriFlux_Neon_Sites_2033_1 Land Surface Phenology, Eddy Covariance Tower Sites, North America, 2017-2021 ORNL_CLOUD STAC Catalog 2017-01-01 2021-12-31 -176.13, 14.34, -57.3, 70.98 https://cmr.earthdata.nasa.gov/search/concepts/C2764693210-ORNL_CLOUD.umm_json This land surface phenology (LSP) dataset provides spatially explicit data related to the timing of phenological changes such as the start, peak, and end of vegetation activity, vegetation index metrics and associated quality assurance flags. The data are for the growing seasons of 2017-2021 for 10-km x 10-km windows centered over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. The dataset is derived at 3-m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. These LSP data can be used to assess satellite-based LSP products, to evaluate predictions from land surface models, and to analyze processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes. The data are provided in NetCDF format along with geospatial area-of-interest information and visualizations of the analysis window for each site in GeoJSON and HTML formats. proprietary
@@ -13320,20 +13321,20 @@ Pleiades.HiRI.archive.and.new_9.0 Pleiades full archive and tasking ESA STAC Cat
Pleiades.Neo.full.archive.and.tasking_9.0 Pléiades Neo full archive and tasking ESA STAC Catalog 2021-04-28 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2547572735-ESA.umm_json "Very High Resolution optical Pléiades Neo data at 30 cm PAN resolution (1.2 m 6-bands Multispectral) are available as part of the Airbus provision with twice daily revisit capability over the entire globe. The swath width is 14 km (footprint at nadir). Band combinations: • Panchromatic one band Black & White image at 0.3 m resolution • Pansharpened colour image at 0.3 m resolution: Natural colour (3 bands RGB), false colour (3 bands NIRRG), 4 bands (RGB+NIR), 6 bands • Multispectral colour image in 4 bands (RGB+NIR) or 6 bands (also Deep blue and Red Edge) at 1.2 m resolution • Bundle 0.3 m panchromatic image and 1.2 m multispectral image (4 or 6 bands) simultaneously acquired Geometric processing levels: • Primary: The Primary product is the processing level closest to the natural image acquired by the sensor. This product restores perfect collection conditions: the sensor is placed in rectilinear geometry, and the image is clear of all radiometric distortion. • Ortho: The Ortho product is a georeferenced image in Earth geometry, corrected from acquisition and terrain off-nadir effects. Acquisition modes: • Mono • Stereo • Tristereo • HD15: 15cm resolution for Panchromatic, 60cm resolution for Multispectral: Mono image resampled by using machine learning model which increase sharpness and fineness of details without introducing any fake data. To complement the traditional and fully customised ordering and download of selected SPOT, Pleiades or Pleiades Neo images in a variety of data formats, you can also subscribe to the OneAtlas Living Library package where the entire OneAtlas optical archive of ortho images is updated on a daily basis and made available for streaming or download. The Living Library consist of: • less-than-18-months-old Pansharpened and Bundle imagery • a curation of SPOT images with no cloud cover and less than 30° incidence angle • Pléiades images acquired worldwide with maximum 15% cloud cover and 30° Incidence Angle • Pléiades Neo premium imagery selection with 2% cloud cover and 30° incidence angle These are the available subscription packages (to be consumed withing one year from the activation) OneAtlas Living Library subscription package 1: up to 230 km2 Pleiades Neo or 430 km2 Pleiades or 1.500 km2 SPOT in download, up to 500 km2 Pleiades Neo or 2.000 km2 Pleiades or 7.500 km2 SPOT in streaming OneAtlas Living Library subscription package 2: up to 654 km2 Pleiades Neo or 1.214 km2 Pleiades or 4.250 km2 SPOT in download, up to 1417 km2 Pleiades Neo or 5.666 km2 Pleiades or 21.250 km2 SPOT in streaming OneAtlas Living Library subscription package 3: up to 1.161 km2 Pleiades Neo or 2.156 km2 Pleiades or 7.545 km2 SPOT in download, up to 2.515 km2 Pleiades Neo or 10.060 km2 Pleiades or 37.723 km2 SPOT in streaming All details about the data provision, data access conditions and quota assignment procedure are described in the _$$Terms of Applicability$$ https://earth.esa.int/eogateway/documents/20142/37627/SPOT-Pleiades-data-terms-of-applicability.pdf available in the Resources section. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary
Plot_Data_Noatak_Seward_AK_1919_1 Burned and Unburned Field Site Data, Noatak, Seward, and North Slope, AK, 2016-2018 ORNL_CLOUD STAC Catalog 2016-07-22 2018-08-27 -164.93, 65.02, -148.64, 69.66 https://cmr.earthdata.nasa.gov/search/concepts/C2240727642-ORNL_CLOUD.umm_json This dataset includes field measurements from unburned and burned 10 m x 10 m and 1 m x 1 m plots in the Noatak, Seward, and North Slope regions of the Alaskan tundra during July through August in the years 2016-2018. The data include vegetation coverage, soil moisture, soil temperature, soil thickness, thaw depth, and weather measurements. Measurements were recorded using ocular assessments and standard equipment. Plot photographs are included. proprietary
Plumes_and_Blooms_0 Plumes and Blooms OB_DAAC STAC Catalog 1996-08-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360616-OB_DAAC.umm_json The Plumes and Blooms program is a joint collaboration among UCSB faculty, student and staff researchers at the Institute of Computational Earth System Science (ICESS), NOAA researchers at the Coastal Services Center (Charleston, SC) and the NOAA sanctuary managers of the Channel Islands National Marine Sanctuary (CINMS). Since August, 1996, monthly research cruises have been conducted to collect measurements. These measurements include temperature and salinity, ocean color spectra, and water column profiles of red light transmission and chlorophyll fluorescence (indexes of suspended particulate load and phytoplankton abundance). The transect observations begin at the shelf waters north of Santa Rosa island and end at an area off Goleta Point. These repeat observations are combined with satellite imagery to build a time-series of the changing ocean color conditions in the Santa Barbara Channel. proprietary
-PolInSAR_Canopy_Height_1589_1 AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon ORNL_CLOUD STAC Catalog 2016-02-27 2016-03-08 9.29, -0.35, 11.83, 0.24 https://cmr.earthdata.nasa.gov/search/concepts/C2734258687-ORNL_CLOUD.umm_json This dataset provides estimates of forest canopy height and canopy height uncertainty for study areas in the Pongara National Park and the Lope National Park, Gabon. Two canopy height products are included: 1) Canopy height was derived from multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data using an inversion of the random volume over ground (RVoG) model and Kapok, an open source Python library. 2) Canopy height was derived from a fusion of PolInSAR and Land, Vegetation, and Ice Sensor (LVIS) Lidar data. This dataset also includes various intermediate parameters of the PolInSAR data (including radar backscatter, coherence, and viewing and terrain geometry) which provide additional insight into the input data used to invert the RVoG model and accuracy of the canopy height estimates. The AfriSAR campaign was flown from 2016-02-27 to 2016-03-08. AfriSAR data were collected by NASA, in collaboration with the European Space Agency (ESA) and the Gabonese Space Agency. proprietary
PolInSAR_Canopy_Height_1589_1 AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon ALL STAC Catalog 2016-02-27 2016-03-08 9.29, -0.35, 11.83, 0.24 https://cmr.earthdata.nasa.gov/search/concepts/C2734258687-ORNL_CLOUD.umm_json This dataset provides estimates of forest canopy height and canopy height uncertainty for study areas in the Pongara National Park and the Lope National Park, Gabon. Two canopy height products are included: 1) Canopy height was derived from multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data using an inversion of the random volume over ground (RVoG) model and Kapok, an open source Python library. 2) Canopy height was derived from a fusion of PolInSAR and Land, Vegetation, and Ice Sensor (LVIS) Lidar data. This dataset also includes various intermediate parameters of the PolInSAR data (including radar backscatter, coherence, and viewing and terrain geometry) which provide additional insight into the input data used to invert the RVoG model and accuracy of the canopy height estimates. The AfriSAR campaign was flown from 2016-02-27 to 2016-03-08. AfriSAR data were collected by NASA, in collaboration with the European Space Agency (ESA) and the Gabonese Space Agency. proprietary
+PolInSAR_Canopy_Height_1589_1 AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon ORNL_CLOUD STAC Catalog 2016-02-27 2016-03-08 9.29, -0.35, 11.83, 0.24 https://cmr.earthdata.nasa.gov/search/concepts/C2734258687-ORNL_CLOUD.umm_json This dataset provides estimates of forest canopy height and canopy height uncertainty for study areas in the Pongara National Park and the Lope National Park, Gabon. Two canopy height products are included: 1) Canopy height was derived from multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data using an inversion of the random volume over ground (RVoG) model and Kapok, an open source Python library. 2) Canopy height was derived from a fusion of PolInSAR and Land, Vegetation, and Ice Sensor (LVIS) Lidar data. This dataset also includes various intermediate parameters of the PolInSAR data (including radar backscatter, coherence, and viewing and terrain geometry) which provide additional insight into the input data used to invert the RVoG model and accuracy of the canopy height estimates. The AfriSAR campaign was flown from 2016-02-27 to 2016-03-08. AfriSAR data were collected by NASA, in collaboration with the European Space Agency (ESA) and the Gabonese Space Agency. proprietary
Polar-VPRM_Alaskan-NEE_1314_1 CARVE Modeled Gross Ecosystem CO2 Exchange and Respiration, Alaska, 2012-2014 ORNL_CLOUD STAC Catalog 2012-01-01 2014-12-31 -179, 55, -134, 73 https://cmr.earthdata.nasa.gov/search/concepts/C2236236883-ORNL_CLOUD.umm_json This data set provides 3-hourly estimates of gross ecosystem CO2 exchange (GEE) and respiration (autotrophic and heterotrophic) for the state of Alaska from 2012 to 2014. The data were generated using the Polar Vegetation Photosynthesis and Respiration Model (PolarVPRM) and are provided at ~ 1 km2 [1/4-degree (longitude) by 1/6-degree (latitude)] pixel resolution. The PolarVPRM produces high-frequency estimates of GEE of CO2 for North American biomes from remotely-sensed data sets. For Alaska, the model used meteorological inputs from the North American regional re-analysis (NARR) and inputs of fractional snow cover and land surface water index (LSWI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Land surface greenness was factored into the model from three sources: 1) Enhanced Vegetation Index (EVI) from MODIS; 2) Solar Induced Florescence (SIF) from the Orbiting Carbon Observatory 2 (OCO-2); and 3) SIF from the Global Ozone Monitoring Experiment 2 (GOME-2). Three independent estimates of GEE are included in the data set, one for each source of greenness observations. proprietary
PolarWindsII_DAWN_DC8_1 Polar Winds II - Doppler Aerosol WiNd (DAWN) - DC8 LARC_ASDC STAC Catalog 2015-05-11 2015-05-25 -59, 49, 15.5, 70.5 https://cmr.earthdata.nasa.gov/search/concepts/C1440079415-LARC_ASDC.umm_json PolarWindsII_DAWN_DC8_1 is the Polar Winds II - Doppler Aerosol WiNd (DAWN) - DC8 data product. Data collection for this product is complete. Beginning in the fall of 2014, NASA sponsored two airborne field campaigns, collectively called Polar Winds, designed to fly the Doppler Aerosol WiNd (DAWN) lidar and other instruments to take airborne wind measurements of the Arctic atmosphere, specifically over and off the coasts of Greenland during Oct-Nov 2014 and May 2015. In particular, Polar Winds conducted a series of science experiments focusing on the measurement and analyses of lower tropospheric winds and aerosols associated with coastal katabatic flows, barrier winds, the Greenland Tip Jet, boundary layer circulations such as rolls and OLEs (Organized Large Eddies), and near surface winds over open water, transitional ice zones and the Greenland Ice Cap. Polar Winds I was based in Kangerlussuaq, Greenland and flew DAWN on board the NASA King Air UC-12B during Oct-Nov 2014 while Polar Winds II was based in Keflavik, Iceland and utilized the NASA DC-8 aircraft to fly DAWN and Dropsondes over the Arctic in May 2015. In total, twenty-four individual missions with over 80 hours of research flights were flown in the Arctic region near Greenland and Iceland during Polar Winds. The focus instrument for the wind measurements taken over the Arctic during Polar Winds was the DAWN airborne wind lidar. At a wavelength of 2.05 microns and at 250 mj per pulse, DAWN is the most powerful airborne Doppler Wind Lidar available today for airborne missions. DAWN has previously been flown on the NASA DC-8 during the 2010 Genesis and Rapid Intensification Processes (GRIP) campaign and on the NASA C-12 for wind field characterization off the coast of Virginia. In addition to DAWN, Polar Winds utilized the High Definition Sounding System (HDSS) dropsonde delivery system developed by Yankee Environmental Services to drop almost 100 dropsondes during Polar Wind II to obtain additional high-resolution vertical wind profiles during most missions. These dropsondes also provided needed calibration/validation for the much newer DAWN measurements. proprietary
PolarWindsI_DAWN_KingAirUC-12B_1 Polar Winds I - Doppler Aerosol WiNd (DAWN) - KingAirUC-12B LARC_ASDC STAC Catalog 2014-10-29 2014-11-13 -58, 59, -42, 69 https://cmr.earthdata.nasa.gov/search/concepts/C1457763994-LARC_ASDC.umm_json PolarWindsI_DAWN_KingAirUC-12B is the Polar Winds I - Doppler Aerosol WiNd (DAWN) - KingAirUC-12B data product. Data for this was collected using the DAWN instrument flown on the NASA Langley Beechcraft UC-12B Huron aircraft. Data collection for this product is complete. Polar Winds I was based in Kangerlussuaq, Greenland and flew DAWN on board the NASA King Air UC-12B during Oct-Nov 2014 while Polar Winds II was based in Keflavik, Iceland and utilized the NASA DC-8 aircraft to fly DAWN and Dropsondes over the Arctic in May 2015. In total, twenty-four individual missions with over 80 hours of research flights were flown in the Arctic region near Greenland and Iceland during Polar Winds. The focus instrument for the wind measurements taken over the Arctic during Polar Winds was the DAWN airborne wind lidar. At a wavelength of 2.05 microns and at 250 mj per pulse, DAWN is the most powerful airborne Doppler Wind Lidar available today for airborne missions. DAWN has previously been flown on the NASA DC-8 during the 2010 Genesis and Rapid Intensification Processes (GRIP) campaign and on the NASA UC-12 for wind field characterization off the coast of Virginia. In addition to DAWN, Polar Winds utilized the High Definition Sounding System (HDSS) dropsonde delivery system developed by Yankee Environmental Services to drop almost 100 dropsondes during Polar Wind II to obtain additional high-resolution vertical wind profiles during most missions. These dropsondes also provided needed calibration/validation for the much newer DAWN measurements. Beginning in the fall of 2014, NASA sponsored two airborne field campaigns, collectively called Polar Winds, designed to fly the Doppler Aerosol WiNd (DAWN) lidar and other instruments to take airborne wind measurements of the Arctic atmosphere, specifically over and off the coasts of Greenland during Oct-Nov 2014 and May 2015. In particular, Polar Winds conducted a series of science experiments focusing on the measurement and analyses of lower tropospheric winds and aerosols associated with coastal katabatic flows, barrier winds, the Greenland Tip Jet, boundary layer circulations such as rolls and OLEs (Organized Large Eddies), and near surface winds over open water, transitional ice zones and the Greenland Ice Cap. proprietary
-Polarimetric_CT_1601_1 AfriSAR: Canopy Structure Derived from PolInSAR and Coherence TomoSAR NISAR tools ALL STAC Catalog 2016-02-25 2016-03-08 9.17, -2.08, 11.86, 0.61 https://cmr.earthdata.nasa.gov/search/concepts/C2734261393-ORNL_CLOUD.umm_json This dataset contains forest vertical structure and associated uncertainty products derived by applying multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) and Polarimetric Coherence Tomographic SAR (PCT or PC-TomoSAR) techniques. The data were collected from multiple repeat-pass flights over Gabonese forests using the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument in February-March 2016. In addition, supplementary data products based on various intermediate parameters of the UAVSAR data are provided and include radar backscatter, coherence, and viewing and terrain geometry. These data were collected by NASA as part of the joint NASA/ESA AfriSAR campaign. proprietary
Polarimetric_CT_1601_1 AfriSAR: Canopy Structure Derived from PolInSAR and Coherence TomoSAR NISAR tools ORNL_CLOUD STAC Catalog 2016-02-25 2016-03-08 9.17, -2.08, 11.86, 0.61 https://cmr.earthdata.nasa.gov/search/concepts/C2734261393-ORNL_CLOUD.umm_json This dataset contains forest vertical structure and associated uncertainty products derived by applying multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) and Polarimetric Coherence Tomographic SAR (PCT or PC-TomoSAR) techniques. The data were collected from multiple repeat-pass flights over Gabonese forests using the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument in February-March 2016. In addition, supplementary data products based on various intermediate parameters of the UAVSAR data are provided and include radar backscatter, coherence, and viewing and terrain geometry. These data were collected by NASA as part of the joint NASA/ESA AfriSAR campaign. proprietary
+Polarimetric_CT_1601_1 AfriSAR: Canopy Structure Derived from PolInSAR and Coherence TomoSAR NISAR tools ALL STAC Catalog 2016-02-25 2016-03-08 9.17, -2.08, 11.86, 0.61 https://cmr.earthdata.nasa.gov/search/concepts/C2734261393-ORNL_CLOUD.umm_json This dataset contains forest vertical structure and associated uncertainty products derived by applying multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) and Polarimetric Coherence Tomographic SAR (PCT or PC-TomoSAR) techniques. The data were collected from multiple repeat-pass flights over Gabonese forests using the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument in February-March 2016. In addition, supplementary data products based on various intermediate parameters of the UAVSAR data are provided and include radar backscatter, coherence, and viewing and terrain geometry. These data were collected by NASA as part of the joint NASA/ESA AfriSAR campaign. proprietary
Polarimetric_height_profile_1577_1 AfriSAR: Polarimetric Height Profiles by TomoSAR, Lope and Rabi Forests, Gabon, 2016 ORNL_CLOUD STAC Catalog 2016-02-25 2016-02-28 9.67, -2.08, 11.86, 0.1 https://cmr.earthdata.nasa.gov/search/concepts/C2734257089-ORNL_CLOUD.umm_json This dataset provides height profiles derived from UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar; JPL) data acquired over Lope National Park and Rabi Forest in Gabon as part of the AfriSAR campaign in 2016. These data were produced using synthetic aperture radar tomography (TomoSAR), a method that reveals three-dimensional forest structures by extending the conventional two-dimensional imaging capabilities of radars using multiple images acquired from slightly different antenna positions. AfriSAR was an airborne campaign that collected radar, lidar, and field measurements of forests in Gabon, West Africa, as part of a collaborative mission between NASA, the European Space Agency, and the Gabonese Space Agency. These data will help prepare for and calibrate current and upcoming spaceborne missions that aim to gauge the role of forests in Earth's carbon cycle, such as the Global Ecosystem Dynamics Investigation (GEDI). proprietary
Polarimetric_height_profile_1577_1 AfriSAR: Polarimetric Height Profiles by TomoSAR, Lope and Rabi Forests, Gabon, 2016 ALL STAC Catalog 2016-02-25 2016-02-28 9.67, -2.08, 11.86, 0.1 https://cmr.earthdata.nasa.gov/search/concepts/C2734257089-ORNL_CLOUD.umm_json This dataset provides height profiles derived from UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar; JPL) data acquired over Lope National Park and Rabi Forest in Gabon as part of the AfriSAR campaign in 2016. These data were produced using synthetic aperture radar tomography (TomoSAR), a method that reveals three-dimensional forest structures by extending the conventional two-dimensional imaging capabilities of radars using multiple images acquired from slightly different antenna positions. AfriSAR was an airborne campaign that collected radar, lidar, and field measurements of forests in Gabon, West Africa, as part of a collaborative mission between NASA, the European Space Agency, and the Gabonese Space Agency. These data will help prepare for and calibrate current and upcoming spaceborne missions that aim to gauge the role of forests in Earth's carbon cycle, such as the Global Ecosystem Dynamics Investigation (GEDI). proprietary
Poplar_Veg_Plots_1376_1 Arctic Vegetation Plots, Poplars, Arctic and Interior AK and YT, Canada, 2003-2005 ORNL_CLOUD STAC Catalog 2003-06-18 2005-08-17 -162.74, 61.08, -135.22, 69.47 https://cmr.earthdata.nasa.gov/search/concepts/C2170969941-ORNL_CLOUD.umm_json This data set provides vegetation cover and environmental plot data collected from 32 balsam poplar (Populus balsamifera L., Salicaceae) vegetation plots located on the Arctic Slope of Alaska and in the interior boreal forests of Alaska and the Yukon from 2003 to 2005. The estimated percent land cover by species per plot are according to the older Braun-Blanquet cover-abundance scale. Plot data includes moisture, topographic position, slope, aspect, shape, and soil data. proprietary
-PostFire_Tree_Regeneration_1955_1.1 ABoVE: Synthesis of Post-Fire Regeneration Across Boreal North America ALL STAC Catalog 1989-01-01 2018-12-31 -152.2, 49.12, -71.01, 66.96 https://cmr.earthdata.nasa.gov/search/concepts/C2539840222-ORNL_CLOUD.umm_json This dataset is a synthesis of species-specific pre- and post-fire tree stem density estimates, field plot characterization data, and acquired climate moisture deficit data for sites from Alaska, USA eastward to Quebec, Canada in fires that burned between 1989 and 2014. Data are from 1,538 sites across 58 fire perimeters encompassing 4.52 Mha of forest and all major boreal ecozones in North America. To be included in this synthesis, a site had to contain information on species-specific post-fire seedling densities. This included sites where seedlings had been counted 2-13 years post-fire, a timeframe over which there was little change in relative dominance of species based on densities. Plot characterization data includes stand age, site drainage, disturbance history, crown combustion severity, seedbed conditions, and stand structural attributes. Gridded values of Hargreaves Climate Moisture Deficit (CMD) were obtained for each plot where plot coordinates were available. These values included 30-year normals (1981-2010) and CMD in the two years immediately following the fire year. CMD anomalies were calculated as the difference between the 30-year normal and the single year values for each of the first two years after a fire. These synthesis data are provided in comma-separated values (CSV) format. proprietary
PostFire_Tree_Regeneration_1955_1.1 ABoVE: Synthesis of Post-Fire Regeneration Across Boreal North America ORNL_CLOUD STAC Catalog 1989-01-01 2018-12-31 -152.2, 49.12, -71.01, 66.96 https://cmr.earthdata.nasa.gov/search/concepts/C2539840222-ORNL_CLOUD.umm_json This dataset is a synthesis of species-specific pre- and post-fire tree stem density estimates, field plot characterization data, and acquired climate moisture deficit data for sites from Alaska, USA eastward to Quebec, Canada in fires that burned between 1989 and 2014. Data are from 1,538 sites across 58 fire perimeters encompassing 4.52 Mha of forest and all major boreal ecozones in North America. To be included in this synthesis, a site had to contain information on species-specific post-fire seedling densities. This included sites where seedlings had been counted 2-13 years post-fire, a timeframe over which there was little change in relative dominance of species based on densities. Plot characterization data includes stand age, site drainage, disturbance history, crown combustion severity, seedbed conditions, and stand structural attributes. Gridded values of Hargreaves Climate Moisture Deficit (CMD) were obtained for each plot where plot coordinates were available. These values included 30-year normals (1981-2010) and CMD in the two years immediately following the fire year. CMD anomalies were calculated as the difference between the 30-year normal and the single year values for each of the first two years after a fire. These synthesis data are provided in comma-separated values (CSV) format. proprietary
-Post_Fire_C_Emissions_1787_1 ABoVE: Spatial Estimates of Carbon Combustion from Wildfires across SK, Canada, 2015 ORNL_CLOUD STAC Catalog 2015-04-06 2015-08-11 -116.06, 51.19, -100.17, 61.24 https://cmr.earthdata.nasa.gov/search/concepts/C2143401918-ORNL_CLOUD.umm_json This dataset provides spatial estimates of carbon combustion from all 2015 wildfire burned areas across Saskatchewan, Canada, on a 30-m grid. Carbon combustion (kg C/m2) was derived from post-fire field measurements of carbon stocks completed in 2016 at 47 stands that burned during three 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in adjacent areas. The study areas covered two ecozones (Boreal Plains and Boreal Shield), two stand-replacing history types (fire and timber harvest), three soil moisture classes (xeric, mesic, and subhygric), and three stand dominance classifications (coniferous, deciduous, and mixed). To spatially extrapolate estimates of combustion to all 2015 fires in Saskatchewan, a predictive radial support vector machine model was trained on the 47 burned stands with associated environmental variables and geospatial predictors and applied to historical fire areas. The dataset also includes uncertainty estimates represented as per pixel standard deviations of model estimates derived using a Monte Carlo analysis. proprietary
+PostFire_Tree_Regeneration_1955_1.1 ABoVE: Synthesis of Post-Fire Regeneration Across Boreal North America ALL STAC Catalog 1989-01-01 2018-12-31 -152.2, 49.12, -71.01, 66.96 https://cmr.earthdata.nasa.gov/search/concepts/C2539840222-ORNL_CLOUD.umm_json This dataset is a synthesis of species-specific pre- and post-fire tree stem density estimates, field plot characterization data, and acquired climate moisture deficit data for sites from Alaska, USA eastward to Quebec, Canada in fires that burned between 1989 and 2014. Data are from 1,538 sites across 58 fire perimeters encompassing 4.52 Mha of forest and all major boreal ecozones in North America. To be included in this synthesis, a site had to contain information on species-specific post-fire seedling densities. This included sites where seedlings had been counted 2-13 years post-fire, a timeframe over which there was little change in relative dominance of species based on densities. Plot characterization data includes stand age, site drainage, disturbance history, crown combustion severity, seedbed conditions, and stand structural attributes. Gridded values of Hargreaves Climate Moisture Deficit (CMD) were obtained for each plot where plot coordinates were available. These values included 30-year normals (1981-2010) and CMD in the two years immediately following the fire year. CMD anomalies were calculated as the difference between the 30-year normal and the single year values for each of the first two years after a fire. These synthesis data are provided in comma-separated values (CSV) format. proprietary
Post_Fire_C_Emissions_1787_1 ABoVE: Spatial Estimates of Carbon Combustion from Wildfires across SK, Canada, 2015 ALL STAC Catalog 2015-04-06 2015-08-11 -116.06, 51.19, -100.17, 61.24 https://cmr.earthdata.nasa.gov/search/concepts/C2143401918-ORNL_CLOUD.umm_json This dataset provides spatial estimates of carbon combustion from all 2015 wildfire burned areas across Saskatchewan, Canada, on a 30-m grid. Carbon combustion (kg C/m2) was derived from post-fire field measurements of carbon stocks completed in 2016 at 47 stands that burned during three 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in adjacent areas. The study areas covered two ecozones (Boreal Plains and Boreal Shield), two stand-replacing history types (fire and timber harvest), three soil moisture classes (xeric, mesic, and subhygric), and three stand dominance classifications (coniferous, deciduous, and mixed). To spatially extrapolate estimates of combustion to all 2015 fires in Saskatchewan, a predictive radial support vector machine model was trained on the 47 burned stands with associated environmental variables and geospatial predictors and applied to historical fire areas. The dataset also includes uncertainty estimates represented as per pixel standard deviations of model estimates derived using a Monte Carlo analysis. proprietary
+Post_Fire_C_Emissions_1787_1 ABoVE: Spatial Estimates of Carbon Combustion from Wildfires across SK, Canada, 2015 ORNL_CLOUD STAC Catalog 2015-04-06 2015-08-11 -116.06, 51.19, -100.17, 61.24 https://cmr.earthdata.nasa.gov/search/concepts/C2143401918-ORNL_CLOUD.umm_json This dataset provides spatial estimates of carbon combustion from all 2015 wildfire burned areas across Saskatchewan, Canada, on a 30-m grid. Carbon combustion (kg C/m2) was derived from post-fire field measurements of carbon stocks completed in 2016 at 47 stands that burned during three 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in adjacent areas. The study areas covered two ecozones (Boreal Plains and Boreal Shield), two stand-replacing history types (fire and timber harvest), three soil moisture classes (xeric, mesic, and subhygric), and three stand dominance classifications (coniferous, deciduous, and mixed). To spatially extrapolate estimates of combustion to all 2015 fires in Saskatchewan, a predictive radial support vector machine model was trained on the 47 burned stands with associated environmental variables and geospatial predictors and applied to historical fire areas. The dataset also includes uncertainty estimates represented as per pixel standard deviations of model estimates derived using a Monte Carlo analysis. proprietary
Post_Fire_SOC_NWT_2235_1 Post-fire Recovery of Soil Organic Layer Carbon in Canadian Boreal Forests, 2015-2018 ORNL_CLOUD STAC Catalog 2015-06-11 2018-08-24 -132.67, 59.79, -104.19, 68.33 https://cmr.earthdata.nasa.gov/search/concepts/C2854211353-ORNL_CLOUD.umm_json This dataset provides site moisture, soil organic layer thickness, soil organic carbon, nonvascular plant functional group, stand dominance, ecozone, time-after-fire, jack pine proportion, and deciduous proportion for 511 forested plots spanning ~140,000 km2 across two ecozones of the Northwest Territories, Canada (NWT). The plots were established during 2015-2018 across 41 wildfire scars and unburned areas (no burn history prior to 1965), with 317 plots in the Plains and 194 plots in the Shield regions. At each plot, two adjacent 30-m transects were established 2 m apart, running north from the plot origin. Soil organic layer (SOL) depth (cm) was measured every 3 m and the mean was taken from the 10 measurements to calculate a plot-level SOL thickness. Three soil organic layer profiles were destructively sampled at 0, 12, and 24 m using a corer that was custom designed for NWT soils. Within the transects, all stems taller than 1.37 m were identified to species to calculate tree density (stems / m2). Nonvascular plant percent cover was identified to functional group at five, 1-m2 quadrats spaced 6 m apart along the belt transect. A subset of 2,067 of 5,137 total increments from 1,803 profiles from 421 plots were analyzed for total percent C using a CHN analyzer. Time-after-fire was established using fire history records. For older plots where no known fire history is recorded, tree age was used. Data are for the period 2015-06-11 to 2018-08-24 and are provided in comma-separated values (CSV) format. proprietary
PreABoVE_AirMOSS_L1_Alaska_1678_1 Pre-ABoVE: L1 S-0 Polarimetric Data from AirMOSS P-band SAR, Alaska, 2014-2015 ORNL_CLOUD STAC Catalog 2014-08-16 2015-10-01 -165.32, 57.22, -135.54, 71.48 https://cmr.earthdata.nasa.gov/search/concepts/C2143402734-ORNL_CLOUD.umm_json This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multi-look complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over 10 study sites across Northern Alaska, USA. Flight campaigns took place in August 2014, October 2014, April 2015, August 2015, September 2015, and October 2015. The acquired L1 P-band radar backscatter data will be used to derive estimates of soil water content and permafrost state at the study sites. proprietary
PreDeltaX_ADCP_Measurements_1806_1 Pre-Delta-X: River Discharge Channel Surveys across Atchafalaya Basin, LA, USA, 2016 ORNL_CLOUD STAC Catalog 2016-10-15 2016-10-20 -91.44, 29.44, -91.21, 29.74 https://cmr.earthdata.nasa.gov/search/concepts/C2025124066-ORNL_CLOUD.umm_json This dataset provides river discharge measurements collected at selected locations across the Atchafalaya River Basin, within the Mississippi River Delta (MRD) floodplain in coastal Louisiana, USA. The measurements were made during the Pre-Delta-X campaign on October 15 to 20, 2016. Seventy-five channel surveys were conducted with a SonTek RiverSurveyor M9 acoustic doppler current profiler (ADCP) on selected wide channels (~100 m) and a few selected (~10 m) narrow channels. ADCP data provide near-instantaneous estimates of river discharge across the sampled channels. Sites coincided with AirSWOT swaths in the Atchafalaya River Basin and water level measurement locations. This in situ dataset was used to calibrate and validate Delta-X hydrodynamic models. proprietary
@@ -13353,8 +13354,8 @@ PreDeltaX_Vegetation_Structure_1805_1 Pre-Delta-X: Vegetation Species, Structure
PreDeltaX_Water_Level_Data_1801_1 Pre-Delta-X: Water Levels across Wax Lake Outlet, Atchafalaya Basin, LA, USA, 2016 ORNL_CLOUD STAC Catalog 2016-10-13 2016-10-20 -91.45, 29.51, -91.36, 29.74 https://cmr.earthdata.nasa.gov/search/concepts/C2025123345-ORNL_CLOUD.umm_json This dataset provides absolute water level elevations derived for 10 locations across the Wax Lake Delta, Atchafalaya Basin, in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Field measurements were made during the Pre-Delta-X campaign on October 13-20, 2016. Relative water level measurements were recorded every five minutes during a one-week period using in situ pressure transducers (Solinst) to measure water surface elevation change with millimeter accuracy. The Solinst system combines a total pressure transducer (TPT) and a temperature detector. Once underwater, the TPT measures the sum of the atmosphere and water pressure above the sensor. Atmospheric pressure fluctuations must be accounted for to obtain the height of the water column above the TPT. An absolute elevation correction was applied to the water level data using an iterative approach with the USGS Calumet Station water level height and Airborne Snow Observatory (ASO) lidar water level profiles. These Pre-Delta-X water level measurements served to calibrate and validate the campaign's remote sensing observations and hydrodynamic models. proprietary
Pre_LBA_ABRACOS_899_1.1 Pre-LBA Anglo-Brazilian Amazonian Climate Observation Study (ABRACOS) Data ORNL_CLOUD STAC Catalog 1991-01-01 1996-12-31 -75, -18, -46, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2762262185-ORNL_CLOUD.umm_json The data set presents the principal data from the Anglo-BRazilian Amazonian Climate Observation Study (ABRACOS) (Gash et al, 1996) and provides quality controlled information from five of the study topics considered by the project in five zipped files containing ASCII text data. The five study topics include Micrometeorology, Climate, Carbon Dioxide and Water Vapor, Plant Physiology, and Soil Moisture. The objectives of the ABRACOS were to monitor Amazonian climate and improve the understanding of the consequences of deforestation and to provide data for the calibration and validation of GCMs and GCM sub-models of Amazonian forest and post-deforestation pasture (Shuttleworth et al, 1991). Three areas were instrumented, each with different soils, dry season intensities and deforestation densities (Gash et al, 1996). In each area, an automatic weather station and soil moisture measurement equipment were installed: in a primary forest site and in nearby cattle pasture, for monitoring climate and soil status throughout the year. Additional intensive periods of study (or Missions), of varying duration, were operated at these sites: for calibration purposes, to understand the physical processes relevant to each site, and for detailed comparisons between sites. These data were collected under the ABRACOS project and made available by the UK Institute of Hydrology and the Instituto Nacional de Pesquisas Espaciais (Brazil). ABRACOS is a collaboration between the Agencia Brasileira de Cooperacao and the UK Overseas Development Administration. The processed, quality controlled and integrated data in the documented Pre-LBA data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually. proprietary
Proantar_0 Measurements off James Ross Island, Antarctica OB_DAAC STAC Catalog 2005-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360623-OB_DAAC.umm_json Measurements made off James Ross Island near Antarctica in 2005. proprietary
-Profile_based_PBL_heights_1706_1.1 ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern USA ORNL_CLOUD STAC Catalog 2016-07-18 2019-07-26 -106.36, 28.65, -73.13, 49.49 https://cmr.earthdata.nasa.gov/search/concepts/C2677222693-ORNL_CLOUD.umm_json This dataset provides profile-based estimates of the height to the top of the planetary boundary layer (PBL), also known as the atmospheric boundary layer (ABL), in meters above mean sea level estimated from meteorological measurements acquired during ascending or descending vertical profile flight segments during NASA's Atmospheric Carbon and Transport - America (ACT-America) airborne campaign. ACT-America flights sampled the atmosphere over the central and eastern United States seasonally from 2016 - 2019. Two aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. proprietary
Profile_based_PBL_heights_1706_1.1 ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern USA ALL STAC Catalog 2016-07-18 2019-07-26 -106.36, 28.65, -73.13, 49.49 https://cmr.earthdata.nasa.gov/search/concepts/C2677222693-ORNL_CLOUD.umm_json This dataset provides profile-based estimates of the height to the top of the planetary boundary layer (PBL), also known as the atmospheric boundary layer (ABL), in meters above mean sea level estimated from meteorological measurements acquired during ascending or descending vertical profile flight segments during NASA's Atmospheric Carbon and Transport - America (ACT-America) airborne campaign. ACT-America flights sampled the atmosphere over the central and eastern United States seasonally from 2016 - 2019. Two aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. proprietary
+Profile_based_PBL_heights_1706_1.1 ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern USA ORNL_CLOUD STAC Catalog 2016-07-18 2019-07-26 -106.36, 28.65, -73.13, 49.49 https://cmr.earthdata.nasa.gov/search/concepts/C2677222693-ORNL_CLOUD.umm_json This dataset provides profile-based estimates of the height to the top of the planetary boundary layer (PBL), also known as the atmospheric boundary layer (ABL), in meters above mean sea level estimated from meteorological measurements acquired during ascending or descending vertical profile flight segments during NASA's Atmospheric Carbon and Transport - America (ACT-America) airborne campaign. ACT-America flights sampled the atmosphere over the central and eastern United States seasonally from 2016 - 2019. Two aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. proprietary
Prudhoe_Bay_ArcSEES_Veg_Plots_1555_1 Arctic Vegetation Plots, Prudhoe Bay ArcSEES Road Study, Lake Colleen, Alaska, 2014 ORNL_CLOUD STAC Catalog 2014-08-06 2014-08-13 -148.47, 70.22, -148.47, 70.22 https://cmr.earthdata.nasa.gov/search/concepts/C2162122325-ORNL_CLOUD.umm_json This dataset provides environmental, soil, and vegetation data collected from study plots in the vicinity of Lake Colleen off the Spine Road at Prudhoe Bay, Alaska, during August of 2014. Data include vegetation species, leaf area index (LAI), percent cover classes, soil moisture and color, and plot characteristics including geology, topographic position, slope, aspect, and plot disturbance. proprietary
Prudhoe_Bay_Veg_Maps_1387_1 Geobotanical and Impact Map Collection for Prudhoe Bay Oilfield, Alaska, 1972-2010 ORNL_CLOUD STAC Catalog 1949-01-01 2010-07-31 -150.17, 69.97, -146.97, 71.03 https://cmr.earthdata.nasa.gov/search/concepts/C2162616071-ORNL_CLOUD.umm_json This data set provides a collection of maps of geoecological characteristics of areas within the Beechey Point quadrangle near Prudhoe Bay on the North slope of Alaska: a geobotanical atlas of the Prudhoe Bay region, a land cover map of the Beechey Point quadrangle, and cumulative impact maps in the Prudhoe Bay Oilfield for ten dates from 1968 to 2010. The geobotanical atlas is based on aerial photographs and covers 145 square kilometers of the Prudhoe Bay Oilfield. The land cover map of the Beechey Point quadrangle was derived from the Landsat multispectral scanner, aerial photography, and other field and cartographic methods. The cumulative impact maps of the Prudhoe Bay Oilfield show historical infrastructure and natural changes digitized from aerial photos taken in each successive analysis year (1968, 1970, 1972, 1973, 1977, 1979, 1983, 1990, 2001, and 2010). Nine geoecological attributes are included: dominant vegetation, secondary vegetation, tertiary vegetation, percentage open water, landform, dominant surface form, secondary surface form, dominant soil, and secondary soil. These data document environmental changes in an Arctic region that is affected by both climate change and rapid industrial development. proprietary
Prudhoe_Bay_Veg_Plots_1360_1 Arctic Vegetation Plots at Prudhoe Bay, Alaska, 1973-1980 ORNL_CLOUD STAC Catalog 1973-01-01 1980-12-31 -148.95, 70.25, -148.29, 70.38 https://cmr.earthdata.nasa.gov/search/concepts/C2170969598-ORNL_CLOUD.umm_json This data set provides environmental, soil, and vegetation data collected between 1973 and 1980 from 89 study plots in the Prudhoe Bay region of Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for study plots subjectively located in 43 plant communities and 4 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation, species, and cover; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for classification, mapping, and analysis of geobotanical factors in the Prudhoe Bay region and across Alaska. proprietary
@@ -13433,10 +13434,10 @@ RSFDCE_KLIM5 Air Temperature 01.00 P.M. Year By Year Date SCIOPS STAC Catalog 18
RSS18_AVIRIS_L1B_449_1 BOREAS RSS-18 Level 1B AVIRIS At-Sensor Radiance Imagery ORNL_CLOUD STAC Catalog 1996-08-14 1996-08-14 -106.49, 53.45, -105.03, 54.32 https://cmr.earthdata.nasa.gov/search/concepts/C2929128157-ORNL_CLOUD.umm_json This dataset holds Level 1B (L1B) radiance data collected by the AVIRIS-Classic instrument near Prince Albert, Saskatchewan, Canada, on August 14, 1996. This imagery was acquired for the Boreal Ecosystem-Atmosphere Study (BOREAS) project in the boreal forests of central Canada. BOREAS focused on improving the understanding of exchanges of radiative energy, sensible heat, water, CO2 and trace gases between the boreal forest and the lower atmosphere. NASA's AVIRIS-Classic is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-Classic measures reflected radiance in 224 contiguous bands at approximately 10-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 400-2500 nm. The AVIRIS-Classic sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub meter range. For these data, AVIRIS-Classic was deployed on NASA's ER-2 high altitude aircraft. These spectra are acquired as images with 20-meter spatial resolution, 11 km swath width, and flight lines up to 800 km in length. The measurements are spectrally, radiometrically, and geometrically calibrated. There are seven flight lines subdivided into 66 scenes. The dataset includes the radiance imagery cube for each scene along with calibration and navigation information. The radiance data are in instrument coordinates, georeferenced by center of each scan line, and provided in a binary file. Metadata are included in a mixture of binary and text file formats. proprietary
RSS_WindSat_L1C_TB_V08.0_8.0 RSS WindSat L1C Calibrated TB Version 8 POCLOUD STAC Catalog 2003-02-01 2020-10-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2559430954-POCLOUD.umm_json The WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO). The dataset contains the Level 1C WindSat Top of the Atmosphere (TOA) TB processed by RSS. The WindSat radiances are turned into TOA TB after correction for hot and cold calibration anomalies, receiver non-linearities, sensor pointing errors, antenna cross-polarization contamination, spillover, Faraday rotation and polarization alignment. The data are resampled on a fixed regular 0.125 deg Earth grid using Backus-Gilbert Optimum Interpolation. The sampling is done separately for fore and aft looks. The 10.7, 18.7, 23.8, 37.0 GHz channels are resampled to the 10.7 GHz spatial resolution. The 6.8 GHz channels are given at their native spatial resolution. The 10.7, 18.7, 23.8, 37.0 GHz channels are absolutely calibrated using the GMI sensor as calibration reference. The 6.8 GHz channels are calibrated using the open ocean with the RSS ocean emission model and the Amazon rain forest as calibration targets. The Faraday rotation angle (FRA) and geometric polarization basis rotation angle (PRA) were added in the last run. proprietary
Radarsat-2_8.0 RADARSAT-2 ESA Archive ESA STAC Catalog 2008-07-27 2021-04-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689631-ESA.umm_json The RADARSAT-2 ESA archive collection consists of RADARSAT-2 products requested by ESA supported projects over their areas of interest around the world. The dataset regularly grows as ESA collects new products over the years. Following Beam modes are available: Standard, Wide Swath, Fine Resolution, Extended Low Incidence, Extended High Incidence, ScanSAR Narrow and ScanSAR Wide. Standard Beam Mode allows imaging over a wide range of incidence angles with a set of image quality characteristics which provides a balance between fine resolution and wide coverage, and between spatial and radiometric resolutions. Standard Beam Mode operates with any one of eight beams, referred to as S1 to S8, in single and dual polarisation . The nominal incidence angle range covered by the full set of beams is 20 degrees (at the inner edge of S1) to 52 degrees (at the outer edge of S8). Each individual beam covers a nominal ground swath of 100 km within the total standard beam accessibility swath of more than 500 km. BEAM MODE: Standard PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 8.0 or 11.8 x 5.1 (SLC), 8.0 x 8.0 (SGX), 12.5 x 12.5 (SSG, SPG) Resolution - Range x Azimuth (m): 9.0 or 13.5 x 7.7 (SLC), 26.8 - 17.3 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 100 x 100 Range of Angle of Incidence (deg): 20 - 52 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: • Single: HH or VV or HV or VH • Dual: HH + HV or VV + VH Wide Swath Beam Mode allows imaging of wider swaths than Standard Beam Mode, but at the expense of slightly coarser spatial resolution. The three Wide Swath beams, W1, W2 and W3, provide coverage of swaths of approximately 170 km, 150 km and 130 km in width respectively, and collectively span a total incidence angle range from 20 degrees to 45 degrees. Polarisation can be single and dual. BEAM MODE: Wide PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 11.8 x 5.1 (SLC), 10 x 10 (SGX), 12.5 x 12.5 (SSG, SPG) Resolution - Range x Azimuth (m): 13.5 x 7.7 (SLC), 40.0 - 19.2 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 150 x 150 Range of Angle of Incidence (deg): 20 - 45 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: • Single: HH or VV or HV or VH • Dual: HH + HV or VV + VH Fine Resolution Beam Mode is intended for applications which require finer spatial resolution. Products from this beam mode have a nominal ground swath of 50 km. Nine Fine Resolution physical beams, F23 to F21, and F1 to F6 are available to cover the incidence angle range from 30 to 50 degrees. For each of these beams, the swath can optionally be centred with respect to the physical beam or it can be shifted slightly to the near or far range side. Thanks to these additional swath positioning choices, overlaps of more than 50% are provided between adjacent swaths. RADARSAT-2 can operate in single and dual polarisation for this beam mode. BEAM MODE: Fine PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 4.7 x 5.1 (SLC), 3.13 x 3.13 (SGX), 6.25 x 6.25 (SSG, SPG) Resolution - Range x Azimuth (m): 5.2 x 7.7 (SLC), 10.4 - 6.8 x 7.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 50 x 50 Range of Angle of Incidence (deg): 30 - 50 No. of Looks - Range x Azimuth: 1 x 1 (SLC,SGX, SGF, SSG, SPG) Polarisations - Options: • Single: HH or VV or HV or VH • Dual: HH + HV or VV + VH In the Extended Low Incidence Beam Mode, a single Extended Low Incidence Beam, EL1, is provided for imaging in the incidence angle range from 10 to 23 degrees with a nominal ground swath coverage of 170 km. Some minor degradation of image quality can be expected due to operation of the antenna beyond its optimum scan angle range. Only single polarisation is available. BEAM MODE: Extended Low PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 8.0 x 5.1 (SLC), 10.0 x 10.0 (SGX), 12.5 x 12.5 (SSG, SPG) Nominal Resolution - Range x Azimuth (m): 9.0 x 7.7 (SLC), 52.7 - 23.3 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 170 x 170 Range of Angle of Incidence (deg): 10 - 23 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: Single Pol HH In the Extended High Incidence Beam Mode, six Extended High Incidence Beams, EH1 to EH6, are available for imaging in the 49 to 60 degree incidence angle range. Since these beams operate outside the optimum scan angle range of the SAR antenna, some degradation of image quality, becoming progressively more severe with increasing incidence angle, can be expected when compared with the Standard Beams. Swath widths are restricted to a nominal 80 km for the inner three beams, and 70 km for the outer beams. Only single polarisation available. BEAM MODE: Extended High PRODUCT: SLC, SGX, SGF, SSG, SPG Nominal Pixel Spacing - Range x Azimuth (m) : 11.8 x 5.1 (SLC), 8.0 x 8.0 (SGX), 12.5 x 12.5 (SSG, SPG) Resolution - Range x Azimuth (m): 13.5 x 7.7 (SLC), 18.2 - 15.9 x 24.7 (SGX, SGF, SSG, SPG) Nominal Scene Size - Range x Azimuth (km): 75 x 75 Range of Angle of Incidence (deg): 49 - 60 No. of Looks - Range x Azimuth: 1 x 1 (SLC), 1 x 4 (SGX, SGF, SSG, SPG) Polarisations - Options: Single Pol HH ScanSAR Narrow Beam Mode provides coverage of a ground swath approximately double the width of the Wide Swath Beam Mode swaths. Two swath positions with different combinations of physical beams can be used: SCNA, which uses physical beams W1 and W2, and SCNB, which uses physical beams W2, S5, and S6. Both options provide coverage of swath widths of about 300 km. The SCNA combination provides coverage over the incidence angle range from 20 to 39 degrees. The SCNB combination provides coverage over the incidence angle range 31 to 47 degrees. RADARSAT-2 can operate in single and dual polarisation for this beam mode. BEAM MODE: ScanSAR Narrow PRODUCT: SCN, SCF, SCS Nominal Pixel Spacing - Range x Azimuth (m) : 25 x 25 Nominal Resolution - Range x Azimuth (m):81-38 x 40-70 Nominal Scene Size - Range x Azimuth (km): 300 x 300 Range of Angle of Incidence (deg): 20 - 46 No. of Looks - Range x Azimuth: 2 x 2 Polarisations - Options: • Single Co or Cross: HH or VV or HV or VH • Dual: HH + HV or VV + VH ScanSAR Wide Beam Mode provides coverage of a ground swath approximately triple the width of the Wide Swath Beam Mode swaths. Two swath positions with different combinations of physical beams can be used: SCWA, which uses physical beams W1, W2, W3, and S7, and SCWB, which uses physical beams W1, W2, S5 and S6. The SCWA combination allows imaging of a swath of more than 500 km covering an incidence angle range of 20 to 49 degrees. The SCWB combination allows imaging of a swath of more than 450 km covering the incidence angle. Polarisation can be single and dual. BEAM MODE: ScanSAR Wide PRODUCT: SCW, SCF, SCS Nominal Pixel Spacing - Range x Azimuth (m) : 50 x 50 Resolution - Range x Azimuth (m): 163.0 - 73 x 78-106 Nominal Scene Size - Range x Azimuth (km): 500 x 500 Range of Angle of Incidence (deg): 20 - 49 No. of Looks - Range x Azimuth: 4 x 2 Polarisations - Options: • Single Co or Cross: HH or VV or HV or VH • Dual: HH + HV or VV + VH These are the different products : SLC (Single Look Complex): Amplitude and phase information is preserved. Data is in slant range. Georeferenced and aligned with the satellite track SGF (Path Image): Data is converted to ground range and may be multi-look processed. Scene is oriented in direction of orbit path. Georeferenced and aligned with the satellite track. SGX (Path Image Plus): Same as SGF except processed with refined pixel spacing as needed to fully encompass the image data bandwidths. Georeferenced and aligned with the satellite track SSG(Map Image): Image is geocorrected to a map projection. SPG (Precision Map Image): Image is geocorrected to a map projection. Ground control points (GCP) are used to improve positional accuracy. SCN(ScanSAR Narrow)/SCF(ScanSAR Wide) : ScanSAR Narrow/Wide beam mode product with original processing options and metadata fields (for backwards compatibility only). Georeferenced and aligned with the satellite track SCF (ScanSAR Fine): ScanSAR product equivalent to SGF with additional processing options and metadata fields. Georeferenced and aligned with the satellite track SCS(ScanSAR Sampled) : Same as SCF except with finer sampling. Georeferenced and aligned with the satellite track proprietary
-Radial_Growth_PRI_1781_1 ABoVE: Photochemical Reflectance and Tree Growth, Brooks Range, Alaska, 2018-2019 ORNL_CLOUD STAC Catalog 2018-05-01 2019-09-13 -149.76, 67.97, -149.72, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401854-ORNL_CLOUD.umm_json This dataset provides simultaneous in-situ measurements of the photochemical reflectance index (PRI) and radial tree growth of selected white spruce trees (Picea glauca (Moench) Voss) at the northern treeline in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. PRI and dendrometer measurements were simultaneously collected on 29 trees from six plots spaced along a 5.5 km transect from south to north where tree density becomes increasingly sparse. Measurements were made throughout the 2018 and 2019 growing seasons (May 1 to September 15) with a sampling interval of 5 minutes. The data were collected to better understand the suitability of the PRI to remotely track radial tree growth dynamics. proprietary
Radial_Growth_PRI_1781_1 ABoVE: Photochemical Reflectance and Tree Growth, Brooks Range, Alaska, 2018-2019 ALL STAC Catalog 2018-05-01 2019-09-13 -149.76, 67.97, -149.72, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401854-ORNL_CLOUD.umm_json This dataset provides simultaneous in-situ measurements of the photochemical reflectance index (PRI) and radial tree growth of selected white spruce trees (Picea glauca (Moench) Voss) at the northern treeline in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. PRI and dendrometer measurements were simultaneously collected on 29 trees from six plots spaced along a 5.5 km transect from south to north where tree density becomes increasingly sparse. Measurements were made throughout the 2018 and 2019 growing seasons (May 1 to September 15) with a sampling interval of 5 minutes. The data were collected to better understand the suitability of the PRI to remotely track radial tree growth dynamics. proprietary
-Rain-on-Snow_Data_1611_1 ABoVE: Rain-on-Snow Frequency and Distribution during Cold Seasons, Alaska, 2003-2016 ORNL_CLOUD STAC Catalog 2002-11-01 2016-12-31 -175.4, 48.62, -111.54, 73.85 https://cmr.earthdata.nasa.gov/search/concepts/C2162145449-ORNL_CLOUD.umm_json This dataset provides maps of rain-on-snow (ROS) events across Alaska for the individual months of November to March 2002-2011 and November to March 2012-2016, and annual water year summary maps for 2003-2011 and 2013-2016. ROS events were defined as changes in passive microwave (PM) detection in surface snow wetness and isothermal states induced by atmospheric processes often associated with winter rainfall. The data are summations of the number of days with ROS events per pixel at 6-km spatial resolution per month or per 5-month water year. The daily ROS record encompassed the months when snowmelt from solar irradiance is minimal and snow cover is widespread and relatively consistent throughout the region. Daily ROS geospatial classification across Alaska was derived by combining snow cover and daily microwave brightness temperature retrievals sensitive to landscape freeze-thaw dynamics from overlapping (1) Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A2 eight-day maximum snow cover extent (SCE) product and (2) Advanced Microwave Scanning Radiometer for EOS (AMSR-E) (2002-2011) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) (2012-to present) Microwave Radiation Imager (MWRI) observations at 19 GHz and 37 GHz. proprietary
+Radial_Growth_PRI_1781_1 ABoVE: Photochemical Reflectance and Tree Growth, Brooks Range, Alaska, 2018-2019 ORNL_CLOUD STAC Catalog 2018-05-01 2019-09-13 -149.76, 67.97, -149.72, 68.02 https://cmr.earthdata.nasa.gov/search/concepts/C2143401854-ORNL_CLOUD.umm_json This dataset provides simultaneous in-situ measurements of the photochemical reflectance index (PRI) and radial tree growth of selected white spruce trees (Picea glauca (Moench) Voss) at the northern treeline in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. PRI and dendrometer measurements were simultaneously collected on 29 trees from six plots spaced along a 5.5 km transect from south to north where tree density becomes increasingly sparse. Measurements were made throughout the 2018 and 2019 growing seasons (May 1 to September 15) with a sampling interval of 5 minutes. The data were collected to better understand the suitability of the PRI to remotely track radial tree growth dynamics. proprietary
Rain-on-Snow_Data_1611_1 ABoVE: Rain-on-Snow Frequency and Distribution during Cold Seasons, Alaska, 2003-2016 ALL STAC Catalog 2002-11-01 2016-12-31 -175.4, 48.62, -111.54, 73.85 https://cmr.earthdata.nasa.gov/search/concepts/C2162145449-ORNL_CLOUD.umm_json This dataset provides maps of rain-on-snow (ROS) events across Alaska for the individual months of November to March 2002-2011 and November to March 2012-2016, and annual water year summary maps for 2003-2011 and 2013-2016. ROS events were defined as changes in passive microwave (PM) detection in surface snow wetness and isothermal states induced by atmospheric processes often associated with winter rainfall. The data are summations of the number of days with ROS events per pixel at 6-km spatial resolution per month or per 5-month water year. The daily ROS record encompassed the months when snowmelt from solar irradiance is minimal and snow cover is widespread and relatively consistent throughout the region. Daily ROS geospatial classification across Alaska was derived by combining snow cover and daily microwave brightness temperature retrievals sensitive to landscape freeze-thaw dynamics from overlapping (1) Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A2 eight-day maximum snow cover extent (SCE) product and (2) Advanced Microwave Scanning Radiometer for EOS (AMSR-E) (2002-2011) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) (2012-to present) Microwave Radiation Imager (MWRI) observations at 19 GHz and 37 GHz. proprietary
+Rain-on-Snow_Data_1611_1 ABoVE: Rain-on-Snow Frequency and Distribution during Cold Seasons, Alaska, 2003-2016 ORNL_CLOUD STAC Catalog 2002-11-01 2016-12-31 -175.4, 48.62, -111.54, 73.85 https://cmr.earthdata.nasa.gov/search/concepts/C2162145449-ORNL_CLOUD.umm_json This dataset provides maps of rain-on-snow (ROS) events across Alaska for the individual months of November to March 2002-2011 and November to March 2012-2016, and annual water year summary maps for 2003-2011 and 2013-2016. ROS events were defined as changes in passive microwave (PM) detection in surface snow wetness and isothermal states induced by atmospheric processes often associated with winter rainfall. The data are summations of the number of days with ROS events per pixel at 6-km spatial resolution per month or per 5-month water year. The daily ROS record encompassed the months when snowmelt from solar irradiance is minimal and snow cover is widespread and relatively consistent throughout the region. Daily ROS geospatial classification across Alaska was derived by combining snow cover and daily microwave brightness temperature retrievals sensitive to landscape freeze-thaw dynamics from overlapping (1) Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A2 eight-day maximum snow cover extent (SCE) product and (2) Advanced Microwave Scanning Radiometer for EOS (AMSR-E) (2002-2011) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) (2012-to present) Microwave Radiation Imager (MWRI) observations at 19 GHz and 37 GHz. proprietary
RapidEye.ESA.archive_7.0 RapidEye ESA archive ESA STAC Catalog 2009-02-22 -180, -84, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C1965336937-ESA.umm_json The RapidEye ESA archive is a subset of the RapidEye Full archive that ESA collected over the years. The dataset regularly grows as ESA collects new RapidEye products. proprietary
RapidEye.Full.archive_6.0 RapidEye Full Archive ESA STAC Catalog 2009-02-01 2020-03-31 -180, -84, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C2547572717-ESA.umm_json The RapidEye Level 3A Ortho Tile, both Visual (in natural colour) and Analytic (multispectral), full archive and new tasking products are available as part of Planet imagery offer. The RapidEye Ortho Tile product (L3A) is radiometric, sensor and geometrically corrected (by using DEMs with a post spacing of between 30 and 90 meters) and aligned to a cartographic map projection. Ground Control Points (GCPs) are used in the creation of every image and the accuracy of the product will vary from region to region based on available GCPs. Product Components and Format: • Image File – GeoTIFF file that contains image data and geolocation information • Metadata File – XML format metadata file • Unusable Data Mask (UDM) file – GeoTIFF format Bands: 3-band natural color (blue, green, red) or 5-band multispectral image (blue, green, red, red edge, near-infrared) Ground Sampling Distance (nadir): 6.5 m at nadir (average at reference altitude 475 km) Projection: UTM WGS84 Accuracy: depends on the quality of the reference data used (GCPs and DEMs) The products are available as part of the Planet provision from RapidEye, Skysat and PlanetScope constellations.RapidEye collection has worldwide coverage: the Planet Explorer Catalogue (https://www.planet.com/explorer/) can be accessed (Planet registration requested) to discover and check the data readiness. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/Access-to-ESAs-Planet-Missions-Terms-of-Applicability.pdf). proprietary
RapidEye.South.America_6.0 RapidEye South America ESA STAC Catalog 2012-07-12 2015-12-13 -81, -41, 54, 1 https://cmr.earthdata.nasa.gov/search/concepts/C1965336940-ESA.umm_json ESA, in collaboration with BlackBridge, has collected this RapidEye dataset of level 3A tiles covering more than 6 million km2 of South American countries: Paraguay, Ecuador, Chile, Bolivia, Peru, Uruguay and Argentina. The area is fully covered with low cloud coverage proprietary
@@ -13451,12 +13452,12 @@ RemSensPOC_0 Remote-sensing-derived particulate organic carbon (POC) validation
ResourceSat-1-IRS-P6.archive_6.0 ResourceSat-1/IRS-P6 full archive ESA STAC Catalog 2003-11-01 2013-09-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336942-ESA.umm_json ResourceSat-1 (also known as IRS-P6) archive products are available as below. • LISS-IV MN: Mono-Chromatic, Resolution 5 m, Coverage 70 km x 70 km, Radiometrically and Ortho (DN) corrected, Acquisition in Neustrelitz 2004 - 2010, Global Archive 2003 - 2013 • LISS-III: Multi-spectral, Resolution 20 m, Coverage 140 km x 140 km, Radiometrically and Ortho (DN) corrected (ortho delivered without Band 5), Acquisition in Neustrelitz 2004 - 2013, Global Archive 2003 - 2013 • AWiFS: Multi-spectral, Resolution 60 m, Coverage 370 km x 370 km, Radiometrically and Ortho (DN) corrected, Acquisition in Neustrelitz 2004 - 2013, Global Archive 2003 - 2013 Note: • LISS-IV: Mono-Chromatic, the band is selectable. In practice the red is used. • For LISS-IV MN and LISS-III ortho corrected: If unavailable, user has to supply ground control information and DEM in suitable qualityFor AWiFS ortho corrected: service based on in house available ground control information and DEM The products are available as part of the GAF Imagery products from the Indian missions: IRS-1C, IRS-1D, CartoSat-1 (IRS-P5), ResourceSat-1 (IRS-P6) and ResourceSat-2 (IRS-R2) missions. ‘ResourceSat-1 archive’ collection has worldwide coverage: for data acquired over Neustrelitz footprint, the users can browse the EOWEB GeoPortal catalogue (http://www.euromap.de/products/serv_003.html) to search archived products; worldwide data (out the Neustrelitz footprint) can be requested by contacting GAF user support to check the readiness since no catalogue is not available. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/Indian-Data-Terms-Of-Applicability.pdf). proprietary
ResourceSat-2.archive.and.tasking_6.0 ResourceSat-2 full archive and tasking ESA STAC Catalog 2011-05-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336944-ESA.umm_json ResourceSat-2 (also known as IRS-R2) archive and tasking products are available as below: Sensor: LISS-IV Type: Mono-Chromatic Resolution (m): 5 Coverage (km x km): 70 x 70 System or radiometrically corrected and Ortho corrected (DN) Neustralitz archive: 2014 Global archive: 2011 Sensor: LISS-III Type: Multi-spectral Resolution (m): 20 Coverage (km x km): 140 x 140 System or radiometrically corrected, Ortho corrected (DN) and Ortho corrected (TOA reflectance) Neustralitz archive: 2014 Global archive: 2011 Sensor: AWiFS Type: Multi-spectral Resolution (m): 60 Coverage (km x km): 370 x 370 System or radiometrically corrected, Ortho corrected (DN) and Ortho corrected (TOA reflectance) Neustralitz archive: 2014 Global archive: 2011 Note: • LISS-IV: Mono-Chromatic, the band is selectable. In practice the red is used.For LISS-IV MN and LISS-III ortho corrected: If unavailable, user has to supply ground control information and DEM in suitable qualityFor AWiFS ortho corrected: service based on in house available ground control information and DEM The products are available as part of the GAF Imagery products from the Indian missions: IRS-1C, IRS-1D, CartoSat-1 (IRS-P5), ResourceSat-1 (IRS-P6) and ResourceSat-2 (IRS-R2) missions. ‘ResourceSat-2 archive and tasking’ collection has worldwide coverage: for data acquired over Neustrelitz footprint, the users can browse the EOWEB GeoPortal catalogue (http://www.euromap.de/products/serv_003.html) to search archived products; worldwide data (out the Neustrelitz footprint) can be requested by contacting GAF user support to check the readiness since no catalogue is not available. All details about the data provision, data access conditions and quota assignment procedure are described in the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/Indian-Data-Terms-Of-Applicability.pdf). proprietary
Respiration_622_1 Global Annual Soil Respiration Data (Raich and Schlesinger 1992) ORNL_CLOUD STAC Catalog 1963-01-01 1992-01-01 -156.4, -37.5, 146.5, 71.18 https://cmr.earthdata.nasa.gov/search/concepts/C2216863171-ORNL_CLOUD.umm_json This data set is a compilation of soil respiration rates (g C m-2 yr-1) from terrestrial and wetland ecosystems reported in the literature prior to 1992. These rates were measured in a variety of ecosystems to examine rates of microbial activity, nutrient turnover, carbon cycling, root dynamics, and a variety of other soil processes. Also included in the data set are biome type, vegetation type, locality, and geographic coordinates. proprietary
-RiSCC_Outcomes_Bibliography_1 A bibliography containing references to the outcomes of the RiSCC project from the Antarctic and subantarctic regions ALL STAC Catalog 1994-01-01 2006-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311230-AU_AADC.umm_json A bibliography of references relating to the outcomes of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1994 to 2006. The bibliography was compiled by Dana Bergstrom, and contains 162 references. proprietary
RiSCC_Outcomes_Bibliography_1 A bibliography containing references to the outcomes of the RiSCC project from the Antarctic and subantarctic regions AU_AADC STAC Catalog 1994-01-01 2006-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311230-AU_AADC.umm_json A bibliography of references relating to the outcomes of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1994 to 2006. The bibliography was compiled by Dana Bergstrom, and contains 162 references. proprietary
-RiSCC_Research_Support_Bibliography_1 A bibliography containing references to the research support of the RiSCC project from the Antarctic and subantarctic regions ALL STAC Catalog 1875-01-01 2004-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311231-AU_AADC.umm_json A bibliography of references relating to the research support of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1875 to 2004. The bibliography was compiled by Dana Bergstrom, and contains 76 references. proprietary
+RiSCC_Outcomes_Bibliography_1 A bibliography containing references to the outcomes of the RiSCC project from the Antarctic and subantarctic regions ALL STAC Catalog 1994-01-01 2006-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311230-AU_AADC.umm_json A bibliography of references relating to the outcomes of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1994 to 2006. The bibliography was compiled by Dana Bergstrom, and contains 162 references. proprietary
RiSCC_Research_Support_Bibliography_1 A bibliography containing references to the research support of the RiSCC project from the Antarctic and subantarctic regions AU_AADC STAC Catalog 1875-01-01 2004-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311231-AU_AADC.umm_json A bibliography of references relating to the research support of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1875 to 2004. The bibliography was compiled by Dana Bergstrom, and contains 76 references. proprietary
-River_Ice_Breakup_Freezeup_1697_1 ABoVE: River Ice Breakup and Freeze-up Stages, Yukon River Basin, Alaska, 1972-2016 ORNL_CLOUD STAC Catalog 1972-11-04 2016-11-30 -160.07, 62.9, -142.99, 66.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143403517-ORNL_CLOUD.umm_json This dataset provides estimates of river ice breakup and freeze-up stages along selected reaches of the Yukon and Tanana Rivers in the Yukon River Basin in interior Alaska from 1972-2016. Time series of Landsat satellite images were visually interpreted to identify the day of year and characteristics of the different stages of river ice seasonality. The stages of breakup or freeze-up were distinguished from one another based on the spatial extent and patterns of open water and ice cover. Images were displayed as false color composites, with the shortwave infrared (SWIR), near infrared (NIR), and green bands represented by red, green, and blue. proprietary
+RiSCC_Research_Support_Bibliography_1 A bibliography containing references to the research support of the RiSCC project from the Antarctic and subantarctic regions ALL STAC Catalog 1875-01-01 2004-12-31 -180, -70, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214311231-AU_AADC.umm_json A bibliography of references relating to the research support of the RiSCC project (Regional Sensitivity to Climate Change in Antarctic Terrestrial Ecosystems) from the Antarctic and subantarctic regions, dating from 1875 to 2004. The bibliography was compiled by Dana Bergstrom, and contains 76 references. proprietary
River_Ice_Breakup_Freezeup_1697_1 ABoVE: River Ice Breakup and Freeze-up Stages, Yukon River Basin, Alaska, 1972-2016 ALL STAC Catalog 1972-11-04 2016-11-30 -160.07, 62.9, -142.99, 66.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143403517-ORNL_CLOUD.umm_json This dataset provides estimates of river ice breakup and freeze-up stages along selected reaches of the Yukon and Tanana Rivers in the Yukon River Basin in interior Alaska from 1972-2016. Time series of Landsat satellite images were visually interpreted to identify the day of year and characteristics of the different stages of river ice seasonality. The stages of breakup or freeze-up were distinguished from one another based on the spatial extent and patterns of open water and ice cover. Images were displayed as false color composites, with the shortwave infrared (SWIR), near infrared (NIR), and green bands represented by red, green, and blue. proprietary
+River_Ice_Breakup_Freezeup_1697_1 ABoVE: River Ice Breakup and Freeze-up Stages, Yukon River Basin, Alaska, 1972-2016 ORNL_CLOUD STAC Catalog 1972-11-04 2016-11-30 -160.07, 62.9, -142.99, 66.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143403517-ORNL_CLOUD.umm_json This dataset provides estimates of river ice breakup and freeze-up stages along selected reaches of the Yukon and Tanana Rivers in the Yukon River Basin in interior Alaska from 1972-2016. Time series of Landsat satellite images were visually interpreted to identify the day of year and characteristics of the different stages of river ice seasonality. The stages of breakup or freeze-up were distinguished from one another based on the spatial extent and patterns of open water and ice cover. Images were displayed as false color composites, with the shortwave infrared (SWIR), near infrared (NIR), and green bands represented by red, green, and blue. proprietary
RoyalPenguin1955-1969_1 Breeding biology of the Royal Penguin (Eudypted chrysolophus)at Macquarie Island 1955-1969 AU_AADC STAC Catalog 1955-01-01 1969-12-31 158.76892, -54.78247, 158.95569, -54.48201 https://cmr.earthdata.nasa.gov/search/concepts/C1214313721-AU_AADC.umm_json The data are contained in a number of log books in hand written form (now scanned onto CD ROM. They were gathered according to a protocol updated annually by the Principal Investigator, DR Robert Carrick (now deceased). Details are contained in the paper Carrick R (1972) Population ecology of the Australian black-backed magpie, royal penguin, and silver gull. in: Population ecology of migratory birds - A symposium. US Dept of the Interior, Fish and wildlife service. Wildlife Research Report 2. pp 41-99. The only other information on the Royal penguin population to come from these investigations is the PhD Thesis of G.T. Smith, Studies on the behaviour and reproduction of the Royal penguin Eudyptes chrysolophus schlegeli. Australian National University April 1970. The log books contain a vast array of observations on the Royal penguin. Major observations/studies include banding of chicks and adults, breeding chronology, egg laying, breeding success, arrival weights, movements within and between colonies. The protocols for the collection of the data are missing although some instructions and notes are included in the volumes. Some data have also been entered into an excel spreadsheet. proprietary
Ruker_rymill_sat_1 Mount Ruker and Mount Rymill Satellite Image Maps 1:100 000 AU_AADC STAC Catalog 1989-03-18 1989-11-29 63, -74, 66.67, -72.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311244-AU_AADC.umm_json Two satellite images maps of Mt Ruker and Mt Rymill in the Australian Antarctic Territory were produced by the Australian Antarctic Division in 1998. Both maps are at a scale of 1:100 000 using Landsat TM imagery. Data source: Mount Ruker - Landsat TM imagery, scenes 128/112, acquired 29 November 1989. Mount Rymill - Landsat TM imagery, scenes 128/111 and 128/112, acquired 18 March 1989 and 29 November 1989 respectively. Nomenclature: Names have been approved by the Antarctic Names Committee of Australia. Please see the URL link for details on the images and processes used to produce these maps. proprietary
Russian_Forest_Disturbance_1294_1 Russian Boreal Forest Disturbance Maps Derived from Landsat Imagery, 1984-2000 ORNL_CLOUD STAC Catalog 1984-06-01 2000-08-31 30.98, 43.76, 138.63, 65.32 https://cmr.earthdata.nasa.gov/search/concepts/C2773247983-ORNL_CLOUD.umm_json This data set provides Boreal forest disturbance maps at 30-m resolution for 55 selected sites across Northern Eurasia within the Russian Federation. Disturbance events were derived from selected high-quality multi-year time series of Landsat Thematic Mapper and Enhanced Thematic Mapper Plus images (stacks) over the 1984 to 2000 time period. Forest pixels were classified by year of latest disturbance or as undisturbed. proprietary
@@ -13738,11 +13739,11 @@ SIMBAD_DESCHAMPS_LOA_0 Measurements using the SIMBAD radiometer by the Laboratoi
SIO-Pier_0 Scripps Ocean Institute (SOI) pier measurements OB_DAAC STAC Catalog 2007-04-04 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360662-OB_DAAC.umm_json Measurements made from the Scripps Ocean Institute pier in 2007. proprietary
SIPEX_ASPECT_1 ASPeCt Sea Ice Data from the SIPEX Voyage of the Aurora Australis in 2007-2008 AU_AADC STAC Catalog 2007-09-09 2007-10-11 116.43, -65.6, 129.133, -61.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214311291-AU_AADC.umm_json ASPeCt is an expert group on multi-disciplinary Antarctic sea ice zone research within the SCAR Physical Sciences program. Established in 1996, ASPeCt has the key objective of improving our understanding of the Antarctic sea ice zone through focussed and ongoing field programs, remote sensing and numerical modelling. The program is designed to complement, and contribute to, other international science programs in Antarctica as well as existing and proposed research programs within national Antarctic programs. ASPeCt also includes a component of data rescue of valuable historical sea ice zone information. The overall aim of ASPeCt is to understand and model the role of Antarctic sea ice in the coupled atmosphere-ice-ocean system. This requires an understanding of key processes, and the determination of physical, chemical, and biological properties of the sea ice zone. These are addressed by objectives which are: 1) To establish the distribution of the basic physical properties of sea ice that are important to air-sea interaction and to biological processes within the Antarctic sea-ice zone (ice and snow cover thickness distributions; structural, chemical and thermal properties of the snow and ice; upper ocean hydrography; floe size and lead distribution). These data are required to derive forcing and validation fields for climate models and to determine factors controlling the biology and ecology of the sea ice-associated biota. 2) To understand the key sea-ice zone processes necessary for improved parameterization of these processes in coupled models. These ASPeCt measurements were taken onboard the Aurora Australis during the SIPEX voyage in the 2007-2008 summer season. proprietary
SIPEX_II_ASPECT_1 ASPeCt ship-based observations during the SIPEX II voyage of the Aurora Australis, 2012 AU_AADC STAC Catalog 2012-09-22 2012-11-11 113, -66, 125, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214311294-AU_AADC.umm_json This dataset contains observations of ice conditions taken from the bridge of the RV Aurora Australis during SIPEX 2012, following the Scientific Committee on Antarctic Research/CliC Antarctic Sea Ice Processes and Climate [ASPeCt] protocols. See aspect.antarctica.gov.au Observations include total and partial concentration, ice type, thickness, floe size, topography, and snow cover in each of three primary ice categories; open water characteristics, and weather summary. The dataset is comprised of the scanned pages of a single logbook, which holds hourly observations taken by observers while the ship was moving through sea-ice zone. The following persons assisted in the collection of these data: Dr R. Massom, AAD, Member of observation team Mr A. Steer, AAD, Member of observation team Prof S. Warren, UW(Seattle), USA, Member of observation team Dr J. Hutchings, IARC, UAF, USA, Member of observation team Dr T. Toyota, Inst Low Temp Science, Japan, Member of observation team Dr T. Tamura, NIPR, Japan, Member of EM observation team Dr G. Dieckmann, AWI, Germany, Member of observation team Dr E. Maksym, WHOI, USA, Member of observation team Mr R. Stevens, IMAS, Trainee on observation team Dr J. Melbourne-Thomas, ACE CRC, Trainee on observation team Dr A. Giles, ACE CRC, Trainee on observation team Ms M. Zhia, IMAS, Trainee on observation team Ms J. Jansens, IMAS, Trainee on observation team Mr R. Humphries, Univ Wollengong, Trainee on observation team Mr C. Sampson, Univ Utah, USA, Trainee on observation team Mr Olivier Lecomte, Univ Catholique, Louvain-la-Neuve, Belgium, Trainee on observation team Mr D. Lubbers, Univ Utah, USA, Trainee on observation team Ms M. Zatko, UW(Seattle), USA, Trainee on observation team Ms C. Gionfriddo, Uni Melbourne, Trainee on observation team Mr K. Nakata, EES, Japan, Trainee on observation team proprietary
-SIPEX_II_AUV_1 3-D mapping of sea ice draft with an autonomous underwater vehicle AU_AADC STAC Catalog 2012-09-28 2012-10-13 115, -65, 125, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214311295-AU_AADC.umm_json We set out to achieve floe-scale 3-D mapping of sea ice draft and bio-optical parameters using a Multibeam SONAR and Hyperspectral radiometer mounted to an Autonomous Underwater Vehicle (AUV). The AUV utilised was the 'JAGUAR' Seabed-class vehicle from the Deep Submergence Laboratory at the WoodsHole Oceanographic Institution. The AUV comes with a CTD and ADCP. However these are not deployed as scientific sensors and therefore are unsupported in terms of metadata. In particular the CTD was not calibrated before or during the voyage. The AUV used a LongBaseLine system formed by three transponders to navigate to and from the survey grid. Two were located on the ice and the third was deployed from the back of the ship with an acoustic communications modem. Once at the survey grid beneath the sea ice, the AUV used the DVL to navigate using bottom-tracking of the underside of the sea ice. We conducted 4 missions beneath sea-ice during the SIPEX-II voyage. The current status of the data is that is in un-processed and unavailable until final processing is completed in 2013. Persons interested in the data should contact Dr Guy Williams directly for further information and preliminary figures relating to the AUV missions. The files currently in the archive are in raw form. Some preliminary data is provided for stations 2, 3, 4 and 6 as: floe-2-20120926.mat floe-3-20121003.mat floe-4-20121006.mat floe-6-20121013.mat These can be accessed using the Seabed_plot routines (MATLAB) in this folder. There is a readme file provided called what-is-this.txt Also included is the video footage taken from the AUV using a GoPro HD Hero. Video Codec: avc1 Resolution: 1920x1080 pixels Frame Rate: 29.970030 f/s Audio Codec: mp4a Audio Bitrate: 1536 kb/s Finally, plots of the data for ice stations 2,3,4 and 6 are included in the preliminary figures folder. The file names indicate which ice station the plots are from. proprietary
SIPEX_II_AUV_1 3-D mapping of sea ice draft with an autonomous underwater vehicle ALL STAC Catalog 2012-09-28 2012-10-13 115, -65, 125, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214311295-AU_AADC.umm_json We set out to achieve floe-scale 3-D mapping of sea ice draft and bio-optical parameters using a Multibeam SONAR and Hyperspectral radiometer mounted to an Autonomous Underwater Vehicle (AUV). The AUV utilised was the 'JAGUAR' Seabed-class vehicle from the Deep Submergence Laboratory at the WoodsHole Oceanographic Institution. The AUV comes with a CTD and ADCP. However these are not deployed as scientific sensors and therefore are unsupported in terms of metadata. In particular the CTD was not calibrated before or during the voyage. The AUV used a LongBaseLine system formed by three transponders to navigate to and from the survey grid. Two were located on the ice and the third was deployed from the back of the ship with an acoustic communications modem. Once at the survey grid beneath the sea ice, the AUV used the DVL to navigate using bottom-tracking of the underside of the sea ice. We conducted 4 missions beneath sea-ice during the SIPEX-II voyage. The current status of the data is that is in un-processed and unavailable until final processing is completed in 2013. Persons interested in the data should contact Dr Guy Williams directly for further information and preliminary figures relating to the AUV missions. The files currently in the archive are in raw form. Some preliminary data is provided for stations 2, 3, 4 and 6 as: floe-2-20120926.mat floe-3-20121003.mat floe-4-20121006.mat floe-6-20121013.mat These can be accessed using the Seabed_plot routines (MATLAB) in this folder. There is a readme file provided called what-is-this.txt Also included is the video footage taken from the AUV using a GoPro HD Hero. Video Codec: avc1 Resolution: 1920x1080 pixels Frame Rate: 29.970030 f/s Audio Codec: mp4a Audio Bitrate: 1536 kb/s Finally, plots of the data for ice stations 2,3,4 and 6 are included in the preliminary figures folder. The file names indicate which ice station the plots are from. proprietary
+SIPEX_II_AUV_1 3-D mapping of sea ice draft with an autonomous underwater vehicle AU_AADC STAC Catalog 2012-09-28 2012-10-13 115, -65, 125, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214311295-AU_AADC.umm_json We set out to achieve floe-scale 3-D mapping of sea ice draft and bio-optical parameters using a Multibeam SONAR and Hyperspectral radiometer mounted to an Autonomous Underwater Vehicle (AUV). The AUV utilised was the 'JAGUAR' Seabed-class vehicle from the Deep Submergence Laboratory at the WoodsHole Oceanographic Institution. The AUV comes with a CTD and ADCP. However these are not deployed as scientific sensors and therefore are unsupported in terms of metadata. In particular the CTD was not calibrated before or during the voyage. The AUV used a LongBaseLine system formed by three transponders to navigate to and from the survey grid. Two were located on the ice and the third was deployed from the back of the ship with an acoustic communications modem. Once at the survey grid beneath the sea ice, the AUV used the DVL to navigate using bottom-tracking of the underside of the sea ice. We conducted 4 missions beneath sea-ice during the SIPEX-II voyage. The current status of the data is that is in un-processed and unavailable until final processing is completed in 2013. Persons interested in the data should contact Dr Guy Williams directly for further information and preliminary figures relating to the AUV missions. The files currently in the archive are in raw form. Some preliminary data is provided for stations 2, 3, 4 and 6 as: floe-2-20120926.mat floe-3-20121003.mat floe-4-20121006.mat floe-6-20121013.mat These can be accessed using the Seabed_plot routines (MATLAB) in this folder. There is a readme file provided called what-is-this.txt Also included is the video footage taken from the AUV using a GoPro HD Hero. Video Codec: avc1 Resolution: 1920x1080 pixels Frame Rate: 29.970030 f/s Audio Codec: mp4a Audio Bitrate: 1536 kb/s Finally, plots of the data for ice stations 2,3,4 and 6 are included in the preliminary figures folder. The file names indicate which ice station the plots are from. proprietary
SIPEX_II_Aerosols_1 In-situ total aerosol number using condensation particle counters as observed during the SIPEX II voyage of the Aurora Australis, 2012 AU_AADC STAC Catalog 2012-09-23 2012-10-24 119, -67, 150, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214311293-AU_AADC.umm_json "The current dataset includes total aerosol count from two different Condensation Particle Counters (CPCs). The two CPCs measure total aerosol number in two different size ranges: - TSI Model 3025A measures particles with diameters larger than 3 nm (files are in the 3025_3nm folder) - TSI Model 3772 measures particles with diameters larger than 10 nm (files are in the 3772_10nm folder) The two CPCs are measuring from the same sample air and as such, the difference between the two measurements gives a measurement of total aerosol concentration in the 3-10 nm size range, known as the nucleation mode. Instrument setup: The instruments are setup inside an insulated shipping container mounted on the hatch covers directly aft of the forecastle. A 100 L pump is used to pull sample air from a 3 m high mast located on the starboard side of the forecastle. The air is pulled through 17 m of 50 mm antistatic (copper coil) polyurethane tubing and 2 m of 50 mm stainless steel pipe for connection and extensions. A 1 m length of one quarter inch stainless steel tubing penetrates into the container and directly through the wall of the polyurethane tubing for sampling off the primary flow to the CPCs. The inserted stainless steel tubing is oriented in such a way that sampled aerosol experience minimal turns to avoid sample loss. Approximately 1.7 m of flexible conductive tubing extends to a Y-piece which directs flow into each CPC. Butanol contaminated exhaust from the CPCs is pushed out of the container by two 10 LPM pumps. Data Processing: Raw data is calibrated for each instrument's recorded flow rate, and an inlet efficiency to correct for losses in the long inlet. Data is then resampled to minute time resolution, and filtered for logged events, wind directions which sampled ship exhaust, and outliers in the dataset. This produced a dataset which represented the sampling of clean Antarctic background atmosphere. The dataset includes both aerosol number concentrations from each instrument giving total number of particles above 3 nm and 10 nm respectively, as well as the different between these values, which gives a measure of newly formed particles in the nucleation mode between 3-10 nm (New Particle Formation, NPF). Associated uncertainties are included in the dataset." proprietary
-SIPEX_II_Albedo_1 Albedos for 300-2500nm for thin sea ice covered with frost flowers, nilas, snow, and slush collected during SIPEX II ALL STAC Catalog 2012-09-14 2012-11-04 113, -66, 147, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214311265-AU_AADC.umm_json This dataset contains albedo data for several varieties of sea ice and snow from 300-2500 nm measured during the SIPEX II voyage (2012). An Analytical Spectral Device (ASD) spectrophotometer records the amount of radiation impingent on a cosine collector, which contains a spectralon diffuser plate. The radiation that hits the diffuser plate is scattered equally in all directions (isotropically). A portion of the radiation incident on the plate is scattered in the direction of a fiber optic cable, which is connected to the ASD. The ASD separates the incoming radiation into 3-10 nm wavelength bins, thus creating a radiation spectrum spanning 300-2500 nm. The cosine collector can be oriented both upwards towards the sky and downward towards the snow and/or sea ice to measure the spectral signature of both the downwelling (from the sky) and upwelling (from the snow/ice) radiation. For each site, we record 5 upwelling and 5 downwelling spectral signatures. MATLAB or a similar analysis package is required to open the spectrum files that are created by the ASD. The ASD files are raw files and named in a sequence, starting with 'spectrum.000'. MATLAB or similar scripts can been written to convert the ASD spectrum data to .mat files. The spectra in the processed files are used to calculate the albedos for various snow and ice types when the ratio of upwelling to downwelling radiation is computed. We use two upwelling scans per one downwelling scan to compute the albedo. Also included is some photography of frost flowers and other examples of ice that was observed. proprietary
SIPEX_II_Albedo_1 Albedos for 300-2500nm for thin sea ice covered with frost flowers, nilas, snow, and slush collected during SIPEX II AU_AADC STAC Catalog 2012-09-14 2012-11-04 113, -66, 147, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214311265-AU_AADC.umm_json This dataset contains albedo data for several varieties of sea ice and snow from 300-2500 nm measured during the SIPEX II voyage (2012). An Analytical Spectral Device (ASD) spectrophotometer records the amount of radiation impingent on a cosine collector, which contains a spectralon diffuser plate. The radiation that hits the diffuser plate is scattered equally in all directions (isotropically). A portion of the radiation incident on the plate is scattered in the direction of a fiber optic cable, which is connected to the ASD. The ASD separates the incoming radiation into 3-10 nm wavelength bins, thus creating a radiation spectrum spanning 300-2500 nm. The cosine collector can be oriented both upwards towards the sky and downward towards the snow and/or sea ice to measure the spectral signature of both the downwelling (from the sky) and upwelling (from the snow/ice) radiation. For each site, we record 5 upwelling and 5 downwelling spectral signatures. MATLAB or a similar analysis package is required to open the spectrum files that are created by the ASD. The ASD files are raw files and named in a sequence, starting with 'spectrum.000'. MATLAB or similar scripts can been written to convert the ASD spectrum data to .mat files. The spectra in the processed files are used to calculate the albedos for various snow and ice types when the ratio of upwelling to downwelling radiation is computed. We use two upwelling scans per one downwelling scan to compute the albedo. Also included is some photography of frost flowers and other examples of ice that was observed. proprietary
+SIPEX_II_Albedo_1 Albedos for 300-2500nm for thin sea ice covered with frost flowers, nilas, snow, and slush collected during SIPEX II ALL STAC Catalog 2012-09-14 2012-11-04 113, -66, 147, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214311265-AU_AADC.umm_json This dataset contains albedo data for several varieties of sea ice and snow from 300-2500 nm measured during the SIPEX II voyage (2012). An Analytical Spectral Device (ASD) spectrophotometer records the amount of radiation impingent on a cosine collector, which contains a spectralon diffuser plate. The radiation that hits the diffuser plate is scattered equally in all directions (isotropically). A portion of the radiation incident on the plate is scattered in the direction of a fiber optic cable, which is connected to the ASD. The ASD separates the incoming radiation into 3-10 nm wavelength bins, thus creating a radiation spectrum spanning 300-2500 nm. The cosine collector can be oriented both upwards towards the sky and downward towards the snow and/or sea ice to measure the spectral signature of both the downwelling (from the sky) and upwelling (from the snow/ice) radiation. For each site, we record 5 upwelling and 5 downwelling spectral signatures. MATLAB or a similar analysis package is required to open the spectrum files that are created by the ASD. The ASD files are raw files and named in a sequence, starting with 'spectrum.000'. MATLAB or similar scripts can been written to convert the ASD spectrum data to .mat files. The spectra in the processed files are used to calculate the albedos for various snow and ice types when the ratio of upwelling to downwelling radiation is computed. We use two upwelling scans per one downwelling scan to compute the albedo. Also included is some photography of frost flowers and other examples of ice that was observed. proprietary
SIPEX_II_Boundary_Layer_Met_1 Boundary Layer Meteorology measurements collected from ice stations during the SIPEX II voyage of the Aurora Australis, 2012 AU_AADC STAC Catalog 2012-09-27 2012-10-21 118.3249, -65.2643, 121.0287, -64.392 https://cmr.earthdata.nasa.gov/search/concepts/C1214313769-AU_AADC.umm_json "Note - these data should be used with caution. The chief investigator for the dataset has indicated that a better quality dataset exists, but the AADC have been unable to attain it for archive. Matlab files containing raw data collected using the program ""HC2S3snowwind.CR1"" running on Campbell Scientific CR1000 dataloggers. Datalogger ""C"" was used during all ice stations. On the 8th of October a second mast and logger (""A"") were installed on what became the final day of Ice Station 4, and both loggers were deployed at stations 6 and 7, with ""C"" containing the longer records for each station as it was always installed first and (conditions permitting) left out longer. The sensors on these masts consist of: RM Young ""Wind Sentry"" Vane and Anemometer set (on top of each mast), no serial numbers Rotronics HC2S3 temperature and relative humidity sensors with standard polyethylene filters Upper sensor, mast ""C"": s/n 60837541 Lower sensor, mast ""C"": s/n 60837536 Upper sensor, mast ""A"": s/n 60837468 Lower sensor, mast ""A"": s/n 60834204 RM Young ""Wind Sentry"" anemometers (without vane) at 3 additional elevations on each mast Wenglor YHO3NCT8 photoelectric sensors at 4 heights on each mast. The upper sensor and the third sensor from the top were oriented facing up, while the others faced down. The upper three sensors were purchased in 2012, from a batch of these sensors manufactured in a new Eastern European factory while the lowest sensor on each mast came from a lot purchased in 2007, manufactured in Wenglor's German factory and extensively tested for use in snow. Data contained in these .mat files includes the following variables, with units: Textdates: CSI formatted dates, UTC except for station 2, which was (accidentally) UTC+12 Datenm: Matlab ""datenumber"", all UTC except for station 2, which is also UTC+12 hours. Battvolt: battery voltage Wptemp: temperature of the Wiring Panel thermister, degrees C Temp 1: air temperature above approximately 50cm, ventilated HC2S3 rotronics sensor, degrees C RH1: relative humidity (WRT water) above approximately 50cm, ventilated HC2S3 rotronics sensor, % Temp 2: air temperature above approximately 200cm, ventilated HC2S3 rotronics sensor, degrees C RH2: relative humidity (WRT water) above 197cm, ventilated HC2S3 rotronics sensor, % Snow1: snow particles per 10second interval at approximately 10cm Snow2: snow particles per 10second interval at approximately 50cm Snow3: snow particles per 10second interval at approximately 100cm Snow4: snow particles per 10second interval at approximately 200cm Wind1: average speed (m/s) at approximately 250cm during 10s interval Wind1max: maximum speed at approximately 250cm during 10s interval Wind2: average speed (m/s) at approximately 100cm during 10s interval Wind2max: maximum speed at approximately 100cm during 10s interval Wind3: average speed (m/s) at approximately 120cm during 10s interval Wind3max: maximum speed at approximately 120cm during 10s interval Wind4: average speed (m/s) at approximately 50cm during 10s interval Wind4max: maximum speed at approximately 50cm during 10s interval WindDir: wind direction at approximately 250cm, degrees, relative to mast orientation (needs correction to true) Measurement heights varied by ice station and by mast being used." proprietary
SIPEX_II_Buoys_1 In situ Lagrangian drifting buoy data off East Antarctica for the austral spring of 2012, deployed during the SIPEX II voyage of the Aurora Australis AU_AADC STAC Catalog 2012-09-01 2012-11-09 114.25781, -66, 121.64063, -63.84067 https://cmr.earthdata.nasa.gov/search/concepts/C2102891777-AU_AADC.umm_json In situ Lagrangian drifter positions were collected from nine expendable sea-ice buoys. Positions were collected by GPS receivers aboard each buoy and relayed via the CLS Argos satellite data system. The scientific proposal for this project was based on the deployment of two meso-scale buoy arrays over the continental shelf break in the SIPEX 2012 experimental region. Resolving of ice motion over the continental shelf and the shelf break is expected to provide crucial information on sea-ice deformation and ice strength. However, due to the unfavourable cruise track and also due to operational issues with helicopter support, it was not possible to deploy any of the meso-scale buoy arrays. Instead buoys were deployed to resolve ice deformation within the wider SIPEX 2012 region. Position data are available hourly from most buoys. CLS Argos transmitted data suffer from a data transmission blackspot just prior to local none, when there will be no data available. Data processing will be carried out as described in Heil et al. [2008] The dataset is build from ASCII files for each buoy with time stamps and observed latitude and longitude. The format (by column [C] for each file is as following: C1: Program ID C2: Buoy ID C3: Year C4: Month C5: Day C6: Hour C7: Minute C8: Second C9: Day-of-year C10: Lat (degN) C11: Lon (degE) proprietary
SIPEX_II_CO2_Flux_1 Atmospheric carbon dioxide (CO2) concentrations for CO2 flux AU_AADC STAC Catalog 2012-09-26 2012-10-22 118.65, -65.22, 120.19, -64.45 https://cmr.earthdata.nasa.gov/search/concepts/C1214311267-AU_AADC.umm_json During the ice stations, measurements of the air CO2, concentration for CO2 flux between sea ice and atmosphere were made with the chamber technique. Air-sea ice CO2 fluxes were measured over the sea ice with semi-automated chambers. Sample air from the chamber is passed through Teflon tubes connected to non-dispersive infrared (NDIR) analyzer (Model 800, LICOR Inc., USA) that was connected to a system controller and data logger (Model 10x, Campbell Scientific Inc., USA), that controls the opening/closing of the chambers as well. During the observation period, the CO2 flux was measured under three different conditions or surface types: (1) a chamber was installed above snow; (2) over the bare ice after removing the snow; (3) slush layer after removing the snow and slush crystals. The CO2 concentration in the chamber was measured every 5 s during experiments lasting 20 minutes for each chamber. A one hour cycle of measurements therefore consist of three 20 minute periods from each chamber (i.e. surface type). Data available: excel files containing sampling station name for each spreadsheet, dates, sampling time and air CO2 concentration as output voltage from NDIR (to indicated as ppm we need to calculate, but, not yet done this process) in the air and chamber for CO2 flux measurement. Also see the record - SIPEX_II_Gas_Flux proprietary
@@ -13783,8 +13784,8 @@ SIR-C_PRECISION Spaceborne Imaging Radar-C Precision USGS_LTA STAC Catalog 1994-
SIRSN3L1_001 SIRS/Nimbus-3 Level 1 Radiance Data V001 (SIRSN3L1) at GES DISC GES_DISC STAC Catalog 1969-04-14 1970-06-19 -180, -80.15, 180, 80.15 https://cmr.earthdata.nasa.gov/search/concepts/C1622768257-GES_DISC.umm_json SIRSN3L1 is the Nimbus-3 Satellite Infrared Spectrometer (SIRS) Level 1 Radiance Data product. SIRS measured infrared radiation (11 to 36 micrometers) emitted from the earth and its atmosphere in 13 selected spectral intervals in the carbon dioxide and water vapor bands plus one channel in the 11-micrometer atmospheric window. The radiances were used to determine the vertical temperature and water vapor profiles of the atmosphere. The data were recovered from the original 6250 tapes, and are now stored online as daily files in their original proprietary binary format each with about 14 orbits per day. The Nimbus-3 SIRS only viewed the nadir of the subsatellite track. Spatial coverage is near global from about latitude -80 to +80 degrees. The data are available from 08 April 1970 (day of year 98) to 08 April 1971. The principal investigator for the SIRS experiment was Dr. David Q. Wark from the NOAA National Environmental Satellite Data and Information Service. This product was previously available from the NSSDC with the identifier ESAD-00130 (old ID 70-025A-04A). proprietary
SIRSN4L1_001 SIRS/Nimbus-4 Level 1 Radiance Data V001 (SIRSN4L1) at GES DISC GES_DISC STAC Catalog 1970-04-08 1971-04-08 -180, -85, 180, 85 https://cmr.earthdata.nasa.gov/search/concepts/C1622768259-GES_DISC.umm_json SIRSN4L1 is the Nimbus-4 Satellite Infrared Spectrometer (SIRS) Level 1 Radiance Data product. SIRS measured infrared radiation (11 to 36 micrometers) emitted from the earth and its atmosphere in 13 selected spectral intervals in the carbon dioxide and water vapor bands plus one channel in the 11-micrometer atmospheric window. The radiances were used to determine the vertical temperature and water vapor profiles of the atmosphere. The data were recovered from the original 6250 tapes, and are now stored online as daily files in their original proprietary binary format each with about 14 orbits per day. The Nimbus-4 SIRS used a scan mirror to observe 12.5 deg to either side of the subsatellite track. Spatial coverage is near global from latitude -85 to +85 degrees. The data are available from 08 April 1970 (day of year 98) to 08 April 1971. The principal investigator for the SIRS experiment was Dr. David Q. Wark from the NOAA National Environmental Satellite Data and Information Service. This product was previously available from the NSSDC with the identifier ESAD-00130 (old ID 70-025A-04A). proprietary
SISTER_Workflow_V004_2335_4 SISTER: Experimental Workflows, Product Generation Environment, and Sample Data, V004 ORNL_CLOUD STAC Catalog 2011-05-13 2018-01-26 -158.05, 21.2, -107.96, 39.08 https://cmr.earthdata.nasa.gov/search/concepts/C3114843226-ORNL_CLOUD.umm_json The Space-based Imaging Spectroscopy and Thermal pathfindER (SISTER) activity originated in support of the NASA Earth System Observatory's Surface Biology and Geology (SBG) mission to develop prototype workflows with community algorithms and generate prototype data products envisioned for SBG. SISTER focused on developing a data system that is open, portable, scalable, standards-compliant, and reproducible. This collection contains EXPERIMENTAL workflows and sample data products, including (a) the Common Workflow Language (CWL) process file and a Jupyter Notebook that run the entire SISTER workflow capable of generating experimental sample data products spanning terrestrial ecosystems, inland and coastal aquatic ecosystems, and snow, (b) the archived algorithm steps (as OGC Application Packages) used to generate products at each step of the workflow, (c) a small number of experimental sample data products produced by the workflow which are based on the Airborne Visible/Infrared Imaging Spectrometer-Classic (AVIRIS or AVIRIS-CL) instrument, and (d) instructions for reproducing the sample products included in this dataset. DISCLAIMER: This collection contains experimental workflows, experimental community algorithms, and experimental sample data products to demonstrate the capabilities of an end-to-end processing system. The experimental sample data products provided have not been fully validated and are not intended for scientific use. The community algorithms provided are placeholders which can be replaced by any user's algorithms for their own science and application interests. These algorithms should not in any capacity be considered the algorithms that will be implemented in the upcoming Surface Biology and Geology mission. proprietary
-SIZEX-89-SAR Airborne X- and C-band SAR Images of Sea Ice in the Barents Sea SCIOPS STAC Catalog 1989-02-15 1989-02-27 15, 74, 25, 77 https://cmr.earthdata.nasa.gov/search/concepts/C1214584391-SCIOPS.umm_json SIZEX-89 was an official pre-launch ERS-1 program where the main objectives were to perform ERS-1 type sensors signature studies of different ice types in order to develop SAR algorithms for ice variables such as ice types, ice concentrations and ice kinematics. SIZEX-89 was a multidisciplinary, international winter experiment carried out in the Barents and the Greenland Seas during February and March 1989. During the experiment, 130 CCT tape of airborne X-band and C-band SAR data were obtained by the CCRS CV-580 in the Barents Sea, in February 1989. Remote Sensing, oceanographical, ocean acoustical, meteorological and sea ice data were collected. Several platforms were used: one ice-strengthened vessel (R/V Polarbjorn), one open ocean ship (R/V Hakon Mosby), helicopter drifting buoys, bottom-moored buoys, aircraft and satellites (NOAA, DMSP). In addition to data collection, an ice-forecasting model was run operationally to predict ice motion, ice thickness and ice concentration. The integrated data set obtained in SIZEX-89 is a pilot data set suitable to develop and improve methods for ice monitoring and prediction. proprietary
SIZEX-89-SAR Airborne X- and C-band SAR Images of Sea Ice in the Barents Sea ALL STAC Catalog 1989-02-15 1989-02-27 15, 74, 25, 77 https://cmr.earthdata.nasa.gov/search/concepts/C1214584391-SCIOPS.umm_json SIZEX-89 was an official pre-launch ERS-1 program where the main objectives were to perform ERS-1 type sensors signature studies of different ice types in order to develop SAR algorithms for ice variables such as ice types, ice concentrations and ice kinematics. SIZEX-89 was a multidisciplinary, international winter experiment carried out in the Barents and the Greenland Seas during February and March 1989. During the experiment, 130 CCT tape of airborne X-band and C-band SAR data were obtained by the CCRS CV-580 in the Barents Sea, in February 1989. Remote Sensing, oceanographical, ocean acoustical, meteorological and sea ice data were collected. Several platforms were used: one ice-strengthened vessel (R/V Polarbjorn), one open ocean ship (R/V Hakon Mosby), helicopter drifting buoys, bottom-moored buoys, aircraft and satellites (NOAA, DMSP). In addition to data collection, an ice-forecasting model was run operationally to predict ice motion, ice thickness and ice concentration. The integrated data set obtained in SIZEX-89 is a pilot data set suitable to develop and improve methods for ice monitoring and prediction. proprietary
+SIZEX-89-SAR Airborne X- and C-band SAR Images of Sea Ice in the Barents Sea SCIOPS STAC Catalog 1989-02-15 1989-02-27 15, 74, 25, 77 https://cmr.earthdata.nasa.gov/search/concepts/C1214584391-SCIOPS.umm_json SIZEX-89 was an official pre-launch ERS-1 program where the main objectives were to perform ERS-1 type sensors signature studies of different ice types in order to develop SAR algorithms for ice variables such as ice types, ice concentrations and ice kinematics. SIZEX-89 was a multidisciplinary, international winter experiment carried out in the Barents and the Greenland Seas during February and March 1989. During the experiment, 130 CCT tape of airborne X-band and C-band SAR data were obtained by the CCRS CV-580 in the Barents Sea, in February 1989. Remote Sensing, oceanographical, ocean acoustical, meteorological and sea ice data were collected. Several platforms were used: one ice-strengthened vessel (R/V Polarbjorn), one open ocean ship (R/V Hakon Mosby), helicopter drifting buoys, bottom-moored buoys, aircraft and satellites (NOAA, DMSP). In addition to data collection, an ice-forecasting model was run operationally to predict ice motion, ice thickness and ice concentration. The integrated data set obtained in SIZEX-89 is a pilot data set suitable to develop and improve methods for ice monitoring and prediction. proprietary
SLAR Side Looking Airborne Radar (SLAR) Imagery USGS_LTA STAC Catalog 1980-07-18 1993-11-30 -180, 24, -60, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1220566112-USGS_LTA.umm_json Side-Looking Airborne Radar (SLAR) imagery is available from the U.S. Geological Survey (USGS) for selected project areas in the conterminous United States and Alaska. Data are X-band synthetic aperture radar (horizontally transmitted, horizontally received) with the exception of some test sites. Coverage was contracted on a yearly basis. The USGS SLAR images most often consist of contact strip images and 1:250,000-scale, map-controlled mosaics. Greater than half of the available SLAR image strips are distributed on 8-mm cassettes, while some image strips are distributed on CD-ROM. In addition, ancillary products such as indexes (on paper, film, or microfiche) and custom photographic products may also be available. Due to the geographically non-searchable nature of the SLAR inventory, customer assistance may be obtained to determine availability of SLAR data over the user's area of interest. Customer knowledge of USGS 1:250,000-scale map names is beneficial in expediting orders. A scale of 1:50,000 only applies to Alaska coverage. proprietary
SLOPE_GPP_CONUS_1786_1 MODIS-based GPP, PAR, fC4, and SANIRv estimates from SLOPE for CONUS, 2000-2019 ORNL_CLOUD STAC Catalog 2000-01-01 2020-01-01 -155.57, 19.99, -52.22, 50.01 https://cmr.earthdata.nasa.gov/search/concepts/C2266194621-ORNL_CLOUD.umm_json This dataset contains estimated gross primary productivity (GPP), photosynthetically active radiation (PAR), soil adjusted near infrared reflectance of vegetation (SANIRv), the fraction of C4 crops in vegetation (fC4), and their uncertainties for the conterminous United States (CONUS) from 2000 to 2019. The daily estimates are SatelLite Only Photosynthesis Estimation (SLOPE) products at 250-m resolution. There are three distinct features of the GPP estimation algorithm: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR, (2) SLOPE couples gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRv (SANIRv) dataset, and (3) SLOPE couples a temporal pattern recognition approach with a long-term Crop Data Layer (CDL) product to predict dynamic C4 crop fraction. PAR, SANIRv and C4 fraction are used to drive a parsimonious model with only two parameters to estimate GPP, along with a quantitative uncertainty, on a per-pixel and daily basis. The slope GPP product has an R2 = 0.84 and a root-mean-square error (RMSE) of 1.65 gC m-2 d-1. proprietary
SMAP_JPL_L2B_NRT2_SSS_CAP_V5_5.0 JPL SMAP Level 2B Near Real-time CAP Sea Surface Salinity V5.0 Validated Dataset (2 hour latency) POCLOUD STAC Catalog 2015-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2681262364-POCLOUD.umm_json The SMAP-SSS V5.0, level 2B (NRT CAP) dataset produced by the Jet Propulsion Laboratory Combined Active-Passive (CAP) project , is a validated product that provides near real-time orbital/swath data on sea surface salinity (SSS) and extreme winds, derived from the NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015. This mission, initially designed to measure and map Earth's soil moisture and freeze/thaw state to better understand terrestrial water, carbon and energy cycles has been adapted to measure ocean SSS and ocean wind speed using its passive microwave instrument. The SMAP instrument is in a near polar orbiting, sun synchronous orbit with a nominal 8 day repeat cycle.
The dataset includes derived SMAP SSS, SSS uncertainty, wind speed and direction data for extreme winds, as well as brightness temperatures for each radiometer polarization. Furthermore, it contains ancillary reference surface salinity, ice concentration, wind and wave height data, quality flags, and navigation data. This broad range of parameters stems from the observatory's version 5.0 (V5) CAP retrieval algorithm, initially developed for the Aquarius/SAC-D mission and subsequently extended to SMAP. Datafrom April 1, 2015 to present, is available with a latency of about 6 hours. The observations are global, provided on a 25km swath grid with an approximate spatial resolution of 60 km. Each data file covers one 98-minute orbit, with 15 files generated per day. The data are based on the near-real-time SMAP V5 Level-1 Brightness Temperatures (TB) and benefits from an enhanced calibration methodology, which improves the absolute radiometric calibration and minimizes biases between ascending and descending passes. These improvements also enrich the applicability of SMAP Level-1 data for other uses, such as further sea surface salinity and wind assessments. Due to a malfunction of the SMAP scatterometer on July 7, 2015, collocated wind speed data has been utilized for the necessary surface roughness correction for salinity retrieval.
This JPL SMAP-SSS V5.0 dataset holds tremendous potential for scientific research and various applications. Given the SMAP satellite's near-polar orbit and sun-synchronous nature, it achieves global coverage in approximately three days , enabling researchers to monitor and model global oceanic and climatic phenomena with unprecedented detail and timeliness. These data can inform and enhance understanding of global weather patterns, the Earth’s hydrological cycle, ocean circulation, and climate change. proprietary
@@ -13828,14 +13829,14 @@ SMAP_RSS_L3_SSS_SMI_MONTHLY_V5.3_5.3 RSS SMAP Level 3 Sea Surface Salinity Stand
SMAP_RSS_L3_SSS_SMI_MONTHLY_V5_5.0 RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V5.0 Validated Dataset POCLOUD STAC Catalog 2015-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2208416221-POCLOUD.umm_json The version 5.0 SMAP-SSS level 3, monthly gridded product is based on the fourth release of the validated standard mapped sea surface salinity (SSS) data from the NASA Soil Moisture Active Passive (SMAP) observatory, produced operationally by Remote Sensing Systems (RSS) with a one-month latency. The major changes in Version 5.0 from Version 4 are: (1) the addition of formal uncertainty estimates to all salinity retrieval products. (2) Sea-ice flagging and sea-ice side-lobe correction based on direct ingestion of AMSR-2 brightness temperature (TB) measurements. This is in contrast to Version 4 and earlier versions in which the sea-ice correction was based on an external sea-ice concentration product. The use of AMSR-2 TB measurements in the SMAP Version 5 products allows for salinity retrievals closer to the sea-ice edge and aids in the detection of large icebergs near the Antarctic. Monthly data files for this product are averages over one-month time intervals. SMAP data begins on April 1,2015 and is ongoing, with a one-month latency in processing and availability. L3 products are global in extent with a default spatial resolution of approximately 70KM. The datasets are gridded at 0.25degree x 0.25degree. Note that while a SSS 40KM variable is also included in the product, for most open ocean applications, the default SSS variable (70KM) is best used as they are significantly less noisy than the 40KM data. The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval. proprietary
SMAP_RSS_L3_SSS_SMI_MONTHLY_V6_6.0 RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V6.0 Validated Dataset POCLOUD STAC Catalog 2015-04-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2832226365-POCLOUD.umm_json The RSS SMAP Level 3 Sea Surface Salinity Standard Mapped Image Monthly V6.0 Validated Dataset produced by the Remote Sensing Systems (RSS) and sponsored by the NASA Ocean Salinity Science Team, is a validated product that provides orbital/swath data on sea surface salinity (SSS) derived from the NASA's Soil Moisture Active Passive (SMAP) mission. The SMAP satellite was launched on 31 January 2015 with a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. Malfunction of the SMAP scatterometer on 7 July, 2015, has necessitated the use of collocated wind speed, primarily from WindSat, for the surface roughness correction required for the surface salinity retrieval.
The major changes in Version 6.0 from Version 5.0 are: (1) Removal of biases during the first few months of the SMAP mission that are related to the operation of the SMAP radar during that time. (2) Mitigation of biases that depend on the SMAP look angle. (3) Mitigation of salty biases at high Northern latitudes. (4) Revised sun-glint flag. The RSS SMAP L3 monthly product includes data for a range of parameters: derived sea surface salinity (SSS) with SSS-uncertainty, rain filtered SMAP sea surface salinity, collocated wind speed, data and ancillary reference surface salinity data from HYCOM. Each data file is available in netCDF-4 file format and is averaged over one-month time intervals with about 7-day latency (after the end of the averaging period). Data begins on April 1,2015 and is ongoing. Observations are global in extent with an approximate spatial resolution of 40KM. Note that while a SSS 40KM variable is also included in the product for most open ocean applications, The standard product of the SMAP Version 6.0 release is the smoothed salinity product with a spatial resolution of approximately 70 km. proprietary
SMERGE_RZSM0_40CM_2.0 Smerge-Noah-CCI root zone soil moisture 0-40 cm L4 daily 0.125 x 0.125 degree V2.0 (SMERGE_RZSM0_40CM) at GES DISC GES_DISC STAC Catalog 1979-01-02 2019-05-10 -125, 25, -67, 53 https://cmr.earthdata.nasa.gov/search/concepts/C1569839798-GES_DISC.umm_json Smerge-Noah-CCI root zone soil moisture 0-40 cm L4 daily 0.125 x 0.125 degree V2.0 is a multi-decadal root-zone soil moisture product. Smerge is developed by merging the North American Land Data Assimilation System (NLDAS) land surface model output with surface satellite retrievals from the European Space Agency Climate Change Initiative. The data have a 0.125 degree resolution at a daily time-step, covering the entire continental United States and spanning nearly four decades (January 1979 to May 2019). This data product contains root-zone soil moisture of 0 - 40 cm layer, Climate Change Initiative (CCI) derived soil moisture anomalies of 0-40 cm layer, and a soil moisture data estimation flag. This data product is the recommended replacement for the AMSR-E/Aqua root zone soil moisture L3 1 day 25 km x 25 km descending and 2-Layer Palmer Water Balance Model V001 product (LPRM_AMSRE_D_RZSM3), which will be removed from archive on June 27, 2022. Smerge provides a better root zone soil moisture estimation because it has higher data quality and longer temporal coverage. proprietary
-SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0 ACEX 2004 ODEN TRACK SCIOPS STAC Catalog 2004-08-08 2004-09-13 19.045, 69.727, 175.94, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595274-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary
SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0 ACEX 2004 ODEN TRACK ALL STAC Catalog 2004-08-08 2004-09-13 19.045, 69.727, 175.94, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595274-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary
-SMHI_IPY_ACEX-2004-Seismic ACEX 2004 Seismic SCIOPS STAC Catalog 2004-08-08 2004-09-13 139.0632, 87.917, 140.31, 87.977 https://cmr.earthdata.nasa.gov/search/concepts/C1214595276-SCIOPS.umm_json Reflection seismic profiles aquired during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary
+SMHI_IPY_ACEX-2004-ODEN-TRACK_1.0 ACEX 2004 ODEN TRACK SCIOPS STAC Catalog 2004-08-08 2004-09-13 19.045, 69.727, 175.94, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595274-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary
SMHI_IPY_ACEX-2004-Seismic ACEX 2004 Seismic ALL STAC Catalog 2004-08-08 2004-09-13 139.0632, 87.917, 140.31, 87.977 https://cmr.earthdata.nasa.gov/search/concepts/C1214595276-SCIOPS.umm_json Reflection seismic profiles aquired during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary
-SMHI_IPY_ACEX-2004-Sites_1.0 ACEX 2004 Sites SCIOPS STAC Catalog 2004-08-08 2004-09-13 -4.05029, 69.727, 19.045, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595252-SCIOPS.umm_json The site location for the cores retrieved during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary
+SMHI_IPY_ACEX-2004-Seismic ACEX 2004 Seismic SCIOPS STAC Catalog 2004-08-08 2004-09-13 139.0632, 87.917, 140.31, 87.977 https://cmr.earthdata.nasa.gov/search/concepts/C1214595276-SCIOPS.umm_json Reflection seismic profiles aquired during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary
SMHI_IPY_ACEX-2004-Sites_1.0 ACEX 2004 Sites ALL STAC Catalog 2004-08-08 2004-09-13 -4.05029, 69.727, 19.045, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595252-SCIOPS.umm_json The site location for the cores retrieved during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary
-SMHI_IPY_AGAVE2007-track_1.0 AGAVE2007 track ALL STAC Catalog 2007-07-01 2007-08-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595299-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the Arctic Gakkel Vents Expedition (AGAVE) 2007. proprietary
+SMHI_IPY_ACEX-2004-Sites_1.0 ACEX 2004 Sites SCIOPS STAC Catalog 2004-08-08 2004-09-13 -4.05029, 69.727, 19.045, 89.999 https://cmr.earthdata.nasa.gov/search/concepts/C1214595252-SCIOPS.umm_json The site location for the cores retrieved during the International Ocean Drilling Program (IODP) Leg 302, also known as Arctic Coring Expedition (ACEX). proprietary
SMHI_IPY_AGAVE2007-track_1.0 AGAVE2007 track SCIOPS STAC Catalog 2007-07-01 2007-08-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595299-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the Arctic Gakkel Vents Expedition (AGAVE) 2007. proprietary
+SMHI_IPY_AGAVE2007-track_1.0 AGAVE2007 track ALL STAC Catalog 2007-07-01 2007-08-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595299-SCIOPS.umm_json Icebreaker Oden\\\\\\\\\\\\\\\'s trackline during the Arctic Gakkel Vents Expedition (AGAVE) 2007. proprietary
SMHI_IPY_ALIS ALIS, Auroral Large Imaging System SCIOPS STAC Catalog 1993-12-23 2009-02-18 18.8, 67.3, 21.7, 69.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214595251-SCIOPS.umm_json ALIS consists of unmanned imaging stations located in Northern Scandinavia in a grid of about 50Ã50 km. Each station is equipped with an imager having a high-resolution monochrome 1024Ã1024 pixel CCD detector and a filter wheel with six positions for narrow-band interference filters. The field of view is 70 degrees diagonally for most imagers, but there are also two units with a 90 degrees field of view. The imagers are mounted in a positioning system and can be pointed so that several imagers can view a common volume. ALIS is operated on campaign basis. Filter sequences and pointing directions are freely selectable. proprietary
SMHI_IPY_ALIS ALIS, Auroral Large Imaging System ALL STAC Catalog 1993-12-23 2009-02-18 18.8, 67.3, 21.7, 69.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214595251-SCIOPS.umm_json ALIS consists of unmanned imaging stations located in Northern Scandinavia in a grid of about 50Ã50 km. Each station is equipped with an imager having a high-resolution monochrome 1024Ã1024 pixel CCD detector and a filter wheel with six positions for narrow-band interference filters. The field of view is 70 degrees diagonally for most imagers, but there are also two units with a 90 degrees field of view. The imagers are mounted in a positioning system and can be pointed so that several imagers can view a common volume. ALIS is operated on campaign basis. Filter sequences and pointing directions are freely selectable. proprietary
SMMRN7IM_001 SMMR/Nimbus-7 Color Images V001 (SMMRN7IM) at GES DISC GES_DISC STAC Catalog 1978-10-30 1983-11-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1616514843-GES_DISC.umm_json "SMMRN7IM is the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) Color Image data product scanned from 17"" x 15"" color prints and saved as JPEG-2000 files. Sea surface temperature, sea surface winds, total atmospheric water vapor over oceans, total atmospheric liquid water over oceans, including brightness temperature parameters are available as both 6-day composites and 1-month averages between 64 south and north latitudes in Mercator projection. Sea ice fraction, sea ice and ocean surface temperature, sea ice concentration, including brightness temperature parameters are available as both 3-day and 1-month averages in north and south polar stereographic projections. Images may contain between one and three measured parameters. These SMMR images are available from 30 October 1978 through 2 November 1983. The principal investigator for the SMMR experiment was Dr. Per Gloersen from NASA GSFC. These products were previously available from the NSSDC under the ids ESAD-00007, ESAD-00056, ESAD-00123, ESAD-00124, ESAD-00162, ESAD-00172, ESAD-00173, ESAD-00176 ESAD-00177, ESAD-00178, and ESAD-00241 (old ids 78-098A-08I-S)." proprietary
@@ -14046,6 +14047,8 @@ SNEX23_Lidar_Raw_1 SnowEx23 Airborne Lidar Scans Raw V001 NSIDC_ECS STAC Catalog
SNEX23_MAR22_SD_1 SnowEx23 Mar22 IOP Snow Depth Measurements V001 NSIDC_ECS STAC Catalog 2022-03-08 2022-03-23 -149.597, 64.699, -147.49, 70.085 https://cmr.earthdata.nasa.gov/search/concepts/C3154261714-NSIDC_ECS.umm_json "The data set contains snow depth measurements from two regions of Alaska, USA collected during the March 2022 intensive observation period (IOP) as part of the NASA SnowEx 2023 field campaign. The study sites include three boreal forest sites in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) and a coastal tundra site in the North Slope region (Arctic coastal plain). Snow depth measurements collected from the study sampling sites during the subsequent field season are available as SnowEx23 Mar23 IOP Snow Depth Measurements, Version 1." proprietary
SNEX23_MAR23_SD_1 SnowEx23 Mar23 IOP Community Snow Depth Measurements V001 NSIDC_ECS STAC Catalog 2023-03-07 2023-03-16 -149.597, 64.699, -147.49, 70.085 https://cmr.earthdata.nasa.gov/search/concepts/C3172387010-NSIDC_ECS.umm_json "The data set contains snow depth measurements from five study sites in Alaska, USA; data were collected during the March 2023 intensive observation period (IOP) as part of the NASA SnowEx 2023 field campaign. The study sites include three boreal forest sites in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) and two coastal tundra sites in the North Slope region (Arctic coastal plain and Upper Kuparuk Toolik). Snow depth measurements collected from the study sampling sites during the previous field season are available as SnowEx23 Mar22 IOP Snow Depth Measurements, Version 1." proprietary
SNEX23_MAR23_SP_1 SnowEx23 Mar23 Snow Pit Measurements V001 NSIDC_ECS STAC Catalog 2023-03-07 2023-03-16 -149.59716, 64.69925, -147.48583, 70.08434 https://cmr.earthdata.nasa.gov/search/concepts/C3306985060-NSIDC_ECS.umm_json The data set presents snow pit measurements collected during the NASA SnowEx March 2023 Intensive Observation Period (IOP) in Alaska, USA to use for calibration and validation with coincident airborne SWESARR and lidar measurements as part of the strategy focused on snow water equivalence (SWE) and snow depth (HS). In total, 170 snow pits were excavated between the five sites at locations representing a range of snow depth, vegetation, and topographic conditions. Three study areas represented boreal forest snow near Fairbanks, AK: Farmers Loop Creamers Field (FLCF), Caribou Poker Creek Research Watershed (CPCRW), and Bonanza Creek Experimental Forest (BCEF). Two study areas represented Arctic tundra snow: Arctic Coastal Plain (ACP) and Upper Kuparuk Toolik (UKT). proprietary
+SNEX23_OCT23_GSR_1 SnowEx23 October 23 Ground Surface Roughness Reconstruction V001 NSIDC_ECS STAC Catalog 2023-10-17 2023-10-28 -149.6, 64.8, -147.5, 68.7 https://cmr.earthdata.nasa.gov/search/concepts/C3333536572-NSIDC_ECS.umm_json "This data set presents ground surface roughness data collected during the NASA SnowEx 2023 field campaign between 17 and 28 October 2023. The data are formatted as point clouds, compiled from images acquired using a digital camera. Images were collected from 22 snow pits located across three study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Caribou Poker Creek watershed (CPCW) and Farmers Loop Creamers Field (FLCF), two boreal forest sites near Fairbanks, Alaska. The raw imagery from which these data are derived are available as SnowEx23 Oct23 Ground Surface Roughness Imagery, Version 1." proprietary
+SNEX23_OCT23_GSR_Raw_1 SnowEx23 Oct23 Ground Surface Roughness Imagery V001 NSIDC_ECS STAC Catalog 2023-10-17 2023-10-28 -149.6, 64.8, -147.5, 68.7 https://cmr.earthdata.nasa.gov/search/concepts/C3333536645-NSIDC_ECS.umm_json "This data set presents photographs of snow pit ground surface collected using a digital camera during the NASA SnowEx 2023 field campaign between 17 and 28 October 2023. The images were collected from 22 snow pits located across three study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Caribou Poker Creek watershed (CPCW) and Farmers Loop Creamers Field (FLCF), two boreal forest sites near Fairbanks, Alaska. These photographs were used to derive point cloud data representative of ground surface roughness, which are available as SnowEx23 Oct23 Ground Surface Roughness Reconstruction, Version 1." proprietary
SNEX23_SSA_1 SnowEx23 Laser Snow Microstructure Specific Surface Area Data V001 NSIDC_ECS STAC Catalog 2023-03-06 2023-03-16 -149.597, 64.701, -147.4905, 70.085 https://cmr.earthdata.nasa.gov/search/concepts/C2735033831-NSIDC_ECS.umm_json "This data set contains vertical profiles of snow reflectance and specific surface area (SSA) from the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, the Caribou Poker Creek watershed and Farmers Loop/Creamer’s Field), and a coastal tundra environment in the North Slope region of northern Alaska (the Arctic coastal plain and Upper Kuparuk Toolik), collected as part of the NASA SnowEx 2023 field campaign in March 2023. Reflectance was measured in snow pits using three different integrating sphere laser devices: an A2 Photonic Sensor IceCube (1310 nm), an IRIS (InfraRed Integrating Sphere) system (1310 nm), and an InfraSnow SSA sensor (945 nm). Measured reflectance values were converted to SSA during data processing. It is recommended that data users work with either the IceCube or IRIS data, as the InfraSnow data was collected primarily for testing of the instrument’s capabilities. Snow-off SSA data from these same study sites are available as SnowEx23 Laser Snow Microstructure Specific Surface Area Snow-off Data, Version 1." proprietary
SNEX23_SSA_SO_1 SnowEx23 Laser Snow Microstructure Specific Surface Area Snow-off Data V001 NSIDC_ECS STAC Catalog 2023-10-17 2023-10-28 -149.5964, 64.701, -147.4906, 70.084 https://cmr.earthdata.nasa.gov/search/concepts/C2881748646-NSIDC_ECS.umm_json "This data set reports vertical profiles of snow reflectance and specific surface area (SSA) from two study sites in Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, the Caribou Poker Creek watershed and Farmers Loop/Creamer’s Field), and a coastal tundra environment in the North Slope region of northern Alaska (the Arctic coastal plain and Upper Kuparuk Toolik). Reflectance was measured in situ using an A2 Photonic Sensor IceCube (1310 nm). Measured reflectance values were converted to SSA during data processing following the methods of Gallet et al., (2009). Snow-on SSA data from these same study sites were collected in March 2023 and are available as SnowEx23 Laser Snow Microstructure Specific Surface Area Data, Version 1." proprietary
SNEX23_SWE_1 SnowEx23 Snow Water Equivalent V001 NSIDC_ECS STAC Catalog 2023-03-13 2023-03-16 -149.494, 64.8677, -147.6745, 68.615 https://cmr.earthdata.nasa.gov/search/concepts/C3041011983-NSIDC_ECS.umm_json This data set presents snow depth, snow water equivalent (SWE), and bulk snow density data collected during the NASA SnowEx 2023 field campaign between March 13-16 2023. Samples were collected using an Adirondack snow sampler (SWE tube) from two study sites: Upper Kuparuk and Toolik (UKT), an arctic tundra environment in Northern Alaska, and Farmers Loop Creamers Field (FLCF), a boreal forest near Fairbanks, Alaska. proprietary
@@ -14093,8 +14096,8 @@ SNF_SITE_86_188_1 SNF Site Characterization Validation ORNL_CLOUD STAC Catalog 1
SNF_TAB3_3T_182_1 SNF Forest Understory Cover Data (Table) ORNL_CLOUD STAC Catalog 1976-01-01 1986-12-31 -92.51, 47.66, -91.77, 48.17 https://cmr.earthdata.nasa.gov/search/concepts/C2884983060-ORNL_CLOUD.umm_json SNF study location measurements of percent ground coverage provided by each understory species; percentages are averages of five 2-meter-diameter subsamples in each site (presented in table format) proprietary
SNF_UND_CVR_181_1 SNF Forest Understory Cover Data ORNL_CLOUD STAC Catalog 1976-01-01 1986-12-31 -92.51, 47.66, -91.77, 48.17 https://cmr.earthdata.nasa.gov/search/concepts/C2884982848-ORNL_CLOUD.umm_json SNF study location measurements of percent ground coverage provided by each understory species; percentages are averages of five 2-meter-diameter subsamples in each site (presented as list format) proprietary
SNOWPETRELSURVEYSCASEY0203_1 Detailed information on 196 grid sites used for snow petrel surveys in the Windmill Islands during the 2002/2003 season AU_AADC STAC Catalog 2002-11-12 2003-02-16 110.3, -66.5, 110.75, -66.2333 https://cmr.earthdata.nasa.gov/search/concepts/C1214313758-AU_AADC.umm_json Very little information is known about the distribution and abundance of snow petrels at the regional scale. This dataset contains locations of grid sites used to survey for snow petrels in the Windmill Islands during the 2002-2003 season. Descriptive information relating to each grid site was recorded and a detailed description of data fields is provided in the attached dataset. Survey methodology used 200*200 m grid squares in which exhaustive searches were conducted (FO). Search effort for these is provided in the dataset. The fields in this dataset are: Site Nest Region Date Time Ice free area UTM Coordinates proprietary
-SNPEMAWSON04-05_1 A GIS dataset of Snow Petrel nests mapped in the Mawson region during the 2004-2005 season AU_AADC STAC Catalog 2004-12-10 2005-04-25 62.25, -67.6, 63.5, -67.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214313800-AU_AADC.umm_json Very little information is known about the distribution and abundance of Snow petrels at the regional and local scales. This dataset contains the locations of Snow petrel nests, mapped in the Mawson region during the 2004-2005 season. Location of nests were recorded with handheld Trimble Geoexplorer GPS receivers, differentially corrected and stored as an Arcview point shapefile (ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of the data fields is provided in the description of the shapefile (word document). A text file also provides the attribute information (formatted for input into R statistical software). This work has been completed as part of ASAC project 2704 (ASAC_2704). Fields recorded. Species Activity Type Entrances Slope Remnants Latitude Longitude Date Snow Eggchick Cavitysize Cavitydepth Distnn Substrate Comments SitedotID Aspect Firstfred proprietary
SNPEMAWSON04-05_1 A GIS dataset of Snow Petrel nests mapped in the Mawson region during the 2004-2005 season ALL STAC Catalog 2004-12-10 2005-04-25 62.25, -67.6, 63.5, -67.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214313800-AU_AADC.umm_json Very little information is known about the distribution and abundance of Snow petrels at the regional and local scales. This dataset contains the locations of Snow petrel nests, mapped in the Mawson region during the 2004-2005 season. Location of nests were recorded with handheld Trimble Geoexplorer GPS receivers, differentially corrected and stored as an Arcview point shapefile (ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of the data fields is provided in the description of the shapefile (word document). A text file also provides the attribute information (formatted for input into R statistical software). This work has been completed as part of ASAC project 2704 (ASAC_2704). Fields recorded. Species Activity Type Entrances Slope Remnants Latitude Longitude Date Snow Eggchick Cavitysize Cavitydepth Distnn Substrate Comments SitedotID Aspect Firstfred proprietary
+SNPEMAWSON04-05_1 A GIS dataset of Snow Petrel nests mapped in the Mawson region during the 2004-2005 season AU_AADC STAC Catalog 2004-12-10 2005-04-25 62.25, -67.6, 63.5, -67.3 https://cmr.earthdata.nasa.gov/search/concepts/C1214313800-AU_AADC.umm_json Very little information is known about the distribution and abundance of Snow petrels at the regional and local scales. This dataset contains the locations of Snow petrel nests, mapped in the Mawson region during the 2004-2005 season. Location of nests were recorded with handheld Trimble Geoexplorer GPS receivers, differentially corrected and stored as an Arcview point shapefile (ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of the data fields is provided in the description of the shapefile (word document). A text file also provides the attribute information (formatted for input into R statistical software). This work has been completed as part of ASAC project 2704 (ASAC_2704). Fields recorded. Species Activity Type Entrances Slope Remnants Latitude Longitude Date Snow Eggchick Cavitysize Cavitydepth Distnn Substrate Comments SitedotID Aspect Firstfred proprietary
SNPPATMSL1B_2 Suomi NPP ATMS Sounder Science Investigator-led Processing System (SIPS) Level 1B Brightness Temperature V2 (SNPPATMSL1B) at GES DISC GES_DISC STAC Catalog 2011-12-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1442068516-GES_DISC.umm_json The Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP). The ATMS instrument is a cross-track scanner with 22 microwave channels in the range 23.8-183.31 Gigahertz (GHz). The beam width is 1.1 degrees for the channels in the 160-183 GHz range, 2.2 degrees for the 80 GHz and 50-60 GHz channels, and 5.2 degrees for the 23.8 and 31.4 GHz channels. Since the SNPP satellite is orbiting at an altitude of about 830 km, the instantaneous spatial resolution on the ground at nadir is about 16 km, 32 km, or 75 km depending upon the channel. The brightness temperature data are contained in an array with 135 rows in the along-track direction, 96 columns in the cross-track direction, and a 3rd dimension for each of the 22 channels. The ATMS cross-track scan interval is 0.018 seconds and the along-track scan period is 8/3 seconds. Data products are constructed on six minute boundaries. The ATMS (Advanced Technology Microwave Sounder) and CrIS (Crosstrack InfraRed Sounder) instruments are meant to operate together as a system, thus providing coverage of a much broader range of atmospheric conditions. The ATMS-CrIS system is referred to as CrIMSS (Cross-Track Infrared and Microwave Sounder Suite). If you were redirected to this page from a DOI from an older version, please note this is the current version of the product. Please contact the GES DISC user support if you need information about previous data collections. proprietary
SNPPATMSL1B_3 Suomi NPP ATMS Sounder Science Investigator-led Processing System (SIPS) Level 1B Brightness Temperature V3 (SNPPATMSL1B) at GES DISC GES_DISC STAC Catalog 2011-12-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1952167462-GES_DISC.umm_json The Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP). The ATMS instrument is a cross-track scanner with 22 microwave channels in the range 23.8-183.31 Gigahertz (GHz). The beam width is 1.1 degrees for the channels in the 160-183 GHz range, 2.2 degrees for the 80 GHz and 50-60 GHz channels, and 5.2 degrees for the 23.8 and 31.4 GHz channels. Since the SNPP satellite is orbiting at an altitude of about 830 km, the instantaneous spatial resolution on the ground at nadir is about 16 km, 32 km, or 75 km depending upon the channel. The brightness temperature data are contained in an array with 135 rows in the along-track direction, 96 columns in the cross-track direction, and a 3rd dimension for each of the 22 channels. The ATMS cross-track scan interval is 0.018 seconds and the along-track scan period is 8/3 seconds. Data products are constructed on six minute boundaries. The ATMS (Advanced Technology Microwave Sounder) and CrIS (Crosstrack InfraRed Sounder) instruments are meant to operate together as a system, thus providing coverage of a much broader range of atmospheric conditions. The ATMS-CrIS system is referred to as CrIMSS (Cross-Track Infrared and Microwave Sounder Suite). If you were redirected to this page from a DOI from an older version, please note this is the current version of the product. Please contact the GES DISC user support if you need information about previous data collections. proprietary
SNPPCrISL1BNSR_2 Suomi NPP CrIS Level 1B Normal Spectral Resolution V2 (SNPPCrISL1BNSR) at GES DISC GES_DISC STAC Catalog 2012-01-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1442068519-GES_DISC.umm_json The Cross-track Infrared Sounder (CrIS) Level 1B Normal Spectral Resolution (NSR) data files contain radiance measurements along with ancillary spacecraft, instrument, and geolocation data of the CrIS instrument on the Suomi National Polar-orbiting Partnership Project (SNPP). In December 2014, the CrIS instrument on the SNPP satellite doubled the spectral resolution of shortwave infrared data being transmitted to the ground. In November 2015, additional points were included at the ends of the longwave and shortwave interferograms to improve the quality of the calibration. Prior to November 2, 2015 the data are only available in Normal Spectral Resolution, after November 2, 2015 at 16:06 UTC, the data are available in both NSR and Full Spectral Resolution (FSR). The NSR files have 1,317 channels: 163 shortwave channels from 3.9 to 4.7 microns (2555 to 2150 cm-1), 437 midwave channels from 5.7 to 8.05 microns (1752.5 to 1242.5 cm-1), and 717 longwave channels from 9.1 to 15.41 microns (1096.25 to 648.75 cm-1). Each CrIS field-of-regard (FOR) contains 9 field-of-views (FOVs) arranged in a 3X3 array. The Level 1B files contain 30 FORs in the cross track direction and 45 in the along track direction. Data products are constructed on six minute boundaries. CrIS is designed to be used with the ATMS (Advanced Technology Microwave Sounder) instrument. Processing the data from both of these instruments together is referred to as CrIMSS (Cross-Track Infrared and Microwave Sounder Suite). If you were redirected to this page from a DOI from an older version, please note this is the current version of the product. Please contact the GES DISC user support if you need information about previous data collections. proprietary
@@ -14123,8 +14126,8 @@ SOE_elephant_seals_4 Annual population estimates of Southern Elephant Seals at M
SOE_fast_ice_thickness_1 Fast ice thickness at Davis, Mawson and Casey AU_AADC STAC Catalog 1954-01-01 1992-10-21 62.8738, -68.5766, 110.5276, -66.2818 https://cmr.earthdata.nasa.gov/search/concepts/C1214311316-AU_AADC.umm_json This indicator is no longer maintained, and is considered OBSOLETE. INDICATOR DEFINITION Regular measurements of the thickness of the fast ice, and of the snow cover that forms on it, are made through drilled holes at several sites near both Mawson and Davis. TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system. This indicator is one of: CONDITION RATIONALE FOR INDICATOR SELECTION Each season around the end of March, the ocean surface around Antarctica freezes to form sea ice. Close to the coast in some regions (e.g. near Mawson and Davis stations) this ice remains fastened to the land throughout the winter and is called fast ice. The thickness and growth rate of fast ice are determined purely by energy exchanges at the air-ice and ice-water interfaces. This contrasts with moving pack ice where deformational processes of rafting and ridging also determine the ice thickness. The maximum thickness that the fast ice reaches, and the date on which it reaches that maximum, represent an integration of the atmospheric and oceanic conditions. Changes in ice thickness represent changes in either oceanic or atmospheric heat transfer. Thicker fast ice reflects either a decrease in air temperature or decreasing oceanic heat flux. These effects can be extrapolated to encompass large-scale ocean-atmosphere processes and potentially, global climate change. DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial Scale: At sites near Australian Antarctic continental stations: Davis; Mawson. Frequency: at least weekly, reported annually Measurement Technique: Tape measurements through freshly drilled 5 cm diameter holes in the ice at marked sites. RESEARCH ISSUES To more effectively analyse the changes in Antarctic fast ice a detailed long-term dataset of sea ice conditions needs to be established. This would provide a baseline for future comparisons and contribute important data for climate modelling and aid the detection of changes that may occur due to climate or environmental change. LINKS TO OTHER INDICATORS SOE Indicator 1 - Monthly mean air temperatures at Australian Antarctic stations SOE Indicator 40 - Average sea surface temperatures in latitude bands 40-50oS, 50-60oS, 60oS-continent SOE Indicator 41 - Average sea surface salinity in latitude bands: 40-50oS, 50-60oS, 60oS-continent SOE Indicator 42 - Antarctic sea ice extent and concentration The fast ice data are also available as a direct download via the url given below. The data are in word documents, and are divided up by year and site (there are three sites (a,b,c) at each station). Snow thickness data have also been included. A pdf document detailing how the observations are collected is also available for download. proprietary
SOE_fur_seals_1 Environmental determinants of fecundity and pup growth in fur seals AU_AADC STAC Catalog 1990-01-01 1999-12-31 158.76343, -54.78327, 158.9653, -54.47882 https://cmr.earthdata.nasa.gov/search/concepts/C1214313694-AU_AADC.umm_json This indicator is no longer maintained, and is considered OBSOLETE. INDICATOR DEFINITION The fecundity (pupping rates) of female fur seals and the growth rates of their pups relative to changes in sea surface temperatures (local primary production) in the vicinity of Macquarie Island. TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system. This indicator is one of: CONDITION RATIONALE FOR INDICATOR SELECTION A highly negative correlation has been detected between sea surface temperatures in the vicinity of Macquarie Island and fur seal fecundity and pup growth. A dataset of over ten years has shown that autumn sea-surface temperatures are highly negatively correlated with female fecundity in the following breeding season. Rather than the reproductive success in terms of fecundity and pup growth being seen simply as a correlate of SST and presumably ocean productivity, the measure is much more than this. What the dataset from the Macquarie Island fur seal populations is rather more unique, in that they indicate how environmental variability effects the reproductive success of animals at annual and lifetime scales. This is especially important as we can now show what impacts environmental/climatic phenomena such as the Antarctic Circumpolar Wave, and global warming will have on fur seals, and how changes in the environment may impact on the viability of populations. In this situation, the data clearly suggest that warmer ocean temperatures significantly effect the reproductive success of fur seals. Sustained warmer temperatures would therefore impose demographic constraints on populations. DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial scale: SST data are obtained from a 1 degree square just north of the island that represents the region in which most females obtain food throughout their lactation period. Frequency: Data on the reproductive success of fur seals is to be collected annually. Measurement technique: Each breeding season (November-January), the reproductive success of tagged females is monitored, including their pupping success, and the growth rates of their pups. RESEARCH ISSUES LINKS TO OTHER INDICATORS proprietary
SOE_generator_boiler_fuel_usage_1 Monthly fuel usage of the generator sets and boilers at Australian Antarctic Stations AU_AADC STAC Catalog 1990-01-01 2016-02-29 62.8738, -68.5766, 158.8609, -54.6198 https://cmr.earthdata.nasa.gov/search/concepts/C1214311317-AU_AADC.umm_json INDICATOR DEFINITION The quantity of fuel used by generator sets and boilers at Casey, Davis, Mawson and Macquarie Island stations as measured on a monthly basis and reported in the monthly reports from the Station Plant Inspectors to the Kingston (Head Office) Mechanical Supervisor. TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system. This indicator is one of: PRESSURE RATIONALE FOR INDICATOR SELECTION The amount of fuel used in Antarctica for power generation and heating is proportional to environmental impact due to the emissions released. Special Antarctic Blend (SAB), a light diesel like fuel, is used at the stations to power the station generator sets, to provide heat through boilers, and to run plant and equipment including the station incinerator and vehicles. DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial scale: Australian Antarctic stations: Casey (lat 66 deg 16' 54.5& S, long 110 deg 31' 39.4& E), Davis (lat 68 deg 34' 35.8& S, long 77 deg 58' 02.6& E), Mawson (lat 67 deg 36' 09.7& S, long 62 deg 52' 25.7& E) and Macquarie Island (lat 54 deg 37' 59.9& S, long 158 deg 52' 59.9& E). Frequency: Monthly reports Measurement technique: The figures are obtained by direct reading of gauges on the stations on a regular basis. The data are recorded in the Plant Inspectors monthly reports. RESEARCH ISSUES In the future, it is planned to automate the collection of most of this data. LINKS TO OTHER INDICATORS SOE Indicator 1 - Monthly mean air temperatures at Australian Antarctic stations. SOE Indicator 2 - Highest monthly air temperatures at Australian Antarctic Stations SOE Indicator 3 - Lowest monthly air temperatures at Australian Antarctic Stations SOE Indicator 4 - Monthly mean lower stratospheric temperatures above Australian Antarctic Stations SOE Indicator 7 - Monthly mean of three-hourly wind speeds (m/s) SOE Indicator 48 - Station and ship person days SOE Indicator 57 - Monthly total of fuel used by station incinerators SOE Indicator 58 - Monthly total of fuel used by station vehicles SOE Indicator 59 - Monthly electricity usage SOE Indicator 60 - Total helicopter hours SOE Indicator 61 - Total potable water consumption SOE Indicator 65 - Station footprint for Australian Antarctic stations proprietary
-SOE_greenhouse_gas_1 Air sampling for greenhouse gas concentrations and associated species AU_AADC STAC Catalog 1984-11-01 62, -90, 159, -41 https://cmr.earthdata.nasa.gov/search/concepts/C1214311318-AU_AADC.umm_json INDICATOR DEFINITION Measurement of air samples for values of the primary greenhouse gases (carbon dioxide, methane and nitrous oxide) and associated species (carbon monoxide, hydrogen and isotopes of carbon dioxide) in the Southern Hemisphere atmosphere. TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system. This indicator is one of: CONDITION RATIONALE FOR INDICATOR SELECTION Over the last century the concentration of greenhouse gases has risen in the atmosphere. The average rise is about half that expected from human activities, predominantly the burning of fossil fuel. Thus observations of the concentration of these gases provides a measure of anthropogenic greenhouse forcing in the atmosphere, and for example, monitors the effectiveness of oceans and terrestrial biomes in removing the excess CO2. Measurements of long-lived trace gas levels in Antarctic air generally provide an accurate integration of global exchanges between the surface and the atmosphere. The climate-influencing gases of main interest are gases released as a result of human activity, as well as from (climate-driven) physical, chemical and biological processes in both land and oceans. The Antarctic monitoring, in concert with other global network results, exploits trace gas ratios to identify and locate globally significant exchanges. DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial Scale: High latitude Southern Hemisphere air samples are collected from AAD sites by BoM personnel at Mawson station, Casey station and Macquarie Island, and by NOAA staff at South Pole. These complement CSIRO supervised sites at Cape Grim, Tasmania and ~7 other globally distributed locations. Frequency: Typical sites collect ~4 flasks of air per month for subsequent analysis at CSIRO. Measurement Technique: Various chemical analysis techniques (Francey et al. 1996). RESEARCH ISSUES For global trace gas monitoring, improvements are sought in network intercalibration and in increased sampling, e.g. continuous CO2 monitoring, vertical profiles, continental sites. More generally, improved coordination of atmospheric composition modeling, surface flux measurements and atmospheric transport representations are sought to serve new 'multiple-constraint modeling frameworks'. LINKS TO OTHER INDICATORS Monthly averages of daily maximum and minimum temperatures for Australian Antarctic Stations Mean sea level Average Summer chlorophyll concentrations in the Southern Ocean, from latitude bands 40-50 deg S, 50-60 deg S, 60 deg S-continent Average sea surface temperatures in latitude bands 40-50 deg S, 50-60 deg S, 60 deg S-continent Antarctic sea ice extent and concentration proprietary
SOE_greenhouse_gas_1 Air sampling for greenhouse gas concentrations and associated species ALL STAC Catalog 1984-11-01 62, -90, 159, -41 https://cmr.earthdata.nasa.gov/search/concepts/C1214311318-AU_AADC.umm_json INDICATOR DEFINITION Measurement of air samples for values of the primary greenhouse gases (carbon dioxide, methane and nitrous oxide) and associated species (carbon monoxide, hydrogen and isotopes of carbon dioxide) in the Southern Hemisphere atmosphere. TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system. This indicator is one of: CONDITION RATIONALE FOR INDICATOR SELECTION Over the last century the concentration of greenhouse gases has risen in the atmosphere. The average rise is about half that expected from human activities, predominantly the burning of fossil fuel. Thus observations of the concentration of these gases provides a measure of anthropogenic greenhouse forcing in the atmosphere, and for example, monitors the effectiveness of oceans and terrestrial biomes in removing the excess CO2. Measurements of long-lived trace gas levels in Antarctic air generally provide an accurate integration of global exchanges between the surface and the atmosphere. The climate-influencing gases of main interest are gases released as a result of human activity, as well as from (climate-driven) physical, chemical and biological processes in both land and oceans. The Antarctic monitoring, in concert with other global network results, exploits trace gas ratios to identify and locate globally significant exchanges. DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial Scale: High latitude Southern Hemisphere air samples are collected from AAD sites by BoM personnel at Mawson station, Casey station and Macquarie Island, and by NOAA staff at South Pole. These complement CSIRO supervised sites at Cape Grim, Tasmania and ~7 other globally distributed locations. Frequency: Typical sites collect ~4 flasks of air per month for subsequent analysis at CSIRO. Measurement Technique: Various chemical analysis techniques (Francey et al. 1996). RESEARCH ISSUES For global trace gas monitoring, improvements are sought in network intercalibration and in increased sampling, e.g. continuous CO2 monitoring, vertical profiles, continental sites. More generally, improved coordination of atmospheric composition modeling, surface flux measurements and atmospheric transport representations are sought to serve new 'multiple-constraint modeling frameworks'. LINKS TO OTHER INDICATORS Monthly averages of daily maximum and minimum temperatures for Australian Antarctic Stations Mean sea level Average Summer chlorophyll concentrations in the Southern Ocean, from latitude bands 40-50 deg S, 50-60 deg S, 60 deg S-continent Average sea surface temperatures in latitude bands 40-50 deg S, 50-60 deg S, 60 deg S-continent Antarctic sea ice extent and concentration proprietary
+SOE_greenhouse_gas_1 Air sampling for greenhouse gas concentrations and associated species AU_AADC STAC Catalog 1984-11-01 62, -90, 159, -41 https://cmr.earthdata.nasa.gov/search/concepts/C1214311318-AU_AADC.umm_json INDICATOR DEFINITION Measurement of air samples for values of the primary greenhouse gases (carbon dioxide, methane and nitrous oxide) and associated species (carbon monoxide, hydrogen and isotopes of carbon dioxide) in the Southern Hemisphere atmosphere. TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system. This indicator is one of: CONDITION RATIONALE FOR INDICATOR SELECTION Over the last century the concentration of greenhouse gases has risen in the atmosphere. The average rise is about half that expected from human activities, predominantly the burning of fossil fuel. Thus observations of the concentration of these gases provides a measure of anthropogenic greenhouse forcing in the atmosphere, and for example, monitors the effectiveness of oceans and terrestrial biomes in removing the excess CO2. Measurements of long-lived trace gas levels in Antarctic air generally provide an accurate integration of global exchanges between the surface and the atmosphere. The climate-influencing gases of main interest are gases released as a result of human activity, as well as from (climate-driven) physical, chemical and biological processes in both land and oceans. The Antarctic monitoring, in concert with other global network results, exploits trace gas ratios to identify and locate globally significant exchanges. DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial Scale: High latitude Southern Hemisphere air samples are collected from AAD sites by BoM personnel at Mawson station, Casey station and Macquarie Island, and by NOAA staff at South Pole. These complement CSIRO supervised sites at Cape Grim, Tasmania and ~7 other globally distributed locations. Frequency: Typical sites collect ~4 flasks of air per month for subsequent analysis at CSIRO. Measurement Technique: Various chemical analysis techniques (Francey et al. 1996). RESEARCH ISSUES For global trace gas monitoring, improvements are sought in network intercalibration and in increased sampling, e.g. continuous CO2 monitoring, vertical profiles, continental sites. More generally, improved coordination of atmospheric composition modeling, surface flux measurements and atmospheric transport representations are sought to serve new 'multiple-constraint modeling frameworks'. LINKS TO OTHER INDICATORS Monthly averages of daily maximum and minimum temperatures for Australian Antarctic Stations Mean sea level Average Summer chlorophyll concentrations in the Southern Ocean, from latitude bands 40-50 deg S, 50-60 deg S, 60 deg S-continent Average sea surface temperatures in latitude bands 40-50 deg S, 50-60 deg S, 60 deg S-continent Antarctic sea ice extent and concentration proprietary
SOE_incinerated_waste_1 Amount of incinerated waste from Australian Antarctic Stations AU_AADC STAC Catalog 1999-01-01 62.8738, -68.5766, 158.8609, -54.6198 https://cmr.earthdata.nasa.gov/search/concepts/C1214311277-AU_AADC.umm_json INDICATOR DEFINITION This indicator identifies the total weight of material incinerated, and the weights of the major components on Casey, Davis, Mawson and Macquarie Island stations. The figures are reported monthly, in the station plumbers' reports to the Building Services Supervisor in Kingston, and to the Operations Environment Officer. TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system. This indicator is one of: PRESSURE RATIONALE FOR INDICATOR SELECTION Waste minimization is an important element of Australia's Antarctic program, so the total weight of waste produced, and any trends, provide an important management tool. Approximately 10% of waste is incinerated, so incineration statistics are an important part of this assessment. A separate aim of Australia's program is reduction in the amount of material incinerated on the stations, either reduction in the amounts of certain materials sent to the stations or by diverting materials from incineration to reuse or recycling. In either case it will be necessary to target individual materials incinerated, as different materials are likely to respond to different management practices. To properly target these materials it is important to know the amounts of each of the materials incinerated, and trends over time. DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial scale: Australian Antarctic continental stations and Macquarie Island station. Frequency: Weights are recorded each time materials are incinerated, which is every few days in winter and daily in summer and reported monthly. Measurement technique: Weights are recorded for the following categories: (1) food scraps, (2) spoiled fruit and vegetables, (3) wood and wood products (not treated wood), (4) cardboard, (5) paper products (poor quality paper, books and magazines), (6) medical waste, (7) science waste, (8) hydroponics waste, (9) human waste from the field and (10) miscellaneous. In addition, weights of specific materials may be recorded separately, if burnt in unusually large amounts, for example if large amounts of particular types of fruit and vegetables have been spoiled. RESEARCH ISSUES Chemical analysis of emissions as a pollution index and also to assess the efficiency of the burn. This information could be used to indicate the need to change the components of burns or to adjust the equipment. It may also highlight the release of toxic materials into the atmosphere which may be overcome by eliminating certain materials from incineration. A major audit of total waste production, leading to recommendations on how to achieve maximum waste reduction. LINKS TO OTHER INDICATORS SOE Indicator 47 - Number and nature of incidents resulting in environmental impact SOE Indicator 48 - Station and ship person days SOE Indicator 49 - Medical consultations per 1000 person years SOE Indicator 53 - Recycled and quarantine waste returned to Australia SOE Indicator 57 - Monthly total of fuel used by station incinerators SOE Indicator 69 - Resources committed to environmental issues proprietary
SOE_incinerator_fuel_usage_1 Monthly incinerator fuel usage of Australian Antarctic Stations AU_AADC STAC Catalog 1995-01-01 2016-02-29 62.8738, -68.5766, 158.8609, -54.6198 https://cmr.earthdata.nasa.gov/search/concepts/C1214311321-AU_AADC.umm_json INDICATOR DEFINITION The quantity of fuel used for incinerators at Casey, Davis, Mawson and Macquarie Island stations as measured on a monthly basis and reported in the monthly reports from the Station Plant Inspectors to the Kingston (Head Office) Mechanical Supervisor. TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system. This indicator is one of: PRESSURE RATIONALE FOR INDICATOR SELECTION The amount of fuel used in Antarctica for waste incineration contributes to environmental impact due to the emissions released. Special Antarctic Blend (SAB), a light diesel like fuel, is used at the stations for the incinerators. DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial scale: Australian Antarctic stations: Casey (lat 66 deg 16' 54.5& S, long 110 deg 31' 39.4& E), Davis (lat 68 deg 34' 35.8& S, long 77 deg 58' 02.6& E), Mawson (lat 67 deg 36' 09.7& S, long 62 deg 52' 25.7& E) and Macquarie Island (lat 54 deg 37' 59.9& S, long 158 deg 52' 59.9& E). Frequency: Monthly reports Measurement technique: The figures are obtained by direct reading of gauges on the stations on a regular basis. The data are recorded in the Plant Inspectors monthly reports. RESEARCH ISSUES In the future, it is planned to automate the collection of most of this data. LINKS TO OTHER INDICATORS SOE Indicator 47 - Number and nature of incidents resulting in environmental impact SOE Indicator 48 - Station and ship person days SOE Indicator 53 - Recycled and quarantine waste returned to Australia SOE Indicator 54 - Amount of waste incinerated at Australian stations SOE Indicator 56 - Monthly fuel usage of the generator sets and boilers SOE Indicator 58 - Monthly total of fuel used by station vehicles SOE Indicator 60 - Total helicopter hours proprietary
SOE_low_strato_1 Monthly mean lower stratospheric temperatures above Australian Antarctic stations. AU_AADC STAC Catalog 1948-04-01 2019-02-01 61, -69, 159, -54 https://cmr.earthdata.nasa.gov/search/concepts/C1214313695-AU_AADC.umm_json "INDICATOR DEFINITION Monthly means of daily temperatures at the 100hPa level (lower stratosphere), from radiosonde soundings above Australian Antarctic stations Casey, Davis, Mawson and Macquarie Island. TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system. This indicator is one of: CONDITION RATIONALE FOR INDICATOR SELECTION Global climate models show warming in response to increased greenhouse gas (carbon dioxide, methane etc) concentrations in the atmosphere; this is called the 'enhanced greenhouse effect'. There is interest in climate variability and change not just at the surface, but extending up into the atmosphere. There is evidence of warming in the lower troposphere, but cooling in the lower stratosphere. Ozone depletion processes are also closely linked to stratospheric temperatures. DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial Scale: Australian Antarctic stations: Casey (lat 66 degrees 16' 54.5"" S, long 110 degrees 31' 39.4"" E), Davis (lat 68 degrees 34' 35.8"" S, long 77 degrees 58' 02.6"" E), Mawson (lat 67 degrees 36' 09.7"" S, long 62 degrees 52' 25.7"" E) and Macquarie Island (lat 54 degrees 37' 59.9"" S, long 158 degrees 52' 59.9"" E). Temporal scale: Monthly. Measurement technique: Radiosonde. RESEARCH ISSUES There is need to develop a high-quality data set from the available data, correcting erroneous data and estimating missing data. Adjustment may be necessary for changes in instrumentation or observing practices. Some of these changes are documented in the station history files held by the Regional Observations Section. These history files are currently held as paper records, although more recent information is held electronically and there is an effort to digitise the older records. Before the data can be used for the detection of change, a concerted effort will need to be made to identify deficiencies in the data, and then make compensations where possible. This is made more difficult by the lack of suitable comparison sites. Over recent years satellite data exist, which could be used in conjunction with radiosonde data. Satellite data and radiosonde data from other nations should lead to a greater coverage. LINKS TO OTHER INDICATORS SOE Indicators 1 - Monthly mean air temperatures for Australian Antarctic Stations SOE Indicators 2 - Monthly highest air temperatures for Australian Antarctic Stations SOE Indicators 3 - Monthly lowest air temperatures for Australian Antarctic Stations SOE Indicators 5 - Monthly mean mid-tropospheric temperature above Australian Antarctic stations SOE Indicators 6 - Daily mean 10m Firn Temperatures at AWS sites in the AAT (deg C) SOE Indicators 8 - Monthly mean of three-hourly mean sea level pressures (hPa) SOE Indicators 11 - Atmospheric concentrations of greenhouse gas species SOE Indicators 12 - Noctilucent cloud observations at Davis SOE Indicators 13 - Polar stratospheric cloud observations at Davis SOE Indicators 14 - Midwinter atmospheric temperature at altitude 87km SOE Indicators 16 - Extent of summer surface glacial melt (sq km) SOE Indicators 42 - Antarctic sea ice extent and concentration SOE Indicators 43 - Fast ice thickness at Davis and Mawson SOE Indicators 56 - Monthly fuel usage of the generator sets and boilers SOE Indicators 59 - Monthly electricity usage Note - Station codes in the data are as follows: 300000 - Davis 300001 - Mawson 300004 - Macquarie Island 300017 - Casey The fields in this dataset are: Mean 100hPa Temperature Year Month Station Station Code Value Enough Observations Number Observations" proprietary
@@ -14204,8 +14207,8 @@ SOR4XPSD_LOW_012 SORCE XPS Level 4 Solar Spectral Irradiance 1.0nm Res 24-Hour M
SORTIE_0 Spectral Ocean Radiance Transfer Investigation and Experiment (SORTIE) program OB_DAAC STAC Catalog 2007-03-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360665-OB_DAAC.umm_json Measurements made under the SORTIE (Spectral Ocean Radiance Transfer Investigation and Experiment) program between 2007 and 2009. proprietary
SPACE_PHOTOS Space Acquired Photography USGS_LTA STAC Catalog 1965-03-23 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220566702-USGS_LTA.umm_json Gemini photography was acquired between March 23, 1965 and November 15, 1966. The images were collected as part of the Synoptic Terrain Photography and the Synoptic Weather Photography experiments during Gemini Missions III through XII. Hand-held cameras were used to obtain photographs of geologic, oceanic, and meteorologic targets. The Gemini archive consists primarily of 70-mm black and white (B/W), color, and color-infrared (CIR) film. All Gemini photographs are distributed by the USGS Earth Resources Observation and Science (EROS) Center as digital products only. Skylab photography was acquired between May 22, 1973 and February 8, 1974 during three manned flights. The Skylab Earth Resources Experiment Package used two photographic remote sensing systems. The Multispectral Photographic Camera (S190A), was a six-camera array, in which each camera used 70-mm film and a six-inch focal length lens. The acquired film ranges from narrow-band B/W to broad-band color and CIR. The Earth Terrain Camera (S190B) consisted of a single high-resolution camera which used five-inch film and an 18-inch focal length lens. The acquired film includes B/W, black and white infrared (BIR), color, and CIR. All Skylab photographs are distributed by the USGS EDC as digital products only. Shuttle Large Format Camera (LFC) images were acquired from the Space Shuttle Challenger Mission on October 5-13, 1984. The LFC was mounted in the cargo bay, and was operated via signals from ground controllers. The archived imagery includes 9 x 18 inch B/W, natural color, and CIR film. Shuttle LFC photographs are distributed by the USGS EDC as digital products only. proprietary
SPANBR Automatic Atmospheric Sun Photometer Data for Brazil CEOS_EXTRA STAC Catalog 1992-06-01 -65, -28, -45, -2 https://cmr.earthdata.nasa.gov/search/concepts/C2227456147-CEOS_EXTRA.umm_json A network of 9 automatic sunphotometers operates in Brazil. Direct sun and sky radiances are acquired every hour by a weather resistant Cimel spectral radiometer in the wavelengths of 340, 440, 670,870, 940, and 1020 nm and transmitted automatically through the NOAA data collection system geostationary link for near real-time processing into spectral aerosol optical thickness, wavelength exponent and precipitable water. Evaluation of the atmospheric effects of biomass burning emissions from June-November are among the primary targets of the measurements. ftp://ftp.pmel.noaa.gov proprietary
-SPL1AP_002 SMAP L1A Radiometer Time-Ordered Parsed Telemetry V002 NSIDC_ECS STAC Catalog 2015-03-31 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C1000001801-NSIDC_ECS.umm_json Each Level-1A (L1A) granule incorporates all radiometer data downlinked from the Soil Moisture Active Passive (SMAP) spacecraft for one specific half orbit. The data are scaled instrument counts of the following:
- The first four raw moments of the fullband channel for both vertical and horizontal polarizations
- The complex cross-correlations of the fullband channel
- The 16 subband channels for both vertical and horizontal polarizations
proprietary
SPL1AP_002 SMAP L1A Radiometer Time-Ordered Parsed Telemetry V002 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2938661641-NSIDC_CPRD.umm_json Each Level-1A (L1A) granule incorporates all radiometer data downlinked from the Soil Moisture Active Passive (SMAP) spacecraft for one specific half orbit. The data are scaled instrument counts of the following:
- The first four raw moments of the fullband channel for both vertical and horizontal polarizations
- The complex cross-correlations of the fullband channel
- The 16 subband channels for both vertical and horizontal polarizations
proprietary
+SPL1AP_002 SMAP L1A Radiometer Time-Ordered Parsed Telemetry V002 NSIDC_ECS STAC Catalog 2015-03-31 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C1000001801-NSIDC_ECS.umm_json Each Level-1A (L1A) granule incorporates all radiometer data downlinked from the Soil Moisture Active Passive (SMAP) spacecraft for one specific half orbit. The data are scaled instrument counts of the following:
- The first four raw moments of the fullband channel for both vertical and horizontal polarizations
- The complex cross-correlations of the fullband channel
- The 16 subband channels for both vertical and horizontal polarizations
proprietary
SPL1A_001_1 SMAP_L1A_RADAR_V001 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214473171-ASF.umm_json SMAP Level 1A Radar Product proprietary
SPL1A_002_2 SMAP_L1A_RADAR_V002 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243149604-ASF.umm_json SMAP Level 1A Radar Product Version 2 proprietary
SPL1A_METADATA_001_1 SMAP_L1A_RADAR_METADATA_V001 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214473426-ASF.umm_json SMAP Level 1A Radar Product Metadata proprietary
@@ -14221,9 +14224,9 @@ SPL1A_RO_METADATA_003_3 SMAP_L1A_RADAR_RECEIVE_ONLY_METADATA_V003 ASF STAC Catal
SPL1A_RO_QA_001_1 SMAP_L1A_RADAR_RECEIVE_ONLY_QA_V001 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243168733-ASF.umm_json SMAP Level 1A Radar Receive Only Data Quality Information Version 1 proprietary
SPL1A_RO_QA_002_2 SMAP_L1A_RADAR_RECEIVE_ONLY_QA_V002 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243168866-ASF.umm_json SMAP Level 1A Radar Receive Only Data Quality Information Version 2 proprietary
SPL1A_RO_QA_003_3 SMAP_L1A_RADAR_RECEIVE_ONLY_QA_V003 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243124139-ASF.umm_json SMAP Level 1A Radar Receive Only Data Quality Information Version 3 proprietary
-SPL1BTB_006 SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2776463679-NSIDC_ECS.umm_json This Level-1B (L1B) product provides calibrated estimates of time-ordered geolocated brightness temperatures measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band brightness temperatures are referenced to the Earth's surface with undesired and erroneous radiometric sources removed. proprietary
SPL1BTB_006 SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2938661904-NSIDC_CPRD.umm_json This Level-1B (L1B) product provides calibrated estimates of time-ordered geolocated brightness temperatures measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band brightness temperatures are referenced to the Earth's surface with undesired and erroneous radiometric sources removed. proprietary
-SPL1BTB_NRT_105 Near Real-time SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V105 NSIDC_ECS STAC Catalog 2024-12-05 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2257958430-NSIDC_ECS.umm_json "This Near Real-Time (NRT) data set corresponds to the standard SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures (SPL1BTB) product. The data provide calibrated estimates of time-ordered geolocated brightness temperature data measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product, SPL1BTB (https://doi.org/10.5067/ZHHBN1KQLI20)." proprietary
+SPL1BTB_006 SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2776463679-NSIDC_ECS.umm_json This Level-1B (L1B) product provides calibrated estimates of time-ordered geolocated brightness temperatures measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band brightness temperatures are referenced to the Earth's surface with undesired and erroneous radiometric sources removed. proprietary
+SPL1BTB_NRT_105 Near Real-time SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures V105 NSIDC_ECS STAC Catalog 2024-12-23 -180, -86.4, 180, 86.4 https://cmr.earthdata.nasa.gov/search/concepts/C2257958430-NSIDC_ECS.umm_json "This Near Real-Time (NRT) data set corresponds to the standard SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures (SPL1BTB) product. The data provide calibrated estimates of time-ordered geolocated brightness temperature data measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product, SPL1BTB (https://doi.org/10.5067/ZHHBN1KQLI20)." proprietary
SPL1B_SO_LoRes_001_1 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_V001 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214473308-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Product proprietary
SPL1B_SO_LoRes_002_2 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_V002 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243253631-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Product Version 2 proprietary
SPL1B_SO_LoRes_003_3 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_V003 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243133445-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Product Version 3 proprietary
@@ -14235,8 +14238,8 @@ SPL1B_SO_LoRes_QA_002_2 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_QA_V002 ASF STAC Catalog 2
SPL1B_SO_LoRes_QA_003_3 SMAP_L1B_SIGMA_NAUGHT_LOW_RES_QA_V003 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243129847-ASF.umm_json SMAP Level 1B Sigma Naught Low Res Data Quality Info Version 3 proprietary
SPL1CTB_006 SMAP L1C Radiometer Half-Orbit 36 km EASE-Grid Brightness Temperatures V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938663268-NSIDC_CPRD.umm_json This Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP L-band Level-1B time-ordered brightness temperatures resampled to an Earth-fixed, 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. This L1C product is a gridded version of the SMAP time-ordered Level-1B radiometer brightness temperature product. proprietary
SPL1CTB_006 SMAP L1C Radiometer Half-Orbit 36 km EASE-Grid Brightness Temperatures V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463699-NSIDC_ECS.umm_json This Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP L-band Level-1B time-ordered brightness temperatures resampled to an Earth-fixed, 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. This L1C product is a gridded version of the SMAP time-ordered Level-1B radiometer brightness temperature product. proprietary
-SPL1CTB_E_004 SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures V004 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463717-NSIDC_ECS.umm_json This enhanced Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract enhanced information from SMAP antenna temperatures before they are converted to brightness temperatures. The resulting brightness temperatures are posted to an Earth-fixed, 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. proprietary
SPL1CTB_E_004 SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures V004 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938663435-NSIDC_CPRD.umm_json This enhanced Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract enhanced information from SMAP antenna temperatures before they are converted to brightness temperatures. The resulting brightness temperatures are posted to an Earth-fixed, 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. proprietary
+SPL1CTB_E_004 SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures V004 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463717-NSIDC_ECS.umm_json This enhanced Level-1C (L1C) product contains calibrated and geolocated brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract enhanced information from SMAP antenna temperatures before they are converted to brightness temperatures. The resulting brightness temperatures are posted to an Earth-fixed, 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in three projections: global cylindrical, Northern Hemisphere azimuthal, and Southern Hemisphere azimuthal. proprietary
SPL1C_S0_HiRes_001_1 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_V001 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214473367-ASF.umm_json SMAP Level 1C Sigma Naught High Res Product proprietary
SPL1C_S0_HiRes_002_2 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_V002 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243268956-ASF.umm_json SMAP Level 1C Sigma Naught High Res Product Version 2 proprietary
SPL1C_S0_HiRes_003_3 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_V003 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243144528-ASF.umm_json SMAP Level 1C Sigma Naught High Res Product Version 3 proprietary
@@ -14248,35 +14251,35 @@ SPL1C_S0_HiRes_QA_002_2 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_QA_V002 ASF STAC Catalog
SPL1C_S0_HiRes_QA_003_3 SMAP_L1C_SIGMA_NAUGHT_HIGH_RES_QA_V003 ASF STAC Catalog 2015-02-12 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1243140611-ASF.umm_json SMAP Level 1C Sigma Naught High Res Data Quality Info Version 3 proprietary
SPL2SMAP_003 SMAP L2 Radar/Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303829-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer during 6:00 a.m. descending half-orbit passes. SMAP L-band backscatter and brightness temperatures are used to derive soil moisture data, which are then resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary
SPL2SMAP_003 SMAP L2 Radar/Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2830464428-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer during 6:00 a.m. descending half-orbit passes. SMAP L-band backscatter and brightness temperatures are used to derive soil moisture data, which are then resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary
-SPL2SMAP_S_003 SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-03-31 -180, -60, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution. proprietary
SPL2SMAP_S_003 SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -60, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2938663471-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution. proprietary
+SPL2SMAP_S_003 SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-03-31 -180, -60, 180, 60 https://cmr.earthdata.nasa.gov/search/concepts/C1931663473-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes and the Sentinel-1A and -1B radar. SMAP L-band brightness temperatures and Copernicus Sentinel-1 C-band backscatter coefficients are used to derive soil moisture data, which are then resampled to an Earth-fixed, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). While the 3 km data product has undergone validation, the 1 km product has not and should be used with caution. proprietary
SPL2SMA_003 SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2812935277-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary
SPL2SMA_003 SMAP L2 Radar Half-Orbit 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303826-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) active radar during 6:00 a.m. descending half-orbit passes, as well as ancillary data such as surface temperature and vegetation water content. Input backscatter data used to derive soil moisture are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary
SPL2SMP_009 SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V009 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938663609-NSIDC_CPRD.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data. proprietary
SPL2SMP_009 SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V009 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463734-NSIDC_ECS.umm_json This Level-2 (L2) soil moisture product provides estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are resampled to an Earth-fixed, global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) [and made available as the SPL1CTB product], and the gridded brightness temperatures are then used to derive gridded soil moisture data. proprietary
-SPL2SMP_E_006 SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.umm_json This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product]. proprietary
SPL2SMP_E_006 SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776463773-NSIDC_ECS.umm_json This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product]. proprietary
-SPL2SMP_NRT_107 Near Real-time SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V107 NSIDC_ECS STAC Catalog 2024-12-05 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2312096175-NSIDC_ECS.umm_json "This Near Real-Time (NRT) data set corresponds to the standard SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture (SPL2SMP) product. The data provide estimates of global land surface conditions measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product SPL2SMP (https://doi.org/10.5067/LPJ8F0TAK6E0)." proprietary
+SPL2SMP_E_006 SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2938663676-NSIDC_CPRD.umm_json This enhanced Level-2 (L2) product contains calibrated, geolocated, brightness temperatures acquired by the Soil Moisture Active Passive (SMAP) radiometer during 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP Level-1B (L1B) interpolated antenna temperatures. Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection [available as the SPl1CTB_E product]. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. These 9-km brightness temperatures are then used to retrieve surface soil moisture posted on the 9-km grid [this SPL2SMP_E product]. proprietary
+SPL2SMP_NRT_107 Near Real-time SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture V107 NSIDC_ECS STAC Catalog 2024-12-23 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2312096175-NSIDC_ECS.umm_json "This Near Real-Time (NRT) data set corresponds to the standard SMAP L2 Radiometer Half-Orbit 36 km EASE-Grid Soil Moisture (SPL2SMP) product. The data provide estimates of global land surface conditions measured by the Soil Moisture Active Passive (SMAP) passive microwave radiometer, the SMAP L-band radiometer. These Near Real-Time data are available within three hours of satellite observation. The data are created using the latest available ancillary data and spacecraft and antenna attitude data to reduce latency. The SMAP satellite orbits Earth every two to three days, providing half-orbit, ascending and descending, coverage from 86.4°S to 86.4°N in swaths 1000 km across. Data are stored for approximately two to three weeks. Thus, at any given time, users have access to at least fourteen consecutive days of Near Real-Time data through the NSIDC DAAC. Users deciding between the NRT and standard SMAP products should consider the immediacy of their needs versus the quality of the data required. Near real-time data are provided for operational needs whereas standard products meet the quality needs of scientific research. If latency is not a primary concern, users are encouraged to use the standard science product SPL2SMP (https://doi.org/10.5067/LPJ8F0TAK6E0)." proprietary
SPL3FTA_003 SMAP L3 Radar Northern Hemisphere Daily 3 km EASE-Grid Freeze/Thaw State V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, 45, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303849-NSIDC_ECS.umm_json This Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band backscatter data are used to derive freeze/thaw data, which are then resampled to an Earth-fixed, Northern Hemisphere azimuthal 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary
SPL3FTA_003 SMAP L3 Radar Northern Hemisphere Daily 3 km EASE-Grid Freeze/Thaw State V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, 45, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2872766057-NSIDC_CPRD.umm_json This Level-3 (L3) product provides a daily composite of Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radar from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band backscatter data are used to derive freeze/thaw data, which are then resampled to an Earth-fixed, Northern Hemisphere azimuthal 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary
-SPL3FTP_004 SMAP L3 Radiometer Global and Northern Hemisphere Daily 36 km EASE-Grid Freeze/Thaw State V004 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938664170-NSIDC_CPRD.umm_json This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0. proprietary
SPL3FTP_004 SMAP L3 Radiometer Global and Northern Hemisphere Daily 36 km EASE-Grid Freeze/Thaw State V004 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463838-NSIDC_ECS.umm_json This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0. proprietary
-SPL3FTP_E_004 SMAP Enhanced L3 Radiometer Global and Northern Hemisphere Daily 9 km EASE-Grid Freeze/Thaw State V004 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463920-NSIDC_ECS.umm_json This enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP enhanced Level-1C brightness temperatures (SPL1CTB_E). Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures. The data are then posted to two 9 km Earth-fixed, Equal-Area Scalable Earth Grids, Version 2.0 (EASE-Grid 2.0): a global cylindrical and a Northern Hemisphere azimuthal. proprietary
+SPL3FTP_004 SMAP L3 Radiometer Global and Northern Hemisphere Daily 36 km EASE-Grid Freeze/Thaw State V004 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938664170-NSIDC_CPRD.umm_json This Level-3 (L3) product provides a daily composite of landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. SMAP L-band brightness temperatures are used to derive freeze/thaw state and transition data, which are then resampled to both an Earth-fixed, Northern Hemisphere azimuthal 36 km Equal-Area Scalable Earth Grid (EASE-Grid 2.0), and to an Earth-fixed global 36 km EASE-Grid 2.0. proprietary
SPL3FTP_E_004 SMAP Enhanced L3 Radiometer Global and Northern Hemisphere Daily 9 km EASE-Grid Freeze/Thaw State V004 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938664439-NSIDC_CPRD.umm_json This enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP enhanced Level-1C brightness temperatures (SPL1CTB_E). Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures. The data are then posted to two 9 km Earth-fixed, Equal-Area Scalable Earth Grids, Version 2.0 (EASE-Grid 2.0): a global cylindrical and a Northern Hemisphere azimuthal. proprietary
+SPL3FTP_E_004 SMAP Enhanced L3 Radiometer Global and Northern Hemisphere Daily 9 km EASE-Grid Freeze/Thaw State V004 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463920-NSIDC_ECS.umm_json This enhanced Level-3 (L3) product provides a daily composite of global and Northern Hemisphere landscape freeze/thaw conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer from 6:00 a.m. descending and 6:00 p.m. ascending half-orbit passes. This product is derived from SMAP enhanced Level-1C brightness temperatures (SPL1CTB_E). Backus-Gilbert optimal interpolation techniques are used to extract maximum information from SMAP antenna temperatures and convert them to brightness temperatures. The data are then posted to two 9 km Earth-fixed, Equal-Area Scalable Earth Grids, Version 2.0 (EASE-Grid 2.0): a global cylindrical and a Northern Hemisphere azimuthal. proprietary
SPL3SMAP_003 SMAP L3 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2872767144-NSIDC_CPRD.umm_json This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary
SPL3SMAP_003 SMAP L3 Radar/Radiometer Global Daily 9 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303847-NSIDC_ECS.umm_json This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by both the Soil Moisture Active Passive (SMAP) radar and radiometer. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary
-SPL3SMA_003 SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303828-NSIDC_ECS.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary
SPL3SMA_003 SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture V003 NSIDC_CPRD STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2872766452-NSIDC_CPRD.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary
+SPL3SMA_003 SMAP L3 Radar Global Daily 3 km EASE-Grid Soil Moisture V003 NSIDC_ECS STAC Catalog 2015-04-13 2015-07-07 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C1236303828-NSIDC_ECS.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary
SPL3SMP_009 SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture V009 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2776463935-NSIDC_ECS.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary
SPL3SMP_009 SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture V009 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938664585-NSIDC_CPRD.umm_json This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) passive microwave radiometer. SMAP L-band soil moisture data are resampled to a global, cylindrical 36 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0). proprietary
-SPL3SMP_E_006 SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776463943-NSIDC_ECS.umm_json This enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures. Backus-Gilbert optimal interpolation techniques are used to extract information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. proprietary
SPL3SMP_E_006 SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture V006 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2938664763-NSIDC_CPRD.umm_json This enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures. Backus-Gilbert optimal interpolation techniques are used to extract information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. proprietary
+SPL3SMP_E_006 SMAP Enhanced L3 Radiometer Global and Polar Grid Daily 9 km EASE-Grid Soil Moisture V006 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2776463943-NSIDC_ECS.umm_json This enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures. Backus-Gilbert optimal interpolation techniques are used to extract information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection. proprietary
SPL4CMDL_007 SMAP L4 Global Daily 9 km EASE-Grid Carbon Net Ecosystem Exchange V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938665243-NSIDC_CPRD.umm_json The Level-4 (L4) carbon product (SPL4CMDL) provides global gridded daily estimates of net ecosystem carbon (CO2) exchange derived using a satellite data based terrestrial carbon flux model informed by the following: Soil Moisture Active Passive (SMAP) L-band microwave observations, land cover and vegetation inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Goddard Earth Observing System Model, Version 5 (GEOS-5) land model assimilation system. Parameters are computed using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary
SPL4CMDL_007 SMAP L4 Global Daily 9 km EASE-Grid Carbon Net Ecosystem Exchange V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2534576405-NSIDC_ECS.umm_json The Level-4 (L4) carbon product (SPL4CMDL) provides global gridded daily estimates of net ecosystem carbon (CO2) exchange derived using a satellite data based terrestrial carbon flux model informed by the following: Soil Moisture Active Passive (SMAP) L-band microwave observations, land cover and vegetation inputs from the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Goddard Earth Observing System Model, Version 5 (GEOS-5) land model assimilation system. Parameters are computed using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary
-SPL4SMAU_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2537927247-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: - SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D)
- SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3)
- SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG).
For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary
SPL4SMAU_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938665508-NSIDC_CPRD.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: - SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D)
- SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3)
- SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG).
For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary
-SPL4SMGP_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary
+SPL4SMAU_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2537927247-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: - SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D)
- SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3)
- SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG).
For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary
SPL4SMGP_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938665761-NSIDC_CPRD.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary
+SPL4SMGP_007 SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2531308461-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary
SPL4SMLM_007 SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants V007 NSIDC_CPRD STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2938666109-NSIDC_CPRD.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary
SPL4SMLM_007 SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants V007 NSIDC_ECS STAC Catalog 2015-03-31 -180, -85.044, 180, 85.044 https://cmr.earthdata.nasa.gov/search/concepts/C2537926833-NSIDC_ECS.umm_json SMAP Level-4 (L4) surface and root zone soil moisture data are provided in three products: * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data (SPL4SMGP, DOI: 10.5067/EVKPQZ4AFC4D) * SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update (SPL4SMAU, DOI: 10.5067/LWJ6TF5SZRG3) * SMAP L4 Global 9 km EASE-Grid Surface and Root Zone Soil Moisture Land Model Constants (SPL4SMLM, DOI: 10.5067/KN96XNPZM4EG). For each product, SMAP L-band brightness temperature data from descending and ascending half-orbit satellite passes (approximately 6:00 a.m. and 6:00 p.m. local solar time, respectively) are assimilated into a land surface model that is gridded using an Earth-fixed, global cylindrical 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) projection. proprietary
SPOT-6.and.7.ESA.archive_9.0 SPOT-6 and 7 ESA archive ESA STAC Catalog 2012-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336951-ESA.umm_json The SPOT 6 and 7 ESA archive is a dataset of SPOT 6 and SPOT 7 products that ESA collected over the years. The dataset regularly grows as ESA collects new SPOT 6 and 7 products. SPOT 6 and 7 Primary and Ortho products can be available in the following modes: Panchromatic image at 1.5m resolution Pansharpened colour image at 1.5m resolution Multispectral image in 4 spectral bands at 6m resolution Bundle (1.5m panchromatic image + 6m multispectral image) Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/socat/SPOT6-7 available on the Third Party Missions Dissemination Service. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided. proprietary
@@ -14325,8 +14328,8 @@ SPURS2_WAVEGLIDER_1.0 SPURS-2 Waveglider data for the E. Tropical Pacific field
SPURS2_XBAND_1.0 SPURS-2 shipboard X-band radar backscatter data for the E. Tropical Pacific field campaign POCLOUD STAC Catalog 2017-10-21 2017-11-13 -129.131, 8.927, -122.151, 10.355 https://cmr.earthdata.nasa.gov/search/concepts/C2781659132-POCLOUD.umm_json The SPURS-2 X-band marine navigation radar image dataset was collected from the ship during both the 2016 and 2017 cruises. The dataset consists of screenshots of rain echoes captured directly from the science-use X-band marine navigation radar. Raw data could not be saved. The screenshots show qualitative (uncalibrated) echoes of backscatter from rain. For full details on the screenshots, how they should be used, and what they show about rainfall, please refer to our publication: Thompson, E.J., W.E. Asher, A.T. Jessup, and K. Drushka. 2019. High-Resolution Rain Maps from an X-band Marine Radar and Their Use in Understanding Ocean Freshening. Oceanography 32(2):58–65, https://doi.org/10.5670/oceanog.2019.213 . The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aims to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. proprietary
SPURS2_XBAND_IMG_1.0 SPURS-2 shipboard X-band radar backscatter images for the 2016 E. Tropical Pacific field campaign POCLOUD STAC Catalog 2016-08-31 2016-09-22 -129.131, 8.927, -122.151, 10.355 https://cmr.earthdata.nasa.gov/search/concepts/C2931233351-POCLOUD.umm_json The SPURS-2 X-band marine navigation radar image dataset was collected from the ship during both the 2016 and 2017 cruises. The dataset consists of screenshots of rain echoes captured directly from the science-use X-band marine navigation radar. Raw data could not be saved. The screenshots show qualitative (uncalibrated) echoes of backscatter from rain. For full details on the screenshots, how they should be used, and what they show about rainfall, please refer to our publication: Thompson, E.J., W.E. Asher, A.T. Jessup, and K. Drushka. 2019. High-Resolution Rain Maps from an X-band Marine Radar and Their Use in Understanding Ocean Freshening. Oceanography 32(2):58–65, https://doi.org/10.5670/oceanog.2019.213 . The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is a NASA-funded oceanographic process study and associated field program that aims to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. proprietary
SPURS2_XBT_1.0 SPURS-2 research vessel Expendable Bathythermograph (XBT) profile data for E. Tropical Pacific R/V Revelle cruises POCLOUD STAC Catalog 2016-08-14 2017-11-15 -157.88, 5.06, -118.32, 21.26 https://cmr.earthdata.nasa.gov/search/concepts/C2491772372-POCLOUD.umm_json The SPURS (Salinity Processes in the Upper Ocean Regional Study) project is NASA-funded oceanographic process study and associated field program that aim to elucidate key mechanisms responsible for near-surface salinity variations in the oceans. The project involves two field campaigns and a series of cruises in regions of the Atlantic and Pacific Oceans exhibiting salinity extremes. SPURS employs a suite of state-of-the-art in-situ sampling technologies that, combined with remotely sensed salinity fields from the Aquarius/SAC-D, SMAP and SMOS satellites, provide a detailed characterization of salinity structure over a continuum of spatio-temporal scales. The SPURS-2 campaign involved two month-long cruises by the R/V Revelle in August 2016 and October 2017 combined with complementary sampling on a more continuous basis over this period by the schooner Lady Amber. Focused around a central mooring located near 10N,125W, the objective of SPURS-2 was to study the dynamics of the rainfall-dominated surface ocean at the western edge of the eastern Pacific fresh pool subject to high seasonal variability and strong zonal flows associated with the North Equatorial Current and Countercurrent. Expendable bathythermograph (XBT) casts were undertaken at stations during both of the SPURS-2 R/V Revelle cruises. Launched off the side of the ship, XBT probes provide vertical profile measurements of the water column at fixed locations. There were a total of 25 and 11 XBT deployments made during the first and second R/V Revelle cruises respectively. There is one XBT data file per cruise, each containing the temperature profile data from all instrument deployments undertaken during that cruise. proprietary
-SRDB_V5_1827_5 A Global Database of Soil Respiration Data, Version 5.0 ORNL_CLOUD STAC Catalog 1961-01-01 2017-12-31 -163.71, -78.02, 175.9, 81.8 https://cmr.earthdata.nasa.gov/search/concepts/C2216864433-ORNL_CLOUD.umm_json The Soil Respiration Database (SRDB) is a near-universal compendium of published soil respiration (Rs) data. The database encompasses published studies that report at least one of the following data measured in the field (not laboratory): annual soil respiration, mean seasonal soil respiration, a seasonal or annual partitioning of soil respiration into its source fluxes, soil respiration temperature response (Q10), or soil respiration at 10 degrees C. The SRDB's orientation is to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. Version 5 (V5) is the compilation of 2,266 published studies with measurements taken between 1961-2017. V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. The database is also restructured to have better interoperability with other datasets related to carbon-cycle science. proprietary
SRDB_V5_1827_5 A Global Database of Soil Respiration Data, Version 5.0 ALL STAC Catalog 1961-01-01 2017-12-31 -163.71, -78.02, 175.9, 81.8 https://cmr.earthdata.nasa.gov/search/concepts/C2216864433-ORNL_CLOUD.umm_json The Soil Respiration Database (SRDB) is a near-universal compendium of published soil respiration (Rs) data. The database encompasses published studies that report at least one of the following data measured in the field (not laboratory): annual soil respiration, mean seasonal soil respiration, a seasonal or annual partitioning of soil respiration into its source fluxes, soil respiration temperature response (Q10), or soil respiration at 10 degrees C. The SRDB's orientation is to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. Version 5 (V5) is the compilation of 2,266 published studies with measurements taken between 1961-2017. V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. The database is also restructured to have better interoperability with other datasets related to carbon-cycle science. proprietary
+SRDB_V5_1827_5 A Global Database of Soil Respiration Data, Version 5.0 ORNL_CLOUD STAC Catalog 1961-01-01 2017-12-31 -163.71, -78.02, 175.9, 81.8 https://cmr.earthdata.nasa.gov/search/concepts/C2216864433-ORNL_CLOUD.umm_json The Soil Respiration Database (SRDB) is a near-universal compendium of published soil respiration (Rs) data. The database encompasses published studies that report at least one of the following data measured in the field (not laboratory): annual soil respiration, mean seasonal soil respiration, a seasonal or annual partitioning of soil respiration into its source fluxes, soil respiration temperature response (Q10), or soil respiration at 10 degrees C. The SRDB's orientation is to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. Version 5 (V5) is the compilation of 2,266 published studies with measurements taken between 1961-2017. V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. The database is also restructured to have better interoperability with other datasets related to carbon-cycle science. proprietary
SRE4_SAB_gammaclones_1 Clone library using primers for gammaproteobacteria from an SAB treatment in the SRE4 experiment AU_AADC STAC Catalog 2002-12-01 2002-12-31 110, -66, 110, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313841-AU_AADC.umm_json A clone library was created from DNA extracted from an SAB-treated sample from the SRE4 in situ biodegradation experiment. The clone libary was created using one universal primer and one primer designed to be specific for the gammaproteobacteria. Sequences of approximately 600 bp were obtained. The samples used in this experiment were collected from O'Brien Bay, near Casey Station in the Windmill Islands. Gammaproteobacteria clone library Clone library created from SRE4 T2 SAB sample using primers 10F (GAG TTT GAT CCT GGC TCA G ) and GAMR (GGT AAG GTT CTT CGC GTT GCA T). Clones sequenced on a CEQ8000 Genetic Analysis system (Beckman-Coulter) and alignments were done in BioEdit v 5.0.9. Text file SRE4gammaclonesalign is a text version of BioEdit file SRE4gammaclones. This work was completed as part of ASAC project 2672 (ASAC_2672). proprietary
SRE4_desulfobaculaDGGE_1 Band pattern data from Desulfobacula-group specific DGGE for the SRE4 experiment AU_AADC STAC Catalog 2001-10-25 2003-03-30 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313816-AU_AADC.umm_json Samples are from the SRE4 experiment - an in situ experiment to determine fate and effects of different types of oils in the Antarctic marine environment. For details see: Powell S.M., Snape I., Bowman J.P., Thompson B.A.W., Stark J.S., McCammon S.A., Riddle M.J. 2005. A comparison of the short term effects of diesel fuel and lubricant oils on Antarctic benthic microbial communities. Journal of Experimental Marine Biology and Ecology 322:53-65. Samples were analysed by denaturing gradient gel electrophoresis (DGGE) with primers specific for the Desulfobacula group. Samples A,B,C,D,E,F,G,H,I are all initial samples collected different days Samples beginning T0 are predeployment samples, the next number refers to the batch. Samples beginning T2 are 1 year samples with: C = control S = SAB L = lubricant U = used lubricant B = biodegradable lubricant PCR conditions were as follows: Primers: 764F: ACAATGGTAAATGAGGGCA 1392RC: CGCCCGCCGCGCCCCGCGCCCGGCCCGCCGCCCCCGCCCCACGGGCGG TGTGTAC 50 ul (micro litre) reactions with Advantage II taq (Clontech) following manufacturer's recommendations with 20 pmol (pico mol) each primer and 20 ng (nano gram) template DNA. Cycling: 94C 5 minutes 10 cycles of: 94C 1 minutes 65C 1 minutes (-1C per cycle) 72C 2 minutes 20 cycles of: 94C 1 minutes 55C 1 minutes 72C 2 minutes 72C 30 minutes DGGE carried out using the D-Code system (BioRad). Gel: 8% acrylamide 30 - 65% denaturant with 2 cm stacking gel (15% acrylamide) 1 x TAE, 60 degrees C, 70V 16 hours The gels were pre-run for 20 minutes then half reaction volume was loaded and the lanes flushed out after 15 minutes. Gels were stained with SYBRGold. Images were captured using Storm Phosphorimager and ImageQuant v5.2 software(.gel files). Samples were only compared within a gel. Band pattern results are in the file desulfodgge.xls. For each comparison made there is a separate sheet in this file (see below). The first column in each sheet is the band position (or band name) and the remaining columns are samples with the first row being the sample name. '0' '1' indicate the band was 'absent' or 'present'. Comparison Image files (.gel and .tif) results sheets Background variation 140704f; 140704b 140704f and 140704b predeployment batches 180604f; 180406b 180604f and 180604b effect of setup 150704 150704 immediate effect of oil 250604f; 250604b 250604f and 250604b 1 year samples (T2) 040804f; 040804b 040804f and 040804b This work was completed as part of ASAC projects 1228 and 2201 (ASAC_1228, ASAC_2201). proprietary
SRE4_gammaproteobacteriaDGGE_1 Band pattern data from Gammaproteobacteria-group specific DGGE AU_AADC STAC Catalog 2001-10-25 2003-03-30 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313817-AU_AADC.umm_json Samples are from the SRE4 experiment - an in situ experiment to determine fate and effects of different types of oils in the Antarctic marine environment. For details see: Powell S.M., Snape I., Bowman J.P., Thompson B.A.W., Stark J.S., McCammon S.A., Riddle M.J. 2005. A comparison of the short term effects of diesel fuel and lubricant oils on Antarctic benthic microbial communities. Journal of Experimental Marine Biology and Ecology 322:53-65. Samples were analysed by denaturing gradient gel electrophoresis (DGGE) with primers specific for the Gammaproteobacteria. Samples used were from Time2 (1 year) Initial: T-1C; T-1E Control: T2C SAB treatment: T2S PCR conditions: Primers: GAMFC: CGC CCG CCG CGC CCC GCG CCC GGC CCG CCG CCC CCG CCC GGG TTA ATC GGA ATT ACT GG GAMR: GGT AAG GTT CTT CGC GTT GCA T 50 ul (micro litre) reactions with HotStar (qiagen) mix, 5ul Q solution, 10 pmol (pico mol) each primer and 20 ng (nano gram) template DNA cycling: 94C 15 minutes 35 cycles of: 94C 1 minutes 55C 1 minutes 72C 1 minutes 72C 20 minutes DGGE was performed using D-Code system (BioRad). Gel: 8% acryloamide, 30 - 65% denaturant with 2 cm stacking gel 1 x TAE, 60 degrees C, 80V 16 hours Gel was pre-run for 20 minutes and lanes were flushed out after 15 minutes. Gel was stained with Sybrgold. Image captured using Storm Phosphorimager and ImageQuant v5.2 software (.gel files). The image files are called 151105#2.gel and 151105.tif Band pattern results are in gammadgge.xls. The first column is the band position (or band name) and the remaining columns are samples with the first row being the sample name. The numbers indicates how many times the band appeared for that sample out of 2 DGGE runs. This work was completed as part of ASAC projects 1228 and 2201 (ASAC_1228, ASAC_2201). proprietary
@@ -14432,11 +14435,13 @@ SV16M_V_1 SMAPVEX16 Manitoba In Situ Vegetation Data V001 NSIDC_ECS STAC Catalog
SV19MA_DEM_1 SMAPVEX19-22 Massachusetts Lidar Derived Digital Elevation Model V001 NSIDC_ECS STAC Catalog 2022-04-02 2022-08-09 -72.33, 42.32, -71.91, 42.72 https://cmr.earthdata.nasa.gov/search/concepts/C2697180518-NSIDC_ECS.umm_json "These digital elevation model (DEM) data consist of ground surface elevations derived from source lidar measurements collected in April and August 2022 in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods occurred to characterize differences during ""leaf-off” and ""leaf-on"" conditions." proprietary
SV19MA_DSM_1 SMAPVEX19-22 Massachusetts Lidar Derived Digital Surface Model V001 NSIDC_ECS STAC Catalog 2022-08-03 2022-08-05 -72.33, 42.32, -71.91, 42.72 https://cmr.earthdata.nasa.gov/search/concepts/C2709181228-NSIDC_ECS.umm_json These digital surface model (DSM) data consist of surface elevations derived from source lidar measurements collected in August 2022 in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. The location was selected due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The August collection period was selected to characterize ‘leaf-on’ conditions. DSM data represents the highest elevation of features on the Earth’s surface, which may include bare-earth, vegetation, and human-made objects. proprietary
SV19MA_LID_1 SMAPVEX19-22 Massachusetts Airborne Lidar V001 NSIDC_ECS STAC Catalog 2022-04-02 2022-08-09 -72.33, 42.32, -71.91, 42.72 https://cmr.earthdata.nasa.gov/search/concepts/C2746987854-NSIDC_ECS.umm_json "These lidar measurements were collected in April and August 2022 in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods were selected to characterize differences during ""leaf-off” and ""leaf-on"" conditions." proprietary
+SV19MA_SAR_1 SMAPVEX19-22 Massachusetts UAVSAR Mosaics V001 NSIDC_ECS STAC Catalog 2022-04-25 2022-07-25 -72.33, 42.32, -71.91, 42.72 https://cmr.earthdata.nasa.gov/search/concepts/C3306692478-NSIDC_ECS.umm_json This data set consists of mosaicked Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images corrected for terrain-flattened gamma. Data image files at three different polarization configurations were composited daily between April to July 2022 in the vicinity of Petersham, Massachusetts during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign. The location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. proprietary
SV19MA_TNET_1 SMAPVEX19-22 Massachusetts Temporary Soil Moisture Network V001 NSIDC_ECS STAC Catalog 2019-05-01 2021-10-31 -72.29, 42.34, -71.95, 42.69 https://cmr.earthdata.nasa.gov/search/concepts/C2654469973-NSIDC_ECS.umm_json These data consist of ground-based, soil moisture, soil temperature, and air temperature measurements recorded by twenty-five temporary stations located in the vicinity of Petersham, MA during the SMAPVEX19-22 campaign. The stations were installed across an area of approximately 23 km by 36 km in May 2019 and operated through 2022. Note that the product is named SMAPVEX19-22 because, although the current coverage is through 2021, it is projected to include 2022 data in the future. proprietary
SV19MA_VOD_1 SMAPVEX19-22 Massachusetts Vegetation Optical Depth V001 NSIDC_ECS STAC Catalog 2019-04-28 2019-10-17 -72.17, 42.54, -72.17, 42.54 https://cmr.earthdata.nasa.gov/search/concepts/C3028784585-NSIDC_ECS.umm_json As part of the SMAPVEX19-22 campaign, an L-band radiometer was deployed on top of a tower at Harvard Forest,Massachusetts, looking down at a stand of red oak forest. The radiometer collected data in V-polarization from late April to mid October 2019. Over 4 days in early July 2019, the water potential and L-band complex dielectric constant of canopy leaves were measured at various times of day. Other instruments were installed within the radiometer's field of view to measure soil moisture and temperature, air temperature, tree xylem apparent dielectric permittivity at 70 MHz, tree xylem water potential, and canopy wetness. The goal of this experiment was to study the sensitivity of L-band vegetation optical depth (VOD) to changing vegetation water potential over a growing season. proprietary
SV19MB_DEM_1 SMAPVEX19-22 Millbrook Lidar Derived Digital Elevation Model V001 NSIDC_ECS STAC Catalog 2022-04-02 2022-08-09 -73.81, 41.66, -73.42, 42.05 https://cmr.earthdata.nasa.gov/search/concepts/C2697180532-NSIDC_ECS.umm_json "These digital elevation model (DEM) data consist of ground surface elevations derived from source lidar measurements collected in April and August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods occurred to characterize differences during ""leaf-off"" and ""leaf-on"" conditions." proprietary
SV19MB_DSM_1 SMAPVEX19-22 Millbrook Lidar Derived Digital Surface Model V001 NSIDC_ECS STAC Catalog 2022-08-02 2022-08-09 -73.81, 41.66, -73.42, 42.05 https://cmr.earthdata.nasa.gov/search/concepts/C2709181275-NSIDC_ECS.umm_json These digital surface model (DSM) data consist of surface elevations derived from source lidar measurements collected in August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. The location was selected due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The August collection period was selected to characterize ‘leaf-on’ conditions. DSM data represents the highest elevation of features on the Earth’s surface, which may include bare-earth, vegetation, and human-made objects. proprietary
SV19MB_LID_1 SMAPVEX19-22 Millbrook Airborne Lidar V001 NSIDC_ECS STAC Catalog 2022-04-02 2022-08-09 -73.81, 41.66, -73.42, 42.05 https://cmr.earthdata.nasa.gov/search/concepts/C2747348757-NSIDC_ECS.umm_json "These lidar measurements were collected in April and August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods were selected to characterize differences during ""leaf-off"" and ""leaf-on"" conditions." proprietary
+SV19MB_SAR_1 SMAPVEX19-22 Millbrook UAVSAR Mosaics V001 NSIDC_ECS STAC Catalog 2022-04-28 2022-07-25 -73.81, 41.66, -73.42, 42.05 https://cmr.earthdata.nasa.gov/search/concepts/C3306692820-NSIDC_ECS.umm_json This data set consists of mosaicked Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images corrected for terrain-flattened gamma. Data image files at three different polarization configurations were composited daily between April to July 2022 in the vicinity of Millbrook, New York during the SMAPVEX19-22 (Soil Moisture Active Passive Validation Experiment 2019-2022) field campaign. The location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. proprietary
SV19MB_TNET_1 SMAPVEX19-22 Millbrook Temporary Soil Moisture Network V001 NSIDC_ECS STAC Catalog 2019-05-01 2021-10-31 -73.79, 41.68, -73.44, 42.03 https://cmr.earthdata.nasa.gov/search/concepts/C2654470154-NSIDC_ECS.umm_json These data consist of soil moisture sensor, soil temperature and air temperature measurements recorded by 25 temporary stations in the Millbrook (MB) experiment location during SMAPVEX19-22. The stations were spread out over the experiment domain of about 30 by 40 km located around Millbrook, NY. The stations were installed in May 2019 and are to continue operation until 2022. Note that the product is named SMAPVEX19-22 because, although the current coverage is through 2021, it is projected to include 2022 data in the future. proprietary
SWDB_L2_004 SeaWiFS Deep Blue Aerosol Optical Depth and Angstrom Exponent Level 2 Data V004 (SWDB_L2) at GES DISC V004 GES_DISC STAC Catalog 1997-09-04 2010-12-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239900189-GES_DISC.umm_json The SeaWiFS Deep Blue (SWDB) Level 2 Product contains data corresponding to a single SeaWiFS swath using Deep Blue algorithm. There are about 15 Level 2 data files produced per day. Each contains retrieved aerosol properties averaged to a resolution of 3x3 SeaWiFS pixels (13.5x13.5 km at the center of the swath given 4.5km SeaWiFS pixels). The primary data parameters are aerosol optical thickness, and Angstrom exponent. proprietary
SWDB_L305_004 SeaWiFS Deep Blue Aerosol Optical Depth and Angstrom Exponent Daily Level 3 Data Gridded at 0.5 Degrees V004 (SWDB_L305) at GES DISC GES_DISC STAC Catalog 1997-09-04 2010-12-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1239900190-GES_DISC.umm_json The SeaWiFS Deep Blue (SWDB) Level 3 daily global gridded (0.5 x 0.5 deg) data is derived from SeaWiFS Deep Blue Level 2 data. In most cases, each data field represents the arithmetic mean of all cells whose latitude and longitude places it within the bounds of each grid element. Furthermore, only cells measured on the day of interest are included in this calculation. The local date based on the longitude of the cell is calculated from the time of measurement. If the local date equals the day of interest, the cell is included in the L3 data processing. The primary data parameters are aerosol optical thickness, and Angstrom exponent. proprietary
@@ -14530,16 +14535,16 @@ SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1_1.0 SWOT Simulated Level 2 North Amer
SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1_1.0 SWOT Simulated Level 2 North America Continent High Rate Raster Product Version 1.0 POCLOUD STAC Catalog 2022-08-01 2022-08-22 -113, 24, -82, 52 https://cmr.earthdata.nasa.gov/search/concepts/C2263383790-POCLOUD.umm_json This dataset contains a simulated rasterized water surface elevation and inundation-extent product to be provided by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this simulated subset focuses on the North America continent. This is a derived product through resampling the upstream dataset L2_HR_PIXC_V1 and L2_HR_PIXCVEC_V1 onto a uniform grid over the North America continent. A uniform grid is superimposed onto the pixel cloud from the source products, and all pixel-cloud samples within each grid cell are aggregated to produce a single value per raster cell. The raster data are produced geographically fixed tiles at resolutions of 100 m and 250 m in a Universal Transverse Mercator projection grid. Note that this is a simulated SWOT product and not suited for any scientific exploration. proprietary
SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1_1.0 SWOT Simulated Level 2 North America Continent High Rate River Vectors Product Version 1.0 POCLOUD STAC Catalog 2022-08-01 2022-08-22 -113, 24, -82, 52 https://cmr.earthdata.nasa.gov/search/concepts/C2263384307-POCLOUD.umm_json This dataset contains a simulated river data product to be provided by the Surface Water and Ocean Topography (SWOT) mission. SWOT will provide a global coverage but this dataset is a subset for the North America continent. This product is derived from the measurements produced by the main SWOT instrument, the Ka-band Interferometer. They are produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. This product contains two shapefiles: 1) river reaches (approximately 10 km long) identified in the prior river database (PRD); and 2) river nodes (approximately 200 m spacing) identified in prior river database (PRD). Each river reach is divided into a number of nodes. Attributes include water surface elevation, slope, width, and uncertainty estimates. As they are derived from SWOT KaRIn measurements, each granule covers an area that is approximately 128 km wide in the cross-track direction with a 20-km nadir gap. Note that this is a simulated SWOT product and not suited for any scientific exploration. proprietary
Sahel_Water_Bodies_1269_1 Location and Permanency of Water Bodies in the African Sahel Region from 2003-2011 ORNL_CLOUD STAC Catalog 2003-01-01 2011-12-31 -20, 10, 40, 20 https://cmr.earthdata.nasa.gov/search/concepts/C2756239079-ORNL_CLOUD.umm_json This data set provides an estimate of the spatial and temporal extent of surface water at 250-m resolution over nine years (2003-2011) for the African Sahel region (10-20 degrees N) using imagery from the Moderate-resolution Imaging Spectroradiometer (MODIS). Water bodies were identified by a spectral analysis of MODIS vegetation indices with the aim to improve existing regional to global mapping products. This data set can be used to enhance the understanding of Earth system processes, and to support global change studies, agricultural planning, and disease prevention. These data provide a gridded (250-m) estimate of the number of years (during 2003-2011) that a pixel was covered by water. The data are presented in a single netCDF (*.nc) file. proprietary
-Salt_Marsh_Biomass_CONUS_2348_1 Aboveground Biomass Estimates for Salt Marsh for the Contiguous United States, 2020 ORNL_CLOUD STAC Catalog 2020-01-01 2020-12-31 -124.74, 24.52, -66.93, 49 https://cmr.earthdata.nasa.gov/search/concepts/C3126460246-ORNL_CLOUD.umm_json This dataset provides estimates of aboveground biomass (AGB) and salt marsh extent in the contiguous United States for 2020 and includes all coastal watersheds across the contiguous United States at 10-m resolution. Estimates were generated by XGBoost machine learning regression. Salt marsh extent was classified using an ensemble of XGBoost, random forests, and support vector machines, trained with salt marsh location identified with the National Wetland Inventory (NWI). The data are organized by Hydrologic Unit Code (HUC) 6-digit basin. Within each HUC, the spatial extent of salt marsh and its uncertainty were estimated by machine learning and input data from NWI maps, the National Elevation Dataset, along with Sentinel-1 and Sentinel-2 imagery. Estimates were compared to in situ biomass data from salt marshes in Georgia and Massachusetts. The data are provided in cloud-optimized GeoTIFF format. proprietary
Salt_Marsh_Biomass_CONUS_2348_1 Aboveground Biomass Estimates for Salt Marsh for the Contiguous United States, 2020 ALL STAC Catalog 2020-01-01 2020-12-31 -124.74, 24.52, -66.93, 49 https://cmr.earthdata.nasa.gov/search/concepts/C3126460246-ORNL_CLOUD.umm_json This dataset provides estimates of aboveground biomass (AGB) and salt marsh extent in the contiguous United States for 2020 and includes all coastal watersheds across the contiguous United States at 10-m resolution. Estimates were generated by XGBoost machine learning regression. Salt marsh extent was classified using an ensemble of XGBoost, random forests, and support vector machines, trained with salt marsh location identified with the National Wetland Inventory (NWI). The data are organized by Hydrologic Unit Code (HUC) 6-digit basin. Within each HUC, the spatial extent of salt marsh and its uncertainty were estimated by machine learning and input data from NWI maps, the National Elevation Dataset, along with Sentinel-1 and Sentinel-2 imagery. Estimates were compared to in situ biomass data from salt marshes in Georgia and Massachusetts. The data are provided in cloud-optimized GeoTIFF format. proprietary
+Salt_Marsh_Biomass_CONUS_2348_1 Aboveground Biomass Estimates for Salt Marsh for the Contiguous United States, 2020 ORNL_CLOUD STAC Catalog 2020-01-01 2020-12-31 -124.74, 24.52, -66.93, 49 https://cmr.earthdata.nasa.gov/search/concepts/C3126460246-ORNL_CLOUD.umm_json This dataset provides estimates of aboveground biomass (AGB) and salt marsh extent in the contiguous United States for 2020 and includes all coastal watersheds across the contiguous United States at 10-m resolution. Estimates were generated by XGBoost machine learning regression. Salt marsh extent was classified using an ensemble of XGBoost, random forests, and support vector machines, trained with salt marsh location identified with the National Wetland Inventory (NWI). The data are organized by Hydrologic Unit Code (HUC) 6-digit basin. Within each HUC, the spatial extent of salt marsh and its uncertainty were estimated by machine learning and input data from NWI maps, the National Elevation Dataset, along with Sentinel-1 and Sentinel-2 imagery. Estimates were compared to in situ biomass data from salt marshes in Georgia and Massachusetts. The data are provided in cloud-optimized GeoTIFF format. proprietary
San_Diego_Coastal_Project_0 San Diego Coastal Project OB_DAAC STAC Catalog 2004-11-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360636-OB_DAAC.umm_json Measurements near the Southern Californias coast made under the San Diego Coastal Project between 2004 and 2006. proprietary
Sargassum_GOM_0 Importance of pelagic Sargassum to fisheries management in the Northern Gulf of Mexico OB_DAAC STAC Catalog 2017-07-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360637-OB_DAAC.umm_json Measurements made under the Linking habitat to recruitment: evaluating the importance of pelagic Sargassum to fisheries management in the Gulf of Mexico, in the Northern Gulf of Mexico. Collaboration with USF and USM. proprietary
Saskatchewan_Soils_125m_SSA_1346_2 BOREAS Agriculture Canada Central Saskatchewan Vector Soils Data, R1 ORNL_CLOUD STAC Catalog 1980-01-01 2001-02-06 -110.45, 52.86, -99.87, 55.06 https://cmr.earthdata.nasa.gov/search/concepts/C2773240578-ORNL_CLOUD.umm_json This data set provides soil descriptions for forested areas in the BOREAS southern study area (SSA) in central Saskatchewan, Canada provided by Agriculture Canada. The data contain soil code, modifiers, extent, and soil names for the primary, secondary, and tertiary soil units within each polygon. proprietary
-Sat_ActiveLayer_Thickness_Maps_1760_1 ABoVE: Active Layer Thickness from Remote Sensing Permafrost Model, Alaska, 2001-2015 ORNL_CLOUD STAC Catalog 2001-01-01 2015-12-31 -179.18, 55.57, -132.58, 70.21 https://cmr.earthdata.nasa.gov/search/concepts/C2143402571-ORNL_CLOUD.umm_json This dataset provides annual estimates of active layer thickness (ALT) at 1 km resolution across Alaska from 2001-2015. The ALT was estimated using a remote sensing-based soil process model incorporating global satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover extent (SCE), and Soil Moisture Active and Passive (SMAP) satellite soil moisture records. The study area covers the majority land area of Alaska except for areas of perennial ice/snow cover or open water. The ALT was defined as the maximum soil thawing depth throughout the year. The mean ALT and mean uncertainty from 2001 to 2015 are also provided. proprietary
Sat_ActiveLayer_Thickness_Maps_1760_1 ABoVE: Active Layer Thickness from Remote Sensing Permafrost Model, Alaska, 2001-2015 ALL STAC Catalog 2001-01-01 2015-12-31 -179.18, 55.57, -132.58, 70.21 https://cmr.earthdata.nasa.gov/search/concepts/C2143402571-ORNL_CLOUD.umm_json This dataset provides annual estimates of active layer thickness (ALT) at 1 km resolution across Alaska from 2001-2015. The ALT was estimated using a remote sensing-based soil process model incorporating global satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover extent (SCE), and Soil Moisture Active and Passive (SMAP) satellite soil moisture records. The study area covers the majority land area of Alaska except for areas of perennial ice/snow cover or open water. The ALT was defined as the maximum soil thawing depth throughout the year. The mean ALT and mean uncertainty from 2001 to 2015 are also provided. proprietary
+Sat_ActiveLayer_Thickness_Maps_1760_1 ABoVE: Active Layer Thickness from Remote Sensing Permafrost Model, Alaska, 2001-2015 ORNL_CLOUD STAC Catalog 2001-01-01 2015-12-31 -179.18, 55.57, -132.58, 70.21 https://cmr.earthdata.nasa.gov/search/concepts/C2143402571-ORNL_CLOUD.umm_json This dataset provides annual estimates of active layer thickness (ALT) at 1 km resolution across Alaska from 2001-2015. The ALT was estimated using a remote sensing-based soil process model incorporating global satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover extent (SCE), and Soil Moisture Active and Passive (SMAP) satellite soil moisture records. The study area covers the majority land area of Alaska except for areas of perennial ice/snow cover or open water. The ALT was defined as the maximum soil thawing depth throughout the year. The mean ALT and mean uncertainty from 2001 to 2015 are also provided. proprietary
SatelliteDerived_Forest_Mexico_2320_1 Satellite-Derived Forest Extent Likelihood Map for Mexico ORNL_CLOUD STAC Catalog 2010-01-01 2020-12-31 -120.31, 12.48, -84.29, 34.51 https://cmr.earthdata.nasa.gov/search/concepts/C2905454214-ORNL_CLOUD.umm_json This dataset provides a comparison of forest extent agreement from seven remote sensing-based products across Mexico. These satellite-derived products include European Space Agency 2020 Land Cover Map for Mexico (ESA), Globeland30 2020 (Globeland30), Commission for Environmental Cooperation 2015 Land Cover Map (CEC), Impact Observatory 2020 Land Cover Map (IO), NAIP Trained Mean Percent Cover Map (NEX-TC), Global Land Analysis and Discovery Global 2010 Tree Cover (Hansen-TC), and Global Forest Cover Change Tree Cover 30 m Global (GFCC-TC). All products included data at 10-30 m resolution and represented the state of forest or tree cover from 2010 to 2020. These seven products were chosen based on: a) feedback from end-users in Mexico; b) availability and FAIR (findable, accessible, interoperable, and replicable) data principles; and c) products representing different methodological approaches from global to regional scales. The combined agreement map documents forest cover for each satellite-derived product at 30-m resolution across Mexico. The data are in cloud optimized GeoTIFF format and cover the period 2010-2020. A shapefile is included that outlines Mexico mainland areas. proprietary
-Scambos_PLR1441432 A Low-power, Quick-install Polar Observation System ('AMIGOS-II') for Monitoring Climate-ice-ocean Interactions SCIOPS STAC Catalog 2014-06-01 2015-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214604828-SCIOPS.umm_json The investigators propose to build and test a multi-sensor, automated measurement station for monitoring Arctic and Antarctic ice-ocean environments. The system, based on a previously successful design, will incorporate weather and climate sensors, camera, snow and firn sensors, instruments to measure ice motion, ice and ocean thermal profilers, hydrophone, and salinity sensors. This new system will have two-way communications for real-time data delivery and is designed for rapid deployment by a small field group. proprietary
Scambos_PLR1441432 A Low-power, Quick-install Polar Observation System ('AMIGOS-II') for Monitoring Climate-ice-ocean Interactions ALL STAC Catalog 2014-06-01 2015-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214604828-SCIOPS.umm_json The investigators propose to build and test a multi-sensor, automated measurement station for monitoring Arctic and Antarctic ice-ocean environments. The system, based on a previously successful design, will incorporate weather and climate sensors, camera, snow and firn sensors, instruments to measure ice motion, ice and ocean thermal profilers, hydrophone, and salinity sensors. This new system will have two-way communications for real-time data delivery and is designed for rapid deployment by a small field group. proprietary
+Scambos_PLR1441432 A Low-power, Quick-install Polar Observation System ('AMIGOS-II') for Monitoring Climate-ice-ocean Interactions SCIOPS STAC Catalog 2014-06-01 2015-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214604828-SCIOPS.umm_json The investigators propose to build and test a multi-sensor, automated measurement station for monitoring Arctic and Antarctic ice-ocean environments. The system, based on a previously successful design, will incorporate weather and climate sensors, camera, snow and firn sensors, instruments to measure ice motion, ice and ocean thermal profilers, hydrophone, and salinity sensors. This new system will have two-way communications for real-time data delivery and is designed for rapid deployment by a small field group. proprietary
SciSat-1.Ace.FTS.and.Maestro_4.0 SciSat-1: ACE-FTS and MAESTRO ESA STAC Catalog 2003-08-13 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336954-ESA.umm_json SCISAT-1 data aim at monitoring and analysing the chemical processes that control the distribution of ozone in the upper troposphere and stratosphere. It provides acquisitions from the 2 instruments MAESTRO and ACE-FTS. • MAESTRO: Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation. Dual-channel optical spectrometer in the spectral region of 285-1030 nm. The objective is to measure ozone, nitrogen dioxide and aerosol/cloud extinction (solar occultation measurements of atmospheric attenuation during satellite sunrise and sunset with the primary objective of assessing the stratospheric ozone budget). Solar occultation spectra are being used for retrieving vertical profiles of temperature and pressure, aerosols, and trace gases (O3, NO2, H2O, OClO, and BrO) involved in middle atmosphere ozone distribution. The use of two overlapping spectrometers (280 - 550 nm, 500 - 1030 nm) improves the stray-light performance. The spectral resolution is about 1-2 nm. • ACE-FTS: Fourier Transform Spectrometer The objective is to measure the vertical distribution of atmospheric trace gases, in particular of the regional polar O3 budget, as well as pressure and temperature (derived from CO2 lines). The instrument is an adapted version of the classical sweeping Michelson interferometer, using an optimized optical layout. The ACE-FTS measurements are recorded every 2 s. This corresponds to a measurement spacing of 2-6 km which decreases at lower altitudes due to refraction. The typical altitude spacing changes with the orbital beta angle. For historical reasons, the retrieved results are interpolated onto a 1 km "grid" using a piecewise quadratic method. For ACE-FTS version 1.0, the results were reported only on the interpolated grid (every 1 km from 0.5 to 149.5 km). For versions 2.2, both the "retrieval" grid and the "1 km" grid profiles are available. SCISAT-1 collection provides ACE-FTS and MAESTRO Level 2 Data. As of today, ACE-FTS products are available in version 4.1, while MAESTRO products are available in version 3.13. proprietary
Scotia_Prince_ferry_0 Scotia Prince ferry dataset OB_DAAC STAC Catalog 1998-06-06 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360640-OB_DAAC.umm_json Although the ferry that data were collected from no longer operates, longstanding data collection methods continue. The Scotia Prince ferry dataset has been reorganized and added to the GNATS experiment dataset (Gulf of Maine North Atlantic Time Series, 10.5067/SeaBASS/GNATS/DATA001). Please refer to that dataset to find data that were originally listed here. proprietary
Scotts_Fuel_1 Composition and origin of fuel from the hut of explorer Robert Falcon Scott, Cape Evans, Antarctica AU_AADC STAC Catalog 1910-08-15 1912-03-29 166.4, -77.633, 166.4, -77.633 https://cmr.earthdata.nasa.gov/search/concepts/C1214311239-AU_AADC.umm_json As a direct result of the 1989-90 trip as part of ASAC 245, a sample of petrol used by Scott on his ill-fated expedition to the South Pole was obtained. This petrol sample was supplied by the late Garth Varcoe of the New Zealand Antarctic Division following a discussion ensuing from a lecture given whilst on the Icebird when stuck in the ice off Davis. This sample is of intense historical interest and the results of the studies are in the download file. The material in the file reports the studies on the composition of the petrol which was left by the remaining members of Scott's group when they departed their base at Evans Head. The aim of this work was to identify the source of the fuel. A later study will attempt to comment on its suitability as a fuel for use under Antarctic conditions. There are five files on the CD. a)a poster presented at the Australian Organic Geochemistry Conference held in Leura, NSW in February of this year, b)a brief description highlighting some salient points of the poster; presented orally, c)an abstract of this work included in the conference proceedings, d)the conference proceedings and e)manuscript of a full paper submitted for publication in the Journal of Organic Geochemistry, including a table of data Geochemical analyses of the fuel used for the motor driven sledges used by the explorer Robert Falcon Scott for his 1911/1912 quest to the South Pole indicates that it is a straight run gasoline. The presence of bicadinanes, oleanane and other oleanoid angiosperm markers indicate that the feedstock oil was likely to be sourced from terrestrial source rocks of Tertiary age in the South East Asian region. The overall chemical composition of the fuel in its present state indicates that it may have been too heavy for usage in polar regions. proprietary
@@ -14599,8 +14604,8 @@ Seabirds_AAT_1 Distribution and abundance of breeding seabirds in the AAT AU_AAD
Seabirds_HIMI_1 Distribution and abundance of breeding seabirds at Heard Island and the McDonald Islands AU_AADC STAC Catalog 1901-01-01 70, -55, 75, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214313740-AU_AADC.umm_json Distribution and abundance of breeding seabirds at Heard I and the McDonald Is. This dataset comprises a broad range of component datasets derived from ground surveys aerial photography and oblique photography. Since the data have also been derived from old station logs for the 1947-54 period, and from published and unpublished records for the 1947-present day period. Aerial and oblique photography has been used to obtain supplementary information on distribution and abundance of seabirds in the region. Recent surveys, 2000/01 onwards, have made use of GPS for more precise geographic information on seabird nests and colonies. At present there are a number of child metadata records attached to this record. See the link above for details. proprietary
Seagrass_Mapping_Florida_0 Water quality measurements near the Big Bend Seagrasses Aquatic Preserve, Florida OB_DAAC STAC Catalog 2010-05-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360643-OB_DAAC.umm_json Water quality measurements taken near the Big Bend Seagrasses Aquatic Preserve in Florida. proprietary
Searcher_0 Measurements from the Baltic Sea in 1999 OB_DAAC STAC Catalog 1999-07-24 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360656-OB_DAAC.umm_json Measurements from the Baltic Sea in 1999. proprietary
-Seasonality_Tundra_Vegetation_1606_1 ABoVE: Climate Drivers of Pan-Arctic Tundra Vegetation Productivity, 1982-2015 ORNL_CLOUD STAC Catalog 1982-01-01 2015-12-31 -180, 70, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2162145436-ORNL_CLOUD.umm_json This dataset provides a summary of potential climate drivers of Arctic tundra vegetation productivity that have been compiled for growing seasons from 1982 to 2015. The scale of interest is the entire pan-arctic non-alpine tundra and the continental subdivisions of the North American and the Eurasian Arctic North of 70 degrees. These climate drivers include (1) maximum normalized difference vegetation index (MaxNDVI) and time-integrated NDVI (TI-NDVI), (2) summer sea ice concentrations, (3) oceanic heat content, (4) land surface temperature, and (5) summer warmth index (SWI). Data are provided variously as timeseries and weekly and bi-weekly averages over selected time ranges and study regions with calculated trends and trend significance. Data collected over 33 years were compiled to observe seasonal trends of vegetation productivity and to detect dynamics between arctic vegetation and climate drivers. proprietary
Seasonality_Tundra_Vegetation_1606_1 ABoVE: Climate Drivers of Pan-Arctic Tundra Vegetation Productivity, 1982-2015 ALL STAC Catalog 1982-01-01 2015-12-31 -180, 70, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2162145436-ORNL_CLOUD.umm_json This dataset provides a summary of potential climate drivers of Arctic tundra vegetation productivity that have been compiled for growing seasons from 1982 to 2015. The scale of interest is the entire pan-arctic non-alpine tundra and the continental subdivisions of the North American and the Eurasian Arctic North of 70 degrees. These climate drivers include (1) maximum normalized difference vegetation index (MaxNDVI) and time-integrated NDVI (TI-NDVI), (2) summer sea ice concentrations, (3) oceanic heat content, (4) land surface temperature, and (5) summer warmth index (SWI). Data are provided variously as timeseries and weekly and bi-weekly averages over selected time ranges and study regions with calculated trends and trend significance. Data collected over 33 years were compiled to observe seasonal trends of vegetation productivity and to detect dynamics between arctic vegetation and climate drivers. proprietary
+Seasonality_Tundra_Vegetation_1606_1 ABoVE: Climate Drivers of Pan-Arctic Tundra Vegetation Productivity, 1982-2015 ORNL_CLOUD STAC Catalog 1982-01-01 2015-12-31 -180, 70, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2162145436-ORNL_CLOUD.umm_json This dataset provides a summary of potential climate drivers of Arctic tundra vegetation productivity that have been compiled for growing seasons from 1982 to 2015. The scale of interest is the entire pan-arctic non-alpine tundra and the continental subdivisions of the North American and the Eurasian Arctic North of 70 degrees. These climate drivers include (1) maximum normalized difference vegetation index (MaxNDVI) and time-integrated NDVI (TI-NDVI), (2) summer sea ice concentrations, (3) oceanic heat content, (4) land surface temperature, and (5) summer warmth index (SWI). Data are provided variously as timeseries and weekly and bi-weekly averages over selected time ranges and study regions with calculated trends and trend significance. Data collected over 33 years were compiled to observe seasonal trends of vegetation productivity and to detect dynamics between arctic vegetation and climate drivers. proprietary
Secret_0 Studies of Ecological and Chemical Responses to Environmental Trends (SECRET) OB_DAAC STAC Catalog 1998-08-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360657-OB_DAAC.umm_json Measurements spanning from the California coast to Hawaii in the mid-Pacific Ocean from 1998 to 2006. proprietary
Semantic Segmentation of Crop Type in Ghana_1 Semantic Segmentation of Crop Type in Ghana MLHUB STAC Catalog 2020-01-01 2023-01-01 -2, 8, 1, 11 https://cmr.earthdata.nasa.gov/search/concepts/C2781412078-MLHUB.umm_json Automatic, accurate crop type maps can provide unprecedented information for understanding food systems, especially in developing countries where ground surveys are infrequent. However, little work has applied existing methods to these data scarce environments, which also have unique challenges of irregularly shaped fields, frequent cloud coverage, small plots, and a severe lack of training data. To address this gap in the literature, we provide the first crop type semantic segmentation dataset of small holder farms, specifically in Ghana and South Sudan. We are also the first to utilize high resolution, high frequency satellite data in segmenting small holder farms. The dataset includes time series of satellite imagery from Sentinel-1, Sentinel-2, and PlanetScope satellites throughout 2016 and 2017. For each tile/chip in the dataset, there are time series of imagery from each of the satellites, as well as a corresponding label that defines the crop type at each pixel. The label has only one value at each pixel location, and assumes that the crop type remains the same across the full time span of the satellite image time series. In many cases where ground truth was not available, pixels have no label and are set to a value of 0. proprietary
Semantic Segmentation of Crop Type in South Sudan_1 Semantic Segmentation of Crop Type in South Sudan MLHUB STAC Catalog 2020-01-01 2023-01-01 24, 1, 36, 13 https://cmr.earthdata.nasa.gov/search/concepts/C2781412590-MLHUB.umm_json Automatic, accurate crop type maps can provide unprecedented information for understanding food systems, especially in developing countries where ground surveys are infrequent. However, little work has applied existing methods to these data scarce environments, which also have unique challenges of irregularly shaped fields, frequent cloud coverage, small plots, and a severe lack of training data. To address this gap in the literature, we provide the first crop type semantic segmentation dataset of small holder farms, specifically in Ghana and South Sudan. We are also the first to utilize high resolution, high frequency satellite data in segmenting small holder farms. The dataset includes time series of satellite imagery from Sentinel-1, Sentinel-2, and PlanetScope satellites throughout 2016 and 2017. For each tile/chip in the dataset, there are time series of imagery from each of the satellites, as well as a corresponding label that defines the crop type at each pixel. The label has only one value at each pixel location, and assumes that the crop type remains the same across the full time span of the satellite image time series. In many cases where ground truth was not available, pixels have no label and are set to a value of 0. proprietary
@@ -14615,43 +14620,43 @@ SiB4_Global_HalfDegree_Hourly_1847_1 SiB4 Modeled Global 0.5-Degree Hourly Carbo
SiB4_Global_HalfDegree_Monthly_1848_1 SiB4 Modeled Global 0.5-Degree Monthly Carbon Fluxes and Pools, 2000-2018 ORNL_CLOUD STAC Catalog 2000-01-01 2018-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2345882961-ORNL_CLOUD.umm_json "This dataset provides global monthly output predicted by the Simple Biosphere Model, Version 4.2 (SiB4), at a 0.5-degree spatial resolution covering the time period 2000 through 2018. SiB4 is a mechanistic land surface model that integrates heterogeneous land cover, environmentally responsive phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter. Monthly output includes carbon, carbonyl sulfide (COS), and energy fluxes; solar-induced fluorescence (SIF); carbon pools; soil moisture and temperatures in the top three layers; total column soil water and plant available water; and environmental potentials used to scale photosynthesis. The SiB4 output is per plant functional type (PFT) within each 0.5-degree grid cell. SiB4 partitions variable output to 15 PFTs in each grid cell that are indexed by the ""npft"" dimension (01-15) in each data file. The PFT three-character abbreviations (""pft_names"" variable) are listed in the same order as the ""npft"" dimension. To combine the PFT-specific output into grid cell totals, users must compute the area-weighted mean across the vector of PFT-specific values for each cell. Fractional areal coverages are given in the ""pft_area"" variable for each cell." proprietary
Siberian_Biomass_Wildfire_1321_1 Siberian Boreal Forest Aboveground Biomass and Fire Scar Maps, Russia, 1969-2007 ORNL_CLOUD STAC Catalog 1969-07-01 2007-07-26 156.61, 64.77, 166.47, 69.9 https://cmr.earthdata.nasa.gov/search/concepts/C2773255198-ORNL_CLOUD.umm_json This data set provides 30-meter resolution mapped estimates of Cajander larch (Larix cajanderi) aboveground biomass (AGB), circa 2007, and a map of burn perimeters for 116 forest fires that occurred from 1966-2007. The data cover ~100,000 km2 of the Kolyma River Basin in northeastern Siberia, Sakha Republic, Russia. proprietary
Siberian_Larch_Stand_Age_1364_1 Distribution of Estimated Stand Age Across Siberian Larch Forests, 1989-2012 ORNL_CLOUD STAC Catalog 1989-01-01 2012-12-31 90, 49, 143, 67 https://cmr.earthdata.nasa.gov/search/concepts/C2767498872-ORNL_CLOUD.umm_json This data set provides mapped estimates of the stand age of young (less than 25 years old) larch forests across Siberia from 1989-2012 at 30-m resolution. The age estimates were derived from Landsat-based composites and tree cover for years 2000 and 2012 developed by the Global Forest Change (GFC) project and the stand-replacing fire mapping (SRFM) data set. This approach is based on the assumption that the relationship between the spectral signature of a burned or unburned forest stand acquired by Landsat ETM+ and TM sensors and stand age before and after the year 2000 is similar, thus allowing for training an algorithm on the data from the post-2000 era and applying the algorithm to infer stand age for the pre-2000 era. The output map combines the modeled forest disturbances before 2000 and direct observations of forest loss after 2000 to deliver a 24-year stand age distribution map. proprietary
-Skelton_Aeromag_Data Aeromagnetic data centered over Skelton Neve, Antarctica: A Web Site for Distribution of Data and Maps (on-line edition) CEOS_EXTRA STAC Catalog 1997-01-01 1998-12-31 153.5, -79.7, 166.7, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552348-CEOS_EXTRA.umm_json The Transantarctic Mountains (TAM) rift-flank uplift has developed along the ancestral margin of the East Antarctic craton, and forms the boundary between the craton and the thinned lithosphere of the West Antarctic rift system. Geodynamic processes associated with the exceptionally large-magnitude uplift of the mountain belt remain poorly constrained, but may involve interaction of rift-related mechanical and thermal processes and the inherited mechanical elements of the cratonic lithosphere. The Transantarctic Mountain Aerogeophysical Research Activities (TAMARA) program proposes to document the regional structural architecture of a key segment of the Transantarctic Mountains in the region around the Royal Society Range where the rift flank is offset along a transverse accommodation zone. proprietary
Skelton_Aeromag_Data Aeromagnetic data centered over Skelton Neve, Antarctica: A Web Site for Distribution of Data and Maps (on-line edition) ALL STAC Catalog 1997-01-01 1998-12-31 153.5, -79.7, 166.7, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552348-CEOS_EXTRA.umm_json The Transantarctic Mountains (TAM) rift-flank uplift has developed along the ancestral margin of the East Antarctic craton, and forms the boundary between the craton and the thinned lithosphere of the West Antarctic rift system. Geodynamic processes associated with the exceptionally large-magnitude uplift of the mountain belt remain poorly constrained, but may involve interaction of rift-related mechanical and thermal processes and the inherited mechanical elements of the cratonic lithosphere. The Transantarctic Mountain Aerogeophysical Research Activities (TAMARA) program proposes to document the regional structural architecture of a key segment of the Transantarctic Mountains in the region around the Royal Society Range where the rift flank is offset along a transverse accommodation zone. proprietary
+Skelton_Aeromag_Data Aeromagnetic data centered over Skelton Neve, Antarctica: A Web Site for Distribution of Data and Maps (on-line edition) CEOS_EXTRA STAC Catalog 1997-01-01 1998-12-31 153.5, -79.7, 166.7, -77.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231552348-CEOS_EXTRA.umm_json The Transantarctic Mountains (TAM) rift-flank uplift has developed along the ancestral margin of the East Antarctic craton, and forms the boundary between the craton and the thinned lithosphere of the West Antarctic rift system. Geodynamic processes associated with the exceptionally large-magnitude uplift of the mountain belt remain poorly constrained, but may involve interaction of rift-related mechanical and thermal processes and the inherited mechanical elements of the cratonic lithosphere. The Transantarctic Mountain Aerogeophysical Research Activities (TAMARA) program proposes to document the regional structural architecture of a key segment of the Transantarctic Mountains in the region around the Royal Society Range where the rift flank is offset along a transverse accommodation zone. proprietary
SkySat.Full.Archive.and.New.Tasking_9.0 SkySat Full Archive and New Tasking ESA STAC Catalog 2013-11-13 -180, -84, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C1965336955-ESA.umm_json "The SkySat Level 1 Basic Scene, Level 3B Ortho Scene and Level 3B Consolidated full archive and new tasking products are available as part of the Planet imagery offer. The SkySat Basic Scene product is uncalibrated and in a raw digital number format, not corrected for any geometric distortions inherent to the imaging process. Rational Polynomial Coefficients (RPCs) are provided to enable orthorectification by the user. • Basic Panchromatic Scene product – unorthorectified, radiometrically corrected, panchromatic (PAN) imagery. • Basic Panchromatic DN Scene product – unorthorectified, panchromatic (PAN) imagery. • Basic L1A Panchromatic DN Scene product – unorthorectified, pre-super resolution, panchromatic (PAN) imagery. • Basic Analytic Scene product – unorthorectified, radiometrically corrected, 4-band multispectral (BGR-NIR) imagery. • Basic Analytic DN Scene product – unorthorectified, 4-band multispectral (BGR-NIR) imagery. Basic Scene Product Components and Format Product Components and Format • Image File (GeoTIFF format) • Metadata File (JSON format) • Rational Polynomial Coefficients (Text File) • UDM File (GeoTIFF format) Image Configurations • 1-band Panchromatic/Panchromatic DN Image (PAN) • 4-band Analytic/Analytic DN Image (Blue, Green, Red, NIR) Ground Sampling Distance (nadir) • SkySat-1 & -2: 0.86 m (PAN), 1.0 m (MS) • SkySat-3 to -15: 0.65 m (PAN), 0.8 m (MS). 0.72 m (PAN) and 1.0 m (MS) for data acquired prior to 30/06/2020 • SkySat-16 to -21: 0.57 m (PAN), 0.75 m (MS) Geolocation Accuracy <50 m RMSE The SkySat Ortho Scene product is sensor- and geometrically-corrected (using DEMs with a post spacing of 30 – 90 m) and is projected to a cartographic map projection; the accuracy of the product varies from region-to-region based on available GCPs. • Ortho Panchromatic Scene product – orthorectified, radiometrically corrected, panchromatic (PAN) imagery. • Ortho Panchromatic DN Scene product – orthorectified, panchromatic (PAN), uncalibrated digital number imagery. • Ortho Analytic Scene product – orthorectified, 4-band multispectral (BGR-NIR) imagery. Radiometric corrections are applied to correct for any sensor artifacts and transformation to top-of-atmosphere radiance. • Ortho Analytic DN Scene product – orthorectified, 4-band multispectral (BGR-NIR), uncalibrated digital number imagery. Radiometric corrections are applied to correct for any sensor artifacts. • Ortho Pansharpened Multispectral Scene product – orthorectified, pansharpened, 4-band (BGR-NIR) imagery. • Ortho Visual Scene product – orthorectified, pansharpened, colour-corrected (using a colour curve) 3-band (RGB) imagery. Ortho Scene Product Components and Format Product Components and Format • Image File (GeoTIFF format) • Metadata File (JSON format) • Rational Polynomial Coefficients (Text File) • UDM File (GeoTIFF format) Image Configurations • 1-band Panchromatic/Panchromatic DN Image (PAN) • 4-band Analytic/Analytic DN Image (Blue, Green, Red, NIR) • 4-band Pansharpened Multispectral Image (Blue, Green, Red, NIR) • 3-band Pansharpened (Visual) Image (Red, Green, Blue) Orthorectified Pixel Size 50 cm Projection UTM WGS84 Geolocation Accuracy <10 m RMSE The SkySat Ortho Collect product is created by composing SkySat Ortho Scene products along an imaging strip into segments typically unifying ~60 individual SkySat Ortho Scenes, resulting in an image with a footprint of approximately 20 km x 5.9 km. The products may contain artifacts resulting from the composing process, particular offsets in areas of stitched source scenes. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary
SkySatESAarchive_8.0 Skysat ESA archive ESA STAC Catalog 2016-02-29 -180, -84, 180, 84 https://cmr.earthdata.nasa.gov/search/concepts/C2547572338-ESA.umm_json "The SkySat ESA archive collection consists of SkySat products requested by ESA supported projects over their areas of interest around the world and that ESA collected over the years. The dataset regularly grows as ESA collects new products. Two different product types are offered, Ground Sampling Distance at nadir up to 65 cm for panchromatic and up to 0.8m for multi-spectral. EO-SIP Product Type Product Description Content SSC_DEF_SC Basic and Ortho scene Level 1B 4-bands Analytic /DN Basic scene Level 1B 4-bands Panchromatic /DN Basic scene Level 1A 1-band Panchromatic DN Pre Sup resolution Basic scene Level 3B 3-bands Visual Ortho Scene Level 3B 4-bands Pansharpened Multispectral Ortho Scene Level 3B 4-bands Analytic/DN/SR Ortho Scene Level 3B 1-band Panchromatic /DN Ortho Scene SSC_DEF_CO Ortho Collect Visual 3-band Pansharpened Image Multispectral 4-band Pansharpened Image Multispectral 4-band Analytic/DN/SR Image (B, G, R, N) 1-band Panchromatic Image The Basic Scene product is uncalibrated, not radiometrically corrected for atmosphere or for any geometric distortions inherent in the imaging process: Analytic - unorthorectified, radiometrically corrected, multispectral BGRN Analytic DN - unorthorectified, multispectral BGRN Panchromatic - unorthorectified, radiometrically corrected, panchromatic (PAN) Panchromatic DN - unorthorectified, panchromatic (PAN) L1A Panchromatic DN - unorthorectified, pre-super resolution, panchromatic (PAN) The Ortho Scene product is sensor and geometrically corrected, and is projected to a cartographic map projection: Visual - orthorectified, pansharpened, and colour-corrected (using a colour curve) 3-band RGB Imagery Pansharpened Multispectral - orthorectified, pansharpened 4-band BGRN Imagery Analytic SR - orthorectified, multispectral BGRN. Atmospherically corrected Surface Reflectance product. Analytic - orthorectified, multispectral BGRN. Radiometric corrections applied to correct for any sensor artifacts and transformation to top-of-atmosphere radiance. Analytic DN - orthorectified, multispectral BGRN, uncalibrated digital number imagery product Radiometric corrections applied to correct for any sensor artifacts Panchromatic - orthorectified, radiometrically correct, panchromatic (PAN) Panchromatic DN - orthorectified, panchromatic (PAN), uncalibrated digital number imagery product The Ortho Collect product is created by composing SkySat Ortho Scenes along an imaging strip. The product may contain artifacts resulting from the composing process, particular offsets in areas of stitched source scenes. Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/SkySat/ available on the Third Party Missions Dissemination Service. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary
Smallholder Cashew Plantations in Benin_1 Smallholder Cashew Plantations in Benin MLHUB STAC Catalog 2020-01-01 2023-01-01 2.4636579, 9.0570625, 2.5618896, 9.1603783 https://cmr.earthdata.nasa.gov/search/concepts/C2781412245-MLHUB.umm_json This dataset contains labels for cashew plantations in a 120 km^2 area in the center of Benin. Each pixel is classified for Well-managed plantation, Poorly-managed plantation, No plantation and other classes. The labels are generated using a combination of ground data collection with a handheld GPS device, and final corrections based on Airbus Pléiades imagery. proprietary
-SnowMeltDuration_PMicrowave_1843_1.1 ABoVE: Passive Microwave-derived Annual Snow Melt Duration Date Maps, 1988-2018 ALL STAC Catalog 1988-02-09 2018-07-20 -180, 51.6, -107.83, 72.41 https://cmr.earthdata.nasa.gov/search/concepts/C2223093928-ORNL_CLOUD.umm_json This dataset provides the annual period of snowpack melting (i.e., snow melt duration, SMD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. SMD is the number of days between the main melt onset date (MMOD) and the last day of seasonal snow cover when the melting of snow is complete. These dates were derived from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). This dataset documents variability in SMD across space and the 31-year temporal period. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary
SnowMeltDuration_PMicrowave_1843_1.1 ABoVE: Passive Microwave-derived Annual Snow Melt Duration Date Maps, 1988-2018 ORNL_CLOUD STAC Catalog 1988-02-09 2018-07-20 -180, 51.6, -107.83, 72.41 https://cmr.earthdata.nasa.gov/search/concepts/C2223093928-ORNL_CLOUD.umm_json This dataset provides the annual period of snowpack melting (i.e., snow melt duration, SMD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. SMD is the number of days between the main melt onset date (MMOD) and the last day of seasonal snow cover when the melting of snow is complete. These dates were derived from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). This dataset documents variability in SMD across space and the 31-year temporal period. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary
-Snow_Cover_Extent_and_Depth_1757_1 ABoVE: High Resolution Cloud-Free Snow Cover Extent and Snow Depth, Alaska, 2001-2017 ORNL_CLOUD STAC Catalog 2001-01-01 2017-12-30 -179.18, 55.57, -132.58, 71.42 https://cmr.earthdata.nasa.gov/search/concepts/C2143402490-ORNL_CLOUD.umm_json This dataset provides estimates of maximum snow cover extent (SCE) and snow depth for each 8-day composite period from 2001 to 2017 at 1 km resolution across Alaska. The study area covers the majority land area of Alaska except for areas covered by perennial ice/snow or open water. A downscaling scheme was used in which Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global reanalysis 0.5 degree snow depth data were interpolated to a finer 1 km spatial grid. In the methods used, the downscaling scheme incorporated MODIS SCE (MOD10A2) to better account for the influence of local topography on the 1km snow distribution patterns. For MODIS cloud-contaminated pixels, persistent and patchy cloud cover conditions were improved by applying an elevation-based spatial filtering algorithm to predict snow occurrence. Cloud-free MODIS SCE data were then used to downscale MERRA-2 snow depth data. For each snow-covered 1 km pixel indicated by the MODIS data, the snow depth was estimated based on the snow depth of the neighboring MERRA-2 0.5 grid cell, with weights predicted using a spatial filter. proprietary
+SnowMeltDuration_PMicrowave_1843_1.1 ABoVE: Passive Microwave-derived Annual Snow Melt Duration Date Maps, 1988-2018 ALL STAC Catalog 1988-02-09 2018-07-20 -180, 51.6, -107.83, 72.41 https://cmr.earthdata.nasa.gov/search/concepts/C2223093928-ORNL_CLOUD.umm_json This dataset provides the annual period of snowpack melting (i.e., snow melt duration, SMD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. SMD is the number of days between the main melt onset date (MMOD) and the last day of seasonal snow cover when the melting of snow is complete. These dates were derived from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). This dataset documents variability in SMD across space and the 31-year temporal period. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies. proprietary
Snow_Cover_Extent_and_Depth_1757_1 ABoVE: High Resolution Cloud-Free Snow Cover Extent and Snow Depth, Alaska, 2001-2017 ALL STAC Catalog 2001-01-01 2017-12-30 -179.18, 55.57, -132.58, 71.42 https://cmr.earthdata.nasa.gov/search/concepts/C2143402490-ORNL_CLOUD.umm_json This dataset provides estimates of maximum snow cover extent (SCE) and snow depth for each 8-day composite period from 2001 to 2017 at 1 km resolution across Alaska. The study area covers the majority land area of Alaska except for areas covered by perennial ice/snow or open water. A downscaling scheme was used in which Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global reanalysis 0.5 degree snow depth data were interpolated to a finer 1 km spatial grid. In the methods used, the downscaling scheme incorporated MODIS SCE (MOD10A2) to better account for the influence of local topography on the 1km snow distribution patterns. For MODIS cloud-contaminated pixels, persistent and patchy cloud cover conditions were improved by applying an elevation-based spatial filtering algorithm to predict snow occurrence. Cloud-free MODIS SCE data were then used to downscale MERRA-2 snow depth data. For each snow-covered 1 km pixel indicated by the MODIS data, the snow depth was estimated based on the snow depth of the neighboring MERRA-2 0.5 grid cell, with weights predicted using a spatial filter. proprietary
+Snow_Cover_Extent_and_Depth_1757_1 ABoVE: High Resolution Cloud-Free Snow Cover Extent and Snow Depth, Alaska, 2001-2017 ORNL_CLOUD STAC Catalog 2001-01-01 2017-12-30 -179.18, 55.57, -132.58, 71.42 https://cmr.earthdata.nasa.gov/search/concepts/C2143402490-ORNL_CLOUD.umm_json This dataset provides estimates of maximum snow cover extent (SCE) and snow depth for each 8-day composite period from 2001 to 2017 at 1 km resolution across Alaska. The study area covers the majority land area of Alaska except for areas covered by perennial ice/snow or open water. A downscaling scheme was used in which Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global reanalysis 0.5 degree snow depth data were interpolated to a finer 1 km spatial grid. In the methods used, the downscaling scheme incorporated MODIS SCE (MOD10A2) to better account for the influence of local topography on the 1km snow distribution patterns. For MODIS cloud-contaminated pixels, persistent and patchy cloud cover conditions were improved by applying an elevation-based spatial filtering algorithm to predict snow occurrence. Cloud-free MODIS SCE data were then used to downscale MERRA-2 snow depth data. For each snow-covered 1 km pixel indicated by the MODIS data, the snow depth was estimated based on the snow depth of the neighboring MERRA-2 0.5 grid cell, with weights predicted using a spatial filter. proprietary
Snow_Depth_Data_Images_1656_1 Snow Depth, Stratigraphy, and Temperature in Wrangell St Elias NP, Alaska, 2016-2018 ORNL_CLOUD STAC Catalog 2016-09-01 2018-03-20 -143.32, 62.26, -143, 62.39 https://cmr.earthdata.nasa.gov/search/concepts/C2170971586-ORNL_CLOUD.umm_json This dataset includes data from late-March snow surveys and hourly digital camera images from two study areas within the Wrangell St Elias National Park, Alaska. These data comprise snow density, stratigraphy, and temperature profiles obtained by snow pits; and snow depth data obtained from transects between snow pits. Daily snow depths, adjacent to each pit, were derived from hourly camera images of snow stakes placed adjacent to each pit. These data were collected to constrain and validate a physically-based, spatially-distributed snow evolution model used to simulate snow conditions in Dall sheep habitat. The two study areas are both located within the Jacksina Park Unit (JPU). The first study area, surveyed in 2017, included the northern end of Jaeger Mesa and an area near Rambler mine in the North East of the JPU. The second study area, surveyed in 2018, was within the upper watershed of Pass Creek in the North of the JPU. The remote cameras operated from September 2016 to August 2017 on Jaeger Mesa/Rambler Mine and from September 2017 to July 2018 at Pass Creek. proprietary
Snow_Wildlife_Tracks_AK_WA_2188_1 Snow Properties and Wildlife Tracks in Washington and Alaska ORNL_CLOUD STAC Catalog 2021-01-09 2023-03-13 -150.01, 48.05, -117.17, 63.97 https://cmr.earthdata.nasa.gov/search/concepts/C2772851281-ORNL_CLOUD.umm_json This dataset contains three field seasons of snow-wildlife observations conducted at 707 sites from January 2021 to March 2023 in Washington and Alaska, spanning a broad range of snow conditions. Relatively fresh tracks (usually <24 h) of common large mammal predators (bobcats, coyotes, cougars, and wolves) and their ungulate prey (caribou, Dall sheep, moose, mule deer, and white-tailed deer) were investigated to determine how snow affects predator-prey interactions. The track sink depth and dimensions (width and length) of three consecutive footprints were measured from one individual. Age class was recorded for moose based either on visual confirmation of an individual creating snow tracks or based on track dimensions. The ability to differentiate age classes for smaller ungulates was more uncertain, so age classes for deer, caribou, or sheep were not specified. Animal gait was identified using a simple classification scheme. Data also include animal species, snow density, hardness, total ice, surface temperature, and vegetation type. To best capture snow hardness, surface penetrability and hand-hardness were measured throughout the snowpack. The data are provided in comma-separated values (CSV) format. proprietary
Snowmelt_timing_maps_V2_1712_2 Snowmelt Timing Maps Derived from MODIS for North America, Version 2, 2001-2018 ORNL_CLOUD STAC Catalog 2001-01-01 2018-12-31 -180, 10, 0, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2764725108-ORNL_CLOUD.umm_json This data set provides snowmelt timing maps (STMs), cloud interference maps, and a map with the count of calculated snowmelt timing values for North America. The STMs are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard 8-day composite snow-cover product MOD10A2 collection 6 for the period 2001-01-01 to 2018-12-31. The STMs were created by conducting a time-series analysis of the MOD10A2 snow maps to identify the DOY of snowmelt on a per-pixel basis. Snowmelt timing (no-snow) was defined as a snow-free reading following two consecutive snow-present readings for a given 500-m pixel. The count of STM values is also reported, which represents the number of years on record in the STMs from 2001-2018. proprietary
-Snowpack_Dall_Sheep_Track_1583_1 ABoVE: Dall Sheep Track Sinking Depths, Snow Depth, Hardness, and Density, 2017 ORNL_CLOUD STAC Catalog 2017-03-19 2017-03-22 -143.06, 62.26, -143.01, 62.28 https://cmr.earthdata.nasa.gov/search/concepts/C2162140002-ORNL_CLOUD.umm_json This dataset contains Dall sheep (Ovis dalli dalli) hoof sinking depths in snow tracks, and snow depth, hardness, and density measurements in snow pits adjacent to the tracks. Snow measurements were collected between March 19-22, 2017 at sites on Jaeger Mesa in the Wrangell Mountains (WRST), Alaska. Estimated sheep age classes and track site coordinates are also provided. proprietary
Snowpack_Dall_Sheep_Track_1583_1 ABoVE: Dall Sheep Track Sinking Depths, Snow Depth, Hardness, and Density, 2017 ALL STAC Catalog 2017-03-19 2017-03-22 -143.06, 62.26, -143.01, 62.28 https://cmr.earthdata.nasa.gov/search/concepts/C2162140002-ORNL_CLOUD.umm_json This dataset contains Dall sheep (Ovis dalli dalli) hoof sinking depths in snow tracks, and snow depth, hardness, and density measurements in snow pits adjacent to the tracks. Snow measurements were collected between March 19-22, 2017 at sites on Jaeger Mesa in the Wrangell Mountains (WRST), Alaska. Estimated sheep age classes and track site coordinates are also provided. proprietary
+Snowpack_Dall_Sheep_Track_1583_1 ABoVE: Dall Sheep Track Sinking Depths, Snow Depth, Hardness, and Density, 2017 ORNL_CLOUD STAC Catalog 2017-03-19 2017-03-22 -143.06, 62.26, -143.01, 62.28 https://cmr.earthdata.nasa.gov/search/concepts/C2162140002-ORNL_CLOUD.umm_json This dataset contains Dall sheep (Ovis dalli dalli) hoof sinking depths in snow tracks, and snow depth, hardness, and density measurements in snow pits adjacent to the tracks. Snow measurements were collected between March 19-22, 2017 at sites on Jaeger Mesa in the Wrangell Mountains (WRST), Alaska. Estimated sheep age classes and track site coordinates are also provided. proprietary
SoilResp_HeterotrophicResp_1928_1 Global Gridded 1-km Soil and Soil Heterotrophic Respiration Derived from SRDB v5 ORNL_CLOUD STAC Catalog 1961-01-01 2016-06-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2345796019-ORNL_CLOUD.umm_json This dataset provides global gridded estimates of annual soil respiration (Rs) and soil heterotrophic respiration (Rh) and associated uncertainties at 1 km resolution. Mean soil respiration was estimated using a quantile regression forest model utilizing data from the global Soil Respiration Database Version 5 (SRDB-V5) and covariates of mean annual temperature, seasonal precipitation, and vegetative cover. The SRDB holds results of field studies of soil respiration from around the globe. A total of 4,115 records from 1,036 studies were selected from SRDB-V5. SRDB-V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. These soil respiration records were combined with global meteorological, land cover, and topographic data and then evaluated with variable selection using random forests. The standard deviation and coefficient of variation of Rs are included and were also derived from the same model. Global heterotrophic respiration was calculated from Rs estimates. The data are produced in part from SRDB-V5 inputs that cover the period 1961-2016. proprietary
SoilSCAPE_1339_1 Soil Moisture Profiles and Temperature Data from SoilSCAPE Sites, USA ORNL_CLOUD STAC Catalog 2011-08-03 2019-12-14 -120.99, 31.74, -83.66, 42.3 https://cmr.earthdata.nasa.gov/search/concepts/C2736724942-ORNL_CLOUD.umm_json This data set contains in-situ soil moisture profile and soil temperature data collected at 20-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites in four states (California, Arizona, Oklahoma, and Michigan) in the United States. SoilSCAPE used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data at up to 12 sites over varying durations since August 2011. At its maximum, the network consisted of over 200 wireless sensor installations (nodes), with a range of 6 to 27 nodes per site. The soil moisture sensors (EC-5 and 5-TM from Decagon Devices) were installed at three to four depths, nominally at 5, 20, and 50 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. Temperature sensors were installed at 5 cm depth at six of the sites. Data collection started in August 2011 and continues at eight sites through the present. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional (airborne, e.g. NASA's Airborne Microwave Observation of Subcanopy and Subsurface Mission - AirMOSS) and national (spaceborne, e.g. NASA's Soil Moisture Active Passive - SMAP) scales. proprietary
SoilSCAPE_V2_2049_2 Soil Moisture Profiles and Temperature Data from SoilSCAPE Sites, Version 2 ORNL_CLOUD STAC Catalog 2021-12-03 2023-02-03 -110.05, -36.72, 174.62, 37.2 https://cmr.earthdata.nasa.gov/search/concepts/C2736725173-ORNL_CLOUD.umm_json This dataset contains in-situ soil moisture profile and soil temperature data collected at 30-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites since 2021 in the United States and New Zealand. The SoilSCAPE network has used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data over varying durations since 2011. Since 2021, the SoilSCAPE has upgraded the two previously active sites in Arizona and added several new sites in the United States and New Zealand. These new sites typically use the METER Teros-12 soil moisture sensor. At its maximum, the new network consisted of 57 wireless sensor installations (nodes), with a range of 6 to 8 nodes per site. Each SoilSCAPE site contains multiple wireless end-devices (EDs). Each ED supports up to five soil moisture probes typically installed at 5, 10, 20, and 30 cm below the surface. Sites in Arizona have soil moisture probes installed at up to 75 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional and national (e.g. NASA's Cyclone Global Navigation Satellite System - CYGNSS and Soil Moisture Active Passive - SMAP) scales. The data are provided in NetCDF format. proprietary
-Soil_ActiveLayer_Properties_AK_2315_1 ABoVE: Active Layer Soil Characteristics at Selected Sites Across Alaska ORNL_CLOUD STAC Catalog 2016-08-09 2018-07-07 -149.53, 63.88, -146.56, 68.56 https://cmr.earthdata.nasa.gov/search/concepts/C2849255421-ORNL_CLOUD.umm_json This dataset provides soil active layer characteristics from nine locations across Alaska. Soil samples were collected in 2016 except for one site which was sampled in 2018. Soil cores were collected from each site using a steel barrel and plastic sample tube attached to a hand drill. At the majority of sites, samples were taken from each end of three 30-m transects (i.e. samples collected at the 0 m and 30 m location of each transect). The entire thawed horizon (active layer) was sampled where possible, and the length of cores varies among sites. Cores were kept frozen until analysis in the lab. Samples were sectioned by horizon (organic and mineral), and the organic horizon was split into subsections so that no section was longer than approximately 10 cm. Coarse roots were removed, dried and weighed. Soils were measured for gravimetric water content, percent soil organic matter (SOM), pH, and bulk density. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma-separated values (CSV) format. proprietary
Soil_ActiveLayer_Properties_AK_2315_1 ABoVE: Active Layer Soil Characteristics at Selected Sites Across Alaska ALL STAC Catalog 2016-08-09 2018-07-07 -149.53, 63.88, -146.56, 68.56 https://cmr.earthdata.nasa.gov/search/concepts/C2849255421-ORNL_CLOUD.umm_json This dataset provides soil active layer characteristics from nine locations across Alaska. Soil samples were collected in 2016 except for one site which was sampled in 2018. Soil cores were collected from each site using a steel barrel and plastic sample tube attached to a hand drill. At the majority of sites, samples were taken from each end of three 30-m transects (i.e. samples collected at the 0 m and 30 m location of each transect). The entire thawed horizon (active layer) was sampled where possible, and the length of cores varies among sites. Cores were kept frozen until analysis in the lab. Samples were sectioned by horizon (organic and mineral), and the organic horizon was split into subsections so that no section was longer than approximately 10 cm. Coarse roots were removed, dried and weighed. Soils were measured for gravimetric water content, percent soil organic matter (SOM), pH, and bulk density. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma-separated values (CSV) format. proprietary
+Soil_ActiveLayer_Properties_AK_2315_1 ABoVE: Active Layer Soil Characteristics at Selected Sites Across Alaska ORNL_CLOUD STAC Catalog 2016-08-09 2018-07-07 -149.53, 63.88, -146.56, 68.56 https://cmr.earthdata.nasa.gov/search/concepts/C2849255421-ORNL_CLOUD.umm_json This dataset provides soil active layer characteristics from nine locations across Alaska. Soil samples were collected in 2016 except for one site which was sampled in 2018. Soil cores were collected from each site using a steel barrel and plastic sample tube attached to a hand drill. At the majority of sites, samples were taken from each end of three 30-m transects (i.e. samples collected at the 0 m and 30 m location of each transect). The entire thawed horizon (active layer) was sampled where possible, and the length of cores varies among sites. Cores were kept frozen until analysis in the lab. Samples were sectioned by horizon (organic and mineral), and the organic horizon was split into subsections so that no section was longer than approximately 10 cm. Coarse roots were removed, dried and weighed. Soils were measured for gravimetric water content, percent soil organic matter (SOM), pH, and bulk density. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma-separated values (CSV) format. proprietary
Soil_Carbon_Flux_Maps_1683_1 Gridded Winter Soil CO2 Flux Estimates for pan-Arctic and Boreal Regions, 2003-2100 ORNL_CLOUD STAC Catalog 1993-01-01 2100-11-30 -180, -84.69, 179.9, 89.98 https://cmr.earthdata.nasa.gov/search/concepts/C2143812328-ORNL_CLOUD.umm_json This dataset provides gridded estimates of soil CO2 flux (g C m-2 d-1) for the winter non-growing season (NGS) across pan-Arctic and Boreal permafrost regions (>49 Deg N), at 25 km spatial resolution. The data are the daily average flux over a monthly period for two climate periods: the baseline climate period represents 2003-2018 and the future climate scenarios period represents 2018-2100 under Representative Concentration Pathways (RCP) 4.5 and 8.5. The data were produced by applying a Boosted Regression Tree machine learning approach to create gridded estimates of emissions based on in situ observations of NGS fluxes provided in a related dataset. The resulting monthly average flux data records can be used to calculate annual NGS soil CO2 flux budgets from 2003-2100. proprietary
Soil_Moisture_Alaska_Alberta_2123_1 Hourly Soil Moisture Logger Data, Alberta and Alaska, 2017-2021 ORNL_CLOUD STAC Catalog 2017-07-24 2021-07-29 -148.81, 56.66, -115.11, 69.63 https://cmr.earthdata.nasa.gov/search/concepts/C2633820284-ORNL_CLOUD.umm_json This dataset includes hourly in-situ soil moisture measurements from data loggers in predominantly organic soils (very low bulk density) at two locations: 1) along the Sag River in Alaska, U.S., and 2) near Red Earth Creek in Alberta, Canada. The dataset also provides soil moisture probe periods, temperature probe readings, as well as calibration coefficients and soil profile measurements used to create per probe calibrations for derived volumetric moisture content. The Campbell Scientific CR200 data loggers used CS625 water content reflectometers and temperature probe 109. Further details to the derivation of the calibrations are provided in a supplementary document. The purpose of the dataset is to provide field measurements that can be used for calibration/validation for satellite-based soil moisture retrieval algorithms. With some interruptions, the dataset exists from July 2017 to July 2021. The data are provided in comma-separated values (CSV) format. proprietary
Soil_Sensors_1 Data collected from in-situ soil sensors placed at Macquarie Island and Casey Station AU_AADC STAC Catalog 2005-01-01 110.52394, -66.28192, 158.9392, -54.498737 https://cmr.earthdata.nasa.gov/search/concepts/C1214313810-AU_AADC.umm_json "Data are collected for the purposes of monitoring on-ground works at Australian Antarctic stations associated with the remediation of petroleum hydrocarbon contaminated soil. Output datasets consist of soil oxygen (%), soil temperature (C), soil moisture content (VWC - Volumetric Water Content %), and aeration manifold pressure as measured by buried sensors (O2, T C, VWC) or manifold instruments (pressure). Sensor types are either: AD590 (temperature C) AD592 (temperature C) Figaro KE25 (% oxygen) Vegetronix VH400 (Volumetric Water Content %) 26PCD (Pressure, kPa) Sensors are attached via instrument cables to Datataker dt80 series loggers, which are housed in waterproof containers mounted on buildings, or inside buildings at Australian Antarctic stations. At the Macquarie Island isthmus, oxygen sensors are attached to buried groundwater monitoring wells (screened PVC tubes, known as mini-piezometers). Pressure sensors are attached to air distribution manifolds (part of an in-situ aeration distribution network), and temperature sensors are buried in the soil profile. Sensor nomenclature is as follows: FF0807/1/O2 (Fuel Farm, 2008 installation, mini-piezometer number 07, Sensor 1, Oxygen sensor) MPH_PS_3 (Main Power House, pressure sensor number 03) Biopiles consist of excavated soil placed in temporary, geo-engineered liner cells. Soil oxygen, soil temperature, and soil moisture content are typically measured at 50 cm height intervals from within the soil piles. Temperature and moisture are also typically measured from within the subgrade and liner materials - common nomenclature for sensor names are as follows: BP1/0.5SS_G11/O2 (Biopile 1, buried 0.5 m in soil profile, location G11, Oxygen sensor) BP1/AGM_G1/T(Biopile 1, Above GeoMembrane, Location G1, Temperature sensor) BP6/AGCL_N1/M (Biopile 6, Above Geosynthetic Clay Liner, Location N1, Moisture sensor) BP6/IGCL_N9/M (Biopile 6, Inside Geosynthetic Clay Liner, Location N9, Moisture sensor) EXT/-30SS_E1/M (External soil location, 30 cm below sediment surface, Sensor 1, Moisture sensor) Permeable Reactive Barrier (PRB's) are permeable gates emplaced within the regolith to treat hydrocarbon contaminated groundwater/meltwater and prevent offsite migration of contaminants (primarily hydrocarbons). The barriers have undergone several design iterations, but have consisted of staged (3 sections) permeable reactive or non-reactive filter media (Granular Activated Carbon, Silica sand, Zeolite, MaxBac (TM), Zeopro (TM), Zero Valent Iron), which are placed in buried galvanised shipping cages. The original PRB (installed 2005/06) is named ""PRB"", the second smaller PRB (named the Upper PRB or ""UPRB"" due to its higher elevation in the ) was installed in 2010/11 to treat contaminated groundwater around the MPH settling tank bund and protected the area cleaned as part of the MPH excavation. From this date, the original PRB has also been referred to as the ""lower PRB"". Sensor nomenclature is as follows: C_MP9/700/T (MiniPiezometer 9, 700 mm below ground surface, Temperature sensor) C_CG3_3/600/02 (Cage 3,Section 3, 600 mm below ground surface, Oxygen sensor) These data are downloaded from the sensors to the Australian Antarctic Division on a daily basis. Data are collected by the sensors every 5-20 minutes. As of 2013-03-04, the following personnel have been involved in the project: Greg Hince (AAD) - Project Manager, Field Remediation (11/12-ongoing). Principle Contact Ian Snape (AAD) - Project Principal (Macquarie Island and Casey Station), Macquarie Island 2008 field team. Geoff Stevens (University of Melbourne) - Project Principal - Casey Lower PRB installation Ben Raymond (AAD) - Calibration and Installation of sensors for Macquarie Island 08/09 field season, maintenance of database and remote troubleshooting of dataloggers. Tim Spedding (ex AAD) - Field Project Manager (08/09-10/11), Macquarie Island 2008 field team Dan Wilkins (AAD) - Datalogger management and system design (2009 onwards), Casey station sensor installation 10/11 and 11/12. John Rayner (ex AAD) - System design - Oxygen sensors. Macquarie Island 2008 field team. Installation of lower PRB (Casey) in 05/06. Lauren Wise (AAD) - Field maintenance and system operation (Macquarie Island, 10/11 and 12/13) Rebecca McWatters (AAD)- Casey Station sensors installation 10/11, 11/12, 12/13 Susan Ferguson (ex AAD) - Macquarie Island 2008 field team, Macquarie Island system maintenance 2009. Brett Quinton (ex AAD) - Macquarie Island system maintenance 2009 Charles Sutherland (AAD contractor/expeditioner) - Macquarie Island system maintenance 12/13 field season Robby Kilpatrick (AAD contractor/expeditioner) - Calibration and Installation of sensors for Macquarie Island 11/12 field season Kathryn Mumford (AAS Project Co-investigator, University of Melbourne) - Installation of lower PRB (Casey) in 05/06. Tom Statham (University of Melbourne, PhD student) - System installation, Casey 10/11 Warren Nichols - Oxygen sensor modifications (resin encasement) Rebecca Miller (AAD contractor/expeditioner) - Calibration and Installation of sensors for Casey EPH biopile - 12/13 Field Season Dan Jones (Queens University, Canada) - Calibration and Installation of sensors for Casey EPH biopile - 12/13 Field Season Various members of AAD Telecommunications Team (on ground troubleshooting and maintenance)" proprietary
-Soil_Temp_Moisture_Alaska_1869_1 ABoVE: Soil Temperature and VWC at Unburned and Burned Sites Across Alaska, 2016-2023 ORNL_CLOUD STAC Catalog 2016-08-11 2023-09-02 -163.24, 61.27, -146.56, 68.99 https://cmr.earthdata.nasa.gov/search/concepts/C2143401688-ORNL_CLOUD.umm_json This dataset provides soil temperature and volumetric water content (VWC) measurements at 15 cm depth collected at 12 selected boreal and tundra sites located across Alaska. Each site is equipped with a HOBO MicroStation Data Logger that hosts two soil temperature sensors (HOBO S-TMB-M006 Temperature Smart Sensor), and two soil moisture sensors (HOBO S-SMD-M005 10HS Soil Moisture Smart Sensor). Each sensor was installed horizontally at a depth of 15 cm within the soil profile. Samples of soil from seven sites were taken to a laboratory for determination of site-specific soil moisture sensor calibration curves to correct raw measurements. Data were nominally recorded at an hourly frequency and downloaded from the sites at least annually for the period 2016-08-11 to 2023-09-02, but data coverage varies by site. These measurements were collected at the same sites as previously archived CO2 efflux and thaw depth data. The data are provided in comma-separated values (CSV) format. proprietary
Soil_Temp_Moisture_Alaska_1869_1 ABoVE: Soil Temperature and VWC at Unburned and Burned Sites Across Alaska, 2016-2023 ALL STAC Catalog 2016-08-11 2023-09-02 -163.24, 61.27, -146.56, 68.99 https://cmr.earthdata.nasa.gov/search/concepts/C2143401688-ORNL_CLOUD.umm_json This dataset provides soil temperature and volumetric water content (VWC) measurements at 15 cm depth collected at 12 selected boreal and tundra sites located across Alaska. Each site is equipped with a HOBO MicroStation Data Logger that hosts two soil temperature sensors (HOBO S-TMB-M006 Temperature Smart Sensor), and two soil moisture sensors (HOBO S-SMD-M005 10HS Soil Moisture Smart Sensor). Each sensor was installed horizontally at a depth of 15 cm within the soil profile. Samples of soil from seven sites were taken to a laboratory for determination of site-specific soil moisture sensor calibration curves to correct raw measurements. Data were nominally recorded at an hourly frequency and downloaded from the sites at least annually for the period 2016-08-11 to 2023-09-02, but data coverage varies by site. These measurements were collected at the same sites as previously archived CO2 efflux and thaw depth data. The data are provided in comma-separated values (CSV) format. proprietary
-Soil_Temperature_Profiles_AK_1767_1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, AK and Canada, 2016-2019 ORNL_CLOUD STAC Catalog 2016-06-25 2019-08-22 -163.18, 63.89, -134.34, 69.92 https://cmr.earthdata.nasa.gov/search/concepts/C2143402511-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 16 monitoring sites in Alaska, USA, and at one site in Yukon, Canada. The six sites are collocated with seismic stations of the USArray program. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m. Measurements were made from 2016-2018 at two sites, 2017-2019 at four sites, and 2018-2019 at 11 sites using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. proprietary
+Soil_Temp_Moisture_Alaska_1869_1 ABoVE: Soil Temperature and VWC at Unburned and Burned Sites Across Alaska, 2016-2023 ORNL_CLOUD STAC Catalog 2016-08-11 2023-09-02 -163.24, 61.27, -146.56, 68.99 https://cmr.earthdata.nasa.gov/search/concepts/C2143401688-ORNL_CLOUD.umm_json This dataset provides soil temperature and volumetric water content (VWC) measurements at 15 cm depth collected at 12 selected boreal and tundra sites located across Alaska. Each site is equipped with a HOBO MicroStation Data Logger that hosts two soil temperature sensors (HOBO S-TMB-M006 Temperature Smart Sensor), and two soil moisture sensors (HOBO S-SMD-M005 10HS Soil Moisture Smart Sensor). Each sensor was installed horizontally at a depth of 15 cm within the soil profile. Samples of soil from seven sites were taken to a laboratory for determination of site-specific soil moisture sensor calibration curves to correct raw measurements. Data were nominally recorded at an hourly frequency and downloaded from the sites at least annually for the period 2016-08-11 to 2023-09-02, but data coverage varies by site. These measurements were collected at the same sites as previously archived CO2 efflux and thaw depth data. The data are provided in comma-separated values (CSV) format. proprietary
Soil_Temperature_Profiles_AK_1767_1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, AK and Canada, 2016-2019 ALL STAC Catalog 2016-06-25 2019-08-22 -163.18, 63.89, -134.34, 69.92 https://cmr.earthdata.nasa.gov/search/concepts/C2143402511-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 16 monitoring sites in Alaska, USA, and at one site in Yukon, Canada. The six sites are collocated with seismic stations of the USArray program. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m. Measurements were made from 2016-2018 at two sites, 2017-2019 at four sites, and 2018-2019 at 11 sites using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. proprietary
+Soil_Temperature_Profiles_AK_1767_1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, AK and Canada, 2016-2019 ORNL_CLOUD STAC Catalog 2016-06-25 2019-08-22 -163.18, 63.89, -134.34, 69.92 https://cmr.earthdata.nasa.gov/search/concepts/C2143402511-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 16 monitoring sites in Alaska, USA, and at one site in Yukon, Canada. The six sites are collocated with seismic stations of the USArray program. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m. Measurements were made from 2016-2018 at two sites, 2017-2019 at four sites, and 2018-2019 at 11 sites using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. proprietary
Sonoma_County_Forest_AGB_1764_1 CMS: LiDAR Biomass Improved for High Biomass Forests, Sonoma County, CA, USA, 2013 ORNL_CLOUD STAC Catalog 2013-09-01 2013-09-01 -123.54, 38.11, -122.34, 38.85 https://cmr.earthdata.nasa.gov/search/concepts/C2389021440-ORNL_CLOUD.umm_json This data set provides estimates of above-ground woody biomass and uncertainty at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha), were generated using a combination of airborne LiDAR data and field plot measurements with a parametric modeling approach. The relationship between field estimated and airborne LiDAR estimated aboveground biomass density is represented as a parametric model that predicts biomass as a function of canopy cover and 50th percentile and 90th percentile LiDAR heights at a 30-m resolution. To estimate uncertainty, the biomass model was re-fit 1,000 times through a sampling of the variance-covariance matrix of the fitted parametric model. This produced 1,000 estimates of biomass per pixel. The 5th and 95th percentiles, and the standard deviation of these pixel biomass estimates, were calculated. proprietary
South Africa Crop Type Competition_1 South Africa Crop Type Competition MLHUB STAC Catalog 2020-01-01 2023-01-01 17.818514, -34.1538276, 19.7650866, -30.7480751 https://cmr.earthdata.nasa.gov/search/concepts/C2781412651-MLHUB.umm_json This dataset was produced as part of the [Radiant Earth Spot the Crop Challenge](https://zindi.africa/hackathons/radiant-earth-spot-the-crop-hackathon). The objective of the competition was to create a machine learning model to classify fields by crop type from images collected during the growing season by the Sentinel-2 and Sentinel-1 satellites. proprietary
-Southern_Boreal_Plot_Attribute_1740_1 ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016 ALL STAC Catalog 2016-05-30 2016-06-16 -109.17, 54.09, -104.69, 57.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143402623-ORNL_CLOUD.umm_json This dataset provides the results of field measurements and estimates of carbon stocks and combustion rates that characterize burned and unburned southern boreal forest stands near the La Ronge and Weyakwin communities in central Saskatchewan (SK), Canada. Measurements were completed in 2016 at 47 stands that burned in the 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in comparable adjacent areas. Stands were characterized through field observations and sampling of the vegetative community (i.e., tree species, abundance, and biophysical measurements, stand age, coarse woody debris, history of fires or logging), soils (i.e., soil moisture class, unburned and burned soil organic layer depth, samples for bulk density and carbon analyses), and basic landscape geophysical traits. From these results, the pre-fire carbon stocks and carbon combustion values from both the above- and below-ground pools were estimated using a combination of linear and mixed-effects modeling and were calibrated against carbons stocks from the unburned stands. Estimates of uncertainty were generated for above- and below-ground carbon stocks and combustion values using a Monte Carlo framework paired with classic uncertainty propagation techniques. proprietary
Southern_Boreal_Plot_Attribute_1740_1 ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016 ORNL_CLOUD STAC Catalog 2016-05-30 2016-06-16 -109.17, 54.09, -104.69, 57.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143402623-ORNL_CLOUD.umm_json This dataset provides the results of field measurements and estimates of carbon stocks and combustion rates that characterize burned and unburned southern boreal forest stands near the La Ronge and Weyakwin communities in central Saskatchewan (SK), Canada. Measurements were completed in 2016 at 47 stands that burned in the 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in comparable adjacent areas. Stands were characterized through field observations and sampling of the vegetative community (i.e., tree species, abundance, and biophysical measurements, stand age, coarse woody debris, history of fires or logging), soils (i.e., soil moisture class, unburned and burned soil organic layer depth, samples for bulk density and carbon analyses), and basic landscape geophysical traits. From these results, the pre-fire carbon stocks and carbon combustion values from both the above- and below-ground pools were estimated using a combination of linear and mixed-effects modeling and were calibrated against carbons stocks from the unburned stands. Estimates of uncertainty were generated for above- and below-ground carbon stocks and combustion values using a Monte Carlo framework paired with classic uncertainty propagation techniques. proprietary
+Southern_Boreal_Plot_Attribute_1740_1 ABoVE: Characterization of Burned and Unburned Boreal Forest Stands, SK, Canada, 2016 ALL STAC Catalog 2016-05-30 2016-06-16 -109.17, 54.09, -104.69, 57.36 https://cmr.earthdata.nasa.gov/search/concepts/C2143402623-ORNL_CLOUD.umm_json This dataset provides the results of field measurements and estimates of carbon stocks and combustion rates that characterize burned and unburned southern boreal forest stands near the La Ronge and Weyakwin communities in central Saskatchewan (SK), Canada. Measurements were completed in 2016 at 47 stands that burned in the 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in comparable adjacent areas. Stands were characterized through field observations and sampling of the vegetative community (i.e., tree species, abundance, and biophysical measurements, stand age, coarse woody debris, history of fires or logging), soils (i.e., soil moisture class, unburned and burned soil organic layer depth, samples for bulk density and carbon analyses), and basic landscape geophysical traits. From these results, the pre-fire carbon stocks and carbon combustion values from both the above- and below-ground pools were estimated using a combination of linear and mixed-effects modeling and were calibrated against carbons stocks from the unburned stands. Estimates of uncertainty were generated for above- and below-ground carbon stocks and combustion values using a Monte Carlo framework paired with classic uncertainty propagation techniques. proprietary
Southern_Ocean_Drifter_0 Southern Pacific Ocean drifter measurements in 1996 OB_DAAC STAC Catalog 1996-09-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360666-OB_DAAC.umm_json Measurements taken by a drifter in the Southern Pacific Ocean in 1996. proprietary
Spire.live.and.historical.data_8.0 Spire live and historical data ESA STAC Catalog 2016-06-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689697-ESA.umm_json "The data collected by Spire from it's 100 satellites launched into Low Earth Orbit (LEO) has a diverse range of applications, from analysis of global trade patterns and commodity flows to aircraft routing to weather forecasting. The data also provides interesting research opportunities on topics as varied as ocean currents and GNSS-based planetary boundary layer height. The following products can be requested: GNSS Polarimetric Radio Occultation (STRATOS) Novel Polarimetric Radio Occultation (PRO) measurements collected by three Spire satellites are available over 15-May-2023 to 30-November-2023. PRO differ from regular RO (described below) in that the H and V polarizations of the signal are available, as opposed to only Right-Handed Circularly Polarized (RHCP) signals in regular RO. The differential phase shift between H and V correlates with the presence of hydrometeors (ice crystals, rain, snow, etc.). When combined, the H and V information provides the same information on atmospheric thermodynamic properties as RO: temperature, humidity, and pressure, based on the signal’s bending angle. Various levels of the products are provided. GNSS Reflectometry (STRATOS) GNSS Reflectometry (GNSS-R) is a technique to measure Earth’s surface properties using reflections of GNSS signals in the form of a bistatic radar. Spire collects two types of GNSS-R data: Near-Nadir incidence LHCP reflections collected by the Spire GNSS-R satellites, and Grazing-Angle GNSS-R (i.e., low elevation angle) RHCP reflections collected by the Spire GNSS-RO satellites. The Near-Nadir GNSS-R collects DDM (Delay Doppler Map) reflectivity measurements. These are used to compute ocean wind / wave conditions and soil moisture over land. The Grazing-Angle GNSS-R collects 50 Hz reflectivity and additionally carrier phase observations. These are used for altimetry and characterization of smooth surfaces (such as ice and inland water). Derived Level 1 and Level 2 products are available, as well as some special Level 0 raw intermediate frequency (IF) data. Historical grazing angle GNSS-R data are available from May 2019 to the present, while near-nadir GNSS-R data are available from December 2020 to the present. Name Temporal coverage Spatial coverage Description Data format and content Application Polarimetric Radio Occultation (PRO) measurements 15-May-2023 to 30-November-2023 Global PRO measurements observe the properties of GNSS signals as they pass through by Earth's atmosphere, similar to regular RO measurements. The polarization state of the signals is recorded separately for H and V polarizations to provide information on the anisotropy of hydrometeors along the propagation path. leoOrb.sp3. This file contains the estimated position, velocity and receiver clock error of a given Spire satellite after processing of the POD observation file PRO measurements add a sensitivity to ice and precipitation content alongside the traditional RO measurements of the atmospheric temperature, pressure, and water vapor. proObs. Level 0 - Raw open loop carrier phase measurements at 50 Hz sampling for both linear polarization components (horizontal and vertical) of the occulted GNSS signal. h(v)(c)atmPhs. Level 1B - Atmospheric excess phase delay computed for each individual linear polarization component (hatmPhs, vatmPhs) and for the combined (“H” + “V”) signal (catmPhs). Also contains values for signal-to-noise ratio, transmitter and receiver positions and open loop model information. polPhs. Level 1C - Combines the information from the hatmPhs and vatmPhs files while removing phase continuities due to phase wrapping and navigation bit modulation. patmPrf. Level 2 - Bending angle, dry refractivity, and dry temperature as a function of mean sea level altitude and impact parameter derived from the “combined” excess phase delay (catmPhs) Near-Nadir GNSS Reflectometry (NN GNSS-R) measurements 25-January-2024 to 24-July-2024 Global Tracks of surface reflections as observed by the near-nadir pointing GNSS-R antennas, based on Delay Doppler Maps (DDMs). gbrRCS.nc. Level 1B - Along-track calibrated bistatic radar cross-sections measured by Spire conventional GNSS-R satellites. NN GNSS-R measurements are used to measure ocean surface winds and characterize land surfaces for applications such as soil moisture, freeze/thaw monitoring, flooding detection, inland water body delineation, sea ice classification, etc. gbrNRCS.nc. Level 1B - Along-track calibrated bistatic and normalized radar cross-sections measured by Spire conventional GNSS-R satellites. gbrSSM.nc. Level 2 - Along-track SNR, reflectivity, and retrievals of soil moisture (and associated uncertainties) and probability of frozen ground. gbrOcn.nc. Level 2 - Along-track retrievals of mean square slope (MSS) of the sea surface, wind speed, sigma0, and associated uncertainties. Grazing angle GNSS Reflectometry (GA GNSS-R) measurements 25-January-2024 to 24-July-2024 Global Tracks of surface reflections as observed by the limb-facing RO antennas, based on open-loop tracking outputs: 50 Hz collections of accumulated I/Q observations. grzRfl.nc. Level 1B - Along-track SNR, reflectivity, phase delay (with respect to an open loop model) and low-level observables and bistatic radar geometries such as receiver, specular reflection, and the transmitter locations. GA GNSS-R measurements are used to 1) characterize land surfaces for applications such as sea ice classification, freeze/thaw monitoring, inland water body detection and delineation, etc., and 2) measure relative altimetry with dm-level precision for inland water bodies, river slopes, sea ice freeboard, etc., but also water vapor characterization from delay based on tropospheric delays. grzIce.nc. Level 2 - Along-track water vs sea ice classification, along with sea ice type classification. grzAlt.nc. Level 2 - Along-track phase-delay, ionosphere-corrected altimetry, tropospheric delay, and ancillary models (mean sea surface, tides). Additionally, the following products (better detailed in the ToA) can be requested but the acceptance is not guaranteed and shall be evaluated on a case-by-case basis: Other STRATOS measurements: profiles of the Earth’s atmosphere and ionosphere, from December 2018 ADS-B Data Stream: monthly subscription to global ADS-B satellite data, available from December 2018 AIS messages: AIS messages observed from Spire satellites (S-AIS) and terrestrial from partner sensor stations (T-AIS), monthly subscription available from June 2016 The products are available as part of the Spire provision with worldwide coverage. All details about the data provision, data access conditions and quota assignment procedure are described in the _$$Terms of Applicability$$ https://earth.esa.int/eogateway/documents/20142/37627/SPIRE-Terms-Of-Applicability.pdf/0dd8b3e8-05fe-3312-6471-a417c6503639 ." proprietary
Stream_GIS_USGS Digital Line Graphs of U.S. Streams for the EPA Clean Air Mapping and Analysis Program (C-MAP) CEOS_EXTRA STAC Catalog 1970-01-01 -127.77, 23.25, -65.71, 48.15 https://cmr.earthdata.nasa.gov/search/concepts/C2231553171-CEOS_EXTRA.umm_json This is a 1:2,000,000 coverage of streams for the conterminous United States. This coverage was intended for use as a background display for the National Water Summary program. The stream layer was extracted from the 1:2,000,000 Digital Line Graph files. Originally, each state was stored as a separate coverage. In this version, the individual state coverages all have been appended. [Summary provided by EPA] proprietary
Surface_Oligo_Med_Sea_0 Surface oligotrophic measurements in the West-central Mediterranean Sea OB_DAAC STAC Catalog 2008-07-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360667-OB_DAAC.umm_json Measurements taken in the west-central Mediterranean Sea of surface oligotrophic water in 2008. proprietary
Survey_1980_81_Ingrid_Christenson_1 Gravity and Miscellaneous Fieldwork Report - Ingrid Christenson Coast 1980-81 AU_AADC STAC Catalog 1980-10-01 1981-02-28 75, -69.5, 79, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313835-AU_AADC.umm_json Report on field season on Ingrid Christenson coast summer 1980-81. Program aims: Helicopter Geophysical (gravity) Glaciological Survey; Palaeomagnetism, Vertical Air Photography. See the report for more details. proprietary
-Survey_1988_89_Mawson_npcms_1 1988/89 Summer season, surveying and mapping program, Mawson - North Prince Charles Mountains - Davis AU_AADC STAC Catalog 1988-10-01 1989-02-28 62, -70, 79, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313847-AU_AADC.umm_json Field season report of these programs: 1988/89 Summer Season surveying and mapping North Prince Charles Mountains; ...mapping program Northern PCM's - Mawson Doppler Translocation Support; ....mapping program Voyage 6 stopover Davis. Includes maps and mapsheet layouts. See the report for full details on the program. Contents are: Introduction Preparation Voytage to Antarctica 1988/89 Summer Season Surveying and Mapping Program, Northern Prince Charles Mountains 1988/89 Summer Season Surveying and Mapping Program, Voyage 6 Stopover, Davis Performance of Equipment Station Marking Field Camping Climatic Conditions Conclusion Appendices proprietary
Survey_1988_89_Mawson_npcms_1 1988/89 Summer season, surveying and mapping program, Mawson - North Prince Charles Mountains - Davis ALL STAC Catalog 1988-10-01 1989-02-28 62, -70, 79, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313847-AU_AADC.umm_json Field season report of these programs: 1988/89 Summer Season surveying and mapping North Prince Charles Mountains; ...mapping program Northern PCM's - Mawson Doppler Translocation Support; ....mapping program Voyage 6 stopover Davis. Includes maps and mapsheet layouts. See the report for full details on the program. Contents are: Introduction Preparation Voytage to Antarctica 1988/89 Summer Season Surveying and Mapping Program, Northern Prince Charles Mountains 1988/89 Summer Season Surveying and Mapping Program, Voyage 6 Stopover, Davis Performance of Equipment Station Marking Field Camping Climatic Conditions Conclusion Appendices proprietary
+Survey_1988_89_Mawson_npcms_1 1988/89 Summer season, surveying and mapping program, Mawson - North Prince Charles Mountains - Davis AU_AADC STAC Catalog 1988-10-01 1989-02-28 62, -70, 79, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313847-AU_AADC.umm_json Field season report of these programs: 1988/89 Summer Season surveying and mapping North Prince Charles Mountains; ...mapping program Northern PCM's - Mawson Doppler Translocation Support; ....mapping program Voyage 6 stopover Davis. Includes maps and mapsheet layouts. See the report for full details on the program. Contents are: Introduction Preparation Voytage to Antarctica 1988/89 Summer Season Surveying and Mapping Program, Northern Prince Charles Mountains 1988/89 Summer Season Surveying and Mapping Program, Voyage 6 Stopover, Davis Performance of Equipment Station Marking Field Camping Climatic Conditions Conclusion Appendices proprietary
Survey_1989_90_Casey_airfield_1 Antarctic Survey Report, Casey Summer 1989/90 AU_AADC STAC Catalog 1989-12-15 1990-02-24 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313880-AU_AADC.umm_json Report on surveys with major tasks: Casey Airfield Survey; Casey Engineering Surveys; Tunnel Terrestrial Photography. Includes tables, diagrams and colour photographic prints. The aims of the 1989/90 summer season surveying and mapping program at Casey are as set out in priority order below: 1) Provide surveying support as and when required to the RAAF ground contingent charged with the responsibility of preparing an ice runway for the proposed RAAF C130 Hercules sorties in mid-February 1990. 2) Carry out the following engineering surveys for the Australian Construction Services: - Detail survey of proposed helipad site approximately centred on 2040E, 7135N on Casey Master Plan Issue No. 9. - Detail survey of proposed helipad site approximately centred on 2254E, 7132N on Casey Master Plan Issue No. 9. - Old-New Casey link road movement monitoring survey. - Hydrographic survey of the melt water lake at 1900E, 6900N on Casey Master Plan Issue No. 9. - Hydrographic survey of the melt water lake at 2000E, 7200N on Casey Master Plan Issue No. 9. 3) Observe horizontal and vertical angles and EDM distances which will enable the strengthening of the geodetic control network in the Bailey and Clark Peninsula areas. 4) Carry out a topographic survey of the Bailey Peninsula area which will enable the preparation of the Casey Management Plan. proprietary
Survey_1989_90_Lambert_1 Lambert Glacier Basin Traverse 1989/90 summer season survey report AU_AADC STAC Catalog 1989-12-04 1990-02-13 55.7, -73.8, 62, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214313861-AU_AADC.umm_json Report by survey staff on the Lambert Glacier Basin Traverse in summer of 1989/90. Includes original photographic prints. The Lambert Glacier Basin Traverse was one of the projects included in the 1989/90 summer programme of the Australian National Antarctic Research Expedition (ANARE) that involved significant survey involvement. The project is an important part of the on-going research programme of the Glaciology section, Antarctic Division. The Lambert Glacier is the largest glacier on Earth. Lying in MacRobertson Land of the Australian Antarctic Territory it drains an area almost half the size of Australia. Recent programs by the Antarctic Division have investigated the glacier itself, however to achieve the overall objective of establishing the Mass Budget of the Lambert system and all its related mechanisms, a study of the catchment was necessary. To this end the first of a series of glaciological traverses was undertaken in the 1989/1990 summer season to make various measurements including ice movement, ice thickness, gravity, magnetometer and snow accumulation. The over snow traverse was effected using three specially built D7H tractors hauling a series of sleds for transport and manned by a party of six. In two and a half months the party, often as two separate units, travelled eight hundred kilometres into the interior of MacRobertson Land along the 2500 metre contour in temperatures ranging between -15 and -38 degrees centigrade. The first priorities were to set up and accurately position ice movement stations to establish rates of flow into the glacier, and to depot fuel to facilitate further traversing over the next few years. Geodetic measurements were effected using four WM102 dual frequency GPS receivers and two MX1502 Transit receivers in a survey network carefully planned to overcome a series of anticipated problems, many being peculiar to operations in polar regions. proprietary
Survey_1989_90_mawson_1 Mawson Blue Ice Runway Reconnaissance Survey, February 1990 AU_AADC STAC Catalog 1990-02-22 1990-02-26 62.22656, -68.1061, 63.45703, -67.49175 https://cmr.earthdata.nasa.gov/search/concepts/C1214313882-AU_AADC.umm_json Report of reconnaissance of selected areas of blue ice in the Mawson hinterland - regarding possible future runway sites suitable for C130 Hercules type aircraft. Program 22 - 26 February 1990. Includes colour print copies, diagrams, slopes of blue ice areas and maps. Aim - To carry out a preliminary reconnaissance of selected area of blue ice in the Mawson hinterland in order to determine which, if any, of these areas may be suitable for further detailed investigation as possible future runway sites capable of handling C130 Hercules type aircraft. Personnel - Mr P. Murphy, Surveyor Class 1, Mr J. Hyslop, Surveyor Class 1, Mr N. Peters, Technical Officer Grade 2, Mr P. Malcolm, Glaciology, Mr R. Kiernan, Glaciology. Time Frame - 22-26 February, 1990 (approx). Mr Phil Barnaart carried out a reconnaissance of possible blue ice runway sites in 1988 related to the proposed operation of Russian aircraft. He prepared a reconnaissance report. More information available in the download file. proprietary
@@ -15307,8 +15312,8 @@ TerraSAR-X_8.0 TerraSAR-X ESA archive ESA STAC Catalog 2007-07-01 -180, -90, 18
TerraSAR-X_TanDEM-X.full.archive.and.tasking_7.0 TerraSAR-X/TanDEM-X full archive and tasking ESA STAC Catalog 2007-11-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689619-ESA.umm_json TerraSAR-X/TanDEM-X full archive and new tasking products can be acquired in six image modes with flexible resolutions (from 0.25 m to 40 m) and scene sizes and are provided in different packages: Staring SpotLight (basic, Interferometric pack, and Maritime pack) High Resolution SpotLight (basic, Interferometric pack, and Maritime pack) SpotLight (basic, Interferometric pack, and Maritime pack) StripMap (basic, Interferometric pack, and Maritime pack) ScanSAR (basic and Maritime pack) Wide ScanSAR (basic and Maritime pack) Product Overview: >> Product: SAR-ST • Instrument mode: Staring SpotLight • Available resolutions (up to): 0.25 m • Scene size: 4x3.7 km2 >> Product: SAR-HS • Instrument mode: High Resolution SpotLight • Available resolutions (up to): 1 m • Scene size: 10x5 km2 >> Product: SAR-SL • Instrument mode: SpotLight • Available resolutions (up to): 2 m • Scene size: 10x10 km2 >> Product: SAR-SM • Instrument mode: StripMap • Available resolutions (up to): 3 m • Scene size: 30x50 km2 (up to 30x1650) • Basic products (SAR-SM) are intended as the products delivered as a standard scene. The available processing levels are: SSC (Single Look Slant Range Complex) in DLR-defined COSAR binary format, MGD (Multi Look Ground Range Detected) in GeoTiff format, GEC (Geocoded Ellipsoid Corrected) in GeoTiff format, EEC (Enhanced Ellipsoid Corrected) in GeoTiff format. >> Product: SAR-SC • Instrument mode: ScanSAR • Available resolutions (up to): 18 m • Scene size: 100x150 km2 (up to 100x1650) >> Product: SAR-WS • Instrument mode: Wide ScanSAR • Available resolutions (up to): 40 m • Scene size: 270x200 km2 (up to 270x1500) >> Available processing levels: • SSC (Single Look Slant Range Complex): azimuth - slant range (time domain) • MGD (Multi Look Ground Range Detected): azimuth - ground range (without terrain correction) • GEC (Geocoded Ellipsoid Corrected): map geometry with ellipsoidal corrections only (no terrain correction performed) • EEC (Enhanced Ellipsoid Corrected): map geometry with terrain correction, using a DEM >> Format: • SSC: DLR-defined COSAR binary • MGD: GeoTiff • GEC: GeoTiff • EEC: GeoTiff >> Spatial coverage: Worldwide >> Interferometry package: • InSAR-ST, InSAR-HS, InSAR-SL, InSAR-SM • Only SSC • At least five ordered scenes within six months from first order • N/A for SAR-SC and SAR-WS >> Maritime Monitoring package: • MmSAR-ST, MmSAR-HS, MmSAR-SL, MmSAR-SM, MmSAR-SC, MmSAR-WS • Only SSC, MGD, GEC • At least 75% of the scene area is water • More than five ordered scenes in three months The following WorldDEM products can be requested: Product: WorldDEMcore Description: WorldDEMcore is output of interferometric processing of StripMap data pairs without any post-processing Product: WorldDEMTM Description: WorldDEMTM is produced based on WorldDEMcore, representing the surface of the Earth (including buildings, infrastructure and vegetation). Hydrological consistency is ensured Product: WorldDEM DTM Description: In additional editing steps, WorldDEMTMis transformed into a Digital Terrain Model (DTM) representing bare Earth elevation Product: WorldDEM Bundle Description: Includes WorldDEMTM, WorldDEM DTM, and Quality Layers The main specifications of the WorldDEM products are: - Horizontal Coordinate Reference System: World Geodetic System 1984 (WGS84-G1150) - Vertical Coordinate Reference System: Earth Gravitational Model 2008 (EGM2008) - Absolute Horizontal Accuracy: <6 m - Vertical Accuracy: 2 m Relative, 4 m Absolute - Quality Layers (including water body mask) can be requested as an option with the WorldDEM and WorldDEM DTM - Auxiliary Layers are delivered together with the WorldDEMcore product The products are available as part of the Airbus provision from TerraSAR-X and Tandem-X missions with worldwide coverage: the TerraSAR-X/TanDEM-X Catalogue (https://terrasar-x-archive.terrasar.com/) can be accessed to discover and check the basic product data readiness; using the WorldDEM database viewers (https://worlddem-database.terrasar.com/ ). All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability (https://earth.esa.int/eogateway/documents/20142/37627/TSX-TDX-Terms-Of-Applicability.pdf/265d10ac-6900-45de-8d31-ccfe3dd8d6e6) available in Resources section. proprietary
Thermokarst_Circumpolar_Map_1332_1 Arctic Circumpolar Distribution and Soil Carbon of Thermokarst Landscapes, 2015 ORNL_CLOUD STAC Catalog 2015-01-01 2015-12-31 -180, 45.54, 180, 83.62 https://cmr.earthdata.nasa.gov/search/concepts/C2216864090-ORNL_CLOUD.umm_json This data set provides the distribution of thermokarst landscapes in the boreal and tundra ecoregions within the northern circumpolar permafrost zones. This dataset provides an areal estimate of wetland, lake, and hillslope thermokarst landscapes as of 2015. Estimates of soil organic carbon (SOC) content associated with thermokarst and non-thermokarst landscapes were based on available circumpolar 0 to 3 meter SOC storage data. proprietary
ThreeRivers_0 Watershed sampling of the Kennebec, Androscoggin, St. John Rivers, Maine, and New Brunswick, Canada OB_DAAC STAC Catalog 2010-08-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360683-OB_DAAC.umm_json The NASA_3Rivers_NNX11AF22G cruise consisted of watershed sampling of the Kennebec, Androscoggin, St. John Rivers in Maine, USA and New Brunswick, Canada. Water samples were collected from a nominal depth of 0.5 m and then processed as described by the accompanying documentation. Each station was visited monthly from early spring, as soon as accessible, to late fall, November. Stations were accessed by land and samples were collected by foot in the deepest water available, usually 1 to 1.5 meters in depth. proprietary
-Tidal_Marsh_Biomass_US_V1-1_1879_1.1 Aboveground Biomass High-Resolution Maps for Selected US Tidal Marshes, 2015 ORNL_CLOUD STAC Catalog 2015-08-01 2015-09-01 -122.73, 25.09, -69.93, 47.12 https://cmr.earthdata.nasa.gov/search/concepts/C2345876612-ORNL_CLOUD.umm_json This dataset provides maps of aboveground tidal marsh biomass (g/m2) at 30 m resolution for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Estuarine and palustrine emergent tidal marsh areas were based on a 2010 NOAA Coastal Change Analysis Program (C-CAP) map. Aboveground biomass maps were generated from a random forest model driven by Landsat vegetation indices and a national scale dataset of field-measured aboveground biomass. The final model, driven by six Landsat vegetation indices, with the soil adjusted vegetation index as the most important, successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle, and growth form. Biomass can be converted to carbon stocks using a mean plant carbon content of 44.1%. proprietary
Tidal_Marsh_Biomass_US_V1-1_1879_1.1 Aboveground Biomass High-Resolution Maps for Selected US Tidal Marshes, 2015 ALL STAC Catalog 2015-08-01 2015-09-01 -122.73, 25.09, -69.93, 47.12 https://cmr.earthdata.nasa.gov/search/concepts/C2345876612-ORNL_CLOUD.umm_json This dataset provides maps of aboveground tidal marsh biomass (g/m2) at 30 m resolution for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Estuarine and palustrine emergent tidal marsh areas were based on a 2010 NOAA Coastal Change Analysis Program (C-CAP) map. Aboveground biomass maps were generated from a random forest model driven by Landsat vegetation indices and a national scale dataset of field-measured aboveground biomass. The final model, driven by six Landsat vegetation indices, with the soil adjusted vegetation index as the most important, successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle, and growth form. Biomass can be converted to carbon stocks using a mean plant carbon content of 44.1%. proprietary
+Tidal_Marsh_Biomass_US_V1-1_1879_1.1 Aboveground Biomass High-Resolution Maps for Selected US Tidal Marshes, 2015 ORNL_CLOUD STAC Catalog 2015-08-01 2015-09-01 -122.73, 25.09, -69.93, 47.12 https://cmr.earthdata.nasa.gov/search/concepts/C2345876612-ORNL_CLOUD.umm_json This dataset provides maps of aboveground tidal marsh biomass (g/m2) at 30 m resolution for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD, Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Estuarine and palustrine emergent tidal marsh areas were based on a 2010 NOAA Coastal Change Analysis Program (C-CAP) map. Aboveground biomass maps were generated from a random forest model driven by Landsat vegetation indices and a national scale dataset of field-measured aboveground biomass. The final model, driven by six Landsat vegetation indices, with the soil adjusted vegetation index as the most important, successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle, and growth form. Biomass can be converted to carbon stocks using a mean plant carbon content of 44.1%. proprietary
Tidal_Marsh_Vegetation_US_1608_1 Green Vegetation Fraction High-Resolution Maps for Selected US Tidal Marshes, 2015 ORNL_CLOUD STAC Catalog 2013-09-25 2015-08-27 -122.75, 25.08, -69.93, 47.12 https://cmr.earthdata.nasa.gov/search/concepts/C2389082183-ORNL_CLOUD.umm_json This dataset provides 30m resolution maps of the fraction of green vegetation within tidal marshes for six estuarine regions of the conterminous United States: Cape Cod, MA; Chesapeake Bay, MD; Everglades, FL; Mississippi Delta, LA; San Francisco Bay, CA; and Puget Sound, WA. Maps were derived from a 1m classification of 2013 to 2015 National Agriculture Imagery Program (NAIP) images as tidal marsh green vegetation, non-vegetation, and open water. Using this high-resolution map, the percent of each class within Landsat pixel extents was calculated to produce a 30m fraction of green vegetation map for each region. proprietary
Tidal_Wetland_Estuaries_Data_1742_1 Tidal Wetlands Soil Organic Carbon and Estuarine Characteristics, USA, 1972-2015 ORNL_CLOUD STAC Catalog 1972-01-01 2015-12-31 -124.39, 25.19, -67.05, 47.82 https://cmr.earthdata.nasa.gov/search/concepts/C2517345906-ORNL_CLOUD.umm_json This dataset provides a synthesis of soil organic carbon (SOC) estimates and a variety of other environmental information from tidal wetlands within estuaries in the conterminous United States for the period 1972-2015. The data were compiled from several existing data resources and include the following: soil organic carbon stock estimates, the proportion of the catchment area containing the wetlands that is barren, tidal wetland area, nontidal wetland land, open water, saltwater zone, mixed zone, agricultural, urban, forest, and wetland areas, land elevation, ocean salinity, sea surface temperature, ocean dissolved inorganic phosphorus, estuary latitude, longitude, depth, perimeter, salinity, and estuary volume, river flow, carbon, nitrogen, and phosphorus river flux, sediment organic carbon content, windspeed, mean temperature, daily and mean precipitation, frost days, and the population within each catchment. Estuaries were also classified to one of six typological categories. Coastal locations were determined by natural environmental and political divisions within the US. The data were used to investigate how tidal wetland soil organic carbon density is distributed across the continental US among various coastal locations, estuarine typologies, vegetation types, water regimes, and management regimes, and to identify whether SOC density is correlated with different environmental variables. The analytical results are not included with this dataset. proprietary
Tidal_Wetland_GPP_CONUS_1792_1 Gross Primary Production Maps of Tidal Wetlands across Conterminous USA, 2000-2019 ORNL_CLOUD STAC Catalog 2000-03-05 2019-11-17 -128.03, 23.5, -65.9, 47.7 https://cmr.earthdata.nasa.gov/search/concepts/C2389119490-ORNL_CLOUD.umm_json "This dataset provides mapped tidal wetland gross primary production (GPP) estimates (g C/m2/day) derived from multiple wetland types at 250-m resolution across the conterminous United States at 16-day intervals from March 5, 2000, through November 17, 2019. GPP was derived with the spatially explicit Blue Carbon (BC) model, which combined tidal wetland cover and field-based eddy covariance (EC) tower GPP data into a single Bayesian framework along with Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets. Tidal wetlands are a critical component of global climate regulation. Tidal wetland-based carbon, or ""blue carbon,"" is a valued resource that is increasingly important for restoration and conservation purposes." proprietary
@@ -15326,8 +15331,8 @@ Tropical Cyclone Wind Estimation Competition_1 Tropical Cyclone Wind Estimation
TundraTransect_VegRefl_Soil_2232_1 Spectral Reflectance and Ancillary Data, Tundra Transect, North Slope, AK, 2000-2022 ORNL_CLOUD STAC Catalog 2000-06-30 2022-08-08 -156.6, 71.32, -156.6, 71.32 https://cmr.earthdata.nasa.gov/search/concepts/C2840820936-ORNL_CLOUD.umm_json This dataset provides visible-near infrared spectral reflectance, descriptions of vegetation cover, surface temperature, the total fraction of absorbed photosynthetically active radiation (fAPAR, 2001 only), permafrost active layer depth, elevation, and soil temperature at 5 cm depth. Measurements were made at every meter along a 100-m transect aligned mainly in an east-west direction, located approximately 300 m southeast of the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) baseline observatory near Utqiagvik, Alaska. Reflectance measurements were collected at nearly weekly intervals through the growing seasons of 2000 to 2002 to describe characteristics of green-up, peak growth, and senescence. Reflectance measurements were also collected once near peak growth in 2022. Ancillary measurements were collected at intervals through the 2001 and 2002 growing seasons. proprietary
TundraVeg_Reflectance_Soil_CO2_1960_1 Tundra Plant Reflectance, CO2 Exchange, PAM Fluorometry, and Pigments, AK, 2001-2002 ORNL_CLOUD STAC Catalog 2001-06-08 2002-08-16 -157.41, 70.45, -156.6, 71.32 https://cmr.earthdata.nasa.gov/search/concepts/C2262495116-ORNL_CLOUD.umm_json This dataset provides measurements at tundra plots collected near Utqiagvik and Atqasuk, AK, including visible-near infrared spectral reflectance, chamber gas exchange measurements of CO2, pulse amplitude modulated (PAM) fluorometry, chlorophyll pigment contents, along with surface temperature, permafrost active layer depth, and soil temperature at 5 cm, through the growing seasons of 2001 and 2002. At all plots, spectral reflectance was measured using a portable spectrometer configured with a straight fiber optic foreoptic, surface temperatures were measured using a handheld Everest Infrared Thermometer, and thaw depth (or active layer depth) was measured using a metal rod graduated in centimeter intervals. At small plots (~15 cm) at Utqiagvik (referred to as Patch plots) chambers were constructed that enclosed an individual patch to determine photosynthetic rate and estimate respiration rate (made by covering the chamber in a dark cloth). Efficiency using PAM fluorometer, ambient yield estimations, and rapid light curve measurements were taken. Chlorophyll concentration was measured with a portable spectrometer configured as a spectrophotometer. At larger plots (approximately 1 m2) which were part of the International Tundra EXperiment (ITEX plots) at Utqiagvik (referred to as Barrow) and Atqasuk, a sub-sample of five control and five warmed plots at each site were fitted with 0.45 m diameter polyvinyl chloride collars for chamber flux measurements. To determine the total fraction of absorbed photosynthetically active radiation (fAPAR), a series of photosynthetically active radiation (PAR) measurements were made using a custom-made light bar consisting of a linear array of GaAsP sensors mounted within an aluminum U-bar under a white plastic diffuser. In addition, a visual estimate was made of the fraction of standing dead vegetation based on percent cover. The data are provided in comma-separated values (*.csv) format. In addition, photographs of plots and instruments are provided. proprietary
Tundra_Fire_Veg_Plots_1547_1 Arctic Vegetation Plots in Burned and Unburned Tundra, Alaska, 2011-2012 ORNL_CLOUD STAC Catalog 2011-07-14 2012-07-30 -164.69, 65.36, -146.65, 70.09 https://cmr.earthdata.nasa.gov/search/concepts/C2162122251-ORNL_CLOUD.umm_json This dataset provides environmental and vegetation data collected in late June and July of 2011 and of 2012 from study plots located in tundra fire scars and adjacent unburned tundra areas on the Seward Peninsula and the northern foothills of the Brooks Range in Arctic Alaska. The surveys focused on upland tundra settings and provide information on vegetative differences between the burned and unburned sites. The sampling design established a chronosequence of sites that varied in time since last fire to better understand post-fire vegetation successional trajectories. Complete species lists and their cover abundance data are provided for both study areas. Environmental data include the baseline plot descriptive information for vegetation, soils, and site factors. No soil samples were collected. proprietary
-Tundra_Greeness_Temp_Trends_1893_1 ABoVE: Landsat Tundra Greenness and Summer Air Temperatures, Arctic Tundra, 1985-2016 ALL STAC Catalog 1985-07-01 2016-08-31 -180, 31.49, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2143401680-ORNL_CLOUD.umm_json This dataset provides annual tundra greenness and summer air temperatures at a resolution of 50 km over the pan-Arctic tundra biome above 31.5 degrees over the time period 1985 to 2016. Annual tundra greenness was assessed using the maximum Normalized Difference Vegetation Index (NDVImax) derived from surface reflectance measured by sensors on the Landsat satellites. Summer air temperatures were quantified using the Summer Warmth Index (SWI) derived from an ensemble of five global temperature datasets. Tabular data include NDVImax, SWI, and estimates of uncertainty using Monte Carlo simulations at 45,334 vegetated sampling sites. Raster data provide (1) annual SWI from 1985 to 2016; (2) temporal trends in annual NDVImax and SWI from 1985 to 2016 and from 2000 to 2016; and (3) temporal correlations between annual NDVImax - SWI during these two periods. Each raster also includes estimates of uncertainty that were generated using Monte Carlo simulations. This dataset provides a new pan-Arctic product for assessing inter-annual variability in tundra using moderate resolution observations from the Landsat satellites. proprietary
Tundra_Greeness_Temp_Trends_1893_1 ABoVE: Landsat Tundra Greenness and Summer Air Temperatures, Arctic Tundra, 1985-2016 ORNL_CLOUD STAC Catalog 1985-07-01 2016-08-31 -180, 31.49, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2143401680-ORNL_CLOUD.umm_json This dataset provides annual tundra greenness and summer air temperatures at a resolution of 50 km over the pan-Arctic tundra biome above 31.5 degrees over the time period 1985 to 2016. Annual tundra greenness was assessed using the maximum Normalized Difference Vegetation Index (NDVImax) derived from surface reflectance measured by sensors on the Landsat satellites. Summer air temperatures were quantified using the Summer Warmth Index (SWI) derived from an ensemble of five global temperature datasets. Tabular data include NDVImax, SWI, and estimates of uncertainty using Monte Carlo simulations at 45,334 vegetated sampling sites. Raster data provide (1) annual SWI from 1985 to 2016; (2) temporal trends in annual NDVImax and SWI from 1985 to 2016 and from 2000 to 2016; and (3) temporal correlations between annual NDVImax - SWI during these two periods. Each raster also includes estimates of uncertainty that were generated using Monte Carlo simulations. This dataset provides a new pan-Arctic product for assessing inter-annual variability in tundra using moderate resolution observations from the Landsat satellites. proprietary
+Tundra_Greeness_Temp_Trends_1893_1 ABoVE: Landsat Tundra Greenness and Summer Air Temperatures, Arctic Tundra, 1985-2016 ALL STAC Catalog 1985-07-01 2016-08-31 -180, 31.49, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2143401680-ORNL_CLOUD.umm_json This dataset provides annual tundra greenness and summer air temperatures at a resolution of 50 km over the pan-Arctic tundra biome above 31.5 degrees over the time period 1985 to 2016. Annual tundra greenness was assessed using the maximum Normalized Difference Vegetation Index (NDVImax) derived from surface reflectance measured by sensors on the Landsat satellites. Summer air temperatures were quantified using the Summer Warmth Index (SWI) derived from an ensemble of five global temperature datasets. Tabular data include NDVImax, SWI, and estimates of uncertainty using Monte Carlo simulations at 45,334 vegetated sampling sites. Raster data provide (1) annual SWI from 1985 to 2016; (2) temporal trends in annual NDVImax and SWI from 1985 to 2016 and from 2000 to 2016; and (3) temporal correlations between annual NDVImax - SWI during these two periods. Each raster also includes estimates of uncertainty that were generated using Monte Carlo simulations. This dataset provides a new pan-Arctic product for assessing inter-annual variability in tundra using moderate resolution observations from the Landsat satellites. proprietary
Tundra_Leaf_Spectra_2005_1 Tundra Plant Leaf-level Spectral Reflectance and Chlorophyll Fluorescence, 2019-2021 ORNL_CLOUD STAC Catalog 2019-07-19 2021-09-30 -156.6, 64.83, -147.81, 71.31 https://cmr.earthdata.nasa.gov/search/concepts/C2262495547-ORNL_CLOUD.umm_json This dataset provides leaf-level visible-near infrared spectral reflectance, chlorophyll fluorescence spectra, species, plant functional type (PFT), and chlorophyll content of common high latitude plant samples collected near Fairbanks, Utqiagvik, and Toolik, Alaska, U.S., during the summers of 2019, 2020, and 2021. A FluoWat leaf clip was used to measure leaf-level visible-near infrared spectral reflectance and chlorophyll fluorescence spectra. Fluorescence yield (Fyield) was calculated as the ratio of the emitted fluorescence divided by the absorbed radiation for the wavelengths from 400 nm up to the wavelength of the cut off for the FluoWat low pass filter (either 650 or 700 nm). Chlorophyll content of samples was measured using a CCM-300 Chlorophyll Content. The data are provided in comma-separated values (.csv) format. proprietary
Turbid9_0 2004 Measurements made in the Chesapeake Bay ALL STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360689-OB_DAAC.umm_json Measurements made in the Chesapeake Bay in 2004. proprietary
Turbid9_0 2004 Measurements made in the Chesapeake Bay OB_DAAC STAC Catalog 2004-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360689-OB_DAAC.umm_json Measurements made in the Chesapeake Bay in 2004. proprietary
@@ -15394,10 +15399,10 @@ UAVSAR_POL_SLOPE_1 UAVSAR_POLSAR_SLOPE ASF STAC Catalog 2008-07-24 165.585938,
UAVSAR_POL_STOKES_1 UAVSAR_POLSAR_STOKES ASF STAC Catalog 2008-07-24 165.585938, -47.989922, 137.636719, 83.84881 https://cmr.earthdata.nasa.gov/search/concepts/C1214419355-ASF.umm_json UAVSAR PolSAR Scene Stokes proprietary
UAV_Imagery_BigLakeTrail_1834_1 Multispectral Imagery, NDVI, and Terrain Models, Big Trail Lake, Fairbanks, AK, 2019 ORNL_CLOUD STAC Catalog 2019-08-04 2019-08-04 -147.83, 64.92, -147.81, 64.92 https://cmr.earthdata.nasa.gov/search/concepts/C2761782139-ORNL_CLOUD.umm_json This dataset provides multispectral reflectance imagery (green at 550 nm, red at 660 nm, red edge at 735 nm, and near-infrared at 790 nm), normalized difference vegetation index (NDVI), and digital surface and terrain models for a 0.5 km2 area surrounding Big Trail Lake (BTL) in the Goldstream Creek Valley north of Fairbanks, Alaska. These high spatial resolution maps (13 cm x 13 cm) were generated by unmanned aerial vehicle (UAV) imagery collected on 2019-08-04. Raw images (n=908) were combined into mosaic layers that incorporated ground control points with centimeter accuracy. These layers were then used to generate vegetation, water body, and elevation maps and then combined with in situ measurements of methane flux to improve upscaling models of greenhouse gas emissions. proprietary
UCLA_DEALIASED_SASS_L3_1 SEASAT SCATTEROMETER DEALIASED OCEAN WIND VECTORS (JPL-UCLA-AES) POCLOUD STAC Catalog 1978-07-07 1978-10-11 -180, -70, 180, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2617197672-POCLOUD.umm_json Contains dealiased ocean wind vector components (zonal and meridional) derived from the Seasat-A Scatterometer (SASS) provided on a global 1x1 degree grid. Dealiasing of the SASS data was achieved manually using ship observations in a joint effort between JPL, UCLA and AES. This data set underwent restoration in 1997. Data are provided in ASCII text files at six hour intervals. proprietary
-UKASSEL_GLOBAL_IRRIGATED_AREA A Digital Global Map of Irrigated Areas ALL STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214608839-SCIOPS.umm_json "For the purpose of global modeling of water use and crop production, a digital global map of irrigated areas was developed. The map depicts the areal percentage of each 0.5 deg. by 0.5 deg grid cell that was equipped for irrigation in 1995. It was derived by combininginformation from large-scale maps with outlines of irrigated areas (one or more countries per map), FAO data on total irrigated area per country in 1995 and national data on total irrigated area per county, drainage basin or federal state. In the documentation of the map, the data and map sources as well as the map generation process is described, and the data uncertainty is discussed. ""http://www.usf.uni-kassel.de/usf/archiv/dokumente/kwws/kwws.4.pdf"" We plan to improve this map in the future. Therefore, comments, information and data that might contribute to this effort are highly welcome." proprietary
UKASSEL_GLOBAL_IRRIGATED_AREA A Digital Global Map of Irrigated Areas SCIOPS STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214608839-SCIOPS.umm_json "For the purpose of global modeling of water use and crop production, a digital global map of irrigated areas was developed. The map depicts the areal percentage of each 0.5 deg. by 0.5 deg grid cell that was equipped for irrigation in 1995. It was derived by combininginformation from large-scale maps with outlines of irrigated areas (one or more countries per map), FAO data on total irrigated area per country in 1995 and national data on total irrigated area per county, drainage basin or federal state. In the documentation of the map, the data and map sources as well as the map generation process is described, and the data uncertainty is discussed. ""http://www.usf.uni-kassel.de/usf/archiv/dokumente/kwws/kwws.4.pdf"" We plan to improve this map in the future. Therefore, comments, information and data that might contribute to this effort are highly welcome." proprietary
-UM0405_26_aerosol_optical Aerosol optical thickness - UM0405_26_aerosol_optical SCIOPS STAC Catalog 2004-12-31 2005-01-25 18, -68, 115, -32 https://cmr.earthdata.nasa.gov/search/concepts/C1221420727-SCIOPS.umm_json The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition. proprietary
+UKASSEL_GLOBAL_IRRIGATED_AREA A Digital Global Map of Irrigated Areas ALL STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214608839-SCIOPS.umm_json "For the purpose of global modeling of water use and crop production, a digital global map of irrigated areas was developed. The map depicts the areal percentage of each 0.5 deg. by 0.5 deg grid cell that was equipped for irrigation in 1995. It was derived by combininginformation from large-scale maps with outlines of irrigated areas (one or more countries per map), FAO data on total irrigated area per country in 1995 and national data on total irrigated area per county, drainage basin or federal state. In the documentation of the map, the data and map sources as well as the map generation process is described, and the data uncertainty is discussed. ""http://www.usf.uni-kassel.de/usf/archiv/dokumente/kwws/kwws.4.pdf"" We plan to improve this map in the future. Therefore, comments, information and data that might contribute to this effort are highly welcome." proprietary
UM0405_26_aerosol_optical Aerosol optical thickness - UM0405_26_aerosol_optical ALL STAC Catalog 2004-12-31 2005-01-25 18, -68, 115, -32 https://cmr.earthdata.nasa.gov/search/concepts/C1221420727-SCIOPS.umm_json The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition. proprietary
+UM0405_26_aerosol_optical Aerosol optical thickness - UM0405_26_aerosol_optical SCIOPS STAC Catalog 2004-12-31 2005-01-25 18, -68, 115, -32 https://cmr.earthdata.nasa.gov/search/concepts/C1221420727-SCIOPS.umm_json The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition. proprietary
UM0506_26_aerosol_optical Aerosol optical thickness SCIOPS STAC Catalog 2006-01-03 2006-01-30 18, -68, 115, -32 https://cmr.earthdata.nasa.gov/search/concepts/C1214595208-SCIOPS.umm_json The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition. proprietary
UM0506_26_aerosol_optical Aerosol optical thickness ALL STAC Catalog 2006-01-03 2006-01-30 18, -68, 115, -32 https://cmr.earthdata.nasa.gov/search/concepts/C1214595208-SCIOPS.umm_json The aerosol optical thickness was measured with a sunphotometer. The measurement was conducted only clear sky condition. proprietary
UM0708_25_multi-frequency_acoustic Acoustic data of multi-frequency acoustic system SCIOPS STAC Catalog 2007-12-24 2008-02-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214595173-SCIOPS.umm_json Vertical profiles of volume backscattering strength recorded by multi-frequency acoustic system for estimate size-abundance spectra of small zooplankton. The system was horizontally mounted on CTD frame and the observation was vertically performed from surface to 200 m at 23 stations. proprietary
@@ -15405,8 +15410,8 @@ UM0708_25_multi-frequency_acoustic Acoustic data of multi-frequency acoustic sys
UM0809_33_nano Abundance and composition of nano, picoplankton, microzooplankton SCIOPS STAC Catalog 2009-01-12 2009-01-25 38, -70, 75, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214595104-SCIOPS.umm_json Water samples from 5 depths (0-100 m) were collected by Niskin bottles at 9 stations (L1, L3, L5, L9, L12, L37, L33, Ⅰ-10, Ⅱ-7) off Lützow-Holm Bay during Umitaka-maru cruise (Jan-Feb. 2008). The waters were fixed by 0.2% of lugol's acid solution (500 ml), 0.3% of bouin solution (500 ml) and 20 % of glutaraldehyde (100ml). http://biows.ac.jp/~plankton/um0809-1a.png proprietary
UM0809_33_nano Abundance and composition of nano, picoplankton, microzooplankton ALL STAC Catalog 2009-01-12 2009-01-25 38, -70, 75, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214595104-SCIOPS.umm_json Water samples from 5 depths (0-100 m) were collected by Niskin bottles at 9 stations (L1, L3, L5, L9, L12, L37, L33, Ⅰ-10, Ⅱ-7) off Lützow-Holm Bay during Umitaka-maru cruise (Jan-Feb. 2008). The waters were fixed by 0.2% of lugol's acid solution (500 ml), 0.3% of bouin solution (500 ml) and 20 % of glutaraldehyde (100ml). http://biows.ac.jp/~plankton/um0809-1a.png proprietary
UMD_GEOL388_0 Measurements from the Atlantic Ocean made by the University of Maryland (UMD) OB_DAAC STAC Catalog 2003-01-05 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360691-OB_DAAC.umm_json Measurements from the Atlantic Ocean made by the University of Maryland between New England, Bermuda, and Brazil in 2003. proprietary
-UNEP_GRID_SF_AFRICA_third version Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls ALL STAC Catalog 1960-01-01 1990-12-31 -18, -35, 52, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2232848311-CEOS_EXTRA.umm_json The African administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change This documentation describes the third version of a database of administrative units with associated population figures for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP 1992, Deichmann and Eklundh 1991), while the second version represented an update and expansion of this first product (Deichmann 1994, WRI 1995). The work discussed in the following paragraphs is also related to NCGIA activities to produce a global database of subnational population estimates (Tobler et al. 1995), and an improved database for the Asian continent (Deichmann 1996a). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. proprietary
UNEP_GRID_SF_AFRICA_third version Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1960-01-01 1990-12-31 -18, -35, 52, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2232848311-CEOS_EXTRA.umm_json The African administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change This documentation describes the third version of a database of administrative units with associated population figures for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP 1992, Deichmann and Eklundh 1991), while the second version represented an update and expansion of this first product (Deichmann 1994, WRI 1995). The work discussed in the following paragraphs is also related to NCGIA activities to produce a global database of subnational population estimates (Tobler et al. 1995), and an improved database for the Asian continent (Deichmann 1996a). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. proprietary
+UNEP_GRID_SF_AFRICA_third version Africa Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls ALL STAC Catalog 1960-01-01 1990-12-31 -18, -35, 52, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2232848311-CEOS_EXTRA.umm_json The African administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change This documentation describes the third version of a database of administrative units with associated population figures for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP 1992, Deichmann and Eklundh 1991), while the second version represented an update and expansion of this first product (Deichmann 1994, WRI 1995). The work discussed in the following paragraphs is also related to NCGIA activities to produce a global database of subnational population estimates (Tobler et al. 1995), and an improved database for the Asian continent (Deichmann 1996a). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. proprietary
UNEP_GRID_SF_ASIA Asia Population Distribution Database and Administrative Units from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 26, -12, 155, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2232847540-CEOS_EXTRA.umm_json The Asian administrative boundaries and population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change. This project (which has been carried out as a cooperative activity between NCGIA, CGIAR and UNEP/GRID between Oct. 1995 and present) has pooled available data sets, many of which had been assembled for the global demography project. All data were checked, international boundaries and coastlines were replaced with a standard template, the attribute database was redesigned, and new, more reliable population estimates for subnational units were produced for all countries. From the resulting data sets, raster surfaces representing population distribution and population density were created in collaboration between NCGIA and GRID-Geneva. proprietary
UNEP_GRID_SF_GLOBAL Global Population Distribution Database from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1990-01-01 1990-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232849256-CEOS_EXTRA.umm_json Population databases are forming the backbone of many important studies modelling the complex interactions between population growth and environmental degradation, predicting the effects of global climate change on humans, and assessing the risks of various hazards such as floods, air pollution and radiation. Detailed information on population size, growth and distribution (along with many other environmental parameters) is of fundamental importance to such efforts. This database includes rural population distributions, population distrbution for cities and gridded global population distributions. This project has provided a population database depicting the worldwide distribution of population in a 1X1 latitude/longitude grid system. The database is unique, firstly, in that it makes use of the most recent data available (1990). Secondly, it offers true apportionment for each grid cell that is, if a cell contains populations from two different countries, each is assigned a percentage of the grid cell area, rather than artificially assigning the whole cell to one or the other country (this is especially important for European countries). Thirdly, the database gives the percentage of a country's total population accounted for in each cell. So if a country's total in a given year around 1990 (1989 or 1991, for example) is known, then population in each cell can be calculated by using the percentage given in the database with the assumption that the growth rate in each cell of the country is the same. And lastly, this dataset is easy to be updated for each country as new national population figures become available. proprietary
UNEP_GRID_SF_LATINAMERICA_1.0 Latin America and Caribbean Population Distribution Database from UNEP/GRID-Sioux Falls CEOS_EXTRA STAC Catalog 1960-01-01 1990-12-31 -120, -60, -31, 36 https://cmr.earthdata.nasa.gov/search/concepts/C2232848778-CEOS_EXTRA.umm_json The Latin America population database is part of an ongoing effort to improve global, spatially referenced demographic data holdings. Such databases are useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change. This documentation describes the Latin American Population Database, a collaborative effort between the International Center for Tropical Agriculture (CIAT), the United Nations Environment Program (UNEP-GRID, Sioux Falls) and the World Resources Institute (WRI). This work is intended to provide a population database that compliments previous work carried out for Asia and Africa. This data set is more detailed than the Africa and Asia data sets. Population estimates for 1960, 1970, 1980, 1990 and 2000 are also provided. The work discussed in the following paragraphs is also related to NCGIA activities to produce a global database of subnational population estimates (Tobler et al. 1995), and an improved database for the Asian continent (Deichmann 1996a). proprietary
@@ -15417,8 +15422,8 @@ USAP-0732711 Collaborative Research in IPY: Abrupt Environmental Change in the L
USAP-0732917_1 Collaborative Research in IPY: Abrupt Environmental Change in the Larsen Ice Shelf System, a Multidisciplinary Approach - Marine Ecosystems AMD_USAPDC STAC Catalog 2007-09-15 2015-08-31 -60.5, -65, -55.4, -63.1 https://cmr.earthdata.nasa.gov/search/concepts/C2534800063-AMD_USAPDC.umm_json A profound transformation in ecosystem structure and function is occurring in coastal waters of the western Weddell Sea, with the collapse of the Larsen B ice shelf. This transformation appears to be yielding a redistribution of energy flow between chemoautotrophic and photosynthetic production, and to be causing the rapid demise of the extraordinary seep ecosystem discovered beneath the ice shelf. This event provides an ideal opportunity to examine fundamental aspects of ecosystem transition associated with climate change. We propose to test the following hypotheses to elucidate the transformations occurring in marine ecosystems as a consequence of the Larsen B collapse: (1) The biogeographic isolation and sub-ice shelf setting of the Larsen B seep has led to novel habitat characteristics, chemoautotrophically dependent taxa and functional adaptations. (2) Benthic communities beneath the former Larsen B ice shelf are fundamentally different from assemblages at similar depths in the Weddell sea-ice zone, and resemble oligotrophic deep-sea communities. Larsen B assemblages are undergoing rapid change. (3) The previously dark, oligotrophic waters of the Larsen B embayment now support a thriving phototrophic community, with production rates and phytoplankton composition similar to other productive areas of the Weddell Sea. To document rapid changes occurring in the Larsen B ecosystem, we will use a remotely operated vehicle, shipboard samplers, and moored sediment traps. We will characterize microbial, macrofaunal and megafaunal components of the seep community; evaluate patterns of surface productivity, export flux, and benthic faunal composition in areas previously covered by the ice shelf, and compare these areas to the open sea-ice zone. These changes will be placed within the geological, glaciological and climatological context that led to ice-shelf retreat, through companion research projects funded in concert with this effort. Together these projects will help predict the likely consequences of ice-shelf collapse to marine ecosystems in other regions of Antarctica vulnerable to climate change. The research features international collaborators from Argentina, Belgium, Canada, Germany, Spain and the United Kingdom. The broader impacts include participation of a science writer; broadcast of science segments by members of the Jim Lehrer News Hour (Public Broadcasting System); material for summer courses in environmental change; mentoring of graduate students and postdoctoral fellows; and showcasing scientific activities and findings to students and public through podcasts. proprietary
USAP-0944266 Climate, Ice Dynamics and Biology using a Deep Ice Core from the West Antarctic Ice Sheet Ice Divide (0944266) AMD_USAPDC STAC Catalog 2010-08-01 2015-07-31 -112.1115, -79.481, -112.1115, -79.481 https://cmr.earthdata.nasa.gov/search/concepts/C2532070632-AMD_USAPDC.umm_json This award supports renewal of funding of the WAIS Divide Science Coordination Office (SCO). The Science Coordination Office (SCO) was established to represent the research community and facilitates the project by working with support organizations responsible for logistics, drilling, and core curation. During the last five years, 26 projects have been individually funded to work on this effort and 1,511 m of the total 3,470 m of ice at the site has been collected. This proposal seeks funding to continue the SCO and related field operations needed to complete the WAIS Divide ice core project. Tasks for the SCO during the second five years include planning and oversight of logistics, drilling, and core curation; coordinating research activities in the field; assisting in curation of the core in the field; allocating samples to individual projects; coordinating the sampling effort; collecting, archiving, and distributing data and other information about the project; hosting an annual science meeting; and facilitating collaborative efforts among the research groups. The intellectual merit of the WAIS Divide project is to better predict how human-caused increases in greenhouse gases will alter climate requires an improved understanding of how previous natural changes in greenhouse gases influenced climate in the past. Information on previous climate changes is used to validate the physics and results of climate models that are used to predict future climate. Antarctic ice cores are the only source of samples of the paleo-atmosphere that can be used to determine previous concentrations of carbon dioxide. Ice cores also contain records of other components of the climate system such as the paleo air and ocean temperature, atmospheric loading of aerosols, and indicators of atmospheric transport. The WAIS Divide ice core project has been designed to obtain the best possible record of greenhouse gases during the last glacial cycle (last ~100,000 years). The site was selected because it has the best balance of high annual snowfall (23 cm of ice equivalent/year), low dust Antarctic ice that does not compromise the carbon dioxide record, and favorable glaciology. The main science objectives of the project are to investigate climate forcing by greenhouse gases, initiation of climate changes, stability of the West Antarctic Ice Sheet, and cryobiology in the ice core. The project has numerous broader impacts. An established provider of educational material (Teachers' Domain) will develop and distribute web-based resources related to the project and climate change for use in K-12 classrooms. These resources will consist of video and interactive graphics that explain how and why ice cores are collected, and what they tell us about future climate change. Members of the national media will be included in the field team and the SCO will assist in presenting information to the general public. Video of the project will be collected and made available for general use. Finally, an opportunity will be created for cryosphere students and early career scientists to participate in field activities and core analysis. An ice core archive will be available for future projects and scientific discoveries from the project can be used by policy makers to make informed decisions. proprietary
USAP-0944348 Climate, Ice Dynamics and Biology using a Deep Ice Core from the West Antarctic Ice Sheet Ice Divide AMD_USAPDC STAC Catalog 2010-08-01 2015-07-31 -112.1115, -79.481, -112.1115, -79.481 https://cmr.earthdata.nasa.gov/search/concepts/C2532070599-AMD_USAPDC.umm_json This award supports renewal of funding of the WAIS Divide Science Coordination Office (SCO). The Science Coordination Office (SCO) was established to represent the research community and facilitates the project by working with support organizations responsible for logistics, drilling, and core curation. During the last five years, 26 projects have been individually funded to work on this effort and 1,511 m of the total 3,470 m of ice at the site has been collected. This proposal seeks funding to continue the SCO and related field operations needed to complete the WAIS Divide ice core project. Tasks for the SCO during the second five years include planning and oversight of logistics, drilling, and core curation; coordinating research activities in the field; assisting in curation of the core in the field; allocating samples to individual projects; coordinating the sampling effort; collecting, archiving, and distributing data and other information about the project; hosting an annual science meeting; and facilitating collaborative efforts among the research groups. The intellectual merit of the WAIS Divide project is to better predict how human-caused increases in greenhouse gases will alter climate requires an improved understanding of how previous natural changes in greenhouse gases influenced climate in the past. Information on previous climate changes is used to validate the physics and results of climate models that are used to predict future climate. Antarctic ice cores are the only source of samples of the paleo-atmosphere that can be used to determine previous concentrations of carbon dioxide. Ice cores also contain records of other components of the climate system such as the paleo air and ocean temperature, atmospheric loading of aerosols, and indicators of atmospheric transport. The WAIS Divide ice core project has been designed to obtain the best possible record of greenhouse gases during the last glacial cycle (last ~100,000 years). The site was selected because it has the best balance of high annual snowfall (23 cm of ice equivalent/year), low dust Antarctic ice that does not compromise the carbon dioxide record, and favorable glaciology. The main science objectives of the project are to investigate climate forcing by greenhouse gases, initiation of climate changes, stability of the West Antarctic Ice Sheet, and cryobiology in the ice core. The project has numerous broader impacts. An established provider of educational material (Teachers' Domain) will develop and distribute web-based resources related to the project and climate change for use in K-12 classrooms. These resources will consist of video and interactive graphics that explain how and why ice cores are collected, and what they tell us about future climate change. Members of the national media will be included in the field team and the SCO will assist in presenting information to the general public. Video of the project will be collected and made available for general use. Finally, an opportunity will be created for cryosphere students and early career scientists to participate in field activities and core analysis. An ice core archive will be available for future projects and scientific discoveries from the project can be used by policy makers to make informed decisions. proprietary
-USAP-1043471 A Study of Atmospheric Dust in the WAIS Divide Ice Core Based on Sr-Nd-Pb-He Isotopes AMD_USAPDC STAC Catalog 2011-08-01 2015-07-31 -112.5, -79.5, -112.086, -79.468 https://cmr.earthdata.nasa.gov/search/concepts/C2532071870-AMD_USAPDC.umm_json This award supports a project to obtain the first set of isotopic-based provenance data from the WAIS divide ice core. A lack of data from the WAIS prevents even a basic knowledge of whether different sources of dust blew around the Pacific and Atlantic sectors of the southern latitudes. Precise isotopic measurements on dust in the new WAIS ice divide core are specifically warranted because the data will be synergistically integrated with other high frequency proxies, such as dust concentration and flux, and carbon dioxide, for example. Higher resolution proxies will bridge gaps between our observations on the same well-dated, well-preserved core. The intellectual merit of the project is that the proposed analyses will contribute to the WAIS Divide Project science themes. Whether an active driver or passive recorder, dust is one of the most important but least understood components of regional and global climate. Collaborative and expert discussion with dust-climate modelers will lead to an important progression in understanding of dust and past atmospheric circulation patterns and climate around the southern latitudes, and help to exclude unlikely air trajectories to the ice sheets. The project will provide data to help evaluate models that simulate the dust patterns and cycle and the relative importance of changes in the sources, air trajectories and transport processes, and deposition to the ice sheet under different climate states. The results will be of broad interest to a range of disciplines beyond those directly associated with the WAIS ice core project, including the paleoceanography and dust- paleoclimatology communities. The broader impacts of the project include infrastructure and professional development, as the proposed research will initiate collaborations between LDEO and other WAIS scientists and modelers with expertise in climate and dust. Most of the researchers are still in the early phase of their careers and hence the project will facilitate long-term relationships. This includes a graduate student from UMaine, an undergraduate student from Columbia University who will be involved in lab work, in addition to a LDEO Postdoctoral scientist, and possibly an additional student involved in the international project PIRE-ICETRICS. The proposed research will broaden the scientific outlooks of three PIs, who come to Antarctic ice core science from a variety of other terrestrial and marine geology perspectives. Outreach activities include interaction with the science writers of the Columbia's Earth Institute for news releases and associated blog websites, public speaking, and involvement in an arts/science initiative between New York City's arts and science communities to bridge the gap between scientific knowledge and public perception. proprietary
USAP-1043471 A Study of Atmospheric Dust in the WAIS Divide Ice Core Based on Sr-Nd-Pb-He Isotopes ALL STAC Catalog 2011-08-01 2015-07-31 -112.5, -79.5, -112.086, -79.468 https://cmr.earthdata.nasa.gov/search/concepts/C2532071870-AMD_USAPDC.umm_json This award supports a project to obtain the first set of isotopic-based provenance data from the WAIS divide ice core. A lack of data from the WAIS prevents even a basic knowledge of whether different sources of dust blew around the Pacific and Atlantic sectors of the southern latitudes. Precise isotopic measurements on dust in the new WAIS ice divide core are specifically warranted because the data will be synergistically integrated with other high frequency proxies, such as dust concentration and flux, and carbon dioxide, for example. Higher resolution proxies will bridge gaps between our observations on the same well-dated, well-preserved core. The intellectual merit of the project is that the proposed analyses will contribute to the WAIS Divide Project science themes. Whether an active driver or passive recorder, dust is one of the most important but least understood components of regional and global climate. Collaborative and expert discussion with dust-climate modelers will lead to an important progression in understanding of dust and past atmospheric circulation patterns and climate around the southern latitudes, and help to exclude unlikely air trajectories to the ice sheets. The project will provide data to help evaluate models that simulate the dust patterns and cycle and the relative importance of changes in the sources, air trajectories and transport processes, and deposition to the ice sheet under different climate states. The results will be of broad interest to a range of disciplines beyond those directly associated with the WAIS ice core project, including the paleoceanography and dust- paleoclimatology communities. The broader impacts of the project include infrastructure and professional development, as the proposed research will initiate collaborations between LDEO and other WAIS scientists and modelers with expertise in climate and dust. Most of the researchers are still in the early phase of their careers and hence the project will facilitate long-term relationships. This includes a graduate student from UMaine, an undergraduate student from Columbia University who will be involved in lab work, in addition to a LDEO Postdoctoral scientist, and possibly an additional student involved in the international project PIRE-ICETRICS. The proposed research will broaden the scientific outlooks of three PIs, who come to Antarctic ice core science from a variety of other terrestrial and marine geology perspectives. Outreach activities include interaction with the science writers of the Columbia's Earth Institute for news releases and associated blog websites, public speaking, and involvement in an arts/science initiative between New York City's arts and science communities to bridge the gap between scientific knowledge and public perception. proprietary
+USAP-1043471 A Study of Atmospheric Dust in the WAIS Divide Ice Core Based on Sr-Nd-Pb-He Isotopes AMD_USAPDC STAC Catalog 2011-08-01 2015-07-31 -112.5, -79.5, -112.086, -79.468 https://cmr.earthdata.nasa.gov/search/concepts/C2532071870-AMD_USAPDC.umm_json This award supports a project to obtain the first set of isotopic-based provenance data from the WAIS divide ice core. A lack of data from the WAIS prevents even a basic knowledge of whether different sources of dust blew around the Pacific and Atlantic sectors of the southern latitudes. Precise isotopic measurements on dust in the new WAIS ice divide core are specifically warranted because the data will be synergistically integrated with other high frequency proxies, such as dust concentration and flux, and carbon dioxide, for example. Higher resolution proxies will bridge gaps between our observations on the same well-dated, well-preserved core. The intellectual merit of the project is that the proposed analyses will contribute to the WAIS Divide Project science themes. Whether an active driver or passive recorder, dust is one of the most important but least understood components of regional and global climate. Collaborative and expert discussion with dust-climate modelers will lead to an important progression in understanding of dust and past atmospheric circulation patterns and climate around the southern latitudes, and help to exclude unlikely air trajectories to the ice sheets. The project will provide data to help evaluate models that simulate the dust patterns and cycle and the relative importance of changes in the sources, air trajectories and transport processes, and deposition to the ice sheet under different climate states. The results will be of broad interest to a range of disciplines beyond those directly associated with the WAIS ice core project, including the paleoceanography and dust- paleoclimatology communities. The broader impacts of the project include infrastructure and professional development, as the proposed research will initiate collaborations between LDEO and other WAIS scientists and modelers with expertise in climate and dust. Most of the researchers are still in the early phase of their careers and hence the project will facilitate long-term relationships. This includes a graduate student from UMaine, an undergraduate student from Columbia University who will be involved in lab work, in addition to a LDEO Postdoctoral scientist, and possibly an additional student involved in the international project PIRE-ICETRICS. The proposed research will broaden the scientific outlooks of three PIs, who come to Antarctic ice core science from a variety of other terrestrial and marine geology perspectives. Outreach activities include interaction with the science writers of the Columbia's Earth Institute for news releases and associated blog websites, public speaking, and involvement in an arts/science initiative between New York City's arts and science communities to bridge the gap between scientific knowledge and public perception. proprietary
USAP-1043623_1 Air-Sea Fluxes of Momentum, Heat, and Carbon Dioxide at High Wind Speeds in the Southern Ocean AMD_USAPDC STAC Catalog 2011-06-15 2015-05-31 117.5, -67.4, 146, -47 https://cmr.earthdata.nasa.gov/search/concepts/C2532072248-AMD_USAPDC.umm_json Accurate parameterizations of the air-sea fluxes of CO2 into the Southern Ocean, in particular at high wind velocity, are needed to better assess how projections of global climate warming in a windier world could affect the ocean carbon uptake, and alter the ocean heat budget at high latitudes. Air-sea fluxes of momentum, sensible and latent heat (water vapor) and carbon dioxide (CO2) are to be measured continuously underway on cruises using micrometeorological eddy covariance techniques adapted to ship-board use. The measured gas transfer velocity (K) is then to be related to other parameters known to affect air-sea-fluxes. A stated goal of this work is the collection of a set of direct air-sea flux measurements at high wind speeds, conditions where parameterization of the relationship of gas exchange to wind-speed remains contentious. The studies will be carried out at sites in the Southern Ocean using the USAP RV Nathaniel B Palmer as measurment platform. Co-located pCO2 data, to be used in the overall analysis and enabling internal consistency checks, are being collected from existing underway systems aboard the USAP research vessel under other NSF awards. proprietary
USAP-1043623_1 Air-Sea Fluxes of Momentum, Heat, and Carbon Dioxide at High Wind Speeds in the Southern Ocean ALL STAC Catalog 2011-06-15 2015-05-31 117.5, -67.4, 146, -47 https://cmr.earthdata.nasa.gov/search/concepts/C2532072248-AMD_USAPDC.umm_json Accurate parameterizations of the air-sea fluxes of CO2 into the Southern Ocean, in particular at high wind velocity, are needed to better assess how projections of global climate warming in a windier world could affect the ocean carbon uptake, and alter the ocean heat budget at high latitudes. Air-sea fluxes of momentum, sensible and latent heat (water vapor) and carbon dioxide (CO2) are to be measured continuously underway on cruises using micrometeorological eddy covariance techniques adapted to ship-board use. The measured gas transfer velocity (K) is then to be related to other parameters known to affect air-sea-fluxes. A stated goal of this work is the collection of a set of direct air-sea flux measurements at high wind speeds, conditions where parameterization of the relationship of gas exchange to wind-speed remains contentious. The studies will be carried out at sites in the Southern Ocean using the USAP RV Nathaniel B Palmer as measurment platform. Co-located pCO2 data, to be used in the overall analysis and enabling internal consistency checks, are being collected from existing underway systems aboard the USAP research vessel under other NSF awards. proprietary
USAP-1056396_1 CAREER: Protist Nutritional Strategies in Permanently Stratified Antarctic Lakes AMD_USAPDC STAC Catalog 2011-05-01 2016-04-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2532071892-AMD_USAPDC.umm_json This project supported an integrated research and education program in the fields of polar biology and environmental microbiology, focusing on single-celled eukaryotes (protists) in high latitude ice-covered Antarctic lakes systems. Protists play important roles in energy flow and material cycling, and act as both primary producers (fixing inorganic carbon by photosynthesis) and consumers (preying on bacteria by phagotrophic digestion). The McMurdo Dry Valleys (MDV) located in Victoria Land, Antarctica, harbor microbial communities which are isolated in the unique aquatic ecosystem of perennially ice-capped lakes. The project studied: (1) the impact of permanent biogeochemical gradients on protist trophic strategy, (2) the effect of major abiotic drivers (light and nutrients) on the distribution of two key mixotrophic and photoautotrophic protist species, and (3) the effect of episodic nutrient pulses on mixotroph communities in high latitude (ultraoligotrophic) MDV lakes versus low latitude (eutrophic) watersheds. Sampling dates: February 4 – April 10, 2008; November 11- 28, 2012; December 12, 2012 Sampling locations/depths: East Lobe Lake Bonney/5m, 10m, 13m, 15m, 20m, 25m, 30m West Lobe Lake Bonney/5m, 10m, 13m, 15m, 20m, 25m, 30m Lake Fryxell/5m, 7m, 9m, 11m, 12m, 15m Lake Vanda/10m, 20m, 30m, 40m, 50m, 60m, 70m, 75m, 80m Two kinds of metadata from this project are available: 1) DNA sequence data – DNA was extracted from filtered lake water (1-2L) collected from sampling locations and dates reported above. Environmental DNA was PCR-amplified using primers specific for the following genes: 16S rRNA, 18S rRNA, rbcL, cbbM, nifJ, psbA. Genes were sequenced on an Applied Biosystems DNA analyzer or an Illumina MiSeq or HiSeq instruments. All DNA sequences from this project are available via GenBank. 2) Limnological metadata - Limnological data was collected from sampling locations and dates reported above. Data includes PAR, conductivity, temperature, Chlorophyll a, and macronutrients and is available via the McMurdo Dry Valleys LTER Data Center. proprietary
@@ -15444,14 +15449,14 @@ USAP-1543498_1 A Full Lifecycle Approach to Understanding Adélie Penguin Respon
USAP-1544526_1 Activity, Preservation and Fossilization of Cryptoendolithic Microorganisms in Antarctica AMD_USAPDC STAC Catalog 2016-09-01 2017-08-31 160, -77.8, 163.7, -76.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069950-AMD_USAPDC.umm_json Cryptoendoliths are organisms that colonize microscopic cavities of rocks, which give them protection and allow them to inhabit extreme environments, such as the cold, arid desert of the Dry Valleys of Antarctica. Fossilized cryptoendoliths preserve the forms and features of organisms from the past and thus provide a unique opportunity to study the organisms' life histories and environments. To study this fossil record, there needs to be a better understanding of what environmental conditions allow these fossils to form. A climate gradient currently exists in the Dry Valleys that allows us to study living, dead, and fossilized cryptoendoliths from mild to increasingly harsh environments; providing insight to the limits of life and how these fossils are formed. This project will develop instruments to detect the biological activity of the live microorganisms and conduct laboratory experiments to determine the environmental limits of their survival. The project also will characterize the chemical and structural features of the living, dead, and fossilized cryptoendoliths to understand how they become fossilized. Knowing how microorganisms are preserved as fossils in cold and dry environments like Antarctica can help to refine methods that can be used to search for and identify evidence for extraterrestrial life in similar habitats on planets such as Mars. This project includes training of graduate and undergraduate students. Little is known about cryptoendolithic microfossils and their formation processes in cold, arid terrestrial habitats of the Dry Valleys of Antarctica, where a legacy of activity is discernible in the form of biosignatures including inorganic materials and microbial fossils that preserve and indicate traces of past biological activity. The overarching goals of the proposed work are: (1) to determine how rates of microbial respiration and biodegradation of organic matter control microbial fossilization; and (2) to characterize microbial fossils and their living counterparts to elucidate mechanisms for fossilization. Using samples collected across an increasingly harsher (more cold and dry) climatic gradient that encompasses living, dead, and fossilized cryptoendolithic microorganisms, the proposed work will: (1) develop an instrument to be used in the field that can measure small concentrations of CO2 in cryptoendolithic habitats in situ; (2) use microscopy techniques to characterize endolithic microorganisms as well as the chemical and morphological characteristics of biosignatures and microbial fossils. A metagenomic survey of microbial communities in these samples will be used to characterize differences in diversity, identify if specific microorganisms (e.g. prokaryotes, eukaryotes) are more capable of surviving under these harsh climatic conditions, and to corroborate microscopic observations of the viability states of these microorganisms. proprietary
USAP-1544526_1 Activity, Preservation and Fossilization of Cryptoendolithic Microorganisms in Antarctica ALL STAC Catalog 2016-09-01 2017-08-31 160, -77.8, 163.7, -76.5 https://cmr.earthdata.nasa.gov/search/concepts/C2532069950-AMD_USAPDC.umm_json Cryptoendoliths are organisms that colonize microscopic cavities of rocks, which give them protection and allow them to inhabit extreme environments, such as the cold, arid desert of the Dry Valleys of Antarctica. Fossilized cryptoendoliths preserve the forms and features of organisms from the past and thus provide a unique opportunity to study the organisms' life histories and environments. To study this fossil record, there needs to be a better understanding of what environmental conditions allow these fossils to form. A climate gradient currently exists in the Dry Valleys that allows us to study living, dead, and fossilized cryptoendoliths from mild to increasingly harsh environments; providing insight to the limits of life and how these fossils are formed. This project will develop instruments to detect the biological activity of the live microorganisms and conduct laboratory experiments to determine the environmental limits of their survival. The project also will characterize the chemical and structural features of the living, dead, and fossilized cryptoendoliths to understand how they become fossilized. Knowing how microorganisms are preserved as fossils in cold and dry environments like Antarctica can help to refine methods that can be used to search for and identify evidence for extraterrestrial life in similar habitats on planets such as Mars. This project includes training of graduate and undergraduate students. Little is known about cryptoendolithic microfossils and their formation processes in cold, arid terrestrial habitats of the Dry Valleys of Antarctica, where a legacy of activity is discernible in the form of biosignatures including inorganic materials and microbial fossils that preserve and indicate traces of past biological activity. The overarching goals of the proposed work are: (1) to determine how rates of microbial respiration and biodegradation of organic matter control microbial fossilization; and (2) to characterize microbial fossils and their living counterparts to elucidate mechanisms for fossilization. Using samples collected across an increasingly harsher (more cold and dry) climatic gradient that encompasses living, dead, and fossilized cryptoendolithic microorganisms, the proposed work will: (1) develop an instrument to be used in the field that can measure small concentrations of CO2 in cryptoendolithic habitats in situ; (2) use microscopy techniques to characterize endolithic microorganisms as well as the chemical and morphological characteristics of biosignatures and microbial fossils. A metagenomic survey of microbial communities in these samples will be used to characterize differences in diversity, identify if specific microorganisms (e.g. prokaryotes, eukaryotes) are more capable of surviving under these harsh climatic conditions, and to corroborate microscopic observations of the viability states of these microorganisms. proprietary
USAP-1643534_1 Biological and Physical Drivers of Oxygen Saturation and Net Community Production Variability along the Western Antarctic Peninsula AMD_USAPDC STAC Catalog 2016-06-15 2023-07-15 -83, -73, -56, -62 https://cmr.earthdata.nasa.gov/search/concepts/C2532075509-AMD_USAPDC.umm_json "This project seeks to make detailed measurements of the oxygen content of the surface ocean along the Western Antarctic Peninsula. Detailed maps of changes in net oxygen content will be combined with measurements of the surface water chemistry and phytoplankton distributions. The project will determine the extent to which on-shore or offshore phytoplankton blooms along the peninsula are likely to lead to different amounts of carbon being exported to the deeper ocean. The project will analyze oxygen in relation to argon that will allow determination of the physical and biological contributions to surface ocean oxygen dynamics. These assessments will be combined with spatial and temporal distributions of nutrients (iron and macronutrients) and irradiances. This will allow the investigators to unravel the complex interplay between ice dynamics, iron and physical mixing dynamics as they relate to Net Community Production (NCP) in the region. NCP measurements will be normalized to Particulate Organic Carbon (POC) and be used to help identify area of ""High Biomass and Low NCP"" and those with ""Low Biomass and High NCP"" as a function of microbial plankton community composition. The team will use machine learning methods- including decision tree assemblages and genetic programming- to identify plankton groups key to facilitating biological carbon fluxes. Decomposing the oxygen signal along the West Antarctic Peninsula will also help elucidate biotic and abiotic drivers of the O2 saturation to further contextualize the growing inventory of oxygen measurements (e.g. by Argo floats) throughout the global oceans." proprietary
-USAP-1643722_1 A High Resolution Atmospheric Methane Record from the South Pole Ice Core ALL STAC Catalog 2017-02-01 2019-01-31 180, -90, 180, -90 https://cmr.earthdata.nasa.gov/search/concepts/C2534799946-AMD_USAPDC.umm_json This award supports a project to measure the concentration of the gas methane in air trapped in an ice core collected from the South Pole. The data will provide an age scale (age as a function of depth) by matching the South Pole methane changes with similar data from other ice cores for which the age vs. depth relationship is well known. The ages provided will allow all other gas measurements made on the South Pole core (by the PI and other NSF supported investigators) to be interpreted accurately as a function of time. This is critical because a major goal of the South Pole coring project is to understand the history of rare gases in the atmosphere like carbon monoxide, carbon dioxide, ethane, propane, methyl chloride, and methyl bromide. Relatively little is known about what controls these gases in the atmosphere despite their importance to atmospheric chemistry and climate. Undergraduate assistants will work on the project and be introduced to independent research through their work. The PI will continue visits to local middle schools to introduce students to polar science, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) as part of the project. Methane concentrations from a major portion (2 depth intervals, excluding the brittle ice-zone which is being measured at Penn State University) of the new South Pole ice core will be used to create a gas chronology by matching the new South Pole ice core record with that from the well-dated WAIS Divide ice core record. In combination with measurements made at Penn State, this will provide gas dating for the entire 50,000-year record. Correlation will be made using a simple but powerful mid-point method that has been previously demonstrated, and other methods of matching records will be explored. The intellectual merit of this work is that the gas chronology will be a fundamental component of this ice core project, and will be used by the PI and other investigators for dating records of atmospheric composition, and determining the gas age-ice age difference independently of glaciological models, which will constrain processes that affected firn densification in the past. The methane data will also provide direct stratigraphic markers of important perturbations to global biogeochemical cycles (e.g., rapid methane variations synchronous with abrupt warming and cooling in the Northern Hemisphere) that will tie other ice core gas records directly to those perturbations. A record of the total air content will also be produced as a by-product of the methane measurements and will contribute to understanding of this parameter. The broader impacts include that the work will provide a fundamental data set for the South Pole ice core project and the age scale (or variants of it) will be used by all other investigators working on gas records from the core. The project will employ an undergraduate assistant(s) in both years who will conduct an undergraduate research project which will be part of the student's senior thesis or other research paper. The project will also offer at least one research position for the Oregon State University Summer REU site program. Visits to local middle schools, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) will also be part of the project. proprietary
USAP-1643722_1 A High Resolution Atmospheric Methane Record from the South Pole Ice Core AMD_USAPDC STAC Catalog 2017-02-01 2019-01-31 180, -90, 180, -90 https://cmr.earthdata.nasa.gov/search/concepts/C2534799946-AMD_USAPDC.umm_json This award supports a project to measure the concentration of the gas methane in air trapped in an ice core collected from the South Pole. The data will provide an age scale (age as a function of depth) by matching the South Pole methane changes with similar data from other ice cores for which the age vs. depth relationship is well known. The ages provided will allow all other gas measurements made on the South Pole core (by the PI and other NSF supported investigators) to be interpreted accurately as a function of time. This is critical because a major goal of the South Pole coring project is to understand the history of rare gases in the atmosphere like carbon monoxide, carbon dioxide, ethane, propane, methyl chloride, and methyl bromide. Relatively little is known about what controls these gases in the atmosphere despite their importance to atmospheric chemistry and climate. Undergraduate assistants will work on the project and be introduced to independent research through their work. The PI will continue visits to local middle schools to introduce students to polar science, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) as part of the project. Methane concentrations from a major portion (2 depth intervals, excluding the brittle ice-zone which is being measured at Penn State University) of the new South Pole ice core will be used to create a gas chronology by matching the new South Pole ice core record with that from the well-dated WAIS Divide ice core record. In combination with measurements made at Penn State, this will provide gas dating for the entire 50,000-year record. Correlation will be made using a simple but powerful mid-point method that has been previously demonstrated, and other methods of matching records will be explored. The intellectual merit of this work is that the gas chronology will be a fundamental component of this ice core project, and will be used by the PI and other investigators for dating records of atmospheric composition, and determining the gas age-ice age difference independently of glaciological models, which will constrain processes that affected firn densification in the past. The methane data will also provide direct stratigraphic markers of important perturbations to global biogeochemical cycles (e.g., rapid methane variations synchronous with abrupt warming and cooling in the Northern Hemisphere) that will tie other ice core gas records directly to those perturbations. A record of the total air content will also be produced as a by-product of the methane measurements and will contribute to understanding of this parameter. The broader impacts include that the work will provide a fundamental data set for the South Pole ice core project and the age scale (or variants of it) will be used by all other investigators working on gas records from the core. The project will employ an undergraduate assistant(s) in both years who will conduct an undergraduate research project which will be part of the student's senior thesis or other research paper. The project will also offer at least one research position for the Oregon State University Summer REU site program. Visits to local middle schools, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) will also be part of the project. proprietary
+USAP-1643722_1 A High Resolution Atmospheric Methane Record from the South Pole Ice Core ALL STAC Catalog 2017-02-01 2019-01-31 180, -90, 180, -90 https://cmr.earthdata.nasa.gov/search/concepts/C2534799946-AMD_USAPDC.umm_json This award supports a project to measure the concentration of the gas methane in air trapped in an ice core collected from the South Pole. The data will provide an age scale (age as a function of depth) by matching the South Pole methane changes with similar data from other ice cores for which the age vs. depth relationship is well known. The ages provided will allow all other gas measurements made on the South Pole core (by the PI and other NSF supported investigators) to be interpreted accurately as a function of time. This is critical because a major goal of the South Pole coring project is to understand the history of rare gases in the atmosphere like carbon monoxide, carbon dioxide, ethane, propane, methyl chloride, and methyl bromide. Relatively little is known about what controls these gases in the atmosphere despite their importance to atmospheric chemistry and climate. Undergraduate assistants will work on the project and be introduced to independent research through their work. The PI will continue visits to local middle schools to introduce students to polar science, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) as part of the project. Methane concentrations from a major portion (2 depth intervals, excluding the brittle ice-zone which is being measured at Penn State University) of the new South Pole ice core will be used to create a gas chronology by matching the new South Pole ice core record with that from the well-dated WAIS Divide ice core record. In combination with measurements made at Penn State, this will provide gas dating for the entire 50,000-year record. Correlation will be made using a simple but powerful mid-point method that has been previously demonstrated, and other methods of matching records will be explored. The intellectual merit of this work is that the gas chronology will be a fundamental component of this ice core project, and will be used by the PI and other investigators for dating records of atmospheric composition, and determining the gas age-ice age difference independently of glaciological models, which will constrain processes that affected firn densification in the past. The methane data will also provide direct stratigraphic markers of important perturbations to global biogeochemical cycles (e.g., rapid methane variations synchronous with abrupt warming and cooling in the Northern Hemisphere) that will tie other ice core gas records directly to those perturbations. A record of the total air content will also be produced as a by-product of the methane measurements and will contribute to understanding of this parameter. The broader impacts include that the work will provide a fundamental data set for the South Pole ice core project and the age scale (or variants of it) will be used by all other investigators working on gas records from the core. The project will employ an undergraduate assistant(s) in both years who will conduct an undergraduate research project which will be part of the student's senior thesis or other research paper. The project will also offer at least one research position for the Oregon State University Summer REU site program. Visits to local middle schools, and other outreach activities (e.g. laboratory tours, talks to local civic or professional organizations) will also be part of the project. proprietary
USAP-1643864_1 Collaborative Research: Borehole Logging to Classify Volcanic Signatures in Antarctic Ice AMD_USAPDC STAC Catalog 2017-05-08 -112.085, -79.467, -112.085, -79.467 https://cmr.earthdata.nasa.gov/search/concepts/C2532074603-AMD_USAPDC.umm_json This dataset comprises new photographs and measurements of a WAIS Divide vertical thin section, WDC-06A 420 VTS, previously prepared and measured by J. Fitzpatrick, D. E. Voigt, and R. Alley (dataset DOI: 10.7265/N5W093VM; http://www.usap-dc.org/view/dataset/609605) as part of a larger study of the WAIS Divide ice core (Fitzpatrick, J. et al, 2014, Physical properties of the WAIS Divide ice core, Journal of Glaciology, 60, 224, 1181-1198. (doi:10.3189/2014JoG14J100). These images were taken as a design test of our new automated lightweight c-axis analyzer, dubbed ALPACA, which implements the ice fabric analysis functionality of the Wilen system used by Fitzpatrick et al. in an easily-portable, field-deployable form factor. proprietary
USAP-1644004_1 Collaborative Research: Foraging Ecology and Physiology of the Leopard Seal AMD_USAPDC STAC Catalog 2017-10-01 2022-09-30 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C2560369942-AMD_USAPDC.umm_json This research project is a multidisciplinary effort that brings together a diverse team of scientists from multiple institutions together to understand the foraging behavior and physiology of leopard seals and their role in the Southern Ocean food web. The project will examine the physiology and behavior of leopard seals to in an effort to determine their ability to respond to potential changes in their habitat and foraging areas. Using satellite tracking devices the team will examine the movement and diving behavior of leopard seals and couple this information with measurements of their physiological capacity. The project will determine whether leopard seals- who feed on diverse range of prey- are built differently than their deep diving relatives the Weddell and elephant seal who feed on fish and squid. The team will also determine whether leopard seals are operating at or near their physiological capability to determine how much, if any, ?reserve capacity? they might have to forage and live in changing environments. A better understanding of their home ranges, movement patterns, and general behavior will also be informative to help in managing human-leopard seal interactions. The highly visual nature of the data and analysis for this project lends itself to public and educational display and outreach, particularly as they relate to the changing Antarctic habitats. The project will use the research results to educate the public on the unique physiological and ecological adaptations to extreme environments seen in diving marine mammals, including adaptations to exercise under low oxygen conditions and energy utilization, which affect and dictate the lifestyle of these exceptional organisms. The results of the project will also contribute to the broader understanding that may enhance the aims of managing marine living resources. The leopard seal is an apex predator in the Antarctic ecosystem. This project seeks to better understand the ability of the leopard seal to cope with a changing environment. The project will first examine the foraging behavior and habitat utilization of leopard seals using satellite telemetry. Specifically, satellite telemetry tags will be used to obtain dive profiles and movement data for individuals across multiple years. Diet and trophic level positions across multiple temporal scales will then be determined from physiological samples (e.g., blood, vibrissae, blubber fatty acids, stable isotopes, fecal matter). Oceanographic data will be integrated with these measures to develop habitat models that will be used to assess habitat type, habitat utilization, habitat preference, and home range areas for individual animals. Diet composition for individual seals will be evaluated to determine whether specific animals are generalists or specialists. Second, the team will investigate the physiological adaptations that allow leopard seals to be apex predators and determine to what extent leopard seals are working at or near their physiological limit. Diving behavior and physiology of leopard seals will be evaluated (for instance the aerobic dive limit for individual animals and skeletal muscle adaptations will be determined for diving under hypoxic conditions). Data from time-depth recorders will be used to determine foraging strategies for individual seals, and these diving characteristics will be related to physiological variables (e.g., blood volume, muscle oxygen stores) to better understand the link between foraging behavior and physiology. The team will compare myoglobin storage in swimming muscles associated with both forelimb and hind limb propulsion and the use of anaerobic versus aerobic metabolic systems while foraging. proprietary
USAP-1644073_1 Collaborative Research: Cobalamin and Iron Co-Limitation Of Phytoplankton Species in Terra Nova Bay AMD_USAPDC STAC Catalog 2017-08-18 2020-08-31 -116, -79, 160, -72 https://cmr.earthdata.nasa.gov/search/concepts/C2532074465-AMD_USAPDC.umm_json Phytoplankton blooms in the coastal waters of the Ross Sea, Antarctica are typically dominated by either diatoms or Phaeocystis Antarctica (a flagellated algae that often can form large colonies in a gelatinous matrix). The project seeks to determine if an association of bacterial populations with Phaeocystis antarctica colonies can directly supply Phaeocystis with Vitamin B12, which can be an important co-limiting micronutrient in the Ross Sea. The supply of an essential vitamin coupled with the ability to grow at lower iron concentrations may put Phaeocystis at a competitive advantage over diatoms. Because Phaeocystis cells can fix more carbon than diatoms and Phaeocystis are not grazed as efficiently as diatoms, the project will help in refining understanding of carbon dynamics in the region as well as the basis of the food web webs. Such understanding also has the potential to help refine predictive ecological models for the region. The project will conduct public outreach activities and will contribute to undergraduate and graduate research. Engagement of underrepresented students will occur during summer student internships. A collaboration with Italian Antarctic researchers, who have been studying the Terra Nova Bay ecosystem since the 1980s, aims to enhance the project and promote international scientific collaborations. The study will test whether a mutualistic symbioses between attached bacteria and Phaeocystis provides colonial cells a mechanism for alleviating chronic Vitamin B12 co-limitation effects thereby conferring them with a competitive advantage over diatom communities. The use of drifters in a time series study will provide the opportunity to track in both space and time a developing algal bloom in Terra Nova Bay and to determine community structure and the physiological nutrient status of microbial populations. A combination of flow cytometry, proteomics, metatranscriptomics, radioisotopic and stable isotopic labeling experiments will determine carbon and nutrient uptake rates and the role of bacteria in mitigating potential vitamin B12 and iron limitation. Membrane inlet and proton transfer reaction mass spectrometry will also be used to estimate net community production and release of volatile organic carbon compounds that are climatically active. Understanding how environmental parameters can influence microbial community dynamics in Antarctic coastal waters will advance an understanding of how changes in ocean stratification and chemistry could impact the biogeochemistry and food web dynamics of Southern Ocean ecosystems. proprietary
USAP-1644197_1 Collaborative Research: New Constraints on Post-Glacial Rebound and Holocene Environmental History along the Northern Antarctic Peninsula from Raised Beaches AMD_USAPDC STAC Catalog 2017-08-08 2021-08-31 -65, -65, -55, -61 https://cmr.earthdata.nasa.gov/search/concepts/C2605088269-AMD_USAPDC.umm_json Glacier ice loss from Antarctica has the potential to lead to a significant rise in global sea level. One line of evidence for accelerated glacier ice loss has been an increase in the rate at which the land has been rising across the Antarctic Peninsula as measured by GPS receivers. However, GPS observations of uplift are limited to the last two decades. One goal of this study is to determine how these newly observed rates of uplift compare to average rates of uplift across the Antarctic Peninsula over a longer time interval. Researchers reconstructed past sea levels using the age and elevation of ancient beaches now stranded above sea level on the low-lying coastal hills of the Antarctica Peninsula and determined the rate of uplift over the last 5,000 years. The researchers analyzed the structure of the beaches using ground-penetrating radar and the characteristics of beach sediments to understand how sea-level rise and past climate changes are recorded in beach deposits. We found that unlike most views of how sea level changed across Antarctica over the last 5,000 years, its history is complex with periods of increasing rates of sea-level fall as well as short periods of potential sea-level rise. We attribute these oscillations in the nature of sea-level change across the Antarctic Peninsula to changes in the ice sheet over the last 5,000 years. These changes in sea level also suggest our understanding of the Earth structure beneath the Antarctic Peninsula need to be revised. The beach deposits themselves also record periods of climate change as reflected in the size and shape of their cobbles. This project has lead to the training of five graduate students, three undergraduate students, and outreach talks to k-12 schools in three communities. proprietary
-USAP-1644234_1 A Test of Global and Antarctic Models for Cosmogenic-nuclide Production Rates using High-precision Dating of 40Ar/39Ar Lava Flows from Mount Erebus AMD_USAPDC STAC Catalog 2017-07-15 2022-06-30 166.17, -77.7, 167.75, -77.3 https://cmr.earthdata.nasa.gov/search/concepts/C2586847142-AMD_USAPDC.umm_json Nontechnical Description: The age of rocks and soils at the surface of the Earth can help answer multiple questions that are important for human welfare, including: when did volcanoes erupt and are they likely to erupt again? when did glaciers advance and what do they tell us about climate? what is the frequency of hazards such as landslides, floods, and debris flows? how long does it take soils to form and is erosion of soils going to make farming unsustainable? One method that is used thousands of times every year to address these questions is called 'cosmogenic surface-exposure dating'. This method takes advantage of cosmic rays, which are powerful protons and neutrons produced by supernova that constantly bombard the Earth's atmosphere. Some cosmic rays reach Earth's surface and produce nuclear reactions that result in rare isotopes. Measuring the quantity of the rare isotopes enables the length of time that the rock or soil has been exposed to the atmosphere to be calculated. The distribution of cosmic rays around the globe depends on Earth's magnetic field, and this distribution must be accurately known if useful exposure ages are to be obtained. Currently there are two remaining theories, narrowed down from many, of how to calculate this distribution. Measurements from a site that is at both high altitude and high latitude (close to the poles) are needed to test the two theories. This study involves both field and lab research and includes a Ph.D. student and an undergraduate student. The research team will collect rocks from lava flows on an active volcano in Antarctica named Mount Erebus and measure the amounts of two rare isotopes: 36Cl and 3He. The age of eruption of the samples will be determined using a highly accurate method that does not depend on cosmic rays, called 40Ar/39Ar dating. The two cosmic-ray theories will be used to calculate the ages of the samples using the 36Cl and 3He concentrations and will then be compared to the ages calculated from the 40Ar/39Ar dating. The accurate cosmic-ray theory will be the one that gives the same ages as the 40Ar/39Ar dating. Identification of the accurate theory will enable use of the cosmogenic surface dating methods anywhere on earth. Technical Description: Nuclides produced by cosmic rays in rocks at the surface of the earth are widely used for Quaternary geochronology and geomorphic studies and their use is increasing every year. The recently completed CRONUS-Earth Project (Cosmic-Ray Produced Nuclides on Earth) has systematically evaluated the production rates and theoretical underpinnings of cosmogenic nuclides. However, the CRONUS-Earth Project was not able to discriminate between the two leading theoretical approaches: the original Lal model (St) and the new Lifton-Sato-Dunai model (LSD). Mathematical models used to scale the production of the nuclides as a function of location on the earth, elevation, and magnetic field configuration are an essential component of this dating method. The inability to distinguish between the two models was because the predicted production rates did not differ sufficiently at the location of the calibration sites. The cosmogenic-nuclide production rates that are predicted by the two models differ significantly from each other at Erebus volcano, Antarctica. Mount Erebus is therefore an excellent site for testing which production model best describes actual cosmogenic-nuclide production variations over the globe. The research team recently measured 3He and 36Cl in mineral separates extracted from Erebus lava flows. The exposure ages for each nuclide were reproducible within each flow (~2% standard deviation) and in very good agreement between the 3He and the 36Cl ages. However, the ages calculated by the St and LSD scaling methods differ by ~15-25% due to the sensitivity of the production rate to the scaling at this latitude and elevation. These results lend confidence that Erebus qualifies as a suitable high- latitude/high-elevation calibration site. The remaining component that is still lacking is accurate and reliable independent (i.e., non-cosmogenic) ages, however, published 40Ar/39Ar ages are too imprecise and typically biased to older ages due to excess argon contained in melt inclusions. The research team's new 40Ar/39Ar data show that previous problems with Erebus anorthoclase geochronology are now overcome with modern mass spectrometry and better sample preparation. This indicates a high likelihood of success for this proposal in defining an accurate global scaling model. Although encouraging, much remains to be accomplished. This project will sample lava flows over 3 km in elevation and determine their 40Ar/39Ar and exposure ages. These combined data will discriminate between the two scaling methods, resulting in a preferred scaling model for global cosmogenic geochronology. The LSD method contains two sub-methods, the 'plain' LSD scales all nuclides the same, whereas LSDn scales each nuclide individually. The project can discriminate between these models using 3He and 36Cl data from lava flows at different elevations, because the first model predicts that the production ratio for these two nuclides will be invariant with elevation and the second that there should be ~10% difference over the range of elevations to be sampled. Finally, the project will provide a local, finite-age calibration site for cosmogenic-nuclide investigations in Antarctica. proprietary
USAP-1644234_1 A Test of Global and Antarctic Models for Cosmogenic-nuclide Production Rates using High-precision Dating of 40Ar/39Ar Lava Flows from Mount Erebus ALL STAC Catalog 2017-07-15 2022-06-30 166.17, -77.7, 167.75, -77.3 https://cmr.earthdata.nasa.gov/search/concepts/C2586847142-AMD_USAPDC.umm_json Nontechnical Description: The age of rocks and soils at the surface of the Earth can help answer multiple questions that are important for human welfare, including: when did volcanoes erupt and are they likely to erupt again? when did glaciers advance and what do they tell us about climate? what is the frequency of hazards such as landslides, floods, and debris flows? how long does it take soils to form and is erosion of soils going to make farming unsustainable? One method that is used thousands of times every year to address these questions is called 'cosmogenic surface-exposure dating'. This method takes advantage of cosmic rays, which are powerful protons and neutrons produced by supernova that constantly bombard the Earth's atmosphere. Some cosmic rays reach Earth's surface and produce nuclear reactions that result in rare isotopes. Measuring the quantity of the rare isotopes enables the length of time that the rock or soil has been exposed to the atmosphere to be calculated. The distribution of cosmic rays around the globe depends on Earth's magnetic field, and this distribution must be accurately known if useful exposure ages are to be obtained. Currently there are two remaining theories, narrowed down from many, of how to calculate this distribution. Measurements from a site that is at both high altitude and high latitude (close to the poles) are needed to test the two theories. This study involves both field and lab research and includes a Ph.D. student and an undergraduate student. The research team will collect rocks from lava flows on an active volcano in Antarctica named Mount Erebus and measure the amounts of two rare isotopes: 36Cl and 3He. The age of eruption of the samples will be determined using a highly accurate method that does not depend on cosmic rays, called 40Ar/39Ar dating. The two cosmic-ray theories will be used to calculate the ages of the samples using the 36Cl and 3He concentrations and will then be compared to the ages calculated from the 40Ar/39Ar dating. The accurate cosmic-ray theory will be the one that gives the same ages as the 40Ar/39Ar dating. Identification of the accurate theory will enable use of the cosmogenic surface dating methods anywhere on earth. Technical Description: Nuclides produced by cosmic rays in rocks at the surface of the earth are widely used for Quaternary geochronology and geomorphic studies and their use is increasing every year. The recently completed CRONUS-Earth Project (Cosmic-Ray Produced Nuclides on Earth) has systematically evaluated the production rates and theoretical underpinnings of cosmogenic nuclides. However, the CRONUS-Earth Project was not able to discriminate between the two leading theoretical approaches: the original Lal model (St) and the new Lifton-Sato-Dunai model (LSD). Mathematical models used to scale the production of the nuclides as a function of location on the earth, elevation, and magnetic field configuration are an essential component of this dating method. The inability to distinguish between the two models was because the predicted production rates did not differ sufficiently at the location of the calibration sites. The cosmogenic-nuclide production rates that are predicted by the two models differ significantly from each other at Erebus volcano, Antarctica. Mount Erebus is therefore an excellent site for testing which production model best describes actual cosmogenic-nuclide production variations over the globe. The research team recently measured 3He and 36Cl in mineral separates extracted from Erebus lava flows. The exposure ages for each nuclide were reproducible within each flow (~2% standard deviation) and in very good agreement between the 3He and the 36Cl ages. However, the ages calculated by the St and LSD scaling methods differ by ~15-25% due to the sensitivity of the production rate to the scaling at this latitude and elevation. These results lend confidence that Erebus qualifies as a suitable high- latitude/high-elevation calibration site. The remaining component that is still lacking is accurate and reliable independent (i.e., non-cosmogenic) ages, however, published 40Ar/39Ar ages are too imprecise and typically biased to older ages due to excess argon contained in melt inclusions. The research team's new 40Ar/39Ar data show that previous problems with Erebus anorthoclase geochronology are now overcome with modern mass spectrometry and better sample preparation. This indicates a high likelihood of success for this proposal in defining an accurate global scaling model. Although encouraging, much remains to be accomplished. This project will sample lava flows over 3 km in elevation and determine their 40Ar/39Ar and exposure ages. These combined data will discriminate between the two scaling methods, resulting in a preferred scaling model for global cosmogenic geochronology. The LSD method contains two sub-methods, the 'plain' LSD scales all nuclides the same, whereas LSDn scales each nuclide individually. The project can discriminate between these models using 3He and 36Cl data from lava flows at different elevations, because the first model predicts that the production ratio for these two nuclides will be invariant with elevation and the second that there should be ~10% difference over the range of elevations to be sampled. Finally, the project will provide a local, finite-age calibration site for cosmogenic-nuclide investigations in Antarctica. proprietary
+USAP-1644234_1 A Test of Global and Antarctic Models for Cosmogenic-nuclide Production Rates using High-precision Dating of 40Ar/39Ar Lava Flows from Mount Erebus AMD_USAPDC STAC Catalog 2017-07-15 2022-06-30 166.17, -77.7, 167.75, -77.3 https://cmr.earthdata.nasa.gov/search/concepts/C2586847142-AMD_USAPDC.umm_json Nontechnical Description: The age of rocks and soils at the surface of the Earth can help answer multiple questions that are important for human welfare, including: when did volcanoes erupt and are they likely to erupt again? when did glaciers advance and what do they tell us about climate? what is the frequency of hazards such as landslides, floods, and debris flows? how long does it take soils to form and is erosion of soils going to make farming unsustainable? One method that is used thousands of times every year to address these questions is called 'cosmogenic surface-exposure dating'. This method takes advantage of cosmic rays, which are powerful protons and neutrons produced by supernova that constantly bombard the Earth's atmosphere. Some cosmic rays reach Earth's surface and produce nuclear reactions that result in rare isotopes. Measuring the quantity of the rare isotopes enables the length of time that the rock or soil has been exposed to the atmosphere to be calculated. The distribution of cosmic rays around the globe depends on Earth's magnetic field, and this distribution must be accurately known if useful exposure ages are to be obtained. Currently there are two remaining theories, narrowed down from many, of how to calculate this distribution. Measurements from a site that is at both high altitude and high latitude (close to the poles) are needed to test the two theories. This study involves both field and lab research and includes a Ph.D. student and an undergraduate student. The research team will collect rocks from lava flows on an active volcano in Antarctica named Mount Erebus and measure the amounts of two rare isotopes: 36Cl and 3He. The age of eruption of the samples will be determined using a highly accurate method that does not depend on cosmic rays, called 40Ar/39Ar dating. The two cosmic-ray theories will be used to calculate the ages of the samples using the 36Cl and 3He concentrations and will then be compared to the ages calculated from the 40Ar/39Ar dating. The accurate cosmic-ray theory will be the one that gives the same ages as the 40Ar/39Ar dating. Identification of the accurate theory will enable use of the cosmogenic surface dating methods anywhere on earth. Technical Description: Nuclides produced by cosmic rays in rocks at the surface of the earth are widely used for Quaternary geochronology and geomorphic studies and their use is increasing every year. The recently completed CRONUS-Earth Project (Cosmic-Ray Produced Nuclides on Earth) has systematically evaluated the production rates and theoretical underpinnings of cosmogenic nuclides. However, the CRONUS-Earth Project was not able to discriminate between the two leading theoretical approaches: the original Lal model (St) and the new Lifton-Sato-Dunai model (LSD). Mathematical models used to scale the production of the nuclides as a function of location on the earth, elevation, and magnetic field configuration are an essential component of this dating method. The inability to distinguish between the two models was because the predicted production rates did not differ sufficiently at the location of the calibration sites. The cosmogenic-nuclide production rates that are predicted by the two models differ significantly from each other at Erebus volcano, Antarctica. Mount Erebus is therefore an excellent site for testing which production model best describes actual cosmogenic-nuclide production variations over the globe. The research team recently measured 3He and 36Cl in mineral separates extracted from Erebus lava flows. The exposure ages for each nuclide were reproducible within each flow (~2% standard deviation) and in very good agreement between the 3He and the 36Cl ages. However, the ages calculated by the St and LSD scaling methods differ by ~15-25% due to the sensitivity of the production rate to the scaling at this latitude and elevation. These results lend confidence that Erebus qualifies as a suitable high- latitude/high-elevation calibration site. The remaining component that is still lacking is accurate and reliable independent (i.e., non-cosmogenic) ages, however, published 40Ar/39Ar ages are too imprecise and typically biased to older ages due to excess argon contained in melt inclusions. The research team's new 40Ar/39Ar data show that previous problems with Erebus anorthoclase geochronology are now overcome with modern mass spectrometry and better sample preparation. This indicates a high likelihood of success for this proposal in defining an accurate global scaling model. Although encouraging, much remains to be accomplished. This project will sample lava flows over 3 km in elevation and determine their 40Ar/39Ar and exposure ages. These combined data will discriminate between the two scaling methods, resulting in a preferred scaling model for global cosmogenic geochronology. The LSD method contains two sub-methods, the 'plain' LSD scales all nuclides the same, whereas LSDn scales each nuclide individually. The project can discriminate between these models using 3He and 36Cl data from lava flows at different elevations, because the first model predicts that the production ratio for these two nuclides will be invariant with elevation and the second that there should be ~10% difference over the range of elevations to be sampled. Finally, the project will provide a local, finite-age calibration site for cosmogenic-nuclide investigations in Antarctica. proprietary
USAP-1656344_1 A Preliminary Assessment of the Influence of Ice Cover on Microbial Carbon and Energy Acquisition during the Antarctic Winter-spring Seasonal Transition AMD_USAPDC STAC Catalog 2016-08-01 2018-07-31 -64.1, -65, -63.9, -64.75 https://cmr.earthdata.nasa.gov/search/concepts/C2532071951-AMD_USAPDC.umm_json "This EAGER project will compare gene expression patterns in the planktonic communities under ice covers that form in coastal embayment's in the Antarctic Peninsula. Previous efforts taking advantage of unique ice conditions in November and December of 2015 allowed researchers to conduct an experiment to examine the role of sea ice cover on microbial carbon and energy transfer during the winter-spring transition. The EAGER effort will enable the researchers to conduct the ""omics"" analyses of the phytoplankton to determine predominant means by which energy is acquired and used in these settings. This EAGER effort will apply new expertise to fill an existing gap in ecological observations along the West Antarctic Peninsula. The principle product of the proposed work will be a novel dataset to be analyzed and by an early career researcher from an underserved community (veteran). The critical baseline data contained in this dataset enable a comparison of eukaryotic and prokaryotic gene expression patterns to establish the relative importance of chemoautotrophy, heterotrophy, mixotrophy, and phototrophy during the experiments. this information and data will be made immediately available to the broader scientific community, and will enable the development of further hypotheses on ecosystem change as sea ice cover changes in the region. Very little gene expression data is currently available for the Antarctic marine environment, and no gene expression data is available during the ecologically critical winter to spring transition. Moreover, ice cover in bays is common along the West Antarctic Peninsula yet the opportunity to study cryptophyte phytoplankton physiology beneath such ice conditions in coastal embayments is rare." proprietary
USAP-1656344_1 A Preliminary Assessment of the Influence of Ice Cover on Microbial Carbon and Energy Acquisition during the Antarctic Winter-spring Seasonal Transition ALL STAC Catalog 2016-08-01 2018-07-31 -64.1, -65, -63.9, -64.75 https://cmr.earthdata.nasa.gov/search/concepts/C2532071951-AMD_USAPDC.umm_json "This EAGER project will compare gene expression patterns in the planktonic communities under ice covers that form in coastal embayment's in the Antarctic Peninsula. Previous efforts taking advantage of unique ice conditions in November and December of 2015 allowed researchers to conduct an experiment to examine the role of sea ice cover on microbial carbon and energy transfer during the winter-spring transition. The EAGER effort will enable the researchers to conduct the ""omics"" analyses of the phytoplankton to determine predominant means by which energy is acquired and used in these settings. This EAGER effort will apply new expertise to fill an existing gap in ecological observations along the West Antarctic Peninsula. The principle product of the proposed work will be a novel dataset to be analyzed and by an early career researcher from an underserved community (veteran). The critical baseline data contained in this dataset enable a comparison of eukaryotic and prokaryotic gene expression patterns to establish the relative importance of chemoautotrophy, heterotrophy, mixotrophy, and phototrophy during the experiments. this information and data will be made immediately available to the broader scientific community, and will enable the development of further hypotheses on ecosystem change as sea ice cover changes in the region. Very little gene expression data is currently available for the Antarctic marine environment, and no gene expression data is available during the ecologically critical winter to spring transition. Moreover, ice cover in bays is common along the West Antarctic Peninsula yet the opportunity to study cryptophyte phytoplankton physiology beneath such ice conditions in coastal embayments is rare." proprietary
USAP-1744755_1 A mechanistic study of bio-physical interaction and air-sea carbon transfer in the Southern Ocean ALL STAC Catalog 2018-05-01 2022-04-30 -80, -70, -30, -45 https://cmr.earthdata.nasa.gov/search/concepts/C2545372297-AMD_USAPDC.umm_json Current generation of coupled climate models, that are used to make climate projections, lack the resolution to adequately resolve ocean mesoscale (10 - 100km) processes, exhibiting significant biases in the ocean carbon uptake. Mesoscale processes include many features including jets, fronts and eddies that are crucial for bio-physical interactions, air-sea CO2 exchange and the supply of iron to the surface ocean. This modeling project will support the eddy resolving regional simulations to understand the mechanisms that drives bio-physical interaction and air-sea exchange of carbon dioxide. proprietary
@@ -15497,8 +15502,8 @@ USAP-2324998_1 ANT LIA: Collaborative Research: Evolutionary Patterns and Mechan
USAP-9615281_1 Air-Ground Study of Tectonics at the Boundary Between the Eastern Ross Embayment and Western Marie Byrd Land, Antarctica: Basement Geology and Structure AMD_USAPDC STAC Catalog 1997-08-15 2002-07-31 -170, -84, -135, -76 https://cmr.earthdata.nasa.gov/search/concepts/C2532072225-AMD_USAPDC.umm_json This award supports a collaborative project that combines air and ground geological-geophysical investigations to understand the tectonic and geological development of the boundary between the Ross Sea Rift and the Marie Byrd Land (MBL) volcanic province. The project will determine the Cenozoic tectonic history of the region and whether Neogene structures that localized outlet glacier flow developed within the context of Cenozoic rifting on the eastern Ross Embayment margin, or within the volcanic province in MBL. The geological structure at the boundary between the Ross Embayment and western MBL may be a result of: 1) Cenozoic extension on the eastern shoulder of the Ross Sea rift; 2) uplift and crustal extension related to Neogene mantle plume activity in western MBL; or a combination of the two. Faulting and volcanism, mountain uplift, and glacier downcutting appear to now be active in western MBL, where generally East-to-West-flowing outlet glaciers incise Paleozoic and Mesozoic bedrock, and deglaciated summits indicate a previous North-South glacial flow direction. This study requires data collection using SOAR (Support Office for Aerogeophysical Research, a facility supported by Office of Polar Programs which utilizes high precision differential GPS to support a laser altimeter, ice-penetrating radar, a towed proton magnetometer, and a Bell BGM-3 gravimeter). This survey requires data for 37,000 square kilometers using 5.3 kilometer line spacing with 15.6 kilometer tie lines, and 86,000 square kilometers using a grid of 10.6 by 10.6 kilometer spacing. Data will be acquired over several key features in the region including, among other, the eastern edge of the Ross Sea rift, over ice stream OEO, the transition from the Edward VII Peninsula plateau to the Ford Ranges, the continuation to the east of a gravity high known from previous reconnaissance mapping over the Fosdick Metamorphic Complex, an d the extent of the high-amplitude magnetic anomalies (volcanic centers?) detected southeast of the northern Ford Ranges by other investigators. SOAR products will include glaciology data useful for studying driving stresses, glacial flow and mass balance in the West Antarctic Ice Sheet (WAIS). The ground program is centered on the southern Ford Ranges. Geologic field mapping will focus on small scale brittle structures for regional kinematic interpretation, on glaciated surfaces and deposits, and on datable volcanic rocks for geochronologic control. The relative significance of fault and joint sets, the timing relationships between them, and the probable context of their formation will also be determined. Exposure ages will be determined for erosion surfaces and moraines. Interpretation of potential field data will be aided by on ground sampling for magnetic properties and density as well as ground based gravity measurements. Oriented samples will be taken for paleomagnetic studies. Combined airborne and ground investigations will obtain basic data for describing the geology and structure at the eastern boundary of the Ross Embayment both in outcrop and ice covered areas, and may be used to distinguish between Ross Sea rift- related structural activity from uplift and faulting on the perimeter of the MBL dome and volcanic province. Outcrop geology and structure will be extrapolated with the aerogeophysical data to infer the geology that resides beneath the WAIS. The new knowledge of Neogene tectonics in western MBL will contribute to a comprehensive model for the Cenozoic Ross rift and to understanding of the extent of plume activity in MBL. Both are important for determining the influence of Neogene tectonics on the ice streams and WAIS. proprietary
USAP-9615281_1 Air-Ground Study of Tectonics at the Boundary Between the Eastern Ross Embayment and Western Marie Byrd Land, Antarctica: Basement Geology and Structure ALL STAC Catalog 1997-08-15 2002-07-31 -170, -84, -135, -76 https://cmr.earthdata.nasa.gov/search/concepts/C2532072225-AMD_USAPDC.umm_json This award supports a collaborative project that combines air and ground geological-geophysical investigations to understand the tectonic and geological development of the boundary between the Ross Sea Rift and the Marie Byrd Land (MBL) volcanic province. The project will determine the Cenozoic tectonic history of the region and whether Neogene structures that localized outlet glacier flow developed within the context of Cenozoic rifting on the eastern Ross Embayment margin, or within the volcanic province in MBL. The geological structure at the boundary between the Ross Embayment and western MBL may be a result of: 1) Cenozoic extension on the eastern shoulder of the Ross Sea rift; 2) uplift and crustal extension related to Neogene mantle plume activity in western MBL; or a combination of the two. Faulting and volcanism, mountain uplift, and glacier downcutting appear to now be active in western MBL, where generally East-to-West-flowing outlet glaciers incise Paleozoic and Mesozoic bedrock, and deglaciated summits indicate a previous North-South glacial flow direction. This study requires data collection using SOAR (Support Office for Aerogeophysical Research, a facility supported by Office of Polar Programs which utilizes high precision differential GPS to support a laser altimeter, ice-penetrating radar, a towed proton magnetometer, and a Bell BGM-3 gravimeter). This survey requires data for 37,000 square kilometers using 5.3 kilometer line spacing with 15.6 kilometer tie lines, and 86,000 square kilometers using a grid of 10.6 by 10.6 kilometer spacing. Data will be acquired over several key features in the region including, among other, the eastern edge of the Ross Sea rift, over ice stream OEO, the transition from the Edward VII Peninsula plateau to the Ford Ranges, the continuation to the east of a gravity high known from previous reconnaissance mapping over the Fosdick Metamorphic Complex, an d the extent of the high-amplitude magnetic anomalies (volcanic centers?) detected southeast of the northern Ford Ranges by other investigators. SOAR products will include glaciology data useful for studying driving stresses, glacial flow and mass balance in the West Antarctic Ice Sheet (WAIS). The ground program is centered on the southern Ford Ranges. Geologic field mapping will focus on small scale brittle structures for regional kinematic interpretation, on glaciated surfaces and deposits, and on datable volcanic rocks for geochronologic control. The relative significance of fault and joint sets, the timing relationships between them, and the probable context of their formation will also be determined. Exposure ages will be determined for erosion surfaces and moraines. Interpretation of potential field data will be aided by on ground sampling for magnetic properties and density as well as ground based gravity measurements. Oriented samples will be taken for paleomagnetic studies. Combined airborne and ground investigations will obtain basic data for describing the geology and structure at the eastern boundary of the Ross Embayment both in outcrop and ice covered areas, and may be used to distinguish between Ross Sea rift- related structural activity from uplift and faulting on the perimeter of the MBL dome and volcanic province. Outcrop geology and structure will be extrapolated with the aerogeophysical data to infer the geology that resides beneath the WAIS. The new knowledge of Neogene tectonics in western MBL will contribute to a comprehensive model for the Cenozoic Ross rift and to understanding of the extent of plume activity in MBL. Both are important for determining the influence of Neogene tectonics on the ice streams and WAIS. proprietary
USAP-9725024_1 Circumpolar Deep Water and the West Antarctic Ice Sheet AMD_USAPDC STAC Catalog 1988-03-01 2002-02-28 140, -68, 150, -65 https://cmr.earthdata.nasa.gov/search/concepts/C2532072042-AMD_USAPDC.umm_json This project will study the dynamics of Circumpolar Deep Water intruding on the continental shelf of the West Antarctic coast, and the effect of this intrusion on the production of cold, dense bottom water, and melting at the base of floating glaciers and ice tongues. It will concentrate on the Amundsen Sea shelf, specifically in the region of the Pine Island Glacier, the Thwaites Glacier, and the Getz Ice Shelf. Circumpolar Deep Water (CDW) is a relatively warm water mass (warmer than +1.0 deg Celsius) which is normally confined to the outer edge of the continental shelf by an oceanic front separating this water mass from colder and saltier shelf waters. In the Amundsen Sea however, the deeper parts of the continental shelf are filled with nearly undiluted CDW, which is mixed upward, delivering significant amounts of heat to the base of the floating glacier tongues and the ice shelf. The melt rate beneath the Pine Island Glacier averages ten meters of ice per year with local annual rates reaching twenty meters. By comparison, melt rates beneath the Ross Ice Shelf are typically twenty to forty centimeters of ice per year. In addition, both the Pine Island and the Thwaites Glacier are extremely fast-moving, and have a significant effect on the regional ice mass balance of West Antarctica. This project therefore has an important connection to antarctic glaciology, particularly in assessing the combined effect of global change on the antarctic environment. The particular objectives of the project are (1) to delineate the frontal structure on the continental shelf sufficiently to define quantitatively the major routes of CDW inflow, meltwater outflow, and the westward evolution of CDW influence; (2) to use the obtained data set to validate a three-dimensional model of sub-ice ocean circulation that is currently under construction, and (3) to refine the estiamtes of in situ melting on the mass balance of the antarctic ice sheet. The observational program will be carried out from the research vessel Nathaniel B. Palmer in February and March, 1999. proprietary
-USARC_AERIAL_PHOTOS Aerial Photography of Antarctica CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231551700-CEOS_EXTRA.umm_json "The USARC maintains all US Aerial Antarctic Mapping photography and USGS flight indexes of the Antarctic. There are over 500,000 photographs in the collection. Most photographs are 9"" x 9"" black and white images taken with three Fairchild cameras each with a metrogon lense resulting in trimetrogon photography (left oblique, vertical and right oblique photographs). Special-purpose photographs showing sites of specific scientific interest ""vertical and handheld oblique as well as photographs taken from helicopters"" are also on file. Some color photographs are also available. Line indexes to identify coverage are available for most aerial photographic missions. Contact prints in either matte or glossy finish are available for inspection or stereoscopic viewing. Special feature options, such as ice and rock enhancements, may be special ordered." proprietary
USARC_AERIAL_PHOTOS Aerial Photography of Antarctica ALL STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231551700-CEOS_EXTRA.umm_json "The USARC maintains all US Aerial Antarctic Mapping photography and USGS flight indexes of the Antarctic. There are over 500,000 photographs in the collection. Most photographs are 9"" x 9"" black and white images taken with three Fairchild cameras each with a metrogon lense resulting in trimetrogon photography (left oblique, vertical and right oblique photographs). Special-purpose photographs showing sites of specific scientific interest ""vertical and handheld oblique as well as photographs taken from helicopters"" are also on file. Some color photographs are also available. Line indexes to identify coverage are available for most aerial photographic missions. Contact prints in either matte or glossy finish are available for inspection or stereoscopic viewing. Special feature options, such as ice and rock enhancements, may be special ordered." proprietary
+USARC_AERIAL_PHOTOS Aerial Photography of Antarctica CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -62.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231551700-CEOS_EXTRA.umm_json "The USARC maintains all US Aerial Antarctic Mapping photography and USGS flight indexes of the Antarctic. There are over 500,000 photographs in the collection. Most photographs are 9"" x 9"" black and white images taken with three Fairchild cameras each with a metrogon lense resulting in trimetrogon photography (left oblique, vertical and right oblique photographs). Special-purpose photographs showing sites of specific scientific interest ""vertical and handheld oblique as well as photographs taken from helicopters"" are also on file. Some color photographs are also available. Line indexes to identify coverage are available for most aerial photographic missions. Contact prints in either matte or glossy finish are available for inspection or stereoscopic viewing. Special feature options, such as ice and rock enhancements, may be special ordered." proprietary
USArray_Ground_Temperature_1680_1.1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, 2016-2021 ORNL_CLOUD STAC Catalog 2016-05-13 2021-07-08 -165.35, 59.25, -141.59, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2143403529-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 63 monitoring sites associated with the USArray program, located across the NASA ABoVE domain in interior Alaska. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m from 2016-2021 using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. This station data complement an existing temperature monitoring network, allowing for better characterization of ground temperatures and permafrost conditions in northern and western Alaska. The temperature measurements are provided for each site in 64 data files in comma-separated values (.csv) format. Site descriptive data are also provided for soil, vegetation, and location. proprietary
USArray_Ground_Temperature_1680_1.1 ABoVE: Soil Temperature Profiles, USArray Seismic Stations, 2016-2021 ALL STAC Catalog 2016-05-13 2021-07-08 -165.35, 59.25, -141.59, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2143403529-ORNL_CLOUD.umm_json This dataset includes soil temperature profile measurements taken at 63 monitoring sites associated with the USArray program, located across the NASA ABoVE domain in interior Alaska. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m from 2016-2021 using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. This station data complement an existing temperature monitoring network, allowing for better characterization of ground temperatures and permafrost conditions in northern and western Alaska. The temperature measurements are provided for each site in 64 data files in comma-separated values (.csv) format. Site descriptive data are also provided for soil, vegetation, and location. proprietary
USDA0113 Groundwater Quality in Beaver Creek Watershed, Tennessee CEOS_EXTRA STAC Catalog 1992-07-01 1992-08-31 -90.74, 34.56, -81.22, 37.12 https://cmr.earthdata.nasa.gov/search/concepts/C2232411621-CEOS_EXTRA.umm_json Analysis for 400 domestic wells for selected constituents. Reconnaissance of Ground Water Quality in Beaver Creek Watershed, Shelby, Tipton, Fayette, and Haywood counties, Tennessee. Collection Organization: USDA-CSREES/USGS - University of Tennessee; Institute of Agriculture Collection Methodology: Samples collected by UTAES staff, trained volunteers, and USGS Personnel - USGS conducted field and laboratory analysis. Collection Frequency: One-time. Update Characteristics: N/A STATISTICAL INFORMATION: 400 wells; 20 parameters per sample. LANGUAGE: English ACCESS/AVAILABILITY: Data Center: U.S. Geological Survey Dissemination Media: USGS Data Base Access Instructions: Contact the data center. proprietary
@@ -15511,14 +15516,14 @@ USGS-DDS-11 Geology of the Conterminous United States at 1:2,500,000 Scale -- A
USGS-DDS-18-A_1.0 National Geochemical Database: National Uranium Resource Evaluation Data for the Conterminous United States CEOS_EXTRA STAC Catalog 1970-01-01 -162, 24, -66, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2231552333-CEOS_EXTRA.umm_json This is an online version of a CD-ROM publication. It is intended for use only on DOS-based computer systems. The files must be downloaded onto your computer before they can be used. The files are presented here in two forms: as the original folders that were published on the CD-ROM and as a large zip file that you can use to download the entire product in one step. This publication contains National Uranium Resource Evaluation (NURE) data for the conterminous United States. The data has been compressed and requires GSSEARCH software for access. GSSEARCH, supplied below, runs only under DOS. [Summary provided by the USGS.] proprietary
USGS-DDS-19 Geology and Resource Assessment of Costa Rica at 1:500,000 Scale CEOS_EXTRA STAC Catalog 1970-01-01 -86, 8, -82, 11 https://cmr.earthdata.nasa.gov/search/concepts/C2231554233-CEOS_EXTRA.umm_json PROJECT OVERVIEW Conversion of the information from the original folio to a computerized digital format was undertaken to facilitate the presentation and analysis of earth-science data. Digital maps can be displayed at any scale or projection, whereas a paper map has a fixed scale and projection. However, most of the maps on this disc are not intended to be used at any scale more detailed than 1:500,000. A geographic information system (GIS) allows combining and overlaying of layers for analysis of spatial relations not readily apparent in the standard paper publication. Digital information on geology, geophysics, and geochemistry can be combined to create useful derivative products. HISTORY OF THE MAPS In 1986 and 1987, the U.S. Geological Survey (USGS), the Dirección General de Geología, Minas e Hidrocarburos, and the Universidad de Costa Rica conducted a mineral-resource assessment of the Republic of Costa Rica. The results were published as a large 80- by 50-cm color folio (U.S. Geological Survey and others, 1987). The 75-page document consists of maps as well as descriptive and interpretive text in English and Spanish covering physiographic, geologic, geochemical, geophysical, and mineral site themes as well as a mineral-resource assessment. The following maps are present in the original folio: 1) Physiographic base map at a scale of 1:500,000 with hypsography, place names, and drainage. 2) Geologic map at a scale of 1:500,000. 3) Regional geophysical maps, including gravity, aeromagnetic, and seismicity maps at various scales. 4) Mineral sites map at a scale of 1:500,000 showing mines, prospects, and occurrences. 5) Volcanological framework of the Tilarán region important for epithermal gold deposits at a scale of 1:100,000. 6) Rock sample locations, mining areas, and vein locations for several parts of the country. 7) Permissive areas delineated for selected mineral deposit types. 8) Digital elevation model. This CD-ROM contains most of the above maps; it lacks items 1 and 8 and the seismicity and aeromagnetic maps of item 3. The linework was digitized from preliminary and printed maps with GSMAP (Selner and Taylor, 1987), a USGS-authored program for map editing and publishing. Conversion from GSMAP to ARC/INFO was accomplished through the use of the GSMARC program (Green and Selner, 1988). The arcs and polygons were tagged using Alacarte (Wentworth and Fitzgibbon, 1991). [Summary provided by the USGS.] proprietary
USGS-DDS-27_1 Monthly average polar sea-ice concentration - USGS-DDS-27 CEOS_EXTRA STAC Catalog 1978-10-25 1991-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231553834-CEOS_EXTRA.umm_json The purpose of this data set is to provide paleoclimate researchers with a tool for estimating the average seasonal variation in sea-ice concentration in the modern polar oceans and for estimating the modern monthly sea-ice concentration at any given polar oceanic location. It is expected that these data will be compared with paleoclimate data derived from geological proxy measures such as faunal census analyses and stable-isotope analyses. The results can then be used to constrain general circulation models of climate change. This data set represents the results of calculations carried out on sea-ice-concentration data from the SMMR and SSM/I instruments. The original data were obtained from the National Snow and Ice Data Center (NSIDC). The data set also contains the source code of the programs that made the calculations. The objective was to derive monthly averages for the whole 13.25-year series (1978-1991) and to derive a composite series of monthly averages representing the variation of an average year. The resulting file set contains monthly images for each of the polar regions for each year, yielding 160 files for each pole, and composite monthly averages in which the years are combined, yielding 12 more files. Averaging the images in this way tends to reduce the number of grid cells that lack valid data; the composite averages are designed to suppress interannual variability. Also included in the data set are programs that can retrieve seasonal ice-concentration profiles at user-specified locations. These nongraphical data retrieval programs are provided in versions for UNIX, extended DOS, and Macintosh computers. Graphical browse utilities are included for the same computing platforms but require more sophisticated display systems. The data contained in this data set are derived from the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/ Imager (SSM/I) data produced by the National Snow and Ice Data Center (NSIDC) at the University of Colorado in cooperation with the U.S. National Aeronautics and Space Administration (NASA) and the U.S. National Oceanic and Atmospheric Administration (NOAA). The basic data come from satellites of the U.S. Air Force Defense Meteorological Satellite Program. NSIDC distributes three collections of sea- ice-concentration grids on CD-ROM: data from the Nimbus-7 SMMR (October 25, 1978 through August 20, 1987) are provided on volume 7 of the SMMR Polar Data series (NASA, 1992); data from the SSM/I are provided on two separate volumes, covering the periods from July 9 of 1987 to December 31 of 1989, and from January 1 of 1990 through December 31 of 1991, respectively. The NASATEAM data from revision 2 of the SSM/I CD-ROM's were used to create the present data set. SMMR images were collected every 2 to 3 days, whereas SSM/I data are provided in daily ice-concentration grids. Apart from a number of small gaps (5 or fewer days) in the record, the only long period for which no data are available is December 3 of 1987 through January 12 of 1988, inclusive. As ancillary data, the ETOPO5 global gridded elevation and bathymetry data (Edwards, 1989) were interpolated to the resolution of the NSIDC data; the interpolated topographic data are included. The images are provided in three formats: Hierarchical Data Format (HDF), a flexible scientific data format developed at the National Center for Supercomputing Applications; Graphics Interchange Format (GIF); and Macintosh PICT format. The ice- concentration grids are distributed by NSIDC in HDF format. proprietary
-USGS-DDS-3 A Geologic Map of the Sea Floor in Western Massachusetts Bay, Constructed from Digital Sidescan-Sonar Images, Photography, and Sediment Samples ALL STAC Catalog 1970-01-01 -71.5, 42, -70, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2231550375-CEOS_EXTRA.umm_json This data set describes sea floor characteristics for the Western Massachusetts Bay. This data set was created using sidescan-sonar imagery, photography, and sediment samples. proprietary
USGS-DDS-3 A Geologic Map of the Sea Floor in Western Massachusetts Bay, Constructed from Digital Sidescan-Sonar Images, Photography, and Sediment Samples CEOS_EXTRA STAC Catalog 1970-01-01 -71.5, 42, -70, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2231550375-CEOS_EXTRA.umm_json This data set describes sea floor characteristics for the Western Massachusetts Bay. This data set was created using sidescan-sonar imagery, photography, and sediment samples. proprietary
-USGS-DDS-33_1.0 3-D Reservoir Characterization of the House Creek Oil Field, Powder River Basin, Wyoming, V1.00 ALL STAC Catalog 1970-01-01 -111.4, 40.65, -103.7, 45.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231553827-CEOS_EXTRA.umm_json "The Upper Cretaceous Sussex ""B"" sandstone was deposited as a probable transgressive-marine sand-ridge complex in a mid-shelf position. The ""B"" sandstone is bounded by upper and basal disconformities and encased in mudstones and low-porosity and low-permeability sandstones of the Cody Shale. Reservoir characteristics are controlled primarily by depositional and diagenetic heterogeneity at megascopic (field), macroscopic (well), and microscopic (rock sample) levels. To simplify, this means production of oil is controlled by stacking and interbedding of sandstone and mudstone beds and by geochemical changes through time that affect flow of fluids through the rock. More than 24.8 million barrels of oil (MMBO) have been produced from the Sussex ""B"" sandstone in the House Creek field, Powder River Basin, Wyoming. Greatest oil production, porosity, and permeability, the thickest reservoir sandstone intervals, and best lateral continuity of the primary reservoir facies are all located parallel and proximal to field axes. Decrease in reservoir quality west of the axes is due to greater heterogeneity from interbedding of low- and moderate-depositional-energy facies, with associated drop in porosity and permeability. Decrease in production east of the axes results primarily from a combination of seaward thinning of the primary reservoir facies and non-deposition of sand ridges. The House Creek field has two axis orientations; these are related to depositional patterns of the four sand ridges. Deposition of the ""B"" sandstone began in the southeastern corner of the field with sand ridge 1; axis orientation is about north 20 degrees west. Later-deposited sand ridges 2 through 4 are located west and north of sand ridge 1; their axis orientations are approximately north 32 degrees west. Progressive northward deposition of later sand ridges is probably concurrent with uplift of the northeast-trending Belle Fourche arch. Movement along the arch and of lineaments may have caused topographic highs that localized Sussex and Shannon deposition proximal to the arch. [Summary provided by the USGS.]" proprietary
+USGS-DDS-3 A Geologic Map of the Sea Floor in Western Massachusetts Bay, Constructed from Digital Sidescan-Sonar Images, Photography, and Sediment Samples ALL STAC Catalog 1970-01-01 -71.5, 42, -70, 43 https://cmr.earthdata.nasa.gov/search/concepts/C2231550375-CEOS_EXTRA.umm_json This data set describes sea floor characteristics for the Western Massachusetts Bay. This data set was created using sidescan-sonar imagery, photography, and sediment samples. proprietary
USGS-DDS-33_1.0 3-D Reservoir Characterization of the House Creek Oil Field, Powder River Basin, Wyoming, V1.00 CEOS_EXTRA STAC Catalog 1970-01-01 -111.4, 40.65, -103.7, 45.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231553827-CEOS_EXTRA.umm_json "The Upper Cretaceous Sussex ""B"" sandstone was deposited as a probable transgressive-marine sand-ridge complex in a mid-shelf position. The ""B"" sandstone is bounded by upper and basal disconformities and encased in mudstones and low-porosity and low-permeability sandstones of the Cody Shale. Reservoir characteristics are controlled primarily by depositional and diagenetic heterogeneity at megascopic (field), macroscopic (well), and microscopic (rock sample) levels. To simplify, this means production of oil is controlled by stacking and interbedding of sandstone and mudstone beds and by geochemical changes through time that affect flow of fluids through the rock. More than 24.8 million barrels of oil (MMBO) have been produced from the Sussex ""B"" sandstone in the House Creek field, Powder River Basin, Wyoming. Greatest oil production, porosity, and permeability, the thickest reservoir sandstone intervals, and best lateral continuity of the primary reservoir facies are all located parallel and proximal to field axes. Decrease in reservoir quality west of the axes is due to greater heterogeneity from interbedding of low- and moderate-depositional-energy facies, with associated drop in porosity and permeability. Decrease in production east of the axes results primarily from a combination of seaward thinning of the primary reservoir facies and non-deposition of sand ridges. The House Creek field has two axis orientations; these are related to depositional patterns of the four sand ridges. Deposition of the ""B"" sandstone began in the southeastern corner of the field with sand ridge 1; axis orientation is about north 20 degrees west. Later-deposited sand ridges 2 through 4 are located west and north of sand ridge 1; their axis orientations are approximately north 32 degrees west. Progressive northward deposition of later sand ridges is probably concurrent with uplift of the northeast-trending Belle Fourche arch. Movement along the arch and of lineaments may have caused topographic highs that localized Sussex and Shannon deposition proximal to the arch. [Summary provided by the USGS.]" proprietary
+USGS-DDS-33_1.0 3-D Reservoir Characterization of the House Creek Oil Field, Powder River Basin, Wyoming, V1.00 ALL STAC Catalog 1970-01-01 -111.4, 40.65, -103.7, 45.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231553827-CEOS_EXTRA.umm_json "The Upper Cretaceous Sussex ""B"" sandstone was deposited as a probable transgressive-marine sand-ridge complex in a mid-shelf position. The ""B"" sandstone is bounded by upper and basal disconformities and encased in mudstones and low-porosity and low-permeability sandstones of the Cody Shale. Reservoir characteristics are controlled primarily by depositional and diagenetic heterogeneity at megascopic (field), macroscopic (well), and microscopic (rock sample) levels. To simplify, this means production of oil is controlled by stacking and interbedding of sandstone and mudstone beds and by geochemical changes through time that affect flow of fluids through the rock. More than 24.8 million barrels of oil (MMBO) have been produced from the Sussex ""B"" sandstone in the House Creek field, Powder River Basin, Wyoming. Greatest oil production, porosity, and permeability, the thickest reservoir sandstone intervals, and best lateral continuity of the primary reservoir facies are all located parallel and proximal to field axes. Decrease in reservoir quality west of the axes is due to greater heterogeneity from interbedding of low- and moderate-depositional-energy facies, with associated drop in porosity and permeability. Decrease in production east of the axes results primarily from a combination of seaward thinning of the primary reservoir facies and non-deposition of sand ridges. The House Creek field has two axis orientations; these are related to depositional patterns of the four sand ridges. Deposition of the ""B"" sandstone began in the southeastern corner of the field with sand ridge 1; axis orientation is about north 20 degrees west. Later-deposited sand ridges 2 through 4 are located west and north of sand ridge 1; their axis orientations are approximately north 32 degrees west. Progressive northward deposition of later sand ridges is probably concurrent with uplift of the northeast-trending Belle Fourche arch. Movement along the arch and of lineaments may have caused topographic highs that localized Sussex and Shannon deposition proximal to the arch. [Summary provided by the USGS.]" proprietary
USGS-DDS-74_2.0 Long-term Oceanographic Observations in Western Massachusetts Bay Offshore of Boston, Massachusetts: Data Report for 1989-2002 CEOS_EXTRA STAC Catalog 1989-12-01 2002-12-01 -71, 42, -70.5, 42.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231551840-CEOS_EXTRA.umm_json Long-term oceanographic observations have been made at two locations in western Massachusetts Bay: (1) Site A (42ý 22.6' N, 70ý 47.0' W, 33 m water depth) from from 1989 to 2002, and (2) Site B (42ý 9.8' N, 70ý 38.4' W, 21 m deter depth) from 1997 to 2002. Site A is approximately 1 km south of the new ocean outfall that began discharging treated sewage effluent from the Boston metropolitan area into Massachusetts Bay in September 2000. These long-term oceanographic observations have been collected by the U.S. Geological Survey (USGS) in partnership with the Massachusetts Water Resources Authority (MWRA) and with logistical support from the U. S. Coast Guard (USCG). This report presents time series data collected through December 2002, updating a similar report that presented data through December 2000 (Butman and others, 2002). The long-term observations at these two stations are part of a USGS study designed to understand the transport and long-term fate of sediments and associated contaminants in the Massachusetts Bays (see //woodshole.er.usgs.gov/project-pages/bostonharbor / and Butman and Bothner, 1997). The long-term observations document seasonal and inter-annual changes in currents, hydrography, and suspended-matter concentration in western Massachusetts Bay, and the importance of infrequent catastrophic events, such as major storms or hurricanes, in sediment resuspension and transport. They also provide observations for testing numerical models of circulation. This data report presents a description of the field program and instrumentation, an overview of the data through summary plots and statistics, and the data in NetCDF and ASCII format for the period December 1989 through December 2002. The objective of this report is to make the data available in digital form, and to provide summary plots and statistics to facilitate browsing of the long-term data set . [Summary provided by the USGS.] proprietary
USGS-DDS-79 Coastal Erosion and Wetland Change in Louisiana: Selected USGS Products CEOS_EXTRA STAC Catalog 1970-01-01 -94.3, 28.67, -88.54, 33.29 https://cmr.earthdata.nasa.gov/search/concepts/C2231552152-CEOS_EXTRA.umm_json Louisiana contains 25 percent of the vegetated wetlands and 40 percent of the tidal wetlands in the 48 conterminous States. These critical natural systems are being lost. Louisiana leads the Nation in coastal erosion and wetland loss as a result of a complex combination of natural processes (e.g. storms, sea-level rise, subsidence) and manmade alterations to the Mississippi River and the wetlands over the past 200 years. Erosion of several of the barrier islands, which lie offshore of the estuaries and wetlands and buffer and protect these important ecosystems from the open marine environment, exceeds 20 meters/year. The average rate of shoreline erosion is over 10 meters/year. Within the past 100 years, Louisiana's barrier islands have decreased in area by more than 40 percent, and some islands have lost more than 75 percent of their land area. If these loss rates continue, several of the barriers are expected to erode completely within the next three decades. Their disappearance will contribute to further loss and deterioration of wetlands and back-barrier estuaries and increase the risk to infrastructure. Coastal wetland environments, which include associated bays and estuaries, support a rich harvest of renewable natural resources with an estimated annual value of over $1 billion. More than 30 percent of the Nation's fisheries come from these wetlands, as well as 25 percent of oil and gas coming through the wetlands. Louisiana also has the highest rate of wetland loss: 80 percent of the Nation's total loss of wetlands has occurred in this State. The rate of wetland loss in the Mississippi River delta plain is estimated to be about 70 square kilometers/year -- the equivalent of a football field every 20 minutes. If these rates continue, an estimated 4,000 square kilometers of wetlands will be lost in the next 50 years. Losses of this magnitude have direct implications on the Nation's energy supplies, economic security, and environmental integrity. Over the past two decades, the USGS, working in partnership with other scientists in universities and State agencies, has led the research effort to document barrier erosion and wetland loss and understand the natural and manmade causes responsible. Some products resulting from this research, included in this DVD, are providing the baseline data and information being used for Federal-State wetlands restoration programs underway and being planned. [Summary provided by the USGS.] proprietary
-USGS-DDS_30_P-10_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Joaquin Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231552106-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 10 (San Joaquin Basin) are listed here by play number, type, and name: Number Type Name 1001 conventional Pliocene Non-associated Gas 1002 conventional Southeast Stable Shelf 1003 conventional Lower Bakersfield Arch 1004 conventional West Side Fold Belt Sourced by Post-Lower Miocene Rocks. 1005 conventional West Side Fold Belt Sourced by Pre-Middle Miocene Rocks 1006 conventional Northeast Shelf of Neogene Basin 1007 conventional Northern Area Non-associated Gas 1008 conventional Tejon Platform 1009 conventional South End Thrust Salient 1010 conventional East Central Basin and Slope North of Bakersfield Arch 1011 conventional Deep Overpressured Fractured Rocks of West Side Fold and Overthrust Belt proprietary
USGS-DDS_30_P-10_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Joaquin Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231552106-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 10 (San Joaquin Basin) are listed here by play number, type, and name: Number Type Name 1001 conventional Pliocene Non-associated Gas 1002 conventional Southeast Stable Shelf 1003 conventional Lower Bakersfield Arch 1004 conventional West Side Fold Belt Sourced by Post-Lower Miocene Rocks. 1005 conventional West Side Fold Belt Sourced by Pre-Middle Miocene Rocks 1006 conventional Northeast Shelf of Neogene Basin 1007 conventional Northern Area Non-associated Gas 1008 conventional Tejon Platform 1009 conventional South End Thrust Salient 1010 conventional East Central Basin and Slope North of Bakersfield Arch 1011 conventional Deep Overpressured Fractured Rocks of West Side Fold and Overthrust Belt proprietary
+USGS-DDS_30_P-10_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Joaquin Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231552106-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 10 (San Joaquin Basin) are listed here by play number, type, and name: Number Type Name 1001 conventional Pliocene Non-associated Gas 1002 conventional Southeast Stable Shelf 1003 conventional Lower Bakersfield Arch 1004 conventional West Side Fold Belt Sourced by Post-Lower Miocene Rocks. 1005 conventional West Side Fold Belt Sourced by Pre-Middle Miocene Rocks 1006 conventional Northeast Shelf of Neogene Basin 1007 conventional Northern Area Non-associated Gas 1008 conventional Tejon Platform 1009 conventional South End Thrust Salient 1010 conventional East Central Basin and Slope North of Bakersfield Arch 1011 conventional Deep Overpressured Fractured Rocks of West Side Fold and Overthrust Belt proprietary
USGS-DDS_30_P10_conventional 1995 National Oil and Gas Assessment Conventional Plays within the San Joaquin Basin Province CEOS_EXTRA STAC Catalog 1970-01-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231550316-CEOS_EXTRA.umm_json The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. proprietary
USGS-DDS_30_P10_conventional 1995 National Oil and Gas Assessment Conventional Plays within the San Joaquin Basin Province ALL STAC Catalog 1970-01-01 -121.388916, 34.890034, -118.58517, 37.83907 https://cmr.earthdata.nasa.gov/search/concepts/C2231550316-CEOS_EXTRA.umm_json The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. proprietary
USGS-DS-91_1.1 Depth to the Juan De Fuca Slab Beneath the Cascadia Subduction Margin: A 3-D Model for Sorting Earthquakes CEOS_EXTRA STAC Catalog 1970-01-01 -130, 40, -120, 51 https://cmr.earthdata.nasa.gov/search/concepts/C2231552778-CEOS_EXTRA.umm_json The USGS presents an updated model of the Juan de Fuca slab beneath southern British Columbia, Washington, Oregon, and northern California, and use this model to separate earthquakes occurring above and below the slab surface. The model is based on depth contours previously published by Flück and others (1997). Our model attempts to rectify a number of shortcomings in the original model and to update it with new work. The most significant improvements include (1) a gridded slab surface in geo-referenced (ArcGIS) format, (2) continuation of the slab surface to its full northern and southern edges, (3) extension of the slab surface from 50-km depth down to 110-km beneath the Cascade arc volcanoes, and (4) revision of the slab shape based on new seismic-reflection and seismic-refraction studies. We have used this surface to sort earthquakes and present some general observations and interpretations of seismicity patterns revealed by our analysis. In addition, we provide files of earthquakes above and below the slab surface and a 3-D animation or fly-through showing a shaded-relief map with plate boundaries, the slab surface, and hypocenters for use as a visualization tool. [Summary provided by the USGS.] proprietary
@@ -15554,14 +15559,14 @@ USGS_DDS_P12_conventional 1995 National Oil and Gas Assessment Conventional Play
USGS_DDS_P12_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Santa Maria Basin Province - USGS_DDS_P12_conventional ALL STAC Catalog 1996-01-01 1996-12-31 -121.977486, 34.488464, -119.44189, 36.40565 https://cmr.earthdata.nasa.gov/search/concepts/C2231551861-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 12 (Santa Maria Basin) are listed here by play number and name: Number Name 1201 Anticlinal Trends - Onshore 1202 Basin Margin 1204 Diagenetic 1211 Anticlinal Trends - Offshore State Waters proprietary
USGS_DDS_P13_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Ventura Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -120.58227, 33.84158, -117.37425, 34.824276 https://cmr.earthdata.nasa.gov/search/concepts/C2231554781-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 13 (Ventura Basin) are listed here by play number, type, and name: Number Type Name 1301 conventional Paleogene - Onshore 1302 conventional Neogene - Onshore 1304 conventional Cretaceous 1311 conventional Paleogene - Offshore State Waters 1312 conventional Neogene - Offshore State Waters proprietary
USGS_DDS_P13_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Ventura Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -120.58227, 33.84158, -117.37425, 34.824276 https://cmr.earthdata.nasa.gov/search/concepts/C2231554781-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 13 (Ventura Basin) are listed here by play number, type, and name: Number Type Name 1301 conventional Paleogene - Onshore 1302 conventional Neogene - Onshore 1304 conventional Cretaceous 1311 conventional Paleogene - Offshore State Waters 1312 conventional Neogene - Offshore State Waters proprietary
-USGS_DDS_P13_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Ventura Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -120.58227, 33.84158, -117.37425, 34.824276 https://cmr.earthdata.nasa.gov/search/concepts/C2231550109-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 13 (Ventura Basin) are listed here by play number and name: Number Name 1301 Paleogene - Onshore 1302 Neogene - Onshore 1304 Cretaceous 1311 Paleogene - Offshore State Waters 1312 Neogene - Offshore State Waters proprietary
USGS_DDS_P13_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Ventura Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -120.58227, 33.84158, -117.37425, 34.824276 https://cmr.earthdata.nasa.gov/search/concepts/C2231550109-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 13 (Ventura Basin) are listed here by play number and name: Number Name 1301 Paleogene - Onshore 1302 Neogene - Onshore 1304 Cretaceous 1311 Paleogene - Offshore State Waters 1312 Neogene - Offshore State Waters proprietary
+USGS_DDS_P13_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Ventura Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -120.58227, 33.84158, -117.37425, 34.824276 https://cmr.earthdata.nasa.gov/search/concepts/C2231550109-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 13 (Ventura Basin) are listed here by play number and name: Number Name 1301 Paleogene - Onshore 1302 Neogene - Onshore 1304 Cretaceous 1311 Paleogene - Offshore State Waters 1312 Neogene - Offshore State Waters proprietary
USGS_DDS_P14_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Los Angeles Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -119.63631, 32.7535, -117.52315, 34.17464 https://cmr.earthdata.nasa.gov/search/concepts/C2231552049-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number, type, and name: Number Type Name 1401 conventional Santa Monica Fault System and Las Cienegas Fault and Block 1402 conventional Southwestern Shelf and Adjacent Offshore State Lands 1403 conventional Newport-Inglewood Deformation Zone and Southwestern Flank of Central Syncline 1404 conventional Whittier Fault Zone and Fullerton Embayment 1405 conventional Northern Shelf and Northern Flank of Central Syncline 1406 conventional Anaheim Nose 1407 conventional Chino Marginal Basin, Puente and San Jose Hills, and San Gabriel Valley Marginal Basin proprietary
USGS_DDS_P14_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Los Angeles Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -119.63631, 32.7535, -117.52315, 34.17464 https://cmr.earthdata.nasa.gov/search/concepts/C2231552049-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number, type, and name: Number Type Name 1401 conventional Santa Monica Fault System and Las Cienegas Fault and Block 1402 conventional Southwestern Shelf and Adjacent Offshore State Lands 1403 conventional Newport-Inglewood Deformation Zone and Southwestern Flank of Central Syncline 1404 conventional Whittier Fault Zone and Fullerton Embayment 1405 conventional Northern Shelf and Northern Flank of Central Syncline 1406 conventional Anaheim Nose 1407 conventional Chino Marginal Basin, Puente and San Jose Hills, and San Gabriel Valley Marginal Basin proprietary
-USGS_DDS_P14_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Los Angeles Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -119.63631, 32.7535, -117.52315, 34.17464 https://cmr.earthdata.nasa.gov/search/concepts/C2231554068-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number and name: Number Name 1401 Santa Monica Fault System and Las Cienegas Fault and Block 1402 Southwestern Shelf and Adjacent Offshore State Lands 1403 Newport-Inglewood Deformation Zone and Southwestern Flank of Central Syncline 1404 Whittier Fault Zone and Fullerton Embayment 1405 Northern Shelf and Northern Flank of Central Syncline 1406 Anaheim Nose 1407 Chino Marginal Basin, Puente and San Jose Hills, and San Gabriel Valley Marginal Basin proprietary
USGS_DDS_P14_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Los Angeles Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -119.63631, 32.7535, -117.52315, 34.17464 https://cmr.earthdata.nasa.gov/search/concepts/C2231554068-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number and name: Number Name 1401 Santa Monica Fault System and Las Cienegas Fault and Block 1402 Southwestern Shelf and Adjacent Offshore State Lands 1403 Newport-Inglewood Deformation Zone and Southwestern Flank of Central Syncline 1404 Whittier Fault Zone and Fullerton Embayment 1405 Northern Shelf and Northern Flank of Central Syncline 1406 Anaheim Nose 1407 Chino Marginal Basin, Puente and San Jose Hills, and San Gabriel Valley Marginal Basin proprietary
-USGS_DDS_P15_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Diego - Oceanside Province ALL STAC Catalog 1990-12-01 1990-12-01 -117.75433, 32.527184, -115.904816, 34.236046 https://cmr.earthdata.nasa.gov/search/concepts/C2231553715-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 15 (San Diego - Oceanside) are listed here by play number, type, and name. proprietary
+USGS_DDS_P14_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Los Angeles Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -119.63631, 32.7535, -117.52315, 34.17464 https://cmr.earthdata.nasa.gov/search/concepts/C2231554068-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 14 (Los Angeles Basin) are listed here by play number and name: Number Name 1401 Santa Monica Fault System and Las Cienegas Fault and Block 1402 Southwestern Shelf and Adjacent Offshore State Lands 1403 Newport-Inglewood Deformation Zone and Southwestern Flank of Central Syncline 1404 Whittier Fault Zone and Fullerton Embayment 1405 Northern Shelf and Northern Flank of Central Syncline 1406 Anaheim Nose 1407 Chino Marginal Basin, Puente and San Jose Hills, and San Gabriel Valley Marginal Basin proprietary
USGS_DDS_P15_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Diego - Oceanside Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -117.75433, 32.527184, -115.904816, 34.236046 https://cmr.earthdata.nasa.gov/search/concepts/C2231553715-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 15 (San Diego - Oceanside) are listed here by play number, type, and name. proprietary
+USGS_DDS_P15_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the San Diego - Oceanside Province ALL STAC Catalog 1990-12-01 1990-12-01 -117.75433, 32.527184, -115.904816, 34.236046 https://cmr.earthdata.nasa.gov/search/concepts/C2231553715-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 15 (San Diego - Oceanside) are listed here by play number, type, and name. proprietary
USGS_DDS_P16_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Salton Trough Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -116.66911, 32.634293, -114.74501, 34.02059 https://cmr.earthdata.nasa.gov/search/concepts/C2231548651-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 16 (Salton Trough) are listed here by play number, type, and name. proprietary
USGS_DDS_P16_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Salton Trough Province ALL STAC Catalog 1990-12-01 1990-12-01 -116.66911, 32.634293, -114.74501, 34.02059 https://cmr.earthdata.nasa.gov/search/concepts/C2231548651-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 16 (Salton Trough) are listed here by play number, type, and name. proprietary
USGS_DDS_P17_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Idaho - Snake River Downwarp Province ALL STAC Catalog 1990-12-01 1990-12-01 -117.24303, 41.99332, -111.04548, 49.00115 https://cmr.earthdata.nasa.gov/search/concepts/C2231550494-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 17 (Idaho - Snake River Downwarp) are listed here by play number, type, and name: Number Type Name 1701 conventional Miocene Lacustrine (Lake Bruneau) 1702 conventional Pliocene Lacustrine (Lake Idaho) 1703 conventional Pre-Miocene 1704 conventional Older Tertiary proprietary
@@ -15574,21 +15579,21 @@ USGS_DDS_P18_conventional 1995 National Oil and Gas Assessment Conventional Play
USGS_DDS_P18_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Western Great Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -122.29004, 32.717037, -114.13121, 44.563953 https://cmr.earthdata.nasa.gov/search/concepts/C2231549693-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 18 (Western Great Basin) are listed here by play number and name: Number Name 1801 Hornbrook Basin-Modoc Plateau 1802 Eastern Oregon Neogene Basins 1803 Permian-Triassic Source Rocks Northwestern Nevada and East Central and Eastern Oregon 1804 Cretaceous Source Rocks, Northwestern Nevada 1805 Neogene Source Rocks, Northwestern Nevada and Eastern California proprietary
USGS_DDS_P19_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Eastern Great Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231552402-CEOS_EXTRA.umm_json "The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number, type, and name: Number Type Name 1901 conventional Unconformity ""A"" 1902 conventional Late Paleozoic 1903 conventional Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 conventional Younger Tertiary Basins 1906 conventional Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 conventional Sevier Frontal Zone" proprietary
USGS_DDS_P19_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Eastern Great Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231552402-CEOS_EXTRA.umm_json "The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number, type, and name: Number Type Name 1901 conventional Unconformity ""A"" 1902 conventional Late Paleozoic 1903 conventional Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 conventional Younger Tertiary Basins 1906 conventional Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 conventional Sevier Frontal Zone" proprietary
-USGS_DDS_P19_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Eastern Great Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231551249-CEOS_EXTRA.umm_json "The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number and name: Number Name 1901 Unconformity ""A"" 1902 Late Paleozoic 1903 Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 Younger Tertiary Basins 1906 Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 Sevier Frontal Zone" proprietary
USGS_DDS_P19_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Eastern Great Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231551249-CEOS_EXTRA.umm_json "The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number and name: Number Name 1901 Unconformity ""A"" 1902 Late Paleozoic 1903 Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 Younger Tertiary Basins 1906 Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 Sevier Frontal Zone" proprietary
+USGS_DDS_P19_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Eastern Great Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -117.02622, 35.002083, -111.170425, 43.022377 https://cmr.earthdata.nasa.gov/search/concepts/C2231551249-CEOS_EXTRA.umm_json "The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 19 (Eastern Great Basin) are listed here by play number and name: Number Name 1901 Unconformity ""A"" 1902 Late Paleozoic 1903 Early Tertiary - Late Cretaceous Sheep Pass and Equivalents 1905 Younger Tertiary Basins 1906 Late Paleozoic - Mesozoic (Central Nevada) Thrust Belt 1907 Sevier Frontal Zone" proprietary
USGS_DDS_P20_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Uinta - Piceance Basin Province ALL STAC Catalog 1990-12-01 1990-12-01 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231553991-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number, type, and name: Number Type Name 2001 conventional Piceance Tertiary Conventional 2002 conventional Uinta Tertiary Oil and Gas 2003 conventional Upper Cretaceous Conventional 2004 conventional Cretaceous Dakota to Jurassic 2005 conventional Permian-Pennsylvanian Sandstones and Carbonates 2007 continuous Tight Gas Piceance Mesaverde Williams Fork 2009 continuous Cretaceous Self-Sourced Fractured Shales Oil 2010 continuous Tight Gas Piceance Mesaverde Iles 2014 conventional Basin Margin Subthrusts 2015 continuous Tight Gas Uinta Tertiary East 2016 continuous Tight Gas Uinta Tertiary West 2018 continuous Basin Flank Uinta Mesaverde 2020 continuous Deep Synclinal Uinta Mesaverde 2050 coalbed gas Uinta Basin - Book Cliffs 2051 coalbed gas Uinta Basin - Sego 2052 coalbed gas Uinta Basin - Emery 2053 coalbed gas Piceance Basin - White River Dome 2054 coalbed gas Piceance Basin - Western Basin Margin 2055 coalbed gas Piceance Basin - Grand Hogback 2056 coalbed gas Piceance Basin - Divide Creek Anticline 2057 coalbed gas Piceance Basin - Igneous Intrusion proprietary
USGS_DDS_P20_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Uinta - Piceance Basin Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231553991-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number, type, and name: Number Type Name 2001 conventional Piceance Tertiary Conventional 2002 conventional Uinta Tertiary Oil and Gas 2003 conventional Upper Cretaceous Conventional 2004 conventional Cretaceous Dakota to Jurassic 2005 conventional Permian-Pennsylvanian Sandstones and Carbonates 2007 continuous Tight Gas Piceance Mesaverde Williams Fork 2009 continuous Cretaceous Self-Sourced Fractured Shales Oil 2010 continuous Tight Gas Piceance Mesaverde Iles 2014 conventional Basin Margin Subthrusts 2015 continuous Tight Gas Uinta Tertiary East 2016 continuous Tight Gas Uinta Tertiary West 2018 continuous Basin Flank Uinta Mesaverde 2020 continuous Deep Synclinal Uinta Mesaverde 2050 coalbed gas Uinta Basin - Book Cliffs 2051 coalbed gas Uinta Basin - Sego 2052 coalbed gas Uinta Basin - Emery 2053 coalbed gas Piceance Basin - White River Dome 2054 coalbed gas Piceance Basin - Western Basin Margin 2055 coalbed gas Piceance Basin - Grand Hogback 2056 coalbed gas Piceance Basin - Divide Creek Anticline 2057 coalbed gas Piceance Basin - Igneous Intrusion proprietary
USGS_DDS_P20_continuous 1995 National Oil and Gas Assessment Continuous-Type Plays within the Uinta - Piceance Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231554716-CEOS_EXTRA.umm_json The purpose of the play map is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Continuous oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2007 Tight Gas Piceance Mesaverde Williams Fork 2009 Cretaceous Self-Sourced Fractured Shales Oil 2010 Tight Gas Piceance Mesaverde Iles 2015 Tight Gas Uinta Tertiary East 2016 Tight Gas Uinta Tertiary West 2018 Basin Flank Uinta Mesaverde 2020 Deep Synclinal Uinta Mesaverde 2050 Uinta Basin - Book Cliffs 2051 Uinta Basin - Sego 2052 Uinta Basin - Emery 2053 Piceance Basin - White River Dome 2054 Piceance Basin - Western Basin Margin 2055 Piceance Basin - Grand Hogback 2056 Piceance Basin - Divide Creek Anticline 2057 Piceance Basin - Igneous Intrusion proprietary
USGS_DDS_P20_continuous 1995 National Oil and Gas Assessment Continuous-Type Plays within the Uinta - Piceance Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231554716-CEOS_EXTRA.umm_json The purpose of the play map is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Continuous oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2007 Tight Gas Piceance Mesaverde Williams Fork 2009 Cretaceous Self-Sourced Fractured Shales Oil 2010 Tight Gas Piceance Mesaverde Iles 2015 Tight Gas Uinta Tertiary East 2016 Tight Gas Uinta Tertiary West 2018 Basin Flank Uinta Mesaverde 2020 Deep Synclinal Uinta Mesaverde 2050 Uinta Basin - Book Cliffs 2051 Uinta Basin - Sego 2052 Uinta Basin - Emery 2053 Piceance Basin - White River Dome 2054 Piceance Basin - Western Basin Margin 2055 Piceance Basin - Grand Hogback 2056 Piceance Basin - Divide Creek Anticline 2057 Piceance Basin - Igneous Intrusion proprietary
-USGS_DDS_P20_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Uinta - Piceance Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231552272-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2001 Piceance Tertiary Conventional 2002 Uinta Tertiary Oil and Gas 2003 Upper Cretaceous Conventional 2004 Cretaceous Dakota to Jurassic 2005 Permian-Pennsylvanian Sandstones and Carbonates 2014 Basin Margin Subthrusts proprietary
USGS_DDS_P20_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Uinta - Piceance Basin Province ALL STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231552272-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2001 Piceance Tertiary Conventional 2002 Uinta Tertiary Oil and Gas 2003 Upper Cretaceous Conventional 2004 Cretaceous Dakota to Jurassic 2005 Permian-Pennsylvanian Sandstones and Carbonates 2014 Basin Margin Subthrusts proprietary
+USGS_DDS_P20_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Uinta - Piceance Basin Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -111.486916, 38.14689, -105.87804, 40.85869 https://cmr.earthdata.nasa.gov/search/concepts/C2231552272-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 20 (Uinta - Piceance Basin) are listed here by play number and name: Number Name 2001 Piceance Tertiary Conventional 2002 Uinta Tertiary Oil and Gas 2003 Upper Cretaceous Conventional 2004 Cretaceous Dakota to Jurassic 2005 Permian-Pennsylvanian Sandstones and Carbonates 2014 Basin Margin Subthrusts proprietary
USGS_DDS_P2_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Alaska Province ALL STAC Catalog 1990-12-01 1990-12-01 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231550471-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 2 (Central Alaska) are listed here by play number, type, and name: Number Type Name 201 conventional Central Alaska Cenozoic Gas 202 conventional Central Alaska Mesozoic Gas 203 conventional Central Alaska Paleozoic Oil 204 conventional Kandik Pre-Mid-Cretaceous Strata 205 conventional Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary
USGS_DDS_P2_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Alaska Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231550471-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 2 (Central Alaska) are listed here by play number, type, and name: Number Type Name 201 conventional Central Alaska Cenozoic Gas 202 conventional Central Alaska Mesozoic Gas 203 conventional Central Alaska Paleozoic Oil 204 conventional Kandik Pre-Mid-Cretaceous Strata 205 conventional Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary
USGS_DDS_P2_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Alaska Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231551071-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 2 (Central Alaska) are listed here by play number and name: Number Name 201 Central Alaska Cenozoic Gas 202 Central Alaska Mesozoic Gas 203 Central Alaska Paleozoic Oil 204 Kandik Pre-Mid-Cretaceous Strata 205 Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary
USGS_DDS_P2_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Alaska Province ALL STAC Catalog 1996-01-01 1996-12-31 -173.22636, 58.49761, -140.99017, 68.01999 https://cmr.earthdata.nasa.gov/search/concepts/C2231551071-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 2 (Central Alaska) are listed here by play number and name: Number Name 201 Central Alaska Cenozoic Gas 202 Central Alaska Mesozoic Gas 203 Central Alaska Paleozoic Oil 204 Kandik Pre-Mid-Cretaceous Strata 205 Kandik Upper Cretaceous and Tertiary Non-Marine Stata proprietary
USGS_DOQ USGS Digital Orthophoto Quadrangles USGS_LTA STAC Catalog 1970-01-01 -126, 24, -66, 49 https://cmr.earthdata.nasa.gov/search/concepts/C1220566203-USGS_LTA.umm_json A Digital Orthophoto Quadrangle (DOQ) is a computer-generated image of an aerial photograph in which the image displacement caused by terrain relief and camera tilt has been removed. The DOQ combines the image characteristics of the original photograph with the georeferenced qualities of a map. DOQs are black and white (B/W), natural color, or color-infrared (CIR) images with 1-meter ground resolution. The USGS produces three types of DOQs: 1. 3.75-minute (quarter-quad) DOQs cover an area measuring 3.75-minutes longitude by 3.75-minutes latitude. Most of the U.S. is currently available, and the remaining locations should be complete by 2004. Quarter-quad DOQs are available in both Native and GeoTIFF formats. Native format consists of an ASCII keyword header followed by a series of 8-bit binary image lines for B/W and 24-bit band-interleaved-by-pixel (BIP) for color. DOQs in native format are cast to the Universal Transverse Mercator (UTM) projection and referenced to either the North American Datum (NAD) of 1927 (NAD27) or the NAD of 1983 (NAD83). GeoTIFF format consists of a georeferenced Tagged Image File Format (TIFF), with all geographic referencing information embedded within the .tif file. DOQs in GeoTIFF format are cast to the UTM projection and referenced to NAD83. The average file size of a B/W quarter quad is 40-45 megabytes, and a color file is generally 140-150 megabytes. Quarter-quad DOQs are distributed via File Transfer Protocol (FTP) as uncompressed files. 2. 7.5-minute (full-quad) DOQs cover an area measuring 7.5-minutes longitude by 7.5-minutes latitude. Full-quad DOQs are mostly available for Oregon, Washington, and Alaska. Limited coverage may also be available for other states. Full-quad DOQs are available in both Native and GeoTIFF formats. Native is formatted with an ASCII keyword header followed by a series of 8-bit binary image lines for B/W. DOQs in native format are cast to the UTM projection and referenced to either NAD27 or NAD83. GeoTIFF is a georeferenced Tagged Image File Format with referencing information embedded within the .tif file. DOQs in GeoTIFF format are cast to the UTM projection and referenced to NAD83. The average file size of a B/W full quad is 140-150 megabytes. Full-quad DOQs are distributed via FTP as uncompressed files. 3. Seamless DOQs are available for free download from the Seamless site. DOQs on this site are the most current version and are available for the conterminous U.S. [Summary provided by the USGS.] proprietary
-USGS_DS-845_PierScoutDatabase_1.0 A pier-scour database: 2,427 field and laboratory measurements of pier scour CEOS_EXTRA STAC Catalog 1970-01-01 19.6, 16.916668, -52.62, 83.1 https://cmr.earthdata.nasa.gov/search/concepts/C2231553801-CEOS_EXTRA.umm_json The U.S. Geological Survey conducted a literature review to identify potential sources of published pier-scour data, and selected data were compiled into a digital spreadsheet called the 2014 USGS Pier-Scour Database (PSDb-2014) consisting of 569 laboratory and 1,858 field measurements. These data encompass a wide range of laboratory and field conditions and represent field data from 23 States within the United States and from 6 other countries. The digital spreadsheet is available on the Internet and offers a valuable resource to engineers and researchers seeking to understand pier-scour relations in the laboratory and field. proprietary
USGS_DS-845_PierScoutDatabase_1.0 A pier-scour database: 2,427 field and laboratory measurements of pier scour ALL STAC Catalog 1970-01-01 19.6, 16.916668, -52.62, 83.1 https://cmr.earthdata.nasa.gov/search/concepts/C2231553801-CEOS_EXTRA.umm_json The U.S. Geological Survey conducted a literature review to identify potential sources of published pier-scour data, and selected data were compiled into a digital spreadsheet called the 2014 USGS Pier-Scour Database (PSDb-2014) consisting of 569 laboratory and 1,858 field measurements. These data encompass a wide range of laboratory and field conditions and represent field data from 23 States within the United States and from 6 other countries. The digital spreadsheet is available on the Internet and offers a valuable resource to engineers and researchers seeking to understand pier-scour relations in the laboratory and field. proprietary
+USGS_DS-845_PierScoutDatabase_1.0 A pier-scour database: 2,427 field and laboratory measurements of pier scour CEOS_EXTRA STAC Catalog 1970-01-01 19.6, 16.916668, -52.62, 83.1 https://cmr.earthdata.nasa.gov/search/concepts/C2231553801-CEOS_EXTRA.umm_json The U.S. Geological Survey conducted a literature review to identify potential sources of published pier-scour data, and selected data were compiled into a digital spreadsheet called the 2014 USGS Pier-Scour Database (PSDb-2014) consisting of 569 laboratory and 1,858 field measurements. These data encompass a wide range of laboratory and field conditions and represent field data from 23 States within the United States and from 6 other countries. The digital spreadsheet is available on the Internet and offers a valuable resource to engineers and researchers seeking to understand pier-scour relations in the laboratory and field. proprietary
USGS_DS_2006_171 JAMSTEC multibeam surveys and submersible dives around the Hawaiian Islands: A collaborative Japan-USA exploration of Hawaii's deep seafloor CEOS_EXTRA STAC Catalog 1998-01-01 2002-12-31 -161, 16.75, -152.99988, 25.25005 https://cmr.earthdata.nasa.gov/search/concepts/C2231554487-CEOS_EXTRA.umm_json This database release, USGS Data Series 171, contains data collected during four Japan-USA collaborative cruises that characterize the seafloor around the Hawaiian Islands. The Japan Agency for Marine-Earth Science and Technology (JAMSTEC) sponsored cruises in 1998, 1999, 2001, and 2002, to build a greater understanding of the deep marine geology around the Hawaiian Islands. During these cruises, scientists surveyed over 600,000 square kilometers of the seafloor with a hull-mounted multibeam seafloor-mapping sonar system (SEA BEAM® 2112), observed the seafloor and collected samples using robotic and manned submersible dives, collected dredge and piston-core samples, and performed single-channel seismic surveys. To date, 32 research papers have been published describing results from these cruises. For a list of these articles see the bibliography. This digital database was compiled with ESRI ArcInfo version 7.2.2 and ArcGIS 9.0. The GIS files contain multibeam bathymetry, and acoustic backscatter data in ESRI grid format, and dive, seafloor sampling, and siesmic location data in ESRI shapefile format; ArcInfo-compatible GIS software is therefore required to use the files of this database. Metadata for the GIS files are available as text files. The GIS files were also symbolized and used to create Portable Document Format (PDF) files that are ready to be printed. Adobe Reader or other software that can translate PDFs is necessary to print these files. [Summary provided by the USGS.] proprietary
USGS_DS_2006_177 Digital database of recently active traces of the Hayward Fault, California CEOS_EXTRA STAC Catalog 1970-01-01 -128, 35, -120, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231553624-CEOS_EXTRA.umm_json The purpose of this map is to show the location of and evidence for recent movement on active fault traces within the Hayward Fault Zone, California. The mapped traces represent the integration of the following three different types of data: (1) geomorphic expression, (2) creep (aseismic fault slip),and (3) trench exposures. This publication is a major revision of an earlier map (Lienkaemper, 1992), which both brings up to date the evidence for faulting and makes it available formatted both as a digital database for use within a geographic information system (GIS) and for broader public access interactively using widely available viewing software. The pamphlet describes in detail the types of scientific observations used to make the map, gives references pertaining to the fault and the evidence of faulting, and provides guidance for use of and limitations of the map. [Summary provided by the USGS.] proprietary
USGS_DS_2006_180_1.0 Capitol Lake, Washington, 2004 Data Summary CEOS_EXTRA STAC Catalog 2004-09-21 2005-02-28 -122.9142, 47.0219, -122.9034, 47.0447 https://cmr.earthdata.nasa.gov/search/concepts/C2231548768-CEOS_EXTRA.umm_json At the request of the Washington Department of Ecology (WDOE), the US Geological Survey (USGS) collected bathymetry data in Capital Lake, Olympia, Wash., on September 21, 2004. The data are to be used to calculate sediment infilling rates within the lake as well as for developing the bottom boundary conditions for numerical models of water quality, sediment transport, and morphological change. In addition, the USGS collected sediment samples in Capitol Lake in February, 2005, to help characterize bottom sediment for numerical model calculations and substrate assessment. [Summary provided by the USGS.] proprietary
@@ -15703,8 +15708,8 @@ USGS_Map_MF-2385_1.0 Map and map database of susceptibility to slope failure by
USGS_NAWQA_HG_DEP Atmospheric Deposition of Mercury in the Boston Area CEOS_EXTRA STAC Catalog 1970-01-01 -78, 40, -70, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2231550487-CEOS_EXTRA.umm_json Atmospheric deposition has been found to be the dominant source of mercury (Hg) in New England's aquatic environment (Krabbenhoft and others, 1999; Northeast States for Coordinated Air Use Management (NESCAUM) and others, 1998). Little is known about atmospheric mercury deposition in urban areas because most atmospheric monitoring to date has been done in rural areas. Preliminary water, sediment, and fish tissue data, collected by U.S. Geological Survey's New England Coastal Basins (NECB) study as part of the National Water Quality Assessment (NAWQA) program, shows elevated concentrations of mercury in the Boston metropolitan area. The NECB Mercury Deposition Network is a four-site, 2-year data collection effort by the USGS to help define the levels of mercury in precipitation and identify how atmospheric mercury may be contributing to mercury in the aquatic ecosystem. [Summary provided by the USGS.] proprietary
USGS_NEIC_NEARRT Current and Near Real Time Earthquake Data from the USGS/National Earthquake Information Center (NEIC) CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551913-CEOS_EXTRA.umm_json The National Earthquake Information Center (NEIC of the U.S. Geological Survey provides current earthquake information and data including interactive earthquake maps, near real time earthquake data, fast moment and broadband solutions, and lists of earthquakes for the past 3 weeks. Current earthquake information and data are located at: http://earthquake.usgs.gov/ Near real time earthquake data is located at: http://earthquake.usgs.gov/ Archives of past earthquakes can be found at: http://earthquake.usgs.gov/earthquakes/eqinthenews/ proprietary
USGS_NHD_CATCH National Hydrography Dataset Catchment Delineations CEOS_EXTRA STAC Catalog 1970-01-01 -170, 17, -46, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2231554271-CEOS_EXTRA.umm_json Topographically-based catchments will be delineated for all stream-reach segments of the National Hydrography Dataset (NHD) within the entire conterminous United States. The NHD is a digital hydrographic dataset produced by the USGS, in cooperation with the U.S. Environmental Protection Agency (USEPA), that shows streams, lakes, ponds, and wetlands for the Nation at an initial scale of 1:100,000. This effort is being supported by the USEPA and USGS and is intended to benefit a wide variety of water-quality and stream-flow studies across the nation. The catchment-delineation technique is the same as that developed for use in the New England SPARROW model. The New England SPARROW model was the first to utilize the detail of the National Hydrography Dataset (NHD) as the underlying stream-reach network. Final products for this project will be the completion of NHD catchment delineations for the conterminous United States, which will be part of the NHDPlus project to be completed and made available in 2006. proprietary
-USGS_NPS_AcadiaAccuracy_Final Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points CEOS_EXTRA STAC Catalog 2003-10-01 2003-10-01 -75.262726, 43.99941, -68.044304, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231554200-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). Thematic accuracy requirements of the VMP specify 80% accuracy for each map class (theme) that represents National Vegetation Classification System (NVCS) associations (vegetation communities). The UMESC selected 728 field sites, all within Acadia National Park fee and easement lands, for a thematic accuracy assessment (AA) to the vegetation map. The sites were randomly generated, stratified to map class themes that represent NVCS natural/semi-natural vegetation communities using VMP standards. Certain modifications to the process were necessary to accommodate logistical challenges. Local botanists collected field data for 724 of the sites during the 1999 field season. Thematic AA used 688 sites. Sites not used for the analysis were due to the elimination of an entire map class because of irreconcilable classification concepts (19 sites), or to other reasons including unmanageable error with GPS coordinate, duplicate site location, and incomplete field data (17 sites). Regardless of their use in the analysis, all 724 AA sites collected are represented in the Accuracy Assessment Site Spatial Database. proprietary
USGS_NPS_AcadiaAccuracy_Final Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points ALL STAC Catalog 2003-10-01 2003-10-01 -75.262726, 43.99941, -68.044304, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231554200-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). Thematic accuracy requirements of the VMP specify 80% accuracy for each map class (theme) that represents National Vegetation Classification System (NVCS) associations (vegetation communities). The UMESC selected 728 field sites, all within Acadia National Park fee and easement lands, for a thematic accuracy assessment (AA) to the vegetation map. The sites were randomly generated, stratified to map class themes that represent NVCS natural/semi-natural vegetation communities using VMP standards. Certain modifications to the process were necessary to accommodate logistical challenges. Local botanists collected field data for 724 of the sites during the 1999 field season. Thematic AA used 688 sites. Sites not used for the analysis were due to the elimination of an entire map class because of irreconcilable classification concepts (19 sites), or to other reasons including unmanageable error with GPS coordinate, duplicate site location, and incomplete field data (17 sites). Regardless of their use in the analysis, all 724 AA sites collected are represented in the Accuracy Assessment Site Spatial Database. proprietary
+USGS_NPS_AcadiaAccuracy_Final Acadia National Park Vegetation Mapping Project - Accuracy Assessment Points CEOS_EXTRA STAC Catalog 2003-10-01 2003-10-01 -75.262726, 43.99941, -68.044304, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231554200-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). Thematic accuracy requirements of the VMP specify 80% accuracy for each map class (theme) that represents National Vegetation Classification System (NVCS) associations (vegetation communities). The UMESC selected 728 field sites, all within Acadia National Park fee and easement lands, for a thematic accuracy assessment (AA) to the vegetation map. The sites were randomly generated, stratified to map class themes that represent NVCS natural/semi-natural vegetation communities using VMP standards. Certain modifications to the process were necessary to accommodate logistical challenges. Local botanists collected field data for 724 of the sites during the 1999 field season. Thematic AA used 688 sites. Sites not used for the analysis were due to the elimination of an entire map class because of irreconcilable classification concepts (19 sites), or to other reasons including unmanageable error with GPS coordinate, duplicate site location, and incomplete field data (17 sites). Regardless of their use in the analysis, all 724 AA sites collected are represented in the Accuracy Assessment Site Spatial Database. proprietary
USGS_NPS_AcadiaFieldPlots_Final Acadia National Park Vegetation Mapping Project - Field Plot Points CEOS_EXTRA STAC Catalog 2003-10-01 2003-10-01 -68.65603, 44.017136, -68.045715, 44.404953 https://cmr.earthdata.nasa.gov/search/concepts/C2231549568-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of mapping and classifying the vegetation, vegetation sample plots were collected and analyzed, identifying 53 National Vegetation Classification System natural/semi-natural associations (vegetation communities). Local botanists, via contract with The Nature Conservancy, collected 179 vegetation plot samples at Acadia National Park (NP) during the 1997-1999 field seasons. Maine Natural Areas Program performed ordination analysis using the field plot data and other existing vegetation data of the area. Vegetation communities of Acadia NP are defined and described at the local and global scale. All 179 vegetation plot samples are represented in the Vegetation Field Plot Spatial Database with selected data fields from the Project's PLOTS database. proprietary
USGS_NPS_AcadiaFieldPlots_Final Acadia National Park Vegetation Mapping Project - Field Plot Points ALL STAC Catalog 2003-10-01 2003-10-01 -68.65603, 44.017136, -68.045715, 44.404953 https://cmr.earthdata.nasa.gov/search/concepts/C2231549568-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of mapping and classifying the vegetation, vegetation sample plots were collected and analyzed, identifying 53 National Vegetation Classification System natural/semi-natural associations (vegetation communities). Local botanists, via contract with The Nature Conservancy, collected 179 vegetation plot samples at Acadia National Park (NP) during the 1997-1999 field seasons. Maine Natural Areas Program performed ordination analysis using the field plot data and other existing vegetation data of the area. Vegetation communities of Acadia NP are defined and described at the local and global scale. All 179 vegetation plot samples are represented in the Vegetation Field Plot Spatial Database with selected data fields from the Project's PLOTS database. proprietary
USGS_NPS_AcadiaParkBoundary_Final Acadia National Park Vegetation Mapping Project - Park Boundary CEOS_EXTRA STAC Catalog 2003-10-01 2003-10-01 -68.944374, 43.99941, -68.02303, 44.48051 https://cmr.earthdata.nasa.gov/search/concepts/C2231550835-CEOS_EXTRA.umm_json ABSTRACT: The U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) has produced a vegetation spatial database coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). In support of the mapping project, various spatial database boundary coverages were either produced or modified from their original source. These boundary coverages are: 1) Project Boundary, 2) Map Data Boundary, 3) Park Boundary, and 4) Quad Boundary. The spatial coverages are projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983. proprietary
@@ -15715,8 +15720,8 @@ USGS_NSHMP National Seismic Hazard Maps from the USGS National Seismic Hazard Ma
USGS_NWRC_LA_LandChange_1932-2010 Land Area Change in Coastal Louisiana from 1932 to 2010 CEOS_EXTRA STAC Catalog 1932-01-01 2010-12-31 -94, 29, -89, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2231549617-CEOS_EXTRA.umm_json The analyses of landscape change presented in this dataset use historical surveys, aerial data, and satellite data to track landscape changes in coastal Louisiana. Persistent loss and gain data are presented for 1932-2010. The U.S. Geological Survey (USGS) analyzed landscape changes in coastal Louisiana by determining land and water classifications for 17 datasets. These datasets include survey data from 1932, aerial data from 1956, and Landsat Multispectral Scanner System (MSS) and Thematic Mapper (TM) data from the 1970s to 2010. proprietary
USGS_OF99-535_1.0 Middle Pliocene Paleoenvironmental Reconstruction: PRISM2 CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231553168-CEOS_EXTRA.umm_json As part of the USGS Global Change Research effort, the PRISM (Pliocene Research, Interpretation and Synoptic Mapping) Project has documented the characteristics of middle Pliocene climate on a global scale. The middle Pliocene was selected for detailed study because it spans the transition from relatively warm global climates when glaciers were absent or greatly reduced in the Northern Hemisphere to the generally cooler climates of the Pleistocene with expanded Northern Hemisphere ice sheets and prominent glacial-interglacial cycles. The purpose of this report is to document and explain the PRISM2 mid Pliocene reconstruction. The PRISM2 reconstruction consists of a series of 28 global scale data sets (Table 1) on a 2° latitude by 2° longitude grid. As such, it is the most complete and detailed global reconstruction of climate and environmental conditions older than the last glacial. PRISM2 evolved from a series of studies that summarized conditions at a large number of marine and terrestrial sites and areas (eg. Cronin and Dowsett, 1991; Poore and Sloan, 1996). The first global reconstruction of mid Pliocene climate (PRISM1) was based upon 64 marine sites and 74 terrestrial sites and included data sets representing annual vegetation and land ice, monthly sea surface temperature (SST) and sea-ice, sea level and topography on a 2°x2° grid (Dowsett et al. (1996) and Thompson and Fleming (1996)). The current reconstruction (PRISM2) is a revision of PRISM1 that incorporates several important differences: 1) Additional sites were added to the marine portion of the reconstruction to improve previous coverage. Sites from the Mediterranean Sea and Indian Ocean are incorporated for the first time in PRISM2. 2) All Pliocene sea surface temperature (SST) estimates were recalculated based upon a new core top calibration to the Reynolds and Smith (1995) adjusted optimum interpolation (AOI) SST data set. This reduced some of the problems previously encountered when different fossil groups were calibrated to different modern climatologies (Climate / Long Range Investigation Mapping and Predictions [CLIMAP], Goddard Institute for Space Sciences [GISS], Advanced Very High Resolution Radiometer [AVHRR], etc.). 3) PRISM2 uses a +25m rise in sea level for the Pliocene (PRISM1 used +35m), in keeping with much new data that has become available. 4) Although the change in global ice volume between PRISM1 and PRISM2 is minor, PRISM2 uses model results from Prentice (personal communication) to guide the areal and topographic distribution of Antarctic ice. This results in a more realistic Antarctic ice configuration in tune with the +25m sea level rise. proprietary
USGS_OFR-03-13 Cascadia Tsunami Deposit Database CEOS_EXTRA STAC Catalog 1970-01-01 -130, 36, -116, 52 https://cmr.earthdata.nasa.gov/search/concepts/C2231550569-CEOS_EXTRA.umm_json Abstract The Cascadia Tsunami Deposit Database contains data on the location and sedimentological properties of tsunami deposits found along the Cascadia margin. Data have been compiled from 52 studies, documenting 59 sites from northern California to Vancouver Island, British Columbia that contain known or potential tsunami deposits. Bibliographical references are provided for all sites included in the database. Cascadia tsunami deposits are usually seen as anomalous sand layers in coastal marsh or lake sediments. The studies cited in the database use numerous criteria based on sedimentary characteristics to distinguish tsunami deposits from sand layers deposited by other processes, such as river flooding and storm surges. Several studies cited in the database contain evidence for more than one tsunami at a site. Data categories include age, thickness, layering, grainsize, and other sedimentological characteristics of Cascadia tsunami deposits. The database documents the variability observed in tsunami deposits found along the Cascadia margin. proprietary
-USGS_OFR-97-792 500,000 Year-old Stable Isotopic Record from Devils Hole, USGS OFR-97-792 CEOS_EXTRA STAC Catalog 1970-01-01 -116.3, 36.42, -116.3, 36.42 https://cmr.earthdata.nasa.gov/search/concepts/C2231554597-CEOS_EXTRA.umm_json Devils Hole is a tectonically formed cave developed in the discharge zone of a regional aquifer in south-central Nevada. (See Riggs, et al., 1994.) The walls of this subaqueous cavern are coated with dense vein calcite which provides an ideal material for precise uranium-series dating via thermal ionization mass spectrometry (TIMS). Devils Hole Core DH-11 is a 36-cm-long core of vein calcite from which we obtained an approximately 500,000-year-long continuous record of paleotemperature and other climatic proxies. Data from this core were recently used by Winograd and others (1997) to discuss the length and stability of the last four interglaciations. These data are given in table 1 (http://pubs.usgs.gov/of/1997/ofr97-792/) These records have provided information that has posed several challenges to the orbital theory of the causation of the Pleistocene glaciations, suggested insights regarding the duration of current Holocene climate, provided a new chronology for the Vostok, Antarctica, ice core paleotemperature record, and yielded insights on the age of the groundwater in the principal aquifer of southern Nevada (http://pubs.usgs.gov/of/2002/ofr02-266/) Carbon and oxygen stable isotopic ratios were measured on 285 samples cut at regular intervals inward from the free face of the core (as reported in Winograd et al. ,1992, and in Coplen et al., 1994). Table 1 lists only 284 samples because a sample taken at 114.28 mm was eliminated when post-1994 reanalysis of its delta 18O value indicated an error in the earlier determination. Carbon isotopic ratios are reported in per mill relative to VPDB, defined by assigning a delta 13C of +1.95 per mill to the reference material NBS 19 calcite. Oxygen isotopic ratios are reported relative to VSMOW reference water on a scale normalized such that SLAP reference water is -55.5 per mill relative to VSMOW reference water. The oxygen isotopic fractionation factors employed in this determination are those listed in Coplen and others (1983). The delta 18O value of the isotopic reference material NBS 19 on this scale is +28.65 per mill. The ± 1 sd (standard deviation) error for the delta 18O and delta 13C analyses is ±0.07 and 0.05 per mill, respectively. Ages were estimated by linear interpolation between age control points taken at key intervals in the core and analyzed by TIMS 230Th-234U-238U dating. The age estimates in Table 1 are based on the original 21 control points (see Table 2 in Ludwig, et al., 1992, and Figure 2 in Winograd, et al., 1992) as well as for the recently obtained TIMS age of 143.8±0.9 ka (2 sd analytical error) at 51.5 mm (Winograd, et al., 1997). The later sample was taken specifically for additional control in a critical portion of the core. Errors in the ages vary but are bounded by the errors in the appropriate control points. (See Table 2 in Ludwig, et al., 1992.) proprietary
USGS_OFR-97-792 500,000 Year-old Stable Isotopic Record from Devils Hole, USGS OFR-97-792 ALL STAC Catalog 1970-01-01 -116.3, 36.42, -116.3, 36.42 https://cmr.earthdata.nasa.gov/search/concepts/C2231554597-CEOS_EXTRA.umm_json Devils Hole is a tectonically formed cave developed in the discharge zone of a regional aquifer in south-central Nevada. (See Riggs, et al., 1994.) The walls of this subaqueous cavern are coated with dense vein calcite which provides an ideal material for precise uranium-series dating via thermal ionization mass spectrometry (TIMS). Devils Hole Core DH-11 is a 36-cm-long core of vein calcite from which we obtained an approximately 500,000-year-long continuous record of paleotemperature and other climatic proxies. Data from this core were recently used by Winograd and others (1997) to discuss the length and stability of the last four interglaciations. These data are given in table 1 (http://pubs.usgs.gov/of/1997/ofr97-792/) These records have provided information that has posed several challenges to the orbital theory of the causation of the Pleistocene glaciations, suggested insights regarding the duration of current Holocene climate, provided a new chronology for the Vostok, Antarctica, ice core paleotemperature record, and yielded insights on the age of the groundwater in the principal aquifer of southern Nevada (http://pubs.usgs.gov/of/2002/ofr02-266/) Carbon and oxygen stable isotopic ratios were measured on 285 samples cut at regular intervals inward from the free face of the core (as reported in Winograd et al. ,1992, and in Coplen et al., 1994). Table 1 lists only 284 samples because a sample taken at 114.28 mm was eliminated when post-1994 reanalysis of its delta 18O value indicated an error in the earlier determination. Carbon isotopic ratios are reported in per mill relative to VPDB, defined by assigning a delta 13C of +1.95 per mill to the reference material NBS 19 calcite. Oxygen isotopic ratios are reported relative to VSMOW reference water on a scale normalized such that SLAP reference water is -55.5 per mill relative to VSMOW reference water. The oxygen isotopic fractionation factors employed in this determination are those listed in Coplen and others (1983). The delta 18O value of the isotopic reference material NBS 19 on this scale is +28.65 per mill. The ± 1 sd (standard deviation) error for the delta 18O and delta 13C analyses is ±0.07 and 0.05 per mill, respectively. Ages were estimated by linear interpolation between age control points taken at key intervals in the core and analyzed by TIMS 230Th-234U-238U dating. The age estimates in Table 1 are based on the original 21 control points (see Table 2 in Ludwig, et al., 1992, and Figure 2 in Winograd, et al., 1992) as well as for the recently obtained TIMS age of 143.8±0.9 ka (2 sd analytical error) at 51.5 mm (Winograd, et al., 1997). The later sample was taken specifically for additional control in a critical portion of the core. Errors in the ages vary but are bounded by the errors in the appropriate control points. (See Table 2 in Ludwig, et al., 1992.) proprietary
+USGS_OFR-97-792 500,000 Year-old Stable Isotopic Record from Devils Hole, USGS OFR-97-792 CEOS_EXTRA STAC Catalog 1970-01-01 -116.3, 36.42, -116.3, 36.42 https://cmr.earthdata.nasa.gov/search/concepts/C2231554597-CEOS_EXTRA.umm_json Devils Hole is a tectonically formed cave developed in the discharge zone of a regional aquifer in south-central Nevada. (See Riggs, et al., 1994.) The walls of this subaqueous cavern are coated with dense vein calcite which provides an ideal material for precise uranium-series dating via thermal ionization mass spectrometry (TIMS). Devils Hole Core DH-11 is a 36-cm-long core of vein calcite from which we obtained an approximately 500,000-year-long continuous record of paleotemperature and other climatic proxies. Data from this core were recently used by Winograd and others (1997) to discuss the length and stability of the last four interglaciations. These data are given in table 1 (http://pubs.usgs.gov/of/1997/ofr97-792/) These records have provided information that has posed several challenges to the orbital theory of the causation of the Pleistocene glaciations, suggested insights regarding the duration of current Holocene climate, provided a new chronology for the Vostok, Antarctica, ice core paleotemperature record, and yielded insights on the age of the groundwater in the principal aquifer of southern Nevada (http://pubs.usgs.gov/of/2002/ofr02-266/) Carbon and oxygen stable isotopic ratios were measured on 285 samples cut at regular intervals inward from the free face of the core (as reported in Winograd et al. ,1992, and in Coplen et al., 1994). Table 1 lists only 284 samples because a sample taken at 114.28 mm was eliminated when post-1994 reanalysis of its delta 18O value indicated an error in the earlier determination. Carbon isotopic ratios are reported in per mill relative to VPDB, defined by assigning a delta 13C of +1.95 per mill to the reference material NBS 19 calcite. Oxygen isotopic ratios are reported relative to VSMOW reference water on a scale normalized such that SLAP reference water is -55.5 per mill relative to VSMOW reference water. The oxygen isotopic fractionation factors employed in this determination are those listed in Coplen and others (1983). The delta 18O value of the isotopic reference material NBS 19 on this scale is +28.65 per mill. The ± 1 sd (standard deviation) error for the delta 18O and delta 13C analyses is ±0.07 and 0.05 per mill, respectively. Ages were estimated by linear interpolation between age control points taken at key intervals in the core and analyzed by TIMS 230Th-234U-238U dating. The age estimates in Table 1 are based on the original 21 control points (see Table 2 in Ludwig, et al., 1992, and Figure 2 in Winograd, et al., 1992) as well as for the recently obtained TIMS age of 143.8±0.9 ka (2 sd analytical error) at 51.5 mm (Winograd, et al., 1997). The later sample was taken specifically for additional control in a critical portion of the core. Errors in the ages vary but are bounded by the errors in the appropriate control points. (See Table 2 in Ludwig, et al., 1992.) proprietary
USGS_OFR00-45_1.0 Bedrock Geologic Map of the Hubbard Brook Experimental Forest, Grafton County, New Hampshire, USGS/OFR 00-45 CEOS_EXTRA STAC Catalog 1998-01-01 2000-12-31 -71.875, 43.875, -71.625, 44 https://cmr.earthdata.nasa.gov/search/concepts/C2231550787-CEOS_EXTRA.umm_json Our mapping study was funded by the USGS Toxic Substances Hydrology Program and was undertaken for the following reasons: 1) to ascertain whether the area might have a greater number of mappable lithologic units than shown on Barton's (1997) map, and to verify the stratigraphically higher formations shown on the map; 2) to have sufficient data to draw geologic cross- sections through the Mirror Lake research site; 3) to gather more data on brittle fracture distribution and orientation; and 4) to assess the degree to which the subsurface lithologies, ductile structures, and fractures observed at the two Mirror Lake well fields correlate with the geology of the surrounding region. The bedrock geology of the Hubbard Brook Experimental Forest, Grafton County, New Hampshire is described in this report of new field investigation. The database includes contacts of bedrock geologic units, faults, folds, and other structural geologic information, as well as the base maps on which the mapped geological features are registered. This report supersedes Barton (1997). Data were originally collected in UTM coordinates, zone 19, NAD 1927, and reprojected to geographic coordinates (Lat/Long), NAD 1983. The database is accompanied by two large format color maps, a readme.txt file, and a explanatory pamphlet. proprietary
USGS_OFR00-462 Archive of Chirp Subbottom Data Collected During USGS Cruise MGNM 00014, Central South Carolina, 13-30 March, 2000, USGS/OFR 00-462 CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -79.17, 33.25, -78.5, 33.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231554800-CEOS_EXTRA.umm_json In November 1999, the U. S. Geological Survey, in cooperation with Coastal Carolina University, began a program to produce geologic maps of the nearshore regime off northern South Carolina, utilizing high resolution sidescan sonar, interferometric (direct phase methods) swath bathymetry, and seismic subbottom profiling systems. The study areas extends from the ~7m isobath to about 10km offshore (water depths <12m). The goals of the investigation are to determine regional scale sand resource availability needed for planned beach nourishment programs, to investigate the roles that the inner shelf morphology and geologic framework play in the evolution of this coastal region, and to provide baseline geologic maps for use in proposed biologic habitat studies. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS MGNM 00014 cruise. The coverage is the nearshore of central South Carolina. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. proprietary
USGS_OFR00-463 Archive of Boomer Subbottom Data Collected During USGS Cruise MGNM 00014, Central South Carolina, 13-30 March, 2000, USGS, OFR 00-463 CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -79.17, 33.25, -78.5, 33.83 https://cmr.earthdata.nasa.gov/search/concepts/C2231555400-CEOS_EXTRA.umm_json In November 1999, the U. S. Geological Survey, in cooperation with Coastal Carolina University, began a program to produce geologic maps of the nearshore regime off northern South Carolina, utilizing high resolution sidescan sonar, interferometric (direct phase methods) swath bathymetry, and seismic subbottom profiling systems. The study areas extends from the ~7m isobath to about 10km offshore (water depths <12m). The goals of the investigation are to determine regional scale sand resource availability needed for planned beach nourishment programs, to investigate the roles that the inner shelf morphology and geologic framework play in the evolution of this coastal region, and to provide baseline geologic maps for use in proposed biologic habitat studies. This CD-ROM contains digital high resolution seismic reflection data collected during the USGS MGNM 00014 cruise. The coverage is the nearshore of central South Carolina. The seismic-reflection data are stored as SEG-Y standard format that can be read and manipulated by most seismic-processing software. Much of the information specific to the data are contained in the headers of the SEG-Y format files. The file system format is ISO 9660 which can be read with DOS, Unix, and MAC operating systems with the appropriate CD-ROM driver software installed. proprietary
@@ -15825,8 +15830,8 @@ USGS_OFR_2003_230_1.1 Digital depth horizon compilations of the Alaskan North Sl
USGS_OFR_2003_235 High-resolution seismic-reflection surveys in the nearshore of outer Cape Cod, Massachusetts CEOS_EXTRA STAC Catalog 1970-01-01 -73.68, 41.06, -69.75, 43.07 https://cmr.earthdata.nasa.gov/search/concepts/C2231549794-CEOS_EXTRA.umm_json The U.S. Geological Survey (USGS) Woods Hole Field Center (WHFC), in cooperation with the USGS Water Resources Division conducted high-resolution seismic-reflection surveys along the nearshore areas of outer Cape Cod, Massachusetts from Chatham to Provincetown, Massachusetts. The objectives of this investigation were to determine the stratigraphy of the nearshore in relation to the Quaternary stratigraphy of outer Cape Cod by correlating units between the nearshore and onshore and to define the geologic framework of the region. [Summary provided by the USGS.] proprietary
USGS_OFR_2003_236_1.0 National Geochronological Database CEOS_EXTRA STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231549399-CEOS_EXTRA.umm_json The National Geochronological Data Base (NGDB) was established by the United States Geological Survey (USGS) to collect and organize published isotopic (also known as radiometric) ages of rocks in the United States. The NGDB (originally known as the Radioactive Age Data Base, RADB) was started in 1974. A committee appointed by the Director of the USGS was given the mission to investigate the feasibility of compiling the published radiometric ages for the United States into a computerized data bank for ready access by the user community. A successful pilot program, which was conducted in 1975 and 1976 for the State of Wyoming, led to a decision to proceed with the compilation of the entire United States. For each dated rock sample reported in published literature, a record containing information on sample location, rock description, analytical data, age, interpretation, and literature citation was constructed and included in the NGDB. The NGDB was originally constructed and maintained on a mainframe computer, and later converted to a Helix Express relational database maintained on an Apple Macintosh desktop computer. The NGDB and a program to search the data files were published and distributed on Compact Disc-Read Only Memory (CD-ROM) in standard ISO 9660 format as USGS Digital Data Series DDS-14 (Zartman and others, 1995). As of May 1994, the NGDB consisted of more than 18,000 records containing over 30,000 individual ages, which is believed to represent approximately one-half the number of ages published for the United States through 1991. Because the organizational unit responsible for maintaining the database was abolished in 1996, and because we wanted to provide the data in more usable formats, we have reformatted the data, checked and edited the information in some records, and provided this online version of the NGDB. This report describes the changes made to the data and formats, and provides instructions for the use of the database in geographic information system (GIS) applications. The data are provided in *.mdb (Microsoft Access), *.xls (Microsoft Excel), and *.txt (tab-separated value) formats. We also provide a single non-relational file that contains a subset of the data for ease of use. [Summary provided by the USGS.] proprietary
USGS_OFR_2003_241_1.0 Contaminated Sediments Database for Long Island Sound and the New York Bight CEOS_EXTRA STAC Catalog 1956-01-01 1997-12-31 -74.99, 38.49333, -71, 41.44219 https://cmr.earthdata.nasa.gov/search/concepts/C2231551092-CEOS_EXTRA.umm_json The Contaminated Sediments Database for Long Island Sound and the New York Bight provides a compilation of published and unpublished sediment texture and contaminant data. This report provides maps of several of the contaminants in the database as well as references and a section on using the data to assess the environmental status of these coastal areas. The database contains information collected between 1956-1997; providing an historical foundation for future contaminant studies in the region. [Summary provided by the USGS.] proprietary
-USGS_OFR_2003_247_1.0 A Digital Geological Map Database For the State of Oklahoma ALL STAC Catalog 1970-01-01 -103, 33, -94, 37 https://cmr.earthdata.nasa.gov/search/concepts/C2231550225-CEOS_EXTRA.umm_json This report consists of a compilation of twelve digital geologic maps provided in ARC/INFO interchange (e00) format for the state of Oklahoma. The source maps consisted of nine USGS 1:250,000-scale quadrangle maps and three 1:125,000 scale county maps. This publication presents a digital composite of these data intact and without modification across quadrangle boundaries to resolve geologic unit discontinuities. An ESRI ArcView shapefile formatted version and Adobe Acrobat (pdf) plot file of the compiled digital map are also provided. [Summary provided by the USGS.] proprietary
USGS_OFR_2003_247_1.0 A Digital Geological Map Database For the State of Oklahoma CEOS_EXTRA STAC Catalog 1970-01-01 -103, 33, -94, 37 https://cmr.earthdata.nasa.gov/search/concepts/C2231550225-CEOS_EXTRA.umm_json This report consists of a compilation of twelve digital geologic maps provided in ARC/INFO interchange (e00) format for the state of Oklahoma. The source maps consisted of nine USGS 1:250,000-scale quadrangle maps and three 1:125,000 scale county maps. This publication presents a digital composite of these data intact and without modification across quadrangle boundaries to resolve geologic unit discontinuities. An ESRI ArcView shapefile formatted version and Adobe Acrobat (pdf) plot file of the compiled digital map are also provided. [Summary provided by the USGS.] proprietary
+USGS_OFR_2003_247_1.0 A Digital Geological Map Database For the State of Oklahoma ALL STAC Catalog 1970-01-01 -103, 33, -94, 37 https://cmr.earthdata.nasa.gov/search/concepts/C2231550225-CEOS_EXTRA.umm_json This report consists of a compilation of twelve digital geologic maps provided in ARC/INFO interchange (e00) format for the state of Oklahoma. The source maps consisted of nine USGS 1:250,000-scale quadrangle maps and three 1:125,000 scale county maps. This publication presents a digital composite of these data intact and without modification across quadrangle boundaries to resolve geologic unit discontinuities. An ESRI ArcView shapefile formatted version and Adobe Acrobat (pdf) plot file of the compiled digital map are also provided. [Summary provided by the USGS.] proprietary
USGS_OFR_2003_265 Grand Canyon Riverbed Sediment Changes, Experimental Release of September 2000 - A Sample Data Set CEOS_EXTRA STAC Catalog 2000-08-28 2000-09-18 -112.09242, 36.08593, -111.47837, 36.93602 https://cmr.earthdata.nasa.gov/search/concepts/C2231552397-CEOS_EXTRA.umm_json An experimental water release from the Glen Canyon Dam into the Colorado River above Grand Canyon was conducted in September 2000 by the U.S. Bureau of Reclamation. The U.S. Geological Survey (USGS) conducted sidescan sonar surveys between Glen Canyon Dam (mile -15) and Diamond Creek (mile 220), Arizona (mile designations after Stevens, 1998) to determine the sediment characteristics of the Colorado River bed before and after the release. The first survey (R3-00-GC, 28 Aug to 5 Sep 2000) was conducted before the release when the river was at its Low Summer Steady Flow (LSSF) of 8,000 cfs. The second survey (R4-00-GC, 10 to 18 Sep 2000) was conducted immediately after the September 2000 experimental release when the average daily flow was as high as 30,800 cfs as measured below Glen Canyon Dam (Figure 2). Riverbed sediment properties interpreted from the sidescan sonar images include sediment type and sandwaves; overall changes in these properties between the two surveys were calculated. Sidescan sonar data from the USGS surveys were processed for segments of the Colorado River from Glen Canyon Dam (mile -15) to Phantom Ranch (mile 87.7, Figure 3). The surveys targeted pools between rapids that are part of the Grand Canyon Monitoring and Research Center (GCMRC http://www.gcmrc.gov/) physical sciences study. Maps interpreted from the sidescan sonar images show the distribution of sediment types (bedrock, boulders, pebbles or cobbles, and sand) and the extent of sandwaves for each of the pre- and post-flow surveys. The changes between the two surveys were calculated with spatial arithmetric and had properties of fining, coarsening, erosion, deposition, and the appearance or disappearance of sandwaves. This report describes GIS spatial data files for this project and provides examples of the data from the Colorado River near mile 2 below the confluence of the Paria and Colorado Rivers. The complete data set includes sidescan sonar images and interpreted map files for each of the pre- and post-flow surveys and the changes between the segments of rivers. [Summary provided by the USGS.] proprietary
USGS_OFR_2003_267 Catalog of Earthquake Hypocenters at Alaskan Volcanoes: January 1 through December 31, 2002 CEOS_EXTRA STAC Catalog 2002-01-01 2002-12-31 -170, 51, -130, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2231552354-CEOS_EXTRA.umm_json The Alaska Volcano Observatory (AVO), a cooperative program of the U.S. Geological Survey, the Geophysical Institute of the University of Alaska Fairbanks, and the Alaska Division of Geological and Geophysical Surveys, has maintained seismic monitoring networks at historically active volcanoes in Alaska since 1988 (Power and others, 1993; Jolly and others, 1996; Jolly and others, 2001; Dixon and others, 2002). The primary objectives of this program are the seismic monitoring of active, potentially hazardous, Alaskan volcanoes and the investigation of seismic processes associated with active volcanism. This catalog presents the basic seismic data and changes in the seismic monitoring program for the period January 1, 2002 through December 31, 2002. Appendix G contains a list of publications pertaining to seismicity of Alaskan volcanoes based on these and previously recorded data. The AVO seismic network was used to monitor twenty-four volcanoes in real time in 2002. These include Mount Wrangell, Mount Spurr, Redoubt Volcano, Iliamna Volcano, Augustine Volcano, Katmai Volcanic Group (Snowy Mountain, Mount Griggs, Mount Katmai, Novarupta, Trident Volcano, Mount Mageik, Mount Martin), Aniakchak Crater, Mount Veniaminof, Pavlof Volcano, Mount Dutton, Isanotski Peaks, Shishaldin Volcano, Fisher Caldera, Westdahl Peak, Akutan Peak, Makushin Volcano, Great Sitkin Volcano, and Kanaga Volcano (Figure 1). Monitoring highlights in 2002 include an earthquake swarm at Great Sitkin Volcano in May-June; an earthquake swarm near Snowy Mountain in July-September; low frequency (1-3 Hz) tremor and long-period events at Mount Veniaminof in September-October and in December; and continuing volcanogenic seismic swarms at Shishaldin Volcano throughout the year. Instrumentation and data acquisition highlights in 2002 were the installation of a subnetwork on Okmok Volcano, the establishment of telemetry for the Mount Veniaminof subnetwork, and the change in the data acquisition system to an EARTHWORM detection system. AVO located 7430 earthquakes during 2002 in the vicinity of the monitored volcanoes. This catalog includes: (1) a description of instruments deployed in the field and their locations; (2) a description of earthquake detection, recording, analysis, and data archival systems; (3) a description of velocity models used for earthquake locations; (4) a summary of earthquakes located in 2002; and (5) an accompanying UNIX tar-file with a summary of earthquake origin times, hypocenters, magnitudes, and location quality statistics; daily station usage statistics; and all HYPOELLIPSE files used to determine the earthquake locations in 2002. [Summary provided by the USGS.] proprietary
USGS_OFR_2003_85_1.0 Nearshore Benthic Habitat GIS for the Channel Islands National Marine Sanctuary and Southern California State Fisheries Reserves Volume 1 CEOS_EXTRA STAC Catalog 1970-01-01 -122, 33, -119, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231551552-CEOS_EXTRA.umm_json The nearshore benthic habitat of the Santa Barbara coast and Channel Islands supports diverse marine life that is commercially, recreationally, and intrinsically valuable. Some of these resources are known to be endangered including a variety of rockfish and the white abalone. Agencies of the state of California and the United States have been mandated to preserve and enhance these resources. Data from sidescan sonar, bathymetry, video and dive observations, and physical samples are consolidated in a geographic information system (GIS). The GIS provides researchers and policymakers a view of the relationship among data sets to assist scienctific research and to help with economic and social policy-making decisions regarding this protected environment. [Summary provided by the USGS.] proprietary
@@ -15844,8 +15849,8 @@ USGS_OFR_2004_1038 Inventory of Significant Mineral Deposit Occurrences in the H
USGS_OFR_2004_1039 Location, Age, and Tectonic Significance of the Western Idaho Suture Zone CEOS_EXTRA STAC Catalog 1970-01-01 -118, 43, -112, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2231552012-CEOS_EXTRA.umm_json The Western Idaho Suture Zone (WISZ) represents the boundary between crust overlying Proterozoic North American lithosphere and Late Paleozoic and Mesozoic intraoceanic crust accreted during Cretaceous time. Highly deformed plutons constituted of both arc and sialic components intrude the WISZ and in places are thrust over the accreted terranes. Pronounced variations in Sr, Nd, and O isotope ratios and in major and trace element composition occur across the suture zone in Mesozoic plutons. The WISZ is located by an abrupt west to east increase in initial 87Sr/86Sr ratios, traceable for over 300 km from eastern Washington near Clarkston, east along the Clearwater River thorough a bend to the south of about 110° from Orofino Creek to Harpster, and extending south-southwest to near Ola, Idaho, where Columbia River basalts conceal its extension to the south. K-Ar and 40Ar/39Ar apparent ages of hornblende and biotite from Jurassic and Early Cretaceous plutons in the accreted terranes are highly discordant within about 10 km of the WISZ, exhibiting patterns of thermal loss caused by deformation, subsequent batholith intrusion, and rapid rise of the continental margin. Major crustal movements within the WISZ commenced after about 135 Ma, but much of the displacement may have been largely vertical, during and following emplacement of batholith-scale silicic magmas. Deformation continued until at least 85 Ma and probably until 74 Ma, progressing from south to north. [Summary provided by the USGS.] proprietary
USGS_OFR_2004_1049_1.0 Geologic and Bathymetric Reconnaissance Overview of the San Pedro Shelf Region, Southern California CEOS_EXTRA STAC Catalog 2002-01-01 2002-12-31 -118.33333, 33.46667, -117.83333, 33.78333 https://cmr.earthdata.nasa.gov/search/concepts/C2231548808-CEOS_EXTRA.umm_json This report presents a series of maps that describe the bathymetry and late Quaternary geology of the San Pedro shelf area as interpreted from seismic-reflection profiles and 3.5-kHz and multibeam bathymetric data. Some of the seismic-reflection profiles were collected with Uniboom and 120-kJ sparker during surveys conducted by the U.S. Geological Survey (USGS) in 1973 (K-2-73-SC), 1978 (S-2-78-SC), and 1979 (S-2a-79-SC). The remaining seismic-reflection profiles were collected in 2000 using Geopulse boomer and minisparker during USGS cruise A-1-00-SC. The report consists of seven oversized sheets: 1. Map of 1978 and 1979 uniboom seismic-reflection and 3.5-kHz tracklines used to map faults and folds on San Pedro Shelf. 2. Maps of multibeam shaded bathymetric relief with faults and folds, and bathymetric contours. 3. Isopach map of unconsolidated sediment, seismic-reflection profile across the San Pedro shelf, seismic-reflection profile across San Gabriel paleo-valley. 4. Seismic-reflection profiles across the Palos Verdes Fault Zone. 5. Geologic map and samples of Uniboom and 120-kJ sparker seismic-reflection profiles used to make the map. 6. Map showing thickness of uppermost (Holocene?) sediment layer. 7. Map of San Gabriel Canyon paleo-valley and associated drainage basins. [Summary provided by the USGS.] proprietary
USGS_OFR_2004_1054 Assessment of Hazards Associated with the Bluegill Landslide, South-Central Idaho CEOS_EXTRA STAC Catalog 1970-01-01 -117.59, 41.64, -110.7, 49.35 https://cmr.earthdata.nasa.gov/search/concepts/C2231554051-CEOS_EXTRA.umm_json The Bluegill landslide, located in south-central Idaho, is part of a larger landslide complex that forms an area in the Salmon Falls Creek drainage named Sinking Canyon. The landslide is on public property administered by the U.S. Bureau of Land Management (BLM). As part of ongoing efforts to address possible public safety concerns, the BLM requested that the U.S. Geological Survey (USGS) conduct a preliminary hazard assessment of the landslide, examine possible mitigation options, and identify alternatives for further study and monitoring of the landslide. This report presents the findings of that assessment based on a field reconnaissance of the landslide on September 24, 2003, a review of data and information provided by BLM and researchers from Idaho State University, and information collected from other sources. [Summary provided by the USGS.] proprietary
-USGS_OFR_2004_1058 2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory CEOS_EXTRA STAC Catalog 2002-01-01 -168, 46, -126, 76 https://cmr.earthdata.nasa.gov/search/concepts/C2231549438-CEOS_EXTRA.umm_json The Alaska Volcano Observatory (AVO) tracks activity at the more than 40 historically active volcanoes of the Aleutian Arc. As of December 31, 2002, 24 of these volcanoes are monitored with short-period seismometer networks. AVO's monitoring program also includes daily analysis of satellite imagery supported by occasional over flights and compilation of pilot reports, observations of local residents, and observations of mariners. In 2002, AVO responded to eruptive activity or suspect volcanic activity at 6 volcanic centers in Alaska - Wrangell, the Katmai Group, Veniaminof, Shishaldin, Emmons Lake (Hague), and Great Sitkin volcanoes. In addition to responding to eruptive activity at Alaskan volcanoes, AVO also disseminated information on behalf of the Kamchatkan Volcanic Eruption Response Team (KVERT) about activity at 5 Russian volcanoes - Sheveluch, Klyuchevskoy, Bezymianny, Karymsky, and Chikurachki. [Summary provided by the USGS.] proprietary
USGS_OFR_2004_1058 2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory ALL STAC Catalog 2002-01-01 -168, 46, -126, 76 https://cmr.earthdata.nasa.gov/search/concepts/C2231549438-CEOS_EXTRA.umm_json The Alaska Volcano Observatory (AVO) tracks activity at the more than 40 historically active volcanoes of the Aleutian Arc. As of December 31, 2002, 24 of these volcanoes are monitored with short-period seismometer networks. AVO's monitoring program also includes daily analysis of satellite imagery supported by occasional over flights and compilation of pilot reports, observations of local residents, and observations of mariners. In 2002, AVO responded to eruptive activity or suspect volcanic activity at 6 volcanic centers in Alaska - Wrangell, the Katmai Group, Veniaminof, Shishaldin, Emmons Lake (Hague), and Great Sitkin volcanoes. In addition to responding to eruptive activity at Alaskan volcanoes, AVO also disseminated information on behalf of the Kamchatkan Volcanic Eruption Response Team (KVERT) about activity at 5 Russian volcanoes - Sheveluch, Klyuchevskoy, Bezymianny, Karymsky, and Chikurachki. [Summary provided by the USGS.] proprietary
+USGS_OFR_2004_1058 2002 Volcanic Activity in Alaska and Kamchatka: Summary of Events and Response of the Alaska Volcano Observatory CEOS_EXTRA STAC Catalog 2002-01-01 -168, 46, -126, 76 https://cmr.earthdata.nasa.gov/search/concepts/C2231549438-CEOS_EXTRA.umm_json The Alaska Volcano Observatory (AVO) tracks activity at the more than 40 historically active volcanoes of the Aleutian Arc. As of December 31, 2002, 24 of these volcanoes are monitored with short-period seismometer networks. AVO's monitoring program also includes daily analysis of satellite imagery supported by occasional over flights and compilation of pilot reports, observations of local residents, and observations of mariners. In 2002, AVO responded to eruptive activity or suspect volcanic activity at 6 volcanic centers in Alaska - Wrangell, the Katmai Group, Veniaminof, Shishaldin, Emmons Lake (Hague), and Great Sitkin volcanoes. In addition to responding to eruptive activity at Alaskan volcanoes, AVO also disseminated information on behalf of the Kamchatkan Volcanic Eruption Response Team (KVERT) about activity at 5 Russian volcanoes - Sheveluch, Klyuchevskoy, Bezymianny, Karymsky, and Chikurachki. [Summary provided by the USGS.] proprietary
USGS_OFR_2004_1064 Coastal Vulnerability Assessment of Cape Hatteras National Seashore (CAHA) to Sea-Level Rise CEOS_EXTRA STAC Catalog 1970-01-01 -80, 33, -76, 38 https://cmr.earthdata.nasa.gov/search/concepts/C2231549408-CEOS_EXTRA.umm_json A coastal vulnerability index (CVI) was used to map the relative vulnerability of the coast to future sea-level rise within Cape Hatteras National Seashore (CAHA) in North Carolina. The CVI ranks the following in terms of their physical contribution to sea-level rise-related coastal change: geomorphology, regional coastal slope, rate of relative sea-level rise, historical shoreline change rates, mean tidal range, and mean significant wave height. The rankings for each variable were combined and an index value was calculated for 1-minute grid cells covering the park. The CVI highlights those regions where the physical effects of sea-level rise might be the greatest. This approach combines the coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, yielding a quantitative, although relative, measure of the park's natural vulnerability to the effects of sea-level rise. The CVI provides an objective technique for evaluation and long-term planning by scientists and park managers. Cape Hatteras National Seashore consists of stable and washover dominated segments of barrier beach backed by wetland and marsh. The areas within Cape Hatteras that are likely to be most vulnerable to sea-level rise are those with the highest occurrence of overwash and the highest rates of shoreline change. [Summary provided by the USGS.] proprietary
USGS_OFR_2004_1067 Digital Database of Selected Aggregate and Related Resources in Ada, Boise, Canyon, Elmore, Gem, and Owyhee Counties, Southwestern Idaho CEOS_EXTRA STAC Catalog 1934-01-01 2003-12-31 -117.01154, 42.29952, -115.10053, 44.17547 https://cmr.earthdata.nasa.gov/search/concepts/C2231549777-CEOS_EXTRA.umm_json "The U.S. Geological Survey (USGS) compiled a database of aggregate sites and geotechnical sample data for six counties - Ada, Boise, Canyon, Elmore, Gem, and Owyhee - in southwest Idaho as part of a series of studies in support of the Bureau of Land Management (BLM) planning process. Emphasis is placed on sand and gravel sites in deposits of the Boise River, Snake River, and other fluvial systems and in Neogene lacustrine deposits. Data were collected primarily from unpublished Idaho Transportation Department (ITD) records and BLM site descriptions, published Army Corps of Engineers (ACE) records, and USGS sampling data. The results of this study provides important information needed by land-use planners and resource managers, particularly in the BLM, to anticipate and plan for demand and development of sand and gravel and other mineral material resources on public lands in response to the urban growth in southwestern Idaho. The aggregate database combines two data sets - site information and geotechnical sample data - into an integrated spatial database with 82 unique fields. The material source site data set includes information on 680 sites, and the geotechnical data set consists of selected information from 2,723 laboratory analyses of samples collected from many, but not all, of the sites. The 680 aggregate sites are divided into six classes: sand & gravel (614); rock quarry (43); cinder quarry (9); placer tailings (8); talus (4); and mine waste rock (2). Most importantly, the aggregate database includes detailed location information allowing individual sites to be located at least within a section and most often within a small parcel of a section. Additional information includes, but is not limited to: lithology-mineralogy or geologic formation (if known); surface ownership; size; production; permitting; agency; and number of samples. Geotechnical data include: lab number and test date; field parameters including sample location, type of material, and size; and the results of geotechnical analyses - gradation (grain size distribution), Los Angeles (LA) Degradation, sand equivalent, absorption, density, and several other tests. Ninety-five percent of the 2,723 geotechnical sample records include gradation data, and 72 percent of the samples have sand equivalent data. However, LA Degradation, absorption, and bulk density data are reported only in about 30 percent of the sample records. Large volumes of geotechnical data reside in a variety of accessible but little-used archives maintained by local and county highway districts, state transportation bureaus, and federal engineering, construction and transportation agencies. Integration of good quality geotechnical lithogeochemical information, particularly in digital form suitable for geospatial analysis, can produce profoundly superior databases that may allow more accurate and reliable ""expert"" decision making and improved land use planning. The database that accompanies this report, structured for direct import into geographic information system (GIS) software, is the first step toward producing such an integrated geologic-geotechnical spatial database. [Summary provided by the USGS.]" proprietary
USGS_OFR_2004_1069 A 30-Year Record of Surface Mass Balance (1966-95) and Motion and Surface Altitude (1975-95) at Wolverine Glacier, Alaska ALL STAC Catalog 1966-04-01 1995-12-31 -156, 57, -144, 66 https://cmr.earthdata.nasa.gov/search/concepts/C2231554448-CEOS_EXTRA.umm_json Scientific measurements at Wolverine Glacier, on the Kenai Peninsula in south-central Alaska, began in April 1966. At three long-term sites in the research basin, the measurements included snow depth, snow density, heights of the glacier surface and stratigraphic summer surfaces on stakes, and identification of the surface materials. Calculations of the mass balance of the surface strata-snow, new firn, superimposed ice, and old firn and ice mass at each site were based on these measurements. Calculations of fixed-date annual mass balances for each hydrologic year (October 1 to September 30), as well as net balances and the dates of minimum net balance measured between time-transgressive summer surfaces on the glacier, were made on the basis of the strata balances augmented by air temperature and precipitation recorded in the basin. From 1966 through 1995, the average annual balance at site A (590 meters altitude) was -4.06 meters water equivalent; at site B (1,070 meters altitude), was -0.90 meters water equivalent; and at site C (1,290 meters altitude), was +1.45 meters water equivalent. Geodetic determination of displacements of the mass balance stake, and glacier surface altitudes was added to the data set in 1975 to detect the glacier motion responses to variable climate and mass balance conditions. The average surface speed from 1975 to 1996 was 50.0 meters per year at site A, 83.7 meters per year at site B, and 37.2 meters per year at site C. The average surface altitudes were 594 meters at site A, 1,069 meters at site B, and 1,293 meters at site C; the glacier surface altitudes rose and fell over a range of 19.4 meters at site A, 14.1 meters at site B, and 13.2 meters at site C. [Summary provided by the USGS.] proprietary
@@ -15867,8 +15872,8 @@ USGS_OFR_2004_1221 Los Angeles and San Diego Margin High-Resolution Multibeam Ba
USGS_OFR_2004_1228 Bottom Photographs from the Pulley Ridge Deep Coral Reef CEOS_EXTRA STAC Catalog 2003-04-01 2003-04-30 -86, 25, -82, 29 https://cmr.earthdata.nasa.gov/search/concepts/C2231550612-CEOS_EXTRA.umm_json Pulley Ridge is a series of drowned barrier islands that extend over 100 km in 60-100 m water depths. This drowned ridge is located on the Florida Platform in the southeastern Gulf of Mexico about 250 km west of Cape Sable, Florida (Halley and others, 2003). This barrier island chain formed during the initial stage of the Holocene marine transgression approximately 7000 years before present. These islands were then submerged and left abandoned near the outer edge of the Florida Platform. The southern portion of Pulley Ridge, the focus of this study, hosts zooxanthellate scleractinian corals, green, red and brown macro algae, and a mix of deep and typically shallow-water tropical fishes. This largely photosynthetic community is unique in that it thrives with only 5% of the light typically associated with shallow-water reefs with similar fauna. Several factors help to account for the existence of this unique deep-water community. First, the underlying drowned barrier island provides both elevated topography and lithified substrate for the establishment of the hardbottom community. Second, the region is dominated by the west edge of the Loop Current, which brings relatively clear and warm water to this area. Third, the ridge's position on the continental shelf places it within the thermocline which provides nutrients to the reef during upwelling (Halley and others, 2003). This report presents the still photographs acquired during the April 2003 cruise aboard the Florida Institute of Oceanography's research vessel Suncoaster. These data are just one part of a multi-year study which includes the acquisition of sidescan-sonar imagery, high-resolution bathymetry, high-resolution seismic-reflection profiles, bottom video imagery, and bottom samples. [Summary provided by the USGS.] proprietary
USGS_OFR_2004_1234 Catalog of Earthquake Hypocenters at Alaskan Volcanoes: January 1 through December 31, 2003 CEOS_EXTRA STAC Catalog 2003-01-01 2003-12-31 -180, 50, -140, 66 https://cmr.earthdata.nasa.gov/search/concepts/C2231552741-CEOS_EXTRA.umm_json The Alaska Volcano Observatory (AVO), a cooperative program of the U.S. Geological Survey, the Geophysical Institute of the University of Alaska Fairbanks, and the Alaska Division of Geological and Geophysical Surveys, has maintained seismic monitoring networks at historically active volcanoes in Alaska since 1988. The primary objectives of this program are the near real time seismic monitoring of active, potentially hazardous, Alaskan volcanoes and the investigation of seismic processes associated with active volcanism. This catalog presents the calculated earthquake hypocenter and phase arrival data, and changes in the seismic monitoring program for the period January 1 through December 31, 2003. The AVO seismograph network was used to monitor the seismic activity at twenty-seven volcanoes within Alaska in 2003. These include Mount Wrangell, Mount Spurr, Redoubt Volcano, Iliamna Volcano, Augustine Volcano, Katmai volcanic cluster (Snowy Mountain, Mount Griggs, Mount Katmai, Novarupta, Trident Volcano, Mount Mageik, Mount Martin), Aniakchak Crater, Mount Veniaminof, Pavlof Volcano, Mount Dutton, Isanotski Peaks, Shishaldin Volcano, Fisher Caldera, Westdahl Peak, Akutan Peak, Makushin Volcano, Okmok Caldera, Great Sitkin Volcano, Kanaga Volcano, Tanaga Volcano, and Mount Gareloi. Monitoring highlights in 2003 include: continuing elevated seismicity at Mount Veniaminof in January-April (volcanic unrest began in August 2002), volcanogenic seismic swarms at Shishaldin Volcano throughout the year, and low-level tremor at Okmok Caldera throughout the year. Instrumentation and data acquisition highlights in 2003 were the installation of subnetworks on Tanaga and Gareloi Islands, the installation of broadband installations on Akutan Volcano and Okmok Caldera, and the establishment of telemetry for the Okmok Caldera subnetwork. AVO located 3911 earthquakes in 2003. This catalog includes: (1) a description of instruments deployed in the field and their locations; (2) a description of earthquake detection, recording, analysis, and data archival systems; (3) a description of velocity models used for earthquake locations; (4) a summary of earthquakes located in 2003; and (5) an accompanying UNIX tar-file with a summary of earthquake origin times, hypocenters, magnitudes, phase arrival times, and location quality statistics; daily station usage statistics; and all HYPOELLIPSE files used to determine the earthquake locations in 2003. [Summary provided by the USGS.] proprietary
USGS_OFR_2004_1235 Distribution of Holocene Sediment in Chesapeake Bay CEOS_EXTRA STAC Catalog 1970-01-01 -78, -36, -74, 41 https://cmr.earthdata.nasa.gov/search/concepts/C2231552442-CEOS_EXTRA.umm_json "The distribution of sedimentary environments presents the limited domain of deposits from ""River Input"", the flood tide wedge of ""Atlantic Sediment"", and the extensive region of indigenous, recycled ""Coastal Erosion Sediment"" in the Chesapeake Bay littoral environment. Studies by Miller (1982, 1983, 1987) along selected reaches of the tidewater Potomac River showed that bluff retreat in the littoral environment could be measured and modeled at as much as 0.5 to 1.0 m/yr. During the September 18, 2003 Hurricane Isabel storm surge of nearly 3 m, as much as 8 to 10 m of coastal erosion was measured near some of Miller's sites. Storm-driven coastal erosion is the most extensive source of Holocene sediment in the modern Bay. Although massive amounts were eroded from the terraces and uplands during lowered sea level and cold climates, presently most sediment eroded and transported from terrace and upland source areas has been stored on slopes and alluvial bottoms of the Coastal Plain landscapes that surround the Chesapeake. [Summary provided by the USGS.]" proprietary
-USGS_OFR_2004_1249 A Forest Vegetation Database for Western Oregon CEOS_EXTRA STAC Catalog 1970-01-01 -124.96, 41.58, -116.06, 46.68 https://cmr.earthdata.nasa.gov/search/concepts/C2231548732-CEOS_EXTRA.umm_json Data on forest vegetation in western Oregon were assembled for 2323 ecological survey plots. All data were from fixed-radius plots with the standardized design of the Current Vegetation Survey (CVS) initiated in the early 1990s. For each site, the database includes: 1) live tree density and basal area of common tree species, 2) total live tree density, basal area, estimated biomass, and estimated leaf area; 3) age of the oldest overstory tree examined, 4) geographic coordinates, 5) elevation, 6) interpolated climate variables, and 7) other site variables. The data are ideal for ecoregional analyses of existing vegetation. [Summary provided by the USGS.] proprietary
USGS_OFR_2004_1249 A Forest Vegetation Database for Western Oregon ALL STAC Catalog 1970-01-01 -124.96, 41.58, -116.06, 46.68 https://cmr.earthdata.nasa.gov/search/concepts/C2231548732-CEOS_EXTRA.umm_json Data on forest vegetation in western Oregon were assembled for 2323 ecological survey plots. All data were from fixed-radius plots with the standardized design of the Current Vegetation Survey (CVS) initiated in the early 1990s. For each site, the database includes: 1) live tree density and basal area of common tree species, 2) total live tree density, basal area, estimated biomass, and estimated leaf area; 3) age of the oldest overstory tree examined, 4) geographic coordinates, 5) elevation, 6) interpolated climate variables, and 7) other site variables. The data are ideal for ecoregional analyses of existing vegetation. [Summary provided by the USGS.] proprietary
+USGS_OFR_2004_1249 A Forest Vegetation Database for Western Oregon CEOS_EXTRA STAC Catalog 1970-01-01 -124.96, 41.58, -116.06, 46.68 https://cmr.earthdata.nasa.gov/search/concepts/C2231548732-CEOS_EXTRA.umm_json Data on forest vegetation in western Oregon were assembled for 2323 ecological survey plots. All data were from fixed-radius plots with the standardized design of the Current Vegetation Survey (CVS) initiated in the early 1990s. For each site, the database includes: 1) live tree density and basal area of common tree species, 2) total live tree density, basal area, estimated biomass, and estimated leaf area; 3) age of the oldest overstory tree examined, 4) geographic coordinates, 5) elevation, 6) interpolated climate variables, and 7) other site variables. The data are ideal for ecoregional analyses of existing vegetation. [Summary provided by the USGS.] proprietary
USGS_OFR_2004_1252 Digital Files for Northeast Asia Geodynamics, Mineral Deposit Location, and Metallogenic Belt Maps, Stratigraphic Columns, Descriptions of Map Units, and Descriptions of Metallogenic Belts CEOS_EXTRA STAC Catalog 1970-01-01 60, 27, 170, 81 https://cmr.earthdata.nasa.gov/search/concepts/C2231554750-CEOS_EXTRA.umm_json This publication contains a a series of files for Northeast Asia geodynamics, mineral deposit location, and metallogenic belt maps descriptions of map units and metallogenic belts, and stratigraphic columns. This region includes Eastern Siberia, Russian Far East, Mongolia, Northeast China, South Korea, and Japan. The files include: (1) a geodynamics map at a scale of 1:5,000,000; (2) page-size stratigraphic columns for major terranes; (3) a generalized geodynamics map at a scale of 1:15,000,000; (4) a mineral deposit location map at a scale of 1:7,500,000; (5) metallogenic belt maps at a scale of 1:15,000,000; (6) detailed descriptions of geologic units with references; (7) detailed descriptions of metallogenic belts with references; and (8) summary mineral deposit and metallogenic belt tables. The purpose of this publication is to provide high-quality, digital graphic files for maps and figures, and Word files for explanations, descriptions, and references to customers and users. [Summary provided by the USGS.] proprietary
USGS_OFR_2004_1260 Channel-Morphology Data for the Tongue River and Selected Tributaries, Southeastern Montana, 2001-02 CEOS_EXTRA STAC Catalog 2001-01-01 2002-12-31 -107, 45, -105, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2231548542-CEOS_EXTRA.umm_json Coal-bed methane exploration and production have begun within the Tongue River watershed in southeastern Montana. The development of coal-bed methane requires production of large volumes of ground water, some of which may be discharged to streams, potentially increasing stream discharge and sediment load. Changes in stream discharge or sediment load may result in changes to channel morphology through changes in erosion and vegetation. These changes might be subtle and difficult to detect without baseline data that indicate stream-channel conditions before extensive coal-bed methane development began. In order to provide this baseline channel-morphology data, the U.S. Geological Survey, in cooperation with the Bureau of Land Management, collected channel-morphology data in 2001-02 to document baseline conditions for several reaches along the Tongue River and selected tributaries. This report presents channel-morphology data for five sites on the mainstem Tongue River and four sites on its tributaries. Bankfull, water-surface, and thalweg elevations, channel sections, and streambed-particle sizes were measured along reaches near streamflow-gaging stations. At each site, the channel was classified using methods described by Rosgen. For six sites, bankfull discharge was determined from the stage- discharge relation at the gage for the stage corresponding to the bankfull elevation. For three sites, the step-backwater computer model HEC-RAS was used to estimate bankfull discharge. Recurrence intervals for the bankfull discharge also were estimated for eight of the nine sites. Channel-morphology data for each site are presented in maps, tables, graphs, and photographs. [Summary provided by the USGS.] proprietary
USGS_OFR_2004_1265 Hydrologic Data Summary for the St. Lucie River Estuary, Martin and St. Lucie Counties, Florida, 1998-2001 CEOS_EXTRA STAC Catalog 1998-01-01 2001-12-31 -81, 27, -80, 28 https://cmr.earthdata.nasa.gov/search/concepts/C2231549907-CEOS_EXTRA.umm_json A hydrologic analysis was made at three canal sites and four tidal sites along the St. Lucie River Estuary in southeastern Florida from 1998 to 2001. The data included for analysis are stage, 15-minute flow, salinity, water temperature, turbidity, and suspended-solids concentration. During the period of record, the estuary experienced a drought, major storm events, and high-water discharge from Lake Okeechobee. Flow mainly occurred through the South Fork of the St. Lucie River; however, when flow increased through control structures along the C-23 and C-24 Canals, the North Fork was a larger than usual contributor of total freshwater inflow to the estuary. At one tidal site (Steele Point), the majority of flow was southward toward the St. Lucie Inlet; at a second tidal site (Indian River Bridge), the majority of flow was northward into the Indian River Lagoon. Large-volume stormwater discharge events greatly affected the St. Lucie River Estuary. Increased discharge typically was accompanied by salinity decreases that resulted in water becoming and remaining fresh throughout the estuary until the discharge events ended. Salinity in the estuary usually returned to prestorm levels within a few days after the events. Turbidity decreased and salinity began to increase almost immediately when the gates at the control structures closed. Salinity ranged from less than 1 to greater than 35 parts per thousand during the period of record (1998-2001), and typically varied by several parts per thousand during a tidal cycle. Suspended-solids concentrations were observed at one canal site (S-80) and two tidal sites (Speedy Point and Steele Point) during a discharge event in April and May 2000. Results suggest that most deposition of suspended-solids concentration occurs between S-80 and Speedy Point. The turbidity data collected also support this interpretation. The ratio of inorganic to organic suspended-solids concentration observed at S-80, Speedy Point, and Steele Point during the discharge event indicates that most flocculation of suspended-solids concentration occurs between Speedy Point and Steele Point. [Summary provided by the USGS.] proprietary
@@ -15888,8 +15893,8 @@ USGS_OFR_2005_1070_1.0 Molokai Benthic Habitat Mapping CEOS_EXTRA STAC Catalog 1
USGS_OFR_2005_1132_1.0 Ground-Magnetic Studies of the Amargosa Desert Region, California and Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -124.9, 32.02, -113.61, 42.51 https://cmr.earthdata.nasa.gov/search/concepts/C2231555068-CEOS_EXTRA.umm_json High-resolution aeromagnetic surveys of the Amargosa Desert region, California and Nevada, exhibit a diverse array of magnetic anomalies reflecting a wide range of mid- and upper-crustal lithologies. In most cases, these anomalies can be interpreted in terms of exposed rocks and sedimentary deposits. More difficult to explain are linear magnetic anomalies situated over lithologies that typically have very low magnetizations. Aeromagnetic anomalies are observed, for example, over thick sections of Quaternary alluvial deposits and spring deposits associated with past or modern ground-water discharge in Ash Meadows, Pahrump Valley, and Furnace Creek Wash. Such deposits are typically considered nonmagnetic. To help determine the source of these aeromagnetic anomalies, we conducted ground-magnetic studies at five areas: near Death Valley Junction, at Point of Rocks Spring, at Devils Hole, at Fairbanks Spring, and near Travertine Springs. Depth-to-source calculations show that the sources of these anomalies lie within the Tertiary and Quaternary sedimentary section. We conclude that they are caused by discrete volcanic units lying above the pre-Tertiary basement. At Death Valley Junction and Travertine Springs, these concealed volcanic units are probably part of the Miocene Death Valley volcanic field exposed in the nearby Greenwater Range and Black Mountains. The linear nature of the aeromagnetic anomalies suggests that these concealed volcanic rocks are bounded and offset by near-surface faults. [Summary provided by the USGS.] proprietary
USGS_OFR_2005_1135_1.0 Modified Mercalli Intensity Maps for the 1906 San Francisco Earthquake Plotted in ShakeMap Format CEOS_EXTRA STAC Catalog 1906-04-18 1906-04-18 -124, 34, -120, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231554244-CEOS_EXTRA.umm_json This website presents Modified Mercalli Intensity maps for the great San Francisco earthquake of April 18, 1906. These new maps combine two important developments. First, we have re-evaluated and relocated the damage and shaking reports compiled by Lawson (1908). These reports yield intensity estimates for more than 600 sites and constitute the largest set of intensities ever compiled for a single earthquake. Second, we use the recent ShakeMap methodology to map these intensities. The resulting MMI intensity maps are remarkably detailed and eloquently depict the enormous power and damage potential of this great earthquake. [Summary provided by the USGS.] proprietary
USGS_OFR_2005_1144 Huminite Reflectance Measurements of Paleocene and Upper Cretaceous Coals from Borehole Cuttings, Zavala and Dimmit Counties, South Texas CEOS_EXTRA STAC Catalog 1970-01-01 -107.31, 25.19, -92.85, 37.14 https://cmr.earthdata.nasa.gov/search/concepts/C2231553355-CEOS_EXTRA.umm_json The reflectance of huminite in 19 cuttings samples was determined in support of ongoing investigations into the coal bed methane potential of subsurface Paleocene and Upper Cretaceous coals of South Texas. Coal cuttings were obtained from the Core Research Center of the Bureau of Economic Geology, The University of Texas at Austin. Geophysical logs, mud-gas logs, driller's logs, completion cards, and scout tickets were used to select potentially coal-bearing sample suites and to identify specific sample depths. Reflectance measurements indicate coals of subbituminous rank are present in a wider area in South Texas than previously recognized. [Summary provided by the USGS.] proprietary
-USGS_OFR_2005_1148_1.0 Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004 ALL STAC Catalog 1970-01-01 -80.82, 39.43, -74.41, 42.56 https://cmr.earthdata.nasa.gov/search/concepts/C2231550432-CEOS_EXTRA.umm_json Recent construction for Interstate Highway 99 (I?99) exposed pyrite and associated Zn-Pb sulfide minerals beneath a >10-m thick gossan to oxidative weathering along a 40-60-m deep roadcut through a 270-m long section of the Ordovician Bald Eagle Formation at Skytop, near State College, Centre County, Pennsylvania. Nearby Zn-Pb deposits hosted in associated sandstone and limestone in Blair and Centre Counties were prospected in the past; however, these deposits generally were not viable as commercial mines. The pyritic sandstone from the roadcut was crushed and used locally as road base and fill for adjoining segments of I?99. Within months, acidic (pH<3), metal-laden seeps and runoff from the exposed cut and crushed sandstone raised concerns about surface- and ground-water contamination and prompted a halt in road construction and the beginning of costly remediation. Mineralized sandstones from the cut contain as much as 34 wt. % Fe, 28 wt. % S, 3.5 wt. % Zn, 1% wt. Pb, 88 ppm As, and 32 ppm Cd. A composite of <2 mm material sampled from the cut face contains 8.1 wt. % total sulfide S, 0.6 wt. % sulfate S, and is net acidic by acid-base accounting (net neutralization potential ?234 kg CaCO3/t). Primary sulfide minerals include pyrite, marcasite, sphalerite (2 to 12 wt. % Fe) and traces of chalcopyrite and galena. Pyrite occurs in mm- to cm-scale veinlets and disseminated grains in sandstone, as needles, and in a locally massive pyrite-cemented breccia along a fault. Inclusions (<10 ?m) of CdS and Ni-Co-As minerals in pyrite and minor amounts of Cd in sphalerite (0.1 wt. % or less) explain the primary source of trace metals in the rock and in associated secondary minerals and seepage. Wet/dry cycles associated with intermittent rainfall promoted oxidative weathering and dissolution of primary sulfides and their oxidation products. Resulting sulfate solutions evaporated during dry periods to form intermittent ?blooms? of soluble, yellow and white efflorescent sulfate salts (copiapite, melanterite, and halotrichite) on exposed rock and other surfaces. Salts coating the cut face incorporated Fe, Al, S, and minor Zn. They readily dissolved in deionized water in the laboratory to form solutions with pH <2.5, consistent with field observations. In addition to elevated dissolved Fe and sulfate concentrations (>1,000 mg/L), seep waters at the base of the cut contain >100 mg/L dissolved Zn and >1 mg/L As, Co, Cu, and Ni. Lead is relatively immobile (<10 ?g/L in seep waters). The salts sequester metals and acidity between rainfall events. Episodic salt dissolution then contributes pulses of contamination including acid to surface runoff and ground water. The Skytop experience highlights the need to understand dynamic interactions of mineralogy and hydrology in order to avoid potentially negative environmental impacts associated with excavation in sulfidic rocks. [Summary provided by the USGS.] proprietary
USGS_OFR_2005_1148_1.0 Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004 CEOS_EXTRA STAC Catalog 1970-01-01 -80.82, 39.43, -74.41, 42.56 https://cmr.earthdata.nasa.gov/search/concepts/C2231550432-CEOS_EXTRA.umm_json Recent construction for Interstate Highway 99 (I?99) exposed pyrite and associated Zn-Pb sulfide minerals beneath a >10-m thick gossan to oxidative weathering along a 40-60-m deep roadcut through a 270-m long section of the Ordovician Bald Eagle Formation at Skytop, near State College, Centre County, Pennsylvania. Nearby Zn-Pb deposits hosted in associated sandstone and limestone in Blair and Centre Counties were prospected in the past; however, these deposits generally were not viable as commercial mines. The pyritic sandstone from the roadcut was crushed and used locally as road base and fill for adjoining segments of I?99. Within months, acidic (pH<3), metal-laden seeps and runoff from the exposed cut and crushed sandstone raised concerns about surface- and ground-water contamination and prompted a halt in road construction and the beginning of costly remediation. Mineralized sandstones from the cut contain as much as 34 wt. % Fe, 28 wt. % S, 3.5 wt. % Zn, 1% wt. Pb, 88 ppm As, and 32 ppm Cd. A composite of <2 mm material sampled from the cut face contains 8.1 wt. % total sulfide S, 0.6 wt. % sulfate S, and is net acidic by acid-base accounting (net neutralization potential ?234 kg CaCO3/t). Primary sulfide minerals include pyrite, marcasite, sphalerite (2 to 12 wt. % Fe) and traces of chalcopyrite and galena. Pyrite occurs in mm- to cm-scale veinlets and disseminated grains in sandstone, as needles, and in a locally massive pyrite-cemented breccia along a fault. Inclusions (<10 ?m) of CdS and Ni-Co-As minerals in pyrite and minor amounts of Cd in sphalerite (0.1 wt. % or less) explain the primary source of trace metals in the rock and in associated secondary minerals and seepage. Wet/dry cycles associated with intermittent rainfall promoted oxidative weathering and dissolution of primary sulfides and their oxidation products. Resulting sulfate solutions evaporated during dry periods to form intermittent ?blooms? of soluble, yellow and white efflorescent sulfate salts (copiapite, melanterite, and halotrichite) on exposed rock and other surfaces. Salts coating the cut face incorporated Fe, Al, S, and minor Zn. They readily dissolved in deionized water in the laboratory to form solutions with pH <2.5, consistent with field observations. In addition to elevated dissolved Fe and sulfate concentrations (>1,000 mg/L), seep waters at the base of the cut contain >100 mg/L dissolved Zn and >1 mg/L As, Co, Cu, and Ni. Lead is relatively immobile (<10 ?g/L in seep waters). The salts sequester metals and acidity between rainfall events. Episodic salt dissolution then contributes pulses of contamination including acid to surface runoff and ground water. The Skytop experience highlights the need to understand dynamic interactions of mineralogy and hydrology in order to avoid potentially negative environmental impacts associated with excavation in sulfidic rocks. [Summary provided by the USGS.] proprietary
+USGS_OFR_2005_1148_1.0 Acid-Rock Drainage at Skytop, Centre County, Pennsylvania, 2004 ALL STAC Catalog 1970-01-01 -80.82, 39.43, -74.41, 42.56 https://cmr.earthdata.nasa.gov/search/concepts/C2231550432-CEOS_EXTRA.umm_json Recent construction for Interstate Highway 99 (I?99) exposed pyrite and associated Zn-Pb sulfide minerals beneath a >10-m thick gossan to oxidative weathering along a 40-60-m deep roadcut through a 270-m long section of the Ordovician Bald Eagle Formation at Skytop, near State College, Centre County, Pennsylvania. Nearby Zn-Pb deposits hosted in associated sandstone and limestone in Blair and Centre Counties were prospected in the past; however, these deposits generally were not viable as commercial mines. The pyritic sandstone from the roadcut was crushed and used locally as road base and fill for adjoining segments of I?99. Within months, acidic (pH<3), metal-laden seeps and runoff from the exposed cut and crushed sandstone raised concerns about surface- and ground-water contamination and prompted a halt in road construction and the beginning of costly remediation. Mineralized sandstones from the cut contain as much as 34 wt. % Fe, 28 wt. % S, 3.5 wt. % Zn, 1% wt. Pb, 88 ppm As, and 32 ppm Cd. A composite of <2 mm material sampled from the cut face contains 8.1 wt. % total sulfide S, 0.6 wt. % sulfate S, and is net acidic by acid-base accounting (net neutralization potential ?234 kg CaCO3/t). Primary sulfide minerals include pyrite, marcasite, sphalerite (2 to 12 wt. % Fe) and traces of chalcopyrite and galena. Pyrite occurs in mm- to cm-scale veinlets and disseminated grains in sandstone, as needles, and in a locally massive pyrite-cemented breccia along a fault. Inclusions (<10 ?m) of CdS and Ni-Co-As minerals in pyrite and minor amounts of Cd in sphalerite (0.1 wt. % or less) explain the primary source of trace metals in the rock and in associated secondary minerals and seepage. Wet/dry cycles associated with intermittent rainfall promoted oxidative weathering and dissolution of primary sulfides and their oxidation products. Resulting sulfate solutions evaporated during dry periods to form intermittent ?blooms? of soluble, yellow and white efflorescent sulfate salts (copiapite, melanterite, and halotrichite) on exposed rock and other surfaces. Salts coating the cut face incorporated Fe, Al, S, and minor Zn. They readily dissolved in deionized water in the laboratory to form solutions with pH <2.5, consistent with field observations. In addition to elevated dissolved Fe and sulfate concentrations (>1,000 mg/L), seep waters at the base of the cut contain >100 mg/L dissolved Zn and >1 mg/L As, Co, Cu, and Ni. Lead is relatively immobile (<10 ?g/L in seep waters). The salts sequester metals and acidity between rainfall events. Episodic salt dissolution then contributes pulses of contamination including acid to surface runoff and ground water. The Skytop experience highlights the need to understand dynamic interactions of mineralogy and hydrology in order to avoid potentially negative environmental impacts associated with excavation in sulfidic rocks. [Summary provided by the USGS.] proprietary
USGS_OFR_2005_1153_1.0 Multibeam Bathymetry and Backscatter Data: Northeastern Channel Islands Region, Southern California CEOS_EXTRA STAC Catalog 2004-08-06 2004-08-15 -119.72, 33.88, -119.03, 34.33 https://cmr.earthdata.nasa.gov/search/concepts/C2231553010-CEOS_EXTRA.umm_json The U.S. Geological Survey (USGS) in cooperation with the Minerals Management Service (MMS) conducted multibeam mapping in the eastern Santa Barbara Channel and northeastern Channel Islands region from August 8 to15, 2004 aboard the R/V Maurice Ewing. The survey was directed and funded by the Minerals Management Service, which is interested in maps of hard bottom habitats, particularly natural outcrops, that support reef communities in areas affected by oil and gas activity. The maps are also useful to biologists studying fish that use the platforms and the sea floor beneath them as habitat. The survey collected bathymetry and corrected, co-registered acoustic backscatter using a Kongsberg Simrad EM1002 multibeam echosounder that was mounted on the hull of the R/V Maurice Ewing. Three main regions were mapped during the survey including: (1) the Eastern Santa Barbara Channel adjacent to an area previously mapped with multibeam-sonar by the Monterey Bay Aquarium Research Institute (see the MBARI Santa Barbara Basin Multibeam Survey web page), (2) the Footprint area south of Anacapa Island, which has been studied extensively by rockfish biologists and is considered a good site for a marine protected area, and (3) part of the submarine canyons along the continental slope south of Port Hueneme. These data will be used to support a number of new and ongoing projects including, habitat mapping, shelf and slope processes, and offshore hazards and resources. [Summary provided by the USGS.] proprietary
USGS_OFR_2005_1164_1.0 An Assessment of Volcanic Threat and Monitoring Capabilities in the United States: Framework for a National Volcano Early Warning System CEOS_EXTRA STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231551822-CEOS_EXTRA.umm_json A National Volcano Early Warning System NVEWS is being formulated by the Consortium of U.S. Volcano Observatories (CUSVO) to establish a proactive, fully integrated, national-scale monitoring effort that ensures the most threatening volcanoes in the United States are properly monitored in advance of the onset of unrest and at levels commensurate with the threats posed. Volcanic threat is the combination of hazards (the destructive natural phenomena produced by a volcano) and exposure (people and property at risk from the hazards). The United States has abundant volcanoes, and over the past 25 years the Nation has experienced a diverse range of the destructive phenomena that volcanoes can produce. Hazardous volcanic activity will continue to occur, and because of increasing population, increasing development, and expanding national and international air traffic over volcanic regions the exposure of human life and enterprise to volcano hazards is increasing. Fortunately, volcanoes exhibit precursory unrest that if detected and analyzed in time allows eruptions to be anticipated and communities at risk to be forewarned with reliable information in sufficient time to implement response plans and mitigation measures. In the 25 years since the cataclysmic eruption of Mount St. Helens, scientific and technological advances in volcanology have been used to develop and test models of volcanic behavior and to make reliable forecasts of expected activity a reality. Until now, these technologies and methods have been applied on an ad hoc basis to volcanoes showing signs of activity. However, waiting to deploy a robust, modern monitoring effort until a hazardous volcano awakens and an unrest crisis begins is socially and scientifically unsatisfactory because it forces scientists, civil authorities, citizens, and businesses into playing catch up with the volcano, trying to get instruments and civil-defense measures in place before the unrest escalates and the situation worsens. Inevitably, this manner of response results in our missing crucial early stages of the volcanic unrest and hampers our ability to accurately forecast events. Restless volcanoes do not always progress to eruption; nevertheless, monitoring is necessary in such cases to minimize either over-reacting, which costs money, or under-reacting, which may cost lives. [Summary provided by the USGS.] proprietary
USGS_OFR_2005_1176 Flooding of the Androscoggin River during December 18-19, 2003, in Canton, Maine CEOS_EXTRA STAC Catalog 2003-12-18 2003-12-19 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C2231550802-CEOS_EXTRA.umm_json The Androscoggin River flooded the town of Canton, Maine in December 2003, resulting in damage to and (or) evacuation of 44 homes. Streamflow records at the U.S. Geological Survey (USGS) streamflow-gaging stations at Rumford (USGS station identification number 01054500) and Auburn (01059000) were used to estimate the peak streamflow for the Androscoggin in the town of Canton for this flood (December 18-19, 2003). The estimated peak flood streamflow at Canton was approximately 39,800 ft3/s, corresponding to an estimated recurrence interval of 4.4 years; however, an ice jam downstream from Canton Point on December 18-19 obstructed river flow resulting in a high-water elevation commensurate with an open-water flood approximately equal to a 15-year event. The high water-surface elevations attained during the December 18-19 flood event in Canton were higher than the expected open-water flood water-surface elevations; this verified the assumption that the water-surface elevation was augmented due to the downstream ice jam. The change in slope of the riverbed from upstream of Canton to the impoundment at the downstream corporate limits, and the river bend near Stevens Island are principal factors in ice-jam formation near Canton. The U.S. Army Corps of Engineers Ice Jam Database indicates five ice-jam-related floods (including December 2003) for the town of Canton: March 13, 1936; January 1978; March 12, 1987; January 29, 1996; and December 18-19, 2003. There have been more ice-jam-related flood events in Canton than these five documented events, but the exact number and nature of ice jams in Canton cannot be determined without further research. proprietary
@@ -15941,8 +15946,8 @@ USGS_OFR_2007_1146 Estimated Magnitudes and Recurrence Intervals of Peak Flows o
USGS_OFR_2007_1152 High-Resolution Seismic Imaging Investigations in Salt Lake and Utah Valleys for Earthquake Hazards CEOS_EXTRA STAC Catalog 2003-09-01 2005-09-30 -113, 40, -111.5, 41 https://cmr.earthdata.nasa.gov/search/concepts/C2231549137-CEOS_EXTRA.umm_json In support of earthquake hazards and ground motion studies by researchers at the Utah Geological Survey, University of Utah, Utah State University, Brigham Young University, and San Diego State University, the U.S. Geological Survey Geologic Hazards Team Intermountain West Project conducted three high-resolution seismic imaging investigations along the Wasatch Front between September 2003 and September 2005. These three investigations include: (1) a proof-of-concept P-wave minivib reflection imaging profile in south-central Salt Lake Valley, (2) a series of seven deep (as deep as 400 m) S-wave reflection/refraction soundings using an S-wave minivib in both Salt Lake and Utah Valleys, and (3) an S-wave (and P-wave) investigation to 30 m at four sites in Utah Valley and at two previously investigated S-wave (Vs) minivib sites. In addition, we present results from a previously unpublished downhole S-wave investigation conducted at four sites in Utah Valley. The locations for each of these investigations are shown in figure 1. Coordinates for the investigation sites are listed in Table 1. With the exception of the P-wave common mid-point (CMP) reflection profile, whose end points are listed, these coordinates are for the midpoint of each velocity sounding. Vs30 and Vs100, also shown in Table 1, are defined as the average shear-wave velocities to depths of 30 and 100 m, respectively, and details of their calculation can be found in Stephenson and others (2005). The information from these studies will be incorporated into components of the urban hazards maps along the Wasatch Front being developed by the U.S. Geological Survey, Utah Geological Survey, and numerous collaborating research institutions. [Summary provided by the USGS.] proprietary
USGS_OFR_2007_1159_2007-1159 Estimating Water Storage Capacity of Existing and Potentially Restorable Wetland Depressions in a Subbasin of the Red River of the North CEOS_EXTRA STAC Catalog 1970-01-01 -106, 37, -84, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231553843-CEOS_EXTRA.umm_json Concern over flooding along rivers in the Prairie Pothole Region has stimulated interest in developing spatially distributed hydrologic models to simulate the effects of wetland water storage on peak river flows. Such models require spatial data on the storage volume and interception area of existing and restorable wetlands in the watershed of interest. In most cases, information on these model inputs is lacking because resolution of existing topographic maps is inadequate to estimate volume and areas of existing and restorable wetlands. Consequently, most studies have relied on wetland area to volume or interception area relationships to estimate wetland basin storage characteristics by using available surface area data obtained as a product from remotely sensed data (e.g., National Wetlands Inventory). Though application of areal input data to estimate volume and interception areas is widely used, a drawback is that there is little information available to provide guidance regarding the application, limitations, and biases associated with such approaches. Another limitation of previous modeling efforts is that water stored by wetlands within a watershed is treated as a simple lump storage component that is filled prior to routing overflow to a pour point or gaging station. This approach does not account for dynamic wetland processes that influence water stored in prairie wetlands. Further, most models have not considered the influence of human-induced hydrologic changes, such as land use, that greatly influence quantity of surface water inputs and, ultimately, the rate that a wetland basin fills and spills. The goals of this study were to (1) develop and improve methodologies for estimating and spatially depicting wetland storage volumes and interceptions areas and (2) develop models and approaches for estimating/simulating the water storage capacity of potentially restorable and existing wetlands under various restoration, land use, and climatic scenarios. To address these goals, we developed models and approaches to spatially represent storage volumes and interception areas of existing and potentially restorable wetlands in the upper Mustinka subbasin within Grant County, Minn. We then developed and applied a model to simulate wetland water storage increases that would result from restoring 25 and 50 percent of the farmed and drained wetlands in the upper Mustinka subbasin. The model simulations were performed during the growing season (May October) for relatively wet (1993; 0.67 m of precipitation) and dry (1987; 0.32 m of precipitation) years. Results from the simulations indicated that the 25 percent restoration scenario would increase water storage by 2732 percent and that a 50 percent scenario would increase storage by 5363 percent. Additionally, we estimated that wetlands in the subbasin have potential to store 11.5720.98 percent of the total precipitation that fell over the entire subbasin area (52,758 ha). Our simulation results indicated that there is considerable potential to enhance water storage in the subbasin; however, evaluation and calibration of the model is necessary before simulation results can be applied to management and planning decisions. In this report we present guidance for the development and application of models (e.g., surface area-volume predictive models, hydrology simulation model) to simulate wetland water storage to provide a basis from which to understand and predict the effects of natural or human-induced hydrologic alterations. In developing these approaches, we tried to use simple and widely available input data to simulate wetland hydrology and predict wetland water storage for a specific precipitation event or a series of events. Further, the hydrology simulation model accounted for land use and soil type, which influence surface water inputs to wetlands. Although information presented in this report is specific to the Mustinka subbasin, the approaches and methods developed should be applicable to other regions in the Prairie Pothole Region. [Summary provided by the USGS.] proprietary
USGS_OFR_2007_1161 Historical Changes in the Mississippi-Alabama Barrier Islands and the Roles of Extreme Storms, Sea Level, and Human Activities CEOS_EXTRA STAC Catalog 1970-01-01 -94, 30, -86, 32 https://cmr.earthdata.nasa.gov/search/concepts/C2231555148-CEOS_EXTRA.umm_json An historical analysis of images and documents shows that the Mississippi-Alabama (MS-AL) barrier islands are undergoing rapid land loss and translocation. The barrier island chain formed and grew at a time when there was a surplus of sand in the alongshore sediment transport system, a condition that no longer prevails. The islands, except Cat, display alternating wide and narrow segments. Wide segments generally were products of low rates of inlet migration and spit elongation that resulted in well-defined ridges and swales formed by wave refraction along the inlet margins. In contrast, rapid rates of inlet migration and spit elongation under conditions of surplus sand produced low, narrow, straight barrier segments. [Summary provided by the USGS.] proprietary
-USGS_OFR_2007_1169 2005 Hydrographic Survey of South San Francisco Bay, California CEOS_EXTRA STAC Catalog 1970-01-01 -126, 37, -122, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231550095-CEOS_EXTRA.umm_json An acoustic hydrographic survey of South San Francisco Bay (South Bay) was conducted in 2005. Over 20 million soundings were collected within an area of approximately 250 sq km (97 sq mi) of the bay extending south of Coyote Point on the west shore, to the San Leandro marina on the east, including Coyote Creek and Ravenswood, Alviso, Artesian, and Mud Sloughs. This is the first survey of this scale that has been conducted in South Bay since the National Oceanic and Atmospheric Administration National Ocean Service (NOS) last surveyed the region in the early 1980s. Data from this survey will provide insight to changes in bay floor topography from the 1980s to 2005 and will also serve as essential baseline data for tracking changes that will occur as restoration of the South San Francisco Bay salt ponds progress. This report provides documentation on how the survey was conducted, an assessment of accuracy of the data, and distributes the sounding data with Federal Geographic Data Committee (FGDC) compliant metadata. Reports from NOS and Sea Surveyor, Inc., containing additional survey details are attached as appendices. [Summary provided by the USGS.] proprietary
USGS_OFR_2007_1169 2005 Hydrographic Survey of South San Francisco Bay, California ALL STAC Catalog 1970-01-01 -126, 37, -122, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231550095-CEOS_EXTRA.umm_json An acoustic hydrographic survey of South San Francisco Bay (South Bay) was conducted in 2005. Over 20 million soundings were collected within an area of approximately 250 sq km (97 sq mi) of the bay extending south of Coyote Point on the west shore, to the San Leandro marina on the east, including Coyote Creek and Ravenswood, Alviso, Artesian, and Mud Sloughs. This is the first survey of this scale that has been conducted in South Bay since the National Oceanic and Atmospheric Administration National Ocean Service (NOS) last surveyed the region in the early 1980s. Data from this survey will provide insight to changes in bay floor topography from the 1980s to 2005 and will also serve as essential baseline data for tracking changes that will occur as restoration of the South San Francisco Bay salt ponds progress. This report provides documentation on how the survey was conducted, an assessment of accuracy of the data, and distributes the sounding data with Federal Geographic Data Committee (FGDC) compliant metadata. Reports from NOS and Sea Surveyor, Inc., containing additional survey details are attached as appendices. [Summary provided by the USGS.] proprietary
+USGS_OFR_2007_1169 2005 Hydrographic Survey of South San Francisco Bay, California CEOS_EXTRA STAC Catalog 1970-01-01 -126, 37, -122, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231550095-CEOS_EXTRA.umm_json An acoustic hydrographic survey of South San Francisco Bay (South Bay) was conducted in 2005. Over 20 million soundings were collected within an area of approximately 250 sq km (97 sq mi) of the bay extending south of Coyote Point on the west shore, to the San Leandro marina on the east, including Coyote Creek and Ravenswood, Alviso, Artesian, and Mud Sloughs. This is the first survey of this scale that has been conducted in South Bay since the National Oceanic and Atmospheric Administration National Ocean Service (NOS) last surveyed the region in the early 1980s. Data from this survey will provide insight to changes in bay floor topography from the 1980s to 2005 and will also serve as essential baseline data for tracking changes that will occur as restoration of the South San Francisco Bay salt ponds progress. This report provides documentation on how the survey was conducted, an assessment of accuracy of the data, and distributes the sounding data with Federal Geographic Data Committee (FGDC) compliant metadata. Reports from NOS and Sea Surveyor, Inc., containing additional survey details are attached as appendices. [Summary provided by the USGS.] proprietary
USGS_OFR_2007_1190 Geophysical Data from Spring Valley to Delamar Valley, East-Central Nevada CEOS_EXTRA STAC Catalog 1970-01-01 -115, 37, -113, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2231549251-CEOS_EXTRA.umm_json Cenozoic basins in eastern Nevada and western Utah constitute major ground-water recharge areas in the eastern part of the Great Basin and these were investigated to characterize the geologic framework of the region. Prior to these investigations, regional gravity coverage was variable over the region, adequate in some areas and very sparse in others. Cooperative studies described herein have established 1,447 new gravity stations in the region, providing a detailed description of density variations in the middle to upper crust. All previously available gravity data for the study area were evaluated to determine their reliability, prior to combining with our recent results and calculating an up-to-date isostatic residual gravity map of the area. A gravity inversion method was used to calculate depths to pre-Cenozoic basement rock and estimates of maximum alluvial/volcanic fill in the major valleys of the study area. The enhanced gravity coverage and the incorporation of lithologic information from several deep oil and gas wells yields a much improved view of subsurface shapes of these basins and provides insights useful for the development of hydrogeologic models for the region. [Summary provided by the USGS.] proprietary
USGS_OFR_2007_1202 Geochemistry of Selected Coal Samples from Sumatra, Kalimantan, Sulawesi, and Papua, Indonesia CEOS_EXTRA STAC Catalog 1970-01-01 90, -20, 140, 20 https://cmr.earthdata.nasa.gov/search/concepts/C2231555267-CEOS_EXTRA.umm_json Indonesia is an archipelago of more than 17,000 islands that stretches astride the equator for about 5,200 km in southeast Asia (figure 1) and includes major Cenozoic volcano-plutonic arcs, active volcanoes, and various related onshore and offshore basins. These magmatic arcs have extensive Cu and Au mineralization that has generated much exploration and mining in the last 50 years. Although Au and Ag have been mined in Indonesia for over 1000 years (van Leeuwen, 1994), it was not until the middle of the nineteenth century that the Dutch explored and developed major Sn and minor Au, Ag, Ni, bauxite, and coal resources. The metallogeny of Indonesia includes Au-rich porphyry Cu, porphyry Mo, skarn Cu-Au, sedimentary-rock hosted Au, epithermal Au, laterite Ni, and diamond deposits. For example, the Grasberg deposit in Papua has the world's largest gold reserves and the third-largest copper reserves (Sillitoe, 1994). Coal mining in Indonesia also has had a long history beginning with the initial production in 1849 in the Mahakam coal field near Pengaron, East Kalimantan; in 1891 in the Ombilin area, Sumatra, (van Leeuwen, 1994); and in South Sumatra in 1919 at the Bukit Asam mine (Soehandojo, 1989). Total production from deposits in Sumatra and Kalimantan, from the 19thth century to World War II, amounted to 40 million metric tons (Mt). After World War II, production declined due to various factors including politics and a boom in the world-wide oil economy. Active exploration and increased mining began again in the 1980's mainly through a change in Indonesian government policy of collaboration with foreign companies and the global oil crises (Prijono, 1989). This recent coal revival (van Leeuwen, 1994) has lead Indonesia to become the largest exporter of thermal (steam) coal and the second largest combined thermal and metallurgical (coking) coal exporter in the world market (Fairhead and others, 2006). The exported coal is desirable as it is low sulfur and ash (generally <1 and < 10 wt.%, respectively). Coal mining for both local use and for export has a very strong future in Indonesia although, at present, there are concerns about the strong need for a major revision in mining laws and foreign investment policies (Wahju, 2004; United States Embassy Jakarta, 2004). The World Coal Quality Inventory (WoCQI) program of the U.S. Geological Survey (Tewalt and others, 2005) is a cooperative project with about 50 countries (out of 70 coal-producing countries world-wide). The WoCQI initiative has collected and published extensive coal quality data from the world's largest coal producers and consumers. The important aspects of the WoCQI program are; (1) samples from active mines are collected, (2) the data have a high degree of internal consistency with a broad array of coal quality parameters, and (3) the data are linked to GIS and available through the world-wide-web. The coal quality parameters include proximate and ultimate analysis, sulfur forms, major-, minor-, and trace-element concentrations and various technological tests. This report contains geochemical data from a selected group of Indonesian coal samples from a range of coal types, localities, and ages collected for the WoCQI program. [Summary provided by the USGS.] proprietary
USGS_OFR_2007_1208 Geophysical Characterization of Pre-Cenozoic Basement for Hydrocarbon Assessment, Yukon Flats, Alaska CEOS_EXTRA STAC Catalog 1970-01-01 -170, 52, -132, 79 https://cmr.earthdata.nasa.gov/search/concepts/C2231549660-CEOS_EXTRA.umm_json The Cenozoic basins of interior Alaska are poorly understood, but may host undiscovered hydrocarbon resources in sufficient quantities to serve remote villages and for possible export. Purported oil seeps and the regional occurrence of potential hydrocarbon source and reservoir rocks fuel an exploration interest in the 46,000 km2 Yukon Flats basin. Whether hydrocarbon source rocks are present in the pre-Cenozoic basement beneath Yukon Flats is difficult to determine because vegetation and surficial deposits obscure the bedrock geology, only limited seismic data are available, and no deep boreholes have been drilled. Analysis of regional potential field data (aeromagnetics and gravity) is valuable, therefore, for preliminary characterization of basement lithology and structure. We present our analysis as a red-green-blue composite spectral map consisting of: (1) reduced-to-the-pole magnetics (red), (2) magnetic potential (green), and (3) basement gravity (blue). The color and texture patterns on this composite map highlight domains with common geophysical characteristics and, by inference, lithology. The observed patterns yield the primary conclusion that much of the basin is underlain by Devonian to Jurassic oceanic rocks related to the Angayucham and Tozitna terranes (JDat). These rocks are part of a lithologically diverse assemblage of brittlely deformed, generally low-grade metamorphic rocks of oceanic affinity; such rocks probably have little or no potential for hydrocarbon generation. The JDat geophysical signature extends from the Tintina fault system northward to the Brooks Range. Along the eastern edge of the basin, JDat appears to overlie moderately dense and non-magnetic Proterozoic(?) and Paleozoic continental margin rocks. The western edge of the JDat in subsurface is difficult to distinguish due to the presence of magnetic granites similar to those exposed in the Ruby geanticline. In the southern portion of the basin, geophysical patterns indicate the possibility of overthrusting of Cenozoic sediments and underlying JDat by Paleozoic and Proterozoic rocks of the Schwatka sequence. These structural hypotheses provide the basis for an overthrust play within the Cenozoic section just south of the basin. [Summary provided by the USGS.] proprietary
@@ -16001,8 +16006,8 @@ USGS_OFR_Acid_Deposition Acid Deposition Sensitivity of the Southern Appalachian
USGS_OFR_aqbound_1.0 Digital boundaries of the Antlers aquifer in southeastern Oklahoma CEOS_EXTRA STAC Catalog 1992-01-01 1992-12-31 -97.4976, 33.7288, -94.4684, 34.3644 https://cmr.earthdata.nasa.gov/search/concepts/C2231550862-CEOS_EXTRA.umm_json This data set was created for a project to develop data sets to support ground-water vulnerability analysis. The objective was to create and document a digital geospatial data set from a published report or map, or existing digital geospatial data sets that could be used in ground-water vulnerability analysis. This data set consists of digitized aquifer boundaries of the Antlers aquifer in southeastern Oklahoma. The Early Cretaceous-age Antlers Sandstone is an important source of water in an area that underlies about 4,400-square miles of all or part of Atoka, Bryan, Carter, Choctaw, Johnston, Love, Marshall, McCurtain, and Pushmataha Counties. The Antlers aquifer consists of sand, clay, conglomerate, and limestone in the outcrop area. The upper part of the Antlers aquifer consists of beds of sand, poorly cemented sandstone, sandy shale, silt, and clay. The Antlers aquifer is unconfined where it outcrops in about an 1,800-square-mile area. The data set includes the outcrop area of the Antlers Sandstone in Oklahoma and areas where the Antlers is overlain by alluvial and terrace deposits and a few small thin outcrops of the Goodland Limestone. Most of the aquifer boundary lines were extracted from published digital geology data sets. Some of the lines were interpolated in areas where the Antlers aquifer is overlain by alluvial and terrace deposits near streams and rivers. The interpolated lines are very similar to the aquifer boundaries published in a ground-water modeling report for the Antlers aquifer. The maps from which this data set was derived were scanned or digitized from maps published at a scale of 1:250,000. This data set is one of four digital map data sets being published together for this aquifer. The four data sets are: aqbound - aquifer boundaries cond - hydraulic conductivity recharg - aquifer recharge wlelev - water-level elevation contours proprietary
USGS_P-11_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Coastal Province CEOS_EXTRA STAC Catalog 1990-12-01 1990-12-01 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231552077-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 11 (Central Coastal) are listed here by play number, type, and name: Number Type Name 1101 conventional Point Arena Oil 1102 conventional Point Reyes Oil 1103 conventional Pescadero Oil 1104 conventional La Honda Oil 1105 conventional Bitterwater Oil 1106 conventional Salinas Oil 1107 conventional Western Cuyama Basin 1109 conventional Cox Graben proprietary
USGS_P-11_cells 1995 National Oil and Gas Assessment 1/4-Mile Cells within the Central Coastal Province ALL STAC Catalog 1990-12-01 1990-12-01 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231552077-CEOS_EXTRA.umm_json The purpose of the cell map is to display the exploration maturity, type of production, and distribution of production in quarter-mile cells in each of the oil and gas plays and each of the provinces defined for the 1995 U.S. National Oil and Gas Assessment. Cell maps for each oil and gas play were created by the USGS as a method for illustrating the degree of exploration, type of production, and distribution of production in a play or province. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or dry. The well information was initially retrieved from the Petroleum Information (PI) Well History Control System (WHCS), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary WHCS data. No proprietary data are displayed or included in the cell maps. The data from WHCS were current as of December 1990 when the cell maps were created in 1994. Oil and gas plays within province 11 (Central Coastal) are listed here by play number, type, and name: Number Type Name 1101 conventional Point Arena Oil 1102 conventional Point Reyes Oil 1103 conventional Pescadero Oil 1104 conventional La Honda Oil 1105 conventional Bitterwater Oil 1106 conventional Salinas Oil 1107 conventional Western Cuyama Basin 1109 conventional Cox Graben proprietary
-USGS_P-11_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Coastal Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231551956-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 11 (Central Coastal) are listed here by play number and name: Number Name 1101 Point Arena Oil 1102 Point Reyes Oil 1103 Pescadero Oil 1104 La Honda Oil 1105 Bitterwater Oil 1106 Salinas Oil 1107 Western Cuyama Basin 1109 Cox Graben proprietary
USGS_P-11_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Coastal Province ALL STAC Catalog 1996-01-01 1996-12-31 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231551956-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 11 (Central Coastal) are listed here by play number and name: Number Name 1101 Point Arena Oil 1102 Point Reyes Oil 1103 Pescadero Oil 1104 La Honda Oil 1105 Bitterwater Oil 1106 Salinas Oil 1107 Western Cuyama Basin 1109 Cox Graben proprietary
+USGS_P-11_conventional 1995 National Oil and Gas Assessment Conventional Plays within the Central Coastal Province CEOS_EXTRA STAC Catalog 1996-01-01 1996-12-31 -123.80987, 34.66294, -118.997696, 39.082233 https://cmr.earthdata.nasa.gov/search/concepts/C2231551956-CEOS_EXTRA.umm_json The purpose of these files is to illustrate the geologic boundary of the play as defined for the 1995 U.S. National Assessment. The play was used as the fundamental assessment unit. The fundamental geologic unit used in the 1995 National Oil and Gas Assessment was the play, which is defined as a set of known or postulated oil and or gas accumulations sharing similar geologic, geographic, and temporal properties, such as source rock, migration pathways, timing, trapping mechanism, and hydrocarbon type. The geographic limit of each play was defined and mapped by the geologist responsible for each province. The play boundaries were defined geologically as the limits of the geologic elements that define the play, such as the limits of the reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are plays that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the play boundary. The play boundaries were defined in the period 1993-1994. Conventional oil and gas plays within province 11 (Central Coastal) are listed here by play number and name: Number Name 1101 Point Arena Oil 1102 Point Reyes Oil 1103 Pescadero Oil 1104 La Honda Oil 1105 Bitterwater Oil 1106 Salinas Oil 1107 Western Cuyama Basin 1109 Cox Graben proprietary
USGS_P1650-a_1.0 Atlas of Relations Between Climatic Parameters and Distributions of Important Trees and Shrubs in North America CEOS_EXTRA STAC Catalog 1970-01-01 -170, 20, -80, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552968-CEOS_EXTRA.umm_json This atlas explores the continental-scale relations between the geographic ranges of woody plant species and climate in North America. A 25-km equal-area grid of modern climatic and bioclimatic parameters was constructed from instrumental weather records. The geographic distributions of selected tree and shrub species were digitized, and the presence or absence of each species was determined for each cell on the 25-km grid, thus providing a basis for comparing climatic data and species' distributions. The relations between climate and plant distributions are explored in graphical and tabular form. The results of this effort are primarily intended for use in biogeographic, paleoclimatic, and global-change research. These web pages provide access to the text, digital representations of figures, and supplemental data files from USGS Professional Paper 1650, chapters A and B. A printed set of these volumes can be ordered from the USGS at a cost of US$63.00. To order, please call or write: USGS Information Services Box 25286 Denver Federal Center Denver, CO 80225 Tel: 303-202-4700; Fax: 303-202-4693 [Summary provided by the USGS.] proprietary
USGS_PA_DIGIT_1.0 Digital drainage basin boundaries of named streams in Pennsylvania CEOS_EXTRA STAC Catalog 1970-01-01 -76.4304, 39.7151, -74.6865, 42.0007 https://cmr.earthdata.nasa.gov/search/concepts/C2231548560-CEOS_EXTRA.umm_json "In 1989, the Pennsylvania Department of Environmental Resources (PaDER), in cooperation with the U.S. Geological Survey, Water Resources Division (USGS published the Pennsylvania (PA) Gazetteer of Streams. This publication contains information related to named streams in Pennsylvania. Drainage basin boundaries are delineated on the 7.5-minute series topographic paper quadrangle maps for PA and parts of the bordering states of New York, Maryland, Ohio, West Virginia, and Delaware. These boundaries enclose catchment areas for named streams officially recognized by the Board on Geographic Names and other unofficially named streams that flow through named hollows, using the hollow name, e.g. ""Smith Hollow"". This was done in an effort to name as many of the 64,000 streams as possible. In 1991, work began by USGS to put these drainage basin boundaries into digital form for use in a geographic information system (GIS). Digitizing started with USGS in Lemoyne, PA., but expanded with assistance by PaDER and the Natural Resource Conservation Service (NRCS), formerly the U.S Department of Agriculture, Soil Conservation Service (SCS). USGS performed all editing, attributing, and edgematching. There are 878, 7.5-minute quadrangle maps in PA. This documentation applies to only those maps in the Delaware River basin (164). Parts of the Delaware River drainage originate outside the PA border. At this time, no effort is being made by USGS to include those named stream basins. [Summary provided by the USGS.]" proprietary
USGS_PONTCHARTRAIN Geologic Framework and Processes of the Lake Pontchartrain Basin CEOS_EXTRA STAC Catalog 1970-01-01 -91, 29, -89, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2231549642-CEOS_EXTRA.umm_json Lake Pontchartrain and adjacent lakes in Louisiana form one of the larger estuaries in the Gulf Coast region. The estuary drains the Pontchartrain Basin (at right), an area of over 12,000 km2 situated on the eastern side of the Mississippi River delta plain. In Louisiana, nearly one-third of the State population lives within the 14 parishes of the basin. Over the past 60 years, rapid growth and development within the basin, along with natural processes, have resulted in significant environmental degradation and loss of critical habitat in and around Lake Pontchartrain. Human activities associated with pollutant discharge and surface drainage have greatly affected the water quality in the lake. This change is evident in the bottom sediments, which record the historic health of the lake. Also, land-altering activities such as logging, dredging, and flood control in and around the lake, lead to shoreline erosion and loss of wetlands.The effects of pollution, shoreline erosion and wetland loss on the lake and surrounding areas have become a major public concern. To better understand the basin's origin and the processes driving its development and degradation requires a wide-ranging study involving many organizations and personnel. When the U.S. Geological Survey began the study of Lake Pontchartrain in 1994, information on four topics was needed: -Geologic Framework, or how the various sedimentary layers that make up the basin are put together -Sediment Characterization, that is, what are the sediments made of, where did they come from, and what kinds of pollutants do they contain -Shoreline and Wetland Change over time -what are the processes that control Water Circulation [Summary provided by the USGS.] proprietary
@@ -16015,14 +16020,14 @@ USGS_SESC_ExtinctFish Extinct North American Freshwater Fishes CEOS_EXTRA STAC C
USGS_SESC_ImperiledFish American Fisheries Society Imperiled Freshwater and Diadromous Fishes of North America CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551557-CEOS_EXTRA.umm_json About: This website presents the 2008 American Fisheries Society Endangered Species Committee list of imperiled North American freshwater and diadromous fishes. The committee considered continental fishes native to Canada, Mexico, and the United States, evaluated their conservation status and determined the major threats impacting these taxa. We use the terms taxon (singular) or taxa (plural) to include named species, named subspecies, undescribed forms, and distinct populations as characterized by unique morphological, genetic, ecological, or other attributes warranting taxonomic recognition. Undescribed taxa are included, based on the above diagnostic criteria in combination with known geographic distributions and documentation deemed of scientific merit, as evidenced from publication in peer-reviewed literature, conference abstracts, unpublished theses or dissertations, or information provided by recognized taxonomic experts. Although we did not independently evaluate the taxonomic validity of undescribed taxa, the committee adopted a conservative approach to recognize them on the basis of prevailing evidence which suggests that these forms are sufficiently distinct to warrant conservation and management actions. Summary: This is the third compilation of imperiled (i.e., endangered, threatened, vulnerable) plus extinct freshwater and diadromous fishes of North America prepared by the American Fisheries Society's Endangered Species Committee. Since the last revision in 1989, imperilment of inland fishes has increased substantially. This list includes 700 extant taxa representing 133 genera and 36 families, a 92% increase over the 364 listed in 1989. The increase reflects the addition of distinct populations, previously non-imperiled fishes, and recently described or discovered taxa. Approximately 39% of described fish species of the continent are imperiled. There are 230 vulnerable, 190 threatened, and 280 endangered extant taxa; 61 taxa are presumed extinct or extirpated from nature. Of those that were imperiled in 1989, most (89%) are the same or worse in conservation status; only 6% have improved in status, and 5% were delisted for various reasons. Habitat degradation and nonindigenous species are the main threats to at-risk fishes, many of which are restricted to small ranges. Documenting the diversity and status of rare fishes is a critical step in identifying and implementing appropriate actions necessary for their protection and management. Maps: In collaboration with the World Wildlife Fund, the committee developed a map of freshwater ecoregions that combines spatial and faunistic information derived from Maxwell and others (1995), Abell and others (2000; 2008), U.S. Geological Survey Hydrologic Unit Code maps (Watermolen 2002), Atlas of Canada (2003), and Commission for Environmental Cooperation (2007). Eighty ecoregions were identified based on physiography and faunal assemblages of the Atlantic, Arctic, and Pacific basins. Each taxon on the list was assigned to one or more ecoregions that circumscribes its native distribution. A variety of sources were used to obtain distributional information, most notably Lee and others (1980), Hocutt and Wiley (1986), Page and Burr (1991), Behnke (2002), Miller and others (2005), numerous state and provincial fish books for the United States and Canada, and the primary literature, including original taxonomic descriptions. Taxa were also associated with the states or provinces where they naturally occur or occurred in the past. proprietary
USGS_SESC_ImperiledFreshwaterOrganisms Imperiled Freshwater Organisms of North America CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231549663-CEOS_EXTRA.umm_json This website provides access to maps and lists of imperiled freshwater organisms of North America as determined by the American Fisheries Society (AFS) Endangered Species Committee (ESC). At this website, one can view lists of animals by freshwater ecoregion, by state or province boundary, and plot distributions of these same creatures by ecoregions or political boundaries. Both the AFS and U.S. Geological Survey (USGS) have a long standing commitment to the advancement of aquatic sciences and sharing that information with the public. Since 1972, the ESC has been tracking the status of imperiled fishes and aquatic invertebrates in North America. Recently, the fish (2008) and crayfish (2007) subcommittees provided revised status lists of at-risk taxa, and the mussel and snail subcommittees are in the process of completing similar revisions. Historically, the revised AFS lists of imperiled fauna have been published in Fisheries. With rapid advances in technology and information transfer, there is a growing need to provide to stakeholders immediate and dynamic data on imperiled resources. The USGS is a leader in aquatic resource research that effectively disseminates results from those studies to the public through print and internet media. A Memorandum Of Understanding formally establishes an agreement between the AFS and USGS to create this website that will serve as a conduit for information exchange about imperiled aquatic organisms of North America. proprietary
USGS_SESC_SnailStatus American Fisheries Society List of Freshwater Snails from Canada and the United States CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551686-CEOS_EXTRA.umm_json About: This website presents the 2013 American Fisheries Society Endangered Species Committee list of freshwater snails (Gastropods) of United States and Canada. The committee evaluated the conservation status and determined the major threats impacting these taxa. Summary: This is the first conservation status review for freshwater snails (gastropods) of Canada and the United States by the American Fisheries Society's Endangered Species Freshwater Gastropod Subcommittee. The goals of this contribution are to provide: 1) a current and comprehensive taxonomic authority list for all native freshwater gastropods of Canada and the United States, 2) provincial and state distributions as presently understood, 3) a conservation assessment, and, 4) references on their biology, distribution and conservation. Freshwater gastropods occupy every type of aquatic habitat ranging from subterranean aquifers to brawling montane headwater creeks. Gastropods are ubiquitous invertebrates and frequently dominate aquatic invertebrate biomass. Of the 703 gastropods documented by Johnson et al. (2013), 74% are imperiled or extinct (278 endangered, 102 threatened, 73 vulnerable, and 67 are considered extinct); only 157 species are considered stable. Map queries display species distributions in provinces and states in which they are believed to occur or occurred in the past, but considerable fieldwork is required to determine exact geographic limits of species. We hope this list stimulates a surge in the study of freshwater gastropods. Supporting Literature: Supporting literature for the North American freshwater gastropods assessment are organized alphabetically by state and province, followed by national, regional, and other general references. This literature compilation is not comprehensive, but offers considerable information for individuals interested in freshwater snails. Recovery Examples: Although the gastropod fauna of Canada and the United States is beleaguered by multiple forms of habitat loss, the fauna is resilient and capable of remarkable recovery when suitable habitat is available. Three examples of recovery demonstrate the inherent reviving potential of freshwater gastropods. Images of the incredible diversity of freshwater snails are presented in plates and photo gallery. Maps: Each species on the list was assigned to one or more states or provinces that circumscribe its native distribution. Mapped distributions indicate where taxa naturally occur or occurred in the past. Resources used to obtain distributional information include state and regional publications. proprietary
-USGS_SESC_SturgeonBiblio_3 A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi. CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552134-CEOS_EXTRA.umm_json "This functional bibliography is meant to be a complete and comprehensive bibliography of all discoverable reports containing information on the Gulf Sturgeon (GS). This bibliography contains all known reports presenting, documenting, summarizing, listing, or interpreting information on the GS through 31 December 2013. Report citations are organized into four sections. Section I includes published scientific journal articles, books, dissertations and theses, published and unpublished technical reports, published harvest prohibitions, and online articles reporting substantive scientific information. Section II includes newspaper, newsletter, magazine, book, agency news releases, and online articles reporting on GS occurrences, mortalities, captures, jumping, boat collisions, aquaculture, historical photographs, and other largely non-scientific or anecdotal issues. Section III consists of books, theses, ecotour-guides, media articles, editorials, and blogs reporting a mix of anecdotal information, historical information, and opinion on GS conservation, habitat issues, exploitation, aquaculture, and human interaction - but presenting very limited or no substantive scientific information. Section IV includes videos, films and audio recordings documenting GS life history and behavior. Each reference includes a bibliographic citation, as well as a brief annotation of key topics in brackets, where possible. The names of journals, theses, dissertations, and books are given in bold within each citation, and relevant page numbers are noted in parentheses at the end of citations, where applicable. Newspaper and magazine article titles are placed within parentheses. Key topic annotations are inserted in bracketed italics on a separate line. If the reference reports GS information under a different common or scientific name (e.g., Atlantic Sturgeon, Common Sturgeon, Sturgeon, Sea Sturgeon, Acipenser oxyrinchus oxyrinchus, Acipenser oxyrhynchus or Acipenser sturio), a notation to that effect is given within the key words annotation line, e.g., [Reported as ""Atlantic sturgeon""]. A small number of reports could not be obtained. These include historical reports from newspapers and magazines long out of circulation. In these limited cases, titles are still provided to substantiate their existence. Other reports that are no longer readily available, but which have been obtained during preparation of this bibliography, have been archived in hardcopy and/or as scanned pdf files at USGS, SESC. Copies of such hard to obtain reports, if non-copyrighted, may be available upon request from USGS corresponding author, via email: mrandall@usgs.gov." proprietary
USGS_SESC_SturgeonBiblio_3 A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552134-CEOS_EXTRA.umm_json "This functional bibliography is meant to be a complete and comprehensive bibliography of all discoverable reports containing information on the Gulf Sturgeon (GS). This bibliography contains all known reports presenting, documenting, summarizing, listing, or interpreting information on the GS through 31 December 2013. Report citations are organized into four sections. Section I includes published scientific journal articles, books, dissertations and theses, published and unpublished technical reports, published harvest prohibitions, and online articles reporting substantive scientific information. Section II includes newspaper, newsletter, magazine, book, agency news releases, and online articles reporting on GS occurrences, mortalities, captures, jumping, boat collisions, aquaculture, historical photographs, and other largely non-scientific or anecdotal issues. Section III consists of books, theses, ecotour-guides, media articles, editorials, and blogs reporting a mix of anecdotal information, historical information, and opinion on GS conservation, habitat issues, exploitation, aquaculture, and human interaction - but presenting very limited or no substantive scientific information. Section IV includes videos, films and audio recordings documenting GS life history and behavior. Each reference includes a bibliographic citation, as well as a brief annotation of key topics in brackets, where possible. The names of journals, theses, dissertations, and books are given in bold within each citation, and relevant page numbers are noted in parentheses at the end of citations, where applicable. Newspaper and magazine article titles are placed within parentheses. Key topic annotations are inserted in bracketed italics on a separate line. If the reference reports GS information under a different common or scientific name (e.g., Atlantic Sturgeon, Common Sturgeon, Sturgeon, Sea Sturgeon, Acipenser oxyrinchus oxyrinchus, Acipenser oxyrhynchus or Acipenser sturio), a notation to that effect is given within the key words annotation line, e.g., [Reported as ""Atlantic sturgeon""]. A small number of reports could not be obtained. These include historical reports from newspapers and magazines long out of circulation. In these limited cases, titles are still provided to substantiate their existence. Other reports that are no longer readily available, but which have been obtained during preparation of this bibliography, have been archived in hardcopy and/or as scanned pdf files at USGS, SESC. Copies of such hard to obtain reports, if non-copyrighted, may be available upon request from USGS corresponding author, via email: mrandall@usgs.gov." proprietary
+USGS_SESC_SturgeonBiblio_3 A bibliography of all known publications & reports on the Gulf Sturgeon, Acipenser oxyrinchus desotoi. CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231552134-CEOS_EXTRA.umm_json "This functional bibliography is meant to be a complete and comprehensive bibliography of all discoverable reports containing information on the Gulf Sturgeon (GS). This bibliography contains all known reports presenting, documenting, summarizing, listing, or interpreting information on the GS through 31 December 2013. Report citations are organized into four sections. Section I includes published scientific journal articles, books, dissertations and theses, published and unpublished technical reports, published harvest prohibitions, and online articles reporting substantive scientific information. Section II includes newspaper, newsletter, magazine, book, agency news releases, and online articles reporting on GS occurrences, mortalities, captures, jumping, boat collisions, aquaculture, historical photographs, and other largely non-scientific or anecdotal issues. Section III consists of books, theses, ecotour-guides, media articles, editorials, and blogs reporting a mix of anecdotal information, historical information, and opinion on GS conservation, habitat issues, exploitation, aquaculture, and human interaction - but presenting very limited or no substantive scientific information. Section IV includes videos, films and audio recordings documenting GS life history and behavior. Each reference includes a bibliographic citation, as well as a brief annotation of key topics in brackets, where possible. The names of journals, theses, dissertations, and books are given in bold within each citation, and relevant page numbers are noted in parentheses at the end of citations, where applicable. Newspaper and magazine article titles are placed within parentheses. Key topic annotations are inserted in bracketed italics on a separate line. If the reference reports GS information under a different common or scientific name (e.g., Atlantic Sturgeon, Common Sturgeon, Sturgeon, Sea Sturgeon, Acipenser oxyrinchus oxyrinchus, Acipenser oxyrhynchus or Acipenser sturio), a notation to that effect is given within the key words annotation line, e.g., [Reported as ""Atlantic sturgeon""]. A small number of reports could not be obtained. These include historical reports from newspapers and magazines long out of circulation. In these limited cases, titles are still provided to substantiate their existence. Other reports that are no longer readily available, but which have been obtained during preparation of this bibliography, have been archived in hardcopy and/or as scanned pdf files at USGS, SESC. Copies of such hard to obtain reports, if non-copyrighted, may be available upon request from USGS corresponding author, via email: mrandall@usgs.gov." proprietary
USGS_SIR-5079_MSRiverFloodMaps Development of flood-inundation maps for the Mississippi River in Saint Paul, Minnesota CEOS_EXTRA STAC Catalog 1970-01-01 -93.15028, 44.90479, -92.999855, 44.97016 https://cmr.earthdata.nasa.gov/search/concepts/C2231549022-CEOS_EXTRA.umm_json Digital flood-inundation maps for a 6.3-mile reach of the Mississippi River in Saint Paul, Minnesota, were developed through a multi-agency effort by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers and in collaboration with the National Weather Service. The inundation maps, which can be accessed through the U.S. Geological Survey Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/ and the National Weather Service Advanced Hydrologic Prediction Service site at http://water.weather.gov/ahps/inundation.php , depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the U.S. Geological Survey streamgage at the Mississippi River at Saint Paul (05331000). The National Weather Service forecasted peak-stage information at the streamgage may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, flood profiles were computed for the Mississippi River by means of a one-dimensional step-backwater model. The hydraulic model was calibrated using the most recent stage-discharge relation at the Robert Street location (rating curve number 38.0) of the Mississippi River at Saint Paul (streamgage 05331000), as well as an approximate water-surface elevation-discharge relation at the Mississippi River at South Saint Paul (U.S. Army Corps of Engineers streamgage SSPM5). The model also was verified against observed high-water marks from the recent 2011 flood event and the water-surface profile from existing flood insurance studies. The hydraulic model was then used to determine 25 water-surface profiles for flood stages at 1-foot intervals ranging from approximately bankfull stage to greater than the highest recorded stage at streamgage 05331000. The simulated water-surface profiles were then combined with a geographic information system digital elevation model, derived from high-resolution topography data, to delineate potential areas flooded and to determine the water depths within the inundated areas for each stage at streamgage 05331000. The availability of these maps along with information regarding current stage at the U.S. Geological Survey streamgage and forecasted stages from the National Weather Service provides enhanced flood warning and visualization of the potential effects of a forecasted flood for the city of Saint Paul and its residents. The maps also can aid in emergency management planning and response activities, such as evacuations and road closures, as well as for post-flood recovery efforts. proprietary
USGS_SOFIA_75_29_flows Baseline hydrologic data collection along the I-75 - State Road 29 corridor in the Big Cypress National Preserve CEOS_EXTRA STAC Catalog 2005-11-01 2009-09-30 -81.325, 25.75, -80.75, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231549536-CEOS_EXTRA.umm_json The objectives of this study are to develop and continue a program of surface water flow monitoring across I-75 and SR 29 in the I-75 corridor from Snake Road west to SR 29 and SR 29 from I-75 south to USGS site 02291000 Barron River near Everglades, Florida. Quarterly discharge measurements will be made along both reaches to assess hydrologic flow patterns and evaluate the feasibility of creating a stage-discharge/index-velocity relationship for this area. Data collected in this project will provide baseline information about a major current barrier to sheetflow, I-75. The data are expected to support the research on the existing linkages among geologic, hydrologic, chemical, climatological, and biological processes that currently shape the Everglades and will provide insight into the predrainage Everglades. The baseline flow will contribute to the Southwest Florida Feasibility Study addressing the health of upland and aquatic ecosystems in the 4,300 square mile area. proprietary
USGS_SOFIA_75_29_hydro_data Hydrologic Data Collected along I-75/SR29 corridor in Big Cypress National Preserve CEOS_EXTRA STAC Catalog 2005-11-01 2009-09-30 -81.325, 25.75, -80.75, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231549847-CEOS_EXTRA.umm_json The location of each site is shown on a Google Map. Data are available as a Google Map with links to Station Information and Data for each site. Data are available for 58 sites along I-75 and for 28 sites along State Road 29 in Big Cypress National Preserve. Data collected in this project will provide baseline information about a major current barrier to sheetflow, I-75. The data are expected to support the research on the existing linkages among geologic, hydrologic, chemical, climatological, and biological processes that currently shape the Everglades and will provide insight into the predrainage Everglades. The baseline flow will contribute to the Southwest Florida Feasibility Study addressing the health of upland and aquatic ecosystems in the 4,300 square mile area. proprietary
USGS_SOFIA_ACME_DB Aquatic Cycling of Mercury in the Everglades Project Database CEOS_EXTRA STAC Catalog 1995-01-01 2008-09-01 -80.1, 25, -81.6, 27.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231554301-CEOS_EXTRA.umm_json Between 1995 and 2008, the Aquatic Mercury Cycling in the Everglades (ACME) project examined in detail the biogeochemical parameters that influence methylmercury (MeHg) production in the Florida Everglades. The interdisciplinary ACME team studied Hg cycling in the Everglades through a process-based, biogeochemical lens (Hurley et al. 1998). In the Everglades, as in most other ecosystems, inorganic mercury is transformed into methylmercury primarily by the action of anaerobic bacteria in surficial sediments and soils. The ACME project has been a collaborative research effort designed to understand the biogeochemical drivers of mercury cycling in the Greater Florida Everglades. The project is led be a team of scientists from the USGS and the Smithsonian Institution, with additional collaborators from the University of Wisconsin, Texas A&M, the SFWMD and FL DEP. ACME�s main objective has been to define the key processes that control the fate and transport of Hg in the Everglades. The study has used a process-oriented, multi-disciplinary approach, focusing on a suite of intensively-studied sites across the trophic gradient of the Water Conservation Areas and Everglades National Park. Since 1995, a core set of sites has been examined in detail through time, including changes in season and in hydrology. The biogeochemical parameters examined focus on those that impact net methylmercury (MeHg) production, and include sulfur, carbon and nutrient biogeochemistry. The study examined Hg and MeHg concentrations, and associated biogeochemical parameters in surface waters, soils, periphyton, emergent plants and biota. The core study sites have been supplemented with survey data across many additional sites in the Greater Everglades Ecosystem. The field study was also supplemented with experimental studies of Hg complexation, photochemistry, and bioavailability. The ACME project has been funded by a variety of agencies including the USGS, NSF, EPA, SFWMD and FL DEP. proprietary
-USGS_SOFIA_ASR_04 A retrospective and critical review of aquifer and storage (ASR) sites and conceptual framework of the Upper Floridian aquifer in south Florida CEOS_EXTRA STAC Catalog 1999-10-01 2004-09-30 -82.55795, 24.441917, -79.84407, 27.586416 https://cmr.earthdata.nasa.gov/search/concepts/C2231549469-CEOS_EXTRA.umm_json The objectives of this study are to: (1) inventory and assess the strengths and weaknesses of available hydrogeologic, hydraulic, hydrochemical, well construction, and cycle test information at existing ASR sites, (2) conduct a critical review of the hydrogeology on a site-by-site basis and relate to existing regional hydrogeology frameworks, allowing for the delineation of hydrogeologic factors that may be important to recovery efficiency, (3) identify hydrogeologic, design, and management factors which locally or regionally constrain the efficient storage and recovery of fresh water within the Upper Floridan aquifer, and (4) conduct a comparative analysis of the performance of all ASR sites having adequate data. This five-year study is divided into two phases, the first of which was two years long. The first phase laid the groundwork for data inventory, review, and analysis, and the second phase will allow for collection of additional data as it becomes available, expand the hydrogeologic framework, and perform a more complete comparative analysis of ASR sites. The study is in the second phase. Aquifer storage and recovery (ASR) has been described as 'the storage of water in a suitable aquifer through a well during times when water is available, and recovery of the water from the same well during times when it is needed'. Water can be stored in aquifers with poor water quality. ASR in south Florida is proposed in the Comprehensive Everglades Restoration Plan (CERP) as a cost-effective water-supply alternative that can help meet needs of agricultural, municipal, and recreational users while providing the water critical for Everglades ecosystem restoration. In CERP, plans have been made to utilize ASR in the Floridan aquifer system on an unprecedented scale. Precedence for ASR in southern Florida has been set with wells having been constructed at over 30 sites, mostly by local municipalities or counties in coastal areas. The Upper Floridan aquifer, the aquifer used at most of these sites, is brackish to saline in south Florida, which can have a large impact on the recovery of the fresh or potable water recharged and stored. Few regional investigations of the Floridan aquifer system hydrogeology in south Florida have been conducted, and the focus of those studies was not on ASR. Lacking a regional ASR framework to aid the decision-making process, ASR well sites in south Florida have been primarily located based on factors such as land availability, source-water quality, and source-water proximity (preexisting surface-water bodies, surficial aquifer system well fields, or water treatment plants). Little effort has been made to link information collected from each site as part of a regional hydrogeologic analysis. Results of this study should help the managers of the CERP program in locating, designing, constructing, and cycle testing ASR wells. These results should help establish a standard cycle testing protocol that can be used to measure the performance of individual CERP wells or clusters of wells. proprietary
USGS_SOFIA_ASR_04 A retrospective and critical review of aquifer and storage (ASR) sites and conceptual framework of the Upper Floridian aquifer in south Florida ALL STAC Catalog 1999-10-01 2004-09-30 -82.55795, 24.441917, -79.84407, 27.586416 https://cmr.earthdata.nasa.gov/search/concepts/C2231549469-CEOS_EXTRA.umm_json The objectives of this study are to: (1) inventory and assess the strengths and weaknesses of available hydrogeologic, hydraulic, hydrochemical, well construction, and cycle test information at existing ASR sites, (2) conduct a critical review of the hydrogeology on a site-by-site basis and relate to existing regional hydrogeology frameworks, allowing for the delineation of hydrogeologic factors that may be important to recovery efficiency, (3) identify hydrogeologic, design, and management factors which locally or regionally constrain the efficient storage and recovery of fresh water within the Upper Floridan aquifer, and (4) conduct a comparative analysis of the performance of all ASR sites having adequate data. This five-year study is divided into two phases, the first of which was two years long. The first phase laid the groundwork for data inventory, review, and analysis, and the second phase will allow for collection of additional data as it becomes available, expand the hydrogeologic framework, and perform a more complete comparative analysis of ASR sites. The study is in the second phase. Aquifer storage and recovery (ASR) has been described as 'the storage of water in a suitable aquifer through a well during times when water is available, and recovery of the water from the same well during times when it is needed'. Water can be stored in aquifers with poor water quality. ASR in south Florida is proposed in the Comprehensive Everglades Restoration Plan (CERP) as a cost-effective water-supply alternative that can help meet needs of agricultural, municipal, and recreational users while providing the water critical for Everglades ecosystem restoration. In CERP, plans have been made to utilize ASR in the Floridan aquifer system on an unprecedented scale. Precedence for ASR in southern Florida has been set with wells having been constructed at over 30 sites, mostly by local municipalities or counties in coastal areas. The Upper Floridan aquifer, the aquifer used at most of these sites, is brackish to saline in south Florida, which can have a large impact on the recovery of the fresh or potable water recharged and stored. Few regional investigations of the Floridan aquifer system hydrogeology in south Florida have been conducted, and the focus of those studies was not on ASR. Lacking a regional ASR framework to aid the decision-making process, ASR well sites in south Florida have been primarily located based on factors such as land availability, source-water quality, and source-water proximity (preexisting surface-water bodies, surficial aquifer system well fields, or water treatment plants). Little effort has been made to link information collected from each site as part of a regional hydrogeologic analysis. Results of this study should help the managers of the CERP program in locating, designing, constructing, and cycle testing ASR wells. These results should help establish a standard cycle testing protocol that can be used to measure the performance of individual CERP wells or clusters of wells. proprietary
+USGS_SOFIA_ASR_04 A retrospective and critical review of aquifer and storage (ASR) sites and conceptual framework of the Upper Floridian aquifer in south Florida CEOS_EXTRA STAC Catalog 1999-10-01 2004-09-30 -82.55795, 24.441917, -79.84407, 27.586416 https://cmr.earthdata.nasa.gov/search/concepts/C2231549469-CEOS_EXTRA.umm_json The objectives of this study are to: (1) inventory and assess the strengths and weaknesses of available hydrogeologic, hydraulic, hydrochemical, well construction, and cycle test information at existing ASR sites, (2) conduct a critical review of the hydrogeology on a site-by-site basis and relate to existing regional hydrogeology frameworks, allowing for the delineation of hydrogeologic factors that may be important to recovery efficiency, (3) identify hydrogeologic, design, and management factors which locally or regionally constrain the efficient storage and recovery of fresh water within the Upper Floridan aquifer, and (4) conduct a comparative analysis of the performance of all ASR sites having adequate data. This five-year study is divided into two phases, the first of which was two years long. The first phase laid the groundwork for data inventory, review, and analysis, and the second phase will allow for collection of additional data as it becomes available, expand the hydrogeologic framework, and perform a more complete comparative analysis of ASR sites. The study is in the second phase. Aquifer storage and recovery (ASR) has been described as 'the storage of water in a suitable aquifer through a well during times when water is available, and recovery of the water from the same well during times when it is needed'. Water can be stored in aquifers with poor water quality. ASR in south Florida is proposed in the Comprehensive Everglades Restoration Plan (CERP) as a cost-effective water-supply alternative that can help meet needs of agricultural, municipal, and recreational users while providing the water critical for Everglades ecosystem restoration. In CERP, plans have been made to utilize ASR in the Floridan aquifer system on an unprecedented scale. Precedence for ASR in southern Florida has been set with wells having been constructed at over 30 sites, mostly by local municipalities or counties in coastal areas. The Upper Floridan aquifer, the aquifer used at most of these sites, is brackish to saline in south Florida, which can have a large impact on the recovery of the fresh or potable water recharged and stored. Few regional investigations of the Floridan aquifer system hydrogeology in south Florida have been conducted, and the focus of those studies was not on ASR. Lacking a regional ASR framework to aid the decision-making process, ASR well sites in south Florida have been primarily located based on factors such as land availability, source-water quality, and source-water proximity (preexisting surface-water bodies, surficial aquifer system well fields, or water treatment plants). Little effort has been made to link information collected from each site as part of a regional hydrogeologic analysis. Results of this study should help the managers of the CERP program in locating, designing, constructing, and cycle testing ASR wells. These results should help establish a standard cycle testing protocol that can be used to measure the performance of individual CERP wells or clusters of wells. proprietary
USGS_SOFIA_ASR_coordination Aquifer Storage and Recovery (ASR) Coordination CEOS_EXTRA STAC Catalog 2002-01-01 2004-12-31 -82.5, 25, -80, 27.5 https://cmr.earthdata.nasa.gov/search/concepts/C2231553754-CEOS_EXTRA.umm_json ABSTRACT: The Comprehensive Everglades Restoration Plan (CERP) relies heavily on Aquifer Storage and Recovery (ASR) technology. The CERP includes approximately 333 ASR wells in South Florida with a total capacity of over 1.6 billion gallons per day. Much of the 'new water' in the CERP is derived from storing excess water that was previously discharged to the ocean. However, this new water would not be very useful unless there is a place to store it for use during dry periods. ASR is included in the CERP as one mechanism to provide this storage. Despite construction of some ASR facilities by local utilities, there remains a considerable number of significant technical and engineering-related uncertainties. Key Findings: 1) An analysis was conducted to describe and interpret the lithology of a part of the Upper Floridan aquifer penetrated by the Regional Observation Monitoring Program (ROMP) 29A test corehole in Highlands County, Florida. Information obtained was integrated into a conceptual model that delineates likely CERP ASR storage zones and confining units in the context of sequence stratigraphy. Carbonate sequence stratigraphy correlation strategies appear to reduce risk of miscorrelation of key ground-water flow units and confining units. 2) A hierarchical arrangement of rock unit cycles can be identified; High Frequency Cycle formed of peritidal, subtidal, and deeper subtidal) form High Frequency Sequence, and those can be grouped into Cycle Sequences. There appears to be a spatial relation among wells that penetrate water-bearing rocks having relatively high and low transmissivities. 3) Assuming hydrogeologic conditions observed in the ROMP 29A well are representative of in south-central Florida, the uppermost (Lower Hawthorn-Suwannee) of two likely CERP ASR storage zones does not appear to be viable with respect to the proposed 200 CERP ASR facility planned to be sited northwest of Lake Okeechobee. Insufficient data were available to adequately characterize the lower flow zone contained within the Avon Park Formation. proprietary
USGS_SOFIA_BigCypress_PineIsland_SatMap Big Cypress-Pine Island Satellite Image Map CEOS_EXTRA STAC Catalog 2000-01-27 -82.27, 25.78, -81.13, 26.7 https://cmr.earthdata.nasa.gov/search/concepts/C2231549800-CEOS_EXTRA.umm_json ABSTRACT: The map is a composite image of spectral bands 3 (630-690 nanometers, red), 4 (775-900 nanometers, near-infrared), and 5 (1,550-1750 nanometers, middle-infrared) and the new panchromatic band (520-900, green to near-infrared) acquired by the Landsat 7 enhanced thematic mapper (ETM) sensor on January 27, 2000. proprietary
USGS_SOFIA_Caloos_Franklin_Locks_flow Flow Monitoring Along the Tidal Caloosahatchee River and Tributaries West of Franklin Locks CEOS_EXTRA STAC Catalog 2007-01-01 2011-12-31 -82.04, 26.4, -81.6, 26.8 https://cmr.earthdata.nasa.gov/search/concepts/C2231552489-CEOS_EXTRA.umm_json Monitoring stations established thru this project are designed as part of a larger network needed for the Caloosahatchee River and tributaries that should remain in place long-term (~10 years). Data from monitoring stations included in this project will be evaluated during the third year of data collection in order to assess viability and need for changes . The objective of this study is to quantify freshwater flows into the tidal reach of the Caloosahatchee River, west of Franklin Locks. proprietary
@@ -16166,8 +16171,8 @@ USGS_SOFIA_integrating_manatee Effects of hydrological restoration on manatees:
USGS_SOFIA_karst_model Linking a conceptual karst hydrogeologic model of the Biscayne aquifer to ground-water flow simulations from Everglades National Park to Biscayne National Park - Phase 1 CEOS_EXTRA STAC Catalog 2005-01-01 2009-12-31 -81.5, 25, -80, 26 https://cmr.earthdata.nasa.gov/search/concepts/C2231550454-CEOS_EXTRA.umm_json This project in being undertaken to develop a high-resolution 3-dimensional karst hydrogeologic framework of the Biscayne aquifer between Everglades National Park (ENP) and Biscayne National Park (BNP) using test coreholes, borehole geophysical logging, cyclostratigraphy, hydrostratigraphy, and hydrologic modeling. The development of an expanded conceptual karst hydrogeologic framework in this project will be used to assist development of procedures for numeric simulations to improve the monitoring and assessment of the response of the ground-water system to hydrologic changes caused by CERP-related changes in stage within the Everglades wetlands, including seepage-management pilot project implementation. Specifically, the development of procedures for ground-water modeling of the karst Biscayne aquifer in the area of Northern Shark Slough will help determine the appropriate hydrologic response to rainfall and translate that information into appropriate performance targets for input into the design and operating rules to manage water levels and flow volumes for the two Seepage Management Areas. Mapping of the karstic stratiform ground-water flow passageways in the Biscayne aquifer is recent and limited to a small area of Miami-Dade County adjacent to the Everglades wetlands. Extension of this karst framework between the Everglades wetlands and coastal Biscayne Bay will aid in the simulation of coupled ground-water and surface-water flows to Biscayne Bay. The development of procedures for modeling in the karst Biscayne aquifer will useful to the establishment of minimum flows and levels to the Biscayne Bay and seasonal flow patterns. Also, these improved procedures for simulations will assist in ecologic modeling efforts of Biscayne Bay coastal estuaries. Research is needed to determine how planned Comprehensive Everglades Restoration Plan (CERP) seepage control actions within the triple-porosity karstic Biscayne aquifer in the general area of Northeast Shark Slough will affect ground-water flows and recharge between the Everglades wetlands and Biscayne Bay. A fundamental problem in the simulation of karst ground-water flow and solute transport is how best to represent aquifer heterogeneity as defined by the spatial distribution of porosity, permeability, and storage. The triple porosity of the Biscayne aquifer is principally: (1) matrix of interparticle and separate-vug porosity, providing much of the storage and, under dynamic conditions, diffuse-carbonate flow; (2) touching-vug porosity creating stratiform ground-water flow passageways; and (3) less common conduit porosity composed mainly of bedding plane vugs, thin solution pipes, and cavernous vugs. The objectives of this project are to: (1) build on the Lake Belt area hydrogeologic framework (recently completed by the principal investigator), mainly using cyclostratigraphy and digital optical borehole images to map porosity types and develop the triple-porosity karst framework between the Everglades wetlands and Biscayne Bay; and (2) develop procedures for numerical simulation of ground-water flow within the Biscayne aquifer multi-porosity system. proprietary
USGS_SOFIA_kendall_stable_isotopes Application of Stable Isotope Techniques to Identifying Foodweb Structure, Contaminant Sources, and Biogeochemical Reactions in the Everglades CEOS_EXTRA STAC Catalog 1995-03-01 1999-10-31 -81.0202, 25.2475, -80.3069, 26.6712 https://cmr.earthdata.nasa.gov/search/concepts/C2231553952-CEOS_EXTRA.umm_json "This is the largest isotope foodweb study ever attempted in a marsh ecosystem, and combines detailed, long-term, trophic and biogeochemical studies at selected well-monitored USGS/SFWMD/FGFFC sites with limited synoptic foodweb data from over 300 sites sampled during 1996 and 1999 by a collaboration with the EPA-REMAP program. The preliminary synthesis of the biota isotopes at USGS and 1996 REMAP sites provides a mechanism for extrapolating the detailed foodwebs developed at the intensive USGS sites to the entire marsh system sampled by REMAP. Furthermore, this unique study strongly suggests that biota isotopes provide a simple means for monitoring how future ecosystem changes affect the role of periphyton (vs. macrophyte-dominated detritus) in local foodchains, and for predictive models for foodweb structure and MeHg bioaccumulation under different proposed land-management changes. Data are available for the following sites: Cell 4, ENR-OUT, L7, Cell 3, LOX, North Holeyland, E0, F1, U3/Glory Hole, L35B, 2BS, L67, 3A-15, 3A-TH, Lostmans Creek, North Prong Creek, TS-7, and TS-9 for the plants and animals found at each site. A first step of the Everglades restoration efforts is ""getting the water right"". However, the underlying goal is actually to re-establish, as much as possible, the ""pre-development"" spatial and temporal distribution of ecosystems throughout the Everglades. Stable isotope compositions of dissolved nutrients, biota, and sediments provide critical information about current and historic ecosystem conditions in the Everglades, including temporal and spatial variations in contaminant sources, biogeochemical reactions in the water column and shallow subsurface, and trophic relations. Hence, the scientific focus of this project is to use stable isotope techniques to examine ecosystem responses (especially variations in foodweb base and trophic structure) to temporal and spatial variations in hydroperiod and contaminant loading for the entire freshwater Everglades. The major ""long-term"" objectives of this project have been to: (1) determine the stable C, N, and S isotopic compositions of Everglades biota, (2) use bulk and compound-specific isotopic ratios to determine relative trophic positions for major organisms, (3) examine the spatial and temporal changes in foodweb structures across the ecosystem, especially with respect to the effect of anthropogenically derived nutrients and contaminants from agricultural land uses on foodwebs, (4) evaluate the effectiveness of isotopic techniques vs. gut content analysis for determining trophic relations in the Everglades, (5) evaluate the role of algae vs. detritus/microbial materials in foodwebs for the entire freshwater marsh part of the Everglades, and (6) work with modelers to correctly incorporate food web and MeHg bioaccumulation information into predictive models. More recent and specific objectives include: (1) link our data on seasonal and temporal differences in foodweb bases and trophic levels with SFWMD, FGFFC, and USGS Hg datasets (first for large fish and, more recently, for lower trophic levels), (2) investigate the effects of seasonal/spatial changes in nutrients, water levels, and reactions on the isotopic compositions at the base of the foodweb (that affect our interpretation of relative trophic positions of organisms), and (3) continue our efforts to link our foodweb isotope data from samples collected at USGS-ACME and EPA-REMAP sites with the spatial environmental patterns observed by the REMAP program. This work started as part of the Aquatic Cycling of Mercury in the Everglades (ACME) project in 1996 and was made a separate project in 2000." proprietary
USGS_SOFIA_kitchens_snail_kite Estimation of Critical Parameters in Conjunction with Monitoring the Florida Snail Kite Population CEOS_EXTRA STAC Catalog 2000-10-01 2003-09-30 -83.32674, 24.229189, -79.897285, 29.138569 https://cmr.earthdata.nasa.gov/search/concepts/C2231550848-CEOS_EXTRA.umm_json Life history traits and the population dynamics of the snail kite may vary considerably across space and over time. Understanding the influence of environmental (spatial and temporal) variation on demographic parameters is essential to understanding the population dynamics of a given species. Recognition of information needs for management decisions and conservation strategies has resulted in an increased emphasis on correlations to spatial and temporal environmental variation in relation to demographic studies. The purpose if this study is to provide valid estimates of the demographic parameters of the snail kite, including temporal and spatial variability due to environmental factors. These parameters will be used in a predictive model of the snail kite already developed under the ATLSS Program (Mooij et al. 2002). The snail kite (Rostrhamus sociabilis) is an endangered species that resides in the highly fluctuating ecosystem in the central and southern Florida wetlands. Many demographic traits, such as stage-dependent survival, reproduction, and movement of the snail kite vary both temporally and spatially. How these demographic parameters vary as a function of environmental conditions, hydrology in particular, is crucial for understanding how the snail kite will respond to proposed changes in water regulation in South and Central Florida. In particular, these data are needed for testing and improving the existing spatially-explicit, individual-based ATLSS snail kite model, developed by Mooij and Bennetts, which has recently been delivered to Department of Interior and other agencies (Mooij et al. 2002). From these data and the model, projections can be made on snail kite response to any hydrologic scenario. Also, continued estimates will be made of the rate of population growth. Assessing the demographic parameters is critical for identifying and evaluating the effectiveness of management actions and conservation strategies. In addition, new modeling techniques, such as structural modeling are being explored to better understand the effects of hydrology on the snail kite. The objectives of this project are the following: 1. To monitor the status of the snail kite population trends in central and southern Florida. 2. To provide estimates of demographic parameters for the spatially explicit individual-based model in ATLSS. 3. To collaborate with Dr. Wolf Mooij of the Netherlands Institute of Ecology to use snail kite data to validate the snail kite model. proprietary
-USGS_SOFIA_la_florida "A Land of Flowers on a Latitude of Deserts: Aiding Conservation and Management of Florida's Biodiversity by Using Predictions from ""Down-Scaled"" AOGCM Climate Scenarios in Combination with Ecological Modeling" ALL STAC Catalog 1970-01-01 2000-12-31 -92, 23, -75, 38.24 https://cmr.earthdata.nasa.gov/search/concepts/C2231554072-CEOS_EXTRA.umm_json The objectives of this project are to develop the knowledge necessary to make accurate predictions of the response of species and their ecosystems to climate change. We propose to down-scale predictions from a suite of coupled Atmosphere-Ocean General Circulation Models (AOGCMs) to make regional scale predictions for the southeastern United States. For the time being the hydrologic and biologic models are confined to Florida. Climate outputs will then be used as inputs to a suite of species / habitat / ecosystem models that are currently being used in two key areas: the Greater Everglades and Suwannee River-Big Bend as a proof of concept that down-scaled climate results can work in ecological forecast models. We will run three scenarios of Land Use/Land Cover (LULC): past (circa 1900), present, and future (2041-2070). Additional climate model runs will address the contribution of green house gasses to climate variability and change over the Florida peninsula. Model perturbation experiments will be performed to address sources of variability and their contribution to the output regional climate change scenarios. We will develop scenarios that specifically address potential changes in temperature (land and near sea surface) and rainfall fields over the peninsula. We will then provide these scenarios and modeling results to resource management groups (NGOs, state and federal) via workshops in which the scenarios will be used to predict responses of additional selected species, habitats and ecosystems. Our approach is to develop regional climate predictions and subsequent ecological predictions for two 30-year long time periods as well as for the present. The first 30-year period is the recent past, spanning the period from 1971-2000. This will be used as a control, with copious observations of both climate variables (e.g. rainfall, ET) and species (e.g. densities, ranges) to verify both climate and ecology model outputs and to serve as a baseline to systematically judge the impacts of an altered climate. The second 30-year time period will begin 30 years in the future and extend for the thirty years from 2041-2070. This is a time horizon that is immediately relevant to habitat management. proprietary
USGS_SOFIA_la_florida "A Land of Flowers on a Latitude of Deserts: Aiding Conservation and Management of Florida's Biodiversity by Using Predictions from ""Down-Scaled"" AOGCM Climate Scenarios in Combination with Ecological Modeling" CEOS_EXTRA STAC Catalog 1970-01-01 2000-12-31 -92, 23, -75, 38.24 https://cmr.earthdata.nasa.gov/search/concepts/C2231554072-CEOS_EXTRA.umm_json The objectives of this project are to develop the knowledge necessary to make accurate predictions of the response of species and their ecosystems to climate change. We propose to down-scale predictions from a suite of coupled Atmosphere-Ocean General Circulation Models (AOGCMs) to make regional scale predictions for the southeastern United States. For the time being the hydrologic and biologic models are confined to Florida. Climate outputs will then be used as inputs to a suite of species / habitat / ecosystem models that are currently being used in two key areas: the Greater Everglades and Suwannee River-Big Bend as a proof of concept that down-scaled climate results can work in ecological forecast models. We will run three scenarios of Land Use/Land Cover (LULC): past (circa 1900), present, and future (2041-2070). Additional climate model runs will address the contribution of green house gasses to climate variability and change over the Florida peninsula. Model perturbation experiments will be performed to address sources of variability and their contribution to the output regional climate change scenarios. We will develop scenarios that specifically address potential changes in temperature (land and near sea surface) and rainfall fields over the peninsula. We will then provide these scenarios and modeling results to resource management groups (NGOs, state and federal) via workshops in which the scenarios will be used to predict responses of additional selected species, habitats and ecosystems. Our approach is to develop regional climate predictions and subsequent ecological predictions for two 30-year long time periods as well as for the present. The first 30-year period is the recent past, spanning the period from 1971-2000. This will be used as a control, with copious observations of both climate variables (e.g. rainfall, ET) and species (e.g. densities, ranges) to verify both climate and ecology model outputs and to serve as a baseline to systematically judge the impacts of an altered climate. The second 30-year time period will begin 30 years in the future and extend for the thirty years from 2041-2070. This is a time horizon that is immediately relevant to habitat management. proprietary
+USGS_SOFIA_la_florida "A Land of Flowers on a Latitude of Deserts: Aiding Conservation and Management of Florida's Biodiversity by Using Predictions from ""Down-Scaled"" AOGCM Climate Scenarios in Combination with Ecological Modeling" ALL STAC Catalog 1970-01-01 2000-12-31 -92, 23, -75, 38.24 https://cmr.earthdata.nasa.gov/search/concepts/C2231554072-CEOS_EXTRA.umm_json The objectives of this project are to develop the knowledge necessary to make accurate predictions of the response of species and their ecosystems to climate change. We propose to down-scale predictions from a suite of coupled Atmosphere-Ocean General Circulation Models (AOGCMs) to make regional scale predictions for the southeastern United States. For the time being the hydrologic and biologic models are confined to Florida. Climate outputs will then be used as inputs to a suite of species / habitat / ecosystem models that are currently being used in two key areas: the Greater Everglades and Suwannee River-Big Bend as a proof of concept that down-scaled climate results can work in ecological forecast models. We will run three scenarios of Land Use/Land Cover (LULC): past (circa 1900), present, and future (2041-2070). Additional climate model runs will address the contribution of green house gasses to climate variability and change over the Florida peninsula. Model perturbation experiments will be performed to address sources of variability and their contribution to the output regional climate change scenarios. We will develop scenarios that specifically address potential changes in temperature (land and near sea surface) and rainfall fields over the peninsula. We will then provide these scenarios and modeling results to resource management groups (NGOs, state and federal) via workshops in which the scenarios will be used to predict responses of additional selected species, habitats and ecosystems. Our approach is to develop regional climate predictions and subsequent ecological predictions for two 30-year long time periods as well as for the present. The first 30-year period is the recent past, spanning the period from 1971-2000. This will be used as a control, with copious observations of both climate variables (e.g. rainfall, ET) and species (e.g. densities, ranges) to verify both climate and ecology model outputs and to serve as a baseline to systematically judge the impacts of an altered climate. The second 30-year time period will begin 30 years in the future and extend for the thirty years from 2041-2070. This is a time horizon that is immediately relevant to habitat management. proprietary
USGS_SOFIA_lake_okee_bathy_data Lake Okeechobee Bathymetry data CEOS_EXTRA STAC Catalog 2001-09-01 -81.125, 26.625, -80.5, 27.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231550957-CEOS_EXTRA.umm_json The data from the bathymetric mapping of Lake Okeechobee are provided in two forms: as raw data files and as elevation contour maps. High resolution acoustic bathymetric surveying is a proven method to map sea and lake floor elevations. Of primary interest to the South Florida Water Management District (SFWMD) is the quantification of the present day lakebed in Lake Okeechobee. This information can be used by water-management decision-makers to better assess the water capacity of the lake at various levels. proprietary
USGS_SOFIA_land_margin_ecosystems Dynamics of Land Margin Ecosystems: Historical Change, Hydrology, Vegetation, Sediment, and Climate CEOS_EXTRA STAC Catalog 2002-10-01 2009-12-31 -81.75, 25, -80.25, 26.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231552313-CEOS_EXTRA.umm_json This project has three objectives (tasks): 1) operate and maintain the Mangrove Hydrology sampling network; 2) study the dynamics of coastal vegetation (mangroves, marshes) in relation to sea-level, fire, disturbance and restoration; and, 3) measure rates of sediment surface elevation change and soil accretion or loss in coastal mangrove forests and brackish marshes of the Everglades and determine how sediment elevation varies in relation to hydrology (i.e. the restoration). proprietary
USGS_SOFIA_lbwfbay Ecosystem History: Florida Bay and Southwest Coast CEOS_EXTRA STAC Catalog 1995-02-01 2003-02-06 -80.75, 24.75, -80.33, 25.25 https://cmr.earthdata.nasa.gov/search/concepts/C2231553226-CEOS_EXTRA.umm_json "Recent negative trends in the Florida Bay ecosystem have been attributed to human activities, however, neither the natural patterns of change, nor the pre-human baseline for the environment have been determined. The major objectives of this project are 1) to determine patterns of faunal and floral change over the last 150-200 years, and 2) to explore associations between biotic changes and anthropogenically-induced changes and/or natural changes in the physical environment. Environmental managers and policy makers responsible for restoring the Everglades ecosystem to a ""natural state"" can use these data to make economical and realistic decisions about restoration goals and to determine interim steps to ameliorate further damage to the ecosystem. The history of the ecosystem during the last 150-200 years is studied by analysis of faunal and floral assemblages from a series of shallow cores taken in Florida Bay. Cores are located at strategic sites in Florida Bay, with initial emphasis on the northeast and northern portions of the Bay where the most significant changes are thought to have occurred. These cores are submitted for Pb 210 analysis to determine the age and degree of disruption of the sediments. Cores that present a good stratigraphic record are sampled at closely spaced intervals for all macro-and micro-fauna and flora present. Quantitative down-core assemblage diagrams are drawn up and the various faunal and floral data are compared to look for correlated changes among the groups analyzed. Determinations of salinity, bottom conditions, nutrient supply and various other physical and chemical parameters of the environment are made for each sample based on the fauna and flora present. Data from all cores will be integrated to search for regional patterns of change in diversity and distribution of the fauna and flora, and data from Florida Bay will supplement and be correlated to onshore data and to Biscayne Bay (Ecosystems History: Terrestrial and Fresh Water Ecosystems of Southern Florida Project and Ecosystems History: Biscayne Bay and the southeast coast Project). The integrated data set will be analyzed to see if detected changes in biota correlate to alterations in physical parameters and/or historic records of human-induced modifications of the environment. This project is one component of an interdisciplinary study of the ecosystem history in Florida Bay. A number of USGS and other agencies scientist's are examining a series of shallow cores (~1-2 m) collected from Florida Bay. By studying the patterns of change that have occurred in the ecosystem over the last two centuries, we gain insight into the natural processes, including the natural range of variability that exists within any ecosystem. We can then determine the degree to which anthropogenic-induced change has effected the system. This understanding is critical to the restoration effort; otherwise we will be attempting to restore the system to a targeted snapshot in time, without understanding how realistic or obtainable those goals are. The ecosystem history component of the initiative will save time and money by providing realistic, economical, obtainable goals. Our component of this study is to analyze the down-core faunal and floral assemblages, over the last 150-200 years. Cores are located at strategic sites in Florida Bay, with initial emphasis on the northeast and northern portions of the Bay where the most significant changes are thought to have occurred. These cores are submitted for Pb 210 analysis to determine the age and degree of disruption of the sediments. Cores that present a good stratigraphic record are sampled at closely spaced intervals for all macro- and micro-fauna and flora present. Quantitative down-core assemblage diagrams are drawn up and the various faunal and floral data are compared to look for correlated changes among the groups analyzed. Determinations of salinity, bottom conditions, nutrient supply and various other physical and chemical parameters of the environment are made for each sample based on the fauna and flora present. Data from all cores will be integrated to search for regional patterns of change in diversity and distribution of the fauna and flora, and data from Florida Bay will supplement and be correlated to onshore data and to Biscayne Bay. The integrated data set will be analyzed to see if detected changes in biota correlate to alterations in physical parameters and/or historic records of human-induced modifications of the environment." proprietary
@@ -16228,10 +16233,10 @@ USGS_cir89_Version 1.0 Color-infrared composite of Landsat data for the Sarcobat
USGS_cira92_Version 1.0 Color-infrared composite of Landsat data for the Death Valley regional flow system, Nevada and California, 1992 CEOS_EXTRA STAC Catalog 1992-06-01 1992-06-13 -117.550385, 35.378323, -115.251015, 37.653557 https://cmr.earthdata.nasa.gov/search/concepts/C2231551442-CEOS_EXTRA.umm_json "This data set was created to determine phreatophyte boundaries for use in the report, ""Ground-water discharge determined from estimates of evapotranspiration, Death Valley regional flow system, Nevada and California"". The raster-based, color-infrared composite was derived from Landsat Thematic Mapper imagery data acquired during June 1992 for the Death Valley regional flow system. The image is a single-channel, parallelepiped classification that when displayed using a 256-color color table shows a simulation of a color-infrared composite. The data set was used in determining phreatophyte boundaries for a ground-water evapotranspiration study. The raster-based, color-infrared composite (CIR) was derived from Landsat Thematic Mapper (TM) imagery data acquired during June 1992 for the Death Valley ground-water flow system, Nevada and California. The image is a single-channel, parallelepiped classification that when displayed using a 256-color color table shows a simulation of a color-infrared composite (Beverley and Penton, 1989). TM channels 2, 3, and 4 are used in the classification process. The wavelengths of these channels correspond to those used for a CIR composite. The data range of each channel is divided into eight divisions. The 512 possible combinations are then reduced to 256. A color table of red, green, and blue values is created for display of the image. Sixteen possible color values exist for each color. These values are scaled between 0 and 255. The image is reduced from more than 16 million colors to 256 colors." proprietary
USGS_cont1992 1992 Water-Table Contours of the Mojave River Ground-Water Basin, San Bernardino County, California CEOS_EXTRA STAC Catalog 1970-01-01 -117.652695, 34.364513, -116.55357, 35.081955 https://cmr.earthdata.nasa.gov/search/concepts/C2231553864-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River Basin. The U.S. Geological Survey, in cooperation with the Mojave Water Agency, constructed a water-table map of the Mojave River ground-water basin for ground-water levels measured in November 1992. Water-level data were collected from approximately 300 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,200 to 1,600 feet above sea level. [Summary provided by the USGS.] proprietary
USGS_cont1992 1992 Water-Table Contours of the Mojave River Ground-Water Basin, San Bernardino County, California ALL STAC Catalog 1970-01-01 -117.652695, 34.364513, -116.55357, 35.081955 https://cmr.earthdata.nasa.gov/search/concepts/C2231553864-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River Basin. The U.S. Geological Survey, in cooperation with the Mojave Water Agency, constructed a water-table map of the Mojave River ground-water basin for ground-water levels measured in November 1992. Water-level data were collected from approximately 300 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,200 to 1,600 feet above sea level. [Summary provided by the USGS.] proprietary
-USGS_cont1994 1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California CEOS_EXTRA STAC Catalog 1970-01-01 -117.07194, 34.095333, -115.98976, 34.64026 https://cmr.earthdata.nasa.gov/search/concepts/C2231554677-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Morongo Basin. The U.S. Geological Survey constructed a water-table map of the Morongo ground-water basin for ground-water levels measured during the period January-October 1994. Water-level data were collected from 248 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,400 to 1,500 feet above sea level. [Summary provided by the USGS.] proprietary
USGS_cont1994 1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California ALL STAC Catalog 1970-01-01 -117.07194, 34.095333, -115.98976, 34.64026 https://cmr.earthdata.nasa.gov/search/concepts/C2231554677-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Morongo Basin. The U.S. Geological Survey constructed a water-table map of the Morongo ground-water basin for ground-water levels measured during the period January-October 1994. Water-level data were collected from 248 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,400 to 1,500 feet above sea level. [Summary provided by the USGS.] proprietary
-USGS_cont1996 1996 Water-Table Contours of the Mojave River, the Morongo, and the Fort Irwin Ground-Water Basins, San Bernardino County, California CEOS_EXTRA STAC Catalog 1970-01-01 -117.63461, 34.109745, -115.98707, 35.31552 https://cmr.earthdata.nasa.gov/search/concepts/C2231555091-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins. The U.S. Geological Survey constructed a water-table map of the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins for ground-water levels measured during the period January-September 1996. Water-level data were collected from 632 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:175,512. The contour interval ranges from 3,400 to 1,550 feet above sea level. [Summary provided by the USGS.] proprietary
+USGS_cont1994 1994 Water-Table Contours of the Morongo Ground-Water Basin, San Bernardino County, California CEOS_EXTRA STAC Catalog 1970-01-01 -117.07194, 34.095333, -115.98976, 34.64026 https://cmr.earthdata.nasa.gov/search/concepts/C2231554677-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Morongo Basin. The U.S. Geological Survey constructed a water-table map of the Morongo ground-water basin for ground-water levels measured during the period January-October 1994. Water-level data were collected from 248 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:125,000. The contour interval ranges from 3,400 to 1,500 feet above sea level. [Summary provided by the USGS.] proprietary
USGS_cont1996 1996 Water-Table Contours of the Mojave River, the Morongo, and the Fort Irwin Ground-Water Basins, San Bernardino County, California ALL STAC Catalog 1970-01-01 -117.63461, 34.109745, -115.98707, 35.31552 https://cmr.earthdata.nasa.gov/search/concepts/C2231555091-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins. The U.S. Geological Survey constructed a water-table map of the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins for ground-water levels measured during the period January-September 1996. Water-level data were collected from 632 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:175,512. The contour interval ranges from 3,400 to 1,550 feet above sea level. [Summary provided by the USGS.] proprietary
+USGS_cont1996 1996 Water-Table Contours of the Mojave River, the Morongo, and the Fort Irwin Ground-Water Basins, San Bernardino County, California CEOS_EXTRA STAC Catalog 1970-01-01 -117.63461, 34.109745, -115.98707, 35.31552 https://cmr.earthdata.nasa.gov/search/concepts/C2231555091-CEOS_EXTRA.umm_json This data set consists of digital water-table contours for the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins. The U.S. Geological Survey constructed a water-table map of the Mojave River, the Morongo and the Fort Irwin Ground-Water Basins for ground-water levels measured during the period January-September 1996. Water-level data were collected from 632 wells to construct the contours. The water-table contours were digitized from the paper map which was published at a scale of 1:175,512. The contour interval ranges from 3,400 to 1,550 feet above sea level. [Summary provided by the USGS.] proprietary
USGS_erf1_Version 1.2, August 01, 1999 ERF1 -- Enhanced River Reach File 1.2 CEOS_EXTRA STAC Catalog 1999-01-07 1999-01-07 -127.8169, 23.247017, -65.55541, 48.19323 https://cmr.earthdata.nasa.gov/search/concepts/C2231552175-CEOS_EXTRA.umm_json ERF1 was designed to be a digital data base of river reaches capable of supporting regional and national water-quality and river-flow modeling and transport investigations in the water-resources community. ERF1 has been recently used at the U.S. Geological Survey to support interpretations of stream water-quality monitoring network data (see Alexander and others, 1996; Smith and others, 1995). In these analyses, the reach network has been used to determine flow pathways between the sources of point and nonpoint pollutants (e.g., fertilizer use, municipal wastewater discharges) and downstream water-quality monitoring locations in support of predictive water-quality models of stream nutrient transport. The digital data set ERF1 includes enhancements to the U.S. Environmental Protection Agency's River Reach File 1 (RF1)to ensure the hydrologic integrity of the digital reach traces and to quantify the time of travel of river reaches and reservoirs [see U.S.EPA (1996) for a description of the original RF1]. Any use of trade, product, or firm names is for descriptive proprietary
USGS_erfi-2_2.0, November 19, 2001 ERF1-2 -- Enhanced River Reach File 2.0 CEOS_EXTRA STAC Catalog 1999-01-07 1999-01-07 -127.85945, 23.243486, -65.37739, 48.194405 https://cmr.earthdata.nasa.gov/search/concepts/C2231551816-CEOS_EXTRA.umm_json "This report describes the process of enhancements to the stream reach network, ERF1, which is an enhanced version of EPA's RF1. The U.S. Environmental Protection Agency's reach file (RF1) is a database of interconnected stream segments or ""reaches"" that comprise the surface water drainage system for the United States. A variety of attributes have been assigned to each reach in support of spatial analysis and mapping applications. ERF1-2 was designed to be a digital database of river reaches capable of supporting regional and national water-quality and river-flow modeling by the water-resources community. ERF1, on which ERF1-2 is based, is used at the U.S. Geological Survey to support national-level water-quality monitoring modeling with the SPARROW model (see Alexander and others, 2000; Smith and others, 1997). In the current and earlier analyses, the reach network is used to determine flow pathways between the sources of point and nonpoint pollutants (e.g., fertilizer use, municipal wastewater discharges) and downstream water-quality monitoring locations in support of predictive water- quality models of stream nutrient transport. Acknowledgements The authors would like to thank Richard Smith, a co-developer of the SPARROW approach, Kristine Verdin, and Stephen Char, all of the U.S. Geological Survey, for providing technical assistance. The reviewers of this report, Dave Stewart, and Mike Wieczorek, are also acknowledged for their significant contributions. The digital segmented network based on watershed boundaries, ERF1-2, includes enhancements to the U.S. Environmental Protection Agency's River Reach File 1 (RF1) (USEPA, 1996; DeWald and others, 1985) to support national and regional-scale surface water-quality modeling. Alexander and others (1999) developed ERF1, which assessed the hydrologic integrity of the digital reach traces and calculated the mean water time-of-travel in river reaches and reservoirs. ERF1-2 serves as the foundation for SPARROW (Spatially Referenced Regressions (of nutrient transport) On Watershed) modeling. Within the context of a Geographic Information System, SPARROW estimates the proportion of watersheds in the conterminous U.S. with outflow concentrations of several nutrients, including total nitrogen and total phosphorus, (Smith, R.A., Schwarz, G.E., and Alexander, R.B., 1997). This version of the network expands on ERF1 (version 1.2; Alexander et al. 1999), and includes the incremental and total drainage area derived from 1-kilometer (km) elevation data for North America. Previous estimates of the water time-of-travel were recomputed for reaches with water- quality monitoring sites that included two reaches. The mean flow and velocity estimates for these split reaches are based on previous estimation methods (Alexander et al., 1999) and are unchanged in ERF1-2. Drainage area calculations provide data used to estimate the contribution of a given nutrient to the outflow. Data estimates depend on the accuracy of node connectivity. Reaches split at water- quality or pesticide-monitoring sites indicate the source point for estimating the contribution and transport of nutrients and their loads throughout the watersheds. The ERF1-2 coverage extends the earlier ERF1 coverage by providing digital-elevation-model (DEM-based estimates of reach drainage area founded on the 1-kilometer data for North America (Verdin, 1996; Verdin and Jenson, 1996). A 1-kilometer raster grid of ERF1-2 projected to Lambert Azimuthal Equal Area, NAD 27 Datum (Snyder, 1987), was merged with the HYDRO1K flow direction data set (Verdin and Jenson, 1996) to generate a DEM-based watershed grid, ERF1_2WS. The watershed boundaries are maintained in a raster (grid cell) format as well as a vector (polygon) format for subsequent model analysis. Both the coverage, ERF1-2, and the grid, ERF1-2WS are available at: ""http://water.usgs.gov/orh/nrwww/sparrow_section5_nolan.pdf"". Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ArcInfo format, this metadata file may include some ArcInfo-specific terminology." proprietary
USGS_etsite_Version 1.0 Evapotranspiration sites within the Ash Meadows and Oasis Valley discharge areas, Nevada CEOS_EXTRA STAC Catalog 1993-01-01 1999-01-01 -116.73254, 36.37027, -116.296814, 37.063698 https://cmr.earthdata.nasa.gov/search/concepts/C2231552240-CEOS_EXTRA.umm_json The digital data set was created to display site locations at which micrometeorological data were collected in Ash Meadows and Oasis Valley, Nev. The digital data set provides locations and general descriptions of sites instrumented to collect micrometeorological data from which mean annual ET rates were computed. Sites are located in Ash Meadows and Oasis Valley, Nevada. Data were collected December 1993 through present. Introduction The digital data set was created in cooperation with the U.S. Department of Energy. The data set was created as part of a study to refine current estimates of ground-water discharge from the major discharge areas of the Death Valley regional flow system. This digital data set provides locations and general descriptions of sites instrumented during recent studies of evapotranspiration in Ash Meadows and Oasis Valley, Nevada. Data were collected December 1993 through 2001. Reviews The digital data set has gone through a multi-level, quality-control process to ensure that the data are a reasonable representation of source points. Reviewers were asked to check metadata and other documentation files for completeness and accuracy. Reviewers also were asked to check the topological consistency, tolerances, projections, and geographic extent. Notes Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although the data set has been used by the U.S. Geological Survey, U.S. Department of the Interior, no warranty expressed or implied is made by the U.S. Geological Survey as to the accuracy of the data and related materials. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in non-proprietary form, as well as in ArcInfo format, this metadata file may include some ArcInfo-specific terminology. Users should exercise caution and judgment in applying these data, and be aware that errors may be present in any or all of the digital image data. If errors are encountered in this data set, it will be appreciated if the user would pass this information to the Metadata_Contact. proprietary
@@ -16296,8 +16301,8 @@ USM_pCO2_0 University of Southern Mississippi (USM) - partial pressure of carbon
US_FOREST_FRAGMENTATION Forest Fragmentation in the United States CEOS_EXTRA STAC Catalog 1970-01-01 -128, 24, -65, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231549003-CEOS_EXTRA.umm_json "National Land Cover Data (NLCD) was reclassified into three categories: forest, other natural (e.g., grassland and wetland), and anthropogenic use (e.g., agricultural and urban). Three new grids were created, one for each edge type (forest, forest, forest natural, and forest anthropogenic). The values in these grids were calculated as the number of edges with the appropriate type in the window divided by the total number of forest edges, regardless of neighbor. These grids represented forest connectivity (forest forest edges), naturally caused forest fragmentation (forest natural edges), and human-caused forest fragmentation (forest anthropogenic edges). In the map, forest connectivity is displayed in green, natural fragmentation in blue, and human fragmentation in red. Pure green identifies areas where most or all forest edges are shared by another forest pixel. Pure red areas are where forest edges are largely shared with human land use. Pure blue areas show where most or all forest edges are shared with another natural land cover type. Different mixes of the three edge types can produce other colors. Two common examples in the map are yellow and cyan. Yellow identifies areas with roughly equal amounts of forest connectivity and anthropogenic fragmentation. Cyan is where forest connectivity and natural fragmentation are approximately equal. Black represents areas with no forest in the window, and white represents ignored areas, mostly water, as well as state boundaries. With few exceptions, forest fragmentation by other natural land cover types is confined to the western United States, while most human-caused forest fragmentation is in the East and Midwest. The yellow and red areas around Yellowstone in northwest Wyoming are a result of the wildfires in 1988. The burned areas are classified as ""transitional"" in the NLCD, which are treated as anthropogenic use. The Mississippi River valley was largely forested at one time but has been almost entirely converted to agricultural use, resulting in a display of black and red. Las Vegas, Nevada, is visible as a patch of red in the Mojave Desert due to an ""urban forest"" effect from trees planted by residents. Riparian corridors are highly visible in arid and developed areas, especially the West and Midwest. In arid areas, climate often confines trees to riparian zones that are displayed in shades of blue. In the intensely farmed Midwest, intact and restored riparian vegetation is depicted in yellow or red. Southern Atlantic coastal plain riparian zones are wider; forest is better connected and is shown in green." proprietary
US_MODIS_NDVI_1299_3 MODIS NDVI Data, Smoothed and Gap-filled, for the Conterminous US: 2000-2015 ORNL_CLOUD STAC Catalog 2000-01-01 2015-12-31 -129.89, 20.85, -62.56, 50.56 https://cmr.earthdata.nasa.gov/search/concepts/C2764637520-ORNL_CLOUD.umm_json This data set provides Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, smoothed and gap-filled, for the conterminous US for the period 2000-01-01 through 2015-12-31. The data were generated using the NASA Stennis Time Series Product Tool (TSPT) to generate NDVI data streams from the Terra satellite (MODIS MOD13Q1 product) and Aqua satellite (MODIS MYD13Q1 product) instruments. TSPT produces NDVI data that are less affected by clouds and bad pixels. proprietary
US_MODIS_Veg_Parameters_1539_1 MODIS-derived Vegetation and Albedo Parameters for Agroecosystem-Climate Modeling ORNL_CLOUD STAC Catalog 2003-01-01 2010-12-31 -139.05, 15.15, -51.95, 49.15 https://cmr.earthdata.nasa.gov/search/concepts/C2517700524-ORNL_CLOUD.umm_json This dataset provides MODIS-derived leaf area index (LAI), stem area index (SAI), vegetation area fraction, dominant landcover category, and albedo parameters for the continental US (CONUS), parts of southern Canada, and Mexico at 30 km resolution. The data cover the period 2003-2010 and were developed to be used as surface input data for regional agroecosystem-climate models. MODIS Collection 5 products used to derive these parameters included the Terra yearly water mask, vegetation continuous field products, the combined Terra and Aqua yearly land-cover category (LCC) (MCD12Q1), 8-day composites for LAI (MCD15A2), and albedo parameter (MCD43B1) products. Please note that the MODIS Version 5 land data products used in this dataset have been superseded by Version 6 data products. proprietary
-UTC_1990countyboundaries 1990 County Boundaries of the United States CEOS_EXTRA STAC Catalog 1972-01-01 1990-12-31 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231550562-CEOS_EXTRA.umm_json This data set portrays the 1990 State and county boundaries of the United States, Puerto Rico, and the U.S. Virgin Islands. The data set was created by extracting county polygon features from the individual 1:2,000,000-scale State boundary Digital Line Graph (DLG) files produced by the U.S. Geological Survey. These files were then merged into a single file and the boundaries were modified to what they were in 1990. This is a revised version of the March 2000 data set. proprietary
UTC_1990countyboundaries 1990 County Boundaries of the United States ALL STAC Catalog 1972-01-01 1990-12-31 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231550562-CEOS_EXTRA.umm_json This data set portrays the 1990 State and county boundaries of the United States, Puerto Rico, and the U.S. Virgin Islands. The data set was created by extracting county polygon features from the individual 1:2,000,000-scale State boundary Digital Line Graph (DLG) files produced by the U.S. Geological Survey. These files were then merged into a single file and the boundaries were modified to what they were in 1990. This is a revised version of the March 2000 data set. proprietary
+UTC_1990countyboundaries 1990 County Boundaries of the United States CEOS_EXTRA STAC Catalog 1972-01-01 1990-12-31 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231550562-CEOS_EXTRA.umm_json This data set portrays the 1990 State and county boundaries of the United States, Puerto Rico, and the U.S. Virgin Islands. The data set was created by extracting county polygon features from the individual 1:2,000,000-scale State boundary Digital Line Graph (DLG) files produced by the U.S. Geological Survey. These files were then merged into a single file and the boundaries were modified to what they were in 1990. This is a revised version of the March 2000 data set. proprietary
UTC_TNgeologicmaps Geologic Maps of Tennessee CEOS_EXTRA STAC Catalog 1966-01-01 1966-12-31 -90.31191, 34.983253, -81.64822, 36.679295 https://cmr.earthdata.nasa.gov/search/concepts/C2231549514-CEOS_EXTRA.umm_json This data set is a digital representation of the printed 1:250,000 geologic maps from the Tennessee Department of Environment and Conservation, Division of Geology. The coverage was designed primarily to provide a more detailed geologic base than the 1:2,500,000 King and Beikman (1974). 1:24,000 scale coverage of the state is available for about 40 percent of the state. Formation names and geologic unit codes used in the coverage are from the Tennessee Division of Geology published maps and may not conform to USGS nomenclature. The Tennessee Division of Geology can be contacted at (615) 532-1500. proprietary
UTC_TRIfacilities Facilities in the Toxic Release Inventory CEOS_EXTRA STAC Catalog 1997-12-31 -127.61431, 23.24277, -65.505165, 51.523094 https://cmr.earthdata.nasa.gov/search/concepts/C2231553589-CEOS_EXTRA.umm_json This data set is a subset of the U.S. Environmental Protection Agency (USEPA) Envirofacts point data set which includes facilities included in the the Toxic Release Inventory. Information on total pounds of volatile organic compounds released in 1995 (from USEPA's Toxic Release Inventory CD-ROM) has been included. This data set is designed to locate or plot manufacturing facilities included in the Toxic Release Inventory and display or analysis of volatile organic compounds releases in pounds per year. The following are the volatile organic compounds (VOC's) selected to calculate the total releases at each facility. Not all of these chemicals actually appear in the TRI data set, but this list was used to select releases to sum for each facility. CAS-ID Chemical name > ---------- ---------------------------- > 1 630-20-6 1,1,1,2-Tetrachloroethane > 2 71-55-6 1,1,1-Trichloroethane > 3 79-34-5 1,1,2,2-Tetrachloroethane > 4 76-13-1 1,1,2-Trichloro-1,2,2-trifluoroethane > 5 79-00-5 1,1,2-Trichloroethane > 6 75-34-3 1,1-Dichloroethane > 7 75-35-4 1,1-Dichloroethene > 8 563-58-6 1,1-Dichloropropene > 9 87-61-6 1,2,3-Trichlorobenzene > 10 96-18-4 1,2,3-Trichloropropane > 11 120-82-1 1,2,4-Trichlorobenzene > 12 95-63-6 1,2,4-Trimethylbenzene > 13 96-12-8 1,2-Dibromo-3-chloropropane > 14 106-93-4 1,2-Dibromoethane > 15 95-50-1 1,2-Dichlorobenzene > 16 107-06-2 1,2-Dichloroethane > 17 78-87-5 1,2-Dichloropropane > 18 108-67-8 1,3,5-Trimethylbenzene > 19 541-73-1 1,3-Dichlorobenzene > 20 142-28-9 1,3-Dichloropropane > 21 106-46-7 1,4-Dichlorobenzene > 22 95-49-8 1-Chloro-2-methylbenzene > 23 106-43-4 1-Chloro-4-methylbenzene > 24 594-20-7 2,2-Dichloropropane > 25 71-43-2 Benzene > 26 108-86-1 Bromobenzene > 27 74-97-5 Bromochloromethane > 28 75-27-4 Bromodichloromethane > 29 74-83-9 Bromomethane > 30 108-90-7 Chlorobenzene > 31 75-00-3 Chloroethane > 32 75-01-4 Chloroethene > 33 74-87-3 Chloromethane > 34 124-48-1 Dibromochloromethane > 35 74-95-3 Dibromomethane > 36 75-71-8 Dichlorodifluoromethane > 37 75-09-2 Dichloromethane > 38 1330-20-7 Dimethylbenzenes > 39 100-42-5 Ethenylbenzene > 40 100-41-4 Ethylbenzene > 41 87-68-3 Hexachlorobutadiene > 42 98-82-8 Isopropylbenzene > 43 1634-04-4 Methyl tert-butyl ether > 44 108-88-3 Methylbenzene > 45 91-20-3 Naphthalene > 46 127-18-4 Tetrachloroethene > 47 56-23-5 Tetrachloromethane > 48 75-25-2 Tribromomethane > 49 79-01-6 Trichloroethene > 50 75-69-4 Trichlorofluoromethane > 51 67-66-3 Trichloromethane > 52 156-59-2 cis-1,2-Dichloroethene > 53 10061-01-5 cis-1,3-Dichloropropene > 54 104-51-8 n-Butylbenzene > 55 103-65-1 n-Propylbenzene > 56 99-87-6 p-Isopropyltoluene > 57 135-98-8 sec-Butylbenzene > 58 98-06-6 tert-Butylbenzene > 59 156-60-5 trans-1,2-Dichloroethene > 60 10061-02-6 trans-1,3-Dichloropropene Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in ARC/INFO format, this metadata file may include some ARC/INFO-specific terminology. proprietary
UTC_USdams Major Dams in the United States CEOS_EXTRA STAC Catalog 1995-01-01 1996-12-31 -162.93422, 18.016077, -66.01461, 68.06759 https://cmr.earthdata.nasa.gov/search/concepts/C2231555196-CEOS_EXTRA.umm_json "This data set portrays major dams of the United States, including Puerto Rico and the U.S. Virgin Islands. The data set was created by extracting dams 50 feet or more in height, or with a normal storage capacity of 5,000 acre- feet or more, or with a maximum storage capacity of 25,000 acre-feet or more, from the 75,187 dams in the U.S. Army Corps of Engineers National Inventory of Dams. These data are intended for geographic display and analysis at the national level, and for large regional areas. The data should be displayed and analyzed at scales appropriate for 1:2,000,000-scale data. No responsibility is assumed by the U.S. Geological Survey in the use of these data. In the online, interactive National Atlas of the United States, at scales smaller than 1:4,850,000 the data is thinned for display purposes. For scales between 1: 4,850,000 and 1:22,000,000, dams are only shown if they have a height of 500 feet or more, or a normal storage capacity of 50,000 acre-feet or more, or a maximum storage capacity of 250,000 acre-feet or more (1173 dams). At scales smaller than 1:22,000,000, dams are only shown if they have a height of 5000 feet or more, or a normal storage capacity of 500,000 acre-feet or more, or a maximum storage capacity of 2,500,000 acre-feet or more (240 dams). The dams in this file were selected from the National Inventory of Dams (NID). First, a subset of the attributes contained in the NID was selected based on input from the Army Corps of Engineers. Using an ArcView query, the dams with a height of 50 feet or more were selected, along with the dams with a normal storage capacity of 5,000 acre-feet or more, and those with a maximum storage capacity of 25,000 acre-feet or more. (The International Committee on Large Dams considers dams over 50 feet to be large dams. The USGS Water Resources Division considers large reservoirs to be those with a normal storage capacity of 5,000 acre-feet or more, or with a maximum storage capacity of 25,000 acre-feet or more.) The resulting data set was converted to an ArcView shape file using the ""Convert to Shapefile"" command. 33 dams that fell outside the 50 States were deleted (1 in Guam, 1 in the Trust Territories, and 31 in Puerto Rico), and 78 dams without coordinates were also deleted. Several misspelled county names were corrected, and the entries in the FIPS_cnty (County FIPS) field were cleaned up. For all dams with a valid county name but no County FIPS, the FIPS code was added based on the listed county name. If two county names were given, the FIPS code used was for the first one listed, or for the county in the listed State. Where the county name was invalid or missing, the county was determined by comparing the dam location to the National Atlas counties file. If the dam fell on a State line, the county name and FIPS code used were those appropriate for the listed State. The shape file was converted to an Arc/Info coverage and then converted to NAD 83 for display purposes. The result was then converted back to shapefile format." proprietary
@@ -16606,8 +16611,8 @@ VMS_Bathy_Processing_1 Acoustic depth soundings collected on Australian Antarcti
VMS_Benthic_Photography_1 High resolution still photographs of the seafloor across the Mertz Glacier Region AU_AADC STAC Catalog 2011-01-04 2011-02-06 140, -67, 150, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214314096-AU_AADC.umm_json Geoscience Australia and the Australian Antarctic Division conducted a benthic community survey using underwater still photographs on the shelf around the Mertz Glacier region. The purpose of the work was to collect high resolution still photographs of the seafloor across the shelf to address three main objectives: 1. to investigate benthic community composition in the area previously covered by the Mertz Glacier tongue and to the east, an area previously covered by fast ice 2. to investigate benthic community composition (or lack thereof) in areas of known iceberg scours 3. to investigate the lateral extent of cold water coral communities in canyons along the shelf break. Benthic photos were captured using a Canon EOS 20D SLR 8 megapixel stills camera fitted with a Canon EF 35mm f1.4 L USM lens in a 2500m rated flat port anodised aluminium housing. Two Canon 580EX Speedlight strobes were housed in 6000m rated stainless steel housings with hemispherical acrylic domes. The camera and strobes were powered with a 28V 2.5Ah cyclone SLA battery pack fitted in the camera housing and connected using Brantner Wetconn series underwater connectors. The results were obtained with 100 ASA and a flash compensation value of +2/3 of a stop. The focus was set manually to 7m and the image was typically exposed at f2.8 and a shutter speed of 1/60 sec. The interval between photos was set to 10 or 15 seconds. The camera was fitted to either the CTD frame or the beam trawl frame and lowered to approximately 4-5 m from the bottom. Two laser pointers, set 50 cm apart, were used for scale. The camera was deployed at 93 stations, 7 using the beam trawl frame and 86 using the CTD frame. The stations were named by: 1. Camera deployment frame (e.g. CTD or beam trawl, BT) 2. Frame sequence number (e.g. CTD53) 3. Instrument (e.g. camera = CAM) 4. Sequence of camera deployments through the survey overall (e.g. first deployment = CAM01, second deployment = CAM02 etc). For example, BT5_CAM16 is the sixteenth camera deployment of the survey overall, and was the fifth deployment using the beam trawl frame. From the 93 stations, there were 75 successful camera deployments. There were no photos captured at 9 stations. This was due to the camera or strobes malfunctioning, the camera being too far from the bottom, or the camera or strobes being in the mud at the bottom. The photos at a further 9 stations are considered poor due to the camera being out of focus, the camera being a little too far from the bottom or because very few photos were captured of the bottom. The benthic photo will be used to document the fauna and communities associated with representative habitats in the study area. The post-cruise analysis of the benthic photos will involve recording seabed geology and biology (class or order, and whatever is significant for the habitat) for each image proprietary
VMS_FRRF_1 2010/11 VMS - Fast Repetition Rate Fluorometer (FRRF) sampling on the Aurora Australis AU_AADC STAC Catalog 2011-01-04 2011-02-06 140, -67, 150, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214314029-AU_AADC.umm_json FRRF deployments were conducted at 22 sites in conjunction with ship stop times when the CTD was deployed. See event log for locations. Some underway FRRF sampling was conducted on the return voyage. This work was conducted as part of the VMS (Voyage Marine Science) voyage of the Aurora Australis in the 2010-2011 season. A report providing further details about the FRRF work is available as part of the download file. The download file also contains a word document (also included in the download file for metadata record ASAC_1307) explaining the data columns in the excel spreadsheets. proprietary
VMS_FRRF_1 2010/11 VMS - Fast Repetition Rate Fluorometer (FRRF) sampling on the Aurora Australis ALL STAC Catalog 2011-01-04 2011-02-06 140, -67, 150, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214314029-AU_AADC.umm_json FRRF deployments were conducted at 22 sites in conjunction with ship stop times when the CTD was deployed. See event log for locations. Some underway FRRF sampling was conducted on the return voyage. This work was conducted as part of the VMS (Voyage Marine Science) voyage of the Aurora Australis in the 2010-2011 season. A report providing further details about the FRRF work is available as part of the download file. The download file also contains a word document (also included in the download file for metadata record ASAC_1307) explaining the data columns in the excel spreadsheets. proprietary
-VMS_Genomics_1 2010/11 VMS Geonomics sampling - data collected from the VMS (Voyage Marine Science) voyage of the Aurora Australis ALL STAC Catalog 2011-01-04 2011-02-06 140, -67, 150, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214314097-AU_AADC.umm_json Purpose of future metagenomic (DNA), metaproteomic (protein) and metatranscriptomic (RNA) analysis: For each sample, two drums (~200L each) of seawater were collected. Samples were taken from CTD sites, and surface samples (2m depth) taken at each of these sites. At most of these CTD sites, a deeper sample was taken according to the location of the DCM at that site. The 200L seawater is pumped through a 20 micron mesh to remove the largest particles, then the biomass is collected on three consecutive filters corresponding to decreasing pore size (3.0 microns, 0.8 microns, 0.1 microns). This is repeated for each sample using the second 200L of seawater to generate duplicates for each sample. The overall aim is to determine the identity of microbes present in the Southern Ocean, and what microbial metabolic processes are in operation. In other words: who is there, and what they are doing. Special emphasis was placed on the SR3 transect. Samples were collected as below. For each sample, a total of six filters were obtained (3x pore sizes, 2x replicates). Each filter is stored in a storage buffer in a 50mL tube, and placed at -80 degrees C for the remainder of the voyage. proprietary
VMS_Genomics_1 2010/11 VMS Geonomics sampling - data collected from the VMS (Voyage Marine Science) voyage of the Aurora Australis AU_AADC STAC Catalog 2011-01-04 2011-02-06 140, -67, 150, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214314097-AU_AADC.umm_json Purpose of future metagenomic (DNA), metaproteomic (protein) and metatranscriptomic (RNA) analysis: For each sample, two drums (~200L each) of seawater were collected. Samples were taken from CTD sites, and surface samples (2m depth) taken at each of these sites. At most of these CTD sites, a deeper sample was taken according to the location of the DCM at that site. The 200L seawater is pumped through a 20 micron mesh to remove the largest particles, then the biomass is collected on three consecutive filters corresponding to decreasing pore size (3.0 microns, 0.8 microns, 0.1 microns). This is repeated for each sample using the second 200L of seawater to generate duplicates for each sample. The overall aim is to determine the identity of microbes present in the Southern Ocean, and what microbial metabolic processes are in operation. In other words: who is there, and what they are doing. Special emphasis was placed on the SR3 transect. Samples were collected as below. For each sample, a total of six filters were obtained (3x pore sizes, 2x replicates). Each filter is stored in a storage buffer in a 50mL tube, and placed at -80 degrees C for the remainder of the voyage. proprietary
+VMS_Genomics_1 2010/11 VMS Geonomics sampling - data collected from the VMS (Voyage Marine Science) voyage of the Aurora Australis ALL STAC Catalog 2011-01-04 2011-02-06 140, -67, 150, -42 https://cmr.earthdata.nasa.gov/search/concepts/C1214314097-AU_AADC.umm_json Purpose of future metagenomic (DNA), metaproteomic (protein) and metatranscriptomic (RNA) analysis: For each sample, two drums (~200L each) of seawater were collected. Samples were taken from CTD sites, and surface samples (2m depth) taken at each of these sites. At most of these CTD sites, a deeper sample was taken according to the location of the DCM at that site. The 200L seawater is pumped through a 20 micron mesh to remove the largest particles, then the biomass is collected on three consecutive filters corresponding to decreasing pore size (3.0 microns, 0.8 microns, 0.1 microns). This is repeated for each sample using the second 200L of seawater to generate duplicates for each sample. The overall aim is to determine the identity of microbes present in the Southern Ocean, and what microbial metabolic processes are in operation. In other words: who is there, and what they are doing. Special emphasis was placed on the SR3 transect. Samples were collected as below. For each sample, a total of six filters were obtained (3x pore sizes, 2x replicates). Each filter is stored in a storage buffer in a 50mL tube, and placed at -80 degrees C for the remainder of the voyage. proprietary
VNP01_NRT_2 VIIRS/NPP Raw Radiances in Counts 6-Min L1A Swath NRT LANCEMODIS STAC Catalog 2022-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2208439292-LANCEMODIS.umm_json VIIRS/NPP Raw Radiances in Counts 6-Min L1A Swath - NRT product contains the unpacked, raw VIIRS science, calibration and engineering data; the extracted ephemeris and attitude data from the spacecraft diary packets; and the raw ADCS and bus-critical spacecraft telemetry data from those packets, with a few critical fields extracted. The shortname for this product is VNP01_NRT. For more information download VIIRS Level 1 Product User's Guide at https://oceancolor.gsfc.nasa.gov/docs/format/VIIRS_Level-1_DataProductUsersGuide.pdf file_naming_convention = VNP01_NRT.AYYYYDDD.HHMM.CCC.nc AYYYYDDD = Acquisition Year and Day of Year HHMM = Acquisition Hour and Minute CCC = Collection number nc = NetCDF5 proprietary
VNP02DNB_2 VIIRS/NPP Day/Night Band 6-Min L1B Swath 750 m LAADS STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105091380-LAADS.umm_json The VIIRS/NPP Day/Night Band 6-Min L1B Swath 750 m product, short-name VNP02DNB, is a panchromatic Day-Night band (DNB) calibrated radiance product. The DNB is one of the M-bands with an at-nadir spatial resolution of 750 meters (across the entire scan). The panchromatic DNB’s spectral wavelength ranges from 0.5 µm to 0.9 µm. It facilitates measuring night lights, reflected solar/lunar lights with a large dynamic range between a low of a quarter moon illumination to the brightest daylight. More information is available at product page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/VNP02DNB/ proprietary
VNP02DNB_NRT_2 VIIRS/NPP Day/Night Band 6-Min L1B Swath 750m NRT LANCEMODIS STAC Catalog 2022-01-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2208367854-LANCEMODIS.umm_json The VIIRS/NPP Day/Night Band 6-Min L1B Swath SDR 750m Near Real Time (NRT) product, short-name VNP02DNB_NRT is among the VIIRS Level 1 and Level 2 swath products that are generated from the processing of 6 minutes of VIIRS data acquired during the S-NPP satellite overpass. The Day/Night band (DNB) is a panchromatic channel covering the wavelengths from 500 nm to 900 nm, and sensitive to visible and near-infrared from daylight down to the low-level radiation observed at night. The VIIRS DNB is much improved from previous products due in large part to its complicated continuous on-board calibration. In addition, new-moon Earth observations are used to estimate and remove stray light. These corrections are a first of its kind to provide on-orbit radiometric calibration. The corrections made to the DNB data are provided by the NASA VIIRS Characterization Support Team and are likely to continue to evolve given this new methodology. The spatial resolution of the instrument at viewing nadir is approximately 750 m for the DNB and the Moderate-resolution Bands and and 375m for the Imagery bands. The DNB is aggregated to maintain nearly constant horizontal spatial resolution across the swath. As the DNB is sensitive to nighttime radiation over the full lunar cycle, the incoming solar and lunar radiation must be properly modeled to calculate the reflectance. However, the DNB is sensitive to more sources of radiation than just the sun and moon. proprietary
@@ -16650,6 +16655,7 @@ VNP13A2_001 VIIRS/NPP Vegetation Indices 16-Day L3 Global 1km SIN Grid V001 LPDA
VNP13A2_002 VIIRS/NPP Vegetation Indices 16-Day L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2519127613-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VNP13A2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles, and a quality layer. Two low resolution browse images are also available for each VNP13A2 product: EVI and NDVI. proprietary
VNP13A3_001 VIIRS/NPP Vegetation Indices Monthly L3 Global 1km SIN Grid V001 LPDAAC_ECS STAC Catalog 2012-01-01 2024-05-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1392010615-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A3) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; pixel reliability; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VNP13A3 product: EVI and NDVI. proprietary
VNP13A3_002 VIIRS/NPP Vegetation Indices Monthly L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2631828309-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VNP13A3) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; pixel reliability; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VNP13A3 product: EVI and NDVI. proprietary
+VNP13A4N_2 VIIRS/NPP Vegetation Indices 8-Day L3 Global 500m SIN Grid LANCEMODIS STAC Catalog 2025-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3363997464-LANCEMODIS.umm_json The VIIRS Near Real Time (NRT) Vegetation Indices 8-Day L3 Global 500m SIN Grid data, short-name VNP13A4N are provided everyday at 500-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes and global and regional climate. These data also may be used for characterizing land surface biophysical properties and processes including primary production and land cover conversion. Note: This is a near real-time product only. Standard historical data and imagery for VNP13A4N (8-Day 500m) are not available. The only 500m standard Vegetation Indices product available is a 16-Day composite (VNP13A1). So, users can either use VNP13A1, use the NDVI standard products from LAADS web (https://ladsweb.modaps.eosdis.nasa.gov/search/), or access the science quality VNP09A1 data and create the VI product of their own. proprietary
VNP13C1_001 VIIRS/NPP Vegetation Indices 16-Day L3 Global 0.05Deg CMG V001 LPDAAC_ECS STAC Catalog 2012-01-17 2024-06-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1392010611-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13C1) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VNP13C1 product: EVI and NDVI. proprietary
VNP13C1_002 VIIRS/NPP Vegetation Indices 16-Day L3 Global 0.05Deg CMG V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314532-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (https://lpdaac.usgs.gov/dataset_discovery/viirs) Vegetation Indices (VNP13C1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VNP13C1 product: EVI and NDVI. proprietary
VNP13C2_001 VIIRS/NPP Vegetation Indices Monthly L3 Global 0.05Deg CMG V001 LPDAAC_ECS STAC Catalog 2012-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1392010618-LPDAAC_ECS.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13C2) Version 1 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 0.05 degree (Deg) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices layers, this product also includes layers for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality layer. Two low resolution browse images are also available for each VNP13C2 product: EVI and NDVI. proprietary
@@ -16815,9 +16821,10 @@ VNP43MA4_001 VIIRS/NPP BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 1km S
VNP43MA4_002 VIIRS/NPP BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 1km SIN Grid V002 LPCLOUD STAC Catalog 2012-01-17 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2545314608-LPCLOUD.umm_json The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Nadir Bidirectional Reflectance Distribution Function (BRDF) Adjusted Reflectance (NBAR) Version 2 product provides NBAR estimates at 1 kilometer (km) resolution. The VNP43IA4 product is produced daily using 16-day VIIRS data and is weighted temporally to the 9th day, which is reflected in the file name. The view angle effects are removed from the directional reflectances resulting in a stable and consistent NBAR product. The VNP43 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP43 data products are designed to promote the continuity of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo data product suite. The VNP43 algorithm uses the RossThick/Li-Sparse-Reciprocal (RTLSR) semi-empirical kernel-driven BRDF model, with the three kernel weights from VNP43MA1 to reconstruct surface anisotropic effects, correcting the directional reflectance to a common view geometry (VNP43MA4), while also computing integrated black-sky albedo (BSA) at local solar noon and white-sky albedo (WSA) (VNP43MA3). Researchers can use the BRDF model parameters with a simple polynomial, to obtain black-sky albedo at any solar illumination angle. Likewise, both the BSA and WSA Science Dataset (SDS) layers can be used with a simple polynomial, to manually estimate instantaneous actual albedo (blue-sky albedo). Additional details regarding the methodology are available in the Algorithm Theoretical Basis Document (ATBD). The VNP43MA4 product includes 18 SDS layers for BRDF/Albedo mandatory quality and nadir reflectance for VIIRS nine moderate bands M1-M5, M7-M8, and M10-M11. A low-resolution browse image is also available showing NBAR bands M5, M7, and M5 as an RGB image in JPEG format. proprietary
VNP46A1G_NRT_2 VIIRS/NPP Granular Gridded Day Night Band 500m Linear Lat. Lon. Grid Night NRT LANCEMODIS STAC Catalog 2023-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2780764136-LANCEMODIS.umm_json The Near Real Time (NRT) Suomi National Polar-Orbiting Partnership (S-NPP) NASA Visible Infrared Imaging Radiometer Suite (VIIRS) hourly top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/NPP Granular Gridded Day Night Band 500m Linear Lat Lon Grid Night. Known by its short-name, VNP46A1G_NRT, is same as VNP46A1_NRT except that this product is generated hourly, cumulative from start of day through the hour the file is generated for. This product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. proprietary
VNP46A1_1 VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night LAADS STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1897815356-LAADS.umm_json The first of two VIIRS DNB-based datasets is a daily, top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/NPP Daily Gridded Day Night Band 15 arc-second Linear Lat Lon Grid Night. Known by its short-name, VNP46A1, this product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. proprietary
-VNP46A1_2 VIIRS/NPP Daily Gridded Day Night Band 500 m Linear Lat Lon Grid Night LAADS STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2980666614-LAADS.umm_json The VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night product, short-name VNP46A1 is a daily, top-of-atmosphere, at-sensor nighttime radiance product. This product is available at 15 arc-second spatial resolution from January 2012 onward. The VNP46A1/VJ146A1 product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. proprietary
+VNP46A1_2 VIIRS/NPP Daily Gridded Day Night Band 500 m Linear Lat Lon Grid Night LAADS STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2980666614-LAADS.umm_json The VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night product, short-name VNP46A1 is a daily, top-of-atmosphere, at-sensor nighttime radiance product. This product is available at 15 arc-second spatial resolution from January 2012 onward. The VNP46A1/VJ146A1 product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. The current v2.0 collection contains several changes and differences relative to the previous v1.0 collection. These include radiance data format change from unsigned integer to floating-point, from exclusively for land surfaces coverage to both land and water surfaces, updated Mandatory_Quality_Flag layer, and others. Consult the v2.0-specific Black Marble User Guide for additional details at: https://landweb.modaps.eosdis.nasa.gov/data/userguide/BlackMarbleUserGuide_Collection2.0_20241203.pdf proprietary
VNP46A1_NRT_2 VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat. Lon. Grid Night NRT LANCEMODIS STAC Catalog 2023-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2780753001-LANCEMODIS.umm_json The first of two Visible Infrared Imager Radiometer Suite (VIIRS) Day Night Band (DNB) based Near Real Time (NRT) datasets is a daily, top-of-atmosphere, at-sensor nighttime radiance product called VIIRS/NPP Daily Gridded Day Night Band 500m Linear Lat Lon Grid Night. Known by its short-name, VNP46A1_NRT, this product contains 26 Science Data Sets (SDS) that include sensor radiance, zenith and azimuth angles (at-sensor, solar, and lunar), cloud-mask flags, time, shortwave IR radiance, brightness temperatures, VIIRS quality flags, moon phase angle, and moon illumination fraction. It also provides Quality Flag (QF) information specific to the cloud-mask, VIIRS moderate-resolution bands M10, M11, M12, M13, M15, M16, and DNB. proprietary
VNP46A2_1 VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 500m Linear Lat Lon Grid LAADS STAC Catalog 2012-01-19 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1898025206-LAADS.umm_json The second of the two VIIRS DNB-based datasets is a daily moonlight- and atmosphere-corrected Nighttime Lights (NTL) product called VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 500m Linear Lat Lon Grid. Known by its short-name, VNP46A2, this product contains seven Science Data Sets (SDS) that include DNB BRDF-Corrected NTL, Gap-Filled DNB BRDF-Corrected NTL, DNB Lunar Irradiance, Latest High-Quality Retrieval, Mandatory Quality Flag, Cloud Mask Quality Flag, and Snow Flag. VNP46A2 products are provided in standard Hierarchical Data Format–Earth Observing System (HDF-EOS5) format. proprietary
+VNP46A2_NRT_2 VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 15 arc-second Linear Lat Lon Grid NRT LANCEMODIS STAC Catalog 2025-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3363944097-LANCEMODIS.umm_json The second of the two VIIRS DNB-based datasets is a daily moonlight- and atmosphere-corrected Nighttime Lights (NTL) product called VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Daily L3 Global 500m Linear Lat Lon Grid. Known by its short-name, VNP46A2, this product contains seven Science Data Sets (SDS) that include DNB BRDF-Corrected NTL, Gap-Filled DNB BRDF-Corrected NTL, DNB Lunar Irradiance, Latest High-Quality Retrieval, Mandatory Quality Flag, Cloud Mask Quality Flag, and Snow Flag. VNP46A2 products are provided in standard Hierarchical Data Format–Earth Observing System (HDF-EOS5) format. proprietary
VNP46A3_1 VIIRS/NPP Lunar BRDF-Adjusted Nighttime Lights Monthly L3 Global 15 arc-second Linear Lat Lon Grid LAADS STAC Catalog 2012-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2062201748-LAADS.umm_json VIIRS/NPP Gap-Filled Lunar BRDF-Adjusted Nighttime Lights Monthly L3 Global 500m Linear Lat Lon Grid, with short-name VNP46A3, is the third nighttime lights (NTL) product in the Black Marble suite, which provides monthly composites generated from daily atmospherically- and lunar-BRDF-corrected NTL radiance to remove the influence of extraneous artifacts and biases. The VNP46A3 product contains 28 layers. They provide information on the NTL composite, the number of observations, quality, and standard deviation for multi-view zenith angle categories (near-nadir, off-nadir, and all angles), their snow-covered and snow-free statuses besides land-water mask, latitude and longitude coordinate information. They also include detailed information and description of the quality flags. This description pertains to the SNPP VIIRS Monthly Lunar BRDF-adjusted NTL collection, whose record starts from January 1st 2012. proprietary
VNP46A4_1 VIIRS/NPP Lunar BRDF-Adjusted Nighttime Lights Yearly L3 Global 15 arc-second Linear Lat Lon Grid LAADS STAC Catalog 2012-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2062213246-LAADS.umm_json VIIRS/NPP Lunar BRDF-Adjusted Nighttime Lights Yearly L3 Global 15 arc-second Linear Lat Lon Grid, with short-name VNP46A4, is the third nighttime lights (NTL) product in the Black Marble suite, which provides monthly composites generated from daily atmospherically- and lunar-BRDF-corrected NTL radiance to remove the influence of extraneous artifacts and biases. The VNP46A4 product contains 28 layers. They provide information on the NTL composite, the number of observations, quality, and standard deviation for multi-view zenith angle categories (near-nadir, off-nadir, and all angles), their snow-covered and snow-free statuses besides land-water mask, latitude and longitude coordinate information. They also include detailed information and description of the quality flags. This yearly Lunar BRDF-Adjusted NTL collection record starts from January 1st 2012. proprietary
VNP64A1_001 VIIRS/NPP Burned Area Monthly L4 Global 500m SIN Grid V001 LPDAAC_ECS STAC Catalog 2014-01-01 2019-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1632559364-LPDAAC_ECS.umm_json The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 1 data product is a monthly, global gridded 500-meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1–366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission. VNP64A1 has been released on a limited basis due to concerns over the quality of the data along the edges of inland water bodies and at high latitudes. These regions contain grid cells falsely identified as burned as a result of coarse resolution inputs to the cloud mask used in the generation of the 750 m VIIRS active fire observations. Users are urged to exercise caution when using this provisional data in research. The Version 2 burned area product generated with an improved cloud mask was released on October 22, 2024. Users are encouraged to use the improved V002 burned area product. The data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, and Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format. Notification: VIIRS/NPP Burned Area Monthly L4 Global 500 m SIN Grid data product has been released on a limited basis due to falsely identified burned areas. Users are encouraged to use the improved Version 2 data. proprietary
@@ -16825,8 +16832,8 @@ VNP64A1_002 VIIRS/NPP Burned Area Monthly L4 Global 500m SIN Grid V002 LPCLOUD S
VOLPE_0 Chlorophyll-a measurements off the San Diego coast in 1999 OB_DAAC STAC Catalog 1999-09-14 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360696-OB_DAAC.umm_json Measurements made off the San Diego, Californian coast in 1999. proprietary
VPRM_North_America_Parameters_1349_1 NACP VPRM NEE Parameters Optimized to North American Flux Tower Sites, 2000-2006 ORNL_CLOUD STAC Catalog 2000-01-01 2007-12-31 -156.63, 28.46, -68.74, 71.32 https://cmr.earthdata.nasa.gov/search/concepts/C2517710454-ORNL_CLOUD.umm_json This data set provides Vegetation Photosynthesis Respiration Model (VPRM) net ecosystem exchange (NEE) parameter values optimized to 65 flux tower sites across North America. The parameters include the basal rate of ecosystem respiration (beta), the slope of respiration with respect to temperature (alpha), light-use efficiency (LUE) (lambda), and LUE curve half-saturation (PAR_0). Observed eddy covariance data from the 65 tower sites, locally observed temperature and photosynthetically active radiation (PAR) along with satellite-derived phenology and moisture were used as input data to optimize the VPRM parameters for the 65 sites. The data are provided by individual site, plant functional types (PFTs), and all sites together, and as monthly, annual, and all available data. The data are for the conterminous USA, Alaska, and Canada for the period 2000 to 2006. proprietary
VT_GOCE_Data_5.0 VT GOCE Data ESA STAC Catalog 2009-09-01 2012-07-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336957-ESA.umm_json This collection contains the VT GOCE software and associated data set needed to run the software that is used for GOCE data visualisation. proprietary
-Veg_Soil_Tundra_Burned_Area_2119_1 ABoVE: Post-Fire and Unburned Field Site Data, Anaktuvuk River Fire Area, 2008-2017 ALL STAC Catalog 2008-07-03 2017-07-23 -151.18, 69.02, -150.03, 69.36 https://cmr.earthdata.nasa.gov/search/concepts/C2612823595-ORNL_CLOUD.umm_json This dataset includes field measurements from 26 burned and unburned transects established in 2008 in the region of the Anaktuvuk River tundra fire on the Arctic Slope of Alaska, US. Measurements include plant cover by species, shrub and tussock density, thaw depth, and soil depth. This wildfire occurred in 2007, and sampling took place in 2008-2011 and in 2017. proprietary
Veg_Soil_Tundra_Burned_Area_2119_1 ABoVE: Post-Fire and Unburned Field Site Data, Anaktuvuk River Fire Area, 2008-2017 ORNL_CLOUD STAC Catalog 2008-07-03 2017-07-23 -151.18, 69.02, -150.03, 69.36 https://cmr.earthdata.nasa.gov/search/concepts/C2612823595-ORNL_CLOUD.umm_json This dataset includes field measurements from 26 burned and unburned transects established in 2008 in the region of the Anaktuvuk River tundra fire on the Arctic Slope of Alaska, US. Measurements include plant cover by species, shrub and tussock density, thaw depth, and soil depth. This wildfire occurred in 2007, and sampling took place in 2008-2011 and in 2017. proprietary
+Veg_Soil_Tundra_Burned_Area_2119_1 ABoVE: Post-Fire and Unburned Field Site Data, Anaktuvuk River Fire Area, 2008-2017 ALL STAC Catalog 2008-07-03 2017-07-23 -151.18, 69.02, -150.03, 69.36 https://cmr.earthdata.nasa.gov/search/concepts/C2612823595-ORNL_CLOUD.umm_json This dataset includes field measurements from 26 burned and unburned transects established in 2008 in the region of the Anaktuvuk River tundra fire on the Arctic Slope of Alaska, US. Measurements include plant cover by species, shrub and tussock density, thaw depth, and soil depth. This wildfire occurred in 2007, and sampling took place in 2008-2011 and in 2017. proprietary
Vegetation_Maps_Toolik_Lake_1690_1 High-Resolution Vegetation Community Maps, Toolik Lake Area, Alaska, 2013-2015 ORNL_CLOUD STAC Catalog 2013-08-01 2015-08-31 -149.66, 68.6, -149.29, 68.65 https://cmr.earthdata.nasa.gov/search/concepts/C2143403456-ORNL_CLOUD.umm_json This dataset contains vegetation community maps at 20 cm resolution for three landscapes near the Toolik Lake research area in the northern foothills of the Brooks Range, Alaska, USA. The maps were built using a Random Forest modeling approach using predictor layers derived from airborne lidar data and high-resolution digital airborne imagery collected in 2013, and vegetation community training data collected from 800 reference field plots across the lidar footprints in 2014 and 2015. Vegetation community descriptions were based on the commonly used classifications of existing Toolik area vegetation maps. proprietary
Vegetation_Photos_Toolik_Lake_1718_1 Ground-Based Vegetation Community Photos, Toolik Lake Area, Alaska, 2014-2015 ORNL_CLOUD STAC Catalog 2014-06-17 2015-07-31 -149.66, 68.6, -149.29, 68.65 https://cmr.earthdata.nasa.gov/search/concepts/C2143402747-ORNL_CLOUD.umm_json This dataset contains 731 ground-based nadir vegetation community and ground surface photographs of selected field plots taken as ground reference data for vegetation classification studies at three areas near Toolik Lake, Alaska during the summers of 2014 and 2015. The largest area, 'Toolik', (approximately 6 km2) covers research areas near Toolik Field Station at Toolik Lake, including Arctic LTER installations. The other two areas are each roughly 3 km2: the 'Pipeline' area: a stretch of the Trans-Alaska Pipeline, and the 'Imnavait' area: along Imnavait Creek roughly 10 km east of Toolik Lake. proprietary
Vegetation_greenness_trend_1576_1 ABoVE: NDVI Trends across Alaska and Canada from Landsat, 1984-2012 ALL STAC Catalog 1984-01-01 2012-12-31 -169.97, 41.61, -50.17, 80.51 https://cmr.earthdata.nasa.gov/search/concepts/C2162131333-ORNL_CLOUD.umm_json "This dataset provides the summer NDVI trend and trend significance for the period 1984-2012 over Alaska and Canada. The NDVI were calculated per-pixel from all available peak-summer 30-m Landsat 5 and 7 surface reflectance data for the period. NDVI time series were assembled for each 30-m land location (i.e., non-water, non-snow), from observations that were unaffected by clouds as indicated by data-quality masks and following additional processing to remove anomalous NDVI values. A simple linear regression via ordinary least squares was applied to the per-pixel NDVI time series. The slope of the regression was taken as the annual NDVI trend (unit NDVI change per year) and is reported in the ""trend"" data files. A Student's t-test was used to assess the significance of the trend and the per-pixel significance is reported in the ""trend_sig"" data files. A significant positive slope indicates a greening trend, and a significant negative slope indicates a browning trend." proprietary
@@ -16840,8 +16847,8 @@ Vulcan_V3_Annual_Emissions_1741_1 Vulcan: High-Resolution Annual Fossil Fuel CO2
Vulcan_V3_Hourly_Emissions_1810_1 Vulcan: High-Resolution Hourly Fossil Fuel CO2 Emissions in USA, 2010-2015, Version 3 ORNL_CLOUD STAC Catalog 2010-01-01 2016-01-01 -165.21, 22.86, -65.31, 73.75 https://cmr.earthdata.nasa.gov/search/concepts/C2516155224-ORNL_CLOUD.umm_json The Vulcan version 3.0 hourly dataset quantifies hourly emissions at a 1-km resolution for the 2010-2015 time period. Estimates are provided of hourly carbon dioxide (CO2) emissions from the combustion of fossil fuels (FF) and CO2 emissions from cement production for the conterminous United States and the state of Alaska. Referred to as FFCO2, the emissions from Vulcan are categorized into 10 source sectors including; residential, commercial, industrial, electricity production, onroad, nonroad, commercial marine vessel, airport, rail, and cement. Files for hourly total emissions are also available. Data are represented in space on a 1 km x 1 km grid as hourly totals for 2010-2015. This dataset provides the first bottom-up U.S.-wide FFCO2 emissions data product at 1 km2/hourly for multiple years and is designed to be used as emission estimates in atmospheric transport modeling, policy, mapping, and other data analyses and applications. proprietary
WACS2_0 Western Atlantic Climate Study II OB_DAAC STAC Catalog 2014-05-22 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360697-OB_DAAC.umm_json Sea spray aerosol (SSA) impacts the Earth’s radiation budget indirectly by altering cloud properties including albedo, lifetime, and extent, and directly by scattering solar radiation. Characterization of the properties of SSA in its freshly emitted state is needed for accurate model calculations of climate impacts. In addition, simultaneous measurements of surface seawater are required to assess the impact of ocean properties on sea spray aerosol and to develop accurate parameterizations of the SSA number production flux for use in regional and global scale models.Sea spray aerosol (SSA) impacts the Earth’s radiation budget indirectly by altering cloud properties including albedo, lifetime, and extent, and directly by scattering solar radiation. Characterization of the properties of SSA in its freshly emitted state is needed for accurate model calculations of climate impacts. In addition, simultaneous measurements of surface seawater are required to assess the impact of ocean properties on sea spray aerosol and to develop accurate parameterizations of the SSA number production flux for use in regional and global scale models. proprietary
WAF_DEALIASED_SASS_L2_1 SEASAT SCATTEROMETER DEALIASED OCEAN WIND VECTORS (Wentz et al.) POCLOUD STAC Catalog 1978-07-07 1978-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2617197640-POCLOUD.umm_json Contains Seasat-A Scatterometer (SASS) wind vector measurements for the entire Seasat mission, from July 1978 until October 1978. The data are global and presented chronologically in by swath. Each record contains data binned in 100 km cells. No wind vectors are computed for the cells along the left and right edges of the swath. Wind direction ambiguities are resolved using a global weather prediction model. This complete dataset is the result of the reprocessing efforts on behalf of Frank Wentz, Robert Atlas, and Michael Freilich. proprietary
-WARd0002_108 Administration Division Maps Of Poland ALL STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232846827-CEOS_EXTRA.umm_json Administration division of Poland created on a basis of digitization with manual generalisation proper for specific scales. Projection Albers; points and polygons; ARC/INFO and SINUS systems proprietary
WARd0002_108 Administration Division Maps Of Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232846827-CEOS_EXTRA.umm_json Administration division of Poland created on a basis of digitization with manual generalisation proper for specific scales. Projection Albers; points and polygons; ARC/INFO and SINUS systems proprietary
+WARd0002_108 Administration Division Maps Of Poland ALL STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232846827-CEOS_EXTRA.umm_json Administration division of Poland created on a basis of digitization with manual generalisation proper for specific scales. Projection Albers; points and polygons; ARC/INFO and SINUS systems proprietary
WARd0004_108 Land Use Division Maps of Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232848834-CEOS_EXTRA.umm_json Land use map of Poland acquisited form interpreted Landsat TM, MSS images by digitization. 24 classes of land use grouped in subjects (agriculture, grass lands, settlements and communication areas, forests, surface waters, industry, not used areas). Vector and raster format; projection Albers; ARC/INFO and SINUS systems proprietary
WARd0005_108 Geomorphology Forms of Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232848304-CEOS_EXTRA.umm_json Geomorphological forms of Poland created within Central Scientific Programme 10.4/1989. Digitized from the map of relief types in Poland; Scale 1:1 000 000. proprietary
WARd0006_108 Hunting Unit Border Maps of Poland CEOS_EXTRA STAC Catalog 1970-01-01 24, 14, 49, 54 https://cmr.earthdata.nasa.gov/search/concepts/C2232849207-CEOS_EXTRA.umm_json Borders of hunting units digitized from the maps prepared by Polish Hunting Association within Central Scientific Programme 10.4/1989. proprietary
@@ -16868,11 +16875,11 @@ WENTZ_NIMBUS-7_SMMR_L2_1 NIMBUS-7 SMMR GLOBAL AIR-SEA PARAMETERS IN SWATH (Wentz
WENTZ_SASS_SIGMA0_L2_1 SEASAT SCATTEROMETER BINNED 50KM SIGMA-0 DATA (Wentz) POCLOUD STAC Catalog 1978-07-07 1978-10-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2617197621-POCLOUD.umm_json Contains Seasat-A Scatterometer (SASS) Sigma-0 measurements for the entire Seasat mission, from July 1978 until October 1978, produced by Frank Wentz at Remote Sensing Systems. The data are presented chronologically by swath and consist of the forward and aft values, binned in 50 km cells. For each cell there are 17 parameters including time, location, incidence angle, sigma-0, instrument corrections, and data quality. proprietary
WHITECAPS_0 Influence of Whitecaps on Aerosol and Ocean-Color Remote Sensing OB_DAAC STAC Catalog 2011-02-20 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360700-OB_DAAC.umm_json The influence of whitecaps on ocean color and aerosol remote sensing from space were invistigated onboard the R/V Melville (MV1102) from Cape Town, South Africa to Valparaiso, Chile from February 2, 2011 to March 14, 2011. Satellite imagery has revealed relatively large amounts of aerosols and particulate organic and inorganic carbon in the Southern oceans, but it is not clear whether this is real or the result of not taking into account properly whitecap effects in the retrieval algorithms. By measuring whitecap optical properties and profiles of marine reflectance and backscattering and absorption coefficients, a bulk whitecap reflectance model will be developed. The measurements will allow comparisons of the aerosol optical thickness and marine reflectance one should retrieve (i.e., in the absence of whitecaps and bubbles) with the satellite-derived estimates. The parameters/variables that will be measured include whitecap coverage, surface reflectance, aerosol optical thickness, in situ profiles of marine reflectance, backscattering and attenuation coefficients, and particle size distribution, and absorption and backscattering coefficients and HPLC pigments from water samples. The backscattering and absorption measurements from water samples will characterize conditions without whitecaps. Cruise information can be found in the R2R repository: https://www.rvdata.us/search/cruise/MV1102. proprietary
WILKS_2018_Chatham_sedimenttraps_specieslist_3 Diatom and coccolithophore assemblages from archival sediment trap samples of the Subtropical and Subantarctic Southwest Pacific AU_AADC STAC Catalog 1996-06-17 1997-05-07 174.90234, -45.39845, 179.73633, -40.71396 https://cmr.earthdata.nasa.gov/search/concepts/C1459701888-AU_AADC.umm_json "This spreadsheet contains species lists and counts from four sediment trap records. The sediment traps were deployed for ~1 year north and south of the Chatham Rise, New Zealand, between 1996 and 1997. Sheets 1a and 1b refer to North Chatham Rise (NCR). 1a = the 300m trap. 1b = the 1000m trap. Sheets 2a and 2b are for the South Chatham Rise traps (SCR). 2a= 300m, 2b= 1000m. Counting was undertaken on 1/16th splits. Material was cleaned of organics before diatom counting under light microscopy. Coccolith counting on uncleaned material was only undertaken at the 300m traps. Radiolarians and silicoflagellates were counted but not identified. Diatoms and coccoliths were counted along non-overlapping transects until 300 specimens had been counted per sample, or until 10 transects had been made. This dataset includes counts of diatom, coccolithophores, radiolarians and silicoflagellates for four sediment trap records- North Chatham Rise (NCR) and South Chatham Rise (SCR) at two trap depths each (300 m and 1000 m). It is intended as supplementary material to Wilks et al. 2018 (submitted) ""Diatom and coccolithophore assemblages from archival sediment trap samples of the Subtropical and Subantarctic Southwest Pacific."" Numbers are raw count per sample cup. Authorities are given. Coordinates of traps given in degrees, minutes and seconds." proprietary
-WIND_3DP 3-D Plasma and Energetic Particle Investigation on WIND ALL STAC Catalog 1994-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214615352-SCIOPS.umm_json The main purpose of the Wind spacecraft is to measure the incoming solar wind, magnetic fields and particles, although early on it will also observe the Earth's foreshock region. Wind, together with Geotail, Polar, SOHO, and Cluster projects, constitute a cooperative scientific satellite project designated the International Solar Terrestrial Physics (ISTP) program which aims at gaining improved understanding of the physics of solar terrestrial relations. This experiment is designed to measure the full three-dimensional distribution of suprathermal electrons and ions at energies from a few eV to over several hundred keV on the WIND spacecraft. Its high sensitivity, wide dynamic range, and good energy and angular resolution make it especially capable of detecting and characterizing the numerous populations of particles that are present in interplanetary space at energies above the bulk of the solar wind particles and below the energies typical of most cosmic rays. Data consists of ion moments, energy spectra, electron spectra, electron and ion omni directional energy spectra. Data are available from SSL at University of California, Berkeley (http://sprg.ssl.berkeley.edu/wind3dp/esahome.html) and at the NSSDC CDAWeb (http://cdaweb.gsfc.nasa.gov/cdaweb/) proprietary
WIND_3DP 3-D Plasma and Energetic Particle Investigation on WIND SCIOPS STAC Catalog 1994-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214615352-SCIOPS.umm_json The main purpose of the Wind spacecraft is to measure the incoming solar wind, magnetic fields and particles, although early on it will also observe the Earth's foreshock region. Wind, together with Geotail, Polar, SOHO, and Cluster projects, constitute a cooperative scientific satellite project designated the International Solar Terrestrial Physics (ISTP) program which aims at gaining improved understanding of the physics of solar terrestrial relations. This experiment is designed to measure the full three-dimensional distribution of suprathermal electrons and ions at energies from a few eV to over several hundred keV on the WIND spacecraft. Its high sensitivity, wide dynamic range, and good energy and angular resolution make it especially capable of detecting and characterizing the numerous populations of particles that are present in interplanetary space at energies above the bulk of the solar wind particles and below the energies typical of most cosmic rays. Data consists of ion moments, energy spectra, electron spectra, electron and ion omni directional energy spectra. Data are available from SSL at University of California, Berkeley (http://sprg.ssl.berkeley.edu/wind3dp/esahome.html) and at the NSSDC CDAWeb (http://cdaweb.gsfc.nasa.gov/cdaweb/) proprietary
+WIND_3DP 3-D Plasma and Energetic Particle Investigation on WIND ALL STAC Catalog 1994-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214615352-SCIOPS.umm_json The main purpose of the Wind spacecraft is to measure the incoming solar wind, magnetic fields and particles, although early on it will also observe the Earth's foreshock region. Wind, together with Geotail, Polar, SOHO, and Cluster projects, constitute a cooperative scientific satellite project designated the International Solar Terrestrial Physics (ISTP) program which aims at gaining improved understanding of the physics of solar terrestrial relations. This experiment is designed to measure the full three-dimensional distribution of suprathermal electrons and ions at energies from a few eV to over several hundred keV on the WIND spacecraft. Its high sensitivity, wide dynamic range, and good energy and angular resolution make it especially capable of detecting and characterizing the numerous populations of particles that are present in interplanetary space at energies above the bulk of the solar wind particles and below the energies typical of most cosmic rays. Data consists of ion moments, energy spectra, electron spectra, electron and ion omni directional energy spectra. Data are available from SSL at University of California, Berkeley (http://sprg.ssl.berkeley.edu/wind3dp/esahome.html) and at the NSSDC CDAWeb (http://cdaweb.gsfc.nasa.gov/cdaweb/) proprietary
WIR_98_4105 Major-Ion, Nutrient, and Trace-Element Concentrations in the Steamboat Creek Basin CEOS_EXTRA STAC Catalog 1996-09-09 1996-09-13 -122.7, 42.3, -122.5, 43.6 https://cmr.earthdata.nasa.gov/search/concepts/C2231554333-CEOS_EXTRA.umm_json In September 1996, a water-quality study was done by the U.S. Geological Survey, in coordination with the U.S. Forest Service, in headwater streams of Steamboat Creek, a tributary to the North Umpqua River Basin in southwestern Oregon. Field measurements were made in and surface-water and bottom-sediment samples were collected from three tributaries of Steamboat Creek-Singe Creek, City Creek, and Horse Heaven Creek-and at one site in Steamboat Creek upstream from where the three tributaries flow into Steamboat Creek. Water samples collected in Singe Creek had larger concentrations of most major-ion constituents and smaller concentrations of most nutrient constitu ents than was observed in the other three creeks. City Creek, Horse Heaven Creek, and Steamboat Creek had primarily calcium bicarbonate water, whereas Singe Creek had primarily a calcium sulfate water; the calcium sulfate water detected in Singe Creek, along with the smallest observed alkalinity and pH values, suggests that Singe Creek may be receiving naturally occurring acidic water. Of the 18 trace elements analyzed in filtered water samples, only 6 were detected-aluminum, barium, cobalt, iron, manganese, and zinc. All six of the trace elements were detected in Singe Creek, at concentrations generally larger than those observed in the other three creeks. Of the detected trace elements, only iron and zinc have chronic toxicity criteria established by the U.S. Environmental Protection Agency (USEPA) for the protection of aquatic life; none exceeded the USEPA criterion. Bottom-sediment concentrations of antimony, arsenic, cadmium, copper, lead, mercury, zinc, and organic carbon were largest in City Creek. In City Creek and Horse Heaven Creek, concentrations for 11 constituents--antimony, arsenic, cadmium, copper, lead, manganese (Horse Heaven Creek only), mercury, selenium, silver, zinc, and organic carbon (City Creek only)--exceeded concentrations considered to be enriched in streams of the nearby Willamette River Basin, whereas in Steamboat Creek only two trace elements--antimony and nickel--exceeded Willamette River enriched concentrations. Bottom-sediment concentrations for six of these constituents in City Creek and Horse Heaven Creek--arsenic, cadmium, copper, lead, mercury, and zinc--also exceeded interim Canadian threshold effect level (TEL) concentrations established for the protection of aquatic life, whereas only four constituents between Singe Creek and Steamboat Creek--arsenic, chromium, copper (Singe Creek only), and nickel--exceeded the TEL concentrations. The data set checked for the concentrations of major ions, nutrients, and trace elements in water and bottom sediments collected in the four tributaries during the low-flow conditions of September 9-13, 1996. Stream-water chemistry results were contrasted, and trace-element concentrations were compared with U.S. Environmental Protection Agency chronic aquatic life toxicity criteria. Bottom-sediment trace-element results were also contrasted and compared with concentrations considered to be enriched in streams of the nearby Willamette River Basin and to interim Canadian threshold level (TEL) concentrations established for the protection of aquatic life. The area of study was Headwater streams of Steamboat Creek, a tributary to the north Umpqua River Basin in southwestern Oregon Field measurements and surface-water and bottom-sediment samples at each of the four sites included streamflow, stream temperature, specific conductance, dissolved oxygen, pH, alkalinity, major ions in filtered water (8 constituents), low-level concentrations of trace elements in filtered water (18 elements), and trace elements and carbon in bottom sediment (47 elements). Stream temperature, specific conductance, dissolved oxygen, and pH were measured using a calibrated Hydrolab multiparameter unit. Because stream widths were less than 8 feet, field measurements were made only near the center of flow at 1 foot or less below water surface. The Hydrolab unit was calibrated at each site before and after sampling. Stream temperatures were recorded to the nearest 0.1 degree Centigrade; specific conductance to the nearest 1 microsiemen per centimeter at 25 degrees Centigrade ; dissolved oxygen to the nearest 0.1 milligrams per liter; and pH to the nearest 0.1 pH units. Measurements of streamflow were made in accordance with standard USGS procedures (Rantz and others, 1982). Alkalinity measurements were made on filtered water samples using an incremental titration method (Shelton, 1994), and results were reported to the nearest 1 milligram per liter as calcium carbonate (CaCO3). Water samples were collected using 1-liter narrow-mouth acid-rinsed polyethylene bottles from a minimum of eight verticals in the cross section, suing an equal-width-increment method described by Edwards and Glysson (1988), and composited into a 8-liter polyethylene acid-rinsed churn splitter. Sample and compositing containers were prerinsed with native water prior to sample collection. Water samples were collected using clean procedures as outlined by Horowitz and others (1994). Samples were chilled on ice from time of sample collection until analysis, except when samples were processed. Processing of the field samples was accomplished either in the mobile field laboratory or in an area suitably clean for carrying out the filtering and preservation procedures. Samples for major ions, nutrients, and trace elements in filtered water (operationally defined as dissolved) were passed through 0.45 micrometer pore-size capsule filters into polyethylene bottles using procedures outlined by Horowitz and others (1994). Filtered-water trace-element samples were preserved with 0.5 milliliter of ultra-pure nitric acid per 250 mL of sample; nutrient samples were placed in dark brown polyethylene bottles and were chilled for preservation. All chemical samples were shipped to the USGS National Water Quality Laboratory (NWQL) in Arvada, Colorado, for analysis according to methods outlined by Fishman (1993). The information for this metadata was taken from the Online Publications of the Oregon District at http://oregon.usgs.gov/pubs_dir/online_list.html . proprietary
-WISPMAWSON04-05_1 A GIS dataset of Wilson's storm petrel nests mapped in the Mawson region during the 2004-2005 season AU_AADC STAC Catalog 2004-12-10 2005-04-25 62.18384, -67.68587, 63.40759, -67.47282 https://cmr.earthdata.nasa.gov/search/concepts/C1214314124-AU_AADC.umm_json Very little information is known about the distribution and abundance of Wilson's storm petrels at the regional and local scales. This dataset contains locations of Wilson's storm petrel nests, mapped in the Mawson region during 2004-2005 season. Location of nests were recorded with handheld Trimble Geoexplorer GPS receivers, differentially corrected and stored as an Arcview point shapefile(ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of data fields is provided in description of the shapefile. A text file also provide the attribute information (formatted for input into R statistical software). This work has been completed as part of ASAC project 2704 (ASAC_2704). Fields recorded Species Activity Type Entrances Slope Remnants Latitude Longitude Date Snow Eggchick Cavitysize Cavitydepth Distnn Substrate Comments SitedotID Aspect Firstfred proprietary
WISPMAWSON04-05_1 A GIS dataset of Wilson's storm petrel nests mapped in the Mawson region during the 2004-2005 season ALL STAC Catalog 2004-12-10 2005-04-25 62.18384, -67.68587, 63.40759, -67.47282 https://cmr.earthdata.nasa.gov/search/concepts/C1214314124-AU_AADC.umm_json Very little information is known about the distribution and abundance of Wilson's storm petrels at the regional and local scales. This dataset contains locations of Wilson's storm petrel nests, mapped in the Mawson region during 2004-2005 season. Location of nests were recorded with handheld Trimble Geoexplorer GPS receivers, differentially corrected and stored as an Arcview point shapefile(ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of data fields is provided in description of the shapefile. A text file also provide the attribute information (formatted for input into R statistical software). This work has been completed as part of ASAC project 2704 (ASAC_2704). Fields recorded Species Activity Type Entrances Slope Remnants Latitude Longitude Date Snow Eggchick Cavitysize Cavitydepth Distnn Substrate Comments SitedotID Aspect Firstfred proprietary
+WISPMAWSON04-05_1 A GIS dataset of Wilson's storm petrel nests mapped in the Mawson region during the 2004-2005 season AU_AADC STAC Catalog 2004-12-10 2005-04-25 62.18384, -67.68587, 63.40759, -67.47282 https://cmr.earthdata.nasa.gov/search/concepts/C1214314124-AU_AADC.umm_json Very little information is known about the distribution and abundance of Wilson's storm petrels at the regional and local scales. This dataset contains locations of Wilson's storm petrel nests, mapped in the Mawson region during 2004-2005 season. Location of nests were recorded with handheld Trimble Geoexplorer GPS receivers, differentially corrected and stored as an Arcview point shapefile(ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of data fields is provided in description of the shapefile. A text file also provide the attribute information (formatted for input into R statistical software). This work has been completed as part of ASAC project 2704 (ASAC_2704). Fields recorded Species Activity Type Entrances Slope Remnants Latitude Longitude Date Snow Eggchick Cavitysize Cavitydepth Distnn Substrate Comments SitedotID Aspect Firstfred proprietary
WLDAS_NOAHMP001_DA1_D1.0 WLDAS Noah-MP 3.6 Land Surface Model L4 Daily 0.01 degree x 0.01 degree Version D1.0 (WLDAS_NOAHMP001_DA1) at GES DISC GES_DISC STAC Catalog 1979-01-02 -124.925, 25.065, -89.025, 52.925 https://cmr.earthdata.nasa.gov/search/concepts/C2789781977-GES_DISC.umm_json The Western Land Data Assimilation System (WLDAS), developed at Goddard Space Flight Center (GSFC) and funded by the NASA Western Water Applications Office, provides water managers and stakeholders in the western United States with a long-term record of near-surface hydrology for use in drought assessment and water resources planning. WLDAS leverages advanced capabilities in land surface modeling and data assimilation to furnish a system that is customized for stakeholders’ needs in the region. WLDAS uses NASA’s Land Information System (LIS) to configure and drive the Noah Multiparameterization (Noah-MP) Land Surface Model (LSM) version 3.6 to simulate land surface states and fluxes. WLDAS uses meteorological observables from the North American Land Data Assimilation System (NLDAS-2) including precipitation, incoming shortwave and longwave radiation, near surface air temperature, humidity, wind speed, and surface pressure along with parameters such as vegetation class, soil texture, and elevation as inputs to a model that simulates land surface energy and water budget processes. Outputs of the model include soil moisture, snow depth and snow water equivalent, evapotranspiration, soil temperature, as well as derived quantities such as groundwater recharge and anomalies of the state variables. proprietary
WOCE91_Chlorophyll_1 Chlorophyll a data collected on the 1991 WOCE voyage of the Aurora Australis AU_AADC STAC Catalog 1991-10-08 1991-10-26 136.393, -62.294, 154.937, -45.183 https://cmr.earthdata.nasa.gov/search/concepts/C1214314037-AU_AADC.umm_json Chloropyll a data were collected along the WOCE transect on voyage 1 of the Aurora Australis, during October of 1991. These data were collected as part of ASAC project 40 (The role of antarctic marine protists in trophodynamics and global change and the impact of UV-B on these organisms). proprietary
WOES_Chlorophyll_1 Aurora Australis Voyage 9 (WOES) 1992-93 Chlorophyll a Data AU_AADC STAC Catalog 1993-03-12 1993-05-03 139.71167, -65.888, 155.11171, -43.22 https://cmr.earthdata.nasa.gov/search/concepts/C1214314038-AU_AADC.umm_json This dataset contains chlorophyll a data collected by the Aurora Australis on Voyage 7, 1992-1993 - the WOES (Wildlife Oceanography Ecosystem Survey) cruise. Samples were collected from March-May of 1993. These data were collected as part of ASAC project 40 (The role of antarctic marine protists in trophodynamics and global change and the impact of UV-B on these organisms). proprietary
@@ -16894,8 +16901,8 @@ WV03_SWIR_L1B_1 WorldView-3 Level 1B Shortwave Infrared 8-Band Satellite Imagery
WV04_MSI_L1B_1 WorldView-4 Level 1B Multispectral 4-Band Satellite Imagery CSDA STAC Catalog 2016-12-01 2019-01-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2497446902-CSDA.umm_json The WorldView-4 Multispectral 4-Band Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the DigitalGlobe WorldView-4 satellite using the SpaceView-110 camera across the global land surface from December 2016 to January 2019. This satellite imagery is in the visible and near-infrared waveband range with data in the blue, green, red, and near-infrared wavelengths. The multispectral imagery has a spatial resolution of 1.24m at nadir and has a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a Maxar End User License Agreement for Worldview 4 imagery and investigators must be approved by the CSDA Program. proprietary
WV04_Pan_L1B_1 WorldView-4 Level 1B Panchromatic Satellite Imagery CSDA STAC Catalog 2016-12-01 2019-01-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2497439327-CSDA.umm_json The WorldView-4 Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Imagery was collected by the DigitalGlobe WorldView-4 satellite using the WorldView-110 camera across the global land surface from December 2016 to January 2019. This data product includes panchromatic imagery with a spatial resolution of 0.31m at nadir and a temporal resolution of approximately 1.1 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a Maxar End User License Agreement for Worldview 4 imagery and investigators must be approved by the CSDA Program. proprietary
WV_LCC_SC_FSCA_1 Land Cover Classification, Snow Cover, and Fractional Snow-Covered Area Maps from Maxar WorldView Satellite Images V001 NSIDC_ECS STAC Catalog 2015-05-20 2019-05-05 -121.203708, 38.867847, -108.032283, 48.672717 https://cmr.earthdata.nasa.gov/search/concepts/C2695676729-NSIDC_ECS.umm_json This data set includes: (1) fine-scale snow and land cover maps from two mountainous study sites in the Western U.S., produced using machine-learning models trained to extract land cover data from WorldView-2 and WorldView-3 stereo panchromatic and multispectral images; (2) binary snow maps derived from the land cover maps; and (3) 30 m and 465 m fractional snow-covered area (fSCA) maps, produced via downsampling of the binary snow maps. The land cover classification maps feature between three and six classes common to mountainous regions and integral for accurate stereo snow depth mapping: illuminated snow, shaded snow, vegetation, exposed surfaces, surface water, and clouds. Also included are Landsat and MODSCAG fSCA map products. The source imagery for these data are the Maxar WorldView-2 and Maxar WorldView-3 Level-1B 8-band multispectral images, orthorectified and converted to top-of-atmosphere reflectance. These Level-1B images are available under the NGA NextView/EnhancedView license. proprietary
-WYGISC_HYDRO100K 1:100,000-scale Hydrography for Wyoming (enhanced DLGs) ALL STAC Catalog 1970-01-01 -111.36555, 40.944794, -103.783806, 44.99391 https://cmr.earthdata.nasa.gov/search/concepts/C1214614313-SCIOPS.umm_json The purpose of this data layer was to provide a base layer of water features at a statewide level for riparian/aquatic species distribution modeling for the Wyoming Gap Analysis project. However the data may also be used for a variety of other natural resources management/biological studies at the appropriate scale. Hydrographic features for Wyoming at 1:100,000-scale, including perennial and intermittent designations and Strahler stream order attributes for streams. Does not include man-made ditches, canals and aqueducts. The data was originally produced by USGS, a Digital Line Graph (DLG) product, though this product was enhanced (edgematched, some linework and attributes corrected, stream order attribute added). A subset of this dataset is also available for distribution, including only major streams (order 4 to 7) and major lakes and reservoirs. In order to reduce the size of this subset, the line segments were dissolved to remove unncessary segments. Both datasets are available in Arc export file and shapefile format for download Statewide and tiled data: there is one export file, which when imported into ARC/INFO, will contain one coverage with both polygon (lakes, reservoirs) and line (streams) topology and two feature attribute files (.PAT and .AAT) along with three additional attribute files containing descriptive information. In shapefile format, there will be two shapefiles (polygons and lines separated), with all attribute files in Dbase format. proprietary
WYGISC_HYDRO100K 1:100,000-scale Hydrography for Wyoming (enhanced DLGs) SCIOPS STAC Catalog 1970-01-01 -111.36555, 40.944794, -103.783806, 44.99391 https://cmr.earthdata.nasa.gov/search/concepts/C1214614313-SCIOPS.umm_json The purpose of this data layer was to provide a base layer of water features at a statewide level for riparian/aquatic species distribution modeling for the Wyoming Gap Analysis project. However the data may also be used for a variety of other natural resources management/biological studies at the appropriate scale. Hydrographic features for Wyoming at 1:100,000-scale, including perennial and intermittent designations and Strahler stream order attributes for streams. Does not include man-made ditches, canals and aqueducts. The data was originally produced by USGS, a Digital Line Graph (DLG) product, though this product was enhanced (edgematched, some linework and attributes corrected, stream order attribute added). A subset of this dataset is also available for distribution, including only major streams (order 4 to 7) and major lakes and reservoirs. In order to reduce the size of this subset, the line segments were dissolved to remove unncessary segments. Both datasets are available in Arc export file and shapefile format for download Statewide and tiled data: there is one export file, which when imported into ARC/INFO, will contain one coverage with both polygon (lakes, reservoirs) and line (streams) topology and two feature attribute files (.PAT and .AAT) along with three additional attribute files containing descriptive information. In shapefile format, there will be two shapefiles (polygons and lines separated), with all attribute files in Dbase format. proprietary
+WYGISC_HYDRO100K 1:100,000-scale Hydrography for Wyoming (enhanced DLGs) ALL STAC Catalog 1970-01-01 -111.36555, 40.944794, -103.783806, 44.99391 https://cmr.earthdata.nasa.gov/search/concepts/C1214614313-SCIOPS.umm_json The purpose of this data layer was to provide a base layer of water features at a statewide level for riparian/aquatic species distribution modeling for the Wyoming Gap Analysis project. However the data may also be used for a variety of other natural resources management/biological studies at the appropriate scale. Hydrographic features for Wyoming at 1:100,000-scale, including perennial and intermittent designations and Strahler stream order attributes for streams. Does not include man-made ditches, canals and aqueducts. The data was originally produced by USGS, a Digital Line Graph (DLG) product, though this product was enhanced (edgematched, some linework and attributes corrected, stream order attribute added). A subset of this dataset is also available for distribution, including only major streams (order 4 to 7) and major lakes and reservoirs. In order to reduce the size of this subset, the line segments were dissolved to remove unncessary segments. Both datasets are available in Arc export file and shapefile format for download Statewide and tiled data: there is one export file, which when imported into ARC/INFO, will contain one coverage with both polygon (lakes, reservoirs) and line (streams) topology and two feature attribute files (.PAT and .AAT) along with three additional attribute files containing descriptive information. In shapefile format, there will be two shapefiles (polygons and lines separated), with all attribute files in Dbase format. proprietary
WYGISC_HYDRO24K 1:24,000-scale Hydrography for ortions Wyoming, various sources ALL STAC Catalog 1967-01-01 1971-12-31 -111, 41, -104, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214614312-SCIOPS.umm_json "The purpose of this data layer is to provide a base layer of hydrography at the watershed scale for GIS display and analysis. The hydrography described by this metadata, including streams, lakes, reservoirs and"" ditches, came from three different sources, all at 1:24,000-scale:"" -USGS Digital Line Graphs -USFS Cartographic Feature File -digitized by Wyoming Water Resources Center off of paper topographic maps" proprietary
WYGISC_HYDRO24K 1:24,000-scale Hydrography for ortions Wyoming, various sources SCIOPS STAC Catalog 1967-01-01 1971-12-31 -111, 41, -104, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1214614312-SCIOPS.umm_json "The purpose of this data layer is to provide a base layer of hydrography at the watershed scale for GIS display and analysis. The hydrography described by this metadata, including streams, lakes, reservoirs and"" ditches, came from three different sources, all at 1:24,000-scale:"" -USGS Digital Line Graphs -USFS Cartographic Feature File -digitized by Wyoming Water Resources Center off of paper topographic maps" proprietary
WYGISC_LANDUSE Agricultural Land Use of Wyoming ALL STAC Catalog 1980-01-01 1982-12-31 -111.09, 40.95, -103.88, 45.107 https://cmr.earthdata.nasa.gov/search/concepts/C1214614317-SCIOPS.umm_json The purpose of this data layer is to provide a digital layer showing areas of agriculture and agricultural chemical use in Wyoming. This layer was designed to be applied in the Wyoming Ground-Water Vulnerability Mapping Project. This dataset represents croplands of Wyoming as interpreted from 1:58,200-scale National High Altitude Program (NHAP) color infrared aerial photography. The photos, which were taken in 1980-1982, were interpreted and land use designations were hand-drawn onto plots produced at the same scale as the photos, using a light table. The plots were then digitized as polygons into ARC/INFO 7.0.2. Valid polygons include irrigated croplands, non-irrigated croplands, urban lands, golf-courses, and non-agricultural lands. Golf courses boundaries, which have changed recently, were later updated with 1994 NAPP photos. proprietary
@@ -16914,35 +16921,35 @@ Wetland_VegClassification_PAD_2069_1 ABoVE: Wetland Vegetation Classification fo
WhitePhenoregions_799_1 Phenoregions For Monitoring Vegetation Responses to Climate Change ORNL_CLOUD STAC Catalog 1982-01-01 1999-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2784383305-ORNL_CLOUD.umm_json The overall purpose in this research was to identify the regions of the world best suited for long-term monitoring of biospheric responses to climate change, i.e., monitoring land surface phenology. The user is referred to White et al. [2005] for further details. Using global 8 km 1982 to 1999 Normalized Difference Vegetation Index (NDVI) data and an eight-element monthly climatology, we identified pixels consistently dominated by annual cycles and then created phenologically and climatically self-similar clusters, which we term phenoregions. We then ranked and screened each phenoregion as a function of landcover homogeneity and consistency, evidence of human impacts, and political diversity.This dataset contains material providing users with direct access to data used to construct the figures in White et al. [2005]. Users are referred to this reference for additional information. Data files include ASCII and binary versions of the image files for the 500 elemental phenoregions and the 136 final monitoring phenoregions (shown in figure below) and a corresponding .jpg map. Also included are the classification data in tabular ACSII format for each of the 500 elemental phenoregions.Selected monitoring phenoregions. Phenoregions with fewer than 100 pixels or dominated by crop, urban or barren landcover removed. The 136 remaining phenoregions are those passing the screening factors in Table 1 and are shown with normalized rankings by landcover. (From White et al., 2005) proprietary
WhiteSpruce_Leaf_Traits_Alaska_2124_1 ABoVE: White Spruce Photosynthetic and Leaf Traits, Alaska and New York, 2017 ORNL_CLOUD STAC Catalog 2017-06-19 2017-07-20 -149.75, 41.4, -74.02, 67.99 https://cmr.earthdata.nasa.gov/search/concepts/C2636355463-ORNL_CLOUD.umm_json This dataset provides measurements of gas exchange (light response curves, Kok curves and ACi curves), leaf traits (carbon, nitrogen, and specific leaf area), leaf pigments (Chlorophyll a, Chlorophyll b and Carotenoids), the photochemical reflectance index (PRI), and average photosynthetic photon flux density as collected from hemispherical photographs. Data were collected on white spruce trees (Picea glauca (Moench) Voss) growing at the northern edge of the species' distribution in Alaska and at the southern edge of the species' distribution in Black Rock Forest (BRF), New York. Measurements were taken at high and low canopy positions on each tree at both sites during the 2017 growing season (2017-06-19 to 2017-07-20). Gas exchange, leaf trait, pigment and spectral measurements were obtained using a portable photosynthesis system (LI-6800, LI-COR, Lincoln, NE). Photochemical reflectance index was determined using a spectroradiometer, and hemispherical photographs were taken with a digital camera. These data were collected to better understand how vertical canopy gradients in photosynthetic physiology change from the southernmost to the northernmost range extremes of white spruce. The data are provided in comma-separated value (CSV) format. proprietary
WhiteSpruce_Leaf_Traits_Alaska_2124_1 ABoVE: White Spruce Photosynthetic and Leaf Traits, Alaska and New York, 2017 ALL STAC Catalog 2017-06-19 2017-07-20 -149.75, 41.4, -74.02, 67.99 https://cmr.earthdata.nasa.gov/search/concepts/C2636355463-ORNL_CLOUD.umm_json This dataset provides measurements of gas exchange (light response curves, Kok curves and ACi curves), leaf traits (carbon, nitrogen, and specific leaf area), leaf pigments (Chlorophyll a, Chlorophyll b and Carotenoids), the photochemical reflectance index (PRI), and average photosynthetic photon flux density as collected from hemispherical photographs. Data were collected on white spruce trees (Picea glauca (Moench) Voss) growing at the northern edge of the species' distribution in Alaska and at the southern edge of the species' distribution in Black Rock Forest (BRF), New York. Measurements were taken at high and low canopy positions on each tree at both sites during the 2017 growing season (2017-06-19 to 2017-07-20). Gas exchange, leaf trait, pigment and spectral measurements were obtained using a portable photosynthesis system (LI-6800, LI-COR, Lincoln, NE). Photochemical reflectance index was determined using a spectroradiometer, and hemispherical photographs were taken with a digital camera. These data were collected to better understand how vertical canopy gradients in photosynthetic physiology change from the southernmost to the northernmost range extremes of white spruce. The data are provided in comma-separated value (CSV) format. proprietary
-Wildfire_Effects_Spruce_Field_1595_1 ABoVE: Characterization of Burned and Unburned Spruce Forest Sites, Tanana, AK, 2017 ORNL_CLOUD STAC Catalog 2017-07-26 2017-07-28 -152.42, 65.1, -151.95, 65.23 https://cmr.earthdata.nasa.gov/search/concepts/C2162141870-ORNL_CLOUD.umm_json This dataset provides the results of field observations of soil characteristics and depth to permafrost, survey results for Composite Burn Index (CBI) determination, and Landsat-derived estimates of Relative Difference Normalized Burn Ratio (RdNBR) for 38 burned and unburned forest sites near Tanana, Alaska, in 2017. Forests in the study area, at the confluence of the Yukon and Tanana Rivers about 200 km west of Fairbanks, are predominately black spruce on wetter soils and white spruce on drier soils. The burned areas were from wildfires that occurred in the summer of 2015. proprietary
Wildfire_Effects_Spruce_Field_1595_1 ABoVE: Characterization of Burned and Unburned Spruce Forest Sites, Tanana, AK, 2017 ALL STAC Catalog 2017-07-26 2017-07-28 -152.42, 65.1, -151.95, 65.23 https://cmr.earthdata.nasa.gov/search/concepts/C2162141870-ORNL_CLOUD.umm_json This dataset provides the results of field observations of soil characteristics and depth to permafrost, survey results for Composite Burn Index (CBI) determination, and Landsat-derived estimates of Relative Difference Normalized Burn Ratio (RdNBR) for 38 burned and unburned forest sites near Tanana, Alaska, in 2017. Forests in the study area, at the confluence of the Yukon and Tanana Rivers about 200 km west of Fairbanks, are predominately black spruce on wetter soils and white spruce on drier soils. The burned areas were from wildfires that occurred in the summer of 2015. proprietary
+Wildfire_Effects_Spruce_Field_1595_1 ABoVE: Characterization of Burned and Unburned Spruce Forest Sites, Tanana, AK, 2017 ORNL_CLOUD STAC Catalog 2017-07-26 2017-07-28 -152.42, 65.1, -151.95, 65.23 https://cmr.earthdata.nasa.gov/search/concepts/C2162141870-ORNL_CLOUD.umm_json This dataset provides the results of field observations of soil characteristics and depth to permafrost, survey results for Composite Burn Index (CBI) determination, and Landsat-derived estimates of Relative Difference Normalized Burn Ratio (RdNBR) for 38 burned and unburned forest sites near Tanana, Alaska, in 2017. Forests in the study area, at the confluence of the Yukon and Tanana Rivers about 200 km west of Fairbanks, are predominately black spruce on wetter soils and white spruce on drier soils. The burned areas were from wildfires that occurred in the summer of 2015. proprietary
Wildfire_Impacts_Boreal_Ecosys_2359_1 Impacts of Wildfires on Boreal Forest Ecosystem Carbon Dynamics ORNL_CLOUD STAC Catalog 1986-01-01 2020-12-31 -166, 43.5, -53, 70 https://cmr.earthdata.nasa.gov/search/concepts/C3234724704-ORNL_CLOUD.umm_json This dataset contains simulations of net primary production (NPP), heterotrophic respiration (RH), net ecosystem production (NEP), and soil temperature data in North American boreal forests for the period 1986-2020. Data sources included historical fire sources and Landsat data. The delta Normalized Burn Ratio (dNBR), which can be used to represent burn severity for a fire, was calculated for each individual fire over the time period. The interactions between canopy, fire and soil thermal dynamics were modelled using a soil surface energy balance model incorporated into a previous Terrestrial Ecosystem Model (TEM). Using the revised TEM, two regional simulations were conducted with and without fire disturbance. Fire polygons were dissected into each unit with unique fire history and then intersected with each grid cell to measure fire impacts. The output values for each grid cell are the area-weighted mean of each fire polygon and unburned area within the cell. Two extra simulations without a canopy energy balance scheme were also conducted to quantify the impact of the canopy. Soil temperature was simulated with and without the canopy energy balance scheme in the model in addition to considering fire impacts. proprietary
Wildfires_2014_NWT_Canada_1307_1 ABoVE: Burn Severity, Fire Progression, Landcover and Field Data, NWT, Canada, 2014 ALL STAC Catalog 1997-07-07 2015-07-15 -121.6, 60.33, -110.68, 64.25 https://cmr.earthdata.nasa.gov/search/concepts/C2170968584-ORNL_CLOUD.umm_json This data set provides peatland landcover classification maps, fire progression maps, and vegetation community biophysical data collected from areas that were burned by wildfire in 2014 in the Northwest Territories, Canada. The peatland maps include peatland type (bog, fen, marsh, swamp) and level of biomass (open, forested). The fire progression maps enabled an assessment of wildfire progression rates at a daily time scale. Field data, collected in 2015, include an estimate of burn severity, woody seedling/sprouting data, soil moisture, and tree diameter and height of burned sites and similar vegetation characterization at landcover validation sites. proprietary
Wildfires_2014_NWT_Canada_1307_1 ABoVE: Burn Severity, Fire Progression, Landcover and Field Data, NWT, Canada, 2014 ORNL_CLOUD STAC Catalog 1997-07-07 2015-07-15 -121.6, 60.33, -110.68, 64.25 https://cmr.earthdata.nasa.gov/search/concepts/C2170968584-ORNL_CLOUD.umm_json This data set provides peatland landcover classification maps, fire progression maps, and vegetation community biophysical data collected from areas that were burned by wildfire in 2014 in the Northwest Territories, Canada. The peatland maps include peatland type (bog, fen, marsh, swamp) and level of biomass (open, forested). The fire progression maps enabled an assessment of wildfire progression rates at a daily time scale. Field data, collected in 2015, include an estimate of burn severity, woody seedling/sprouting data, soil moisture, and tree diameter and height of burned sites and similar vegetation characterization at landcover validation sites. proprietary
-Wildfires_Date_of_Burning_1559_1.1 ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019 ORNL_CLOUD STAC Catalog 2001-01-01 2019-12-31 -178.84, 41.75, -53.83, 70.16 https://cmr.earthdata.nasa.gov/search/concepts/C2162122340-ORNL_CLOUD.umm_json This dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases. All scars within these databases were used in this study. The estimated DoB was derived using an algorithm for identifying the first fire occurrence from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection product (MCD14ML, Collection 6) and to subsequently determine all dates of burning within fire scars. The DoB data are provided as polygons and map the daily progression of a fire within each burn scar. As a result, there is one polygon for each DoB detected within an identified burn scar boundary. The MODIS active fire points associated with the burn scar data are also provided. Data for the period 2001-2015 were first published in 2017 and data for the period 2016-2019 were added in January 2021. proprietary
Wildfires_Date_of_Burning_1559_1.1 ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019 ALL STAC Catalog 2001-01-01 2019-12-31 -178.84, 41.75, -53.83, 70.16 https://cmr.earthdata.nasa.gov/search/concepts/C2162122340-ORNL_CLOUD.umm_json This dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases. All scars within these databases were used in this study. The estimated DoB was derived using an algorithm for identifying the first fire occurrence from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection product (MCD14ML, Collection 6) and to subsequently determine all dates of burning within fire scars. The DoB data are provided as polygons and map the daily progression of a fire within each burn scar. As a result, there is one polygon for each DoB detected within an identified burn scar boundary. The MODIS active fire points associated with the burn scar data are also provided. Data for the period 2001-2015 were first published in 2017 and data for the period 2016-2019 were added in January 2021. proprietary
-Wildfires_NWT_Canada_1548_1 ABoVE: Burn Severity, Fire Progression, and Field Data, NWT, Canada, 2015-2016 ORNL_CLOUD STAC Catalog 2015-05-20 2016-08-08 -135.54, 59.93, -106.76, 68.33 https://cmr.earthdata.nasa.gov/search/concepts/C2162122286-ORNL_CLOUD.umm_json This data set provides a fire progression map for year 2015 and measures of burn severity and vegetation community biophysical data collected from areas that were burned by wildfires in 2014 and 2015 in the Northwest Territories, Canada. Field data collected in 2016 include an estimate of burn severity, woody seedling/sprouting data, soil moisture, peat depth, thaw depth, and vegetation cover for selected sites. proprietary
+Wildfires_Date_of_Burning_1559_1.1 ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019 ORNL_CLOUD STAC Catalog 2001-01-01 2019-12-31 -178.84, 41.75, -53.83, 70.16 https://cmr.earthdata.nasa.gov/search/concepts/C2162122340-ORNL_CLOUD.umm_json This dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases. All scars within these databases were used in this study. The estimated DoB was derived using an algorithm for identifying the first fire occurrence from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection product (MCD14ML, Collection 6) and to subsequently determine all dates of burning within fire scars. The DoB data are provided as polygons and map the daily progression of a fire within each burn scar. As a result, there is one polygon for each DoB detected within an identified burn scar boundary. The MODIS active fire points associated with the burn scar data are also provided. Data for the period 2001-2015 were first published in 2017 and data for the period 2016-2019 were added in January 2021. proprietary
Wildfires_NWT_Canada_1548_1 ABoVE: Burn Severity, Fire Progression, and Field Data, NWT, Canada, 2015-2016 ALL STAC Catalog 2015-05-20 2016-08-08 -135.54, 59.93, -106.76, 68.33 https://cmr.earthdata.nasa.gov/search/concepts/C2162122286-ORNL_CLOUD.umm_json This data set provides a fire progression map for year 2015 and measures of burn severity and vegetation community biophysical data collected from areas that were burned by wildfires in 2014 and 2015 in the Northwest Territories, Canada. Field data collected in 2016 include an estimate of burn severity, woody seedling/sprouting data, soil moisture, peat depth, thaw depth, and vegetation cover for selected sites. proprietary
+Wildfires_NWT_Canada_1548_1 ABoVE: Burn Severity, Fire Progression, and Field Data, NWT, Canada, 2015-2016 ORNL_CLOUD STAC Catalog 2015-05-20 2016-08-08 -135.54, 59.93, -106.76, 68.33 https://cmr.earthdata.nasa.gov/search/concepts/C2162122286-ORNL_CLOUD.umm_json This data set provides a fire progression map for year 2015 and measures of burn severity and vegetation community biophysical data collected from areas that were burned by wildfires in 2014 and 2015 in the Northwest Territories, Canada. Field data collected in 2016 include an estimate of burn severity, woody seedling/sprouting data, soil moisture, peat depth, thaw depth, and vegetation cover for selected sites. proprietary
Wildfires_NWT_Canada_2018_1703_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2018 ORNL_CLOUD STAC Catalog 2018-08-12 2018-08-22 -117.43, 60.45, -113.42, 62.57 https://cmr.earthdata.nasa.gov/search/concepts/C2143403376-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics and biophysical data collected in 2018 from areas that were burned by wildfire in 2014 and 2015, and from nine unburned validation sites in the Northwest Territories, Canada. The data include vegetation inventories, ground cover, regrowth, tree diameter and height, and woody seedling/sprouting data at burned sites, and similar vegetation community characterization at unburned validation sites. Additional measurements included soil moisture, collected for validation of the UAVSAR airborne collection, and depth to frozen ground at the nine unburned sites. This 2018 fieldwork completes four years of field sampling at the wildfire areas. proprietary
Wildfires_NWT_Canada_2018_1703_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2018 ALL STAC Catalog 2018-08-12 2018-08-22 -117.43, 60.45, -113.42, 62.57 https://cmr.earthdata.nasa.gov/search/concepts/C2143403376-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics and biophysical data collected in 2018 from areas that were burned by wildfire in 2014 and 2015, and from nine unburned validation sites in the Northwest Territories, Canada. The data include vegetation inventories, ground cover, regrowth, tree diameter and height, and woody seedling/sprouting data at burned sites, and similar vegetation community characterization at unburned validation sites. Additional measurements included soil moisture, collected for validation of the UAVSAR airborne collection, and depth to frozen ground at the nine unburned sites. This 2018 fieldwork completes four years of field sampling at the wildfire areas. proprietary
-Wildfires_NWT_Canada_2019_1900_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2019 ORNL_CLOUD STAC Catalog 2018-08-16 2019-09-05 -117.43, 60.92, -113.02, 62.57 https://cmr.earthdata.nasa.gov/search/concepts/C2445465291-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2019 from 11 study areas, which contained 28 sites that were burned by wildfires in 2014 and 2015, and 14 unburned sites in the Northwest Territories (NWT), Canada. Burn sites included peatland and upland. These field data include vegetation inventories, ground cover, as well as diameter and height for trees and shrubs in the unburned sites. Similar data were collected for the unburned sites in the years 2015-18 and are available in related separate datasets. In 2019, the focus was on woody and non-woody seedling/sprouting regrowth data in the burned sites. Additional measurements collected at all sites included total peat depth, soil moisture, and active layer thickness (ALT). Soil moisture and ALT were collected for validation of the UAVSAR airborne collection and Radarsat-2 overpasses. This 2019 fieldwork completes five years of field sampling at the wildfire areas. proprietary
Wildfires_NWT_Canada_2019_1900_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2019 ALL STAC Catalog 2018-08-16 2019-09-05 -117.43, 60.92, -113.02, 62.57 https://cmr.earthdata.nasa.gov/search/concepts/C2445465291-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2019 from 11 study areas, which contained 28 sites that were burned by wildfires in 2014 and 2015, and 14 unburned sites in the Northwest Territories (NWT), Canada. Burn sites included peatland and upland. These field data include vegetation inventories, ground cover, as well as diameter and height for trees and shrubs in the unburned sites. Similar data were collected for the unburned sites in the years 2015-18 and are available in related separate datasets. In 2019, the focus was on woody and non-woody seedling/sprouting regrowth data in the burned sites. Additional measurements collected at all sites included total peat depth, soil moisture, and active layer thickness (ALT). Soil moisture and ALT were collected for validation of the UAVSAR airborne collection and Radarsat-2 overpasses. This 2019 fieldwork completes five years of field sampling at the wildfire areas. proprietary
+Wildfires_NWT_Canada_2019_1900_1 ABoVE: Post-Fire and Unburned Vegetation Community and Field Data, NWT, Canada, 2019 ORNL_CLOUD STAC Catalog 2018-08-16 2019-09-05 -117.43, 60.92, -113.02, 62.57 https://cmr.earthdata.nasa.gov/search/concepts/C2445465291-ORNL_CLOUD.umm_json This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2019 from 11 study areas, which contained 28 sites that were burned by wildfires in 2014 and 2015, and 14 unburned sites in the Northwest Territories (NWT), Canada. Burn sites included peatland and upland. These field data include vegetation inventories, ground cover, as well as diameter and height for trees and shrubs in the unburned sites. Similar data were collected for the unburned sites in the years 2015-18 and are available in related separate datasets. In 2019, the focus was on woody and non-woody seedling/sprouting regrowth data in the burned sites. Additional measurements collected at all sites included total peat depth, soil moisture, and active layer thickness (ALT). Soil moisture and ALT were collected for validation of the UAVSAR airborne collection and Radarsat-2 overpasses. This 2019 fieldwork completes five years of field sampling at the wildfire areas. proprietary
Willow_Veg_Plots_1368_1 Arctic Vegetation Plots in Willow Communities, North Slope, Alaska, 1997 ORNL_CLOUD STAC Catalog 1997-07-09 1997-08-17 -149.85, 68.03, -148.08, 70.19 https://cmr.earthdata.nasa.gov/search/concepts/C2170969823-ORNL_CLOUD.umm_json This data set provides environmental, soil, and vegetation data collected in July and August 1997 from 85 study plots in willow shrub communities located along a north-south transect from the Brooks Range to Prudhoe Bay on the North Slope of Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in three broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species, cover, indices, and biomass pools; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping, and analysis of geobotanical factors in the region and across Alaska. proprietary
WindSat-REMSS-L3U-v7.0.1a_7.0.1a GHRSST Level 3U Global Subskin Sea Surface Temperature version7.0.1a from the WindSat Polarimetric Radiometer on the Coriolis satellite POCLOUD STAC Catalog 2002-06-01 2020-10-19 -179.99, -39.06, 180, 39.01 https://cmr.earthdata.nasa.gov/search/concepts/C2036878925-POCLOUD.umm_json "The WindSat Polarimetric Radiometer, launched on January 6, 2003 aboard the Department of Defense Coriolis satellite, was designed to measure the ocean surface wind vector from space. It developed by the Naval Research Laboratory (NRL) Remote Sensing Division and the Naval Center for Space Technology for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Integrated Program Office (IPO). In addition to wind speed and direction, the instrument can also measure sea surface temperature, soil moisture, ice and snow characteristics, water vapor, cloud liquid water, and rain rate. Unlike previous radiometers, the WindSat sensor takes observations during both the forward and aft looking scans. This makes the WindSat geometry of the earth view swath quite different and significantly more complicated to work with than the other passive microwave sensors. The Remote Sensing Systems (RSS, or REMSS) WindSat products are the only dataset available that uses both the fore and aft look directions. By using both directions, a wider swath and more complicated swath geometry is obtained. RSS providers of these SST data for the Group for High Resolution Sea Surface Temperature (GHRSST) Project, performs a detailed processing of WindSat instrument data in two stages. The first stage produces a near-real-time (NRT) product (identified by ""rt"" within the file name) which is made as available as soon as possible. This is generally within 3 hours of when the data are recorded. Although suitable for many timely uses the NRT products are not intended to be archive quality. ""Final"" data (currently identified by ""v7.0.1a"" within the file name) are processed when RSS receives the atmospheric mode NCEP FNL analysis. The NCEP wind directions are particularly useful for retrieving more accurate SSTs and wind speeds. The final ""v7.0.1a"" products will continue to accumulate new swaths (half orbits) until the maps are full, generally within 7 days. The version with letter ""a"" refers to the file incompliance with GHRSST format." proprietary
-Wolves_Denning_Pups_Climate_1846_1 ABoVE: Wolf Denning Phenology and Reproductive Success, Alaska and Canada, 2000-2017 ALL STAC Catalog 2000-03-29 2017-08-31 -154.58, 52.97, -112.97, 67.84 https://cmr.earthdata.nasa.gov/search/concepts/C2143401778-ORNL_CLOUD.umm_json This dataset provides annual gray wolf (Canis lupus) denning spatial information and timing, associated climatic and phenologic metrics, and reproductive success (i.e., pup survival) in wolf populations across areas of western Canada and Alaska within the NASA ABoVE Core Domain. The study encompasses 18 years between the period 2000-2017. Wolves were captured from eight populations following standard animal care protocols and released with Global Positioning System (GPS) collars. Data from 388 wolves were used to estimate den initiation dates (n=227 dens of 106 packs) and reproductive success in the eight populations. Each population was monitored from 1 to 12 years between 2000 and 2017. Denning parturition phenology was measured each year as the number of calendar days from January 1st to the initiation date of each documented denning event. Reproductive success was determined as to whether pups survived through the end of August following a reproductive event. To evaluate the effect of climate factors on reproductive phenology, aggregated seasonal climate metrics for temperature, precipitation, and snow water equivalent based on three biological seasons for seasonal wolf home ranges were produced. Normalized Difference Vegetation Index (NDVI) time-series data were used to estimate phenological metrics such as the start of the growing season (SOS), length of the growing season (LOS), and time-integrated NDVI (tiNDVI), and were summarized for the populations' home range. proprietary
Wolves_Denning_Pups_Climate_1846_1 ABoVE: Wolf Denning Phenology and Reproductive Success, Alaska and Canada, 2000-2017 ORNL_CLOUD STAC Catalog 2000-03-29 2017-08-31 -154.58, 52.97, -112.97, 67.84 https://cmr.earthdata.nasa.gov/search/concepts/C2143401778-ORNL_CLOUD.umm_json This dataset provides annual gray wolf (Canis lupus) denning spatial information and timing, associated climatic and phenologic metrics, and reproductive success (i.e., pup survival) in wolf populations across areas of western Canada and Alaska within the NASA ABoVE Core Domain. The study encompasses 18 years between the period 2000-2017. Wolves were captured from eight populations following standard animal care protocols and released with Global Positioning System (GPS) collars. Data from 388 wolves were used to estimate den initiation dates (n=227 dens of 106 packs) and reproductive success in the eight populations. Each population was monitored from 1 to 12 years between 2000 and 2017. Denning parturition phenology was measured each year as the number of calendar days from January 1st to the initiation date of each documented denning event. Reproductive success was determined as to whether pups survived through the end of August following a reproductive event. To evaluate the effect of climate factors on reproductive phenology, aggregated seasonal climate metrics for temperature, precipitation, and snow water equivalent based on three biological seasons for seasonal wolf home ranges were produced. Normalized Difference Vegetation Index (NDVI) time-series data were used to estimate phenological metrics such as the start of the growing season (SOS), length of the growing season (LOS), and time-integrated NDVI (tiNDVI), and were summarized for the populations' home range. proprietary
+Wolves_Denning_Pups_Climate_1846_1 ABoVE: Wolf Denning Phenology and Reproductive Success, Alaska and Canada, 2000-2017 ALL STAC Catalog 2000-03-29 2017-08-31 -154.58, 52.97, -112.97, 67.84 https://cmr.earthdata.nasa.gov/search/concepts/C2143401778-ORNL_CLOUD.umm_json This dataset provides annual gray wolf (Canis lupus) denning spatial information and timing, associated climatic and phenologic metrics, and reproductive success (i.e., pup survival) in wolf populations across areas of western Canada and Alaska within the NASA ABoVE Core Domain. The study encompasses 18 years between the period 2000-2017. Wolves were captured from eight populations following standard animal care protocols and released with Global Positioning System (GPS) collars. Data from 388 wolves were used to estimate den initiation dates (n=227 dens of 106 packs) and reproductive success in the eight populations. Each population was monitored from 1 to 12 years between 2000 and 2017. Denning parturition phenology was measured each year as the number of calendar days from January 1st to the initiation date of each documented denning event. Reproductive success was determined as to whether pups survived through the end of August following a reproductive event. To evaluate the effect of climate factors on reproductive phenology, aggregated seasonal climate metrics for temperature, precipitation, and snow water equivalent based on three biological seasons for seasonal wolf home ranges were produced. Normalized Difference Vegetation Index (NDVI) time-series data were used to estimate phenological metrics such as the start of the growing season (SOS), length of the growing season (LOS), and time-integrated NDVI (tiNDVI), and were summarized for the populations' home range. proprietary
WorldView-1.full.archive.and.tasking_8.0 WorldView-1 full archive and tasking ESA STAC Catalog 2007-10-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336959-ESA.umm_json "WorldView-1 high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. In particular, WorldView-1 offers archive and tasking panchromatic products up to 0.50 m GSD resolution. Band Combination Data Processing Level Resolution Panchromatic Standard(2A)/View Ready STANDARD (OR2A) 50 cm, 30 cm HD View Ready Stereo 50 cm Map-Ready (Ortho) 1:12.000 Orthorectified 50 cm, 30 cm HD Native 50 cm resolution products are processed with MAXAR HD Technology to generate the 30 cm HD products: the initial special resolution (GSD) is unchanged but the HD technique increases the number of pixels and improves the visual clarity achieving aesthetically refined imagery with precise edges and well reconstructed details. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary
WorldView-2.European.Cities_10.0 WorldView-2 European Cities ESA STAC Catalog 2010-07-20 2015-07-19 -19, -26, 35, 66 https://cmr.earthdata.nasa.gov/search/concepts/C1965336961-ESA.umm_json ESA, in collaboration with European Space Imaging, has collected this WorldView-2 dataset covering the most populated areas in Europe at 40 cm resolution. The products have been acquired between July 2010 and July 2015. proprietary
WorldView-2.full.archive.and.tasking_8.0 WorldView-2 full archive and tasking ESA STAC Catalog 2009-11-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336963-ESA.umm_json "WorldView-2 high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. In particular, WorldView-2 offers archive and tasking panchromatic products up to 0.46 m GSD resolution, and 4-Bands/8-Bands Multispectral products up to 1.84 m GSD resolution. Band Combination Data Processing Level Resolution Panchromatic and 4-bands Standard (2A)/View Ready Standard (OR2A) 15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm View Ready Stereo 30 cm, 40 cm, 50/60 cm Map-Ready (Ortho) 1:12.000 Orthorectified 15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm 8-bands Standard(2A)/View Ready Standard (OR2A) 30 cm, 40 cm, 50/60 cm View Ready Stereo 30 cm, 40 cm, 50/60 cm Map-Ready (Ortho) 1:12.000 Orthorectified 30 cm, 40 cm, 50/60 cm 4-Bands being an optional from: 4-Band Multispectral (BLUE, GREEN, RED, NIR1) 4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1) 4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1) 3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED) 3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1). 8-Bands being an optional from: 8-Band Multispectral (COASTAL, BLUE, GREEN, YELLOW, RED, RED EDGE, NIR1, NIR2) 8-Band Bundle (PAN, COASTAL, BLUE, GREEN, YELLOW, RED, RED EDGE, NIR1, NIR2). Native 30 cm and 50/60 cm resolution products are processed with MAXAR HD Technology to generate respectively the 15 cm HD and 30 cm HD products: the initial special resolution (GSD) is unchanged but the HD technique increases the number of pixels, improves the visual clarity and allows to obtain an aesthetically refined imagery with precise edges and well reconstructed details. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary
WorldView-3.full.archive.and.tasking_8.0 WorldView-3 full archive and tasking ESA STAC Catalog 2014-09-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1965336965-ESA.umm_json "WorldView-3 high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. In particular, WorldView-3 offers archive and tasking panchromatic products up to 0.31m GSD resolution, 4-Bands/8-Bands products up to 1.24 m GSD resolution, and SWIR products up to 3.70 m GSD resolution. Band Combination Data Processing Level Resolution High Res Optical: Panchromatic and 4-bands Standard(2A)/View Ready Standard (OR2A) 15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm View Ready Stereo 30 cm, 40 cm, 50/60 cm Map Ready (Ortho) 1:12.000 Orthorectified 15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm High Res Optical: 8-bands Standard(2A)/View Ready Standard (OR2A) 30 cm, 40 cm, 50/60 cm View Ready Stereo 30 cm, 40 cm, 50/60 cm Map Ready (Ortho) 1:12.000 Orthorectified 30 cm, 40 cm, 50/60 cm High Res Optical: SWIR Standard(2A)/View Ready Standard (OR2A) 3.7 m or 7.5 m (depending on the collection date) Map Ready (Ortho) 1:12.000 Orthorectified 4-Bands being an optional from: 4-Band Multispectral (BLUE, GREEN, RED, NIR1) 4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1) 4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1) 3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED) 3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1) 8-Bands being an optional from: 8-Band Multispectral (COASTAL, BLUE, GREEN, YELLOW, RED, RED EDGE, NIR1, NIR2) 8-Band Bundle (PAN, COASTAL, BLUE, GREEN, YELLOW, RED, RED EDGE, NIR1, NIR2) Native 30 cm and 50/60 cm resolution products are processed with MAXAR HD Technology to generate respectively the 15 cm HD and 30 cm HD products: the initial special resolution (GSD) is unchanged but the HD technique increases the number of pixels and improves the visual clarity achieving aesthetically refined imagery with precise edges and well reconstructed details. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary
WorldView-4.full.archive_7.0 WorldView-4 full archive ESA STAC Catalog 2016-12-01 2019-01-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2547572305-ESA.umm_json WorldView-4 high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. In particular, WorldView-4 offers archive panchromatic products up to 0.31m GSD resolution, and 4-Bands Multispectral products up to 1.24m GSD resolution Band Combination: Panchromatic and 4-bands Data Processing Level: STANDARD (2A) / VIEW READY STANDARD (OR2A), VIEW READY STEREO, MAP-READY (ORTHO) 1:12.000 Orthorectified Resolutions: 0.30 m, 0.40 m, 0.50 m. 0.60 m The options for 4-Bands are the following: • 4-Band Multispectral (BLUE, GREEN, RED, NIR1) • 4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1) • 4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1) • 3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED) • 3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1) The list of available archived data can be retrieved using the Image Library (https://www.euspaceimaging.com/image-library/) catalogue. proprietary
WorldView.ESA.archive_9.0 WorldView ESA archive ESA STAC Catalog 2009-02-07 2020-12-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2119689694-ESA.umm_json "The WorldView ESA archive is composed of products acquired by WorldView-1, -2, -3 and -4 satellites and requested by ESA supported projects over their areas of interest around the world Panchromatic, 4-Bands, 8-Bands and SWIR products are part of the offer, with the resolution at Nadir depicted in the table. Band Combination Mission GSD Resolution at Nadir GSD Resolution (20° off nadir) Panchromatic WV-1 50 cm 55 cm WV-2 46 cm 52 cm WV-3 31 cm 34 cm WV-4 31 cm 34 cm 4-Bands WV-2 1.84 m 2.4 m WV-3 1.24 m 1.38 m WV-4 1.24 m 1.38 m 8-Bands WV-2 1.84 m 2.4 m WV-3 1.24 m 1.38 m SWIR WV-3 3.70 m 4.10 m The 4-Bands includes various options such as Multispectral (separate channel for Blue, Green, Red, NIR1), Pan-sharpened (Blue, Green, Red, NIR1), Bundle (separate bands for PAN, Blue, Green, Red, NIR1), Natural Colour (pan-sharpened Blue, Green, Red), Coloured Infrared (pan-sharpened Green, Red, NIR). The 8-Bands being an option from Multispectral (COASTAL, Blue, Green, Yellow, Red, Red EDGE, NIR1, NIR2) and Bundle (PAN, COASTAL, Blue, Green, Yellow, Red, Red EDGE, NIR1, NIR2). The processing levels are: Standard (2A): normalised for topographic relief View Ready Standard: ready for orthorectification (RPB files embedded) View Ready Stereo: collected in-track for stereo viewing and manipulation (not available for SWIR) Map Scale (Ortho) 1:12,000 Orthorectified: additional processing unnecessary Spatial coverage: Check the spatial coverage of the collection on a _$$map$$ https://tpm-ds.eo.esa.int/smcat/WorldView/ available on the Third Party Missions Dissemination Service. The following table summarises the offered product types EO-SIP Product Type Band Combination Processing Level Missions WV6_PAN_2A Panchromatic (PAN) Standard/View Ready Standard WorldView-1 and 4 WV6_PAN_OR Panchromatic (PAN) View Ready Stereo WorldView-1 and 4 WV6_PAN_MP Panchromatic (PAN) Map Scale Ortho WorldView-1 and 4 WV1_PAN__2A Panchromatic (PAN) Standard/View Ready Standard WorldView-2 and 3 WV1_PAN__OR Panchromatic (PAN) View Ready Stereo WorldView-2 and 3 WV1_PAN__MP Panchromatic (PAN) Map Scale Ortho WorldView-2 and 3 WV1_4B__2A 4-Band (4B) Standard/View Ready Standard WorldView-2, 3 and 4 WV1_4B__OR 4-Band (4B) View Ready Stereo WorldView-2, 3 and 4 WV1_4B__MP 4-Band (4B) Map Scale Ortho WorldView-2, 3 and 4 WV1_8B_2A 8-Band (8B) Standard/View Ready Standard WorldView-2 and 3 WV1_8B_OR 8-Band (8B) View Ready Stereo WorldView-2 and 3 WV1_8B_MP 8-Band (8B) Map Scale Ortho WorldView-2 and 3 WV1_S8B__2A SWIR Standard/View Ready Standard WorldView-3 WV1_S8B__MP SWIR Map Scale Ortho WorldView-3 As per ESA policy, very high-resolution imagery of conflict areas cannot be provided." proprietary
-XAERDT_L2_ABI_G16_1 ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS STAC Catalog 2019-01-01 2023-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859273114-LAADS.umm_json The ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G16 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-16 has been serving in the operational GOES-East position (near -75°W) since December 18, 2017. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G16 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G16 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G16 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary
XAERDT_L2_ABI_G16_1 ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2019-01-01 2023-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859273114-LAADS.umm_json The ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G16 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-16 has been serving in the operational GOES-East position (near -75°W) since December 18, 2017. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G16 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G16 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G16 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary
+XAERDT_L2_ABI_G16_1 ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS STAC Catalog 2019-01-01 2023-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859273114-LAADS.umm_json The ABI/GOES-16 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G16 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-16 has been serving in the operational GOES-East position (near -75°W) since December 18, 2017. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G16 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G16 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G16 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary
XAERDT_L2_ABI_G17_1 ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2019-01-01 2023-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859265967-LAADS.umm_json The ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G17 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-17 served in the operational GOES-West position (near -137°W), from February 12, 2019, through January 4, 2023. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G17 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G17 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G17 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary
XAERDT_L2_ABI_G17_1 ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS STAC Catalog 2019-01-01 2023-01-02 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859265967-LAADS.umm_json The ABI/GOES-17 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_ABI_G17 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 144 granules over the daylit hours of a 24-hour period. The Geostationary Operational Environmental Satellite – GOES-17 served in the operational GOES-West position (near -137°W), from February 12, 2019, through January 4, 2023. The GOES-16/ABI collection record spans from January 2019 through December 2022. The XAERDT_L2_ABI_G17 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_ABI_G17 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_ABI_G17 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary
-XAERDT_L2_AHI_H08_1 AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2019-01-01 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859255251-LAADS.umm_json The AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H08 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-8 platform served in the operational Himawari position (near 140.7°E) between October 2014 and 13 December 2022. Himawari-9 replaced Himawari-8 and is currently operational. The Himawari-8/AHI collection record spans from January 2019 through 12th December 2022. The final 19 days of 2022 (December 13 through 31) are served by L2 products derived from the Himawari-9/AHI instrument. The XAERDT_L2_AHI_H08 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H08 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H08 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary
XAERDT_L2_AHI_H08_1 AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS STAC Catalog 2019-01-01 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859255251-LAADS.umm_json The AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H08 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-8 platform served in the operational Himawari position (near 140.7°E) between October 2014 and 13 December 2022. Himawari-9 replaced Himawari-8 and is currently operational. The Himawari-8/AHI collection record spans from January 2019 through 12th December 2022. The final 19 days of 2022 (December 13 through 31) are served by L2 products derived from the Himawari-9/AHI instrument. The XAERDT_L2_AHI_H08 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H08 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H08 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary
+XAERDT_L2_AHI_H08_1 AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2019-01-01 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859255251-LAADS.umm_json The AHI/Himawari-08 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H08 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-8 platform served in the operational Himawari position (near 140.7°E) between October 2014 and 13 December 2022. Himawari-9 replaced Himawari-8 and is currently operational. The Himawari-8/AHI collection record spans from January 2019 through 12th December 2022. The final 19 days of 2022 (December 13 through 31) are served by L2 products derived from the Himawari-9/AHI instrument. The XAERDT_L2_AHI_H08 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H08 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H08 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary
XAERDT_L2_AHI_H09_1 AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km ALL STAC Catalog 2022-12-13 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859261579-LAADS.umm_json The AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H09 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-9 platform currently serves in the operational Himawari position (near 140.7°E) since it was launched November 2, 2016, and replaces Himawari-8. The Himawari-9/AHI collection record spans from 13th December 2022 through 31st December 2022. The XAERDT_L2_AHI_H09 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H09 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H09 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary
XAERDT_L2_AHI_H09_1 AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km LAADS STAC Catalog 2022-12-13 2022-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859261579-LAADS.umm_json The AHI/Himawari-09 Dark Target Aerosol 10-Min L2 Full Disk 10 km product, short-name XAERDT_L2_AHI_H09 is provided at 10-km spatial resolution (at-nadir) and a 10-minute full-disk cadence that typically yields about 142 granules over the daylit hours of a 24-hour period (there are no images produced at 02:20 or 14:20 UTC for navigation purposes). The Himawari-9 platform currently serves in the operational Himawari position (near 140.7°E) since it was launched November 2, 2016, and replaces Himawari-8. The Himawari-9/AHI collection record spans from 13th December 2022 through 31st December 2022. The XAERDT_L2_AHI_H09 product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_AHI_H09 product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_AHI_H09 Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary
XAERDT_L2_MODIS_Aqua_1 MODIS/Aqua Dark Target Aerosol 5-Min L2 Swath 10 km LAADS STAC Catalog 2019-01-01 2023-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2859238768-LAADS.umm_json The MODIS/Aqua Dark Target Aerosol 5-Min L2 Swath 10 km product, short-name XAERDT_L2_MODIS_Aqua is provided at 10-km spatial resolution (at-nadir) and a 5-minute cadence that typically yields about 140 granules over the daylit hours of a 24-hour period. The Aqua/MODIS L2 collection record spans from January 2019 through December 2022. The XAERDT_L2_MODIS_Aqua product is a part of the Geostationary Earth Orbit (GEO)–Low-Earth Orbit (LEO) Dark Target Aerosol project under NASA’s Making Earth System Data Records for Use in Research Environments (MEaSUREs) program, led by Robert Levy, uses a special version of the MODIS Dark Target (DT) aerosol retrieval algorithm to produce Aerosol Optical Depth (AOD) and other aerosol parameters derived independently from seven sensor/platform combinations, where 3 are in GEO and 4 are in LEO. The 3 GEO sensors include Advanced Baseline Imagers (ABI) on both GOES-16 (GOES-East) and GOES-17 (GOES-West), and Advanced Himawari Imager (AHI) on Himawari-8. The 4 LEO sensors include MODIS on both Terra and Aqua, and VIIRS on both Suomi-NPP and NOAA-20. Adding the LEO sensors reinforces a major goal of this project, which is to render a consistent science maturity level across DT aerosol products derived from both types and sources of orbital satellites. The XAERDT_L2_MODIS_Aqua product, in netCDF4 format, contains 45 Science Data Set (SDS) layers that include 8 geolocation and 37 geophysical SDSs. For more information consult LAADS product description page at: https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/XAERDT_L2_MODIS_Aqua Or, Dark Target aerosol team Page at: https://darktarget.gsfc.nasa.gov/ proprietary
@@ -16980,70 +16987,70 @@ aad_ais_gz_modis_slope_break_1 Amery Ice Shelf Grounding Zone defined as interpr
aad_ctd_database_1 Database of CTD data collected in the Southern Ocean by the AAD, ACE CRC and part of the Southern Ocean Atlas data set. AU_AADC STAC Catalog 1900-01-01 2003-03-09 -180, -80, 180, -15.05 https://cmr.earthdata.nasa.gov/search/concepts/C1214311486-AU_AADC.umm_json Microsoft Access database containing a compilation of CTD data collected in the Southern Ocean from Australian Antarctic Division (AAD), Antarctic Climate and Ecosystems Co-operative Research Centre (ACE CRC) and Hydrographic Atlas of the Southern Ocean (SOA) data sources. This SOA data contains discrete CTD (Conductivity, Temperature and Depth) station data along with a 1 x 1 degree gridded CTD data set interpolated in space and time. Parameters include pressure, temperature, salinity, dissolved oxygen, nutrients (phosphate, nitrate+nitrite, and silicate). Ocean Tools software developed by AAD is available in conjunction with this database to manipulate, extract and visualise data (including station map, transect selection, xy plots, vertical cross sections, geostrophic velocity/transport calculations). The download file contains an access database of the compiled CTD data, a word document containing further information about the structure of the database and the data (AAD CTD Data.doc), and a folder of the original source data, including readmes providing reference details, and specific information. proprietary
aae157df-5b91-4a49-b00b-d81729a566d7_NA TerraSAR-X - High Resolution Spotlight Images (TerraSAR-X High Resolution Spotlight) FEDEO STAC Catalog 2007-06-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207457996-FEDEO.umm_json "This collection contains radar image products of the German national TerraSAR-X mission acquired in High Resolution Spotlight mode. High Resolution Spotlight imaging allows for a spatial resolution of up to 1 m at a scene size of 10 km (across swath) x 5 km (in orbit direction). TerraSAR-X is a sun-synchronous polar-orbiting, all-weather, day-and-night X-band radar earth observation mission realized in the frame of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defence and Space. For more information concerning the TerraSAR-X mission, the reader is referred to: https://www.dlr.de/content/de/missionen/terrasar-x.html" proprietary
aae643e1a7614c24b6b604dea82cad93_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Kangerlussuaq Glacier between 2017-07-21 and 2017-08-20, generated using Sentinel-2 data, v1.1 FEDEO STAC Catalog 2017-07-20 2017-08-20 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548143151-FEDEO.umm_json This dataset contains optical ice velocity time series and seasonal product of the Kangerlussuaq Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-07-21 and 2017-08-20. It has been produced as part of the ESA Greenland Ice sheet CCI project. The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid.The data have been produced by S[&]T Norway. proprietary
-aamhcpex_1 AAMH CPEX ALL STAC Catalog 2017-05-26 2017-07-16 154.716, 0.6408, -19.5629, 44.9689 https://cmr.earthdata.nasa.gov/search/concepts/C2645106424-GHRC_DAAC.umm_json The AAMH CPEX dataset contains products obtained from the MetOp-A, MetOp-B, NOAA-18, and NOAA-19 satellites. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May to 25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May to 24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 26, 2017, through July 15, 2017, and are available in netCDF-4 format. proprietary
aamhcpex_1 AAMH CPEX GHRC_DAAC STAC Catalog 2017-05-26 2017-07-16 154.716, 0.6408, -19.5629, 44.9689 https://cmr.earthdata.nasa.gov/search/concepts/C2645106424-GHRC_DAAC.umm_json The AAMH CPEX dataset contains products obtained from the MetOp-A, MetOp-B, NOAA-18, and NOAA-19 satellites. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May to 25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May to 24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 26, 2017, through July 15, 2017, and are available in netCDF-4 format. proprietary
+aamhcpex_1 AAMH CPEX ALL STAC Catalog 2017-05-26 2017-07-16 154.716, 0.6408, -19.5629, 44.9689 https://cmr.earthdata.nasa.gov/search/concepts/C2645106424-GHRC_DAAC.umm_json The AAMH CPEX dataset contains products obtained from the MetOp-A, MetOp-B, NOAA-18, and NOAA-19 satellites. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region from 25 May to 25 June 2017. CPEX conducted a total of sixteen DC-8 missions from 27 May to 24 June. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 26, 2017, through July 15, 2017, and are available in netCDF-4 format. proprietary
ab90030e26c54ba495b1cbec51e137e1_NA ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from AATSR (ADV algorithm), Version 2.31 FEDEO STAC Catalog 2002-07-24 2012-04-08 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142756-FEDEO.umm_json The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics. This dataset comprises Level 3 daily and monthly gridded aerosol products from the AATSR instrument on the ENVISAT satellite, derived using the ADV algorithm, version 2.31. Data is available for the period from 2002 to 2012.For further details about these data products please see the linked documentation. proprietary
-above-and-below-ground-herbivore-communities-along-elevation_1.0 Above- and below-ground herbivore communities along elevation ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814648-ENVIDAT.umm_json Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017. proprietary
above-and-below-ground-herbivore-communities-along-elevation_1.0 Above- and below-ground herbivore communities along elevation ALL STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814648-ENVIDAT.umm_json Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017. proprietary
+above-and-below-ground-herbivore-communities-along-elevation_1.0 Above- and below-ground herbivore communities along elevation ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814648-ENVIDAT.umm_json Despite the common role of above- and below-ground herbivore communities in mediating ecosystem functioning, our understanding of the variation of species communities along natural gradient is largely strongly biased toward aboveground organisms. This dataset enables to study the variations in assemblages of two dominant groups of herbivores, namely, aboveground orthoptera and belowground nematodes together with their food plants. Herbivores and plant surveys were conducted in 48 natural grasslands along six elevation gradients, selected to span the major macro-climatic and environmental conditions of the Swiss Alps. It compiles herbivores and plant surveys, information on the study sites as well as plant and herbivores functional traits sought to be involved in trophic interactions and to respond to climatic variation along the elevation. Plant functional traits considered are the SLA, the LDMC, the C/N content, the punch strength (i.e. force required to pierce the leave lamina), the mandibular strength for Orthoptera insect. Data were collected during the summer 2016 and 2017. proprietary
accessibility-of-the-swiss-forest-for-economic-wood-extraction_1.0 Accessibility of the Swiss forest for economic wood extraction (2021) ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081516-ENVIDAT.umm_json "Two raster maps (10m resolution) of: I) the most suitable extraction method for wood in the Swiss forest, and II) the overall suitability of the Swiss forest for economic wood extraction and transport. A modern forest road system is important for the efficient management of forests. In order to assess the current forest accessibility in Switzerland on a comprehensive basis, the entire Swiss forest was investigated using a consistent methodology. In our model, wood extraction from the stand to the road and on-road transport are analysed in combination. Suitable extraction methods for each forest parcel (10m x 10m) were determined using an approach in which ground-based, cable-based and air-based transport are distinguished. First, the areas for ground- and cable-based extraction were delineated. The trafficability of the forest areas was assessed based on the terrain and soil characteristics; trafficable areas also had to be connected to a forest road. To evaluate the suitability for cable-yarding (up to a maximum distance of 1500 m), terrain and possible obstacles (e.g., power lines) were considered. The remaining forest area, which was not suitable for either ground-based or cable-based methods, was assigned to the ""helicopter"" category. As a result of this analysis, a map of the most suitable skidding method for each plot could be created. When several methods were possible for a parcel, the priority was ground-based over cable-based over air-based. Road transport was investigated using network analysis, based on the data set ""Forest access roads 2013"" from the Swiss National Forest Inventory (NFI), which contains information on width and weight limits of roads in the forest and up to the superordinate main road network. Thus, in addition to the distance, the largest type of vehicle allowed on the respective removal route could also be taken into account. Based on the extraction method and the weight limits for on-road transport, the forest area was divided into three categories: 1) meets the requirements for efficient forest management (all forest parcels with ground-based extraction method or mobile cable-yarding, transport weight limit at least 28 tons); 2) limited suitability for efficient forest management; and 3) not suitable for efficient forest management (forest parcels in the ""helicopter"" category or transport with trucks under 26 tons). The resulting maps cannot provide an accurate classification for each forest parcel. Missing or incorrect roads in the road dataset, insufficient information on ground trafficability or other local factors, the limitation to only three possible extraction systems, and failure to account for anchor trees, extraction methods changing over small distances, and unrealistically short cable-yarding distances can cause the model results to deviate from the assessment by an expert with knowledge of the local conditions. Also, protected areas were not excluded and harvesting intensity was not taken into account. The advantage of the method is that consistent criteria are used for the entire Swiss forest, making the results comparable throughout Switzerland. The data are managed at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and are available to third parties on request. (NFI data policy: https://www.lfi.ch/dienstleist/daten.php) Input data used: - Forest road dataset of the NFI4 (only truck roads from 3.0 m width and 26 t carrying capacity) (2016). - NFI forest mask, 10 m resolution (2015) - Digital elevation model, 10m resolution (based on swissALTI3D 2016) - Slope map, 10m resolution (based on swissALTI3D 2016) - Soil suitability map, 10m resolution (based on soil suitability map BFS 2000) - Obstacles for cable lines, 10m resolution (buildings, major roads, power lines, railroads, based on swissTLM3D 2016)" proprietary
accessibility-of-the-swiss-forest-for-economic-wood-extraction_1.0 Accessibility of the Swiss forest for economic wood extraction (2021) ALL STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081516-ENVIDAT.umm_json "Two raster maps (10m resolution) of: I) the most suitable extraction method for wood in the Swiss forest, and II) the overall suitability of the Swiss forest for economic wood extraction and transport. A modern forest road system is important for the efficient management of forests. In order to assess the current forest accessibility in Switzerland on a comprehensive basis, the entire Swiss forest was investigated using a consistent methodology. In our model, wood extraction from the stand to the road and on-road transport are analysed in combination. Suitable extraction methods for each forest parcel (10m x 10m) were determined using an approach in which ground-based, cable-based and air-based transport are distinguished. First, the areas for ground- and cable-based extraction were delineated. The trafficability of the forest areas was assessed based on the terrain and soil characteristics; trafficable areas also had to be connected to a forest road. To evaluate the suitability for cable-yarding (up to a maximum distance of 1500 m), terrain and possible obstacles (e.g., power lines) were considered. The remaining forest area, which was not suitable for either ground-based or cable-based methods, was assigned to the ""helicopter"" category. As a result of this analysis, a map of the most suitable skidding method for each plot could be created. When several methods were possible for a parcel, the priority was ground-based over cable-based over air-based. Road transport was investigated using network analysis, based on the data set ""Forest access roads 2013"" from the Swiss National Forest Inventory (NFI), which contains information on width and weight limits of roads in the forest and up to the superordinate main road network. Thus, in addition to the distance, the largest type of vehicle allowed on the respective removal route could also be taken into account. Based on the extraction method and the weight limits for on-road transport, the forest area was divided into three categories: 1) meets the requirements for efficient forest management (all forest parcels with ground-based extraction method or mobile cable-yarding, transport weight limit at least 28 tons); 2) limited suitability for efficient forest management; and 3) not suitable for efficient forest management (forest parcels in the ""helicopter"" category or transport with trucks under 26 tons). The resulting maps cannot provide an accurate classification for each forest parcel. Missing or incorrect roads in the road dataset, insufficient information on ground trafficability or other local factors, the limitation to only three possible extraction systems, and failure to account for anchor trees, extraction methods changing over small distances, and unrealistically short cable-yarding distances can cause the model results to deviate from the assessment by an expert with knowledge of the local conditions. Also, protected areas were not excluded and harvesting intensity was not taken into account. The advantage of the method is that consistent criteria are used for the entire Swiss forest, making the results comparable throughout Switzerland. The data are managed at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) and are available to third parties on request. (NFI data policy: https://www.lfi.ch/dienstleist/daten.php) Input data used: - Forest road dataset of the NFI4 (only truck roads from 3.0 m width and 26 t carrying capacity) (2016). - NFI forest mask, 10 m resolution (2015) - Digital elevation model, 10m resolution (based on swissALTI3D 2016) - Slope map, 10m resolution (based on swissALTI3D 2016) - Soil suitability map, 10m resolution (based on soil suitability map BFS 2000) - Obstacles for cable lines, 10m resolution (buildings, major roads, power lines, railroads, based on swissTLM3D 2016)" proprietary
-accum-measurements-domec-traverse-1982_1 Accumulation Measurements from Pioneerskaya to Dome C, 1982-84 ALL STAC Catalog 1982-01-01 1984-12-31 124.5, -78.5, 93, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311710-AU_AADC.umm_json Initial accumulation levels measured on traverse in 1982/83, and re-measurement of some poles on the 1983/84 traverse. These documents have been archived in the records store at the Australian Antarctic Division. proprietary
accum-measurements-domec-traverse-1982_1 Accumulation Measurements from Pioneerskaya to Dome C, 1982-84 AU_AADC STAC Catalog 1982-01-01 1984-12-31 124.5, -78.5, 93, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311710-AU_AADC.umm_json Initial accumulation levels measured on traverse in 1982/83, and re-measurement of some poles on the 1983/84 traverse. These documents have been archived in the records store at the Australian Antarctic Division. proprietary
+accum-measurements-domec-traverse-1982_1 Accumulation Measurements from Pioneerskaya to Dome C, 1982-84 ALL STAC Catalog 1982-01-01 1984-12-31 124.5, -78.5, 93, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311710-AU_AADC.umm_json Initial accumulation levels measured on traverse in 1982/83, and re-measurement of some poles on the 1983/84 traverse. These documents have been archived in the records store at the Australian Antarctic Division. proprietary
accumulation-movement-markers-mirny-domec_1 Detailed Notes on Accumulation/Movement Markers, Mirny-Dome C AU_AADC STAC Catalog 1977-01-01 1978-12-31 124.5, -78.5, 93, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214311711-AU_AADC.umm_json Detailed notes about each of the markers used for movement (and accumulation) measurements along the Mirny-Dome C traverse line. Includes processing notes from the JMR position analysis. These documents have been archived in the records store at the Australian Antarctic Division. proprietary
-accumulation_lawdome_1960_1 Accumulation Measurements, Law Dome 1959-1960 ALL STAC Catalog 1959-01-01 1960-12-31 110, -67, 115, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214305674-AU_AADC.umm_json A collection of information on the position and measurements of snow accumulation via accumulation stakes placed on Law Dome in 1959, and measured over 1959 and 1960. These documents have been archived at the Australian Antarctic Division. proprietary
accumulation_lawdome_1960_1 Accumulation Measurements, Law Dome 1959-1960 AU_AADC STAC Catalog 1959-01-01 1960-12-31 110, -67, 115, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214305674-AU_AADC.umm_json A collection of information on the position and measurements of snow accumulation via accumulation stakes placed on Law Dome in 1959, and measured over 1959 and 1960. These documents have been archived at the Australian Antarctic Division. proprietary
-aces1am_1 ACES Aircraft and Mechanical Data GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977826980-GHRC_DAAC.umm_json The ACES Aircraft and Mechanical Data consist of aircraft (e.g. pitch, roll, yaw) and mechanical (e.g. aircraft engine speed, tail commands, fuel levels) data recorded by the Altus II Unmanned Aerial Vehicle (Altus II UAV) system during the Altus Cumulus Electrification Study (ACES) based at the Naval Air Facility Key West in Florida. ACES aimed to provide extensive observations of the cloud electrification process and its effects by using the Altus II UAV to collect cloud top observations of thunderstorms. The campaign also worked to validate satellite lightning measurements. The Altus II aircraft and mechanical data files are available from July 10 through August 30, 2002 in MATLAB data format (.mat). proprietary
+accumulation_lawdome_1960_1 Accumulation Measurements, Law Dome 1959-1960 ALL STAC Catalog 1959-01-01 1960-12-31 110, -67, 115, -65 https://cmr.earthdata.nasa.gov/search/concepts/C1214305674-AU_AADC.umm_json A collection of information on the position and measurements of snow accumulation via accumulation stakes placed on Law Dome in 1959, and measured over 1959 and 1960. These documents have been archived at the Australian Antarctic Division. proprietary
aces1am_1 ACES Aircraft and Mechanical Data ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977826980-GHRC_DAAC.umm_json The ACES Aircraft and Mechanical Data consist of aircraft (e.g. pitch, roll, yaw) and mechanical (e.g. aircraft engine speed, tail commands, fuel levels) data recorded by the Altus II Unmanned Aerial Vehicle (Altus II UAV) system during the Altus Cumulus Electrification Study (ACES) based at the Naval Air Facility Key West in Florida. ACES aimed to provide extensive observations of the cloud electrification process and its effects by using the Altus II UAV to collect cloud top observations of thunderstorms. The campaign also worked to validate satellite lightning measurements. The Altus II aircraft and mechanical data files are available from July 10 through August 30, 2002 in MATLAB data format (.mat). proprietary
-aces1cont_1 ACES CONTINUOUS DATA V1 GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847043-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August, 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloudelectrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil Magnetometer, Accelerometers, Gerdien Conductivity Probe, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels. proprietary
+aces1am_1 ACES Aircraft and Mechanical Data GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977826980-GHRC_DAAC.umm_json The ACES Aircraft and Mechanical Data consist of aircraft (e.g. pitch, roll, yaw) and mechanical (e.g. aircraft engine speed, tail commands, fuel levels) data recorded by the Altus II Unmanned Aerial Vehicle (Altus II UAV) system during the Altus Cumulus Electrification Study (ACES) based at the Naval Air Facility Key West in Florida. ACES aimed to provide extensive observations of the cloud electrification process and its effects by using the Altus II UAV to collect cloud top observations of thunderstorms. The campaign also worked to validate satellite lightning measurements. The Altus II aircraft and mechanical data files are available from July 10 through August 30, 2002 in MATLAB data format (.mat). proprietary
aces1cont_1 ACES CONTINUOUS DATA V1 ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847043-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August, 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloudelectrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil Magnetometer, Accelerometers, Gerdien Conductivity Probe, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels. proprietary
+aces1cont_1 ACES CONTINUOUS DATA V1 GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847043-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August, 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloudelectrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from seven instruments: the Slow/Fast antenna, Electric Field Mill, Dual Optical Pulse Sensor, Searchcoil Magnetometer, Accelerometers, Gerdien Conductivity Probe, and the Fluxgate Magnetometer. Data consists of sensor reads at 50HZ throughout the flight from all 64 channels. proprietary
aces1efm_1 ACES ELECTRIC FIELD MILL V1 GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847178-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data from Electric Field Mills, which yield information about the atmospheric electrical fields above the instruments. proprietary
aces1efm_1 ACES ELECTRIC FIELD MILL V1 ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977847178-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from it's birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data from Electric Field Mills, which yield information about the atmospheric electrical fields above the instruments. proprietary
-aces1log_1 ACES LOG DATA GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977853903-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of log data from each flight, and yields instrument and aircraft status throughout the flight. proprietary
aces1log_1 ACES LOG DATA ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977853903-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of log data from each flight, and yields instrument and aircraft status throughout the flight. proprietary
+aces1log_1 ACES LOG DATA GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977853903-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of log data from each flight, and yields instrument and aircraft status throughout the flight. proprietary
aces1time_1 ACES TIMING DATA GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977855412-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August or 2002, ACES researchers overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of timing data used for the experiment. When used it provides: syncclock_time = time found at the syncclock (VSI-SYnCCLOCK-32) in seconds from first file name, syncclock_m_time = time found at the syncclock (VSI-SYnCCLOCK-32) in Matlab dateform format, system_time = system time in seconds from first file name, system_m_time = system time in dateform format, gps_time = time found at the GPS unit in seconds from first file name, gps_m_time = time found at GPS unit in dateform, cmos_time = time found at the computer CMOS in seconds from first file name, cmos_m_time = time found at the computer CMOS in dateform. proprietary
aces1time_1 ACES TIMING DATA ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977855412-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August or 2002, ACES researchers overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of timing data used for the experiment. When used it provides: syncclock_time = time found at the syncclock (VSI-SYnCCLOCK-32) in seconds from first file name, syncclock_m_time = time found at the syncclock (VSI-SYnCCLOCK-32) in Matlab dateform format, system_time = system time in seconds from first file name, system_m_time = system time in dateform format, gps_time = time found at the GPS unit in seconds from first file name, gps_m_time = time found at GPS unit in dateform, cmos_time = time found at the computer CMOS in seconds from first file name, cmos_m_time = time found at the computer CMOS in dateform. proprietary
-aces1trig_1 ACES TRIGGERED DATA GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977858342-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from the following instruments: Slow/Fast antenna, Electric Field Mill, Optical Pulse Sensors, Searchcoil Magnetometer, Accelerometer, and Gerdien Conductivity Probe. These data were collected at 200KHz from the first 16 telemetry items collected on the aircraft, were initiated by an operator selected trigger (e.g. DOPS), and continued collecting for as long as the trigger continued. proprietary
aces1trig_1 ACES TRIGGERED DATA ALL STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977858342-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from the following instruments: Slow/Fast antenna, Electric Field Mill, Optical Pulse Sensors, Searchcoil Magnetometer, Accelerometer, and Gerdien Conductivity Probe. These data were collected at 200KHz from the first 16 telemetry items collected on the aircraft, were initiated by an operator selected trigger (e.g. DOPS), and continued collecting for as long as the trigger continued. proprietary
-acoustic_charts_v6_1994_95_1 Acoustic Sounder Charts from Australian Antarctic Division Voyage 6 1994/95 (BANGSS) AU_AADC STAC Catalog 1995-02-06 1995-04-12 60, -69.393, 147.473, -42.882 https://cmr.earthdata.nasa.gov/search/concepts/C1214311712-AU_AADC.umm_json Acoustic sounder charts were collected at six locations during Australian Antarctic Division Voyage 6 1994/95 (BANGSS) using the Kongsberg EA200 Echo Sounder on the Aurora Australis. BANGSS is an acronym for Big ANtarctic Geological and Seismic Survey. The voyage began on 6 February 1995 and finished on 12 April 1995. Each chart is labelled with information about when and where the data was collected: date, time, latitude and longitude. The charts provide a profile of the sea floor and have a time axis with numbers in the following format. the first two digits are the day the next two digits are the month the next five digits are the time (UTC) the last ten digits are the maximum value on the depth axis eg 2402005 360000000500 means 24 February 5:36 UTC and the maximum value on the depth axis is 500 metres See a Related URL for a link to information about the voyage including the voyage report. proprietary
+aces1trig_1 ACES TRIGGERED DATA GHRC_DAAC STAC Catalog 2002-07-10 2002-08-30 -85, 23, -81, 26 https://cmr.earthdata.nasa.gov/search/concepts/C1977858342-GHRC_DAAC.umm_json The ALTUS Cloud Electrification Study (ACES) was based at the Naval Air Facility Key West in Florida. During August 2002, ACES researchers conducted overflights of thunderstorms over the southwestern corner of Florida. For the first time in NASA research, an uninhabited aerial vehicle (UAV) named ALTUS was used to collect cloud electrification data. Carrying field mills, optical sensors, electric field sensors and other instruments, ALTUS allowed scientists to collect cloud electrification data for the first time from above the storm, from its birth through dissipation. This experiment allowed scientists to achieve the dual goals of gathering weather data safely and testing new aircraft technology. This dataset consists of data collected from the following instruments: Slow/Fast antenna, Electric Field Mill, Optical Pulse Sensors, Searchcoil Magnetometer, Accelerometer, and Gerdien Conductivity Probe. These data were collected at 200KHz from the first 16 telemetry items collected on the aircraft, were initiated by an operator selected trigger (e.g. DOPS), and continued collecting for as long as the trigger continued. proprietary
acoustic_charts_v6_1994_95_1 Acoustic Sounder Charts from Australian Antarctic Division Voyage 6 1994/95 (BANGSS) ALL STAC Catalog 1995-02-06 1995-04-12 60, -69.393, 147.473, -42.882 https://cmr.earthdata.nasa.gov/search/concepts/C1214311712-AU_AADC.umm_json Acoustic sounder charts were collected at six locations during Australian Antarctic Division Voyage 6 1994/95 (BANGSS) using the Kongsberg EA200 Echo Sounder on the Aurora Australis. BANGSS is an acronym for Big ANtarctic Geological and Seismic Survey. The voyage began on 6 February 1995 and finished on 12 April 1995. Each chart is labelled with information about when and where the data was collected: date, time, latitude and longitude. The charts provide a profile of the sea floor and have a time axis with numbers in the following format. the first two digits are the day the next two digits are the month the next five digits are the time (UTC) the last ten digits are the maximum value on the depth axis eg 2402005 360000000500 means 24 February 5:36 UTC and the maximum value on the depth axis is 500 metres See a Related URL for a link to information about the voyage including the voyage report. proprietary
+acoustic_charts_v6_1994_95_1 Acoustic Sounder Charts from Australian Antarctic Division Voyage 6 1994/95 (BANGSS) AU_AADC STAC Catalog 1995-02-06 1995-04-12 60, -69.393, 147.473, -42.882 https://cmr.earthdata.nasa.gov/search/concepts/C1214311712-AU_AADC.umm_json Acoustic sounder charts were collected at six locations during Australian Antarctic Division Voyage 6 1994/95 (BANGSS) using the Kongsberg EA200 Echo Sounder on the Aurora Australis. BANGSS is an acronym for Big ANtarctic Geological and Seismic Survey. The voyage began on 6 February 1995 and finished on 12 April 1995. Each chart is labelled with information about when and where the data was collected: date, time, latitude and longitude. The charts provide a profile of the sea floor and have a time axis with numbers in the following format. the first two digits are the day the next two digits are the month the next five digits are the time (UTC) the last ten digits are the maximum value on the depth axis eg 2402005 360000000500 means 24 February 5:36 UTC and the maximum value on the depth axis is 500 metres See a Related URL for a link to information about the voyage including the voyage report. proprietary
acoustic_doppler_current_profiler_data_-_2010 Acoustic Doppler Current Profiler Data - 2010 SCIOPS STAC Catalog 2010-08-21 2010-09-17 -156, 70, -154, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214602088-SCIOPS.umm_json "Acoustic Doppler current profiler data were collected using a RD Instruments, 300 kHz ADCP that was mounted on an acoustic sled and towed alongside the R/V Annika Marie. Deployment was somewhat limited by weather, with higher sea states precluding use of the instrument. Data were processed by Frank Bahr at the Woods Hole Oceanographic Institution. Two files are included: A matlab file and a .zip file containing ascii files for each deployement. 2.) ascii format. The .mat file sos2010_dt.mat contains all deployments in the structure vm_data.
The format is described in a text variable ""readme"" contained in sos2010_dt.mat 2.) ascii format.
The data are also presented in ascii, with one data file per deployment, with files zipped together in to sos2010dt_ascii.zip.
The first line of each file gives the center depth of the ADCP bins in meters.
Note that both the bin depths as well as the number of bins may change
between deployments.
It is followed by one line per ADCP profile, listing
- profile time as year/month/day hour:min:sec,
- profile time in 2010 decimal days (noon on Jan 1 equals decimal day 0.5)
- longitude, latitude in decimal degrees
- N values of zonal velocity, positive eastward, where N is the number of bins
- N values of meridional velocity, positive northward
""Bad"" data are marked with the flag value 999.99." proprietary
acoustic_doppler_current_profiler_data_-_2010 Acoustic Doppler Current Profiler Data - 2010 ALL STAC Catalog 2010-08-21 2010-09-17 -156, 70, -154, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214602088-SCIOPS.umm_json "Acoustic Doppler current profiler data were collected using a RD Instruments, 300 kHz ADCP that was mounted on an acoustic sled and towed alongside the R/V Annika Marie. Deployment was somewhat limited by weather, with higher sea states precluding use of the instrument. Data were processed by Frank Bahr at the Woods Hole Oceanographic Institution. Two files are included: A matlab file and a .zip file containing ascii files for each deployement. 2.) ascii format. The .mat file sos2010_dt.mat contains all deployments in the structure vm_data.
The format is described in a text variable ""readme"" contained in sos2010_dt.mat 2.) ascii format.
The data are also presented in ascii, with one data file per deployment, with files zipped together in to sos2010dt_ascii.zip.
The first line of each file gives the center depth of the ADCP bins in meters.
Note that both the bin depths as well as the number of bins may change
between deployments.
It is followed by one line per ADCP profile, listing
- profile time as year/month/day hour:min:sec,
- profile time in 2010 decimal days (noon on Jan 1 equals decimal day 0.5)
- longitude, latitude in decimal degrees
- N values of zonal velocity, positive eastward, where N is the number of bins
- N values of meridional velocity, positive northward
""Bad"" data are marked with the flag value 999.99." proprietary
acoustic_doppler_current_profiler_data_-_2011 Acoustic Doppler Current Profiler Data - 2011 SCIOPS STAC Catalog 2011-08-22 2011-09-13 -156, 70, -154, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214600594-SCIOPS.umm_json "Acoustic Doppler current profiler data were collected using a RD Instruments, 300 kHz ADCP that was mounted on an acoustic sled and towed alongside the R/V Annika Marie. Deployment was somewhat limited by weather, with higher sea states precluding use of the instrument. Data were processed by Frank Bahr at the Woods Hole Oceanographic Institution. Three files are included: A matlab file and .zip file and .tar files containing ascii files for each deployement. 1.) Matlab format. The .mat file sos2011_dt.mat contains all deployments in the structure vm_data.
The format is described in a text variable ""readme"" contained in sos2010_dt.mat 2.) ascii format.
The data are also presented in ascii, with one data file per deployment, with files zipped together in to sos2011dt_ascii.zip or sos2011dt_asc.tar.
The first line of each file gives the center depth of the ADCP bins in meters.
Note that both the bin depths as well as the number of bins may change
between deployments.
It is followed by one line per ADCP profile, listing
- profile time as year/month/day hour:min:sec,
- profile time in 2010 decimal days (noon on Jan 1 equals decimal day 0.5)
- longitude, latitude in decimal degrees
- N values of zonal velocity, positive eastward, where N is the number of bins
- N values of meridional velocity, positive northward
""Bad"" data are marked with the flag value 999.99." proprietary
acoustic_doppler_current_profiler_data_-_2011 Acoustic Doppler Current Profiler Data - 2011 ALL STAC Catalog 2011-08-22 2011-09-13 -156, 70, -154, 72 https://cmr.earthdata.nasa.gov/search/concepts/C1214600594-SCIOPS.umm_json "Acoustic Doppler current profiler data were collected using a RD Instruments, 300 kHz ADCP that was mounted on an acoustic sled and towed alongside the R/V Annika Marie. Deployment was somewhat limited by weather, with higher sea states precluding use of the instrument. Data were processed by Frank Bahr at the Woods Hole Oceanographic Institution. Three files are included: A matlab file and .zip file and .tar files containing ascii files for each deployement. 1.) Matlab format. The .mat file sos2011_dt.mat contains all deployments in the structure vm_data.
The format is described in a text variable ""readme"" contained in sos2010_dt.mat 2.) ascii format.
The data are also presented in ascii, with one data file per deployment, with files zipped together in to sos2011dt_ascii.zip or sos2011dt_asc.tar.
The first line of each file gives the center depth of the ADCP bins in meters.
Note that both the bin depths as well as the number of bins may change
between deployments.
It is followed by one line per ADCP profile, listing
- profile time as year/month/day hour:min:sec,
- profile time in 2010 decimal days (noon on Jan 1 equals decimal day 0.5)
- longitude, latitude in decimal degrees
- N values of zonal velocity, positive eastward, where N is the number of bins
- N values of meridional velocity, positive northward
""Bad"" data are marked with the flag value 999.99." proprietary
active_layer_arcss_grid_atqasuk_alaska_2010 Active Layer ARCSS grid Atqasuk, Alaska 2010 SCIOPS STAC Catalog 2010-07-10 2010-08-16 -156, 70, -158, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214602289-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe. proprietary
active_layer_arcss_grid_atqasuk_alaska_2010 Active Layer ARCSS grid Atqasuk, Alaska 2010 ALL STAC Catalog 2010-07-10 2010-08-16 -156, 70, -158, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214602289-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe. proprietary
-active_layer_arcss_grid_atqasuk_alaska_2011 Active Layer ARCSS grid Atqasuk, Alaska 2011 ALL STAC Catalog 2011-06-17 2011-08-12 -157, 70, -156, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600393-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2011 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary
active_layer_arcss_grid_atqasuk_alaska_2011 Active Layer ARCSS grid Atqasuk, Alaska 2011 SCIOPS STAC Catalog 2011-06-17 2011-08-12 -157, 70, -156, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600393-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2011 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary
-active_layer_arcss_grid_atqasuk_alaska_2012 Active Layer ARCSS grid Atqasuk, Alaska 2012 SCIOPS STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214601993-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2012 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary
+active_layer_arcss_grid_atqasuk_alaska_2011 Active Layer ARCSS grid Atqasuk, Alaska 2011 ALL STAC Catalog 2011-06-17 2011-08-12 -157, 70, -156, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600393-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2011 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary
active_layer_arcss_grid_atqasuk_alaska_2012 Active Layer ARCSS grid Atqasuk, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214601993-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2012 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary
+active_layer_arcss_grid_atqasuk_alaska_2012 Active Layer ARCSS grid Atqasuk, Alaska 2012 SCIOPS STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214601993-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Atqasuk, Alaska during the 2012 summer field season. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from E2-E6 and J2-J6. The SEL lab's CALM depth probe was used to take the measurements. proprietary
active_layer_arcss_grid_barrow_alaska_2010 Active Layer ARCSS grid Barrow, Alaska 2010 ALL STAC Catalog 2010-06-30 2010-08-11 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600590-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe. proprietary
active_layer_arcss_grid_barrow_alaska_2010 Active Layer ARCSS grid Barrow, Alaska 2010 SCIOPS STAC Catalog 2010-06-30 2010-08-11 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600590-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's long depth probe was used (orange tape on the handle). Data have been corrected by subtracting 3 cm from measurements made in the field to account for the missing tip of the probe. proprietary
active_layer_arcss_grid_barrow_alaska_2011 Active Layer ARCSS grid Barrow, Alaska 2011 ALL STAC Catalog 2011-06-14 2011-07-25 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600390-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's CALM depth probe was used. Depth was measured on the probe as the distance from the frozen active layer to the top of the surface of the vegetation. If water was present, then it was measured to the top of the biomass. proprietary
active_layer_arcss_grid_barrow_alaska_2011 Active Layer ARCSS grid Barrow, Alaska 2011 SCIOPS STAC Catalog 2011-06-14 2011-07-25 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600390-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's CALM depth probe was used. Depth was measured on the probe as the distance from the frozen active layer to the top of the surface of the vegetation. If water was present, then it was measured to the top of the biomass. proprietary
-active_layer_arcss_grid_barrow_alaska_2012 Active Layer ARCSS grid Barrow, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600333-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's CALM depth probe was used. Depth was measured on the probe as the distance from the frozen active layer to the top of the surface of the vegetation. If water was present, then it was measured to the top of the biomass. proprietary
active_layer_arcss_grid_barrow_alaska_2012 Active Layer ARCSS grid Barrow, Alaska 2012 SCIOPS STAC Catalog 2012-06-09 2012-08-18 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600333-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's CALM depth probe was used. Depth was measured on the probe as the distance from the frozen active layer to the top of the surface of the vegetation. If water was present, then it was measured to the top of the biomass. proprietary
+active_layer_arcss_grid_barrow_alaska_2012 Active Layer ARCSS grid Barrow, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600333-SCIOPS.umm_json Active Layer measurements were taken on a 30 plot subset within the ARCSS Grid in Barrow, Alaska. Each measurement was taken on the north eastern most corner of each plot. The chosen subset was located from D2-D7 and H2-H7. The SEL lab's CALM depth probe was used. Depth was measured on the probe as the distance from the frozen active layer to the top of the surface of the vegetation. If water was present, then it was measured to the top of the biomass. proprietary
active_layer_nims_grid_atqasuk_alaska_2011 Active Layer NIMS grid Atqasuk, Alaska 2011 ALL STAC Catalog 2011-06-05 2011-08-12 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600341-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Atqasuk, Alaska throughout the 2011 summer field season. UTEP SELs CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary
active_layer_nims_grid_atqasuk_alaska_2011 Active Layer NIMS grid Atqasuk, Alaska 2011 SCIOPS STAC Catalog 2011-06-05 2011-08-12 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600341-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Atqasuk, Alaska throughout the 2011 summer field season. UTEP SELs CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary
active_layer_nims_grid_atqasuk_alaska_2012 Active Layer NIMS grid Atqasuk, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600318-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Atqasuk, Alaska throughout the 2012 summer field season. UTEP SEL's CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary
active_layer_nims_grid_atqasuk_alaska_2012 Active Layer NIMS grid Atqasuk, Alaska 2012 SCIOPS STAC Catalog 2012-06-09 2012-08-18 -156, 70, -157, 71 https://cmr.earthdata.nasa.gov/search/concepts/C1214600318-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Atqasuk, Alaska throughout the 2012 summer field season. UTEP SEL's CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary
-active_layer_nims_grid_barrow_alaska_2011 Active Layer NIMS grid Barrow, Alaska 2011 SCIOPS STAC Catalog 2011-06-14 2011-08-09 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214602385-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2011 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary
active_layer_nims_grid_barrow_alaska_2011 Active Layer NIMS grid Barrow, Alaska 2011 ALL STAC Catalog 2011-06-14 2011-08-09 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214602385-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2011 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary
-active_layer_nims_grid_barrow_alaska_2012 Active Layer NIMS grid Barrow, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600541-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2012 summer field season. UTEP SELs CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary
+active_layer_nims_grid_barrow_alaska_2011 Active Layer NIMS grid Barrow, Alaska 2011 SCIOPS STAC Catalog 2011-06-14 2011-08-09 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214602385-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2011 summer field season. UTEP SEL’s CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary
active_layer_nims_grid_barrow_alaska_2012 Active Layer NIMS grid Barrow, Alaska 2012 SCIOPS STAC Catalog 2012-06-09 2012-08-18 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600541-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2012 summer field season. UTEP SELs CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary
+active_layer_nims_grid_barrow_alaska_2012 Active Layer NIMS grid Barrow, Alaska 2012 ALL STAC Catalog 2012-06-09 2012-08-18 -156.6, 71, -156.5, 71.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214600541-SCIOPS.umm_json Active Layer measurements were taken at each NIMS (Networked Info-mechanical Systems) grid plot in Barrow, Alaska throughout the 2012 summer field season. UTEP SELs CALM depth probe was used to take measurements. Depth was measured on the probe as the distance from frozen active layer to the top surface of the vegetation. If water was present then it was measured to the top of the biomass. proprietary
ada968fd392d49fbbb07ac84eeb23ac6_NA ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Optical ice velocity of the Zachariae Glacier between 2017-06-25 and 2017-08-10, generated using Sentinel-2 data, v1.1 FEDEO STAC Catalog 2017-06-24 2017-08-10 -80, 60, -10, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142710-FEDEO.umm_json This dataset contains an optical ice velocity time series and seasonal product of the Zachariae Glacier in Greenland, derived from intensity-tracking of Sentinel-2 data acquired between 2017-06-25 and 2017-08-10. It has been produced as part of the ESA Greenland Ice Sheet CCI project.The data are provided on a polar stereographic grid (EPSG 3413:Latitude of true scale 70N, Reference Longitude 45E) with 50m grid spacing. The horizontal velocity is provided in true meters per day, towards EASTING (x) and NORTHING (y) direction of the grid. The product was generated by S[&]T Norway. proprietary
adaptive_long-term_fasting_in_land_and_ice-bound_polar_bears_data_table Adaptive long-term fasting in land and ice-bound polar bears: Data Table SCIOPS STAC Catalog 2008-01-01 2011-12-31 -155, 70, -122, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1214602399-SCIOPS.umm_json The datasets in the data table have been collected as part of a project to understand how reduced sea ice cover in the Arctic will impact polar bear populations. Bears that stay ashore in summer have almost no access to food and tend to be inactive. Those that stay on the ice, however, have continued access to prey and make extensive movements. Over a three year period, scientists from the University of Wyoming and the U. S. Geological Service followed the movements of bears in both habitats and monitored their body temperature, muscle condition, blood chemistry, and metabolism. The physiological data will be added to spatially-explicit individual-based population models to predict population response to reduced ice cover. proprietary
adaptive_long-term_fasting_in_land_and_ice-bound_polar_bears_data_table Adaptive long-term fasting in land and ice-bound polar bears: Data Table ALL STAC Catalog 2008-01-01 2011-12-31 -155, 70, -122, 80 https://cmr.earthdata.nasa.gov/search/concepts/C1214602399-SCIOPS.umm_json The datasets in the data table have been collected as part of a project to understand how reduced sea ice cover in the Arctic will impact polar bear populations. Bears that stay ashore in summer have almost no access to food and tend to be inactive. Those that stay on the ice, however, have continued access to prey and make extensive movements. Over a three year period, scientists from the University of Wyoming and the U. S. Geological Service followed the movements of bears in both habitats and monitored their body temperature, muscle condition, blood chemistry, and metabolism. The physiological data will be added to spatially-explicit individual-based population models to predict population response to reduced ice cover. proprietary
adcp_2 Aurora Australis Southern Ocean ADCP data AU_AADC STAC Catalog 1994-12-13 1999-09-07 75, -69, 165, -41 https://cmr.earthdata.nasa.gov/search/concepts/C1214311719-AU_AADC.umm_json Acoustic Doppler current profiler (ADCP) measurements from a hull mounted 150 kHz narrow band ADCP unit were collected in the Southern Ocean from 1994 to 1999, on the following cruises: au9404, au9501, au9604, au9601, au9701, au9706, au9807 and au9901. The fields in this dataset are: Currents bottom depth cruise number ship speed time velocity GPS proprietary
add104f4c4454b629dbc7648efaa1b50_NA ESA Ozone Climate Change Initiative (Ozone CCI): ODIN/SMR (544.6 GHz) Level 3 Limb Ozone Monthly Zonal Mean (MZM) Profiles, Version 1 FEDEO STAC Catalog 2001-01-01 2013-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2548142584-FEDEO.umm_json This dataset comprises gridded limb ozone monthly zonal mean profiles from the ODIN/SMR (544.6 GHz) instrument. The data are zonal mean time series (10° latitude bin) and include uncertainty/variability of the Monthly Zonal Mean.The monthly zonal mean (MZM) data set provides ozone profiles averaged in 10° latitude zones from 90°S to 90°N, for each month. The monthly zonal mean data are structured into yearly netcdf files, for each instrument separately. The filename indicates the instrument and the year. For example, the file âESACCI-OZONE-L3-LP-MZM-SMR_ODIN-544_6_GHz-2008-fv0001.ncâ contains monthly zonal mean data for ODIN/SMR at 544.6GHz in 2008. proprietary
-adpe-aat-census_1 Adelie penguin census from records from 1931 to 2007 AAT region AU_AADC STAC Catalog 1931-02-13 2006-12-08 38.2, -69.6, 89.5, -65.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214311746-AU_AADC.umm_json A catalogue of adelie penguin colony census records from 1931 to 2007 and limited geographically to the Australian Antarctic Territory (AAT). The present set is from 40E to Gaussberg (89E). The census records have been collected and compiled from a literature search. proprietary
adpe-aat-census_1 Adelie penguin census from records from 1931 to 2007 AAT region ALL STAC Catalog 1931-02-13 2006-12-08 38.2, -69.6, 89.5, -65.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214311746-AU_AADC.umm_json A catalogue of adelie penguin colony census records from 1931 to 2007 and limited geographically to the Australian Antarctic Territory (AAT). The present set is from 40E to Gaussberg (89E). The census records have been collected and compiled from a literature search. proprietary
+adpe-aat-census_1 Adelie penguin census from records from 1931 to 2007 AAT region AU_AADC STAC Catalog 1931-02-13 2006-12-08 38.2, -69.6, 89.5, -65.8 https://cmr.earthdata.nasa.gov/search/concepts/C1214311746-AU_AADC.umm_json A catalogue of adelie penguin colony census records from 1931 to 2007 and limited geographically to the Australian Antarctic Territory (AAT). The present set is from 40E to Gaussberg (89E). The census records have been collected and compiled from a literature search. proprietary
adu_birp Animal Demography Unit - The Birds in Reserves Project (BIRP) CEOS_EXTRA STAC Catalog 1906-02-05 2007-05-20 16.46, -34.77, 32.86, -22.61 https://cmr.earthdata.nasa.gov/search/concepts/C2232477691-CEOS_EXTRA.umm_json BIRP is a joint project of BirdLife South Africa (BLSA), and the Animal Demography Unit (ADU), based at the University of Cape Town (UCT). The basic purpose of BIRP is to compile a comprehensive catalogue of the species of birds which occur and breed in South Africa’s many protected areas. A database of this kind will help to identify the species which are as yet not adequately protected and will also provide the managers of protected areas with information useful in setting management policies. proprietary
adu_cwac Animal Demography Unit - Coordinated Waterbird Counts (CWAC) CEOS_EXTRA STAC Catalog 1983-07-15 2006-09-30 16.46, -34.72, 32.88, -22.22 https://cmr.earthdata.nasa.gov/search/concepts/C2232477679-CEOS_EXTRA.umm_json The Coordinated Waterbird Counts (CWAC) project was launched in 1992. The objective of CWAC is to monitor South Africa's waterbird populations and the conditions of the wetlands which are important for waterbirds. This is being done by means of a programme of regular mid-summer and mid-winter censuses at a large number of South African wetlands. Regular six-monthly counts are conducted; however, we do encourage counters to survey their wetlands on a more regular basis as this provides better data. CWAC currently monitors over 400 wetlands around the country on a regular basis, and furthermore curates waterbird data for close to 600 wetlands. proprietary
adu_safring Animal Demography Unit - South African Bird Ringing Unit (SAFRING) CEOS_EXTRA STAC Catalog 1899-12-30 2004-12-31 -76.33, -71.9, 73.5, 72.25 https://cmr.earthdata.nasa.gov/search/concepts/C2232477669-CEOS_EXTRA.umm_json The South African Bird Ringing Unit (SAFRING) administers bird ringing in southern Africa, supplying rings, ringing equipment and services to volunteer and professional ringers in South Africa and neighbouring countries. All ringing records are curated by SAFRING, which is an essential arm of the Animal Demography Unit. Contact is maintained by the SAFRING Project Coordinator with all ringers (banders in North American or Australian terminology). The Bird Ringing Scheme in South Africa was initiated in 1948, so 1998 saw the 50th anniversary of the scheme. During this period over 1.7 million birds of 852 species were ringed. There have been a total of 16 800 ring recoveries since the inception of the scheme. This gives an overall recovery rate for rings in southern Africa of marginally less than 1%, averaged across all species. This probability varies enormously across species. proprietary
aerial_casa_2010_11_1 Aerial photography flown for the Australian Antarctic Division from CASA 212-400 aircraft, 2010-11 ALL STAC Catalog 2011-01-02 2011-02-06 89.17, -72.37, 112.42, -65.69 https://cmr.earthdata.nasa.gov/search/concepts/C1214305645-AU_AADC.umm_json Digital aerial photography was flown by a contractor for the Australian Antarctic Division (AAD) from CASA 212-400 aircraft during the 2010-11 season. Photographs were taken for various projects or needs: Whales project requested by Natalie Kelly (Science Branch AAD and CSIRO); Cronk Islands, Knox Coast, Wilkes Coast - requested by Colin Southwell (Science Branch AAD, AAS project 2722) - the coverage also includes Bailey Peninsula and part of Clark Peninsula; Frazier Islands - requested by Ian Hay (Strategies Branch AAD, AAS project 3154); Aurora Basin - taken on the return flight from Dome C to Casey of Aurora Basin GC41 position 71 degrees 36'10''S, 111 degrees 15'46''E; Wilkins Aerodrome - to photograph runway and melt; Casey, Wilkes - requested by Gill Slocum (Strategies Branch AAD). The photographs were taken between 2 January 2011 and 6 February 2011. In most cases the images were georeferenced in the camera using the aircraft GPS. Vertical photographs were taken with an in floor camera system using a Nikon D200 digital camera and oblique photographs were taken using a handheld Nikon D700 digital camera in the cockpit. The set of images is too big for download but the images are available upon request from the Australian Antarctic Data Centre. Data extracted from the exif information of the images are available for download as csv files and, in some cases, shapefiles. These data include file name, date, camera, focal length, latitude, longitude and altitude. The images of the Cronk Islands and the Frazier Islands can be viewed in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see a Related URL below. The Film/Digital Series are ANTD1260 (Cronk Islands and Frazier Islands 2 January 2011) and ANTD1261 (Frazier Islands 23 January 2011). proprietary
aerial_casa_2010_11_1 Aerial photography flown for the Australian Antarctic Division from CASA 212-400 aircraft, 2010-11 AU_AADC STAC Catalog 2011-01-02 2011-02-06 89.17, -72.37, 112.42, -65.69 https://cmr.earthdata.nasa.gov/search/concepts/C1214305645-AU_AADC.umm_json Digital aerial photography was flown by a contractor for the Australian Antarctic Division (AAD) from CASA 212-400 aircraft during the 2010-11 season. Photographs were taken for various projects or needs: Whales project requested by Natalie Kelly (Science Branch AAD and CSIRO); Cronk Islands, Knox Coast, Wilkes Coast - requested by Colin Southwell (Science Branch AAD, AAS project 2722) - the coverage also includes Bailey Peninsula and part of Clark Peninsula; Frazier Islands - requested by Ian Hay (Strategies Branch AAD, AAS project 3154); Aurora Basin - taken on the return flight from Dome C to Casey of Aurora Basin GC41 position 71 degrees 36'10''S, 111 degrees 15'46''E; Wilkins Aerodrome - to photograph runway and melt; Casey, Wilkes - requested by Gill Slocum (Strategies Branch AAD). The photographs were taken between 2 January 2011 and 6 February 2011. In most cases the images were georeferenced in the camera using the aircraft GPS. Vertical photographs were taken with an in floor camera system using a Nikon D200 digital camera and oblique photographs were taken using a handheld Nikon D700 digital camera in the cockpit. The set of images is too big for download but the images are available upon request from the Australian Antarctic Data Centre. Data extracted from the exif information of the images are available for download as csv files and, in some cases, shapefiles. These data include file name, date, camera, focal length, latitude, longitude and altitude. The images of the Cronk Islands and the Frazier Islands can be viewed in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see a Related URL below. The Film/Digital Series are ANTD1260 (Cronk Islands and Frazier Islands 2 January 2011) and ANTD1261 (Frazier Islands 23 January 2011). proprietary
-aerial_mosaics_macquarie_2017_2 Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017 ALL STAC Catalog 2017-01-15 2017-02-15 158.874, -54.506, 158.954, -54.483 https://cmr.earthdata.nasa.gov/search/concepts/C1437176029-AU_AADC.umm_json One vertical and two oblique mosaics of The Isthmus at Macquarie Island were created from aerial photographs taken with a UAV (Unmanned Aerial Vehicle) during the course of Australian Antarctic Science Project 4340 in January and February 2017. The oblique mosaics include Wireless Hill and the northern end of the island's plateau. One oblique mosaic is a view from the eastern side of The Isthmus and the other is a view from the western side of The Isthmus. The photographs were taken by Murray Hamilton of the University of Adelaide using a DJI Phantom 3 Advanced UAV (under Monash University's Operators Certificate) which he was using to make temperature and humidity observations. They were taken when the UAV was waiting to descend and measure a temperature profile. The measuring instrument needed some time for the temperature to equilibrate after a rapid ascent. The photographs were taken by rotating the craft, taking snapshots every few tens of degrees. Hugin software was used to create the mosaics. The photographs for the vertical mosaic were taken on 15 January 2017 and the photographs for the oblique mosaics were taken on 7 February 2017 (view from east) and 15 February 2017 (view from west). The vertical mosaic was produced at the request of the Building Services Supervisor at the station. proprietary
aerial_mosaics_macquarie_2017_2 Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017 AU_AADC STAC Catalog 2017-01-15 2017-02-15 158.874, -54.506, 158.954, -54.483 https://cmr.earthdata.nasa.gov/search/concepts/C1437176029-AU_AADC.umm_json One vertical and two oblique mosaics of The Isthmus at Macquarie Island were created from aerial photographs taken with a UAV (Unmanned Aerial Vehicle) during the course of Australian Antarctic Science Project 4340 in January and February 2017. The oblique mosaics include Wireless Hill and the northern end of the island's plateau. One oblique mosaic is a view from the eastern side of The Isthmus and the other is a view from the western side of The Isthmus. The photographs were taken by Murray Hamilton of the University of Adelaide using a DJI Phantom 3 Advanced UAV (under Monash University's Operators Certificate) which he was using to make temperature and humidity observations. They were taken when the UAV was waiting to descend and measure a temperature profile. The measuring instrument needed some time for the temperature to equilibrate after a rapid ascent. The photographs were taken by rotating the craft, taking snapshots every few tens of degrees. Hugin software was used to create the mosaics. The photographs for the vertical mosaic were taken on 15 January 2017 and the photographs for the oblique mosaics were taken on 7 February 2017 (view from east) and 15 February 2017 (view from west). The vertical mosaic was produced at the request of the Building Services Supervisor at the station. proprietary
+aerial_mosaics_macquarie_2017_2 Aerial photograph mosaics of The Isthmus at Macquarie Island, January and February 2017 ALL STAC Catalog 2017-01-15 2017-02-15 158.874, -54.506, 158.954, -54.483 https://cmr.earthdata.nasa.gov/search/concepts/C1437176029-AU_AADC.umm_json One vertical and two oblique mosaics of The Isthmus at Macquarie Island were created from aerial photographs taken with a UAV (Unmanned Aerial Vehicle) during the course of Australian Antarctic Science Project 4340 in January and February 2017. The oblique mosaics include Wireless Hill and the northern end of the island's plateau. One oblique mosaic is a view from the eastern side of The Isthmus and the other is a view from the western side of The Isthmus. The photographs were taken by Murray Hamilton of the University of Adelaide using a DJI Phantom 3 Advanced UAV (under Monash University's Operators Certificate) which he was using to make temperature and humidity observations. They were taken when the UAV was waiting to descend and measure a temperature profile. The measuring instrument needed some time for the temperature to equilibrate after a rapid ascent. The photographs were taken by rotating the craft, taking snapshots every few tens of degrees. Hugin software was used to create the mosaics. The photographs for the vertical mosaic were taken on 15 January 2017 and the photographs for the oblique mosaics were taken on 7 February 2017 (view from east) and 15 February 2017 (view from west). The vertical mosaic was produced at the request of the Building Services Supervisor at the station. proprietary
aerial_photo_sea_ice_1 Aerial photographs of sea ice flown by the Australian Antarctic Division AU_AADC STAC Catalog 2003-09-10 -58.2, -69.67, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1214305646-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the following voyages: Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE); Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05; and Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: ARISE: 10 Sep 2003 to 31 Oct 2003 ISPOL: 6 Nov 2004 to 19 Jan 2005 SIPEX: 29 Aug 2007 to 16 Oct 2007 SIPEX II: 25 Sep 2012 to 6 Nov 2012 The child records include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. proprietary
aerial_photo_sea_ice_1 Aerial photographs of sea ice flown by the Australian Antarctic Division ALL STAC Catalog 2003-09-10 -58.2, -69.67, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1214305646-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the following voyages: Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE); Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05; and Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: ARISE: 10 Sep 2003 to 31 Oct 2003 ISPOL: 6 Nov 2004 to 19 Jan 2005 SIPEX: 29 Aug 2007 to 16 Oct 2007 SIPEX II: 25 Sep 2012 to 6 Nov 2012 The child records include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. proprietary
aerial_photo_sea_ice_ARISE_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the ARISE voyage in 2003 AU_AADC STAC Catalog 2003-09-10 2003-10-31 109.1, -66.7, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611591-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE), 10 Sep 2003 to 31 Oct 2003. The Related URLs in this metadata record include the urls of web pages with information about the voyage, urls for requesting for the photographs and urls for downloading information about the photographs. The ARISE aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis. The collection can be searched in the Australian Antarctic Data Centre's Aerial Photograph Catalogue - see Related URLs. Select ARISE from the Aerial Photography Series picklist. Preview images of the photos may be viewed using this search. Digital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue. The flight lines and photo centres for ARISE are available for download as shapefiles - see metadata record ID: aerial_photo_sea_ice_shapefiles. proprietary
@@ -17053,16 +17060,16 @@ aerial_photo_sea_ice_ISPOL_1 Aerial photographs of sea ice flown by the Australi
aerial_photo_sea_ice_SIPEX_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the SIPEX voyage in 2007 AU_AADC STAC Catalog 2007-08-29 2007-10-16 109.1, -66.7, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611658-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: SIPEX: 29 Aug 2007 to 16 Oct 2007 The Related URLs in this metadata record include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. Some of the SIPEX aerial photographs were taken at ice stations. Refer to the metadata record 'An integrated study of processes linking sea ice and biological ecosystem elements off East Antarctica during winter', Entry ID: ASAC_2767, for information about the ice stations. The metadata record 'RAPPLS Surveys (Radar, Aerial Photography, Pyrometer, and Laser Scanning system) made during the SIPEX II voyage of the Aurora Australis, 2012', Entry ID: SIPEX_II_RAPPLS, describes the aerial photography conducted on SIPEX II, 13 Sep 2012 to 15 Nov 2012. proprietary
aerial_photo_sea_ice_SIPEX_1 Aerial photographs of sea ice flown by the Australian Antarctic Division on the SIPEX voyage in 2007 ALL STAC Catalog 2007-08-29 2007-10-16 109.1, -66.7, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611658-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during Australian Antarctic Division voyage 1 2007/08 - Sea Ice Physics and Ecosystems Experiment (SIPEX). Voyage dates: SIPEX: 29 Aug 2007 to 16 Oct 2007 The Related URLs in this metadata record include the urls of web pages with information about these voyages, urls for requesting for the photographs and urls for downloading information about the photographs. Some of the SIPEX aerial photographs were taken at ice stations. Refer to the metadata record 'An integrated study of processes linking sea ice and biological ecosystem elements off East Antarctica during winter', Entry ID: ASAC_2767, for information about the ice stations. The metadata record 'RAPPLS Surveys (Radar, Aerial Photography, Pyrometer, and Laser Scanning system) made during the SIPEX II voyage of the Aurora Australis, 2012', Entry ID: SIPEX_II_RAPPLS, describes the aerial photography conducted on SIPEX II, 13 Sep 2012 to 15 Nov 2012. proprietary
aerial_photo_sea_ice_shapefiles_1 Flight lines and photo centres of aerial photographs of sea ice flown by the Australian Antarctic Division on the ARISE and ISPOL voyages in 2003 and 2004 AU_AADC STAC Catalog 2003-09-10 2005-01-19 -58.2, -69.67, 118.85, -64.03 https://cmr.earthdata.nasa.gov/search/concepts/C1292611653-AU_AADC.umm_json The Australian Antarctic Division acquired aerial photographs of sea ice from helicopters using a digital Nikon D1X digital camera during the following voyages: Australian Antarctic Division voyage 1 2003/04 - Antarctic Remote Ice Sensing Experiment (ARISE); Alfred Wegener Institute Ice Station Polarstern (ISPOL) voyage 2004/05. Voyage dates: ARISE: 10 Sep 2003 to 31 Oct 2003 ISPOL: 6 Nov 2004 to 19 Jan 2005 The ARISE and ISPOL aerial photographs of sea ice are part of the Australian Antarctic Data Centre's collection of aerial photographs which is described by the metadata record 'The collection of aerial photographs held by the Australian Antarctic Data Centre' with Entry ID: aerial_photo_gis. Digital flight lines and photo centres representing the runs along which the photographs were taken and the centres of the photographs are the basis of the catalogue. proprietary
-aerial_photographs_from_columbia_glacier_1976-2010 Aerial Photographs from Columbia Glacier, 1976-2010 SCIOPS STAC Catalog 1976-07-24 2011-06-15 -146.895, 61.22, -146.895, 61.22 https://cmr.earthdata.nasa.gov/search/concepts/C1214600568-SCIOPS.umm_json Aerial stereophotography missions were flown at least once every year over the Columbia Glacier in 1976-2010, and documented further in the Aerial Inventory. Flight data include all existing scans of the large format diapositives and their derived data products from 2002-2010.
This dataset consists of scanned aerial diapositives in high resolution from a photogrammetric scanner and low resolution JPEG previews. The data are collected into TAR files by year. Data gathered during 2002-2003 are collected into TAR files by day and part (e.g. 20020826_01.tar).
proprietary
aerial_photographs_from_columbia_glacier_1976-2010 Aerial Photographs from Columbia Glacier, 1976-2010 ALL STAC Catalog 1976-07-24 2011-06-15 -146.895, 61.22, -146.895, 61.22 https://cmr.earthdata.nasa.gov/search/concepts/C1214600568-SCIOPS.umm_json Aerial stereophotography missions were flown at least once every year over the Columbia Glacier in 1976-2010, and documented further in the Aerial Inventory. Flight data include all existing scans of the large format diapositives and their derived data products from 2002-2010.
This dataset consists of scanned aerial diapositives in high resolution from a photogrammetric scanner and low resolution JPEG previews. The data are collected into TAR files by year. Data gathered during 2002-2003 are collected into TAR files by day and part (e.g. 20020826_01.tar).
proprietary
-aerial_rpa_nov2016_1 Aerial photographs of Davis and Heidemann Valley taken with Remotely Piloted Aircraft, November 2016 ALL STAC Catalog 2016-11-07 2016-11-20 77.9619, -68.5811, 78.0131, -68.5731 https://cmr.earthdata.nasa.gov/search/concepts/C1367275166-AU_AADC.umm_json The Australian Antarcic Division (AAD) contracted Helicopter Resources to fly remotely piloted aircraft (RPA) on Voyage 1 2016/17. The RPA were used to take aerial photographs for sea ice reconnaisance from the RSV Aurora Australis, aerial photographs of Davis, aerial photographs for building roof inspections at Davis and aerial photographs of part of Heidemann Valley. Video was also recorded from the RSV Aurora Australis and of Heidemann Valley. The flights over Heidemann Valley were done at the request of the AAD's Antarctic Modernisation Taskforce. The roof inspections were done at the request of the AAD's Infrastructure section. The following can be downloaded or requested from this metadata record by AAD staff only (see Related URLs): 1 A report prepared by Doug Thost, the chief RPA pilot; 2 The aerial photographs of Davis and Heidemann Valley; and 3 Some panoramas created from aerial photographs taken at Davis. The AAD's Multimedia section have a copy of the videos. The AAD's Infrastructure section have a copy of the aerial photographs taken for roof inspections. See the report for further details. proprietary
+aerial_photographs_from_columbia_glacier_1976-2010 Aerial Photographs from Columbia Glacier, 1976-2010 SCIOPS STAC Catalog 1976-07-24 2011-06-15 -146.895, 61.22, -146.895, 61.22 https://cmr.earthdata.nasa.gov/search/concepts/C1214600568-SCIOPS.umm_json Aerial stereophotography missions were flown at least once every year over the Columbia Glacier in 1976-2010, and documented further in the Aerial Inventory. Flight data include all existing scans of the large format diapositives and their derived data products from 2002-2010.
This dataset consists of scanned aerial diapositives in high resolution from a photogrammetric scanner and low resolution JPEG previews. The data are collected into TAR files by year. Data gathered during 2002-2003 are collected into TAR files by day and part (e.g. 20020826_01.tar).
proprietary
aerial_rpa_nov2016_1 Aerial photographs of Davis and Heidemann Valley taken with Remotely Piloted Aircraft, November 2016 AU_AADC STAC Catalog 2016-11-07 2016-11-20 77.9619, -68.5811, 78.0131, -68.5731 https://cmr.earthdata.nasa.gov/search/concepts/C1367275166-AU_AADC.umm_json The Australian Antarcic Division (AAD) contracted Helicopter Resources to fly remotely piloted aircraft (RPA) on Voyage 1 2016/17. The RPA were used to take aerial photographs for sea ice reconnaisance from the RSV Aurora Australis, aerial photographs of Davis, aerial photographs for building roof inspections at Davis and aerial photographs of part of Heidemann Valley. Video was also recorded from the RSV Aurora Australis and of Heidemann Valley. The flights over Heidemann Valley were done at the request of the AAD's Antarctic Modernisation Taskforce. The roof inspections were done at the request of the AAD's Infrastructure section. The following can be downloaded or requested from this metadata record by AAD staff only (see Related URLs): 1 A report prepared by Doug Thost, the chief RPA pilot; 2 The aerial photographs of Davis and Heidemann Valley; and 3 Some panoramas created from aerial photographs taken at Davis. The AAD's Multimedia section have a copy of the videos. The AAD's Infrastructure section have a copy of the aerial photographs taken for roof inspections. See the report for further details. proprietary
-aerial_surveys_vestfold_2017-18_1 Aerial surveys of Davis station and an area of the Vestfold Hills to the north-east of the station 2017/18 AU_AADC STAC Catalog 2017-11-19 2018-01-31 77.8923, -68.6067, 78.2235, -68.4809 https://cmr.earthdata.nasa.gov/search/concepts/C1542262550-AU_AADC.umm_json "Three aerial surveys were flown by Helicopter Resources Pty Ltd for the Australian Antarctic Division's Antarctic Modernisation Taskforce during the 2017/18 field season. The photography was done from a helicopter and covered Davis station and an area of the Vestfold Hills to the north-east of the station. The first survey conducted on 19 November 2017 covered an inner higher resolution area with flying heights approximately 300 to 400 metres above sea level. The second survey conducted on 20 November 2017 covered a more extensive area at lower resolution with flying heights approximately 800 metres above sea level. The third survey was conducted on 31 January 2018 over a similar area to the first survey with flying heights approximately 300 to 400 metres above sea level. The report on the third survey states ""As a general comment, in comparison to Survey 1, this survey was flown more accurately, in better lighting conditions, with less snow cover, and by all statistical metrics has resulted in a higher quality survey overall."" The spatial extents given in this metadata record are for the second survey. For each survey there is zip file with a report and the following products generated from the survey data: (i) an orthophoto; (ii) a Digital Surface Model (DSM); and (iii) contours generated from the DSM. The products are stored in the UTM zone 44S coordinate system, based on the horizontal datum ITRF2000. Elevations are in metres above Mean Sea Level. There is also a separate zip file with the aerial photographs from the three surveys and a spreadsheet with latitude and longitude for each photo centre. Ground control points were used to constrain the DSM for each survey. One metre by one metre cross markers were set out across the survey area prior to the aerial surveys being flown. The centre of each cross was surveyed by Australian Defence Force surveyors Sam Kelly and Warwick Cox. Some permanent survey marks were used as an independent check of the overall accuracy of the DSM." proprietary
+aerial_rpa_nov2016_1 Aerial photographs of Davis and Heidemann Valley taken with Remotely Piloted Aircraft, November 2016 ALL STAC Catalog 2016-11-07 2016-11-20 77.9619, -68.5811, 78.0131, -68.5731 https://cmr.earthdata.nasa.gov/search/concepts/C1367275166-AU_AADC.umm_json The Australian Antarcic Division (AAD) contracted Helicopter Resources to fly remotely piloted aircraft (RPA) on Voyage 1 2016/17. The RPA were used to take aerial photographs for sea ice reconnaisance from the RSV Aurora Australis, aerial photographs of Davis, aerial photographs for building roof inspections at Davis and aerial photographs of part of Heidemann Valley. Video was also recorded from the RSV Aurora Australis and of Heidemann Valley. The flights over Heidemann Valley were done at the request of the AAD's Antarctic Modernisation Taskforce. The roof inspections were done at the request of the AAD's Infrastructure section. The following can be downloaded or requested from this metadata record by AAD staff only (see Related URLs): 1 A report prepared by Doug Thost, the chief RPA pilot; 2 The aerial photographs of Davis and Heidemann Valley; and 3 Some panoramas created from aerial photographs taken at Davis. The AAD's Multimedia section have a copy of the videos. The AAD's Infrastructure section have a copy of the aerial photographs taken for roof inspections. See the report for further details. proprietary
aerial_surveys_vestfold_2017-18_1 Aerial surveys of Davis station and an area of the Vestfold Hills to the north-east of the station 2017/18 ALL STAC Catalog 2017-11-19 2018-01-31 77.8923, -68.6067, 78.2235, -68.4809 https://cmr.earthdata.nasa.gov/search/concepts/C1542262550-AU_AADC.umm_json "Three aerial surveys were flown by Helicopter Resources Pty Ltd for the Australian Antarctic Division's Antarctic Modernisation Taskforce during the 2017/18 field season. The photography was done from a helicopter and covered Davis station and an area of the Vestfold Hills to the north-east of the station. The first survey conducted on 19 November 2017 covered an inner higher resolution area with flying heights approximately 300 to 400 metres above sea level. The second survey conducted on 20 November 2017 covered a more extensive area at lower resolution with flying heights approximately 800 metres above sea level. The third survey was conducted on 31 January 2018 over a similar area to the first survey with flying heights approximately 300 to 400 metres above sea level. The report on the third survey states ""As a general comment, in comparison to Survey 1, this survey was flown more accurately, in better lighting conditions, with less snow cover, and by all statistical metrics has resulted in a higher quality survey overall."" The spatial extents given in this metadata record are for the second survey. For each survey there is zip file with a report and the following products generated from the survey data: (i) an orthophoto; (ii) a Digital Surface Model (DSM); and (iii) contours generated from the DSM. The products are stored in the UTM zone 44S coordinate system, based on the horizontal datum ITRF2000. Elevations are in metres above Mean Sea Level. There is also a separate zip file with the aerial photographs from the three surveys and a spreadsheet with latitude and longitude for each photo centre. Ground control points were used to constrain the DSM for each survey. One metre by one metre cross markers were set out across the survey area prior to the aerial surveys being flown. The centre of each cross was surveyed by Australian Defence Force surveyors Sam Kelly and Warwick Cox. Some permanent survey marks were used as an independent check of the overall accuracy of the DSM." proprietary
-aerosol-data-davos-wolfgang_1.0 Aerosol Data Davos Wolfgang ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.853594, 46.835577, 9.853594, 46.835577 https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary
+aerial_surveys_vestfold_2017-18_1 Aerial surveys of Davis station and an area of the Vestfold Hills to the north-east of the station 2017/18 AU_AADC STAC Catalog 2017-11-19 2018-01-31 77.8923, -68.6067, 78.2235, -68.4809 https://cmr.earthdata.nasa.gov/search/concepts/C1542262550-AU_AADC.umm_json "Three aerial surveys were flown by Helicopter Resources Pty Ltd for the Australian Antarctic Division's Antarctic Modernisation Taskforce during the 2017/18 field season. The photography was done from a helicopter and covered Davis station and an area of the Vestfold Hills to the north-east of the station. The first survey conducted on 19 November 2017 covered an inner higher resolution area with flying heights approximately 300 to 400 metres above sea level. The second survey conducted on 20 November 2017 covered a more extensive area at lower resolution with flying heights approximately 800 metres above sea level. The third survey was conducted on 31 January 2018 over a similar area to the first survey with flying heights approximately 300 to 400 metres above sea level. The report on the third survey states ""As a general comment, in comparison to Survey 1, this survey was flown more accurately, in better lighting conditions, with less snow cover, and by all statistical metrics has resulted in a higher quality survey overall."" The spatial extents given in this metadata record are for the second survey. For each survey there is zip file with a report and the following products generated from the survey data: (i) an orthophoto; (ii) a Digital Surface Model (DSM); and (iii) contours generated from the DSM. The products are stored in the UTM zone 44S coordinate system, based on the horizontal datum ITRF2000. Elevations are in metres above Mean Sea Level. There is also a separate zip file with the aerial photographs from the three surveys and a spreadsheet with latitude and longitude for each photo centre. Ground control points were used to constrain the DSM for each survey. One metre by one metre cross markers were set out across the survey area prior to the aerial surveys being flown. The centre of each cross was surveyed by Australian Defence Force surveyors Sam Kelly and Warwick Cox. Some permanent survey marks were used as an independent check of the overall accuracy of the DSM." proprietary
aerosol-data-davos-wolfgang_1.0 Aerosol Data Davos Wolfgang ALL STAC Catalog 2020-01-01 2020-01-01 9.853594, 46.835577, 9.853594, 46.835577 https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary
-aerosol-data-weissfluhjoch_1.0 Aerosol Data Weissfluhjoch ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.806475, 46.832964, 9.806475, 46.832964 https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary
+aerosol-data-davos-wolfgang_1.0 Aerosol Data Davos Wolfgang ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.853594, 46.835577, 9.853594, 46.835577 https://cmr.earthdata.nasa.gov/search/concepts/C2789814678-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Davos Wolfgang (LON: 9.853594, LAT: 46.835577). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs) and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3788 , TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and DRINCZ: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l minˉ¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the DRoplet Ice Nuclei Counter Zurich (DRINCZ, ETH Zurich) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary
aerosol-data-weissfluhjoch_1.0 Aerosol Data Weissfluhjoch ALL STAC Catalog 2020-01-01 2020-01-01 9.806475, 46.832964, 9.806475, 46.832964 https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary
+aerosol-data-weissfluhjoch_1.0 Aerosol Data Weissfluhjoch ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.806475, 46.832964, 9.806475, 46.832964 https://cmr.earthdata.nasa.gov/search/concepts/C2789814736-ENVIDAT.umm_json Aerosol properties were measured between February 8 and March 31 2019 at the measurement site Weissfluhjoch (LON: 9.806475, LAT: 46.832964). Optical and aerodynamic particle counters, as well as a scanning mobility particle size spectrometer and an ice nuclei counter were deployed to report particle concentrations and size distributions in fine (10-1000 nm) and coarse mode (> 1000 nm), cloud condensation nuclei concentrations (CCNCs), and ice nuclei particle concentrations (ICNCs). The ambient particles were transported via a heated inlet to be distributed to the particle detecting devices inside the setup room. Optical Particle Counter (OPC): Light scattering of a diode laser beam caused by travelling particles is used in the both, the OPC-N3 (0.41 - 38.5 μm) and GT-526S (0.3 – 5 μm), to determine their size and number concentration. For the OPC-N3, particle size spectra and concentration data are used afterwards to calculate PM₁, PM₂,₅ and PM₁₀ (assumptions: particle density: 1.65 g cmˉ³, refractive index: 1.5+i0). Aerodynamic Particle Sizer (APS): The APS (3321, TSI Inc.) measured the particle size distribution for aerodynamic diameters between 0.5 μm and ~20 μm by the particle’s time-of-flight and light-scattering intensity (assumptions: particle density 1 g cmˉ³). Scanning Mobility Particle Size Spectrometer (SMPS): Particle number concentrations in a size range between 12 and 460 nm (electrical mobility diameter) were measured at Davos Wolfgang, using a Scanning Mobility Particle Sizer Spectrometer (SMPS 3938, TSI Inc.). The classifier (3082, TSI Inc.) was equipped with a neutralizer (3088, TSI Inc.) and a differential mobility analyzer working with negative polarity (3081, TSI Inc.). The size selected particles were counted by a water-based condensation particle counter (3787 TSI Inc.). The TSI AIM software was used to provide particle size distributions by applying multiple charge and diffusion loss corrections (assumptions: particle density 1 g cmˉ³). Coriolis μ and LINDA: A microbial air sampler (Coriolis μ, bertin Instruments) was used to collect airborne particles for investigating their ice nucleating ability with a droplet freezing device. Particles larger than 0.5 μm were drawn with an air flow rate of up to 300 l min‾¹ into the cone and centrifuged into the wall of the cone due to the forming vortex. The liquid sample was transferred into the LED based Ice Nucleation Detection Apparatus (LINDA, University of Basel) to study heterogeneous ice formation (immersion freezing mode) of ambient airborne particles. proprietary
aerosol_properties_725_1 SAFARI 2000 Physical and Chemical Properties of Aerosols, Dry Season 2000 ORNL_CLOUD STAC Catalog 2000-08-17 2000-09-13 5, -35, 60, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2789011485-ORNL_CLOUD.umm_json SAFARI 2000 provided an opportunity to study aerosol particles produced by savanna burning. We used analytical transmission electron microscopy (TEM), including energy-dispersive X-ray spectrometry (EDS) and electron energy-loss spectroscopy (EELS), to study aerosol particles from several smoke and haze samples and from a set of cloud samples. These aerosol particle samples were collected using the University of Washington Convair CV-580 research aircraft (Posfai et al., 2003). proprietary
aes5davg_236_1 BOREAS AES Five-day Averaged Surface Meteorological and Upper Air Data ORNL_CLOUD STAC Catalog 1976-01-01 1997-01-01 -107.87, 52.17, -97.83, 57.35 https://cmr.earthdata.nasa.gov/search/concepts/C2807614663-ORNL_CLOUD.umm_json Contains 5-day averages of hourly and daily data from 23 meteorological stations across Canada along with full-resolution upper air measurements from 1 station in The Pas, Manitoba. proprietary
aes_upl1_238_1 BOREAS AFM-05 Level-1 Upper Air Network Data, R1 ORNL_CLOUD STAC Catalog 1993-08-16 1996-10-22 -111, 50.09, -93.5, 59.98 https://cmr.earthdata.nasa.gov/search/concepts/C2812433046-ORNL_CLOUD.umm_json Contains basic upper-air parameters collected by the AFM-05 team from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region. proprietary
@@ -17082,12 +17089,12 @@ afm4toas_498_1 BOREAS AFM-04 Twin Otter Aircraft Sounding Data ORNL_CLOUD STAC C
afm4tofx_497_1 BOREAS AFM-04 Twin Otter Aircraft Flux Data ORNL_CLOUD STAC Catalog 1994-05-25 1994-09-19 -104.81, 53.79, -98.4, 55.95 https://cmr.earthdata.nasa.gov/search/concepts/C2808092173-ORNL_CLOUD.umm_json Measurements in the boundary layer of the fluxes of sensible and latent heat, momentum, ozone, methane, and carbon dioxide, plus supporting meteorological parameters such as temperature, humidity, and wind speed and direction. proprietary
afm6gifs_433_1 BOREAS AFM-06 NOAA/ETL 35 GHz Cloud/Turbulence Radar GIF Images ORNL_CLOUD STAC Catalog 1994-07-16 1994-08-08 -110.05, 50.57, -94.08, 59.34 https://cmr.earthdata.nasa.gov/search/concepts/C2928013317-ORNL_CLOUD.umm_json The BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environmental Technology Laboratory (NOAA/ETL) operated a 35 GHz cloud-sensing radar in the Northern Study Area (NSA) near the Old Jack Pine (OJP) tower from 16-Jul-1994 to 08-Aug-1994. proprietary
african_woody_savanna_850_1 Characteristics of African Savanna Biomes for Determining Woody Cover ORNL_CLOUD STAC Catalog 1981-01-01 2003-12-31 -15.84, -27.75, 37.24, 16.76 https://cmr.earthdata.nasa.gov/search/concepts/C2784383572-ORNL_CLOUD.umm_json This data set includes the soil and vegetation characteristics, herbivore estimates, and precipitation measurement data for the 854 sites described and analyzed in Sankaran et al., 2005. Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief determinant of ecosystem properties. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover but perceptions differ on which of these are the primary drivers of savanna structure. Analyses of data from 854 sites across Africa (Figure 1) showed that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than approximately 650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered stable systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of approximately 650 mm, savannas are unstable systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation may considerably affect their distribution and dynamics (Sankaran et al., 2005).This data set includes the site characteristics and measurement data for the 854 sites described and analyzed in Sankaran et al., 2005. The data are provided in two formats, *.xls and *.csv. See the data format section below for more information. A companion document composed of the supplemental documentation and figures provided with Sankaran et al., 2005 is also included (ftp://daac.ornl.gov/data/global_vegetation/african_woody_savanna/comp/Woody_Cover.pdf). proprietary
-agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0 Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using substance and energy flow analysis ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081749-ENVIDAT.umm_json "Supplementary material for the publication "" Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using material substance and energy flow analysis"" Burg, V., b, Rolli, C., Schnorf, V., Scharfy, D., Anspach, V., Bowman, G. Today's agro-food system is typically based on linear fluxes (e.g. mineral fertilizers importation), when a circular approach should be privileged. The production of biogas as a renewable energy source and digestate, used as an organic fertilizer, is essential for the circular economy in the agricultural sector. This study investigates the current utilization of wet biomass in agricultural anaerobic digestion plants in Switzerland in terms of mass, nutrients, and energy flows, to see how biomass use contributes to circular economy and climate change mitigation through the substitution effect of mineral fertilizers and fossil fuels. We quantify the system and its benefits in details and examine future developments of agricultural biogas plants using different scenarios. Our results demonstrate that agricultural anaerobic digestion could be largely increased, as it could provide ten times more biogas by 2050, while saving significant amounts of mineral fertilizer and GHG emissions." proprietary
agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0 Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using substance and energy flow analysis ALL STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081749-ENVIDAT.umm_json "Supplementary material for the publication "" Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using material substance and energy flow analysis"" Burg, V., b, Rolli, C., Schnorf, V., Scharfy, D., Anspach, V., Bowman, G. Today's agro-food system is typically based on linear fluxes (e.g. mineral fertilizers importation), when a circular approach should be privileged. The production of biogas as a renewable energy source and digestate, used as an organic fertilizer, is essential for the circular economy in the agricultural sector. This study investigates the current utilization of wet biomass in agricultural anaerobic digestion plants in Switzerland in terms of mass, nutrients, and energy flows, to see how biomass use contributes to circular economy and climate change mitigation through the substitution effect of mineral fertilizers and fossil fuels. We quantify the system and its benefits in details and examine future developments of agricultural biogas plants using different scenarios. Our results demonstrate that agricultural anaerobic digestion could be largely increased, as it could provide ten times more biogas by 2050, while saving significant amounts of mineral fertilizer and GHG emissions." proprietary
+agricultural-biogas-plants-to-foster-circular-economy-and-bioenergy_1.0 Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using substance and energy flow analysis ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081749-ENVIDAT.umm_json "Supplementary material for the publication "" Agricultural biogas plants as a hub to foster circular economy and bioenergy: An assessment using material substance and energy flow analysis"" Burg, V., b, Rolli, C., Schnorf, V., Scharfy, D., Anspach, V., Bowman, G. Today's agro-food system is typically based on linear fluxes (e.g. mineral fertilizers importation), when a circular approach should be privileged. The production of biogas as a renewable energy source and digestate, used as an organic fertilizer, is essential for the circular economy in the agricultural sector. This study investigates the current utilization of wet biomass in agricultural anaerobic digestion plants in Switzerland in terms of mass, nutrients, and energy flows, to see how biomass use contributes to circular economy and climate change mitigation through the substitution effect of mineral fertilizers and fossil fuels. We quantify the system and its benefits in details and examine future developments of agricultural biogas plants using different scenarios. Our results demonstrate that agricultural anaerobic digestion could be largely increased, as it could provide ten times more biogas by 2050, while saving significant amounts of mineral fertilizer and GHG emissions." proprietary
air_methane_lawdome_1 Dated Readings For Air Composition And Methane From Law Dome Ice Core AU_AADC STAC Catalog 1988-01-01 1993-12-31 112.8, -66.771, 112.81, -66.77 https://cmr.earthdata.nasa.gov/search/concepts/C1214311761-AU_AADC.umm_json "This work was completed as part of ASAC project 757 (ASAC_757). This file comprises three main records compiled for publication in the following: V. Morgan, M. Delmotte, T. van Ommen, J. Jouzel, J. Chappellaz, S. Woon, V. Masson-Delmotte and D. Raynaud. Relative Timing of Deglacial Climate Events in Antarctica and Greenland, Science, 13 September 2002, Vol 297 (5588), pp. 1862-1864, DOI: 10.1126/science.1074257. Supporting Material - http://www.sciencemag.org/cgi/content/full/sci;297/5588/1862/DC1 Law Dome is a small (200 km in diameter) ice sheet located at the edge of the Indian Ocean sector of East Antarctica. The core site, near the summit of Law Dome (66 degrees 46'S, 112 degrees 48'E), is characterised by a high rate of accumulation (late Holocene average, 0.68 m ice equivalent per year) that results in an ice core with a highly tapered time scale in which the Holocene represents some 93% of the ice thickness of 1200 m. However, the full Law Dome isotopic record generally matches the long records from Vostok and Byrd to at least 80 ka, indicating that the record is continuous and undisturbed over this period. The Law Dome record is suited to gas-synchronisation studies because the high accumulation rate and consequent rapid burial give a small age difference (Delta age) between trapped air and the older enclosing ice. Derivation of an age scale for the Law Dome core, is based upon a Dansgaard- Johnsen flow model (S1) matched to the observed layer thinning (S2). Continuously sampled seasonal cycles down to ~1/3 ice-thickness (~1ky) and spot measurements of seasonal layers to ~85% ice-thickness (~4 ky) constrain the ice-flow model through this period in which mean accumulation is assumed to be free of large trends. Chronological control in the lower portion of the ice-sheet prior to 4 ky is through ties to other records. For the period of discussion, namely 10 ky to 17 ky, ties at 9.6 ky, 11.0 ky, 11.6 ky, 12.5 ky, 12.8 ky, 14.3 ky and 16.3 ky, are obtained by matching air composition changes with those of GRIP. The 9.6 ky tie is obtained by matching to d18O of air in GRIP (S3) and GISP2 (S4) data, and the remainder synchronise with the Byrd and GRIP CH4 records (S5). Dust concentration data also provide additional constraints on the 16.3 ky tie. Beyond 16.3 ky control is by a tie at 32 ky (based on both dust and d18Oice matched to the Byrd ice core (S6) on the GRIP timescale (S5)). The mean temporal resolution of the LD isotope data is ~24y through this period. The air-composition age-ties require Delta age computations for sequencing events within the LD record and for synchronisation of the chronology with GRIP. The high accumulation at DSS results in a particularly small Delta age value. The modern difference between ice-age and gas-age is 60 plus or minus 2 years for methane (S7). Note that at such low Delta age values, the diffusive mixing time from the free atmosphere down to seal-off depth becomes significant and must be accounted for; in the case of CH4 this is ~8 years (S7). The absolute chronology derived for the LD record has contributions from both the LD and GRIP Delta age errors, but the relative timing between the LD CH4 and water isotope (d18Oice) signals is only uncertain to within the small errors associated with LD Delta age. While the present-day trapping age at LD is small, lower temperatures and accumulation rates during the deglaciation lead to longer trapping times. To estimate Delta age under past conditions, we use a model (S8) to compute trapping age from accumulation and temperature (this model agrees with precise experimentally determined present day values). Since we have no direct indicators for palaeoaccumulation and palaeotemperature, we adopt two scenarios that use alternative estimation methods. Estimation of palaeotemperature from the isotope data in both scenarios is by application of a calibration slope, ""Beta ppt/degrees C"". For the young chronology, which has minimum Delta age, the commonly applied spatial slope of Beta=0.67 ppt/degrees C is used, giving relatively warm temperatures. The default chronology uses a long-term temporal calibration (S9) for Law Dome, Beta=0.44 ppt/degrees C. This estimate, which is seasonally derived, gives greater temperature sensitivity for isotopic changes than the spatial slope. The use of this lower value for Beta is supported by direct comparisons between annual averages in d18O and temperature at the site and elsewhere on Law Dome. Over several years to a few decades, these yield coefficients of typically ~0.33 ppt/degrees C. We adopt the value 0.44 ppt/degrees C as a conservative choice, based on a longer-term calibration and because the incorporation of seasonal sea-ice variations may better capture glacial-to- Holocene environmental shifts. Estimation of palaeoaccumulation for the young chronology is via the commonly applied method (see, e.g. S5) that scales modern accumulation-rate using the derivative of saturation vapour-pressure versus temperature relationship (also using Beta=0.67 ppt/degrees C). This method explicitly assumes no non-thermodynamic changes to moisture transport during climate variations (such as, e.g., atmospheric circulation changes) that may be important at this near-coastal location. Our alternative palaeoaccumulation estimate used for the default chronology assumes that the flow-model is correct and infers accumulation from the known age-intervals between the gas ties. This leads to considerably larger changes in accumulation which may nonetheless be understandable given the distinctively high Holocene precipitation regime that prevails at Law Dome. In addition, dust concentration data show a larger LGM to Holocene decrease at LD than Vostok. If relative flux changes at the two sites are similar, then the exaggerated dilution at LD is consistent with a large interglacial accumulation shift. Trapped gas measurements were made in France: CH4 measurements at LGGE, Grenoble and d18Oair measurements at LSCE, Saclay. Both analyses were conducted using a wet extraction procedure to release the air of the ice and followed by an injection into a gas chromatograph (CH4 measurement) or by a mass spectrometer isotopic analysis (d18Oair measurements). Both analyses were conducted using established procedures (S10,S11). The methane analytical uncertainty is plus or minus 20 ppbv with values were obtained on a single measurement (in which the sample was exhausted) and are presented on the LGGE scale which differs slightly from the NOAA scale but is well calibrated against it: LGGE = 1.02*NOAA (S12). The d18Oair values arise from means of duplicate measurements (except for one point with an obvious experimental problem, 1127.492 m depth). The analytical precision for d18Oair is around 0.05 ppt with a mean reproducibility of about 0.1 ppt. d18Oice measurements were made in Hobart and have an analytical precision of approximately 0.1 ppt. The results are expressed using the conventional reference of VSMOW (Vienna Standard Mean Ocean Water). Supporting References and Notes S1. W. Dansgaard, S. J. Johnsen, J. Glaciol., 8, 215 (1969). S2. V. Morgan et al., J. Glaciol., 43, 3 (1997). S3. M. Cross, (Compiler) Greenland summit ice cores CD-ROM. Boulder, CO: National Snow and Ice Data Center in association with the World Data Center for Paleoclimatology at NOAA-NGDC, and the Institute of Arctic and Alpine Research (1997). S4. M. Bender et al., Nature 372, 663-666 (1994). S5. T. Blunier, et al., Nature 394, 739 (1998). S6. S. J. Johnsen, W. Dansgaard, H. B. Clausen, C. C. Langway, Nature, 235, 429 (1972). S7. D. M. Etheridge et al., J. Geophys. Res., 101, 4115 (1996). S8. J.-M. Barnola, P. Pimienta, D. Raynaud, Y. S. Korotkevich, Tellus Ser. B, 43, 83 (1991). S9. T. D. van Ommen, V. Morgan, J. Geophys. Res., 102, 9351 (1997). S10. J. Chappellaz, et al., J. Geophys. Res., 102, 15987, (1997). S11. B. Malaize, Analyse isotopique de l'oxygene de l'air piege dans les glaces de l'Antarctique et du Groenland: correlation inter-hemispheriques et effet Dole, PhD thesis, University Paris 6, (1998). S12. T. Sowers et al, J. Geophys. Res., 102, 26527, (1997)." proprietary
air_sea_gas_exchange_xdeg_1208_1 ISLSCP II Air-Sea Carbon Dioxide Gas Exchange ORNL_CLOUD STAC Catalog 1995-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2785340637-ORNL_CLOUD.umm_json This data set contains the calculated net ocean-air carbon dioxide (CO2) flux and sea-air CO2 partial pressure (pCO2) difference. The estimates are based on approximately one million measurements made for the pCO2 in surface waters of the global ocean since the International Geophysical Year, 1956-1959. Only the ocean water pCO2 values measured using direct gas-seawater equilibration methods were used. The results represent the climatological distributions under non-El Nino conditions. Since the measurements were made in different years, during which the atmospheric pCO2 was increasing, they were corrected to a single reference year (arbitrarily chosen to be 1995) on the basis of the following assumptions: -Surface waters in subtropical gyres mix vertically at slow rates with subsurface waters due to the presence of strong stratification at the base of the mixed layer. This will allow a long contact time with the atmosphere to exchange CO2. Therefore, their CO2 chemistry tends to follow the atmospheric CO2 increase. Accordingly, the pCO2 measured in a given month and year is corrected to the same month of the reference year 1995 using changes in the atmospheric CO2 concentration occurred during this period.-Oceanic pCO2 measurements made after the beginning of 1979 have been corrected to 1995 using the atmospheric CO2 concentration data from the GLOBALVIEW-CO2 database (2000), in which the zonal mean atmospheric concentrations (for each 0.05 in sine of latitude) within the planetary boundary layer are summarized for each month since 1979 to 2000.-Pre-1979 oceanic pCO2 data were corrected to 1979 using the annual mean trend for the global mean atmospheric CO2 concentration constructed from the Mauna Loa data of Keeling and Whorf (2000), and then from 1979 to 1995 using the GLOBALVIEW-CO2 database. -Measurements for pCO2 made in the following areas have been corrected for the time of observation; 45 degrees N, 50 degrees S, in the Atlantic Ocean, north of 50 degrees S in the Indian Ocean, 40 degrees N, 50 degrees S in the western Pacific west of the date line, and 40 degrees N, 60 degrees S, in the eastern Pacific east of the date line. proprietary
-air_temperature_observations_in_the_arctic_1979-2004 Air Temperature Observations in the Arctic 1979-2004 ALL STAC Catalog 1979-01-01 2005-12-01 -180, 14.5, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600622-SCIOPS.umm_json The statistics of surface air temperature observations obtained from buoys, manned drifting stations, and meteorological land stations in the Arctic during 1979-2004 are analyzed. Although the basic statistics agree with what has been published in various climatologies, the seasonal correlation length scales between the observations are shorter than the annual correlation length scales, especially during summer when the inhomogeneity between the ice-covered ocean and the land is most apparent. During autumn, winter, and spring, the monthly mean correlation length scales are approximately constant at about 1000 km; during summer, the length scales are much shorter, i.e. as low as 300 km. These revised scales are particularly important in the optimal interpolation of data on surface air temperature (SAT) and are used in the analysis of an improved SAT dataset called IABP/POLES. Compared to observations from land stations and the Russian North Pole drift stations, the IABP/POLES dataset has higher correlations and lower rms errors than previous SAT fields and provides better temperature estimates, especially during summer in the marginal ice zones. In addition, the revised correlation length scales allow data taken at interior land stations to be included in the optimal interpretation analysis without introducing land biases to grid points over the ocean. The new analysis provides 12-hour fields of air temperatures on a 100-km rectangular grid for all land and ocean areas of the Arctic region for the years 1979-2004. proprietary
air_temperature_observations_in_the_arctic_1979-2004 Air Temperature Observations in the Arctic 1979-2004 SCIOPS STAC Catalog 1979-01-01 2005-12-01 -180, 14.5, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600622-SCIOPS.umm_json The statistics of surface air temperature observations obtained from buoys, manned drifting stations, and meteorological land stations in the Arctic during 1979-2004 are analyzed. Although the basic statistics agree with what has been published in various climatologies, the seasonal correlation length scales between the observations are shorter than the annual correlation length scales, especially during summer when the inhomogeneity between the ice-covered ocean and the land is most apparent. During autumn, winter, and spring, the monthly mean correlation length scales are approximately constant at about 1000 km; during summer, the length scales are much shorter, i.e. as low as 300 km. These revised scales are particularly important in the optimal interpolation of data on surface air temperature (SAT) and are used in the analysis of an improved SAT dataset called IABP/POLES. Compared to observations from land stations and the Russian North Pole drift stations, the IABP/POLES dataset has higher correlations and lower rms errors than previous SAT fields and provides better temperature estimates, especially during summer in the marginal ice zones. In addition, the revised correlation length scales allow data taken at interior land stations to be included in the optimal interpretation analysis without introducing land biases to grid points over the ocean. The new analysis provides 12-hour fields of air temperatures on a 100-km rectangular grid for all land and ocean areas of the Arctic region for the years 1979-2004. proprietary
+air_temperature_observations_in_the_arctic_1979-2004 Air Temperature Observations in the Arctic 1979-2004 ALL STAC Catalog 1979-01-01 2005-12-01 -180, 14.5, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214600622-SCIOPS.umm_json The statistics of surface air temperature observations obtained from buoys, manned drifting stations, and meteorological land stations in the Arctic during 1979-2004 are analyzed. Although the basic statistics agree with what has been published in various climatologies, the seasonal correlation length scales between the observations are shorter than the annual correlation length scales, especially during summer when the inhomogeneity between the ice-covered ocean and the land is most apparent. During autumn, winter, and spring, the monthly mean correlation length scales are approximately constant at about 1000 km; during summer, the length scales are much shorter, i.e. as low as 300 km. These revised scales are particularly important in the optimal interpolation of data on surface air temperature (SAT) and are used in the analysis of an improved SAT dataset called IABP/POLES. Compared to observations from land stations and the Russian North Pole drift stations, the IABP/POLES dataset has higher correlations and lower rms errors than previous SAT fields and provides better temperature estimates, especially during summer in the marginal ice zones. In addition, the revised correlation length scales allow data taken at interior land stations to be included in the optimal interpretation analysis without introducing land biases to grid points over the ocean. The new analysis provides 12-hour fields of air temperatures on a 100-km rectangular grid for all land and ocean areas of the Arctic region for the years 1979-2004. proprietary
airmoss_chamela_mexico USGS AirMOSS - Chamela, Mexico USGS_LTA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220567952-USGS_LTA.umm_json North American ecosystems are critical components of the global carbon cycle, exchanging large amounts of carbon dioxide and other gases with the atmosphere. Net ecosystem exchange (NEE) quantifies these carbon fluxes, but current continental-scale estimates contain high levels of uncertainty. Root-zone soil moisture (RZSM) and its spatial and temporal hetergeneity influence NEE and contribute as much as 60-80 percent to the uncertainty. Energy and CO2 Fluxes have been monitored from 1997 to 2007 using Bowen Ratio technique, and since spring of 2004 with eddy covariance. proprietary
airscm3b_448_1 BOREAS RSS-16 Level-3b DC-8 AIRSAR CM Images ORNL_CLOUD STAC Catalog 1993-08-12 1995-07-31 -110.05, 50.57, -94.08, 59.34 https://cmr.earthdata.nasa.gov/search/concepts/C2929127558-ORNL_CLOUD.umm_json Satellite and aircraft SAR data used in conjunction with various ground measurements to determine the moisture regime of the boreal forest. The NASA JPL AIRSAR is a side-looking imaging radar system that utilizes the SAR principle to obtain high-resolution images that represent the radar backscatter of the imaged surface at different frequencies and polarizations. The information contained in each pixel of the AIRSAR data represents the radar backscatter for all possible combinations of horizontal and vertical transmit and receive polarizations (i.e., HH, HV, VH, and VV). proprietary
airscpex_1 Atmospheric Infrared Sounder (AIRS) CPEX GHRC_DAAC STAC Catalog 2017-05-11 2017-07-16 -130.881382, -18.2515803, -14.6008026, 64.1143891 https://cmr.earthdata.nasa.gov/search/concepts/C2721994875-GHRC_DAAC.umm_json The Atmospheric Infrared Sounder (AIRS) CPEX dataset contains products obtained from the Atmospheric Infrared Sounder (AIRS) onboard the NASA Aqua satellite. These data were collected in support of the NASA Convective Processes Experiment (CPEX) field campaign. The CPEX field campaign took place in the North Atlantic-Gulf of Mexico-Caribbean Sea region and conducted a total of sixteen DC-8 missions from May through June 2017. The CPEX campaign collected data to help explain convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data are available from May 11, 2017 through July 16, 2017 and are available in HDF-4 format. proprietary
@@ -17114,8 +17121,8 @@ ames_sunphotometer_643_1 SAFARI 2000 Airborne Sunphotometer Aerosol Optical Dept
amount_of_dead_wood-214_1.0 Amount of dead wood ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814565-ENVIDAT.umm_json Wood volume of all deadwood recorded according to the NFI3 method. For standing trees and shrubs starting at 12 cm dbh, the volume of stemwood reduced due to stem breakage is recorded, and for lying deadwood the merchantable wood ( starting at 7 cm in diameter). Heaps of branches are not included. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary
amphibian-and-landscape-data-swiss-lowlands_1.0 Amphibian and urban-rural landscape data Swiss Lowlands ENVIDAT STAC Catalog 2022-01-01 2022-01-01 7.7124023, 47.0776041, 9.0637207, 47.7983967 https://cmr.earthdata.nasa.gov/search/concepts/C2789814582-ENVIDAT.umm_json "The data includes (1) amphibian occurrence data (2017-2019) for ten species across the cantons of Aargau and Zürich gathered from the Coordination Center for the Protection of Amphibians and Reptiles of Switzerland (http://www.karch.ch), (2) amphibian whole-life cycle environmental predictors (i.e. topographic, hydrologic, edaphic, vegetation, land-use derived, movement-ecology related), and (3) local urban ""green"" and ""grey"" landcover data which can be used to identify opportunities for Blue-Green Infrastructure (through green or grey transitions) in support of regional landscape connectivity." proprietary
amphibian-data-aargau_1.0 Amphibian observation and pond data (Aargau, Switzerland) ENVIDAT STAC Catalog 2021-01-01 2021-01-01 7.7, 47.15, 8.46, 47.62 https://cmr.earthdata.nasa.gov/search/concepts/C2789814599-ENVIDAT.umm_json In the canton of Aargau, hundreds of new ponds have been constructed since the 1990s to benefit declining amphibian populations. This dataset consists of monitoring data for all 12 pond-breeding amphibian species in the canton of Aargau from 1999 to 2019 in 856 ponds, and environmental variables that describe the ponds and the landscape surrounding the ponds. Species observation data is detection/non-detection data from repeat visits during survey years, during which all potentially suitable ponds in an area were surveyed. Environmental variables describing the ponds are whether the pond has been newly constructed since 1991 or not, pond age (if constructed), elevation a.s.l., the water surface area, and whether the water table fluctuates or not. Environmental variables describing the surroundings of the ponds are the percent area of forest within a circular buffer of radius 100m around the pond, the area of large (width ≥6m) roads within a circular buffer of radius 1km around the pond, as well as structural and potential population connectivity, quantified by three different metrics each. The canton of Aargau is the owner of the monitoring data; the original datafile is only disclosed upon request and in consultation with the canton of Aargau. The edited dataset contains cleaned observation data for the 12 amphibian species, as well as compiled and edited covariate data and code to fit dynamic occupancy models. proprietary
-amprimpacts_1 Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS ALL STAC Catalog 2020-01-18 2023-03-02 -124.153, 26.507, -64.366, 49.31 https://cmr.earthdata.nasa.gov/search/concepts/C2004708841-GHRC_DAAC.umm_json The Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS dataset consists of brightness temperature measurements collected by the Advanced Microwave Precipitation Radiometer (AMPR) onboard the NASA ER-2 high-altitude research aircraft. AMPR provides multi-frequency microwave imagery, with high spatial and temporal resolution for deriving cloud, precipitation, water vapor, and surface properties. These measurements were taken during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Data files are available from January 18, 2020, through March 2, 2023, in netCDF-4 format. proprietary
amprimpacts_1 Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS GHRC_DAAC STAC Catalog 2020-01-18 2023-03-02 -124.153, 26.507, -64.366, 49.31 https://cmr.earthdata.nasa.gov/search/concepts/C2004708841-GHRC_DAAC.umm_json The Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS dataset consists of brightness temperature measurements collected by the Advanced Microwave Precipitation Radiometer (AMPR) onboard the NASA ER-2 high-altitude research aircraft. AMPR provides multi-frequency microwave imagery, with high spatial and temporal resolution for deriving cloud, precipitation, water vapor, and surface properties. These measurements were taken during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Data files are available from January 18, 2020, through March 2, 2023, in netCDF-4 format. proprietary
+amprimpacts_1 Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS ALL STAC Catalog 2020-01-18 2023-03-02 -124.153, 26.507, -64.366, 49.31 https://cmr.earthdata.nasa.gov/search/concepts/C2004708841-GHRC_DAAC.umm_json The Advanced Microwave Precipitation Radiometer (AMPR) IMPACTS dataset consists of brightness temperature measurements collected by the Advanced Microwave Precipitation Radiometer (AMPR) onboard the NASA ER-2 high-altitude research aircraft. AMPR provides multi-frequency microwave imagery, with high spatial and temporal resolution for deriving cloud, precipitation, water vapor, and surface properties. These measurements were taken during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Data files are available from January 18, 2020, through March 2, 2023, in netCDF-4 format. proprietary
amprtbcp_2 AMPR BRIGHTNESS TEMPERATURE CAPE EXPERIMENT GHRC_DAAC STAC Catalog 1991-07-21 1991-08-16 -83.2024, 0, 12.6618, 38.1879 https://cmr.earthdata.nasa.gov/search/concepts/C1977858384-GHRC_DAAC.umm_json The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Precipitation/Electrification Experiment (CaPE). AMPR data werecollected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) during the time period of July 21, 1991 - Aug. 16, 1991. CaPE took place in centralFlorida between 43 N - 25.5 N latitude and 86 W - 69 W longitude. proprietary
amprtbcx1_2 AMPR BRIGHTNESS TEMPERATURE CAMEX-1 GHRC_DAAC STAC Catalog 1993-09-26 1993-10-05 -83.8511, 23.9917, -68.2377, 42.6325 https://cmr.earthdata.nasa.gov/search/concepts/C1977858400-GHRC_DAAC.umm_json The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Moisture Experiments (CAMEX-1) conducted at Wallops Island, VA. AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) during the time period of September 26 - October 5, 1993. The geographic domain of the CAMEX region was between 25.5N - 43N latitude and 70W - 83W longitude. proprietary
amprtbcx2_2 AMPR BRIGHTNESS TEMPERATURE CAMEX-2 GHRC_DAAC STAC Catalog 1995-08-23 1995-08-30 -78.907, 30.0262, -72.3661, 41.0703 https://cmr.earthdata.nasa.gov/search/concepts/C1977858440-GHRC_DAAC.umm_json The Advanced Microwave Precipitation Radiometer (AMPR) was deployed during the Convection and Moisture Experiment 2 (CAMEX-2). AMPR data were collected at a combination of frequencies (10.7, 19.35, 37.1, and 85.5 GHz) during the time period of August 23 - August 30, 1995. The geographic domain of the CAMEX-2 region was between 25.5 N - 43 N latitude and 83 W - 70 W longitude. proprietary
@@ -17130,10 +17137,10 @@ ams_cs93_403_1 BOREAS/AES Campbell Scientific 15-minute Surface Meteorological D
ams_cs94_404_1 BOREAS/AES Campbell Scientific 15-minute Surface Meteorological Data: 1994 ORNL_CLOUD STAC Catalog 1994-01-01 1994-12-31 -108.52, 50.95, -94.7, 58.18 https://cmr.earthdata.nasa.gov/search/concepts/C2808090015-ORNL_CLOUD.umm_json Contains data from 1994 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS. proprietary
ams_cs95_405_1 BOREAS/AES Campbell Scientific 15-minute Surface Meteorological Data: 1995 ORNL_CLOUD STAC Catalog 1995-01-01 1995-12-31 -108.52, 50.95, -94.7, 58.18 https://cmr.earthdata.nasa.gov/search/concepts/C2808090046-ORNL_CLOUD.umm_json Contains data from 1995 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS. proprietary
ams_cs96_406_1 BOREAS/AES Campbell Scientific 15-minute Surface Meteorological Data: 1996 ORNL_CLOUD STAC Catalog 1996-01-01 1996-12-31 -108.52, 50.95, -94.7, 58.18 https://cmr.earthdata.nasa.gov/search/concepts/C2808090091-ORNL_CLOUD.umm_json Contains data from 1996 from the Atmospheric Environment Service Campbell Scientific autostations collecting continuous fifteen minute data for BOREAS. proprietary
-amsua15sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15 GHRC_DAAC STAC Catalog 1998-08-03 -180, -90, 180, 89.756 https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NOAA-15 was the first spacecraft to fly AMSU. Launched on 13 May 1998, NOAA-15 is in a sun synchronous near polar orbit. proprietary
-amsua15sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15 ALL STAC Catalog 1998-08-03 -180, -90, 180, 89.756 https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NOAA-15 was the first spacecraft to fly AMSU. Launched on 13 May 1998, NOAA-15 is in a sun synchronous near polar orbit. proprietary
-amsua16sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-16 GHRC_DAAC STAC Catalog 2001-05-27 2009-07-30 -180, -89.91, 180, 89.73 https://cmr.earthdata.nasa.gov/search/concepts/C1979956366-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). Launched on 21 September 2000, NOAA-16 is in a sun synchronous near polar orbit. proprietary
+amsua15sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15 ALL STAC Catalog 1998-08-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NOAA-15 was the first spacecraft to fly AMSU. Launched on 13 May 1998, NOAA-15 is in a sun synchronous near polar orbit. proprietary
+amsua15sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-15 GHRC_DAAC STAC Catalog 1998-08-03 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1996541017-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NOAA-15 was the first spacecraft to fly AMSU. Launched on 13 May 1998, NOAA-15 is in a sun synchronous near polar orbit. proprietary
amsua16sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-16 ALL STAC Catalog 2001-05-27 2009-07-30 -180, -89.91, 180, 89.73 https://cmr.earthdata.nasa.gov/search/concepts/C1979956366-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). Launched on 21 September 2000, NOAA-16 is in a sun synchronous near polar orbit. proprietary
+amsua16sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-16 GHRC_DAAC STAC Catalog 2001-05-27 2009-07-30 -180, -89.91, 180, 89.73 https://cmr.earthdata.nasa.gov/search/concepts/C1979956366-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). Launched on 21 September 2000, NOAA-16 is in a sun synchronous near polar orbit. proprietary
amsua17sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-17 ALL STAC Catalog 2002-07-21 2003-12-13 -180, -89.575, 180, 89.629 https://cmr.earthdata.nasa.gov/search/concepts/C1979975136-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The Third Advanced Microwave Sounding Unit-A was launched on NOAA-17 on 24 June 2002 from Vandenberg AFB, California on a Titan II booster. proprietary
amsua17sp_1 ADVANCED MICROWAVE SOUNDING UNIT-A (AMSU-A) SWATH FROM NOAA-17 GHRC_DAAC STAC Catalog 2002-07-21 2003-12-13 -180, -89.575, 180, 89.629 https://cmr.earthdata.nasa.gov/search/concepts/C1979975136-GHRC_DAAC.umm_json AMSU-A, the Advanced Microwave Sounding Unit, is a 15-channel passive microwave radiometer used to profile atmospheric temperature and moisture from the earth's surface to ~45 km (3 millibars). All orbits beginning in the day (00:00:00 - 23:59:59 UTC) are stored in one daily HDF-EOS file. Each file contains 15 (channel) arrays, as well as corresponding latitude, longitude, and time. AMSU flies on the National Oceanic and Atmospheric Administration (NOAA) polar orbiting spacecraft as part of the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The Third Advanced Microwave Sounding Unit-A was launched on NOAA-17 on 24 June 2002 from Vandenberg AFB, California on a Titan II booster. proprietary
anezet-analysing-net-zero-transformations_1.0 ANEZET: Analysing Net-Zero Transformations ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081289-ENVIDAT.umm_json We have analysed past transformations in Switzerland in four environmental domains, with the aim to draw conclusions for current challenges, such as the net‐zero transformation. The data comprise transcripts of interviews with experts in the field of biodiversity, forests, landscape and natural hazard research. proprietary
@@ -17147,15 +17154,15 @@ anthropogenic-change-and-net-n-mineralization_1.0 Anthropogenic change and soil
aoci0bil_281_1 BOREAS Level-0 AOCI Imagery: Digital Counts in BIL Format ORNL_CLOUD STAC Catalog 1994-07-21 1994-07-21 -105.91, 52.98, -104.93, 54.46 https://cmr.earthdata.nasa.gov/search/concepts/C2927616228-ORNL_CLOUD.umm_json The level-0 AOCI imagery, along with the other remotely sensed images, was collected to provide spatially extensive information about radiant energy over the primary BOREAS study areas. The AOCI was the only remote sensing instrument flown with wavelength bands specific to the investigation of various aquatic parameters such as chlorophyll content and turbidity. proprietary
apr3cpex_1 Airborne Precipitation Radar 3rd Generation (APR-3) CPEX GHRC_DAAC STAC Catalog 2017-05-27 2017-06-24 -96.0262, 16.8091, -69.2994, 28.9042 https://cmr.earthdata.nasa.gov/search/concepts/C2409563129-GHRC_DAAC.umm_json The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment (CPEX) aircraft field campaign. CPEX collected data to help answer questions about convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 27, 2017 through June 24, 2017 in a HDF-5 file, with associated browse imagery in JPG format. proprietary
apr3cpex_1 Airborne Precipitation Radar 3rd Generation (APR-3) CPEX ALL STAC Catalog 2017-05-27 2017-06-24 -96.0262, 16.8091, -69.2994, 28.9042 https://cmr.earthdata.nasa.gov/search/concepts/C2409563129-GHRC_DAAC.umm_json The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment (CPEX) aircraft field campaign. CPEX collected data to help answer questions about convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 27, 2017 through June 24, 2017 in a HDF-5 file, with associated browse imagery in JPG format. proprietary
-apr3cpexaw_1 Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-AW ALL STAC Catalog 2021-08-20 2021-09-04 -80.7804, 11.8615, -45.6417, 34.046 https://cmr.earthdata.nasa.gov/search/concepts/C2269541013-GHRC_DAAC.umm_json The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-AW dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment – Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix. These data files are available from August 20, 2021 through September 4, 2021 in a MatLab file, with associated browse files in JPEG format. proprietary
apr3cpexaw_1 Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-AW GHRC_DAAC STAC Catalog 2021-08-20 2021-09-04 -80.7804, 11.8615, -45.6417, 34.046 https://cmr.earthdata.nasa.gov/search/concepts/C2269541013-GHRC_DAAC.umm_json The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-AW dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment – Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix. These data files are available from August 20, 2021 through September 4, 2021 in a MatLab file, with associated browse files in JPEG format. proprietary
+apr3cpexaw_1 Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-AW ALL STAC Catalog 2021-08-20 2021-09-04 -80.7804, 11.8615, -45.6417, 34.046 https://cmr.earthdata.nasa.gov/search/concepts/C2269541013-GHRC_DAAC.umm_json The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-AW dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment – Aerosols & Winds (CPEX-AW) field campaign. CPEX-AW was a joint effort between the US National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA) with the primary goal of conducting a post-launch calibration and validation activities of the Atmospheric Dynamics Mission-Aeolus (ADM-AEOLUS) Earth observation wind Lidar satellite in St. Croix. These data files are available from August 20, 2021 through September 4, 2021 in a MatLab file, with associated browse files in JPEG format. proprietary
apr3cpexcv_1 Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-CV ALL STAC Catalog 2022-09-02 2022-09-30 -89.6733315, 1.7593585, -14.8189435, 39.1985524 https://cmr.earthdata.nasa.gov/search/concepts/C2708951073-GHRC_DAAC.umm_json The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-CV dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross-section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment – Cabo Verde (CPEX-CV) field campaign. The NASA CPEX-CV field campaign will be based out of Sal Island, Cabo Verde from August through September 2022. The campaign is a continuation of CPEX – Aerosols and Winds (CPEX-AW) and was conducted aboard the NASA DC-8 aircraft equipped with remote sensors and dropsonde-launch capability that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. The overarching CPEX-CV goal was to investigate atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. These data files are available from September 2, 2022, through September 30, 2022, in netCDF-4 format, with associated browse imagery in JPG format. proprietary
apr3cpexcv_1 Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-CV GHRC_DAAC STAC Catalog 2022-09-02 2022-09-30 -89.6733315, 1.7593585, -14.8189435, 39.1985524 https://cmr.earthdata.nasa.gov/search/concepts/C2708951073-GHRC_DAAC.umm_json The Airborne Precipitation Radar 3rd Generation (APR-3) CPEX-CV dataset consists of radar reflectivity, Doppler velocity for all bands, linear depolarization ratio Ku-band, and normalized radar cross-section measurements at Ka- and Ku- bands data collected by the APR-3 onboard the NASA DC-8 aircraft. These data were gathered during the Convective Processes Experiment – Cabo Verde (CPEX-CV) field campaign. The NASA CPEX-CV field campaign will be based out of Sal Island, Cabo Verde from August through September 2022. The campaign is a continuation of CPEX – Aerosols and Winds (CPEX-AW) and was conducted aboard the NASA DC-8 aircraft equipped with remote sensors and dropsonde-launch capability that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. The overarching CPEX-CV goal was to investigate atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. These data files are available from September 2, 2022, through September 30, 2022, in netCDF-4 format, with associated browse imagery in JPG format. proprietary
apuimpacts_1 Autonomous Parsivel Unit (APU) IMPACTS GHRC_DAAC STAC Catalog 2020-01-15 2020-02-29 -75.5894, 37.919, -75.3588, 38.2064 https://cmr.earthdata.nasa.gov/search/concepts/C1995564696-GHRC_DAAC.umm_json The Autonomous Parsivel Unit (APU) IMPACTS data were collected in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. The IMPACTS field campaign addressed providing observations critical to understanding the mechanisms of snowband formation, organization, and evolution, examining how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands, and improving snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. This dataset consists of precipitation data including precipitation amount, precipitation rate, reflectivity in Rayleigh regime, liquid water content, drop diameter, and drop concentration. Data are available in ASCII format from January 15, 2020 through February 29, 2020. proprietary
area_of_shrub_forest-123_1.0 Area of shrub forest ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814712-ENVIDAT.umm_json All plots classified as shrub forest according to the NFI forest definition. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary
arthropod-biomass-abundance-species-richness-trends-limpach_1.0 Arthropod biomass, abundance and species richness trends over 32 years in the agricultural Limpach valley, Switzerland ENVIDAT STAC Catalog 2020-01-01 2020-01-01 7.3819542, 47.0815787, 7.528553, 47.1334543 https://cmr.earthdata.nasa.gov/search/concepts/C2789814758-ENVIDAT.umm_json Recent publications about declines in arthropod biomass, abundance and species diversity raise concerns and call for measures. Agricultural intensification is likely one cause for the negative trends. But rare long-term arthropod surveys conceal trends in arthropod communities in agricultural land. Here, we report about a standardized sampling of arthropod fauna in a Swiss agricultural landscape, repeated over 32 years (1987, 1997 and 2019). We sampled 8 sites covering 4 semi-natural and agricultural habitat types. Four trap types were used to capture a wide range of flying and ground dwelling arthropods between May and July. Over the three sampling periods, 58’255 specimens of 1’343 species were analysed. Mean arthropod biomass, abundance and species richness per trap was significantly higher in 2019 than in prior years and gamma diversity of the study area was highest in 2019. Biomass and abundance increased stronger in the flight traps than in the pitfall traps. The implementation of agri-environmental schemes has improved habitat quality since 1993, while landscape composition and pesticide and fertilizer use remained stable over the study period, both contributing to the findings. The results of this study contrast with outcomes of comparable investigations and highlight the importance of further long-term investigations on arthropod dynamics. Data are provided on request to contact person against bilateral agreement. proprietary
-asas Advanced Solid-state Array Spectroradiometer (ASAS) ALL STAC Catalog 1988-06-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220566261-USGS_LTA.umm_json The Advanced Solid-state Array Spectroradiometer (ASAS) data collection contains data collected by the ASAS sensor flown aboard NASA aircraft. A fundamental use of ASAS data is to characterize and understand the directional variability in solar energy scattered by various land surface cover types (e.g.,crops, forests, prairie grass, snow, or bare soil). The sensor's Bidirectional Reflectance Distribution Function determines the variation in the reflectance of a surface as a function of both the view zenith angle and solar illumination angle. The ASAS sensor is a hyperspectral, multiangle, airborne remote sensing instrument maintained and operated by the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center in Greenbelt, Maryland. The ASAS instrument is mounted on the underside of either NASA C-130 or NASA P-3 aircraft and is capable of off-nadir pointing from approximately 70 degrees forward to 55 degrees aft along the direction of flight. The aircraft is flown at an altitude of 5000 - 6000 meters (approximately 16,000 - 20,000 ft.). Data in the ASAS collection primarily cover areas over the continental United States, but some ASAS data are also available over areas in Canada and western Africa. The ASAS data were collected between 1988 and 1994. proprietary
asas Advanced Solid-state Array Spectroradiometer (ASAS) USGS_LTA STAC Catalog 1988-06-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220566261-USGS_LTA.umm_json The Advanced Solid-state Array Spectroradiometer (ASAS) data collection contains data collected by the ASAS sensor flown aboard NASA aircraft. A fundamental use of ASAS data is to characterize and understand the directional variability in solar energy scattered by various land surface cover types (e.g.,crops, forests, prairie grass, snow, or bare soil). The sensor's Bidirectional Reflectance Distribution Function determines the variation in the reflectance of a surface as a function of both the view zenith angle and solar illumination angle. The ASAS sensor is a hyperspectral, multiangle, airborne remote sensing instrument maintained and operated by the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center in Greenbelt, Maryland. The ASAS instrument is mounted on the underside of either NASA C-130 or NASA P-3 aircraft and is capable of off-nadir pointing from approximately 70 degrees forward to 55 degrees aft along the direction of flight. The aircraft is flown at an altitude of 5000 - 6000 meters (approximately 16,000 - 20,000 ft.). Data in the ASAS collection primarily cover areas over the continental United States, but some ASAS data are also available over areas in Canada and western Africa. The ASAS data were collected between 1988 and 1994. proprietary
+asas Advanced Solid-state Array Spectroradiometer (ASAS) ALL STAC Catalog 1988-06-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1220566261-USGS_LTA.umm_json The Advanced Solid-state Array Spectroradiometer (ASAS) data collection contains data collected by the ASAS sensor flown aboard NASA aircraft. A fundamental use of ASAS data is to characterize and understand the directional variability in solar energy scattered by various land surface cover types (e.g.,crops, forests, prairie grass, snow, or bare soil). The sensor's Bidirectional Reflectance Distribution Function determines the variation in the reflectance of a surface as a function of both the view zenith angle and solar illumination angle. The ASAS sensor is a hyperspectral, multiangle, airborne remote sensing instrument maintained and operated by the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center in Greenbelt, Maryland. The ASAS instrument is mounted on the underside of either NASA C-130 or NASA P-3 aircraft and is capable of off-nadir pointing from approximately 70 degrees forward to 55 degrees aft along the direction of flight. The aircraft is flown at an altitude of 5000 - 6000 meters (approximately 16,000 - 20,000 ft.). Data in the ASAS collection primarily cover areas over the continental United States, but some ASAS data are also available over areas in Canada and western Africa. The ASAS data were collected between 1988 and 1994. proprietary
asas_l1b_562_1 BOREAS RSS-02 Level-1b ASAS Image Data: At-sensor Radiance in BSQ Format ORNL_CLOUD STAC Catalog 1994-04-19 1996-07-20 -106.32, 53.24, -97.23, 56.25 https://cmr.earthdata.nasa.gov/search/concepts/C2813527156-ORNL_CLOUD.umm_json The BOREAS RSS-02 team used the ASAS instrument, mounted on the NASA C-130 aircraft, to create at-sensor radiance images of various sites as a function of spectral wavelength, view geometry (combinations of view zenith angle, view azimuth angle, solar zenith angle, and solar azimuth angle), and altitude. The level-1b ASAS images of the BOREAS study areas were collected from April to September 1994 and March to July 1996. proprietary
asasrefl_287_1 BOREAS RSS-02 Extracted Reflectance Factors Derived from ASAS Imagery ORNL_CLOUD STAC Catalog 1994-05-24 1996-07-20 -106.2, 53.24, -104.62, 53.99 https://cmr.earthdata.nasa.gov/search/concepts/C2813382300-ORNL_CLOUD.umm_json Contains calculated bidirectional reflectance factor means derived from extractions of C130-based ASAS measurements made during BOREAS. proprietary
ascatcpex_1 Advanced Scatterometer (ASCAT) CPEX GHRC_DAAC STAC Catalog 2017-05-24 2017-07-16 160.241, 3.9062, -25.0958, 42.5176 https://cmr.earthdata.nasa.gov/search/concepts/C2428509185-GHRC_DAAC.umm_json The Advanced Scatterometer (ASCAT) CPEX dataset consists of ice probability, wind speed, and wind direction estimates collected by the ASCAT. The ASCAT is onboard the MetOp-A and MetOp-B satellites and uses radar to measure the electromagnetic backscatter from the wind-roughened ocean surface, from which data on wind speed and direction can be derived. These data were gathered during the Convective Processes Experiment (CPEX) field campaign. CPEX collected data to help answer questions about convective storm initiation, organization, growth, and dissipation in the North Atlantic-Gulf of Mexico-Caribbean Oceanic region during the early summer of 2017. These data files are available from May 24, 2017 through July 16, 2017 in netCDF-3 format. proprietary
@@ -17177,8 +17184,8 @@ atree-forest-owner-clearances-offsetting_1.0 ATREE forest owners survey about fo
atree-forest-owners-survey-about-climate-regulation-services-of-forests_1.0 ATREE forest owners survey about climate regulation services of forests ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814546-ENVIDAT.umm_json Forest owners of the Canton of Lucerne were survey about their willingness to employ different forest management measures to provicde climate regulation services by forests. Of the nearly 3000 forest owners that received an invitation to a online-survey and the 900 forest owners that received a paper and pencil survey, 1055 valid responses were received. The questionnaire contained a survey experiment in which 9 choice situations were presented to the respondents in which they had the choice between two options and the status quo. This survey experiment part of the survey was completed by 990 respondents. proprietary
atree-q-methodology-forest-clearances-offsetting_1.0 ATREE Q-methodology statement sorts on forest clearances offsetting in the forest ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814556-ENVIDAT.umm_json "In Novdember 2019 about 19 experts on forest surface protection and forest clearances were invited to a workshop in order to discuss policy design and implementation problems regarding the offsetting of forest clearances. In Switzerland such offsetting can be provided under certain circumstances by implementing forest nature conservation measures in the forest instead of providing in-kind compensation, i.e. reafforestation on agricultural land. The workshop included the sorting of 34 statements – that were elaborated beforehand, partially also with help of the participants – according to the ""Q-methodology"" survey technique (participants arrange given statements about a certain subject into boxes that are normally distributed over a ""agree - do not agree"" answer scale). The participants included representatives from cantonal and national forest administrations, nature conservation NGOs, forest NGOs, spatial planning NGOs, private counseling enterprises as well as national, cantonal and regional forest owner organizations. The data allows a factor analytical differentiation of actors into groups with distinct positions towards forest clearance compensation as well as a positioning of these groups relative to each statement." proprietary
atree-social-network-analysis-carbon-sequestration-lucerne_1.0 ATREE Social Network Analysis survey on policy options regarding CO2 mitigation and sequestration in wood and forest ENVIDAT STAC Catalog 2022-01-01 2022-01-01 8.0859375, 46.9348859, 8.470459, 47.2191951 https://cmr.earthdata.nasa.gov/search/concepts/C2789814569-ENVIDAT.umm_json "In January 2020 a social network analysis survey was conducted among forest policy stakeholders (at the organizational level) from the Canton of Lucerne as well as the national level. The aim was to elicit positions relative to a set of policy options currently discussed with respect to carbon mitigation and sequestration services of the forest, i.e. forest management and to establish information and collaboration network relations in order to identify actor coalitions as inspired by the ""actor coalition framework"" approach to policy analysis. Of the 66 questionnaires sent out, 51 were answered (77%). Only one additional organization was indicated as being missing from the provided list of stakeholder organizations." proprietary
-atrs Airborne Coherant Radar Sounding Data SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, -70 https://cmr.earthdata.nasa.gov/search/concepts/C1214620687-SCIOPS.umm_json "Developmental airborne coherent radar sounding data collected over a variety of sounding targets in Antarctica, including a full gridded survey of subglacial Lake Vostok and its environs. This was an instrument development award, so the data are not of ""production"" quality. Receiver sensitivity documents are provided with the data. The data resides in 6, DLT 4 tapes (~30 Gb each)." proprietary
atrs Airborne Coherant Radar Sounding Data ALL STAC Catalog 1970-01-01 -180, -90, 180, -70 https://cmr.earthdata.nasa.gov/search/concepts/C1214620687-SCIOPS.umm_json "Developmental airborne coherent radar sounding data collected over a variety of sounding targets in Antarctica, including a full gridded survey of subglacial Lake Vostok and its environs. This was an instrument development award, so the data are not of ""production"" quality. Receiver sensitivity documents are provided with the data. The data resides in 6, DLT 4 tapes (~30 Gb each)." proprietary
+atrs Airborne Coherant Radar Sounding Data SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, -70 https://cmr.earthdata.nasa.gov/search/concepts/C1214620687-SCIOPS.umm_json "Developmental airborne coherent radar sounding data collected over a variety of sounding targets in Antarctica, including a full gridded survey of subglacial Lake Vostok and its environs. This was an instrument development award, so the data are not of ""production"" quality. Receiver sensitivity documents are provided with the data. The data resides in 6, DLT 4 tapes (~30 Gb each)." proprietary
au0103_1 Aurora Australis marine science cruise au0103 (CLIVAR_SR3) - CTD and ADCP data AU_AADC STAC Catalog 2001-10-29 2002-12-13 139, -68, 148, -43 https://cmr.earthdata.nasa.gov/search/concepts/C1214306658-AU_AADC.umm_json Oceanographic measurements were conducted along CLIVAR Southern Ocean meridional repeat transect SR3 between Tasmania and Antarctica from October to December 2001. A total of 135 CTD vertical profile stations were taken, more than half to within 20 m of the bottom. Over 2200 Niskin bottle water samples were collected for the measurement of salinity, dissolved oxygen, nutrients, CFC's, CCl4, dissolved inorganic carbon, alkalinity, 13C, DMS/DMSP/DMSO, halocarbons, barium, barite, ammonia, del30Si, dissolved and particulate organic carbon, particulate silica, 15N-nitrate, 18O, 234Th, 230Th, 231Pa, primary productivity and biological parameters, using a 24 bottle rosette sampler. Near surface current data were collected using a ship mounted ADCP. Two sediment trap moorings were serviced, and a third mooring was deployed at a new location. A summary of all CTD data and data quality is presented in the data report. This work was completed as part of ASAC project 1335. proprietary
au0106_1 Aurora Australis Southern Ocean oceanographic data, voyage 6, 2000-2001 - KACTAS AU_AADC STAC Catalog 2001-01-01 2001-03-09 61.875, -68.26939, 148.11719, -43.61071 https://cmr.earthdata.nasa.gov/search/concepts/C1709216539-AU_AADC.umm_json Oceanographic measurements conducted on voyage 6 of the Aurora Australis of the 2000-2001 season. These data comprise CTD (Conductivity, Temperature and Depth) and ADCP (Acoustic Doppler Current Profiler) data. These data were collected by Mark Rosenberg. This metadata record was completed by AADC staff when the data were discovered bundled with acoustics data during a data cleaning exercise. Basic information about voyage 6: The voyage will complete a range of Marine Science activities off the Mawson Coast, and off the Amery Ice Shelf before calling at Davis to retrieve summer personnel and helicopters prior to returning to Hobart. Science equipment calibration will be undertaken at Mawson. (Marine Science activities were interrupted when the Aurora Australis was required to provide assistance in the Polar Bird's attempt to reach Casey, complete the station resupply and return to open water.) Leader: Dr Graham Hosie Deputy Leader: Mr Andrew McEldowney See the readme files in the downloads for more information. proprietary
au0201_1 Aurora Australis Southern Ocean oceanographic data, voyage 1, 2002-2003 - ADCP data AU_AADC STAC Catalog 2002-10-13 2002-11-18 137.6, -66.6, 159.1, -42.8 https://cmr.earthdata.nasa.gov/search/concepts/C1834759929-AU_AADC.umm_json "Oceanographic measurements conducted on voyage 7 of the Aurora Australis of the 2002-2003 season. These data are ADCP (Acoustic Doppler Current Profiler) data. These data were collected/collated by Mark Rosenberg. Final ADCP data for voyage au0201 (SAZ mooring turnaround and iceberg B9B experiment), Aurora Australis Voyage 1 2002/2003, 17th Oct 2002 to 18th Nov 2002. * The complete ADCP data for cruise au0201 are in the file: au020101.cny (ascii format) a0201dop.mat (matlab format) * The ""on station"" ADCP data (specifically, the data for which the ship speed was less than or equal to 0.35 m/s) are in the files: au0201_slow35.cny (ascii format) a0201dop_slow35.mat (matlab format) * The file bindep.dat shows the water depths (in metres) that correspond to the centre of each vertical bin. * The data are 30 minute averages. Each 30 minute averageing period starts from the time indicated. (so, e.g., an ensemble with time 120000 is the average from 120000 to 123000). * ADCP currents are absolute - i.e. ship's motion has been subtracted out. * Note that the top few bins can have bad data from water dragged along by the ship. * Beware of data when the ship is underway - it's often suspect. MATLAB VECTORS AND MATRICES: ============================ header info ----------- for header info: column number corresponds to 30 minute average number botd = mean bottom depth (m) over the 30 minute period cnav = GPS info: don't worry about it cruise = cruise number date = ddmmyy (UTC) ibcover = a bottom track parameter: don't worry about it icover = percentage of 30 minute averageing period covered by acceptable 3 minute ensembles lastgd = deepest accepted bin in this profile lat = mean latitude over the 30 minute period (decimal degrees) lon = mean longitude over the 30 minute period (decimal degrees) nbins = no. of bins logged (=60) shipspeed = scalar resultant of shipu and shipv shipu = ship's E/W velocity over the ground over 30 minute period (m/s, +ve east) shipv = ship's N/S velocity over the ground over 30 minute period (m/s, +ve north) time = hhmmss, time (UTC) at start of 30 minute averageing period dectime = time in decimal days from start of year 2002 (e.g. midday on January 2nd = 1.5000) adcp data --------- for adcp data matrices: row number corresponds to bin number column number corresponds to 30 min. average no. bindep = depth (m) to centre of each bin in the profile (will be the same for all profiles) ipcok = percentage of the profile period for which there was good data in this bin (N.B. data=NaN when ipcok=0) qc = a quality control value for each bin - see below speed = scalar resultant of u and v u = east/west current (m/s, +ve east) v = north/south current (m/s, +ve north) ASCII FORMAT FILE: ================== * The file starts with a 3 line header. * Then comes each 30 min. ensemble, as follows: First, a 1 line header, containing date (UTC) (dd-mmm-yyyy) time (UTC) (hh:mm:ss) % of 30 min average covered by acceptable 3 min. ensembles deepest accepted bin in the profile ship's E/W velocity over the ground over the 30min (m/s) ship's N/S velocity over the ground over the 30min (m/s) P= GPS position-derived velocity (D=direct GPS vel.; B=bottom track vel.) mean longitude over the 30 min. mean latitude over the 30 min. % of interfix period for which there was bottom depth information mean bottom depth over the 30 min. 0 0 Next, the data, from the shallowest bin to the deepest bin: for each bin, there's 4 parameters: u = east/west current (m/s, +ve east) v = north/south current (m/s, +ve north) qc = quality control value - see below ipcok = percentage of the profile period for which there was good data in this bin Note that the data are written left to right across each line, then onto the next line, etc. (so 4 bins on a full line) quality control value: ---------------------- qc = %good / (Verr+0.05) where: %good = percent good pings after logging system screening Verr = RMS error velocity (m/s). Possible range of qc is 0-20, with an expected range of 0-10; values of 0-4 indicate very poor data; values above 8 indicate very good data." proprietary
@@ -17213,8 +17220,8 @@ avalanche-fatalities-european-alps-1969-2015_1.0 Avalanche fatalities in the Eur
avalanche-fatalities-per-calendar-year-since-1936_1.0 Number of avalanche fatalities per calendar year in Switzerland since 1937 ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814645-ENVIDAT.umm_json Attention: this data is not updated after 2022 anymore. This dataset contains the statistics on the number of avalanche fatalities per **calendar year** in Switzerland. The data collection commences with the beginning of the year 1937. After the completion of a hydrological year, which is the standard way avalanche fatalities are summarized in Switzerland and ends on the 30th of September, the new data is appended to the existing dataset. If you require annual statistics per hydrological year, please download the data from here: [https://www.envidat.ch/dataset/avalanche-fatalities-switzerland-1936] The following information is contained (by column and column title): - year - number of fatalities in the backcountry (=tour) - number of fatalities in terrain close to ski areas (=offpiste, away from open and secured ski runs) - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors) - number of fatalities in or around buildings or in settlements (= buildings) - sum (of all four categories) The definitions for these four categories, as described in the guidelines to the avalanche accident database are: __tour:__ activities include back-country ski, snowboard or snow-shoe touring __offpiste:__ access from ski area, generally from the top of a skilift with short hiking distances __transportation.corridors__ (Techel et al., 2016): people travelling or recreating on open or temporarily closed transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams) __buildings__ (Techel et al., 2016): people inside or just outside buildings, and workers on high alpine building sites proprietary
avalanche-fatalities-switzerland-1936_1.0 Number of avalanche fatalities per hydrological year in Switzerland since 1936-1937 ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814658-ENVIDAT.umm_json Attention: this data is not updated after 2022 anymore. This dataset contains the statistics on the number of avalanche fatalities per hydrological year in Switzerland. The data set commences with the beginning of the hydrological year 1936/37 on 01/10/1936. After the completion of a hydrological year, the new data is appended to the existing dataset. The following information is contained (by column and column title): - hydrological year - number of fatalities in the backcountry (=tour) - number of fatalities in terrain close to ski areas (=offpiste) - number of fatalities on transportation corridors including ski runs, roads, railway lines (=transportation.corridors) - number of fatalities in or around buildings or in settlements (= buildings) - sum (of all four categories) The definition for these four categories as described in the guidelines to the avalanche accident database: **tour**: activities include back-country ski, snowboard or snow-shoe touring **offpiste**: access from ski area, generally from the top of a skilift with short hiking distances **transportation.corridors** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )): people travelling or recreating on open or temporarily closed transportation corridors (e.g. a road user or a skier on a ski run) and people working on open or closed transportation corridors (e.g. maintenance crews on roads, professional rescue teams) **buildings** ([Techel et al., 2016](http://www.geogr-helv.net/71/147/2016/ )): people inside or just outside buildings, and workers on high alpine building sites proprietary
avalanche-prediction-snowpack-simulations_1.0 Data-set for prediction of natural dry-snow avalanche activity and avalanche size using physics-based snowpack simulations ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081494-ENVIDAT.umm_json The data set contained in this repository was used in the analysis by Mayer et al. (2023): Mayer, S. I., Techel, F., Schweizer, J., and van Herwijnen, A.: Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations, EGUsphere, https://doi.org/10.5194/egusphere-2023-646, 2023. proprietary
-avapsimpacts_1 Advanced Vertical Atmospheric Profiling System Dropsondes (AVAPS) IMPACTS ALL STAC Catalog 2020-01-12 2023-02-28 -77.815, 33.54, -65.44, 44.17 https://cmr.earthdata.nasa.gov/search/concepts/C2004708338-GHRC_DAAC.umm_json The Advanced Vertical Atmospheric Profiling System (AVAPS) IMPACTS dataset consists of vertical atmospheric profile measurements collected by the Advanced Vertical Atmospheric Profiling System (AVAPS) dropsondes released from the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. AVAPS uses a Global Positioning System (GPS) dropsonde to measure atmospheric state parameters (temperature, humidity, wind speed/direction, pressure) and location in 3-dimensional space during the dropsonde’s descent. The AVAPS dataset files are available from January 12, 2020, through February 28, 2023, in ASCII-ict format. proprietary
avapsimpacts_1 Advanced Vertical Atmospheric Profiling System Dropsondes (AVAPS) IMPACTS GHRC_DAAC STAC Catalog 2020-01-12 2023-02-28 -77.815, 33.54, -65.44, 44.17 https://cmr.earthdata.nasa.gov/search/concepts/C2004708338-GHRC_DAAC.umm_json The Advanced Vertical Atmospheric Profiling System (AVAPS) IMPACTS dataset consists of vertical atmospheric profile measurements collected by the Advanced Vertical Atmospheric Profiling System (AVAPS) dropsondes released from the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. AVAPS uses a Global Positioning System (GPS) dropsonde to measure atmospheric state parameters (temperature, humidity, wind speed/direction, pressure) and location in 3-dimensional space during the dropsonde’s descent. The AVAPS dataset files are available from January 12, 2020, through February 28, 2023, in ASCII-ict format. proprietary
+avapsimpacts_1 Advanced Vertical Atmospheric Profiling System Dropsondes (AVAPS) IMPACTS ALL STAC Catalog 2020-01-12 2023-02-28 -77.815, 33.54, -65.44, 44.17 https://cmr.earthdata.nasa.gov/search/concepts/C2004708338-GHRC_DAAC.umm_json The Advanced Vertical Atmospheric Profiling System (AVAPS) IMPACTS dataset consists of vertical atmospheric profile measurements collected by the Advanced Vertical Atmospheric Profiling System (AVAPS) dropsondes released from the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. AVAPS uses a Global Positioning System (GPS) dropsonde to measure atmospheric state parameters (temperature, humidity, wind speed/direction, pressure) and location in 3-dimensional space during the dropsonde’s descent. The AVAPS dataset files are available from January 12, 2020, through February 28, 2023, in ASCII-ict format. proprietary
avhrr_822_1 SAFARI 2000 AVHRR Daily Site (1.5 km) and 15-Day Regional (1.5- and 6-km) Imagery ORNL_CLOUD STAC Catalog 1998-07-01 2000-10-31 8.73, -35.26, 43.2, -7.49 https://cmr.earthdata.nasa.gov/search/concepts/C2804805089-ORNL_CLOUD.umm_json The Global Inventory Mapping and Modeling (GIMMS) group at NASA/GSFC provided SAFARI 2000 with remotely sensed satellite data products at the site and regional level. These AVHRR data contain two main sets of data: site extracts of SAFARI core sites (Mongu, Etosha, Kasungu, Maun, Skukuza, and Tshane), and regional 15-day composites from sets of single-day images. These AVHRR data contain four main sets of data:1.5 km daily site extracts of SAFARI core sites (2000)1.5 km 15-day composites of SAFARI core sites (1998-2000)1.5 km 15-day composites of the southern African region (Mar, Sept 2000)6 km 15-day composites of the southern African region (1998-2000)The primary data layers for site extracts and regional composites are fire pixel counts and maximum NDVI. The fire product is different for the daily and for the composited products (see readme file) and a fire product is not included in the 1.5 km regional data set. NDVI composite-associated data layers for the regional data sets include land surface temperature, reflectance, solar zenith angle, view zenith angle, and relative azimuth angle. NDVI composite-associated data layers for the site extracts include these same variables as well as brightness temperature, fire mask composite, latitude, and longitude. The data are stored in binary image format files. There is a metadata file for each site and date/compositing period, in ASCII format. proprietary
avhrr_albedo_1995_xdeg_928_1 ISLSCP II AVHRR Albedo and BRDF, 1995 ORNL_CLOUD STAC Catalog 1995-02-01 1995-07-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2784840966-ORNL_CLOUD.umm_json This Albedo and BRDF (Bidirectional Reflectance Distribution Function) data set contains three files containing BRDF parameters, white- sky albedo and black-sky albedo at solar noon for three bands ((350-680nm, 680-3000nm, and 350-30000nm)derived from AVHRR (Advanced Very High Resolution Radiometer). These data are available at spatial resolutions of quarter, half, and one degree. Black-sky albedo (direct beam contribution) and white-sky (Completely diffuse contribution) can be linearly combined as a function of the fraction of diffuse skylight (itself a function of optical depth) to provide an actual or instantaneous albedo at local solar noon. proprietary
avhrrl3b_481_1 BOREAS Level-3b AVHRR-LAC Imagery: Scaled At-Sensor Radiance in LGSOWG Format ORNL_CLOUD STAC Catalog 1994-01-30 1996-09-18 -111, 50.09, -93.5, 59.98 https://cmr.earthdata.nasa.gov/search/concepts/C2929133860-ORNL_CLOUD.umm_json Data acquired from the AVHRR instrument on the NOAA-9, -11, -12, and -14 satellites were processed and archived. A few winter acquisitions are available, but the archive contains primarily growing season imagery. These gridded, at-sensor radiance image data cover the period of 30-Jan-1994 to 18-Sep-1996. Geographically, the data cover the entire 1000 km x 1000 km BOREAS Region. proprietary
@@ -17244,11 +17251,11 @@ basin_border_670_1 LBA Regional Boundary for the Amazon and Tocantins River Basi
bathy_proposedMPAs_eastantarctica_1 Bathymetry Compilation for Proposed Marine Protected Areas in East Antarctica AU_AADC STAC Catalog 1979-10-19 2010-12-02 32, -72.5, 150, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214313157-AU_AADC.umm_json The Australian Antarctic Division (AAD) has developed a proposal for the establishment of seven Marine Protected Areas (MPAs) located around east Antarctica for the purposes of marine ecosystem conservation. As seafloor morphology is a key component of marine ecosystems, this bathymetry compilation for the proposed MPAs was produced to support the AAD proposal. All bathymetry data available to Geoscience Australia at the time of compilation were used. This included multibeam and singlebeam acoustic data which were verified and processed to ensure the data were as accurate as possible. Processing included sound velocity corrections, navigation verification and the rejection of erroneous data points. Once processed, the data were gridded to 100m resolution and projected into suitable WGS84 UTM zones. The gridded data was exported into several formats to facilitate ease of use. The formats include xyz files, ESRI rasters, geoTIFs, CARISTM image files and soundings. The data and the technical report are available for download from URLs below. proprietary
bats-and-nocturnal-insects-in-urban-green-areas_1.0 Bats and nocturnal insects in urban green areas ENVIDAT STAC Catalog 2020-01-01 2020-01-01 1.8237305, 47.2195681, 8.8110352, 51.5360856 https://cmr.earthdata.nasa.gov/search/concepts/C2789814542-ENVIDAT.umm_json Animal biodiversity in cities is generally expected to be uniformly reduced, but recent studies show that this is modulated by the composition and configuration of Urban Green Areas (UGAs). UGAs represent a heterogeneous network of vegetated spaces in urban settings that have repeatedly shown to support a significant part of native diurnal animal biodiversity. However, nocturnal taxa have so far been understudied, constraining our understanding of the role of UGAs on maintaining ecological connectivity and enhancing overall biodiversity. We present a well-replicated multi-city study on the factors driving bat and nocturnal insect biodiversity in three European cities. To achieve this, we sampled bats with ultrasound recorders and flying insects with light traps during the summer of 2018. Results showed a greater abundance and diversity of bats and nocturnal insects in the city of Zurich, followed by Antwerp and Paris. We identified artificial lighting in the UGA to lower bat diversity by probably filtering out light-sensitive species. We also found a negative correlation between both bat activity and diversity and insect abundance, suggesting a top-down control. An in-depth analysis of the Zurich data revealed divergent responses of the nocturnal fauna to landscape variables, while pointing out a bottom-up control of insect diversity on bats. Thus, to effectively preserve biodiversity in urban environments, UGAs management decisions should take into account the combined ecological needs of bats and nocturnal insects and consider the specific spatial topology of UGAs in each city. proprietary
bb9fdc41-1a19-4793-aca1-a6f5f28d592d_NA TerraSAR-X - Staring Spotlight Images (TerraSAR-X Staring Spotlight) FEDEO STAC Catalog 2007-06-15 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2207458066-FEDEO.umm_json "This collection contains radar image products of the German national TerraSAR-X mission acquired in Staring Spotlight mode. Staring Spotlight imaging allows for a spatial resolution of up to 25 cm. The scene size varies depending on the incidence angle. As an example, 4 km (across swath) x 3.7 km (in orbit direction) can be achieved at 60°. TerraSAR-X is a sun-synchronous polar-orbiting, all-weather, day-and-night X-band radar earth observation mission realized in the frame of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defence and Space. For more information concerning the TerraSAR-X mission, the reader is referred to: https://www.dlr.de/content/de/missionen/terrasar-x.html" proprietary
-bds_dragonfly A Checklist of British and Irish Dragonfly Species SCIOPS STAC Catalog 1998-01-01 -8.41, 49.49, 2.39, 59.07 https://cmr.earthdata.nasa.gov/search/concepts/C1214611738-SCIOPS.umm_json "Dragonflies are among the most ancient of living creatures. Fossil records, clearly recognisable as dragonflies, go back to Carboniferous times which means that they date back almost 300 million years, predating pterodactyls by 100 million years and birds by some 150 million. It would he tragic if, after surviving such an unimaginable number of years, it should be our generation that witnesses the decline of these fascinating and beautiful insects. The British Dragonfly Society maintains a checklist of British and Irish dragonflies. This checklist includes all British and Irish species including migrants, vagrants and species now believed extinct in the British Isles. The species name provides a link to a photograph where available. Information was obtained from ""http://www.british-dragonflies.org.uk/content/uk-species""." proprietary
bds_dragonfly A Checklist of British and Irish Dragonfly Species ALL STAC Catalog 1998-01-01 -8.41, 49.49, 2.39, 59.07 https://cmr.earthdata.nasa.gov/search/concepts/C1214611738-SCIOPS.umm_json "Dragonflies are among the most ancient of living creatures. Fossil records, clearly recognisable as dragonflies, go back to Carboniferous times which means that they date back almost 300 million years, predating pterodactyls by 100 million years and birds by some 150 million. It would he tragic if, after surviving such an unimaginable number of years, it should be our generation that witnesses the decline of these fascinating and beautiful insects. The British Dragonfly Society maintains a checklist of British and Irish dragonflies. This checklist includes all British and Irish species including migrants, vagrants and species now believed extinct in the British Isles. The species name provides a link to a photograph where available. Information was obtained from ""http://www.british-dragonflies.org.uk/content/uk-species""." proprietary
+bds_dragonfly A Checklist of British and Irish Dragonfly Species SCIOPS STAC Catalog 1998-01-01 -8.41, 49.49, 2.39, 59.07 https://cmr.earthdata.nasa.gov/search/concepts/C1214611738-SCIOPS.umm_json "Dragonflies are among the most ancient of living creatures. Fossil records, clearly recognisable as dragonflies, go back to Carboniferous times which means that they date back almost 300 million years, predating pterodactyls by 100 million years and birds by some 150 million. It would he tragic if, after surviving such an unimaginable number of years, it should be our generation that witnesses the decline of these fascinating and beautiful insects. The British Dragonfly Society maintains a checklist of British and Irish dragonflies. This checklist includes all British and Irish species including migrants, vagrants and species now believed extinct in the British Isles. The species name provides a link to a photograph where available. Information was obtained from ""http://www.british-dragonflies.org.uk/content/uk-species""." proprietary
beaver_sat_1 Beaver Lake Satellite Image and Topographic Double-sided Map 1:100 000 AU_AADC STAC Catalog 1990-05-01 1990-05-31 67, -71, 69, -70 https://cmr.earthdata.nasa.gov/search/concepts/C1214313272-AU_AADC.umm_json Double-sided satellite image and topographic map of Beaver Lake, Antarctica. These maps were produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1990. Both maps are at a scale of 1:100 000. The satellite image map was produced from SPOT 1 and LANDSAT 5 TM scenes. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, stations/bases and gives some historical text information. The map has both geographical and UTM co-ordinates. Contours on the topographic map were derived from Russian maps (values have not been verified.) This map is also projected on a transverse mercator projection, and shows traverses/routes/foot track charts, bases/stations, glaciers/ice shelves, survey marks, and gives some historical text information. proprietary
-bech_nest_locations_1 Adelie Penguin nest locations on Bechervaise Island AU_AADC STAC Catalog 2000-02-01 2000-02-22 62.8084, -67.5879, 62.8152, -67.5863 https://cmr.earthdata.nasa.gov/search/concepts/C1214313158-AU_AADC.umm_json This dataset represents the locations of Adelie Penguin nests in colonies K, L and Q on Bechervaise Island, Holme Bay, Antarctica. Attributes include colony, nest number and tag colour. The dataset contains three files - an image file and two zip files. The image file, mapping_grid.jpg, is a diagram showing the grid used for plotting the colony L nest locations. The zip file, bech_penguin_nests.zip, contains shapefiles representing the Adelie Penguin nest locations, Bechervaise Island. The zip file, transform_nests_colonyL.zip, provides further information about the georeferencing of the colony L nest locations. proprietary
bech_nest_locations_1 Adelie Penguin nest locations on Bechervaise Island ALL STAC Catalog 2000-02-01 2000-02-22 62.8084, -67.5879, 62.8152, -67.5863 https://cmr.earthdata.nasa.gov/search/concepts/C1214313158-AU_AADC.umm_json This dataset represents the locations of Adelie Penguin nests in colonies K, L and Q on Bechervaise Island, Holme Bay, Antarctica. Attributes include colony, nest number and tag colour. The dataset contains three files - an image file and two zip files. The image file, mapping_grid.jpg, is a diagram showing the grid used for plotting the colony L nest locations. The zip file, bech_penguin_nests.zip, contains shapefiles representing the Adelie Penguin nest locations, Bechervaise Island. The zip file, transform_nests_colonyL.zip, provides further information about the georeferencing of the colony L nest locations. proprietary
+bech_nest_locations_1 Adelie Penguin nest locations on Bechervaise Island AU_AADC STAC Catalog 2000-02-01 2000-02-22 62.8084, -67.5879, 62.8152, -67.5863 https://cmr.earthdata.nasa.gov/search/concepts/C1214313158-AU_AADC.umm_json This dataset represents the locations of Adelie Penguin nests in colonies K, L and Q on Bechervaise Island, Holme Bay, Antarctica. Attributes include colony, nest number and tag colour. The dataset contains three files - an image file and two zip files. The image file, mapping_grid.jpg, is a diagram showing the grid used for plotting the colony L nest locations. The zip file, bech_penguin_nests.zip, contains shapefiles representing the Adelie Penguin nest locations, Bechervaise Island. The zip file, transform_nests_colonyL.zip, provides further information about the georeferencing of the colony L nest locations. proprietary
beech_stress_thresholds_1.0 Stress thresholds of mature European beech trees ENVIDAT STAC Catalog 2020-01-01 2020-01-01 6.5368652, 45.9799133, 9.7009277, 47.6044342 https://cmr.earthdata.nasa.gov/search/concepts/C2789814551-ENVIDAT.umm_json This data set contains the data presented in the figures 1-6 in Walthert et al. (2020): From the comfort zone to crown dieback: sequence of physiological stress thresholds in mature European beech trees across progressive drought. Science of the Total Environment. DOI: 10.1016/j.scitotenv.2020.141792. A detailed methodical description of the data can be found in the Material and Methods section of the paper. Drought responses of mature trees are still poorly understood making it difficult to predict species distributions under a warmer climate. Using mature European beech (Fagus sylvatica L.), a widespread and economically important tree species in Europe, we aimed at developing an empirical stress-level scheme to describe its physiological response to drought. We analysed effects of decreasing soil and leaf water potential on soil water uptake, stem radius, native embolism, early defoliation and crown dieback with comprehensive measurements from overall nine hydrologically distinct beech stands across Switzerland, including records from the exceptional 2018 drought and the 2019/2020 post-drought period. Based on the observed responses to decreasing water potential we derived the following five stress levels: I (predawn leaf water potential >-0.4 MPa): no detectable hydraulic limitations; II (-0.4 to -1.3): persistent stem shrinkage begins and growth ceases; III (-1.3 to -2.1): onset of native embolism and defoliation; IV (-2.1 to -2.8): onset of crown dieback; V (<-2.8): transpiration ceases and crown dieback is >20%. Our scheme provides, for the first time, quantitative thresholds regarding the physiological downregulation of mature European beech trees under drought and therefore synthesises relevant and fundamental information for process-based species distribution models. Moreover, our study revealed that European beech is drought vulnerable, because it still transpires considerably at high levels of embolism and because defoliation occurs rather as a result of embolism than preventing embolism. During the 2018 drought, an exposure to the stress levels III-V of only one month was long enough to trigger substantial crown dieback in beech trees on shallow soils. On deep soils with a high water holding capacity, in contrast, water reserves in deep soil layers prevented drought stress in beech trees. This emphasises the importance to include local data on soil water availability when predicting the future distribution of European beech. proprietary
bender2020_1.0 Changes in climatology, snow cover and ground temperatures at high alpine locations in Switzerland (Bender et al. 2020) ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.7568359, 45.7828484, 10.7336426, 48 https://cmr.earthdata.nasa.gov/search/concepts/C2789814563-ENVIDAT.umm_json This dataset includes all data and simulation files presented in the publication: Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100. This includes: * meteorological forcing, * climate change timeries and * simulation files together with * snow depth * ground temperature __Please refer to the following publication for further details which should be cited when using this dataset:__ __Bender et al. 2020. Changes in climatology, snow cover and ground temperatures at high alpine locations, DOI: 10.3389/feart.2020.00100.__ proprietary
beryllium_10be_isotopes_lawdome_1 High resolution studies of cosmogenic beryllium isotopes (10Be) at Law Dome AU_AADC STAC Catalog 2013-03-01 2013-03-31 112.80535, -66.7059, 112.80534, -66.7058 https://cmr.earthdata.nasa.gov/search/concepts/C1214571598-AU_AADC.umm_json "Energy from the Sun drives the Earth's climate system but this energy varies: there is an 11 year solar cycle and the Sun's intensity has varied over longer timescales. Reconstructing how the Sun's output has varied in past times is crucial to understanding the Earth's past climate which is key to predicting future climate change. Naturally-occurring radioactive isotopes such as 7Be and 10Be are produced in the Earth's atmosphere by cosmic rays, at a rate controlled by the activity of the Sun, and are layered in ice sheets, thus providing a means of reconstructing past solar output. 3 x 3"" PICO firn cores were drilled immediately in front of snow pit. The 3 pico cores were sampled at 14cm intervals and the sections combined resulting in 16 samples. Some length was lost during transit, especially in the top cores. It was assumed that the lost length was from the breaks in the core as the ends rubbed against each other during transport, and was evenly lost from each break, using the field notes to help. The bottom of each core was assumed to be the lengths as measured in the field. The samples were placed in a melting jar with carrier and left to melt overnight. ~10mL of the samples were retained for water isotope analysis. The samples were filtered and pumped onto cation columns." proprietary
@@ -17295,12 +17302,12 @@ brdpier0006 Demography and Movements of the Endangered Akepa and Hawaii Creeper
brdpier0008 Determining age and sex of Oma'o (Myadestes obscurus) CEOS_EXTRA STAC Catalog 1976-01-01 1982-12-31 -155, 19, -155, 19 https://cmr.earthdata.nasa.gov/search/concepts/C2231549047-CEOS_EXTRA.umm_json Methods to determine the age and sex of 'Oma'o (Myadestes obscurus) were developed on the basis of 66 museum speciments and 149 live 'Oma'o captured in mist nets on the island of Hawaii. 'Oma'o in juvenile plumage are heavily spotted with scalloped greater coverts and tertials and are easily distinguished from adults. Birds in their first prebasic plumage usually retain one or more scallped wing coverts or tertials. Wing lengths of adult and immature male 'Oma'o were significantly longer than those of females, but only 80% of adult specimens were accurately sexed by wing length. Geographic Description: Island of Hawaii, Keauhou Ranch (19.50, -155.33; 1800 m elevation) and Kilauea Forest (19.52, -155.32; 1600-1650 m). 1.5.2 Bounding Rectangle Coordinates Methodology: Recorded plumage characteristics and exteral measurements of 55 'Oma'o specimens at the Bernice P. Bishop Museum and 11 'Oma'o specimens loaned by the American Museum of Natural History. 'Oma'op juvenal plumage are dark and below and are easily distinguished from adults. The feathers of the breast, belly, and flanks are buffy white in the center but are broadly bordered with blackish brown, giving the feathers a scalloped pattern (Berger 1981, Pratt 1982). proprietary
brdpier0009 Diets of Owls and Feral Cats in Hawaii CEOS_EXTRA STAC Catalog 1990-01-01 1994-12-31 -155, 19, -155, 19 https://cmr.earthdata.nasa.gov/search/concepts/C2231549329-CEOS_EXTRA.umm_json "The feral house cat (Felis catus), Hawaiian Short-eared Owl or Pueo (Asio flammeus sandwichensis), and Common Barn Owl (Tyto alba) are important predators of birds and introduced rodents in Hawai'i. Cat scats from the island of Hawai'i (n=87), Pueo pellets from Hawai'i, Kaua'i, and Kaho'olawe (n=36), and Barn Owl pellets from Hawai'i, O'ahu and Kaho'olawe (n=301) were examined to determine the incidence of rodent, bird and insect remains in the diets of these predators. Rodents were the main prey of cats, Pueo, and Barn Owls, but the incidence of bird remains in diets of all three predator species was high relative to studies conducted elsewhere in the world. Geographic Description: All cat scats were collected in dry mamane (Sophora chrysophylla)-naio (Myoporum sandwichensis) forests on the western and eastern slopes of Mauna Kea above 2,000 m elevation. Pueo pellets were collected in dry forests on Mauna Kea (n=13), from Kaumana Gulch on Kaho'olawe (n=21), and from the Alakai Swamp on Kaua'i (n=2). Barn Owl pellets were collected at roosts and nests at Kakalau Forest National Wildlife Refuge on Hawai'i (n=207), near the Pu'u La'au cabin on Mauna Kea (n= 73), on O'ahu (n=19), at Ahupi Beach on Kaho'olawe (n=1). Acumulations of Barn Owl pellets were found below roosting sites, whereas single Pueo pellets were found below tall trees or on open ground (Mauna Kea), or on cliff faces on Kaho'olawe. On Kaua'i, Pueo pellets were found in an open bog near the remains of a recent Pacific Golden Plover kill. 1.5.2 Bounding Rectangle Coordinates Methodology: Determined predator diets from analysis of 87 cat scats, 36 Pueo pellets, and 301 Barn Owl pellets. All cat scats were collected in dry mamane-naio forests. Size, appearance, and consistency were used to determine the source of scats and pellets. Cat scats were smaller than pellets and had tapered ends with fewer bones distributed through them. Pueo pellets were smaller than Barn Owl pellets and had a uniformly cylindrical shape. They fit Mikkola's (1983) description as ""elongated, roughly cylindrical dark gray and formed from a tightly-massed conglomeration of fur or feathers with a central core of mammal and bird bones.""" proprietary
brdwerc0002 Comparison of the Sedimentary Record of Fire with the Tree-Ring Record Within and Near Giant Sequoia Groves, Sierra Nevada, California CEOS_EXTRA STAC Catalog 1997-09-18 1998-09-30 -123, 35, -117, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2231553360-CEOS_EXTRA.umm_json "The larger Sierra Nevada Global Change Research Program (SNGCRP) seeks to understand past, present, and possible future changes in Sierran forest structure, composition, and dynamics resulting from changing management practices and anticipated global climate change. Within the larger program, this project (""Comparison of the sedimentary record of fire with the tree-ring record within and near giant sequoia groves, Sierra Nevada, California"") will use high precision carbon dating of charcoal and pollen in sediment cores in order to (1) develop a 10,000-year record of fire history in the southern and central Sierra Nevada, calibrated against multi-millennial, annual-resolution fire histories from tree rings at the same sites, and (2) develop detailed descriptions of changes in forest composition over the last few millennia, to be compared with climate and fire histories developed by other SNGCRP projects. This work will provide data for calibration and testing of fire spread and forest dynamics models currently being developed by other global change research projects, and will provide baseline data on past disturbance regimes, their variability, and consequent forest response. These objectives will be achieved by analyzing four sediment cores. The cores have already been collected from meadows adjacent to sites with multi-millennial, annual-resolution fire histories developed from giant sequoia tree rings: Giant Forest (Sequoia National Park), Mountain Home Grove (Mountain Home State Forest), Mariposa Grove (Yosemite National Park), and Big Stump Grove (Kings Canyon National Park)." proprietary
-breeding_success_BI_1 Adelie penguin breeding success for Bechervaise Island, Mawson AU_AADC STAC Catalog 1990-10-01 2005-02-01 62.8055, -67.5916, 62.825, -67.5861 https://cmr.earthdata.nasa.gov/search/concepts/C1214313363-AU_AADC.umm_json Adelie penguin breeding success records for Bechervaise Island, Mawson since 1990-91. Data include counts of occupied nests and chick counts when either 2/3 of the nests have creched or when all nests have creched. Breeding success values are calculated as the number of chicks per occupied nest. Breeding Success = the number of chicks raised to fledging per nest with eggs Breeding success is calculated from four different whole island counts: 1) the number of incubating nests (i.e. the number of nest with eggs) - 'incubating nest count' 2) the number of brooding nests (i.e. the number of nests brooding chicks) - 'brooding chick count' 3) the number of chicks present when 2/3 of the nests have creched their chicks - '2/3-creche count' 4) the number of chicks present when all the nests have creche their chicks - 'fully-creche count' Each colony on the island is manually counted by field observers, using 'counters', three times each. Counts within 10% of each other are used to average the number of nests or chicks for each colony and then in later calculations to determine breeding success. Incubating nest counts are conducted on or about 2nd December; Brooding chick counts are conducted on or about the 7th January; 2/3-creche counts on or about the 19th January; and Fully-creche chick counts on or about 26th January. Whole island 2/3-creche and fully-creche chick count dates are determined from calculating when 2/3 and all study nests in the census area (study colonies) have creche their chicks. This work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project. The fields in this dataset are: Year Breeding success Occupied nests proprietary
breeding_success_BI_1 Adelie penguin breeding success for Bechervaise Island, Mawson ALL STAC Catalog 1990-10-01 2005-02-01 62.8055, -67.5916, 62.825, -67.5861 https://cmr.earthdata.nasa.gov/search/concepts/C1214313363-AU_AADC.umm_json Adelie penguin breeding success records for Bechervaise Island, Mawson since 1990-91. Data include counts of occupied nests and chick counts when either 2/3 of the nests have creched or when all nests have creched. Breeding success values are calculated as the number of chicks per occupied nest. Breeding Success = the number of chicks raised to fledging per nest with eggs Breeding success is calculated from four different whole island counts: 1) the number of incubating nests (i.e. the number of nest with eggs) - 'incubating nest count' 2) the number of brooding nests (i.e. the number of nests brooding chicks) - 'brooding chick count' 3) the number of chicks present when 2/3 of the nests have creched their chicks - '2/3-creche count' 4) the number of chicks present when all the nests have creche their chicks - 'fully-creche count' Each colony on the island is manually counted by field observers, using 'counters', three times each. Counts within 10% of each other are used to average the number of nests or chicks for each colony and then in later calculations to determine breeding success. Incubating nest counts are conducted on or about 2nd December; Brooding chick counts are conducted on or about the 7th January; 2/3-creche counts on or about the 19th January; and Fully-creche chick counts on or about 26th January. Whole island 2/3-creche and fully-creche chick count dates are determined from calculating when 2/3 and all study nests in the census area (study colonies) have creche their chicks. This work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project. The fields in this dataset are: Year Breeding success Occupied nests proprietary
+breeding_success_BI_1 Adelie penguin breeding success for Bechervaise Island, Mawson AU_AADC STAC Catalog 1990-10-01 2005-02-01 62.8055, -67.5916, 62.825, -67.5861 https://cmr.earthdata.nasa.gov/search/concepts/C1214313363-AU_AADC.umm_json Adelie penguin breeding success records for Bechervaise Island, Mawson since 1990-91. Data include counts of occupied nests and chick counts when either 2/3 of the nests have creched or when all nests have creched. Breeding success values are calculated as the number of chicks per occupied nest. Breeding Success = the number of chicks raised to fledging per nest with eggs Breeding success is calculated from four different whole island counts: 1) the number of incubating nests (i.e. the number of nest with eggs) - 'incubating nest count' 2) the number of brooding nests (i.e. the number of nests brooding chicks) - 'brooding chick count' 3) the number of chicks present when 2/3 of the nests have creched their chicks - '2/3-creche count' 4) the number of chicks present when all the nests have creche their chicks - 'fully-creche count' Each colony on the island is manually counted by field observers, using 'counters', three times each. Counts within 10% of each other are used to average the number of nests or chicks for each colony and then in later calculations to determine breeding success. Incubating nest counts are conducted on or about 2nd December; Brooding chick counts are conducted on or about the 7th January; 2/3-creche counts on or about the 19th January; and Fully-creche chick counts on or about 26th January. Whole island 2/3-creche and fully-creche chick count dates are determined from calculating when 2/3 and all study nests in the census area (study colonies) have creche their chicks. This work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project. The fields in this dataset are: Year Breeding success Occupied nests proprietary
brok_5k_gis_1 Broknes Peninsula 1:5000 Topographic GIS Dataset AU_AADC STAC Catalog 1994-11-03 1994-11-17 76.2, -69.4333, 76.4333, -69.3333 https://cmr.earthdata.nasa.gov/search/concepts/C1214313345-AU_AADC.umm_json Broknes Peninsula, Larsemann Hills, 1:5000 GIS dataset. This dataset has been superseded by the datasets described by the metadata records: 'Larsemann Hills - Mapping from aerial photography captured February 1998' and 'Larsemann Hills - Mapping from Landsat 7 imagery captured January 2000'. These data have been archived as they have been superseded. proprietary
broknes_lake_catchments_gis_1 Lake catchments on Broknes, Larsemann Hills AU_AADC STAC Catalog 1997-05-06 2001-08-14 76.285, -69.4193, 76.42, -69.3698 https://cmr.earthdata.nasa.gov/search/concepts/C1214313378-AU_AADC.umm_json Catchment boundaries of the the lakes on Broknes, Larsemann Hills. These catchments were generated using the FLOWDIRECTION and BASINS routines in the GRID module of ArcInfo GIS. proprietary
-bromwich_0337948_1 A 45-Y Hindcast of Antarctic Surface Mass Balance Using Polar MM5 SCIOPS STAC Catalog 1979-01-01 2002-08-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214586989-SCIOPS.umm_json This 3-year project (June 2004-May 2007) was funded by the National Science Foundation's Office of Polar Programs (Glaciology). We employed the Polar MM5 to model variability and change in the surface mass balance (the net accumulation of moisture) over Antarctica in recent decades. Available here are annually and seasonally resolved grids of atmospheric data simulated by Polar MM5 for the period Jan 1979-Aug 2002. The ERA-40 dataset provided the initial and boundary conditions for the simulations. The burden of validating the data provided is the responsibility of anyone choosing to download it. MODEL CONFIGURATION: The Polar MM5 simulations were performed on a 121 x 121 polar stereographic grid covering the Antarctic and centered over the South Pole. The model resolution is 60-km in each horizontal direction. Vertically, the domain contains 32 sigma levels ranging from the surface to 10 hPa. Atmospheric data (U,V,T,Q,P) and sea surface temperatures were provided by ERA-40. 25-km resolution daily sea ice concentration grids were provided by the National Snow and Ice Data Center to determine fractional ice coverage over ocean gridpoints. The model topography was interpolated from the 1-km resolution digital elevation model of Liu et al. (2001). Images of the model domain, topography and land use specifications can be found here. More information on the physics in Polar MM5 can be found on the Polar MM5 Webpage, http://polarmet.mps.ohio-state.edu/PolarMet/pmm5.html Please reference the following publication if you use the data in a publication: Monaghan, A. J., D. H. Bromwich, and S.-H. Wang, 2006: Recent trends in Antarctic snow accumulation from Polar MM5. Philosophical Trans. Royal. Soc. A, 364, 1683-1708. proprietary
bromwich_0337948_1 A 45-Y Hindcast of Antarctic Surface Mass Balance Using Polar MM5 ALL STAC Catalog 1979-01-01 2002-08-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214586989-SCIOPS.umm_json This 3-year project (June 2004-May 2007) was funded by the National Science Foundation's Office of Polar Programs (Glaciology). We employed the Polar MM5 to model variability and change in the surface mass balance (the net accumulation of moisture) over Antarctica in recent decades. Available here are annually and seasonally resolved grids of atmospheric data simulated by Polar MM5 for the period Jan 1979-Aug 2002. The ERA-40 dataset provided the initial and boundary conditions for the simulations. The burden of validating the data provided is the responsibility of anyone choosing to download it. MODEL CONFIGURATION: The Polar MM5 simulations were performed on a 121 x 121 polar stereographic grid covering the Antarctic and centered over the South Pole. The model resolution is 60-km in each horizontal direction. Vertically, the domain contains 32 sigma levels ranging from the surface to 10 hPa. Atmospheric data (U,V,T,Q,P) and sea surface temperatures were provided by ERA-40. 25-km resolution daily sea ice concentration grids were provided by the National Snow and Ice Data Center to determine fractional ice coverage over ocean gridpoints. The model topography was interpolated from the 1-km resolution digital elevation model of Liu et al. (2001). Images of the model domain, topography and land use specifications can be found here. More information on the physics in Polar MM5 can be found on the Polar MM5 Webpage, http://polarmet.mps.ohio-state.edu/PolarMet/pmm5.html Please reference the following publication if you use the data in a publication: Monaghan, A. J., D. H. Bromwich, and S.-H. Wang, 2006: Recent trends in Antarctic snow accumulation from Polar MM5. Philosophical Trans. Royal. Soc. A, 364, 1683-1708. proprietary
+bromwich_0337948_1 A 45-Y Hindcast of Antarctic Surface Mass Balance Using Polar MM5 SCIOPS STAC Catalog 1979-01-01 2002-08-31 -180, -90, 180, -60 https://cmr.earthdata.nasa.gov/search/concepts/C1214586989-SCIOPS.umm_json This 3-year project (June 2004-May 2007) was funded by the National Science Foundation's Office of Polar Programs (Glaciology). We employed the Polar MM5 to model variability and change in the surface mass balance (the net accumulation of moisture) over Antarctica in recent decades. Available here are annually and seasonally resolved grids of atmospheric data simulated by Polar MM5 for the period Jan 1979-Aug 2002. The ERA-40 dataset provided the initial and boundary conditions for the simulations. The burden of validating the data provided is the responsibility of anyone choosing to download it. MODEL CONFIGURATION: The Polar MM5 simulations were performed on a 121 x 121 polar stereographic grid covering the Antarctic and centered over the South Pole. The model resolution is 60-km in each horizontal direction. Vertically, the domain contains 32 sigma levels ranging from the surface to 10 hPa. Atmospheric data (U,V,T,Q,P) and sea surface temperatures were provided by ERA-40. 25-km resolution daily sea ice concentration grids were provided by the National Snow and Ice Data Center to determine fractional ice coverage over ocean gridpoints. The model topography was interpolated from the 1-km resolution digital elevation model of Liu et al. (2001). Images of the model domain, topography and land use specifications can be found here. More information on the physics in Polar MM5 can be found on the Polar MM5 Webpage, http://polarmet.mps.ohio-state.edu/PolarMet/pmm5.html Please reference the following publication if you use the data in a publication: Monaghan, A. J., D. H. Bromwich, and S.-H. Wang, 2006: Recent trends in Antarctic snow accumulation from Polar MM5. Philosophical Trans. Royal. Soc. A, 364, 1683-1708. proprietary
brownbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of Brown Bay, Windmill Islands AU_AADC STAC Catalog 1997-02-01 2000-02-05 110.54, -66.281, 110.548, -66.279 https://cmr.earthdata.nasa.gov/search/concepts/C1214308318-AU_AADC.umm_json This dataset is a Digital Elevation Model (DEM) of Brown Bay, Windmill Islands and contours and bathymetric contours derived from the DEM. The data are stored in a UTM zone 49 projection. They were created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA ARE NOT FOR NAVIGATION PURPOSES. proprietary
brownbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of Brown Bay, Windmill Islands ALL STAC Catalog 1997-02-01 2000-02-05 110.54, -66.281, 110.548, -66.279 https://cmr.earthdata.nasa.gov/search/concepts/C1214308318-AU_AADC.umm_json This dataset is a Digital Elevation Model (DEM) of Brown Bay, Windmill Islands and contours and bathymetric contours derived from the DEM. The data are stored in a UTM zone 49 projection. They were created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA ARE NOT FOR NAVIGATION PURPOSES. proprietary
bryophyte-observer-bias_1.0 Greater observer expertise leads to higher estimates of bryophyte species richness ENVIDAT STAC Catalog 2024-01-01 2024-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081769-ENVIDAT.umm_json This dataset contains bryophyte species count data and information about the observers bryophyte expertise for 2332 relevés conducted from 2011 to 2021 on 10-m2 plots in a long-term monitoring program in Switzerland. Plots were situated in raised bogs and fens of national importance, which were distributed across the whole country. The majority of the plots is represented by two relevés as sites are revisited every six years. The dataset was used in the paper mentioned below to test if species richness estimates differed among categories of observer expertise. Moser T, Boch S, Bedolla A, Ecker KT, Graf UH, Holderegger R, Küchler H, Pichon NA, Bergamini A (2024) Greater observer expertise leads to higher estimates of bryophyte species richness. _Journal of Vegetation Science_. (submitted) proprietary
@@ -17411,8 +17418,8 @@ chelsa_cmip5_ts_1.0 High resolution monthly precipitation and temperature timese
chelsa_trace_1.0 CHELSA-TraCE21k: Downscaled transient temperature and precipitation data since the last glacial maximum ENVIDAT STAC Catalog 2020-01-01 2020-01-01 179.995693, -89.9959722, -179.9959722, 83.9956937 https://cmr.earthdata.nasa.gov/search/concepts/C2789814958-ENVIDAT.umm_json High resolution, downscaled climate model data are used in a wide variety of applications in environmental sciences. Here we present the CHELSA-TraCE21k downscaling algorithm to create global monthly climatologies for temperature and precipitation at 30 arcsec spatial resolution in 100 year time steps for the last 21,000 years. Paleo orography at high spatial resolution and at each timestep is created by combining high resolution information on glacial cover from current and Last Glacial Maximum (LGM) glacier databases with the interpolation of a dynamic ice sheet model (ICE6G) and a coupling to mean annual temperatures from CCSM3-TraCE21k. Based on the reconstructed paleo orography, mean annual temperature and precipitation was downscaled using the CHELSA V1.2 algorithm. The data is published under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license. proprietary
chelsacruts_1.0 CHELSAcruts - High resolution temperature and precipitation timeseries for the 20th century and beyond ENVIDAT STAC Catalog 2018-01-01 2018-01-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2789814896-ENVIDAT.umm_json CHELSAcruts is a delta change monthly climate dataset for the years 1901-2016 for mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sum. Here we use the delta change method by B-spline interpolation of anomalies (deltas) of the CRU TS 4.01 dataset. Anomalies were interpolated between all CRU TS grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2 (Karger et al. 2017, Scientific Data). This method has the assumption that climate only varies on the scale of the coarser (CRU TS) dataset, and the spatial pattern (from CHELSA) is consistent over time. This is certainly a rather crude assumption, and for time periods for which more accurate data is available CHELSAcruts should be avoided if possible (e.g. use CHELSA V1.2 for 1979-2015). Different to CHELSA V1.2, CHELSAcruts does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages. CHELSAcruts is licensed under a Creative Commons Attribution 2.0 Generic (CC BY 2.0) license. proprietary
chem_26_1 Canopy Chemistry (OTTER) ORNL_CLOUD STAC Catalog 1989-08-23 1991-06-04 -123.94, 44.29, -121.33, 45.06 https://cmr.earthdata.nasa.gov/search/concepts/C2804747736-ORNL_CLOUD.umm_json Canopy characteristics: leaf chemistry, specific leaf area, LAI, PAR, IPAR, NPP, standing biomass--see also: Meteorology (OTTER) for associated meteorological conditions proprietary
-chesapeake_val_2013_0 2013 Chesapeake Bay measurements OB_DAAC STAC Catalog 2013-04-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360188-OB_DAAC.umm_json 2013 Chesapeake Bay measurements. proprietary
chesapeake_val_2013_0 2013 Chesapeake Bay measurements ALL STAC Catalog 2013-04-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360188-OB_DAAC.umm_json 2013 Chesapeake Bay measurements. proprietary
+chesapeake_val_2013_0 2013 Chesapeake Bay measurements OB_DAAC STAC Catalog 2013-04-11 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360188-OB_DAAC.umm_json 2013 Chesapeake Bay measurements. proprietary
chlorophyll_65-02_1 Long-term variation of surface phytoplankton chlorophyll a in the Southern Ocean during 1965-2002 AU_AADC STAC Catalog 1965-11-23 2002-12-08 100.147, -54.985, 137.95, 24.567 https://cmr.earthdata.nasa.gov/search/concepts/C1214313422-AU_AADC.umm_json The variation in the phytoplankton biomass over a decadal time scale, and its relationship with the Antarctic Circumpolar Wave (ACW) and climate change, has been poorly interpreted because of the limited satellite chlorophylla (chl a) data compared with the physical parameters from satellite. We analysed a long-term chl a dataset along the Japanese Antarctic Research Expedition (JARE) cruise tracks since 1965 to investigate inter-annual variation of phytoplankton biomass. In the Southern Ocean, increasing trends of chl a and the spreading of higher chl a area to the north with 3-7 year cycles were found. Although relationships between the decadal change in chl a and climate change such as variation of sea ice extent and the El Nino are still obscure, large variation of primary production in proportion to the chl a is implied. The chl a concentration of sea surface water has been measured routinely on board the icebreakers Fuji and Shirase during almost every cruise of the JARE. The download file contains chlorophyll a data collected from ship tracks on JARE voyages between 1965 and 2002. The field in this dataset are: Date (local time) Year Latitude Longitude Corrected Chlorophyll a See the attached paper for more details. The publications on the data collected during the 1965-1976 and 1988-1993 cruises are listed in Fukuchi [1980] and Suzuki and Fukuchi [1997], respectively. For data on the 1977-1985 and 1994-1997 cruises, see [Kanda and Fukuchi, 1979; Fukuchi and Tamura, 1982; Tanimura, 1981; Watanabe and Nakajima, 1983; Ino and Fukuchi, 1984; Sasaki, 1984; Hamada et al., 1985; Fukuda et al., 1986; Hattori and Fukuchi, 1988; Midorikawa et al., 2000]. Data post 1998-2002 cruises is in Hirawake and Fukuchi [2004]. Data from the 1986-1987 will be published in the JARE data report of digital media, including all cruise data. Auxiliary Material for paper 2004GL021394 Long-term variation of surface phytoplankton chlorophyll a in the Southern Ocean during 1965-2002. Toru Hirawake, Tsuneo Odate and Mitsuo Fukuchi (National Institute of Polar Research, Tokyo) Geophys. Res. Lett., Vol (Num), doi:10.1029/2004GL021394 All of the chl a data have been reported in the publications of the National Institute of Polar Research (NIPR). proprietary
chm-hp-4rtm_1.0 Forest canopy structure data for radiation and snow modelling (CH/FIN) ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.871859, 46.845432, 26.6365886, 67.366827 https://cmr.earthdata.nasa.gov/search/concepts/C2789814990-ENVIDAT.umm_json This dataset contains forest canopy structure data acquired in a spruce forest at Laret, Switzerland, and a pine forest at Sodankylä, Finland. Data include: * Hemispherical photographs taken at transect intersection points of 13 experimental plots (40x40m each) * a Canopy Height Model (tree height map) derived by rasterizing airborne LiDAR data, encompassing the entire simulation domain at Laret (150'000 m2) These data provide the necessary basis for creating canopy structure datasets to be used as input to the forest snow snow model FSM2. These datasets, the model input derivatives and the radiation and snow modelling are described in detail in the following publication: _Mazzotti, G., Webster, C., Essery, R., and Jonas, T. (2021) Improving the physical representation of forest snow processes in coarse-resolution models: lessons learned from upscaling hyper-resolution simulations. Water Resources Research 57, e2020WR029064. [doi: 10.1029/2020WR029064](https://doi.org/10.1029/2020WR029064)_ This publication must be cited when using the data. ### See also: For additional information on the FSM2 model, see the corresponding [GitHub repository](https://github.com/GiuliaMazzotti/FSM2/tree/hyres_enhanced_canopy) The datasets and the model have also been used in _Mazzotti et al. (2020) Process-level evaluation of a hyper-resolution forest snow model using distributed multi-sensor observations. [doi: 10.1029/2020WR027572](https://doi.org/10.1029/2020WR027572) proprietary
climate-change-scenarios-at-hourly-resolution_1.0 Dataset for: Climate change scenarios at hourly time-step over Switzerland from an enhanced temporal downscaling approach ENVIDAT STAC Catalog 2021-01-01 2021-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814547-ENVIDAT.umm_json In fall 2019, a new set of climate change scenarios has been released for Switzerland, the CH2018 dataset (www.climate-scenarios.ch). The data are provided at daily resolution. We produced from the CH2018 dataset a new set of climate change scenarios temporally downscaled at hourly resolution. In addition, we extended this dataset integrating the meteorological stations from the Inter-Cantonal Measurement and Information System (IMIS) network, an alpine network of automatic meteorological stations operated by the WSL Institute for Snow and Avalanche Research SLF. The extension to the IMIS network is obtained using a Quantile Mapping approach in order to perform a spatial transfer of the CH2018 scenarios from the location of the MeteoSwiss stations to the location of the IMIS stations. The temporal downscaling is performed using an enhanced Delta-Change approach. This approach is based on objective criteria for assessing the quality of the determined delta and downscaled time series. In addition, this method also fixes a flaw of common quantile mapping methods (such as used in the CH2018 dataset for spatial downscaling) related to the decrease of correlation between different variables. The idea behind the delta change approach is to take the main seasonal signal (and mean) from climate change scenarios at daily resolution and to map it to a historical time series at hourly resolution in order to modify the historical time series. The obtained time series exhibit the same seasonal signal as the original climate change time series, while it keeps the sub-daily cycle from the historical time series. The applied methods (Quantile Mapping and Delta-Change) have limitations in correctly representing statistically extreme events and changes in the frequency of discontinuous events such as precipitation. In addition, the sub-daily cycle in the data is inherited from the historical time series, so there is no information of the climate change signal in this sub-daily cycle. A careful reading of the paper accompanying the dataset is necessary to understand the limitations and scope of application of this new dataset. This material is distributed under CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/legalcode). proprietary
@@ -17420,8 +17427,8 @@ climate_iceberg_1 Antarctic CRC and Australian Antarctic Division Climate Data S
climate_pressure_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air pressure ALL STAC Catalog 1901-01-01 1998-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313319-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air pressure for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary
climate_pressure_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air pressure AU_AADC STAC Catalog 1901-01-01 1998-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313319-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air pressure for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary
climate_sea_ice_1 Antarctic CRC and Australian Antarctic Division Climate Data Set - Northern extent of Antarctic sea ice AU_AADC STAC Catalog 1973-01-18 1996-12-19 -180, -80, 180, -50 https://cmr.earthdata.nasa.gov/search/concepts/C1214313423-AU_AADC.umm_json This dataset contains the digitisation of one U.S. Navy/NOAA Joint Ice Facility sea ice extent and concentration map monthly to give the latitude and longitude of the northern extent of the Antarctic sea ice. Maps were produced weekly, but have been digitised monthly, since distribution began in January 1973 (except August 1985), until December 1996. Maps were digitised at each 10 degrees of longitude, and the longitude, distance from the south pole to the northern edge of the sea ice at that longitude, and latitude of that edge is given, as well as the mean distance and latitude for that map. Summary tabulations (sea ice northern extent latitudes at each 10 degree of longitude each year, grouped by month) and mean monthly sea ice extent statistics are also available. proprietary
-climate_temps_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures AU_AADC STAC Catalog 1901-01-01 2002-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313410-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air temperature for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary
climate_temps_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures ALL STAC Catalog 1901-01-01 2002-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313410-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air temperature for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary
+climate_temps_1 ACE CRC and Australian Antarctic Division Climate Data Set - Mean monthly surface air temperatures AU_AADC STAC Catalog 1901-01-01 2002-12-31 -180, -80, 180, -17 https://cmr.earthdata.nasa.gov/search/concepts/C1214313410-AU_AADC.umm_json This dataset consists of tabulations of mean monthly surface air temperature for most occupied stations in Antarctic and the Southern Ocean. Some South Pacific Island stations are also included, along with a few continent based stations. The data have been collected from various climate sources world wide, and spans varying years ranging between 1901 and 2002. proprietary
climatological-snow-data-1998-2022-oshd_1.0 Climatological snow data since 1998, OSHD ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226081762-ENVIDAT.umm_json This dataset comprises the climatology on gridded data of snow water equivalent and snow melt runoff spanning 1998-2022, with a spatial resolution of 1 km and daily temporal resolution. This data was produced with the conceptual OSHD model (Temperature Index Model). proprietary
climwat CLIMWAT, A Climatic Database CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2232283619-CEOS_EXTRA.umm_json CLIMWAT is a climatic database to be used in combination with the computer program CROPWAT and allows the ready calculation of crop water requirements, irrigation supply and irrigation scheduling for various crops for a range of climatological stations worldwide. The CLIMWAT database includes data from a total of 3262 meteorological stations from 144 countries. CLIMWAT is published as Irrigation and Drainage paper No 49 in 1994 and includes a Manual with description of the use of the database with CROPWAT The data are contained in five diskettes included in the publication and can be ordered as FAO Irrigation and Drainage Paper 49 through the FAO Sales and Marketing Group. [Summary provided by the FAO.] proprietary
cmar_wh CSIRO Marine Data Warehouse (OBIS Australia) CEOS_EXTRA STAC Catalog 1978-02-05 1997-08-30 114, -44, 155, -8 https://cmr.earthdata.nasa.gov/search/concepts/C2226653616-CEOS_EXTRA.umm_json The CSIRO Marine Data Warehouse is a repository for biological and other marine survey data collected by CSIRO Division of Marine and Atmospheric Research (CMAR), Australia. It contains field (observational) data from numerous research trawls and other fisheries-related surveys conducted in waters around Australia by the Division since the late 1970s. At time of writing (April 2006) the database is serving approximately 106,000 species-level records to OBIS. Multiple species records and those of taxa not identified to species level are presently excluded. Associated data include species counts and/or weights in some but not all cases. proprietary
@@ -17467,12 +17474,12 @@ daily-solute-and-isotope-of-stream-water-and-precipitation_1.0 Daily data of sol
daily_precip_est_793_1 SAFARI 2000 Daily Rainfall Estimates, 0.1-Deg, Southern Africa, 1993-2001 ORNL_CLOUD STAC Catalog 1993-01-01 2001-12-31 10, -34, 50, 0 https://cmr.earthdata.nasa.gov/search/concepts/C2789731186-ORNL_CLOUD.umm_json The Microwave InfraRed Algorithm (MIRA) is used to produce an imagery data set of daily mean rain rates at 0.1 degree spatial resolution over southern Africa for the period 1993-2001. MIRA combines passive microwave (PMW) from the Special Sensor Microwave/Imager (SSM/I) on board the DMSP F10 and F14 satellites at a resolution of 0.5 degrees and infrared (IR) data from the Meteosat 4, 5, 6, and 7 satellites in 2-hour slots at a resolution of 5 km. This approach accounts for the limitations of both data types in estimating precipitation. Rainfall estimates are produced at the high spatial and temporal frequency of the IR data using rainfall information from the PMW data. An IR/rain rate relationship, variable in space and time, is derived from coincident observations of IR and PMW rain rate (accumulated over a calibration domain) using the probability matching method. The IR/rain rate relationship is then applied to IR imagery at full temporal resolution. The results presented here are the daily means of those derived rain rates at 0.1 degree spatial resolution.The rainfall data sets are flat binary images with no headers. They are compressed band sequential (bsq) files that contain all of the daily images for the given year. Each image is an array of 401 lines, each with 341 binary floating-point numbers, containing rainfall at 0.1 degree resolution for the area 10 to 50 degrees longitude and 0 to -34 degrees latitude. The number of band sequential images in each annual file and the associated dates can be found in the file MIRA_data_dates.csv. proprietary
dalmolin_thurmodeling1_1.0 Data for: Understanding dominant controls on streamflow spatial variability to set-up a semi-distributed hydrological model: the case study of the Thur catchment ENVIDAT STAC Catalog 2020-01-01 2020-01-01 8.5830688, 47.1112614, 9.6377563, 47.6246779 https://cmr.earthdata.nasa.gov/search/concepts/C2789814894-ENVIDAT.umm_json This study documents the development of a semi-distributed hydrological model aimed at reflecting the dominant controls on observed streamflow spatial variability. The process is presented through the case study of the Thur catchment (Switzerland, 1702 km2), an alpine and pre–alpine catchment where streamflow (measured at 10 subcatchments) has different spatial characteristics in terms of amounts, seasonal patterns, and dominance of baseflow. In order to appraise the dominant controls on streamflow spatial variability, and build a model that reflects them, we follow a two–stages approach. In a first stage, we identify the main climatic or landscape properties that control the spatial variability of streamflow signatures. This stage is based on correlation analysis, complemented by expert judgment to identify the most plausible cause-effect relationships. In a second stage, the results of the previous analysis are used to develop a set of model experiments aimed at determining an appropriate model representation of the Thur catchment. These experiments confirm that only a hydrological model that accounts for the heterogeneity of precipitation, snow related processes, and landscape features such as geology, produces hydrographs that have signatures similar to the observed ones. This model provides consistent results in space–time validation, which is promising for predictions in ungauged basins. The presented methodology for model building can be transferred to other case studies, since the data used in this work (meteorological variables, streamflow, morphology and geology maps) is available in numerous regions around the globe. proprietary
danger_descriptions_avalanche_bulletin_switzerland_1.0 How is avalanche danger described in textual descriptions in avalanche forecasts in Switzerland? ENVIDAT STAC Catalog 2021-01-01 2021-01-01 5.8886719, 45.7984239, 10.5908203, 47.6804285 https://cmr.earthdata.nasa.gov/search/concepts/C2789814949-ENVIDAT.umm_json The data set contains the danger descriptions (German) of the avalanche forecasts published at 5 pm between 27-Nov-2012 and 13-Feb-2020. proprietary
-darling_sst_00 2000 Seawater Temperatures at the Darling Marine Center ALL STAC Catalog 2000-01-01 2000-12-31 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214621651-SCIOPS.umm_json 2000 Seawater Surface Temperature Data collected off the dock at the Darling Marine Center, Walpole, Maine proprietary
darling_sst_00 2000 Seawater Temperatures at the Darling Marine Center SCIOPS STAC Catalog 2000-01-01 2000-12-31 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214621651-SCIOPS.umm_json 2000 Seawater Surface Temperature Data collected off the dock at the Darling Marine Center, Walpole, Maine proprietary
+darling_sst_00 2000 Seawater Temperatures at the Darling Marine Center ALL STAC Catalog 2000-01-01 2000-12-31 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214621651-SCIOPS.umm_json 2000 Seawater Surface Temperature Data collected off the dock at the Darling Marine Center, Walpole, Maine proprietary
darling_sst_01 2001 Seawater Temperatures at the Darling Marine Center ALL STAC Catalog 2001-01-01 2001-04-20 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214612276-SCIOPS.umm_json 2001 Seawater Surface Temperature Data collected off the dock at the Darling Marine Center Walpole, Maine. proprietary
darling_sst_01 2001 Seawater Temperatures at the Darling Marine Center SCIOPS STAC Catalog 2001-01-01 2001-04-20 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214612276-SCIOPS.umm_json 2001 Seawater Surface Temperature Data collected off the dock at the Darling Marine Center Walpole, Maine. proprietary
-darling_sst_82-93 1982-1989 and 1993 Seawater Temperatures at the Darling Marine Center ALL STAC Catalog 1982-03-01 1993-12-31 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214621676-SCIOPS.umm_json Seawater Surface Temperature Data Collected between the years 1982-1989 and 1993 off the dock at the Darling Marine Center, Walpole, Maine proprietary
darling_sst_82-93 1982-1989 and 1993 Seawater Temperatures at the Darling Marine Center SCIOPS STAC Catalog 1982-03-01 1993-12-31 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214621676-SCIOPS.umm_json Seawater Surface Temperature Data Collected between the years 1982-1989 and 1993 off the dock at the Darling Marine Center, Walpole, Maine proprietary
+darling_sst_82-93 1982-1989 and 1993 Seawater Temperatures at the Darling Marine Center ALL STAC Catalog 1982-03-01 1993-12-31 -71.31, 42.85, -66.74, 47.67 https://cmr.earthdata.nasa.gov/search/concepts/C1214621676-SCIOPS.umm_json Seawater Surface Temperature Data Collected between the years 1982-1989 and 1993 off the dock at the Darling Marine Center, Walpole, Maine proprietary
data-amphibian-monitoring_1.0 Data from: Estimation of breeding probbability can make monitoring data more revealing: a case study of amphibians ENVIDAT STAC Catalog 2021-01-01 2021-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814986-ENVIDAT.umm_json "This dataset includes data from 15 native pond breeding species in Switzerland in addition to observations of any species within the Pelophylax genus of water frogs. 233 sites (obnr) sampled during the 2011-2016 round of the WBS survey, which are listed as the ""first"" round of surveys. Data are also provided at 73 sites which were resurveyed in 2017 or 2018 (""second"" surveyround). The data are filtered as described in Cruickshank et al. (2021) to remove data from surveys carried out after the final sighting of a species within a year, and before the first observation of the species within a year. Observational data are provided as one of 3 observation types; 1 denotes a survey where the species was not detected, 2 denotes surveys where the species was detected but no life stages indicating successful breeding (e.g. the presence of eggs or larvae) were observed. Observation type 3 denotes a survey where evidence of successful breeding was observed (i.e. eggs or larvae). Survey protocols and full descriptions of the data are provided in Cruickshank et al (2021)." proprietary
data-analysis-toolkits_1.0 Data analysis toolkits ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814544-ENVIDAT.umm_json "These are condensed notes covering selected key points in data analysis and statistics. They were developed by James Kirchner for the course ""Analysis of Environmental Data"" at Berkeley in the 1990's and 2000's. They are not intended to be comprehensive, and thus are not a substitute for a good textbook or a good education! License: These notes are released by James Kirchner under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license." proprietary
data-and-code-on-extreme-inflow-and-lowflow-analysis-for-alpine-reservoirs_1.0 Data and Code on Extreme Inflow and Lowflow Analysis for Alpine Reservoirs ENVIDAT STAC Catalog 2023-01-01 2023-01-01 8.9761734, 46.5670779, 8.9761734, 46.5670779 https://cmr.earthdata.nasa.gov/search/concepts/C3226081971-ENVIDAT.umm_json "## Summary * Dataset of daily inflow to Luzzone reservoir in Ticino, Switzerland * R scripts used to generate return levels for low reservoir inflow, low precipitation, high inflow, and extreme high precipitation based on various methods from extreme value analysis ## Data The dataset included here is the ""natural"" reservoir inflow for the Luzzone reservoir. Additional analyses were conducted on daily total precipitation of 6 meteorological stations (abbreviations: TIOLI, TIOLV, COM, VRN, VLS, ZEV). These precipitation data are freely available for teaching and research from the MeteoSwiss IDAweb portal (https://www.meteoswiss.admin.ch/services-and-publications/service/weather-and-climate-products/data-portal-for-teaching-and-research.html). ## Codes R scripts used to determine return levels of the data set are included for both extreme high events and low events. The scripts include the following methods for calculating return levels: * GEV (Generalized Extreme Value) * GPD and GPDd (Generalized Pareto Distribution including declustered version) * eGPD (extended Generalized Pareto Distribution) * MEV (Metastatistical Extreme Value)" proprietary
@@ -17533,15 +17540,15 @@ distribution-maps-of-permanent-grassland-habitats-for-switzerland_1.0 Distributi
diversity-of-ground-beetles-and-spiders-as-well-as-cynipid-oak-gall-formation_1.0 Diversity of ground beetles and spiders as well as cynipid oak gall formation on irrigated and non-irrigated plots in a dry mixed Scots pine forest ENVIDAT STAC Catalog 2022-01-01 2022-01-01 7.6136971, 46.3021928, 7.6136971, 46.3021928 https://cmr.earthdata.nasa.gov/search/concepts/C2789814550-ENVIDAT.umm_json In the dry Pfynwald forest a long-term experiment of WSL was initiated in 2003 with a set of irrigated and non-irrigated plots. Forest Entomologie WSL made several investigations, one of them on the effect of irrigation (or conversely of drought) on the biodiversity of epigaeic arthropods such as ground beetles and spiders. In addition, its effects were also assessed by counting galls formed by gall wasps on pubescent oak. proprietary
diversity_of_woody_species-36_1.0 Diversity of woody species ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789814561-ENVIDAT.umm_json Index based on the number of tree and shrub species starting at 12 cm dbh in the upper layer and the occurrence of especially ecologically valuable tree and shrub species starting at 12 cm dbh in the upper layer. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary
dlhimpacts_1 Diode Laser Hygrometer (DLH) IMPACTS GHRC_DAAC STAC Catalog 2023-01-13 2023-02-28 -95.243, 35.753, -67.878, 48.237 https://cmr.earthdata.nasa.gov/search/concepts/C3247876662-GHRC_DAAC.umm_json The Diode Laser Hygrometer (DLH) dataset is comprised of water vapor mixing ratio measurements as well as relative humidities (both concerning liquid water and ice) which are derived from the water vapor mixing ratio and ambient static temperature and pressure provided by the TAMMS instrument suite. These measurements were made using two separate DLH instruments installed on the NASA P-3B research aircraft, and the data from these instruments were combined to provide the best combination of accuracy, dynamic range, and data coverage. The two DLH instruments are (1) the zenith-mounted system which utilizes an optical path between the zenith port and the aircraft’s vertical tail, and (2) the short-path system, which utilizes an optical path between two fuselage-mounted fins. This dataset was measured during the 2023 campaign of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Earth Venture Suborbital 3 project. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The project aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The DLH data files are available for flights from January 13, 2023, through February 28, 2023, and are in the ASCII format. proprietary
-doi:10.25921/sta3-3b95_Not Applicable 2014-2015 Untrawlable Habitat Strategic Initiative (UHSI) Video and Still Imagery Data Collection NOAA_NCEI STAC Catalog 2014-09-08 2015-05-08 -84.4, 27.7, -83.4, 29.7 https://cmr.earthdata.nasa.gov/search/concepts/C2107094639-NOAA_NCEI.umm_json The data collection deals with the optical data (i.e., video and still imagery) collected by natural light stereo cameras mounted on a MOdular Underwater Sampling System (MOUSS). The data collection consists of natively collected still images (5 frames per second) as well as the full length video and video segments that were created from original still images. Video annotations exist for the video segments; annotations are currently housed within a spreadsheet. The purpose was to execute a testbed study designed to evaluate the performance of transitional advanced technologies. All data are spatially located in the Florida Middle Grounds in the Gulf of Mexico. proprietary
doi:10.25921/sta3-3b95_Not Applicable 2014-2015 Untrawlable Habitat Strategic Initiative (UHSI) Video and Still Imagery Data Collection ALL STAC Catalog 2014-09-08 2015-05-08 -84.4, 27.7, -83.4, 29.7 https://cmr.earthdata.nasa.gov/search/concepts/C2107094639-NOAA_NCEI.umm_json The data collection deals with the optical data (i.e., video and still imagery) collected by natural light stereo cameras mounted on a MOdular Underwater Sampling System (MOUSS). The data collection consists of natively collected still images (5 frames per second) as well as the full length video and video segments that were created from original still images. Video annotations exist for the video segments; annotations are currently housed within a spreadsheet. The purpose was to execute a testbed study designed to evaluate the performance of transitional advanced technologies. All data are spatially located in the Florida Middle Grounds in the Gulf of Mexico. proprietary
+doi:10.25921/sta3-3b95_Not Applicable 2014-2015 Untrawlable Habitat Strategic Initiative (UHSI) Video and Still Imagery Data Collection NOAA_NCEI STAC Catalog 2014-09-08 2015-05-08 -84.4, 27.7, -83.4, 29.7 https://cmr.earthdata.nasa.gov/search/concepts/C2107094639-NOAA_NCEI.umm_json The data collection deals with the optical data (i.e., video and still imagery) collected by natural light stereo cameras mounted on a MOdular Underwater Sampling System (MOUSS). The data collection consists of natively collected still images (5 frames per second) as well as the full length video and video segments that were created from original still images. Video annotations exist for the video segments; annotations are currently housed within a spreadsheet. The purpose was to execute a testbed study designed to evaluate the performance of transitional advanced technologies. All data are spatially located in the Florida Middle Grounds in the Gulf of Mexico. proprietary
doi:10.25921/v3a2-m248_Not Applicable Archival and Discovery of November 27, 1945 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1945-11-15 1945-12-01 66.97, 24.804, 66.97, 24.804 https://cmr.earthdata.nasa.gov/search/concepts/C2105865668-NOAA_NCEI.umm_json These water level data were digitized from a scanned marigram image associated with the tsunami event of 1945-11-27 at a tide gauge located at Karachi, Pakistan, and referenced to station datum. The Karachi marigram is one of the two instrumental records existing of the 1945 Makran tsunami and spans most of the 16 days between November 15 and December 1. The original Karachi analog record belongs to the Survey of India (SOI) and was collected and digitized by the National Institute of Oceanography (NIO) and Indian National Center for Ocean Information Services (INCOIS) for use in the publication of a few scientific papers. This digital marigram scan was reformatted into the accompanying digital, numerical time series by the Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO. Acknowledgement of SOI, NIO, and INCOIS should be included in any future scientific works using this record. proprietary
doi:10.7289/V51R6NQJ_Not Applicable Archival and Discovery of May 22, 1960 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1960-05-18 1960-05-27 144.6539, 8.966667, -149.426667, 60.12 https://cmr.earthdata.nasa.gov/search/concepts/C2105865673-NOAA_NCEI.umm_json NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. The 1946 tsunami is one of four 20th century tsunami events which are historically important but data during each reside only on the marigram records. The 1946 tsunami was the impetus for establishment of the Pacific Tsunami Warning Center after impact to the Hawaiian Islands. The 1952, 1960, and 1964 tsunamis were each generated by three of the greatest of all recorded earthquakes. The 1960 tsunami, in particular, was generated by the largest earthquake ever recorded, a magnitude 9.5 off the central coast of Chile. Measurements of these tsunamis are expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections. proprietary
doi:10.7289/V54X564T_Not Applicable Archival and Discovery of May 16, 1968 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1968-05-13 1968-05-19 141, 13.4387, -124.18333, 41.745 https://cmr.earthdata.nasa.gov/search/concepts/C2105865675-NOAA_NCEI.umm_json NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. As a follow-up to a successful 2016 BEDI project resulting in the archival and discovery of data held on marigrams during four large tsunamis (1946, 1952, 1960, 1964), marigrams from five additional tsunami events in 1854, 1883, 1896, 1933, and 1968 have been digitized. These additional five tsunami events were generated in both the Pacific and Indian Oceans and are rarely cited in research due to lack of data access. The five tsunami events proposed here for reformat, archive, and discovery in 2017 reside only on these same paper marigram records. Each of these datasets are of great importance as very little digital data exists from tsunamis that occurred during this time period, particularly those prior to the turn of the 20th Century. These events are not only historically important but with new research into tsunami probabilities, are statistically important as well. Similar to seismic hazard analyses, the tsunami community is now focused on tsunami recurrence rates through probabilistic tsunami hazard analysis to support land-use and construction decision-making. As a result, measurements of these tsunamis are not only expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics, but will add a significant number of tsunami data points to recurrence rates calculations. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections. proprietary
doi:10.7289/V55H7DGQ_Not Applicable Archival and Discovery of November 4, 1952 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1952-10-29 1952-11-08 167.7383, -18.4758, -159.5916666, 54.317 https://cmr.earthdata.nasa.gov/search/concepts/C2105865672-NOAA_NCEI.umm_json NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. The 1946 tsunami is one of four 20th century tsunami events which are historically important but data during each reside only on the marigram records. The 1946 tsunami was the impetus for establishment of the Pacific Tsunami Warning Center after impact to the Hawaiian Islands. The 1952, 1960, and 1964 tsunamis were each generated by three of the greatest of all recorded earthquakes. The 1960 tsunami, in particular, was generated by the largest earthquake ever recorded, a magnitude 9.5 off the central coast of Chile. Measurements of these tsunamis are expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections. proprietary
doi:10.7289/V57H1GW8_Not Applicable Archival and Discovery of June 15, 1896 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1896-06-13 1896-06-21 -157.86667, 21.30667, -122.47834, 37.85 https://cmr.earthdata.nasa.gov/search/concepts/C2105865667-NOAA_NCEI.umm_json NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. As a follow-up to a successful 2016 BEDI project resulting in the archival and discovery of data held on marigrams during four large tsunamis (1946, 1952, 1960, 1964), marigrams from five additional tsunami events in 1854, 1883, 1896, 1933, and 1968 have been digitized. These additional five tsunami events were generated in both the Pacific and Indian Oceans and are rarely cited in research due to lack of data access. The five tsunami events proposed here for reformat, archive, and discovery in 2017 reside only on these same paper marigram records. Each of these datasets are of great importance as very little digital data exists from tsunamis that occurred during this time period, particularly those prior to the turn of the 20th Century. These events are not only historically important but with new research into tsunami probabilities, are statistically important as well. Similar to seismic hazard analyses, the tsunami community is now focused on tsunami recurrence rates through probabilistic tsunami hazard analysis to support land-use and construction decision-making. As a result, measurements of these tsunamis are not only expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics, but will add a significant number of tsunami data points to recurrence rates calculations. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections. proprietary
-doi:10.7289/V5862DPB_Not Applicable Airborne Magnetic Trackline Database NOAA_NCEI STAC Catalog 1958-12-06 2011-02-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107121616-NOAA_NCEI.umm_json The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) receive airborne magnetic survey data from US and non-US agencies. In an effort to provide a central library for digital aeromagnetic data in the public domain, NCEI is continuing to assimilate new digital data from aeromagnetic surveys in the United States. Major contributors to this important data base include the U.S. Geological Survey, U.S. Naval Oceanographic Office, U.S. Naval Research Laboratory, Woods Hole Oceanographic Institution, the University of Texas, and the Natural Resources Canada (NRCAN). The details of these surveys are contained in an automated inventory system Geophysical Data System (GEODAS). The philosophy of exchange of data from the archive for new contributions has stimulated many organizations to transfer their data holdings to the Data Center. proprietary
doi:10.7289/V5862DPB_Not Applicable Airborne Magnetic Trackline Database ALL STAC Catalog 1958-12-06 2011-02-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107121616-NOAA_NCEI.umm_json The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) receive airborne magnetic survey data from US and non-US agencies. In an effort to provide a central library for digital aeromagnetic data in the public domain, NCEI is continuing to assimilate new digital data from aeromagnetic surveys in the United States. Major contributors to this important data base include the U.S. Geological Survey, U.S. Naval Oceanographic Office, U.S. Naval Research Laboratory, Woods Hole Oceanographic Institution, the University of Texas, and the Natural Resources Canada (NRCAN). The details of these surveys are contained in an automated inventory system Geophysical Data System (GEODAS). The philosophy of exchange of data from the archive for new contributions has stimulated many organizations to transfer their data holdings to the Data Center. proprietary
+doi:10.7289/V5862DPB_Not Applicable Airborne Magnetic Trackline Database NOAA_NCEI STAC Catalog 1958-12-06 2011-02-26 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107121616-NOAA_NCEI.umm_json The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) receive airborne magnetic survey data from US and non-US agencies. In an effort to provide a central library for digital aeromagnetic data in the public domain, NCEI is continuing to assimilate new digital data from aeromagnetic surveys in the United States. Major contributors to this important data base include the U.S. Geological Survey, U.S. Naval Oceanographic Office, U.S. Naval Research Laboratory, Woods Hole Oceanographic Institution, the University of Texas, and the Natural Resources Canada (NRCAN). The details of these surveys are contained in an automated inventory system Geophysical Data System (GEODAS). The philosophy of exchange of data from the archive for new contributions has stimulated many organizations to transfer their data holdings to the Data Center. proprietary
doi:10.7289/V598856F_Not Applicable Archival and Discovery of April 1, 1946 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1946-04-01 1946-04-04 145.583333, 35.017222, -123.3707, 48.424666 https://cmr.earthdata.nasa.gov/search/concepts/C2105865670-NOAA_NCEI.umm_json NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. The 1946 tsunami is one of four 20th century tsunami events which are historically important but data during each reside only on the marigram records. The 1946 tsunami was the impetus for establishment of the Pacific Tsunami Warning Center after impact to the Hawaiian Islands. The 1952, 1960, and 1964 tsunamis were each generated by three of the greatest of all recorded earthquakes. The 1960 tsunami, in particular, was generated by the largest earthquake ever recorded, a magnitude 9.5 off the central coast of Chile. Measurements of these tsunamis are expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections. proprietary
doi:10.7289/V5C827KJ_Not Applicable Archival and Discovery of August 27, 1883 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1883-08-24 1883-09-01 -157.86444, 21.30333, -122.47833, 57.7833 https://cmr.earthdata.nasa.gov/search/concepts/C2105865669-NOAA_NCEI.umm_json NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. As a follow-up to a successful 2016 BEDI project resulting in the archival and discovery of data held on marigrams during four large tsunamis (1946, 1952, 1960, 1964), marigrams from five additional tsunami events in 1854, 1883, 1896, 1933, and 1968 have been digitized. These additional five tsunami events were generated in both the Pacific and Indian Oceans and are rarely cited in research due to lack of data access. The five tsunami events proposed here for reformat, archive, and discovery in 2017 reside only on these same paper marigram records. Each of these datasets are of great importance as very little digital data exists from tsunamis that occurred during this time period, particularly those prior to the turn of the 20th Century. These events are not only historically important but with new research into tsunami probabilities, are statistically important as well. Similar to seismic hazard analyses, the tsunami community is now focused on tsunami recurrence rates through probabilistic tsunami hazard analysis to support land-use and construction decision-making. As a result, measurements of these tsunamis are not only expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics, but will add a significant number of tsunami data points to recurrence rates calculations. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections. proprietary
doi:10.7289/V5GX48VS_Not Applicable Archival and Discovery of December 23, 1854 Tsunami Event on Marigrams NOAA_NCEI STAC Catalog 1854-12-21 1854-12-27 -122.4375, 32.70059, -117.22565, 37.69944 https://cmr.earthdata.nasa.gov/search/concepts/C2105865663-NOAA_NCEI.umm_json NOAA National Centers for Environmental Information have more than 3,000 tsunami marigram (tide gauge) records in both image and paper format. The majority of these tsunami marigram records were scanned to high-resolution digital tiff images during the NOAA Climate Data Modernization Program (CDMP). There still remain shelves full of deteriorating paper records that are in need of rescue reformatting to scanned images before they are lost. As a follow-up to a successful 2016 BEDI project resulting in the archival and discovery of data held on marigrams during four large tsunamis (1946, 1952, 1960, 1964), marigrams from five additional tsunami events in 1854, 1883, 1896, 1933, and 1968 have been digitized. These additional five tsunami events were generated in both the Pacific and Indian Oceans and are rarely cited in research due to lack of data access. The five tsunami events proposed here for reformat, archive, and discovery in 2017 reside only on these same paper marigram records. Each of these datasets are of great importance as very little digital data exists from tsunamis that occurred during this time period, particularly those prior to the turn of the 20th Century. These events are not only historically important but with new research into tsunami probabilities, are statistically important as well. Similar to seismic hazard analyses, the tsunami community is now focused on tsunami recurrence rates through probabilistic tsunami hazard analysis to support land-use and construction decision-making. As a result, measurements of these tsunamis are not only expected to provide researchers with important information linking earthquake rupture to tsunami generation and propagation characteristics, but will add a significant number of tsunami data points to recurrence rates calculations. All data reformatted as part of this project will be brought into compliance with NOAA Data Directives and meet the requirements for Data Management, Discoverability, Accessibility, Documentation, Readability, and Data Preservation and Stewardship as per the Big Earth Data Initiative (BEDI). BEDI is designed to promote interoperability of Earth observation data across Federal agencies, systems and platforms through the improvement of data management practices and increased discoverability, accessibility, and usability of data collections. proprietary
@@ -17622,8 +17629,8 @@ envidat-lwf-30_2019-03-06 EC-5 soil water content measurement LWF ENVIDAT STAC C
envidat-lwf-31_2019-03-06 MPS-2 soil water matric potential LWF ENVIDAT STAC Catalog 2019-01-01 2019-01-01 7.85832, 46.29688, 7.85832, 46.29688 https://cmr.earthdata.nasa.gov/search/concepts/C2789815159-ENVIDAT.umm_json Continuous measurement of soil matrix potential at 15, 50 and 80 cm depth with Decagon MPS-2 sensors ### Purpose: ### Improve the available data for the calibration or validation of the water cycle modells, i.e. the determination of the water flux needed for calculating the leaching fluxes. proprietary
envidat-lwf-32_2019-03-06 MPS-2 on LWF Visp to survey 2017 mortality wave ENVIDAT STAC Catalog 2019-01-01 2019-01-01 7.85832, 46.29688, 7.85832, 46.29688 https://cmr.earthdata.nasa.gov/search/concepts/C2789815187-ENVIDAT.umm_json Continuous measurement of soil matrix potential at 15, 50 and 100 cm depth with Decagon MPS-2 sensors 1 m N, SE and SW from the stem of 3 threes within much and 3 trees within few shrubs ### Purpose: ### Explore the effect of shrubs on the water availability for pine trees in Visp. proprietary
envidat-lwf-33_2019-03-06 TDR Pfynwald ENVIDAT STAC Catalog 2019-01-01 2019-01-01 7.61211, 46.30279, 7.61211, 46.30279 https://cmr.earthdata.nasa.gov/search/concepts/C2789815214-ENVIDAT.umm_json Continuous measurement of soil water content at one control and in one irrigated plot in 10, 40 and 60 cm depth (4 replications) with TDR (Tektronix 1502B cable tester, Beaverton, OR, US). ### Purpose: ### Monitoring of the soil water content ### Paper Citation: ### * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123) proprietary
-envidat-lwf-34_2019-03-06 10-HS Pfynwald ALL STAC Catalog 2019-01-01 2019-01-01 7.61211, 46.30279, 7.61211, 46.30279 https://cmr.earthdata.nasa.gov/search/concepts/C2789815241-ENVIDAT.umm_json Continuous measurement of soil water content at 10 and 80 cm depth (3 replications) with 10-HS soil moisture probes (Decagon Incorporation, Pullman, WA, USA). ### Purpose: ### Monitoring of the soil water matrix potential ### Paper Citation: ### * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123) proprietary
envidat-lwf-34_2019-03-06 10-HS Pfynwald ENVIDAT STAC Catalog 2019-01-01 2019-01-01 7.61211, 46.30279, 7.61211, 46.30279 https://cmr.earthdata.nasa.gov/search/concepts/C2789815241-ENVIDAT.umm_json Continuous measurement of soil water content at 10 and 80 cm depth (3 replications) with 10-HS soil moisture probes (Decagon Incorporation, Pullman, WA, USA). ### Purpose: ### Monitoring of the soil water matrix potential ### Paper Citation: ### * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123) proprietary
+envidat-lwf-34_2019-03-06 10-HS Pfynwald ALL STAC Catalog 2019-01-01 2019-01-01 7.61211, 46.30279, 7.61211, 46.30279 https://cmr.earthdata.nasa.gov/search/concepts/C2789815241-ENVIDAT.umm_json Continuous measurement of soil water content at 10 and 80 cm depth (3 replications) with 10-HS soil moisture probes (Decagon Incorporation, Pullman, WA, USA). ### Purpose: ### Monitoring of the soil water matrix potential ### Paper Citation: ### * Dobbertin, M.; Eilmann, B.; Bleuler, P.; Giuggiola, A.; Graf Pannatier, E.; Landolt, W.; Schleppi, P.; Rigling, A., 2010: Effect of irrigation on needle morphology, shoot and stem growth in a drought-exposed Pinus sylvestris forest. Tree Physiology, 30, 3: 346-360. [doi: 10.1093/treephys/tpp123](http://doi.org/10.1093/treephys/tpp123) proprietary
envidat-lwf-36_2019-03-06 Passive sampling of O3 LWF ENVIDAT STAC Catalog 2019-01-01 2019-01-01 6.29085, 45.86141, 10.23009, 47.6837 https://cmr.earthdata.nasa.gov/search/concepts/C2789815256-ENVIDAT.umm_json Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone measurements are carried out at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone-induced visible symptoms and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling. ### Purpose: ### Ozone risk assessment: Measurements of mean ozone concentrations with passive samplers (passam ag). ### Manual Citation: ### * Schaub M, Calatayud V, Ferretti M, Brunialti G, Lövblad G, Krause G, Sanz MJ, 2016: Part XV: Monitoring of Air Quality. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Thünen Institute of Forest Ecosystems, Eberswalde, Germany, 11 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Schaub M, Häni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu¨nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf) * Cailleret M, Ferretti M, Gessler A, Rigling A, Schaub M (2018) Ozone effects on European forest growth – towards an integrative approach. Journal of Ecology. [doi:10.1111/1365-2745.12941](http://doi.org/10.1111/1365-2745.12941) * Calatayud V, Diéguez JJ, Sicard P, Schaub M, De Marco A (2016) Testing approaches for calculating stomatal ozone fluxes from passive sampler. Science of the Total Environment. [doi:10.1016/j.scitotenv.2016.07.155](http://doi.org/10.1016/j.scitotenv.2016.07.155) * Calatayud V and Schaub M (2013) Methods for Measuring Gaseous Air Pollutants in Forests. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 375-384. [doi:10.1016/B978-0-08-098222-9.00019-4](http://doi.org/10.1016/B978-0-08-098222-9.00019-4) proprietary
envidat-lwf-37_2019-03-06 Continuous measurement of O3 LWF ENVIDAT STAC Catalog 2019-01-01 2019-01-01 6.65804, 45.86141, 9.06707, 47.6837 https://cmr.earthdata.nasa.gov/search/concepts/C2789815274-ENVIDAT.umm_json Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone measurements are carried out at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone-induced visible symptoms and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling. ### Purpose: ### Ozone risk assessment: Continuous measurements of ozone concentrations ### Manual Citation: ### * Schaub M, Calatayud V, Ferretti M, Brunialti G, Lövblad G, Krause G, Sanz MJ, 2016: Part XV: Monitoring of Air Quality. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Thünen Institute of Forest Ecosystems, Eberswalde, Germany, 11 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Schaub M, Häni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu¨nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf) * Cailleret M, Ferretti M, Gessler A, Rigling A, Schaub M (2018) Ozone effects on European forest growth – towards an integrative approach. Journal of Ecology. [doi:10.1111/1365-2745.12941](https://doi.org/10.1111/1365-2745.12941) * Calatayud V and Schaub M (2013) Methods for Measuring Gaseous Air Pollutants in Forests. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 375-384. [doi:10.1016/B978-0-08-098222-9.00019-4](https://doi.org/10.1016/B978-0-08-098222-9.00019-4) proprietary
envidat-lwf-38_2019-03-06 Symptoms of O3 injuries LWF ENVIDAT STAC Catalog 2019-01-01 2019-01-01 6.29085, 46.02261, 10.23009, 47.6837 https://cmr.earthdata.nasa.gov/search/concepts/C2789815286-ENVIDAT.umm_json Measuring air pollutants in forests is important for evaluating the risk for vegetation in areas not covered by conventional air quality monitoring networks. Ozone-induced symptoms are being assessed at LWF, applying the harmonized methodologies from UNECE/ICP Forests and running under the 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone. Data are also collected on ozone concentrations and other ecosystem properties such as tree growth, nutrition, and biodiversity, as well as climate. This makes this long-term monitoring data series essential for impact assessment and air pollution modelling. ### Purpose: ### Ozone risk assessment, i.e. to investigate relationships between ozone exposures and ozone-induced, visible symptoms ### Manual Citation: ### * Schaub M, Calatayud V, Ferretti M, Brunialti G, Lövblad G, Krause G, Sanz MJ, 2016: Part VIII: Monitoring of Ozone Injury. In: UNECE ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. Thünen Institute of Forest Ecosystems, Eberswalde, Germany, 14 p. + Annex [http://icp-forests.net/page/icp-forests-manual](http://icp-forests.net/page/icp-forests-manual) ### Paper Citation: ### * Schaub M, Häni M, Calatayud V, Ferretti M, Gottardini E (2018) ICP Forests Brief No 3 - Ozone concentrations are decreasing but exposure remains high in European forests. Programme Co-ordinating Centre of ICP Forests, Thu¨nen Institute of Forest Ecosystems. [doi: 10.3220/ICP1525258743000](https://icp-forests.org/pdf/ICPForestsBriefNo2.pdf) * Schaub M and Calatayud V (2013) Assessment of Visible Foliar Injury Induced by Ozone. In: Marco Ferretti and Richard Fischer (Eds). Forest Monitoring: Methods for Terrestrial Investigations in Europe with an Overview of North America and Asia, Vol 12, DENS, UK: Elsevier, 2013, pp. 205-221. ISBN: 9780080982229. [doi: 10.1016/B978-0-08-098222-9.00011-X](https://doi.org/10.1016/B978-0-08-098222-9.00011-X) proprietary
@@ -17771,8 +17778,8 @@ fife_biology_soil_gas_106_1 Soil Gas Fluxes Using Soil Cores (FIFE) ORNL_CLOUD S
fife_biology_veg_biop_135_1 Vegetation Biophysical Data (FIFE) ORNL_CLOUD STAC Catalog 1987-05-26 1989-08-18 -96.61, 38.98, -96.45, 39.12 https://cmr.earthdata.nasa.gov/search/concepts/C2980707152-ORNL_CLOUD.umm_json Measurements of leaf area index and biomass of different canopy components proprietary
fife_biology_veg_ref_137_1 Vegetation Species Reference (FIFE) ORNL_CLOUD STAC Catalog 1989-10-31 1989-10-31 -97, 39, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2980719966-ORNL_CLOUD.umm_json LTER species names, codes, types, and other reference information proprietary
fife_biology_veg_spec_136_1 Vegetation Species Data (FIFE) ORNL_CLOUD STAC Catalog 1984-05-07 1989-08-18 -96.61, 38.98, -96.45, 39.12 https://cmr.earthdata.nasa.gov/search/concepts/C2980708363-ORNL_CLOUD.umm_json Species composition data, by site and date proprietary
-fife_hydrology_strm_15m_1_1 15 Minute Stream Flow Data: USGS (FIFE) ALL STAC Catalog 1984-12-25 1988-03-04 -96.6, 39.1, -96.6, 39.1 https://cmr.earthdata.nasa.gov/search/concepts/C2977827088-ORNL_CLOUD.umm_json USGS 15 minute stream flow data for Kings Creek on the Konza Prairie proprietary
fife_hydrology_strm_15m_1_1 15 Minute Stream Flow Data: USGS (FIFE) ORNL_CLOUD STAC Catalog 1984-12-25 1988-03-04 -96.6, 39.1, -96.6, 39.1 https://cmr.earthdata.nasa.gov/search/concepts/C2977827088-ORNL_CLOUD.umm_json USGS 15 minute stream flow data for Kings Creek on the Konza Prairie proprietary
+fife_hydrology_strm_15m_1_1 15 Minute Stream Flow Data: USGS (FIFE) ALL STAC Catalog 1984-12-25 1988-03-04 -96.6, 39.1, -96.6, 39.1 https://cmr.earthdata.nasa.gov/search/concepts/C2977827088-ORNL_CLOUD.umm_json USGS 15 minute stream flow data for Kings Creek on the Konza Prairie proprietary
fife_hydrology_strm_day_119_1 Stream Flow Daily Data: USGS (FIFE) ORNL_CLOUD STAC Catalog 1979-04-01 1988-09-02 -97, 39, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2980681974-ORNL_CLOUD.umm_json USGS daily stream flow data for Kings Creek on the Konza Prairie proprietary
fife_hydrology_strm_st_120_1 Stream Flow Storm Data (FIFE) ORNL_CLOUD STAC Catalog 1987-01-01 1988-01-01 -96.58, 39.07, -96.56, 39.09 https://cmr.earthdata.nasa.gov/search/concepts/C2980689463-ORNL_CLOUD.umm_json USGS stream flow during storm events around Kings Creek on the Konza Prairie proprietary
fife_optical_ot_brug_62_1 Optical Thickness Data: Bruegge (FIFE) ORNL_CLOUD STAC Catalog 1987-05-30 1989-08-08 -96.62, 38.98, -96.54, 39.12 https://cmr.earthdata.nasa.gov/search/concepts/C2980489715-ORNL_CLOUD.umm_json Optical thickness data from Dr. Carol Bruegge, JPL proprietary
@@ -17837,8 +17844,8 @@ fife_sur_refl_se5_unl_82_1 SE-590 Ground Data: UNL (FIFE) ORNL_CLOUD STAC Catalo
fife_sur_refl_soilrefl_114_1 Soil Reflectance Data (FIFE) ORNL_CLOUD STAC Catalog 1989-10-31 1989-10-31 -102, 37, -95, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2980655792-ORNL_CLOUD.umm_json Spectral reflectance of soils, Atlas of Soil Reflectance Properties (Stoner '80) proprietary
fife_sur_refl_unl_long_49_1 Longwave Radiation Data: UNL (FIFE) ORNL_CLOUD STAC Catalog 1987-06-03 1989-08-11 -96.59, 38.98, -96.47, 39.12 https://cmr.earthdata.nasa.gov/search/concepts/C2980474531-ORNL_CLOUD.umm_json Average incoming longwave radiation measured by University of Nebraska proprietary
fife_sur_refl_unl_surf_123_1 Surface Radiance Data: UNL (FIFE) ORNL_CLOUD STAC Catalog 1987-05-30 1989-08-11 -96.59, 38.98, -96.47, 39.12 https://cmr.earthdata.nasa.gov/search/concepts/C2980692342-ORNL_CLOUD.umm_json Canopy IR & air temperature, albedo, incoming and reflected shortwave, humidity proprietary
-finnarp_aerosols Aerosol measurements at ABOA / FINNARP 2009 SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214596474-SCIOPS.umm_json The data set contains: - neutral aerosol size distribution from 10 to 500 nm (8.12.2009-23.1.2010) with 12 min resolution and 25 separate size bins - charged aerosol size distribution from 0.8 to 40 nm (5.12.2009-23.1.2010) with 12 min resolution and 28 separate size bins - tropospheric ozone concentration (5.12.2009-23.1.2010), 1 min averages, unit ppb (parts per billion) - quartz filter samples for later chemical analysis (8.12.2009-23.1.2010), each filter was collecting the sample 2-3 days (filters were changed 3 times a week) proprietary
finnarp_aerosols Aerosol measurements at ABOA / FINNARP 2009 ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214596474-SCIOPS.umm_json The data set contains: - neutral aerosol size distribution from 10 to 500 nm (8.12.2009-23.1.2010) with 12 min resolution and 25 separate size bins - charged aerosol size distribution from 0.8 to 40 nm (5.12.2009-23.1.2010) with 12 min resolution and 28 separate size bins - tropospheric ozone concentration (5.12.2009-23.1.2010), 1 min averages, unit ppb (parts per billion) - quartz filter samples for later chemical analysis (8.12.2009-23.1.2010), each filter was collecting the sample 2-3 days (filters were changed 3 times a week) proprietary
+finnarp_aerosols Aerosol measurements at ABOA / FINNARP 2009 SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214596474-SCIOPS.umm_json The data set contains: - neutral aerosol size distribution from 10 to 500 nm (8.12.2009-23.1.2010) with 12 min resolution and 25 separate size bins - charged aerosol size distribution from 0.8 to 40 nm (5.12.2009-23.1.2010) with 12 min resolution and 28 separate size bins - tropospheric ozone concentration (5.12.2009-23.1.2010), 1 min averages, unit ppb (parts per billion) - quartz filter samples for later chemical analysis (8.12.2009-23.1.2010), each filter was collecting the sample 2-3 days (filters were changed 3 times a week) proprietary
fire-randomizer-first-release_1.0 fire-randomizer: first release ENVIDAT STAC Catalog 2016-01-01 2016-01-01 8.4545978, 47.3606372, 8.4545978, 47.3606372 https://cmr.earthdata.nasa.gov/search/concepts/C3226082141-ENVIDAT.umm_json Tool to assess fire selectivity for topographic (e.g. alitiude, slope, aspect) or land use (forest or vegetation type, distance to infrastructures) categories with Monte Carlo simulations. proprietary
fire_emissions_724_1 SAFARI 2000 Fire Emission Data, Dry Season 2000 ORNL_CLOUD STAC Catalog 2000-08-14 2000-09-14 12, -27, 36, -14 https://cmr.earthdata.nasa.gov/search/concepts/C2788974415-ORNL_CLOUD.umm_json As part of the SAFARI 2000), the University of Montana participated in both ground-based and airborne campaigns during the southern African dry season of 2000 to measure trace gas emissions from biofuel production and use and savanna fires, respectively. During the airborne campaign, stable and reactive trace gases were measured over southern Africa with an airborne Fourier transform infrared spectroscopy (AFTIR) onboard the University of Washington Convair-580 research aircraft in August-September of 2000. The measurements included vertical profiles of CO2, CO, H2O, and CH4 up to 5.5 km on 6 occasions above instrumented ground sites and below the TERRA satellite and ER-2 high-flying research aircraft as well as trace gas emissions from ten African savanna fires. These measurements are the first broad characterization of the most abundant trace gases in nascent smoke from African savanna fires (i.e., including oxygen- and nitrogen-containing species). proprietary
fire_emissions_v4_R1_1293_4.1 Global Fire Emissions Database, Version 4.1 (GFEDv4) ORNL_CLOUD STAC Catalog 1995-06-01 2016-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2763353619-ORNL_CLOUD.umm_json This dataset provides global estimates of monthly burned area, monthly emissions and fractional contributions of different fire types, daily or 3-hourly fields to scale the monthly emissions to higher temporal resolutions, and data for monthly biosphere fluxes. The data are at 0.25-degree latitude by 0.25-degree longitude spatial resolution and are available from June 1995 through 2016, depending on the dataset. Emissions data are available for carbon (C), dry matter (DM), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), hydrogen (H2), nitrous oxide (N2O), nitrogen oxides (NOx), non-methane hydrocarbons (NMHC), organic carbon (OC), black carbon (BC), particulate matter less than 2.5 microns (PM2.5), total particulate matter (TPM), and sulfur dioxide (SO2) among others. These data are yearly totals by region, globally, and by fire source for each region. proprietary
@@ -17848,8 +17855,8 @@ flowering-plants-angiospermae-in-urban-green-areas-in-five-european-cities_1.0 F
fltrepepoch_1 Flight Reports EPOCH GHRC_DAAC STAC Catalog 2017-07-27 2017-08-31 -130, 10, -80, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2175817241-GHRC_DAAC.umm_json The Flight Reports EPOCH dataset consists of flight number, purpose of flight, and flight hours logged during the East Pacific Origins and Characteristics of Hurricanes (EPOCH) project. EPOCH was a NASA program manager training opportunity directed at training NASA young scientists in conceiving, planning, and executing a major airborne science field program. The goals of the EPOCH project were to sample tropical cyclogenesis or intensification of an Eastern Pacific hurricane and to train the next generation of NASA Airborne Science Program leadership. The mission reports are available from July 27, 2017 through August 31, 2017 in PDF format. proprietary
flu-a-bh_1.0 Processed permafrost borehole data (2394 m asl), Fluelapass A, Switzerland ENVIDAT STAC Catalog 2016-01-01 2016-01-01 9.9451, 46.7479, 9.9451, 46.7479 https://cmr.earthdata.nasa.gov/search/concepts/C2789815125-ENVIDAT.umm_json Processed ground temperature measurements at the Fluelapass permafrost borehole A (FLU_0102) in canton Graubunden, Switzerland. The borehole is located at 2394 m asl on a moderate (26°) North-east slope (45°). The surface material is talus and borehole depth is 23 m. Thermistors used YSI 44006. Year of drilling 2002. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied. proprietary
fluxnet_point_1029_1 ISLSCP II Carbon Dioxide Flux at Harvard Forest and Northern BOREAS Sites ORNL_CLOUD STAC Catalog 1992-01-01 1995-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2785312311-ORNL_CLOUD.umm_json This International Satellite Land Surface Climatology Project (ISLSCP II) data set, ISLSCP II Carbon Dioxide Flux at Harvard Forest and Northern BOREAS Sites, contains gapp-filled flux and meterological data for half-hourly, daily, weekly, monthly, and annual time intervals presented for each site and year. The 1992-1995 Harvard Forest, MA site, and the 1994-95 Old Black Spruce, Alberta, Canada site are members of the FLUXNET global network of micrometeorological towers that use eddy covariance methods to measure the excahanges of carbon dioxide (CO2), water vapor, and energy between terrestrial ecosystem and atmosphere. proprietary
-foraging_trip_duration_BI_1 Adelie penguin foraging trip duration, Bechervaise Island, Mawson AU_AADC STAC Catalog 1991-10-01 2005-02-01 62.8055, -67.5916, 62.825, -67.5861 https://cmr.earthdata.nasa.gov/search/concepts/C1214308557-AU_AADC.umm_json Adelie penguin foraging trip duration records for Bechervaise Island, Mawson since 1991-92. Data include average male and female foraging trip durations for both the guard and creche stages of the breeding season. Data based on records of tagged birds crossing the APMS for in and out crossings. Durations determined from difference between out and in crossings in conjunction with nest census records. Data included only for birds which were known to be foraging for a live chick. This work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project. The fields in this dataset are: Year trip duration (hours) Mean , standard error, count and standard deviation for male and female foraging trips during guard and creche stages of the breeding season. proprietary
foraging_trip_duration_BI_1 Adelie penguin foraging trip duration, Bechervaise Island, Mawson ALL STAC Catalog 1991-10-01 2005-02-01 62.8055, -67.5916, 62.825, -67.5861 https://cmr.earthdata.nasa.gov/search/concepts/C1214308557-AU_AADC.umm_json Adelie penguin foraging trip duration records for Bechervaise Island, Mawson since 1991-92. Data include average male and female foraging trip durations for both the guard and creche stages of the breeding season. Data based on records of tagged birds crossing the APMS for in and out crossings. Durations determined from difference between out and in crossings in conjunction with nest census records. Data included only for birds which were known to be foraging for a live chick. This work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project. The fields in this dataset are: Year trip duration (hours) Mean , standard error, count and standard deviation for male and female foraging trips during guard and creche stages of the breeding season. proprietary
+foraging_trip_duration_BI_1 Adelie penguin foraging trip duration, Bechervaise Island, Mawson AU_AADC STAC Catalog 1991-10-01 2005-02-01 62.8055, -67.5916, 62.825, -67.5861 https://cmr.earthdata.nasa.gov/search/concepts/C1214308557-AU_AADC.umm_json Adelie penguin foraging trip duration records for Bechervaise Island, Mawson since 1991-92. Data include average male and female foraging trip durations for both the guard and creche stages of the breeding season. Data based on records of tagged birds crossing the APMS for in and out crossings. Durations determined from difference between out and in crossings in conjunction with nest census records. Data included only for birds which were known to be foraging for a live chick. This work was completed as part of ASAC Project 2205, Adelie penguin research and monitoring in support of the CCAMLR Ecosystem Monitoring Project. The fields in this dataset are: Year trip duration (hours) Mean , standard error, count and standard deviation for male and female foraging trips during guard and creche stages of the breeding season. proprietary
forclim_4.0 ForClim ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815136-ENVIDAT.umm_json "ForClim is a cohort-based model that was developed to analyze successional pathways of various forest types in Central Europe. Following the standard approach of gap models ForClim simulates the establishment; growth and mortality of trees on multiple independent patches (typically n = 200) in annual time steps to derive regional-scale stand dynamics. ForClim is currently parameterized for ca. 180 tree species dominant of temperate forests worldwide. The model has been tested comprehensively for the representation of natural forest dynamics of temperate forests of the Northern Hemisphere, with an emphasis on European forests. ForClim may be freely used under the terms of the ""GNU GENERAL PUBLIC LICENSE v3"" license. ![alt text](https://www.envidat.ch/dataset/a049e6ad-caac-492a-9771-90856c48ed03/resource/e1c9f03a-2e55-444b-afee-fa1f7f50dee0/download/forclim_4submodels.jpg ""ForClim structure"")" proprietary
forecast-avalanche-danger-level-european-alps-2011-2015_1.0 Forecast avalanche danger level European Alps 2011 - 2015 ENVIDAT STAC Catalog 2018-01-01 2018-01-01 4.8779297, 43.2761391, 16.2597656, 48.179762 https://cmr.earthdata.nasa.gov/search/concepts/C2789815158-ENVIDAT.umm_json This dataset contains the data used in the publication by Techel et al., 2018 _Spatial consistency and bias in avalanche forecasts - a case study in the European Alps_ (Nat Haz Earth Syst Sci). For details on the data please refer to this publication. The dataset contains the following: - shape files for the warning regions in the Alps - highest forecast danger level for each warning region and day proprietary
forecomon-proceedings_v14 Forest monitoring to assess forest functioning under air pollution and climate change. Proceedings. FORECOMON 2021 - the 9th forest ecosystem monitoring conference. 7–9 June 2021, Birmensdorf, Switzerland ENVIDAT STAC Catalog 2021-01-01 2021-01-01 8.4549183, 47.3607533, 8.4549183, 47.3607533 https://cmr.earthdata.nasa.gov/search/concepts/C2789815176-ENVIDAT.umm_json Forest monitoring to assess forest functioning under air pollution and climate change. Proceedings. FORECOMON 2021 - the 9th forest ecosystem monitoring conference. 7-9 June 2021, WSL, Birmensdorf, Switzerland The goal of FORECOMON 2021 is to highlight the extensive ICP Forests data series on forest growth, phenology and leaf area index, biodiversity and ground vegetation, foliage and litter fall, ambient air quality, deposition, meteorology, soil and crown condition. We combine novel modeling and assessment approaches and integrate long-term trends to assess air pollution and climate effects on European forests and related ecosystem services. Latest results and conclusions from local scale to European scale studies will be presented and discussed. Copyright © 2021 by WSL, Birmensdorf The authors are responsible for the content of their contribution. proprietary
@@ -17943,8 +17950,8 @@ geodata_0261 Groundwater Produced Internally CEOS_EXTRA STAC Catalog 1958-01-01
geodata_0271 Fishery Production - Marine CEOS_EXTRA STAC Catalog 1960-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848156-CEOS_EXTRA.umm_json TOTAL PRODUCTION The annual series of capture production begin in 1950. Data relate to nominal catch of fish, crustaceans and mollusks*, taken for commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is also included. Data include all quantities caught and landed for both food and feed purposes but exclude discards. Catches of fish, crustaceans and molluscs are expressed in live weight, that is the nominal weight of the aquatic organisms at the time of capture. To assign nationality to catches, the flag of the fishing vessel is used, unless the wording of chartering and joint operation contracts indicates otherwise. * includes all FAOSTAT group excepted aquatic animals nei, aquatic plants, aquatic mammals proprietary
geodata_0278 Exclusive Fishing Zone (EFZ) CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848807-CEOS_EXTRA.umm_json The exclusive fishing zone or fishery zone refers to an area beyond the outer limit of the territorial sea (12 nautical miles from the coast) in which the coastal State has the right to fish, subject to any concessions which may be granted to foreign fishermen. Some countries have made no claim beyond the territorial sea. Some States have claimed an exclusive fishing zone instead of the more encompassing 200 nautical mile Exclusive Economic Zone (EEZ). The United Nations Convention on the Law of the Sea (UNCLOS) is an international agreement that sets conditions and limits on the use and exploitation of the oceans. This Convention also sets the rules for the maritime jurisdictional boundaries of the different member states. The UNCLOS was opened for signature on 10 December 1982 in Montego Bay, Jamaica, and it entered into force on 16 November 1994. As of January 2000, there are 132 countries that have ratified UNCLOS. Under UNCLOS, coastal States can claim sovereign rights in a 200-nautical mile exclusive economic zone (EEZ). This allows for sovereign rights over the EEZ in terms of exploration, exploitation, conservation and management of all natural resources in the seabed, its subsoil, and overlaying waters. UNCLOS allows other states to navigate and fly over the EEZ, as well as to lay submarine cables and pipelines. The inner limit of the EEZ starts at the outer boundary of the Territorial Sea (i.e., 12 nautical miles from the low-water line along the coast). Some States have not ratified UNCLOS and many have not yet claimed their EEZ. Given the uncertainties surrounding much of the delimitation of the fishing zones, these figures should be used with caution. Further information on the Web site: http://www.maritimeboundaries.com/ proprietary
geodata_0279 Claimed Exclusive Economic Zone (EEZ) CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848800-CEOS_EXTRA.umm_json "The United Nations Convention on the Law of the Sea (UNCLOS) is an international agreement that sets conditions and limits on the use and exploitation of the oceans. This Convention also sets the rules for the maritime jurisdictional boundaries of the different member states. The UNCLOS was opened for signature on 10 December 1982 in Montego Bay, Jamaica, and it entered into force on 16 November 1994. As of January 2000, there are 132 countries that have ratified UNCLOS. Under UNCLOS, coastal States can claim sovereign rights in a 200-nautical mile exclusive economic zone (EEZ). This allows for sovereign rights over the EEZ in terms of exploration, exploitation, conservation and management of all natural resources in the seabed, its subsoil and overlaying waters. UNCLOS allows other states to navigate and fly over the EEZ, as well as to lay submarine cables and pipelines. The inner limit of the EEZ starts at the outer boundary of the Territorial Sea (i.e., 12 nautical miles from the low-water line along the coast). In cases where a country's low-water lines is within 400 nautical miles of each other the EEZ boundaries are generally established by treaty, though there are many cases where these are in dispute. Under UNCLOS, ""land-locked and geographically disadvantaged States have the right to participate on an equitable basis in exploitation of an appropriate part of the surplus of the living resources of the EEZ's of coastal States of the same region or sub-region."" Some States have not ratified UNCLOS and many have not yet claimed their EEZ. These areas of unclaimed EEZ are the areas that a State has the right to claim under UNCLOS, but has not done so yet. Given the uncertainties surrounding much of the delimitation of the EEZ, these figures should be used with caution. Further information on the Web site: http://www.maritimeboundaries.com/ " proprietary
-geodata_0290 Administrative Boundaries - First Level (ESRI) CEOS_EXTRA STAC Catalog 1998-01-01 1998-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848662-CEOS_EXTRA.umm_json The sub Country Administrative Units 1998 GeoDataset represents a small-scale political map of the world. The data are generalized and were designed for display at scales to about 1:10,000,000. The data were generalized from ESRI's ArcWorld Supplement Map data. Country codes are from U.S. Federal Information Processing Standards (FIPS) version 10-4. proprietary
geodata_0290 Administrative Boundaries - First Level (ESRI) ALL STAC Catalog 1998-01-01 1998-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848662-CEOS_EXTRA.umm_json The sub Country Administrative Units 1998 GeoDataset represents a small-scale political map of the world. The data are generalized and were designed for display at scales to about 1:10,000,000. The data were generalized from ESRI's ArcWorld Supplement Map data. Country codes are from U.S. Federal Information Processing Standards (FIPS) version 10-4. proprietary
+geodata_0290 Administrative Boundaries - First Level (ESRI) CEOS_EXTRA STAC Catalog 1998-01-01 1998-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848662-CEOS_EXTRA.umm_json The sub Country Administrative Units 1998 GeoDataset represents a small-scale political map of the world. The data are generalized and were designed for display at scales to about 1:10,000,000. The data were generalized from ESRI's ArcWorld Supplement Map data. Country codes are from U.S. Federal Information Processing Standards (FIPS) version 10-4. proprietary
geodata_0291 Dams CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848575-CEOS_EXTRA.umm_json Construction of reservoirs became a worldwide activity in the second half of the twentieth century. The total storage capacity of the large reservoirs is more than 100 million cubic meters, which makes up more than 95% of water accumulated in all the reservoirs of the world. The total area of the more than 60,000 reservoirs that have been built in the last 50 years exceeds more than 100,000 square kilometers. This is an area equivalent to 11 water bodies the size of the Sea of Azov or five the size of Lake Superior. These man-made lakes affect natural and economic conditions over an area of 1.5 million square kilometers. Many of the world's large rivers, such as the Volga, Angara, Missouri, Colorado, and Parana Rivers, have been transformed into cascades of reservoirs. Construction and use of reservoirs cause inevitable changes in the environment, both positive and negative. Environmental changes can include overflowing and swamping; transformation of coasts; changes of soil, vegetation, and fauna; and changes of reproduction and habitat conditions of various aquatic organisms, especially fish and blue-green algae. The impact of reservoirs on the environment is diverse and contradictory. proprietary
geodata_0295 Global Vegetation Index 1983-1990 CEOS_EXTRA STAC Catalog 1991-01-01 1991-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848459-CEOS_EXTRA.umm_json "The NOAA/GVI (Global Vegetation Index; see reference pg. 3) Eight-Year Mean Maximum data set was developed in the following manner. First, eight years of NOAA/GVI Monthly Maximum data were obtained from GRID's Geneva archive of these data*. At GRID-Nairobi, an analyst then used these data files (12 per year) to calculate yearly mean maximum images, and the eight yearly mean images were averaged in their turn, in order to create a single eight-year mean maximum image. The original idea had been to produce an eight-year :hp2.maximum:ehp2. value image, but this was abandoned due to the accretion of ""noise"" from spurious maximum-value pixels in the individual data files (UNEP/GRID, 1990). * - GRID-Geneva has compiled an archive of NOAA/GVI Weekly data from the U.S. National Oceanic and Atmospheric Administration / National Environmental Satellite Data and Information Service / National Climate Data Center / Satellite Data Services Division (or the NOAA / NESDIS / NCDC / SDSD). This collection covers the period from April 1982 to present. At GRID-Geneva, the Weekly data are used to create Monthly, Seasonal and Annual Maximum images, in addition to the archived NOAA/GVI Weekly data. " proprietary
geodata_0331 Agriculture Value Added - Percent of GDP CEOS_EXTRA STAC Catalog 1960-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848745-CEOS_EXTRA.umm_json Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Note: For VAB countries, gross value added at factor cost is used as the denominator. Source: World Bank national accounts data, and OECD National Accounts data files. proprietary
@@ -18081,8 +18088,8 @@ geodata_1649 Irrigated Areas (Europe) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12
geodata_1650 Irrigated Areas (South America) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -122.85, -55.78, -18.14, 30.46 https://cmr.earthdata.nasa.gov/search/concepts/C2232847182-CEOS_EXTRA.umm_json The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). proprietary
geodata_1651 Irrigated Areas - geodata_1651 CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 -178.97, 8.56, -11.98, 87.95 https://cmr.earthdata.nasa.gov/search/concepts/C2232848914-CEOS_EXTRA.umm_json The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). proprietary
geodata_1652 Irrigated Areas (Middle East) CEOS_EXTRA STAC Catalog 1995-01-01 1995-12-31 29.09, 9.24, 63.99, 38.67 https://cmr.earthdata.nasa.gov/search/concepts/C2232846636-CEOS_EXTRA.umm_json The map depicts the fraction of each 5 min by 5 min cell (9.25 km x 9.25 km at the equator) cell that was equipped for irrigation around 1995. It has been derived by combining statistical data on the area quipped for irrigation within administrative units (counties, districts, federal states, countries) and geographical information on the location of irrigated areas (point, polygon and raster format). proprietary
-geodata_1672 Agricultural Area CEOS_EXTRA STAC Catalog 1961-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848984-CEOS_EXTRA.umm_json "Agricultural area, this category is the sum of areas under a) arable land - land under temporary agricultural crops (multiple-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for Arable land are not meant to indicate the amount of land that is potentially cultivable; (b) permanent crops - land cultivated with long-term crops which do not have to be replanted for several years (such as cocoa and coffee); land under trees and shrubs producing flowers, such as roses and jasmine; and nurseries (except those for forest trees, which should be classified under ""forest""); and (c) permanent meadows and pastures - land used permanently (five years or more) to grow herbaceous forage crops, either cultivated or growing wild (wild prairie or grazing land)." proprietary
geodata_1672 Agricultural Area ALL STAC Catalog 1961-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848984-CEOS_EXTRA.umm_json "Agricultural area, this category is the sum of areas under a) arable land - land under temporary agricultural crops (multiple-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for Arable land are not meant to indicate the amount of land that is potentially cultivable; (b) permanent crops - land cultivated with long-term crops which do not have to be replanted for several years (such as cocoa and coffee); land under trees and shrubs producing flowers, such as roses and jasmine; and nurseries (except those for forest trees, which should be classified under ""forest""); and (c) permanent meadows and pastures - land used permanently (five years or more) to grow herbaceous forage crops, either cultivated or growing wild (wild prairie or grazing land)." proprietary
+geodata_1672 Agricultural Area CEOS_EXTRA STAC Catalog 1961-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848984-CEOS_EXTRA.umm_json "Agricultural area, this category is the sum of areas under a) arable land - land under temporary agricultural crops (multiple-cropped areas are counted only once), temporary meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The abandoned land resulting from shifting cultivation is not included in this category. Data for Arable land are not meant to indicate the amount of land that is potentially cultivable; (b) permanent crops - land cultivated with long-term crops which do not have to be replanted for several years (such as cocoa and coffee); land under trees and shrubs producing flowers, such as roses and jasmine; and nurseries (except those for forest trees, which should be classified under ""forest""); and (c) permanent meadows and pastures - land used permanently (five years or more) to grow herbaceous forage crops, either cultivated or growing wild (wild prairie or grazing land)." proprietary
geodata_1685 Land Area CEOS_EXTRA STAC Catalog 1990-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232846605-CEOS_EXTRA.umm_json Land area is the total area of the country excluding area under inland water bodies. Possible variations in the data may be due to updating and revisions of the country data and not necessarily to any change of area. proprietary
geodata_1706 Consumption of Ozone-Depleting Substances - Methyl Bromide CEOS_EXTRA STAC Catalog 1989-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849036-CEOS_EXTRA.umm_json Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. Methyl bromide (CH3Br) is used as a fumigant for high-value crops, pest control, and quarantine treatment of agricultural commodities awaiting export. Total world annual consumption is about 70,000 tonnes, most of it in the industrialized countries. It takes about 0.7 years to break down. proprietary
geodata_1708 Consumption of Ozone-Depleting Substances - Hydrochlorofluorocarbons (HCFCs) CEOS_EXTRA STAC Catalog 1989-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847289-CEOS_EXTRA.umm_json Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. Hydrochlorofluorocarbons (HCFCs) were developed as the first major replacement for CFCs. While much less destructive than CFCs, HCFCs also contribute to ozone depletion. They have an atmospheric lifetime of about 1.4 to 19.5 years. proprietary
@@ -18167,8 +18174,8 @@ geodata_2128 Cadmium (Cd) Consumption CEOS_EXTRA STAC Catalog 1999-01-01 2006-12
geodata_2129 Lead (Pb) Production CEOS_EXTRA STAC Catalog 2003-01-01 2006-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847966-CEOS_EXTRA.umm_json Lead production refers to World mine production (metal content). proprietary
geodata_2130 Lead (Pb) Consumption CEOS_EXTRA STAC Catalog 2003-01-01 2006-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848445-CEOS_EXTRA.umm_json Lead Consumption refers to World refined lead consumption proprietary
geodata_2131 Mercury (Hg) Production CEOS_EXTRA STAC Catalog 1999-01-01 2006-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848487-CEOS_EXTRA.umm_json World metal production (primary metal) proprietary
-geodata_2134 Agricultural Area Irrigated ALL STAC Catalog 2001-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848664-CEOS_EXTRA.umm_json Agricultural area irrigated, part of the full or partial control irrigated Agricultural land which is actually irrigated in a given year. Often, part of the equipped area is not irrigated for various reasons, such as lack of water, absence of farmers, land degradation, damage, organizational problems etc. proprietary
geodata_2134 Agricultural Area Irrigated CEOS_EXTRA STAC Catalog 2001-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848664-CEOS_EXTRA.umm_json Agricultural area irrigated, part of the full or partial control irrigated Agricultural land which is actually irrigated in a given year. Often, part of the equipped area is not irrigated for various reasons, such as lack of water, absence of farmers, land degradation, damage, organizational problems etc. proprietary
+geodata_2134 Agricultural Area Irrigated ALL STAC Catalog 2001-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848664-CEOS_EXTRA.umm_json Agricultural area irrigated, part of the full or partial control irrigated Agricultural land which is actually irrigated in a given year. Often, part of the equipped area is not irrigated for various reasons, such as lack of water, absence of farmers, land degradation, damage, organizational problems etc. proprietary
geodata_2135 Country Area CEOS_EXTRA STAC Catalog 1961-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848571-CEOS_EXTRA.umm_json Country area, area of the country including area under inland water bodies, but excluding offshore territorial waters. Possible variations in the data may be due to updating and revisions of the country data and not necessarily to any change of area. proprietary
geodata_2136 Forest Area CEOS_EXTRA STAC Catalog 1990-01-01 2007-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232848786-CEOS_EXTRA.umm_json Forest area is the land spanning more than 0.5 hectares with trees higher than 5 metres and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use. Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 metres (m) in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate. Includes: areas with bamboo and palms provided that height and canopy cover criteria are met; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 ha and width of more than 20 m; plantations primarily used for forestry or protective purposes, such as: rubber-wood plantations and cork, oak stands. Excludes: tree stands in agricultural production systems, for example in fruit plantations and agroforestry systems. The term also excludes trees in urban parks and gardens. proprietary
geodata_2169 Consumption of Ozone-Depleting Substances - All CEOS_EXTRA STAC Catalog 1989-01-01 2010-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849311-CEOS_EXTRA.umm_json Ozone-depleting substances are any substance containing chlorine or bromine that destroys the stratospheric ozone layer. The stratospheric ozone absorbs most of the biologically damaging ultraviolet radiation. Hydrochlorofluorocarbons (HCFCs) were developed as the first major replacement for CFCs. While much less destructive than CFCs, HCFCs also contribute to ozone depletion. They have an atmospheric lifetime of about 1.4 to 19.5 years. proprietary
@@ -18188,8 +18195,8 @@ geodata_2207 Livestock Production Index Base 1999-2001 - Total CEOS_EXTRA STAC C
geodata_2208 Cereals - Area Harvested CEOS_EXTRA STAC Catalog 1961-01-01 2009-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849210-CEOS_EXTRA.umm_json Cereals also includes other cereals such as mixed grains and buckwheat. Crop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption). When the production data available refers to a production period falling into two successive calendar years and it is not possible to allocate the relative production to each of them, it is usual to refer production data to that year into which the bulk of the production falls. Crop production data are stored in tonnes (T). proprietary
geodata_2215 Hazardous Pesticides - Exports CEOS_EXTRA STAC Catalog 2007-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847283-CEOS_EXTRA.umm_json Refers to the value of the type of pesticide (put up in forms or packings for retail sale or as preparations or articles), provided to (exports) or received (imported) from the rest of the world. Differences between figures given for total exports and total imports at the world level may be due to several factors, e.g. the time lag between the dispatch of goods from exporting country and their arrival in the importing country; the use of different classification of the same product by different countries; or the fact that some countries supply data on general trade while others give data on special trade. proprietary
geodata_2216 Hazardous Pesticides - Imports CEOS_EXTRA STAC Catalog 2007-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847501-CEOS_EXTRA.umm_json Refers to the value of the type of pesticide (put up in forms or packings for retail sale or as preparations or articles), provided to (exports) or received (imported) from the rest of the world. Differences between figures given for total exports and total imports at the world level may be due to several factors, e.g. the time lag between the dispatch of goods from exporting country and their arrival in the importing country; the use of different classification of the same product by different countries; or the fact that some countries supply data on general trade while others give data on special trade. proprietary
-geodata_2217 Agricultural Area Certified Organic ALL STAC Catalog 2003-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847551-CEOS_EXTRA.umm_json Land area exclusively dedicated to organic agriculture and managed by applying organic agriculture methods. It refers to the land area fully converted to organic agriculture. It is the portion of land area (including arable lands, pastures or wild areas) managed (cultivated) or wild harvested in accordance with specific organic standards or technical regulations and that has been inspected and approved by a certification body. proprietary
geodata_2217 Agricultural Area Certified Organic CEOS_EXTRA STAC Catalog 2003-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847551-CEOS_EXTRA.umm_json Land area exclusively dedicated to organic agriculture and managed by applying organic agriculture methods. It refers to the land area fully converted to organic agriculture. It is the portion of land area (including arable lands, pastures or wild areas) managed (cultivated) or wild harvested in accordance with specific organic standards or technical regulations and that has been inspected and approved by a certification body. proprietary
+geodata_2217 Agricultural Area Certified Organic ALL STAC Catalog 2003-01-01 2008-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232847551-CEOS_EXTRA.umm_json Land area exclusively dedicated to organic agriculture and managed by applying organic agriculture methods. It refers to the land area fully converted to organic agriculture. It is the portion of land area (including arable lands, pastures or wild areas) managed (cultivated) or wild harvested in accordance with specific organic standards or technical regulations and that has been inspected and approved by a certification body. proprietary
geodata_2222 Adjusted Human Water Security Threat CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849396-CEOS_EXTRA.umm_json Rivers maintain unique biotic resources and provide critical water supplies to people. The Earth's limited supplies of fresh water and irreplaceable biodiversity are vulnerable to human mismanagement of watersheds and waterways. Multiple environmental stressors, such as agricultural runoff, pollution and invasive species, threaten rivers that serve 80 percent of the world’s population. These same stressors endanger the biodiversity of 65 percent of the world’s river habitats putting thousands of aquatic wildlife species at risk. Efforts to abate fresh water degradation through highly engineered solutions are effective at reducing the impact of threats but at a cost that can be an economic burden and often out of reach for developing nations. proprietary
geodata_2222 Adjusted Human Water Security Threat ALL STAC Catalog 1970-01-01 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849396-CEOS_EXTRA.umm_json Rivers maintain unique biotic resources and provide critical water supplies to people. The Earth's limited supplies of fresh water and irreplaceable biodiversity are vulnerable to human mismanagement of watersheds and waterways. Multiple environmental stressors, such as agricultural runoff, pollution and invasive species, threaten rivers that serve 80 percent of the world’s population. These same stressors endanger the biodiversity of 65 percent of the world’s river habitats putting thousands of aquatic wildlife species at risk. Efforts to abate fresh water degradation through highly engineered solutions are effective at reducing the impact of threats but at a cost that can be an economic burden and often out of reach for developing nations. proprietary
geodata_2223 Global Net Primary Production (NPP) Anomaly from Multi-year Average, 2000 CEOS_EXTRA STAC Catalog 2000-01-01 2000-12-31 -180, -90, 180, -60.5033 https://cmr.earthdata.nasa.gov/search/concepts/C2232849273-CEOS_EXTRA.umm_json Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production. proprietary
@@ -18307,23 +18314,23 @@ goesrpltsolma_1 GOES-R PLT Southern Ontario Lightning Mapping Array (LMA) V1 GHR
goesrpltwtlma_1 GOES-R PLT West Texas Lightning Mapping Array (LMA) V1 GHRC_DAAC STAC Catalog 2017-03-01 2017-06-01 -101.833, 33.597, -101.813, 33.617 https://cmr.earthdata.nasa.gov/search/concepts/C1977516629-GHRC_DAAC.umm_json The GOES-R PLT West Texas Lightning Mapping Array (LMA) dataset consists of total lightning data measured from the West Texas LMA (WTXLMA) network during the GOES-R Post Launch Test (PLT) airborne science field campaign. The GOES-R PLT airborne science field campaign took place in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The LMA measures the arrival time of radiation from a lightning discharge at multiple stations and locates the sources of radiation to produce a three-dimensional map of total lightning activity. These data files are available in compressed ASCII files and are available from March 1, 2017 through June 1, 2017. proprietary
goeswvt_1 GOES WATER VAPOR TRANSPORT V1 GHRC_DAAC STAC Catalog 1987-05-05 1988-11-30 -120, -30, -30, 45 https://cmr.earthdata.nasa.gov/search/concepts/C1995554230-GHRC_DAAC.umm_json The GOES Water Vapor Transport CD contains nineteen months of geostationary satellite-derived products from the GOES-8 satellite spanning the 1987-1988 El Nino Southern Oscillation (ENSO) cycle. Water vapor transport variables was derived using the Marshall Automated Winds (MAW) tracking algorithm from GOES data are provided in daily and monthly gridded and non-gridded formats. Relative humidity was calculated using a modified version of the brightness temperature to relative humidity conversion technique. Pressure heights were assigned to each wind vector using the simple IR window technique. Data are available in binary and McIDAS format. For further information and to obtain this data, please contact GHRC at support-ghrc@earthdata.nasa.gov proprietary
gom_bathymetry Digital Bathymetric Data for the Gulf of Maine CEOS_EXTRA STAC Catalog 1970-01-01 -71.5, 39.5, -63, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2231551983-CEOS_EXTRA.umm_json Gridded bathymetry and topography at 15 arc second (~1/2 km grid cell size) and a 30 arc second (~1 km grid cell size) resolution were constructed for the Gulf of Maine (Longitude = 71.5 - 63 W, Latitude = 39.5 - 46 N) using available digital bathymety datasets. In addition to the grids themselves, valuable ancillary products such as corrected sounding data, digital bathymetric contour lines and shaded-relief maps were generated and are available in a variety of formats, including Arc, Matlab, GMT and ASCII. See http://pubs.usgs.gov/of/1998/of98-801/ proprietary
-gomc_156 Adopt-a-Tide Pool SCIOPS STAC Catalog 1990-01-01 -70.923, 42.489, -70.763, 42.577 https://cmr.earthdata.nasa.gov/search/concepts/C1214586152-SCIOPS.umm_json Salem Sound Coastwatch trains volunteers to monitor tide pools through the Adopt-A-Tide pool program. Volunteers will help us focus special attention on local tide pools and catalog the diversity of both native and invasive species. This information will be passed on to scientists working on strategies to address marine invasive species. Waterbody or Watershed Names: Salem Sound proprietary
gomc_156 Adopt-a-Tide Pool ALL STAC Catalog 1990-01-01 -70.923, 42.489, -70.763, 42.577 https://cmr.earthdata.nasa.gov/search/concepts/C1214586152-SCIOPS.umm_json Salem Sound Coastwatch trains volunteers to monitor tide pools through the Adopt-A-Tide pool program. Volunteers will help us focus special attention on local tide pools and catalog the diversity of both native and invasive species. This information will be passed on to scientists working on strategies to address marine invasive species. Waterbody or Watershed Names: Salem Sound proprietary
+gomc_156 Adopt-a-Tide Pool SCIOPS STAC Catalog 1990-01-01 -70.923, 42.489, -70.763, 42.577 https://cmr.earthdata.nasa.gov/search/concepts/C1214586152-SCIOPS.umm_json Salem Sound Coastwatch trains volunteers to monitor tide pools through the Adopt-A-Tide pool program. Volunteers will help us focus special attention on local tide pools and catalog the diversity of both native and invasive species. This information will be passed on to scientists working on strategies to address marine invasive species. Waterbody or Watershed Names: Salem Sound proprietary
gomc_162 Circulation and Contaminant Transport in Massachusetts Coastal Waters CEOS_EXTRA STAC Catalog 1977-01-01 -70.95037, 42.09017, -70.26193, 42.61774 https://cmr.earthdata.nasa.gov/search/concepts/C2231548638-CEOS_EXTRA.umm_json U.S. Geological Survey studies show that the concentrations of metals in surface sediments of Boston Harbor are decreasing with time. This conclusion is supported by analysis of (1) surface sediments collected at monitoring stations in the outer harbor between 1977 and 1993, (2) sediment cores from depositional areas of the harbor, and (3) historical data from a contaminated-sediment data base, which includes information on metal and organic contaminants and sediment texture. During the 16 years of the continuing study, chromium, lead, mercury, silver, and zinc concentrations in surface sediments have decreased by about 50 percent. Although these trends are indeed encouraging, concentrations of some metals in harbor sediments are still above levels considered toxic to certain bottom-dwelling organisms. Type: Bay Waterbody or Watershed Names: Boston Harbor proprietary
-gomc_219 2001 Long Island Sound Study Ambient Water Quality and Monitoring Program ALL STAC Catalog 1970-01-01 -74.3, 40.5, -71.75, 41.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214585922-SCIOPS.umm_json The Interstate Environmental Commission is a joint agency of the States of New York, New Jersey, and Connecticut. The IEC was established in 1936 under a Compact between New York and New Jersey and approved by Congress. The State of Connecticut joined the Commission in 1941. Waterbody or Watershed Names: Long Island Sound proprietary
gomc_219 2001 Long Island Sound Study Ambient Water Quality and Monitoring Program SCIOPS STAC Catalog 1970-01-01 -74.3, 40.5, -71.75, 41.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214585922-SCIOPS.umm_json The Interstate Environmental Commission is a joint agency of the States of New York, New Jersey, and Connecticut. The IEC was established in 1936 under a Compact between New York and New Jersey and approved by Congress. The State of Connecticut joined the Commission in 1941. Waterbody or Watershed Names: Long Island Sound proprietary
+gomc_219 2001 Long Island Sound Study Ambient Water Quality and Monitoring Program ALL STAC Catalog 1970-01-01 -74.3, 40.5, -71.75, 41.5 https://cmr.earthdata.nasa.gov/search/concepts/C1214585922-SCIOPS.umm_json The Interstate Environmental Commission is a joint agency of the States of New York, New Jersey, and Connecticut. The IEC was established in 1936 under a Compact between New York and New Jersey and approved by Congress. The State of Connecticut joined the Commission in 1941. Waterbody or Watershed Names: Long Island Sound proprietary
gomc_323 ACAP Saint John's Community Environmental Monitoring Program (CEMP) ALL STAC Catalog 1992-01-01 -66.25, 45, -65.25, 46 https://cmr.earthdata.nasa.gov/search/concepts/C1214585928-SCIOPS.umm_json Parameters measured included: ammonia nitrogen, orthophosphate, dissolved oxygen, pH, turbidity, salinity, faecal coliform. proprietary
gomc_323 ACAP Saint John's Community Environmental Monitoring Program (CEMP) SCIOPS STAC Catalog 1992-01-01 -66.25, 45, -65.25, 46 https://cmr.earthdata.nasa.gov/search/concepts/C1214585928-SCIOPS.umm_json Parameters measured included: ammonia nitrogen, orthophosphate, dissolved oxygen, pH, turbidity, salinity, faecal coliform. proprietary
-gomc_40 Air Quality Monitoring In New Brunswick ALL STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C1214586182-SCIOPS.umm_json We know that air pollution can have an effect on the health of our environment and on human health. People who have respiratory difficulties are particularly sensitive to poor air quality. Children are frequently affected because of their physiology and because they tend to be more active outdoors. Monitoring air quality in New Brunswick helps us to better understand the sources, movements and effects of various substances in the air we breathe. The data we collect helps us to control sources of air pollution within our province, and to negotiate with governments in other jurisdictions for controls on air pollution that crosses borders. The more we know, the more effectively we can work to protect and enhance our air quality and our environment. proprietary
gomc_40 Air Quality Monitoring In New Brunswick SCIOPS STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C1214586182-SCIOPS.umm_json We know that air pollution can have an effect on the health of our environment and on human health. People who have respiratory difficulties are particularly sensitive to poor air quality. Children are frequently affected because of their physiology and because they tend to be more active outdoors. Monitoring air quality in New Brunswick helps us to better understand the sources, movements and effects of various substances in the air we breathe. The data we collect helps us to control sources of air pollution within our province, and to negotiate with governments in other jurisdictions for controls on air pollution that crosses borders. The more we know, the more effectively we can work to protect and enhance our air quality and our environment. proprietary
+gomc_40 Air Quality Monitoring In New Brunswick ALL STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C1214586182-SCIOPS.umm_json We know that air pollution can have an effect on the health of our environment and on human health. People who have respiratory difficulties are particularly sensitive to poor air quality. Children are frequently affected because of their physiology and because they tend to be more active outdoors. Monitoring air quality in New Brunswick helps us to better understand the sources, movements and effects of various substances in the air we breathe. The data we collect helps us to control sources of air pollution within our province, and to negotiate with governments in other jurisdictions for controls on air pollution that crosses borders. The more we know, the more effectively we can work to protect and enhance our air quality and our environment. proprietary
gone-wild-grapevines-in-forests_1.0 Gone-wild grapevines in forests may act as a potential habitat for “Flavescence dorée” phytoplasma vectors and inoculum ENVIDAT STAC Catalog 2023-01-01 2023-01-01 8.4347534, 45.8809865, 9.2422485, 46.5159373 https://cmr.earthdata.nasa.gov/search/concepts/C3226082143-ENVIDAT.umm_json Dataset used to test the potential role of gone-wild grapevines (GWGV) in forests of Southern Switzerland as a source of Flavescence dorée phytoplasma (FDp) inoculum and as a habitat for its main and alternative vectors, Scaphoideus titanus and Orientus ishidae. In the first phase, GWGV were located and sampled to test their FDp status. In addition, a set of chromotropic traps were placed to monitor the presence and abundance of FDp vectors. In the second phase, wood from GWGV in forests was collected and placed in cages to test the potential oviposition activity by FDp vectors. The results showed that GWGV in forests are a reservoir of FDp and that they can sustain the whole life cycle of both S.titanus and O.ishidae. Eventually, the need to adapt the current FD management strategies are highlighted. proprietary
gov.noaa.ncdc:C00842_Version 1.2 Blended 6-Hourly Sea Surface Wind Vectors and Wind Stress on a Global 0.25 Degree Grid (1987-2011) NOAA_NCEI STAC Catalog 1987-07-09 2011-09-30 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093688-NOAA_NCEI.umm_json The Blended Global Sea Surface Winds products contain ocean surface wind vectors and wind stress on a global 0.25 degree grid, in multiple time resolutions of 6-hourly and monthly, with an 11-year (1995-2005) monthly climatology. Daily files from a direct average of the 6-hourly data were also produced but are not included in this archive. The period of record is July 9, 1987 to September 30, 2011 for product Version 1.2, released in July 2007. Wind speeds were generated by blending available and selected microwave and scatterometer observations using a Simple spatiotemporally weighted Interpolation (SI) method. The following satellite retrieval datasets from Remote Sensing Systems (RSS) were used for Version 1.2: SSMI Version 6, TMI Version 4, QSCAT Version 3a, and AMSRE Version 5 (updated using the SSMI rain rate). The wind directions are from the NCEP-DOE Reanalysis 2 (NRA-2). The model wind directions are interpolated onto the blended wind speed grids. The 6-hourly satellite-scaled global 0.25-degree grid wind stresses are computed as: taux_s = -[(w_s/w_m)**2]*taux_m tauy_s = -[(w_s/w_m)**2]*tauy_m where 's' indicates satellite-scaled values and 'm' indicates NRA-2 model values interpolated to the satellite grid. Files are in netCDF format and available to users via FTP and THREDDS. A near real-time (NRT) variant of the product is generated quasi-daily to satisfy the needs of real-time users. The publicly available NRT data were replaced by the delayed-mode research quality data on a monthly basis through the end of September 2011, at which time the Seawinds production was impacted by the loss of data from the AMSR-E instrument failure. Production of the delayed-mode research products ends with the loss of AMSR-E in Version 1.2; a future version will extend beyond September 2011. The NRT products are continued after September 2011; however, this archive only includes the delayed-mode research products as the NRT data have a lower maturity rating removing the basis for archiving those data. proprietary
gov.noaa.ncdc:C01381_Not Applicable AVHRR/HIRS Longwave Radiation Budget Data (RBUD) NOAA_NCEI STAC Catalog 2000-03-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107093896-NOAA_NCEI.umm_json Radiation Budget Data - The Radiation Budget product suite is produced from the primary morning and afternoon Polar Orbiters. Product shows a measure of the longwave radiation emitted (W/m^2) by the earth-atmosphere system to space. The observations are displayed on a one degree equal area map for the day and night. The products are: GAC long wave, HIRS long wave, longwave histogram, annual mean, monthly mean, and seasonal mean. This is a NESDIS legacy product and the file naming pattern is as follows: NPR.RBSD.[SatelliteID].D[YYDDD] or NPR.RBMD.[SatelliteID].D[YYDDD] proprietary
gov.noaa.ncdc:C01560_V3 Blended Global Biomass Burning Emissions Product - Extended (GBBEPx) from Multiple Satellites NOAA_NCEI STAC Catalog 2018-01-09 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2107094570-NOAA_NCEI.umm_json The Blended Global Biomass Burning Emissions Product version 3 (GBBEPx V3) system produces global biomass burning emissions. The product contains daily global biomass burning emissions (PM2.5, BC, CO, CO2, OC, and SO2) blended fire observations from MODIS Quick Fire Emission Dataset (QFED), VIIRS (NPP and JPSS-1) fire emissions, and Global Biomass Burning Emission Product from Geostationary satellites (GBBEP-Geo), which are in a grid cell of 0.25 Ã 0.3125 degree and 0.1 x 0.1 degree. It also produces hourly emissions from geostationary satellites, which is at individual fire pixels. The product output also include fire detection record in a HMS format, quality flag in biomass burning emissions, spatial pattern of PM2.5 emissions, and statistic PM2.5 information at continental scale. In Version3, daily biomass burning emissions at a FV3 C384 grid in binary format and daily biomass burning emissions at a 0.1 x 0.1 degree grid that include all the emissions species are added as new output. proprietary
gov.noaa.ncdc:C01598_Beta4 Adaptive Ecosystem Climatology Beta 4 Model and Satellite Climatology ALL STAC Catalog 1980-01-01 2012-12-31 -98, 18.091, -77.36, 30.73 https://cmr.earthdata.nasa.gov/search/concepts/C2107094643-NOAA_NCEI.umm_json The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included. proprietary
gov.noaa.ncdc:C01598_Beta4 Adaptive Ecosystem Climatology Beta 4 Model and Satellite Climatology NOAA_NCEI STAC Catalog 1980-01-01 2012-12-31 -98, 18.091, -77.36, 30.73 https://cmr.earthdata.nasa.gov/search/concepts/C2107094643-NOAA_NCEI.umm_json The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included. proprietary
-gov.noaa.ncdc:C01599_beta6 Adaptive Ecosystem Climatology Beta 6 Satellite Climatology ALL STAC Catalog 1980-01-01 2012-12-31 -135, 22.9276, -62.987, 53 https://cmr.earthdata.nasa.gov/search/concepts/C2107094649-NOAA_NCEI.umm_json The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included. proprietary
gov.noaa.ncdc:C01599_beta6 Adaptive Ecosystem Climatology Beta 6 Satellite Climatology NOAA_NCEI STAC Catalog 1980-01-01 2012-12-31 -135, 22.9276, -62.987, 53 https://cmr.earthdata.nasa.gov/search/concepts/C2107094649-NOAA_NCEI.umm_json The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included. proprietary
+gov.noaa.ncdc:C01599_beta6 Adaptive Ecosystem Climatology Beta 6 Satellite Climatology ALL STAC Catalog 1980-01-01 2012-12-31 -135, 22.9276, -62.987, 53 https://cmr.earthdata.nasa.gov/search/concepts/C2107094649-NOAA_NCEI.umm_json The Adaptive Ecosystem Climatology (AEC) is produced by the Naval Research Laboratory (NRL). It consists of two datasets covering multiple regions of the ocean. One is a climatology derived from satellite data, the other is a climatology derived from a computer model of parts of the ocean that simulates physical and biological phenomena. The satellite climatology has data for chlorophyll concentration and sea surface temperature. The model climatology has fields for sea surface height, temperature, current, and concentrations of various types of plankton on the surface and underwater. Spatial resolution ranges from 1km to 4km depending on the product. These data are in NetCDF version 3 format with metadata attributes included. proprietary
gov.noaa.ngdc.mgg.photos:12_Not Applicable April 1906 San Francisco, USA Images NOAA_NCEI STAC Catalog 1906-04-18 1906-04-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105705777-NOAA_NCEI.umm_json The 1906 San Francisco earthquake was the largest event (magnitude 8.3) to occur in the conterminous United States in the 20th Century. Recent estimates indicate that as many as 3,000 people lost their lives in the earthquake and ensuing fire. In terms of 1906 dollars, the total property damage amounted to about $24 million from the earthquake and $350 million from the fire. The fire destroyed 28,000 buildings in a 520-block area of San Francisco. proprietary
gov.noaa.ngdc.mgg.photos:16_Not Applicable April 1992 Cape Mendocino, USA Images NOAA_NCEI STAC Catalog 1992-04-25 1992-04-25 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105705735-NOAA_NCEI.umm_json On April 25, 1992 at 11:06 am local time (April 25 at 18:06 GMT), a magnitude 7.1 earthquake occurred in the Cape Mendocino area. Two additional earthquakes, magnitudes 6.6 and 6.7 occurred the next morning (April 26 at 00:41 and 04:18 am local time). The first earthquake was located six miles north of Petrolia, California, in a sparsely populated part of southwestern Humboldt County. Five small communities were located within a 50-mile radius of these events: Honeydew, Petrolia, Rio Dell, Scotia, and Ferndale. proprietary
gov.noaa.ngdc.mgg.photos:1_Not Applicable August 1959 Hebgen Lake, USA Images NOAA_NCEI STAC Catalog 1959-08-18 1959-08-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2105705741-NOAA_NCEI.umm_json The magnitude 7.1 earthquake killed 28 people and caused $11 million property damage. Affected area: 1,554,000 sq km proprietary
@@ -18343,10 +18350,10 @@ gov.noaa.nodc:0000015_Not Applicable Alkalinity, dissolved oxygen, nutrients, pH
gov.noaa.nodc:0000028_Not Applicable Benthic species - TAXA counts, identities, and wet weights collected by sediment grab from multiple cruises in Prince William Sound, Alaska, from 10/22/1985 - 8/31/1988 (NCEI Accession 0000028) NOAA_NCEI STAC Catalog 1985-10-22 1998-08-31 -146.597, 61.0802, -146.2983, 61.13 https://cmr.earthdata.nasa.gov/search/concepts/C2089372272-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:0000029_Not Applicable 1990, 1991, 1992 and 1995 CRETM/LMER Zooplankton Data Sets (NCEI Accession 0000029) NOAA_NCEI STAC Catalog 1990-09-26 1995-05-26 -124.041667, 0.766667, -16.25, 46.263167 https://cmr.earthdata.nasa.gov/search/concepts/C2089372282-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:0000029_Not Applicable 1990, 1991, 1992 and 1995 CRETM/LMER Zooplankton Data Sets (NCEI Accession 0000029) ALL STAC Catalog 1990-09-26 1995-05-26 -124.041667, 0.766667, -16.25, 46.263167 https://cmr.earthdata.nasa.gov/search/concepts/C2089372282-NOAA_NCEI.umm_json Not provided proprietary
-gov.noaa.nodc:0000035_Not Applicable 1996 - Early 1998 CRETM/LMER Phytoplankton Data (NCEI Accession 0000035) ALL STAC Catalog 1996-07-09 1998-03-06 -124.003, 46.179833, -123.183167, 46.261667 https://cmr.earthdata.nasa.gov/search/concepts/C2089372325-NOAA_NCEI.umm_json Pump cast sampling, and associated CTD casts took place from a fixed vessel during one 28-35 day cruise per year in 1990, 1991, 1992, 1995, and 1996. In 1997 there were 2 week cruises in May, July, and October. proprietary
gov.noaa.nodc:0000035_Not Applicable 1996 - Early 1998 CRETM/LMER Phytoplankton Data (NCEI Accession 0000035) NOAA_NCEI STAC Catalog 1996-07-09 1998-03-06 -124.003, 46.179833, -123.183167, 46.261667 https://cmr.earthdata.nasa.gov/search/concepts/C2089372325-NOAA_NCEI.umm_json Pump cast sampling, and associated CTD casts took place from a fixed vessel during one 28-35 day cruise per year in 1990, 1991, 1992, 1995, and 1996. In 1997 there were 2 week cruises in May, July, and October. proprietary
-gov.noaa.nodc:0000052_Not Applicable 1988 Resurrection Bay Zooplankton Data Set from 01 March 1988 to 28 June 1988 (NCEI Accession 0000052) NOAA_NCEI STAC Catalog 1988-03-01 1988-06-28 -149.4083, 59.9117, -149.3583, 60.02 https://cmr.earthdata.nasa.gov/search/concepts/C2089372461-NOAA_NCEI.umm_json Zooplantkon and beach tar data were collected using plankton net casts in the Gulf of Alaska from the ALPHA HELIX. Data were collected from 01 March 1988 to 28 June 1988 by University of Alaska in Fairbanks; Institute of Marine Science with support from the Gulf of Alaska - 1 (GAK-1) project. proprietary
+gov.noaa.nodc:0000035_Not Applicable 1996 - Early 1998 CRETM/LMER Phytoplankton Data (NCEI Accession 0000035) ALL STAC Catalog 1996-07-09 1998-03-06 -124.003, 46.179833, -123.183167, 46.261667 https://cmr.earthdata.nasa.gov/search/concepts/C2089372325-NOAA_NCEI.umm_json Pump cast sampling, and associated CTD casts took place from a fixed vessel during one 28-35 day cruise per year in 1990, 1991, 1992, 1995, and 1996. In 1997 there were 2 week cruises in May, July, and October. proprietary
gov.noaa.nodc:0000052_Not Applicable 1988 Resurrection Bay Zooplankton Data Set from 01 March 1988 to 28 June 1988 (NCEI Accession 0000052) ALL STAC Catalog 1988-03-01 1988-06-28 -149.4083, 59.9117, -149.3583, 60.02 https://cmr.earthdata.nasa.gov/search/concepts/C2089372461-NOAA_NCEI.umm_json Zooplantkon and beach tar data were collected using plankton net casts in the Gulf of Alaska from the ALPHA HELIX. Data were collected from 01 March 1988 to 28 June 1988 by University of Alaska in Fairbanks; Institute of Marine Science with support from the Gulf of Alaska - 1 (GAK-1) project. proprietary
+gov.noaa.nodc:0000052_Not Applicable 1988 Resurrection Bay Zooplankton Data Set from 01 March 1988 to 28 June 1988 (NCEI Accession 0000052) NOAA_NCEI STAC Catalog 1988-03-01 1988-06-28 -149.4083, 59.9117, -149.3583, 60.02 https://cmr.earthdata.nasa.gov/search/concepts/C2089372461-NOAA_NCEI.umm_json Zooplantkon and beach tar data were collected using plankton net casts in the Gulf of Alaska from the ALPHA HELIX. Data were collected from 01 March 1988 to 28 June 1988 by University of Alaska in Fairbanks; Institute of Marine Science with support from the Gulf of Alaska - 1 (GAK-1) project. proprietary
gov.noaa.nodc:0000064_Not Applicable Arabian Sea Biogeochemistry from 27 August 1994 to 19 December 1994 (NCEI Accession 0000064) NOAA_NCEI STAC Catalog 1994-08-27 1994-12-19 56.5529, 7.7811, 67.3194, 26.0221 https://cmr.earthdata.nasa.gov/search/concepts/C2089372546-NOAA_NCEI.umm_json Arabesque was a multidisciplinary oceanographic research project focused on the Arabian Sea and Northwest Indian Ocean during the monsoon and intermonsoon season in 1994. proprietary
gov.noaa.nodc:0000085_Not Applicable Benthic taxonomy and benthic biomass data collected by the R/V Alpha Helix in support of the ISHTAR Project in the Bering and Chukchi Seas, 1984-1990 (NCEI Accession 0000085) NOAA_NCEI STAC Catalog 1984-06-19 1990-06-21 -175.00118, 60.014, -163.75, 70 https://cmr.earthdata.nasa.gov/search/concepts/C2089372672-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:0000103_Not Applicable Bering Sea Inner Front zooplankton data sets collected with CalVet net on four cruises from 6/3/1997 - 9/1/1998 (NCEI Accession 0000103) NOAA_NCEI STAC Catalog 1997-06-01 1998-09-01 -168.745, 55.0372, -159.994, 59.1733 https://cmr.earthdata.nasa.gov/search/concepts/C2089372740-NOAA_NCEI.umm_json Zooplankton and other data were collected using CalVet net in Bering sea from ALPHA HELIX. Data were collected from 01 June 1997 to 01 September 1998 by University of Alaska in Fairbanks with support from the Inner Front project. proprietary
@@ -18364,15 +18371,15 @@ gov.noaa.nodc:0000340_Not Applicable Bacteria and other data from the HERMANO GI
gov.noaa.nodc:0000349_Not Applicable Bottom-mounted water level recorder data in the Gulf of Alaska as part of the Inner Shelf Transport and Recycling (ISHTAR) project from 05 July 1985 to 09 October 1988 (NCEI Accession 0000349) NOAA_NCEI STAC Catalog 1985-07-05 1988-10-09 -172.247, 62.815, -168.22, 68.122 https://cmr.earthdata.nasa.gov/search/concepts/C2089373949-NOAA_NCEI.umm_json Depth, pressure, and water temperature data were collected at fixed platforms in the Gulf of Alaska from July 5, 1985 to October 9, 1988. These data were submitted by the University of Alaska - Fairbanks; Institute of Marine Science as part of the Inner Shelf Transfer and Recycling (ISHTAR) project. proprietary
gov.noaa.nodc:0000354_Not Applicable Chemical, physical, and other data from various cruises in the Northeast Pacific Ocean from 08 July 1974 to 21 August 1983 (NCEI Accession 0000354) NOAA_NCEI STAC Catalog 1974-07-08 1983-08-21 -127.633333, 47, -123.166667, 55.95 https://cmr.earthdata.nasa.gov/search/concepts/C2089373979-NOAA_NCEI.umm_json Chemical, physical, and other data were collected from the YAQUINA, CAYUSE, WECOMA, and THOMAS G. THOMPSON from July 8, 1974 to August 21, 1983. Data were submitted by University of Washington using bottle and CTD casts in Coastal Waters of the Washington/Oregon and Northeast Pacific Ocean. proprietary
gov.noaa.nodc:0000358_Not Applicable Barometric pressure, conductivity, temperature, and water level data from tide gauge from the Florida Department of Environmental Protection Tide Station from 01 January 1977 to 31 December 1999 (NCEI Accession 0000358) NOAA_NCEI STAC Catalog 1997-01-01 1999-12-31 -81.68, 27.15, -80.15, 30.4 https://cmr.earthdata.nasa.gov/search/concepts/C2089373989-NOAA_NCEI.umm_json Barometric pressure, conductivity, temperature, and water level data were collected at fixed platforms in the North Atlantic Ocean and Coastal waters of Florida from January 1, 1977 to December 31, 1999. Data were submitted by Florida Department of Environmental Protection. These data were collected using tide gauge at the fixed locations. proprietary
-gov.noaa.nodc:0000366_Not Applicable Air/delta/sea surface temperature, pressure, and other data from MISS GAIL in a world-wide distribution from 21 October 1957 to 18 April 1961 (NCEI Accession 0000366) ALL STAC Catalog 1957-10-21 1961-04-18 18.7, -43.033333, 16.3, 64.033333 https://cmr.earthdata.nasa.gov/search/concepts/C2089374032-NOAA_NCEI.umm_json Air/delta/sea surface temperature, pressure, and other data were collected from the MISS GAIL in a world-wide distribution from October 21, 1957 to April 18, 1961. Data were submitted by the NOAA Oar Climate Monitoring and Diagnostics Lab. proprietary
gov.noaa.nodc:0000366_Not Applicable Air/delta/sea surface temperature, pressure, and other data from MISS GAIL in a world-wide distribution from 21 October 1957 to 18 April 1961 (NCEI Accession 0000366) NOAA_NCEI STAC Catalog 1957-10-21 1961-04-18 18.7, -43.033333, 16.3, 64.033333 https://cmr.earthdata.nasa.gov/search/concepts/C2089374032-NOAA_NCEI.umm_json Air/delta/sea surface temperature, pressure, and other data were collected from the MISS GAIL in a world-wide distribution from October 21, 1957 to April 18, 1961. Data were submitted by the NOAA Oar Climate Monitoring and Diagnostics Lab. proprietary
+gov.noaa.nodc:0000366_Not Applicable Air/delta/sea surface temperature, pressure, and other data from MISS GAIL in a world-wide distribution from 21 October 1957 to 18 April 1961 (NCEI Accession 0000366) ALL STAC Catalog 1957-10-21 1961-04-18 18.7, -43.033333, 16.3, 64.033333 https://cmr.earthdata.nasa.gov/search/concepts/C2089374032-NOAA_NCEI.umm_json Air/delta/sea surface temperature, pressure, and other data were collected from the MISS GAIL in a world-wide distribution from October 21, 1957 to April 18, 1961. Data were submitted by the NOAA Oar Climate Monitoring and Diagnostics Lab. proprietary
gov.noaa.nodc:0000396_Not Applicable Chlorophyll data from the Coastal waters of Hawaii and Northeast Pacific Ocean to study the responses of the ecosystem to the sewage diversion from the the inner bay to an offshore, deep-water location from 24 September 1976 to 15 June 1979 (NCEI Accession 0000396) NOAA_NCEI STAC Catalog 1976-09-24 1979-06-15 -157.76, 21.4, -157.76, 21.4 https://cmr.earthdata.nasa.gov/search/concepts/C2089374658-NOAA_NCEI.umm_json Chlorophyll data were collected at fixed platforms in the Coastal waters of Hawaii and Northeast Pacific Ocean from September 24, 1976 to June 15, 1979. Data were submitted by the University of Hawaii, Maui. Data were collected using pump sampler. proprietary
gov.noaa.nodc:0000411_Not Applicable Aquatic vegetation were photographed from aircraft from Florida Bay, Indian River (Florida), and the Coast of Massachusetts (NCEI Accession 0000411) NOAA_NCEI STAC Catalog 28.15, -81, 71.3, -41.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089374769-NOAA_NCEI.umm_json "Aerial photographs were taken of the aquatic vegetation of Florida Bay, Indian River (Florida), and the Coast of Massachusetts. Photographs were scanned and geo-referenced for the purpose of mapping. Data is contained on a ""DLT"" tape and is stored ""off-site"" as a secure backup copy." proprietary
gov.noaa.nodc:0000422_Not Applicable An Eighteen-Year Time-Series of Chlorophyll Monthly Averages from Kaneohe Bay, Oahu, Hawaii, 1982 - 2001 (NCEI Accession 0000422) NOAA_NCEI STAC Catalog 1982-06-01 2001-01-31 -157.78, 21.41, -157.78, 24.41 https://cmr.earthdata.nasa.gov/search/concepts/C2089374869-NOAA_NCEI.umm_json Chlorophyll data were collected from a sewage outfall site in Kaneohe Bay, Hawaii, from 1982 to 2001. The purpose of the project was to study the responses of the ecosystem to the sewage diversion from the inner bay to an offshore, deep water location and to continue monitoring the location to denote changes associated with natural environmental and anthropogenic forcing on the primary productivity. Data were submitted by the University of Hawaii at Manoa and funding was provided by the Environmental Protective Agency (EPA). proprietary
gov.noaa.nodc:0000425_Not Applicable Biological, chemical, geological, and other data were collected from the R/V KITTIWAKE at 100 sites in Puget Sound from 01 June 1998 to 01 July 1998 as part of a three-year study of toxins (NCEI Accession 0000425) NOAA_NCEI STAC Catalog 1998-06-01 1998-07-01 -122.3, 47.3, -122.3, 47.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089374887-NOAA_NCEI.umm_json Biological, chemical, geological, and other data were collected from the R/V Kittiwait from 01 June 1998 to 01 July 1998. Data were submitted by the Washington State Department of Ecology (WADOE) as part of a 3 year, 100 site, study of toxins in the Puget Sound. Biological data include infauna surveys, amphipod bioassays, and percent urchin fertilization. Chemical data include results of tests for toxins by cytochrome and microtoxology. Geological data include determination of grain fractions. proprietary
gov.noaa.nodc:0000447_Not Applicable Benthic data from bottom grabs from Prince William Sound in support of Exxon Valdez Oil Spill Restoration Project from the R/V DAVIDSON and R/V BIG VALLEY from 03 July 1990 to 25 June of 1991 (NCEI Accession 0000447) NOAA_NCEI STAC Catalog 1990-07-03 1991-06-25 -147.08803, 60.273, -146.92303, 60.332 https://cmr.earthdata.nasa.gov/search/concepts/C2089375015-NOAA_NCEI.umm_json Benthic samples and other data were collected from the R/V DAVIDSON and R/V BIG VALLEY from the Prince William Sound from 03 July 1990 to 25 June of 1991 . Data were collected as part of the Exxon Valdez Oil Spill Restoration Project. Data were collected by the University of Alaska - Fairbanks / Institute of Marine Science (UAK/IMS) with bottom grab sampler and include taxonomic identities and taxonomic counts of benthic animals. proprietary
-gov.noaa.nodc:0000501_Not Applicable A unified, long-term, Caribbean-wide initiative to identity the factors responsible for sustaining mangrove wetland, seagrass meadow, and coral reef productivity, February 1993 - October 1998 (NCEI Accession 0000501) ALL STAC Catalog 1993-02-12 1998-10-15 -90.583333, 9.583333, -59.633333, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2089375341-NOAA_NCEI.umm_json The Caribbean Coastal Marine Productivity (CARICOMP) Program is a Caribbean-wide research and monitoring network of 27 marine laboratories, parks, and reserves in 17 countries. This data set includes data collected from 42 stations at 29 sites in the Caribbean from 1993 to 1998. Line transects were used to determine the abundance of hard and soft corals, algae, sponges, urchins, and biotic material such as substrate type. proprietary
gov.noaa.nodc:0000501_Not Applicable A unified, long-term, Caribbean-wide initiative to identity the factors responsible for sustaining mangrove wetland, seagrass meadow, and coral reef productivity, February 1993 - October 1998 (NCEI Accession 0000501) NOAA_NCEI STAC Catalog 1993-02-12 1998-10-15 -90.583333, 9.583333, -59.633333, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2089375341-NOAA_NCEI.umm_json The Caribbean Coastal Marine Productivity (CARICOMP) Program is a Caribbean-wide research and monitoring network of 27 marine laboratories, parks, and reserves in 17 countries. This data set includes data collected from 42 stations at 29 sites in the Caribbean from 1993 to 1998. Line transects were used to determine the abundance of hard and soft corals, algae, sponges, urchins, and biotic material such as substrate type. proprietary
+gov.noaa.nodc:0000501_Not Applicable A unified, long-term, Caribbean-wide initiative to identity the factors responsible for sustaining mangrove wetland, seagrass meadow, and coral reef productivity, February 1993 - October 1998 (NCEI Accession 0000501) ALL STAC Catalog 1993-02-12 1998-10-15 -90.583333, 9.583333, -59.633333, 24.05 https://cmr.earthdata.nasa.gov/search/concepts/C2089375341-NOAA_NCEI.umm_json The Caribbean Coastal Marine Productivity (CARICOMP) Program is a Caribbean-wide research and monitoring network of 27 marine laboratories, parks, and reserves in 17 countries. This data set includes data collected from 42 stations at 29 sites in the Caribbean from 1993 to 1998. Line transects were used to determine the abundance of hard and soft corals, algae, sponges, urchins, and biotic material such as substrate type. proprietary
gov.noaa.nodc:0000504_Not Applicable Bacteria, plankton, and trace metal, and other data from bottle and CTD casts in the Antarctic from the NATHANIEL B. PALMER and ROGER REVELLE in support of the US Joint Global Ocean Flux Study / Antarctic Environments Southern Ocean Process Study (JGOFS /AESOPS) from 1996-10-17 to 1998-03-15 (NCEI Accession 0000504) NOAA_NCEI STAC Catalog 1996-10-17 1998-03-15 163.34, -78.05, -165.91, -52.95 https://cmr.earthdata.nasa.gov/search/concepts/C2089375350-NOAA_NCEI.umm_json Phytoplankton and other data were collected in the Antarctic from the NATHANIEL B. PALMER and ROGER REVELL from 17 October 1996 to 15 March 1998. Bottle data include enumeration and counts of bacteria, picoplankton, nanoplankton and nano microplankton. Bottle data also include concentrations of trace metals. CTD data include conductivity, temperature, and salinity profiles. Data were collected in support of the US Joint Global Ocean Flux Study / Antarctic Environments Southern Ocean Process Study (JGOFS/AESOPS). proprietary
gov.noaa.nodc:0000525_Not Applicable Chlorophyll and brevetoxin data from the ECOHAB project along the west coast of Florida from 1999-2000 (NCEI Accession 0000525) NOAA_NCEI STAC Catalog 1999-09-10 2000-09-29 -87.23565, 25.44867, -81.71588, 30.39237 https://cmr.earthdata.nasa.gov/search/concepts/C2089375484-NOAA_NCEI.umm_json Water and sediment samples were collected on annual ECOHAB Process cruises and on isolated Mote transects (10/13/99 and 10/20/99). Samples will be analyzed for brevetoxin using a competetive ELISA assay (Naar and Baden, in progress) as well as a receptor-binding assay (VanDolah et al., 1994), and have been analyzed for chlorophyll a (water only) using the Welschmeyer (1994) non-acidification technique. (To be updated when data has been analyzed.) proprietary
gov.noaa.nodc:0000599_Not Applicable Aids to Navigation (ATONS) GIS data from the Gulf of Mexico and coastal waters of Alabama, Florida, Louisiana, Mississippi and Texas as of 1999-10-21 (NCEI Accession 0000599) NOAA_NCEI STAC Catalog 1999-01-01 1999-10-21 -98.320706, 17.398031, -61.876841, 32.288483 https://cmr.earthdata.nasa.gov/search/concepts/C2089376009-NOAA_NCEI.umm_json "This accession contains a GIS database of Aids to Navigation in the Gulf of Mexico and coastal waters of Alabama, Florida, Louisiana, Mississippi and Texas. These data were compiled on 1999-10-21. The term ""Aids to Navigation"" (ATONS or AIDS) refers to a device outside of a vessel used to assist mariners in determining their position or safe course, or to warn them of obstructions. AIDS to navigation include lighthouses, lights, buoy, sound signals, landmarks, racons, radio beacons, LORAN, and omega. These include AIDS which are installed and maintained by the Coast Guard as well as privately installed and maintained aids (permit required). This does not include unofficial AIDS (illegal) such as stakes, PVC pipes, and such placed without permission. Each USCG District Headquarters is responsible for updating their database on an ""as needed"" basis. When existing AIDS are destroyed or relocated and new AIDS are installed the database is updated. Each AID is assigned an official ""light listing number"". The light list is a document listing the current status of ATONS and it is published and distributed on a regular basis. Interim changes to the light list are published in local Notices to Mariners which are the official means which navigators are supposed to keep their charts current. In addition, the USCG broadcasts Notices to Mariners on the marine band radio as soon as changes of the status of individual AIDS are reported. The light list number and local Notices to Mariners reports are suggested ways to keep the database current on a regular or even ""real time"" basis. However, annual (or more frequent) updates of the entire dataset may be obtained from each USCG District Headquarters. Geographic Information System (GIS) software is required to display the data in this NCEI accession." proprietary
@@ -18388,8 +18395,8 @@ gov.noaa.nodc:0000794_Not Applicable A survey of selected coral and fish assembl
gov.noaa.nodc:0000794_Not Applicable A survey of selected coral and fish assemblages near the Waianae Ocean Outfall, Oahu, Hawaii, 1990-1999 (NCEI Accession 0000794) NOAA_NCEI STAC Catalog 1990-10-01 1999-08-31 -158.28, 21.41, -158.26, 21.43 https://cmr.earthdata.nasa.gov/search/concepts/C2089373252-NOAA_NCEI.umm_json During 1990-1999, coral growth and fish abundance were monitored at stations located at and in the vicinity of the Waianae Ocean Outfall. Comparisons of results with fish surveys showed no significant differences in the species composition or relative abundances of fish populations at Station W-2 (the sunken ship Mahi), which is located 1.2 km south of the diffuser. Fish abundance and species richness increased at Station W- 3, which is located at the diffuser, from 1990 to 1995, decreased in 1996, and increased again in 1997 through 1999. At Station WW, an inshore station located 0.8 km from shore, fish were abundant and speciose on the armor rock covering the pipeline. The fish species seen inshore are comparable to fish species seen in similar (boulder) natural biotopes around Hawaii. There were no significant differences in total mean coral cover at selected quadrats from 1994 to 1999 at Station W-2. However, there was a significant increase (approximately 8%) in total mean coral cover at this station from 1991 to 1999. At the diffuser, corals were seen growing on the diffuser pipe and on the riser discharge ports. In 1986, when the diffuser began operation at a discharge rate of 1.5 mgd (0.07 m3/s), no corals were seen at this location. At inshore station WW, corals off the pipeline were sparsely distributed but were numerous and thriving on the armor rock over the pipeline. In 1998 the inshore transect (Alpha), off the armor rock, was covered (30%) with the alga Dictyopteris plagiogramma; however, in 1999 it disappeared. This seaweed was also abundant at this location in 1995, 1996, and 1997. The water was clear at all stations surveyed (13 to 20 m horizontal visibility), and the surrounding sediments were clean and white. No significant deleterious effect due to outfall operation and discharge were seen on the biological community at the stations surveyed. The increase in fish diversity and abundance at the diffuser since 1997 may be due to natural fluctuations in abundance or to environmental conditions suitable to the fish populations living there. proprietary
gov.noaa.nodc:0000820_Not Applicable Bacteria Biomass and Chlorophyll-a depth profiles from bottle casts off the western Antarctic Peninsula from the R/V LAURENCE M. GOULD from 23 April 2001 to 01 September 2001 (NCEI Accession 0000820) NOAA_NCEI STAC Catalog 2001-04-29 2001-09-01 -72.42, -69.88, -67.04, -66.22 https://cmr.earthdata.nasa.gov/search/concepts/C2089373349-NOAA_NCEI.umm_json Bacteria and Chlorophyll data were collected from bottle cast of the western Antarctic peninsula from the R/V Laurence M. Gould. Data were collected by the University of Nevada/Desert Research Institute (DRI) in support of the Global Ocean Ecosystems Dynamic (GLOBEC) project from 23 April 2001 to 01 September 2001. Bacteria data include profiles of bacterial abundance and biomass. Chlorophyll-a data include concentration profiles. proprietary
gov.noaa.nodc:0000829_Not Applicable Broward County Florida thermographic data collected at twelve locations along four eastward lines that cross three offshore reef Tracks during the time period July 2000 to the present using self-recording temperature gauges (NCEI Accession 0000829) NOAA_NCEI STAC Catalog 2000-07-01 2002-11-30 -80.112007, 26.020458, -80.077343, 26.159952 https://cmr.earthdata.nasa.gov/search/concepts/C2089373393-NOAA_NCEI.umm_json "Broward County Florida has responsibility for the resource management of coral reefs in marine waters adjacent to Broward County. The Department of Planning and Environmental Protection is assigned the duties of monitoring the health of the coral reefs. Environmental stresses are a limiting factor in the biomass and diversity, and maintaining these populations of coral species requires an understanding of the environmental factors. One of these factors is the water temperature. Visual surveys are conducted by divers, and the staff has implemented an environmental monitoring program with water temperature as the first measured parameter. The monitoring program is on a ""not to interfere basis"" using self-recording thermographs for data acquisition. The thermographs are placed along coral reef tracks located in three separate bands near the northern most extent of the natural range for corals. The raw data are captured from the recorder by means of a laptop computer using transfer and conversion software provided by the instrument's vendor. Upon return to the office, the raw data are transferred to separate files that are then loaded into spreadsheet files. Each spreadsheet file corresponds to a single location and only one instrument. Twelve spreadsheet files are updated every sixty days for the dynamic raw data; the static geographical information is stored in a separate spreadsheet file." proprietary
-gov.noaa.nodc:0000861_Not Applicable A Hydrographic Survey of the Scotia Sea, 15 March 1999 to 22 April 1999 (NCEI Accession 0000861) NOAA_NCEI STAC Catalog 1999-03-15 1999-04-22 -68.260333, -67.576667, -2.296667, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2089373502-NOAA_NCEI.umm_json CTD and chemical data were collected using CTD and bottle casts in the Drake Passage and Scotia Sea from the JAMES CLARK ROSS. Data were collected from 15 March 1999 to 22 April 1999. Data were collected and submitted by the University of East Anglia with support of the Antarctic Large-scale Box Analysis and the Role of the Scotia Sea (ALBATROSS) project. proprietary
gov.noaa.nodc:0000861_Not Applicable A Hydrographic Survey of the Scotia Sea, 15 March 1999 to 22 April 1999 (NCEI Accession 0000861) ALL STAC Catalog 1999-03-15 1999-04-22 -68.260333, -67.576667, -2.296667, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2089373502-NOAA_NCEI.umm_json CTD and chemical data were collected using CTD and bottle casts in the Drake Passage and Scotia Sea from the JAMES CLARK ROSS. Data were collected from 15 March 1999 to 22 April 1999. Data were collected and submitted by the University of East Anglia with support of the Antarctic Large-scale Box Analysis and the Role of the Scotia Sea (ALBATROSS) project. proprietary
+gov.noaa.nodc:0000861_Not Applicable A Hydrographic Survey of the Scotia Sea, 15 March 1999 to 22 April 1999 (NCEI Accession 0000861) NOAA_NCEI STAC Catalog 1999-03-15 1999-04-22 -68.260333, -67.576667, -2.296667, 10 https://cmr.earthdata.nasa.gov/search/concepts/C2089373502-NOAA_NCEI.umm_json CTD and chemical data were collected using CTD and bottle casts in the Drake Passage and Scotia Sea from the JAMES CLARK ROSS. Data were collected from 15 March 1999 to 22 April 1999. Data were collected and submitted by the University of East Anglia with support of the Antarctic Large-scale Box Analysis and the Role of the Scotia Sea (ALBATROSS) project. proprietary
gov.noaa.nodc:0000879_Not Applicable Abundance data acquired in support of invasive species distribution studies at ten macroalgal ecology and taxonomic assessment sites in Hawaii during 2001 (NCEI Accession 0000879) ALL STAC Catalog 2001-01-26 2001-05-18 -158.14, 19.27, -155.05, 21.37 https://cmr.earthdata.nasa.gov/search/concepts/C2089373608-NOAA_NCEI.umm_json Abundance data represent estimates of percent cover of species type (coral or algal) in 10 randomly placed quadrats along two 50 meter transect lines of each site. Data are available for 10 sites from Oahu to the Island of Hawaii taken in 2001 in support of the Macroalgal Ecology and Taxonomic Assessment (TEAM) Project. The species for abundance estimates include 11 corals, 5 invertebrates, 33 algals, and 2 benthic types (turf or sand). The role that marine algae play in a coral reef system is often overlooked because of lack of knowledge that they are the primary producers in the system. The coral reef ecosystem in Hawaii contains about ten times more algal species than coral species, some of them regulating space that permits coral recruitment. The primary purpose of the TEAM research program is to provide taxonomic and ecological algal expertise for the Coral Reef Monitoring and Assessment Program (CRAMP). Our group also seeks to develop, implement and assess new methodologies for quantitatively surveying benthic algal communities in the Hawaiian Islands. proprietary
gov.noaa.nodc:0000879_Not Applicable Abundance data acquired in support of invasive species distribution studies at ten macroalgal ecology and taxonomic assessment sites in Hawaii during 2001 (NCEI Accession 0000879) NOAA_NCEI STAC Catalog 2001-01-26 2001-05-18 -158.14, 19.27, -155.05, 21.37 https://cmr.earthdata.nasa.gov/search/concepts/C2089373608-NOAA_NCEI.umm_json Abundance data represent estimates of percent cover of species type (coral or algal) in 10 randomly placed quadrats along two 50 meter transect lines of each site. Data are available for 10 sites from Oahu to the Island of Hawaii taken in 2001 in support of the Macroalgal Ecology and Taxonomic Assessment (TEAM) Project. The species for abundance estimates include 11 corals, 5 invertebrates, 33 algals, and 2 benthic types (turf or sand). The role that marine algae play in a coral reef system is often overlooked because of lack of knowledge that they are the primary producers in the system. The coral reef ecosystem in Hawaii contains about ten times more algal species than coral species, some of them regulating space that permits coral recruitment. The primary purpose of the TEAM research program is to provide taxonomic and ecological algal expertise for the Coral Reef Monitoring and Assessment Program (CRAMP). Our group also seeks to develop, implement and assess new methodologies for quantitatively surveying benthic algal communities in the Hawaiian Islands. proprietary
gov.noaa.nodc:0000918_Not Applicable Chemical data from bottle casts in the Arctic Ocean and other Sea areas by the University of Alaska, from 16 April 1948 to 17 September 2000 (NCEI Accession 0000918) NOAA_NCEI STAC Catalog 1948-04-16 2000-09-17 -71, 16, -80.123, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089373877-NOAA_NCEI.umm_json Chemical data were collected using bottle casts from multiple vessels in the Arctic Ocean and other Sea areas from 16 April 1948 to 17 September 2000. Data were submitted by the University of Alaska in Fairbanks, Alaska. Chemical data include alkalinity, nitrate, nitrite, oxygen, silicate, and phosphate. proprietary
@@ -18410,11 +18417,11 @@ gov.noaa.nodc:0001344_Not Applicable Chlorophyll and Plankton data from the Indi
gov.noaa.nodc:0001410_Not Applicable Bathymetric Survey of the West Florida Shelf, Gulf of Mexico 2001 (NCEI Accession 0001410) NOAA_NCEI STAC Catalog 2001-09-03 2001-10-12 -86.71, 28.04, -84.61, 30.06 https://cmr.earthdata.nasa.gov/search/concepts/C2089376038-NOAA_NCEI.umm_json A zone of deep-water reefs is thought to extend from the mid and outer shelf south of Mississippi and Alabama to at least the northwestern Florida shelf off Panama City, Florida. Reefs off Mississippi and Alabama are found in water depths of 60 to 120 m (Ludwick and Walton, 1957, Gardner et al., in press) and were the focus of a multibeam echosounder mapping survey by the U.S. Geological Survey (USGS) in 2000 (Gardner et al., 2000, in press). It is critical to determine the accurate geomorphology and type of the reefs that occur because of their importance as benthic habitats for fisheries. These data are ArcInfo GRID and XYZ ASCII format data generated from a U.S. Geological Survey multibeam sonar survey of the West Florida Shelf, Gulf of Mexico. The data include high-resolution bathymetry and calibrated acoustic backscatter. File types include arc files .dat, .nit, and .adf. Documentation is included as metadata .txt files. Because the area is so large (i.e., the file sizes are very large), the area was subdivided into North, Central, and South regions as reflected in the data subdirectories for this accession. proprietary
gov.noaa.nodc:0001419_Not Applicable Assessment of Nonindigenous Species on Coral Reefs in the Hawaiian Islands, with Emphasis on Introduced Invertebrates, November 2, 2002 - November 5, 2003 (NCEI Accession 0001419) NOAA_NCEI STAC Catalog 2002-11-02 2003-11-05 -159.65, 19.5, -155.83, 21.96 https://cmr.earthdata.nasa.gov/search/concepts/C2089376077-NOAA_NCEI.umm_json Coral reefs on the islands of Kauai, Molokai, Maui, Hawaii and Oahu were surveyed for the presence and impact of marine nonindigenous and cryptogenic species (NIS) using a rapid assessment method that standardized search effort for approximately 312 m2 at each site. A total of 41 sites were surveyed by three investigators for a total of approximately 120 hours search time on the five islands. Algae, invertebrate, and fish taxa were identified on site or returned to laboratory for identity confirmation. Only 26 NIS, comprised of three species of algae, 19 invertebrates, and four fishes were recorded from a total of 486 total taxa on the entire study, and 17 of the NIS occurred at only one or two sites. The most NIS that occurred at any site was six, and 21 of the sites had less than three. If the three species of fish that were introduced in the 1950s and known to occur throughout Hawaii are excluded, over half the sites had less than two NIS. proprietary
gov.noaa.nodc:0001624_Not Applicable Bottle and Pumpcast data collected by CTD casts from the R/V Knorr during cruises 2 through 5 of the 1988 Black Sea Oceanographic Expedition (NCEI Accession 0001624) NOAA_NCEI STAC Catalog 1988-05-14 1988-07-29 28, 41, 42, 46 https://cmr.earthdata.nasa.gov/search/concepts/C2089372426-NOAA_NCEI.umm_json Not provided proprietary
-gov.noaa.nodc:0001746_Not Applicable ALINE time series (NCEI Accession 0001746) ALL STAC Catalog 1989-01-01 2001-01-01 141, 37, 150, 44 https://cmr.earthdata.nasa.gov/search/concepts/C2089372824-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:0001746_Not Applicable ALINE time series (NCEI Accession 0001746) NOAA_NCEI STAC Catalog 1989-01-01 2001-01-01 141, 37, 150, 44 https://cmr.earthdata.nasa.gov/search/concepts/C2089372824-NOAA_NCEI.umm_json Not provided proprietary
+gov.noaa.nodc:0001746_Not Applicable ALINE time series (NCEI Accession 0001746) ALL STAC Catalog 1989-01-01 2001-01-01 141, 37, 150, 44 https://cmr.earthdata.nasa.gov/search/concepts/C2089372824-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:0001756_Not Applicable Assessment of economic benefits and costs of marine managed areas in Hawaii, 1998 - 2003 (NCEI Accession 0001756) NOAA_NCEI STAC Catalog 1998-01-01 2003-12-31 -158.9, 18.8, -154.9, 22.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089372862-NOAA_NCEI.umm_json "This dataset combines the research results from a number of papers carried out under the study ""Assessment of Economic Benefits and Costs of Marine Managed Areas in Hawaii"". The studies included a paper on the fisheries benefits of MMAs (Friedlander and Cesar, 2004), a write-up of the recreational survey at the MMA sites (Van Beukering and Cesar, 2004), a background on the institutional/regulatory framework on MMAs in Hawaii (Cesar, 2004), a paper on the economic value and cost-benefit analysis of management options for MMAs (Van Beukering and Cesar, 2004) and a paper on the international experience of sustainable financing of MMAs (Cesar and van Beukering, 2004). This dataset is basically a set of MS Word documents with mostly social-economic data embedded within tables. The habitat and fish data in this dataset are drawn from other datasets already in the NOAA archives, the NOAA Benthic Habitat Maps and the Coral Reef Assessment and Monitoring Program (CRAMP), respectively." proprietary
-gov.noaa.nodc:0001941_Not Applicable Aerial surveys of bowhead and beluga whales along with incidental sighting of other marine mammals in the Bering, Beaufort and Chukchi Seas for the Bowhead Whale Aerial Survey Project (BWASP), 1979 - 2004 (NCEI Accession 0001941) NOAA_NCEI STAC Catalog 1979-04-01 2004-10-18 -174.01, 57.72, -125.25, 76.14 https://cmr.earthdata.nasa.gov/search/concepts/C2089373265-NOAA_NCEI.umm_json "The Minerals Management Service (MMS), previously Bureau of Land Management, has funded fall bowhead whale aerial surveys in this area each year since 1978, using a repeatable protocol from 1982 to the present. Bowhead monitoring by MMS Environmental Studies Section, Alaska Outer Continental Shelf (OCS) Region, normally overlaps the September-October ""open-water"" season when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Nuiqsut, and Barrow, Alaska. The primary survey aircraft was a de Havilland Twin Otter Series 300. The aircraft was equipped with three medium-size bubble windows that afforded complete viewing of the track-line. Geographic positions of the aircraft were logged onto a laptop computer from a Global Navigation System (1982-1991) or a Global Positioning System (1992-2000). Prior to 1992, many surveys in Block 12 (See Browse Graphic) were conducted from a Grumman Turbo Goose Model G21G. All bowhead (and beluga) whales observed were recorded, along with incidental sightings of other marine mammals. Particular emphasis was placed on regional surveys to assess large-area shifts in the migration pathway of bowhead whales and on the coordination of effort and management of data necessary to support seasonal offshore-drilling and seismic-exploration regulations. The selection of survey blocks to be flown on a given day was nonrandom, based primarily on criteria such as observed and predicted weather conditions over the study area and offshore oil-industry activities. Otherwise, the project attempted to distribute effort fairly evenly east-to-west across the entire study area. Aerial coverage favored inshore survey blocks (See Browse Graphic), since bowheads were rarely sighted north of these blocks in previous surveys (1979-1986). Surveys were flown at a target altitude of 458 m in order to maximize visibility and to minimize potential disturbance to marine mammals. Flights were normally aborted when cloud ceilings were consistently less than 305 m or the wind force was consistently above Beaufort 4. Daily flight patterns were based on sets of non-repeating transect grids computer-generated for each survey block. Transect grids were derived by dividing each survey block into sections 30 minutes of longitude across. One of the minute marks along the northern edge of each section was selected at random then connected by a straight line to a similarly selected endpoint along the southern edge of that same section. This procedure was followed for all sections of that survey block. These transect legs were then connected alternately at their northernmost or southernmost ends to produce one continuous flight grid within each survey block. Gridlines were occasionally lengthened to cover both an inshore block and the block north of it. Lines were occasionally truncated due to extended poor visibility or to avoid potential interference with subsistence whaling activities. For bowheads encountered ""on transect"", the aircraft sometimes circled for a brief (< 10 min) period to observe behavior, obtain better estimates of their numbers, and/or determine whether calves were present. Any new groups sighted when circling were recorded as ""on search""." proprietary
gov.noaa.nodc:0001941_Not Applicable Aerial surveys of bowhead and beluga whales along with incidental sighting of other marine mammals in the Bering, Beaufort and Chukchi Seas for the Bowhead Whale Aerial Survey Project (BWASP), 1979 - 2004 (NCEI Accession 0001941) ALL STAC Catalog 1979-04-01 2004-10-18 -174.01, 57.72, -125.25, 76.14 https://cmr.earthdata.nasa.gov/search/concepts/C2089373265-NOAA_NCEI.umm_json "The Minerals Management Service (MMS), previously Bureau of Land Management, has funded fall bowhead whale aerial surveys in this area each year since 1978, using a repeatable protocol from 1982 to the present. Bowhead monitoring by MMS Environmental Studies Section, Alaska Outer Continental Shelf (OCS) Region, normally overlaps the September-October ""open-water"" season when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Nuiqsut, and Barrow, Alaska. The primary survey aircraft was a de Havilland Twin Otter Series 300. The aircraft was equipped with three medium-size bubble windows that afforded complete viewing of the track-line. Geographic positions of the aircraft were logged onto a laptop computer from a Global Navigation System (1982-1991) or a Global Positioning System (1992-2000). Prior to 1992, many surveys in Block 12 (See Browse Graphic) were conducted from a Grumman Turbo Goose Model G21G. All bowhead (and beluga) whales observed were recorded, along with incidental sightings of other marine mammals. Particular emphasis was placed on regional surveys to assess large-area shifts in the migration pathway of bowhead whales and on the coordination of effort and management of data necessary to support seasonal offshore-drilling and seismic-exploration regulations. The selection of survey blocks to be flown on a given day was nonrandom, based primarily on criteria such as observed and predicted weather conditions over the study area and offshore oil-industry activities. Otherwise, the project attempted to distribute effort fairly evenly east-to-west across the entire study area. Aerial coverage favored inshore survey blocks (See Browse Graphic), since bowheads were rarely sighted north of these blocks in previous surveys (1979-1986). Surveys were flown at a target altitude of 458 m in order to maximize visibility and to minimize potential disturbance to marine mammals. Flights were normally aborted when cloud ceilings were consistently less than 305 m or the wind force was consistently above Beaufort 4. Daily flight patterns were based on sets of non-repeating transect grids computer-generated for each survey block. Transect grids were derived by dividing each survey block into sections 30 minutes of longitude across. One of the minute marks along the northern edge of each section was selected at random then connected by a straight line to a similarly selected endpoint along the southern edge of that same section. This procedure was followed for all sections of that survey block. These transect legs were then connected alternately at their northernmost or southernmost ends to produce one continuous flight grid within each survey block. Gridlines were occasionally lengthened to cover both an inshore block and the block north of it. Lines were occasionally truncated due to extended poor visibility or to avoid potential interference with subsistence whaling activities. For bowheads encountered ""on transect"", the aircraft sometimes circled for a brief (< 10 min) period to observe behavior, obtain better estimates of their numbers, and/or determine whether calves were present. Any new groups sighted when circling were recorded as ""on search""." proprietary
+gov.noaa.nodc:0001941_Not Applicable Aerial surveys of bowhead and beluga whales along with incidental sighting of other marine mammals in the Bering, Beaufort and Chukchi Seas for the Bowhead Whale Aerial Survey Project (BWASP), 1979 - 2004 (NCEI Accession 0001941) NOAA_NCEI STAC Catalog 1979-04-01 2004-10-18 -174.01, 57.72, -125.25, 76.14 https://cmr.earthdata.nasa.gov/search/concepts/C2089373265-NOAA_NCEI.umm_json "The Minerals Management Service (MMS), previously Bureau of Land Management, has funded fall bowhead whale aerial surveys in this area each year since 1978, using a repeatable protocol from 1982 to the present. Bowhead monitoring by MMS Environmental Studies Section, Alaska Outer Continental Shelf (OCS) Region, normally overlaps the September-October ""open-water"" season when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Nuiqsut, and Barrow, Alaska. The primary survey aircraft was a de Havilland Twin Otter Series 300. The aircraft was equipped with three medium-size bubble windows that afforded complete viewing of the track-line. Geographic positions of the aircraft were logged onto a laptop computer from a Global Navigation System (1982-1991) or a Global Positioning System (1992-2000). Prior to 1992, many surveys in Block 12 (See Browse Graphic) were conducted from a Grumman Turbo Goose Model G21G. All bowhead (and beluga) whales observed were recorded, along with incidental sightings of other marine mammals. Particular emphasis was placed on regional surveys to assess large-area shifts in the migration pathway of bowhead whales and on the coordination of effort and management of data necessary to support seasonal offshore-drilling and seismic-exploration regulations. The selection of survey blocks to be flown on a given day was nonrandom, based primarily on criteria such as observed and predicted weather conditions over the study area and offshore oil-industry activities. Otherwise, the project attempted to distribute effort fairly evenly east-to-west across the entire study area. Aerial coverage favored inshore survey blocks (See Browse Graphic), since bowheads were rarely sighted north of these blocks in previous surveys (1979-1986). Surveys were flown at a target altitude of 458 m in order to maximize visibility and to minimize potential disturbance to marine mammals. Flights were normally aborted when cloud ceilings were consistently less than 305 m or the wind force was consistently above Beaufort 4. Daily flight patterns were based on sets of non-repeating transect grids computer-generated for each survey block. Transect grids were derived by dividing each survey block into sections 30 minutes of longitude across. One of the minute marks along the northern edge of each section was selected at random then connected by a straight line to a similarly selected endpoint along the southern edge of that same section. This procedure was followed for all sections of that survey block. These transect legs were then connected alternately at their northernmost or southernmost ends to produce one continuous flight grid within each survey block. Gridlines were occasionally lengthened to cover both an inshore block and the block north of it. Lines were occasionally truncated due to extended poor visibility or to avoid potential interference with subsistence whaling activities. For bowheads encountered ""on transect"", the aircraft sometimes circled for a brief (< 10 min) period to observe behavior, obtain better estimates of their numbers, and/or determine whether calves were present. Any new groups sighted when circling were recorded as ""on search""." proprietary
gov.noaa.nodc:0002013_Not Applicable A mesoscale hydrographic survey off Northwest Africa to examine the horizontal mixing by eddies, March - April 2003 (NCEI Accession 0002013) NOAA_NCEI STAC Catalog 2003-03-26 2003-04-16 -31.5, 6.6, -25, 11 https://cmr.earthdata.nasa.gov/search/concepts/C2089373546-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:0002013_Not Applicable A mesoscale hydrographic survey off Northwest Africa to examine the horizontal mixing by eddies, March - April 2003 (NCEI Accession 0002013) ALL STAC Catalog 2003-03-26 2003-04-16 -31.5, 6.6, -25, 11 https://cmr.earthdata.nasa.gov/search/concepts/C2089373546-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:0002170_Not Applicable 22 Real-time XBT replacements assembled by Canada Department of Fisheries and Oceans (DFO) for the Global Temperature-Salinity Profile Program (GTSPP), dates ranging from 05/26/2004 to 05/27/2004 (NCEI Accession 0002170) NOAA_NCEI STAC Catalog 2004-05-27 2004-05-27 9.106, 31.684, 33.058, 44.043 https://cmr.earthdata.nasa.gov/search/concepts/C2089373990-NOAA_NCEI.umm_json Not provided proprietary
@@ -18425,31 +18432,31 @@ gov.noaa.nodc:0002193_Not Applicable A survey by Texas A & M University to chara
gov.noaa.nodc:0002193_Not Applicable A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002193) NOAA_NCEI STAC Catalog 1999-09-01 2002-08-01 -96, 23.47, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374098-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary
gov.noaa.nodc:0002196_Not Applicable Acoustic doppler current meter data collected in support of the Minerals Management Service-supported Deep Water Program in the the Gulf of Mexico, 1999 - 2003 (NCEI Accession 0002196) ALL STAC Catalog 1999-09-01 2003-08-01 -96, 23.47, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374197-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary
gov.noaa.nodc:0002196_Not Applicable Acoustic doppler current meter data collected in support of the Minerals Management Service-supported Deep Water Program in the the Gulf of Mexico, 1999 - 2003 (NCEI Accession 0002196) NOAA_NCEI STAC Catalog 1999-09-01 2003-08-01 -96, 23.47, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374197-NOAA_NCEI.umm_json "A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled ""The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology."" Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation." proprietary
-gov.noaa.nodc:0002198_Not Applicable A survey to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002198) NOAA_NCEI STAC Catalog 1999-09-01 2002-08-01 -96, 23.49, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374298-NOAA_NCEI.umm_json A research program has been initiated by the Minerals Management Service (Contract No.1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology. Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation. proprietary
gov.noaa.nodc:0002198_Not Applicable A survey to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002198) ALL STAC Catalog 1999-09-01 2002-08-01 -96, 23.49, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374298-NOAA_NCEI.umm_json A research program has been initiated by the Minerals Management Service (Contract No.1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology. Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation. proprietary
+gov.noaa.nodc:0002198_Not Applicable A survey to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002198) NOAA_NCEI STAC Catalog 1999-09-01 2002-08-01 -96, 23.49, -85.47, 29.33 https://cmr.earthdata.nasa.gov/search/concepts/C2089374298-NOAA_NCEI.umm_json A research program has been initiated by the Minerals Management Service (Contract No.1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology. Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation. proprietary
gov.noaa.nodc:0002199_Not Applicable Biological, chemical, and physical data from CTD/XCTD from five Japanese R/Vs in the North Pacific Ocean and other marginal basins from 1993 to 2003 (NCEI Accession 0002199) NOAA_NCEI STAC Catalog 1993-01-01 2003-12-31 179, 20, 130, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2089374415-NOAA_NCEI.umm_json The Japan Meteorological Agency (JMA) has been carrying out oceanographic and marine meteorological observations on board research vessels, at the coastal water temperature observation stations and by ocean data buoys, for the purposes of the better understanding of dynamical processes of the oceanic general circulation affecting climate change, prevention and mitigation of natural disasters, and contributing to international cooperative activities. This Data Report contains the data obtained from the observations made by JMA in 2003 together with the explanations. The observations include the followings: 1. Oceanographic and Marine Meteorological Observations on board Research Vessels Oceanographic observations are conducted in the seas adjacent to Japan and in the western North Pacific on board five vessels: Ryofu Maru, Keifu Maru, Kofu Maru, Chofu Maru and Seifu Maru. 2. Coastal Water Temperature Observations JMA has carried out water temperature observations at the coastal stations. Historical time series of 10 day and monthly mean temperatures, daily observations and hourly observations are available in this CD-ROM. 3. Ocean Data Buoy Observations Operational ocean data buoy observations have been made to obtain marine meteorological and oceanographic observations in the seas around Japan. Correspondence relating to this Data Report may be directed to: Marine Division Climate and Marine Department Japan Meteorological Agency 1-3-4 Otemachi, Chiyoda-ku, Tokyo, 100-8122 JAPAN Facsimile: +81-3-3211-6908 E-mail: seadata@hq.kishou.go.jp proprietary
gov.noaa.nodc:0002270_Not Applicable Assessment of nonindigenous marine species in harbors and nearby coral reefs on Kauai, Molokai, Maui, and Hawaii, 2002 - 2003 (NCEI Accession 0002270) NOAA_NCEI STAC Catalog 2002-11-02 2003-06-28 -159.59, 19.73, -155.02, 21.96 https://cmr.earthdata.nasa.gov/search/concepts/C2089374772-NOAA_NCEI.umm_json Collections and observations in 2002-2003 at harbor and nearby reef sites at Nawilwili and Port Allen, Kauai; Hale O Lono and Kaunakakai, Molokai; Kahului and Maalaea, Maui; and Kawaihae and Hilo, Hawaii recorded a total of 1039 taxa of marine algae, invertebrates, and fishes, 872 of which were identified to the species level. Of these 11 were new reports for Hawaii and 112 were identified as introduced or cryptogenic species (NIS), for an overall NIS component of 10.9% of the total taxa recorded. Contrasting patterns were found between the distributions of the total identified taxa and NIS, with greater numbers of total taxa occurring at reef stations and greater numbers of NIS occurring in harbors, where they composed up to 36% of the total identified taxa. Occurrence and abundance of NIS decreased systematically from maxima in highly used commercial harbors which are isolated from oceanic circulation to relatively exposed small boat harbors to fully exposed reef sites. Only a few NIS that frequently occurred at harbor sites also occurred at reef sites. These results concur with previous studies in Hawaii and the tropical Pacific that have indicated NIS to show maximum numbers in harbors and embayments with restricted oceanic circulation and few introduced or cryptogenic species to occur on coral reefs or other ocean exposed environments. proprietary
-gov.noaa.nodc:0002295_Not Applicable A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002295) NOAA_NCEI STAC Catalog 1999-09-01 2002-08-20 -92.01, 23.79, -85.49, 25.49 https://cmr.earthdata.nasa.gov/search/concepts/C2089374863-NOAA_NCEI.umm_json A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology. Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation. proprietary
gov.noaa.nodc:0002295_Not Applicable A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002295) ALL STAC Catalog 1999-09-01 2002-08-20 -92.01, 23.79, -85.49, 25.49 https://cmr.earthdata.nasa.gov/search/concepts/C2089374863-NOAA_NCEI.umm_json A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology. Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation. proprietary
+gov.noaa.nodc:0002295_Not Applicable A survey by Texas A & M University to characterize the principal components of benthic communities over the entire northern Gulf of Mexico, 1999 - 2002 (NCEI Accession 0002295) NOAA_NCEI STAC Catalog 1999-09-01 2002-08-20 -92.01, 23.79, -85.49, 25.49 https://cmr.earthdata.nasa.gov/search/concepts/C2089374863-NOAA_NCEI.umm_json A research program has been initiated by the Minerals Management Service (Contract No. 1435-01-99-CT-30991) to gain better knowledge of the benthic communities of the deep Gulf of Mexico entitled The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology. Increasing exploration and exploitation of fossil hydrocarbon resources in the deep-sea prompted the Minerals Management Service of the U.S. Department of the Interior to support an investigation of the structure and function of the assemblages of organisms that live in association with the sea floor in the deep-sea. The program, Deep Gulf of Mexico Benthos or DGoMB, is studying the northern Gulf of Mexico (GOM) continental slope from water depths of 300 meters on the upper continental slope out to greater than 3,000 meters water depth seaward of the base of the Sigsbee and Florida Escarpments. The study is focused on areas that are the most likely targets of future resource exploration and exploitation. proprietary
gov.noaa.nodc:0002316_Not Applicable Biological and other data collected from bottle casts in the NW Atlantic Ocean from HERMANO GINES from 16 January 2002 to 18 May 2004 (NCEI Accession 0002316) NOAA_NCEI STAC Catalog 2002-01-16 2004-05-18 -64.66, 10.48, -64.65, 10.48 https://cmr.earthdata.nasa.gov/search/concepts/C2089374930-NOAA_NCEI.umm_json Data collected in support of the CARIACO program, which is studying the relationship between surface primary production, physical forcing variables like the wind, and the settling flux of particulate carbon in the Cariaco Basin on the continental shelf of Venezuela. Data were collected from 16 January 2002 to 18 May 2004. proprietary
gov.noaa.nodc:0002352_Not Applicable ARGO profiling float temperature, salinity, and oxygen data measurements collected using profiling floats in the World Ocean from 1996 to 2005 (NCEI Accession 0002352) NOAA_NCEI STAC Catalog 1996-01-05 2005-08-10 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089375016-NOAA_NCEI.umm_json The U.S. National Oceanographic Data Center (NODC) operates the Global Argo Data Repository (GADR) as the long-term archive for the International Global Argo Project (for additional information about ARGO, see http://www.argo.ucsd.edu (last accessed December 2003)). Argo data archived by the USNODC on a weekly basis starting the second quarter of FY 2003, may include real-time and/or delayed mode profiles of ocean temperature and salinity, as well as related conductivity and/or pressure measurements (if any), collected by Argo profiling floats. proprietary
gov.noaa.nodc:0002449_Not Applicable Bottle data collected for chemical analysis along the coastal waters of Hawai'i as part of the Windward Community College Heeia Stream and Kaneohe Bay Water Quality Assessment Project from May 22, 2004 to March 19, 2005 (NCEI Accession 0002449) NOAA_NCEI STAC Catalog 2004-05-22 2005-03-19 -157.8164, 21.4175, -157.8078, 21.4483 https://cmr.earthdata.nasa.gov/search/concepts/C2089375205-NOAA_NCEI.umm_json Measurements of water quality parameters were taken by Windward Community College faculty and students at eight sites in the Heeia Stream and adjacent Kaneohe Bay waters from May 2004 through March 2005. Parameters include Combined Nitrogen, Photo Oxidized Nitrate, Photo Oxidized Nitrite, Total Nitrogen, and Total Phosphate. Data provided as MS Excel spreadsheets and redundant ASCII copies were made of each with same file name except for a CSV (Comma Separated Version) extension. proprietary
gov.noaa.nodc:0002602_Not Applicable Assessment of invasiveness of the Orange Keyhole Sponge, Mycale Armata, in Kaneohe Bay Oahu, Hawaii, based on surveys 2004-2005 (NCEI Accession 0002602) NOAA_NCEI STAC Catalog 2004-01-02 2005-12-31 -157.85, 21.41, -157.76, 21.51 https://cmr.earthdata.nasa.gov/search/concepts/C2089375498-NOAA_NCEI.umm_json The Orange Keyhole Sponge, Mycale armata Thiele, was unknown in Hawaii prior to 1996. First reported in Pearl Harbor, it now occurs in virtually every commercial harbor in the main Hawaiian islands, where it can be a major component of the fouling community on harbor piers and jetties. It has been reported from a few coral reef locations near harbors, but in Kaneohe Bay it has become a major component of the benthic biota in the south bay in the last 5-10 years. A study was conducted in 2004-2005 to determine Mycale armata's distribution, abundance throughout the bay, its growth rates on permanent quadrats, and whether mechanical removal would be an effective management technique for its control. Results from 190 manta board surveys on 28 reefs and paired 25 m belt transects using photo quadrats on 19 reefs indicated that the sponge had maximal coverage in the south-central part of the bay, in the vicinity of Coconut Island. proprietary
-gov.noaa.nodc:0002650_Not Applicable A survey of the marine biota of the island of Lanai, Hawaii, to determine the presence and impact of marine non-indigenous and cryptogenic species, February - March 2005 (NCEI Accession 0002650) NOAA_NCEI STAC Catalog 2005-02-28 2005-03-04 -157.05, 20.73, -156.88, 20.92 https://cmr.earthdata.nasa.gov/search/concepts/C2089375642-NOAA_NCEI.umm_json A baseline survey of the marine biota of the island of Lanai was conducted in May 2005. This was first comprehensive study that has been made on this island for all components of its marine nearshore community. Samples and observations were taken at seven sites around the island, and all macroalgae, macroinvertebrates and fish species collected or observed were recorded. On-site observations without collections were made at two other sites. Identified species were designated as native, nonindigenous (introduced) or cryptogenic (neither demonstrably native nor introduced) according to criteria used for previous introduced species surveys in Hawaii. A total of 294 taxa were observed or identified from collected specimens, which included 16 introduced or cryptogenic species and three new reports for the Hawaiian Islands. The 16 introduced and cryptogenic species comprised 5.4% of the total identified taxa and included seven cnidarians, one polychaete, two pericards, one decapod, one bryozoan, two ascidians and three fish. By station, the introduced/cryptogenic component ranged 3 to 7 species and 3.8% to 6.8% of the total biota. The stations included two sites at or near Kaumalapau Harbor, Lanai's principal harbor for inter-island shipping. The percent component values are similar to those that have been determined on ocean-exposed reef areas elsewhere in the Hawaiian Islands but the harbor value is well below the values in other Hawaiian harbors that are more isolated from open ocean circulation than Kaumalapau Harbor. No invasive introduced algae and only two invasive introduced invertebrates were found on the surveys. These were a single colony of the octocoral Carijoa riisei in the vicinity of Cathedrals between Manele Bay and Harbor, and a single stomatopod Gonodactylaceous falcatus at the site closest to Manele Harbor. proprietary
gov.noaa.nodc:0002650_Not Applicable A survey of the marine biota of the island of Lanai, Hawaii, to determine the presence and impact of marine non-indigenous and cryptogenic species, February - March 2005 (NCEI Accession 0002650) ALL STAC Catalog 2005-02-28 2005-03-04 -157.05, 20.73, -156.88, 20.92 https://cmr.earthdata.nasa.gov/search/concepts/C2089375642-NOAA_NCEI.umm_json A baseline survey of the marine biota of the island of Lanai was conducted in May 2005. This was first comprehensive study that has been made on this island for all components of its marine nearshore community. Samples and observations were taken at seven sites around the island, and all macroalgae, macroinvertebrates and fish species collected or observed were recorded. On-site observations without collections were made at two other sites. Identified species were designated as native, nonindigenous (introduced) or cryptogenic (neither demonstrably native nor introduced) according to criteria used for previous introduced species surveys in Hawaii. A total of 294 taxa were observed or identified from collected specimens, which included 16 introduced or cryptogenic species and three new reports for the Hawaiian Islands. The 16 introduced and cryptogenic species comprised 5.4% of the total identified taxa and included seven cnidarians, one polychaete, two pericards, one decapod, one bryozoan, two ascidians and three fish. By station, the introduced/cryptogenic component ranged 3 to 7 species and 3.8% to 6.8% of the total biota. The stations included two sites at or near Kaumalapau Harbor, Lanai's principal harbor for inter-island shipping. The percent component values are similar to those that have been determined on ocean-exposed reef areas elsewhere in the Hawaiian Islands but the harbor value is well below the values in other Hawaiian harbors that are more isolated from open ocean circulation than Kaumalapau Harbor. No invasive introduced algae and only two invasive introduced invertebrates were found on the surveys. These were a single colony of the octocoral Carijoa riisei in the vicinity of Cathedrals between Manele Bay and Harbor, and a single stomatopod Gonodactylaceous falcatus at the site closest to Manele Harbor. proprietary
+gov.noaa.nodc:0002650_Not Applicable A survey of the marine biota of the island of Lanai, Hawaii, to determine the presence and impact of marine non-indigenous and cryptogenic species, February - March 2005 (NCEI Accession 0002650) NOAA_NCEI STAC Catalog 2005-02-28 2005-03-04 -157.05, 20.73, -156.88, 20.92 https://cmr.earthdata.nasa.gov/search/concepts/C2089375642-NOAA_NCEI.umm_json A baseline survey of the marine biota of the island of Lanai was conducted in May 2005. This was first comprehensive study that has been made on this island for all components of its marine nearshore community. Samples and observations were taken at seven sites around the island, and all macroalgae, macroinvertebrates and fish species collected or observed were recorded. On-site observations without collections were made at two other sites. Identified species were designated as native, nonindigenous (introduced) or cryptogenic (neither demonstrably native nor introduced) according to criteria used for previous introduced species surveys in Hawaii. A total of 294 taxa were observed or identified from collected specimens, which included 16 introduced or cryptogenic species and three new reports for the Hawaiian Islands. The 16 introduced and cryptogenic species comprised 5.4% of the total identified taxa and included seven cnidarians, one polychaete, two pericards, one decapod, one bryozoan, two ascidians and three fish. By station, the introduced/cryptogenic component ranged 3 to 7 species and 3.8% to 6.8% of the total biota. The stations included two sites at or near Kaumalapau Harbor, Lanai's principal harbor for inter-island shipping. The percent component values are similar to those that have been determined on ocean-exposed reef areas elsewhere in the Hawaiian Islands but the harbor value is well below the values in other Hawaiian harbors that are more isolated from open ocean circulation than Kaumalapau Harbor. No invasive introduced algae and only two invasive introduced invertebrates were found on the surveys. These were a single colony of the octocoral Carijoa riisei in the vicinity of Cathedrals between Manele Bay and Harbor, and a single stomatopod Gonodactylaceous falcatus at the site closest to Manele Harbor. proprietary
gov.noaa.nodc:0002805_Not Applicable Chlorophyll data collected from the old outfall site in the south sector of Kaneohe Bay, Oahu, Hawaii, February 2001 to May 2004 (NCEI Accession 0002805) NOAA_NCEI STAC Catalog 2001-02-07 2004-05-26 -157.77, 21.41, -157.77, 21.41 https://cmr.earthdata.nasa.gov/search/concepts/C2089376053-NOAA_NCEI.umm_json Kaneohe Bay received increasing amounts of sewage from the 1950s through 1977. Most sewage was diverted from the bay in 1977 and early 1978. Data were collected beginning in September 1976 and continued until June 1979. The time series was re-established in June 1982 and continued to December 2005, when it was terminated. The sampling was at 1 m depth in the south sector of Kaneohe Bay, Oahu near the old outfall that ceased in 1977. Previous NODC Accessions 0000396 (1976-1979) and 0000422 (1982-1/2001) contained monthly averages of chlorophyll a, based on weekly to bi-weekly samples. This data set has the weekly to bi-weekly chlorophyll a, pheo, water temperature, secchi depth, and sample site depth. Additional data were taken from June 2004 - December 2005 and these will be available in a separate data set. proprietary
gov.noaa.nodc:0013170_Not Applicable Chemical and biological data collected as part of the CArbon Retention In A Colored Ocean (CARIACO) program in the Cariaco Basin off the coast of Venezuela, January 17, 2005 - January 16, 2006 (NCEI Accession 0013170) NOAA_NCEI STAC Catalog 2005-01-17 2006-01-16 -65.56, 10.45, -64.65, 10.66 https://cmr.earthdata.nasa.gov/search/concepts/C2089372614-NOAA_NCEI.umm_json Chemical and biological data were collected using bottle casts on the continental shelf of Venezuela from the HERMANO GINES from January 17, 2005 to January 16, 2006. Data were collected and submitted by Dr. Mary Scranton of Stony Brook University with support from the CArbon Retention In A Colored Ocean (CARIACO) program. proprietary
gov.noaa.nodc:0014123_Not Applicable Chemical and physical profile data collected from CTD casts from 01 January 2003 to 01 October 2005 aboard the F. G. WALTON SMITH in the Straits of Florida (NCEI Accession 0014123) NOAA_NCEI STAC Catalog 2003-01-01 2005-10-01 -81.299667, 23.249833, -79.017833, 25.627167 https://cmr.earthdata.nasa.gov/search/concepts/C2089372909-NOAA_NCEI.umm_json Not provided proprietary
-gov.noaa.nodc:0014906_Not Applicable Aerial sightings of bowhead whales and other marine mammals by the US Department of the Interior's Minerals Management Service, 1979 - 2006, in the Bering, Chukchi and Beaufort Seas (NCEI Accession 0014906) NOAA_NCEI STAC Catalog 1979-04-01 2006-10-31 -174.01, 57.72, -125.25, 76.14 https://cmr.earthdata.nasa.gov/search/concepts/C2089373613-NOAA_NCEI.umm_json "The Minerals Management Service (MMS), previously Bureau of Land Management, has funded fall bowhead whale aerial surveys in this area each year since 1978, using a repeatable protocol from 1982 to the present. Bowhead monitoring by MMS Environmental Studies Section, Alaska Outer Continental Shelf (OCS) Region, normally overlaps the September-October ""open-water"" season when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Nuiqsut, and Barrow, Alaska. The primary survey aircraft was a de Havilland Twin Otter Series 300. The aircraft was equipped with three medium-size bubble windows that afforded complete viewing of the track-line. Geographic positions of the aircraft were logged onto a laptop computer from a Global Navigation System (1982-1991) or a Global Positioning System (1992-2000). Prior to 1992, many surveys in Block 12 (See Browse Graphic) were conducted from a Grumman Turbo Goose Model G21G. All bowhead (and beluga) whales observed were recorded, along with incidental sightings of other marine mammals. Particular emphasis was placed on regional surveys to assess large-area shifts in the migration pathway of bowhead whales and on the coordination of effort and management of data necessary to support seasonal offshore-drilling and seismic-exploration regulations. The selection of survey blocks to be flown on a given day was nonrandom, based primarily on criteria such as observed and predicted weather conditions over the study area and offshore oil-industry activities. Otherwise, the project attempted to distribute effort fairly evenly east-to-west across the entire study area. Aerial coverage favored inshore survey blocks (See Browse Graphic), since bowheads were rarely sighted north of these blocks in previous surveys (1979-1986). Surveys were flown at a target altitude of 458 m in order to maximize visibility and to minimize potential disturbance to marine mammals. Flights were normally aborted when cloud ceilings were consistently less than 305 m or the wind force was consistently above Beaufort 4. Daily flight patterns were based on sets of non-repeating transect grids computer-generated for each survey block. Transect grids were derived by dividing each survey block into sections 30 minutes of longitude across. One of the minute marks along the northern edge of each section was selected at random then connected by a straight line to a similarly selected endpoint along the southern edge of that same section. This procedure was followed for all sections of that survey block. These transect legs were then connected alternately at their northernmost or southernmost ends to produce one continuous flight grid within each survey block. Gridlines were occasionally lengthened to cover both an inshore block and the block north of it. Lines were occasionally truncated due to extended poor visibility or to avoid potential interference with subsistence whaling activities. For bowheads encountered ""on transect"", the aircraft sometimes circled for a brief (< 10 min) period to observe behavior, obtain better estimates of their numbers, and/or determine whether calves were present. Any new groups sighted when circling were recorded as ""on search""." proprietary
gov.noaa.nodc:0014906_Not Applicable Aerial sightings of bowhead whales and other marine mammals by the US Department of the Interior's Minerals Management Service, 1979 - 2006, in the Bering, Chukchi and Beaufort Seas (NCEI Accession 0014906) ALL STAC Catalog 1979-04-01 2006-10-31 -174.01, 57.72, -125.25, 76.14 https://cmr.earthdata.nasa.gov/search/concepts/C2089373613-NOAA_NCEI.umm_json "The Minerals Management Service (MMS), previously Bureau of Land Management, has funded fall bowhead whale aerial surveys in this area each year since 1978, using a repeatable protocol from 1982 to the present. Bowhead monitoring by MMS Environmental Studies Section, Alaska Outer Continental Shelf (OCS) Region, normally overlaps the September-October ""open-water"" season when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Nuiqsut, and Barrow, Alaska. The primary survey aircraft was a de Havilland Twin Otter Series 300. The aircraft was equipped with three medium-size bubble windows that afforded complete viewing of the track-line. Geographic positions of the aircraft were logged onto a laptop computer from a Global Navigation System (1982-1991) or a Global Positioning System (1992-2000). Prior to 1992, many surveys in Block 12 (See Browse Graphic) were conducted from a Grumman Turbo Goose Model G21G. All bowhead (and beluga) whales observed were recorded, along with incidental sightings of other marine mammals. Particular emphasis was placed on regional surveys to assess large-area shifts in the migration pathway of bowhead whales and on the coordination of effort and management of data necessary to support seasonal offshore-drilling and seismic-exploration regulations. The selection of survey blocks to be flown on a given day was nonrandom, based primarily on criteria such as observed and predicted weather conditions over the study area and offshore oil-industry activities. Otherwise, the project attempted to distribute effort fairly evenly east-to-west across the entire study area. Aerial coverage favored inshore survey blocks (See Browse Graphic), since bowheads were rarely sighted north of these blocks in previous surveys (1979-1986). Surveys were flown at a target altitude of 458 m in order to maximize visibility and to minimize potential disturbance to marine mammals. Flights were normally aborted when cloud ceilings were consistently less than 305 m or the wind force was consistently above Beaufort 4. Daily flight patterns were based on sets of non-repeating transect grids computer-generated for each survey block. Transect grids were derived by dividing each survey block into sections 30 minutes of longitude across. One of the minute marks along the northern edge of each section was selected at random then connected by a straight line to a similarly selected endpoint along the southern edge of that same section. This procedure was followed for all sections of that survey block. These transect legs were then connected alternately at their northernmost or southernmost ends to produce one continuous flight grid within each survey block. Gridlines were occasionally lengthened to cover both an inshore block and the block north of it. Lines were occasionally truncated due to extended poor visibility or to avoid potential interference with subsistence whaling activities. For bowheads encountered ""on transect"", the aircraft sometimes circled for a brief (< 10 min) period to observe behavior, obtain better estimates of their numbers, and/or determine whether calves were present. Any new groups sighted when circling were recorded as ""on search""." proprietary
+gov.noaa.nodc:0014906_Not Applicable Aerial sightings of bowhead whales and other marine mammals by the US Department of the Interior's Minerals Management Service, 1979 - 2006, in the Bering, Chukchi and Beaufort Seas (NCEI Accession 0014906) NOAA_NCEI STAC Catalog 1979-04-01 2006-10-31 -174.01, 57.72, -125.25, 76.14 https://cmr.earthdata.nasa.gov/search/concepts/C2089373613-NOAA_NCEI.umm_json "The Minerals Management Service (MMS), previously Bureau of Land Management, has funded fall bowhead whale aerial surveys in this area each year since 1978, using a repeatable protocol from 1982 to the present. Bowhead monitoring by MMS Environmental Studies Section, Alaska Outer Continental Shelf (OCS) Region, normally overlaps the September-October ""open-water"" season when offshore drilling and geophysical exploration are feasible and when the fall subsistence hunt for bowhead whales takes place near Kaktovik, Nuiqsut, and Barrow, Alaska. The primary survey aircraft was a de Havilland Twin Otter Series 300. The aircraft was equipped with three medium-size bubble windows that afforded complete viewing of the track-line. Geographic positions of the aircraft were logged onto a laptop computer from a Global Navigation System (1982-1991) or a Global Positioning System (1992-2000). Prior to 1992, many surveys in Block 12 (See Browse Graphic) were conducted from a Grumman Turbo Goose Model G21G. All bowhead (and beluga) whales observed were recorded, along with incidental sightings of other marine mammals. Particular emphasis was placed on regional surveys to assess large-area shifts in the migration pathway of bowhead whales and on the coordination of effort and management of data necessary to support seasonal offshore-drilling and seismic-exploration regulations. The selection of survey blocks to be flown on a given day was nonrandom, based primarily on criteria such as observed and predicted weather conditions over the study area and offshore oil-industry activities. Otherwise, the project attempted to distribute effort fairly evenly east-to-west across the entire study area. Aerial coverage favored inshore survey blocks (See Browse Graphic), since bowheads were rarely sighted north of these blocks in previous surveys (1979-1986). Surveys were flown at a target altitude of 458 m in order to maximize visibility and to minimize potential disturbance to marine mammals. Flights were normally aborted when cloud ceilings were consistently less than 305 m or the wind force was consistently above Beaufort 4. Daily flight patterns were based on sets of non-repeating transect grids computer-generated for each survey block. Transect grids were derived by dividing each survey block into sections 30 minutes of longitude across. One of the minute marks along the northern edge of each section was selected at random then connected by a straight line to a similarly selected endpoint along the southern edge of that same section. This procedure was followed for all sections of that survey block. These transect legs were then connected alternately at their northernmost or southernmost ends to produce one continuous flight grid within each survey block. Gridlines were occasionally lengthened to cover both an inshore block and the block north of it. Lines were occasionally truncated due to extended poor visibility or to avoid potential interference with subsistence whaling activities. For bowheads encountered ""on transect"", the aircraft sometimes circled for a brief (< 10 min) period to observe behavior, obtain better estimates of their numbers, and/or determine whether calves were present. Any new groups sighted when circling were recorded as ""on search""." proprietary
gov.noaa.nodc:0033380_Not Applicable Assessment of invasiveness of the Orange Keyhole Sponge Mycale Armata in Kaneohe Bay, Oahu, Hawaii, based on surveys in 2005 - 2006, Year 2 of Hawaii Coral Reef Initiative (NCEI Accession 0033380) NOAA_NCEI STAC Catalog 2005-01-02 2006-03-31 -157.85, 21.41, -157.76, 21.51 https://cmr.earthdata.nasa.gov/search/concepts/C2089374745-NOAA_NCEI.umm_json The purpose of this study was to determine Mycale armata's distribution, abundance throughout the bay, its growth rates on permanent quadrats, and whether mechanical removal would be an effective management technique for its control. The study utilized both quadrat surveys and manta tow boards for data collection. Data files are in Excel, PDF, MS Word, and JPEG image formats. proprietary
gov.noaa.nodc:0038513_Not Applicable Chemical and biological data collected as part of the CArbon Retention In A Colored Ocean (CARIACO) program in the Cariaco Basin off the coast of Venezuela, May 23, 2005 - November 11, 2006 (NCEI Accession 0038513) NOAA_NCEI STAC Catalog 2005-05-23 2006-11-11 -65.58727, 10.49568, -64.5845, 10.71638 https://cmr.earthdata.nasa.gov/search/concepts/C2089375332-NOAA_NCEI.umm_json Chemical and biological data were collected using bottle casts on the continental shelf of Venezuela from the HERMANO GINES from May 23, 2005 to November 11, 2006. Data were collected and submitted by Dr. Mary Scranton of Stony Brook University with support from the CArbon Retention In A Colored Ocean (CARIACO) program. proprietary
gov.noaa.nodc:0040205_Not Applicable Carbon dioxide from surface underway survey in global oceans from 1968 to 2006 (Version 1.0) (NCEI Accession 0040205) NOAA_NCEI STAC Catalog 1966-01-01 2006-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089375975-NOAA_NCEI.umm_json More than 3 million measurements of surface water partial pressure of CO2 obtained over the global oceans during 1968 to 2006 are listed in the Lamont-Doherty Earth Observatory database, which includes open ocean and coastal water measurements. The data assembled include only those measured by equilibrator CO2 analyzer systems and have been quality-controlled based on the stability of the system performance, the reliability of calibrations for CO2 analysis, and the internal consistency of data. Versions up to 2007 are included in this dataset proprietary
gov.noaa.nodc:0043167_Not Applicable Aurora 1993 XBT's temperature measurements collected using XBT from Aurora Australis in the Tasman Sea during 1993 (NCEI Accession 0043167) NOAA_NCEI STAC Catalog 1993-01-05 1993-10-08 61.52, -68.93, 159, -42.83 https://cmr.earthdata.nasa.gov/search/concepts/C2089372431-NOAA_NCEI.umm_json Temperature data received at NODC on April 14, 2008 by Tim Boyer placed on the FTP server by Ann Thresher, CSIRO (COMMONWEALTH SCIENTIFIC AND INDUSTRIAL RESEARCH ORGANIZATION) for XBT/CTD comparisons proprietary
gov.noaa.nodc:0045502_Not Applicable Carbon dioxide, temperature, salinity, and atmospheric pressure from surface underway survey in the North Pacific from January 1998 to January 2004 (NCEI Accession 0045502) NOAA_NCEI STAC Catalog 1998-01-01 2004-01-01 -100, -10, 120, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2089372737-NOAA_NCEI.umm_json Sea surface pCO2, sea surface temperature, sea surface salinity, and atmospheric pressure measurements collected in the North Pacific as part of the NOAA Office of Climate Observations (OCO) and U.S. Carbon Cycle Science Programs. proprietary
gov.noaa.nodc:0045505_Not Applicable AOML VOS pCO2. temperature, salinity, and other underway measurements collected using in the Pacific and Atlantic from 2007 to 2008 (NCEI Accession 0045505) NOAA_NCEI STAC Catalog 2007-04-06 2008-01-15 -90, -40, -20, 20 https://cmr.earthdata.nasa.gov/search/concepts/C2089372759-NOAA_NCEI.umm_json AOML pCO2 underway measurements collected using in the Pacific and Atlantic from 2007 to 2008 proprietary
-gov.noaa.nodc:0046934_Not Applicable Acropora Spatial Survey Data of the Upper Florida Keys National Marine Sanctuary, 2005 - 2007 (NCEI Accession 0046934) NOAA_NCEI STAC Catalog 2005-01-01 2007-12-31 -81.41079, 24.54466, -80.19632, 25.29129 https://cmr.earthdata.nasa.gov/search/concepts/C2089373092-NOAA_NCEI.umm_json These data were collected by the NOAA Southeast Fisheries Science Center to document the presence or absence of Acropora spp at shallow reef sites in the Upper Florida Keys (USA). The presence or absence of acroporid corals was marked by handheld GPS during snorkel or tow surveys of shallow water (<5m) reef habitats in the Upper Florida Keys National Marine Sanctuary. The data are in GIS shape and layer files with associated attribute files, metadata files, and additional .pdf file outputs of the GIS data layers. proprietary
gov.noaa.nodc:0046934_Not Applicable Acropora Spatial Survey Data of the Upper Florida Keys National Marine Sanctuary, 2005 - 2007 (NCEI Accession 0046934) ALL STAC Catalog 2005-01-01 2007-12-31 -81.41079, 24.54466, -80.19632, 25.29129 https://cmr.earthdata.nasa.gov/search/concepts/C2089373092-NOAA_NCEI.umm_json These data were collected by the NOAA Southeast Fisheries Science Center to document the presence or absence of Acropora spp at shallow reef sites in the Upper Florida Keys (USA). The presence or absence of acroporid corals was marked by handheld GPS during snorkel or tow surveys of shallow water (<5m) reef habitats in the Upper Florida Keys National Marine Sanctuary. The data are in GIS shape and layer files with associated attribute files, metadata files, and additional .pdf file outputs of the GIS data layers. proprietary
+gov.noaa.nodc:0046934_Not Applicable Acropora Spatial Survey Data of the Upper Florida Keys National Marine Sanctuary, 2005 - 2007 (NCEI Accession 0046934) NOAA_NCEI STAC Catalog 2005-01-01 2007-12-31 -81.41079, 24.54466, -80.19632, 25.29129 https://cmr.earthdata.nasa.gov/search/concepts/C2089373092-NOAA_NCEI.umm_json These data were collected by the NOAA Southeast Fisheries Science Center to document the presence or absence of Acropora spp at shallow reef sites in the Upper Florida Keys (USA). The presence or absence of acroporid corals was marked by handheld GPS during snorkel or tow surveys of shallow water (<5m) reef habitats in the Upper Florida Keys National Marine Sanctuary. The data are in GIS shape and layer files with associated attribute files, metadata files, and additional .pdf file outputs of the GIS data layers. proprietary
gov.noaa.nodc:0049902_Not Applicable Biological dataset collected from bottle casts from the R/V LAURENCE M. GOULD and the R/V NATHANIEL B. PALMER in the Southern Drake Passage and Scotia Sea in support of National Science Foundation projects OPP 03-30443 and ANT 04-44134 from 15 February 2004 to 09 August 2006 (NCEI Accession 0049902) NOAA_NCEI STAC Catalog 2004-02-15 2006-08-09 -64.9884, -64.675, -52.8742, -54.8127 https://cmr.earthdata.nasa.gov/search/concepts/C2089373417-NOAA_NCEI.umm_json Ocean biology data were collected in Southern Drake Passage and Scotia Sea during two research cruises supported by NSF awards. These two cruises, namely LMG0402 and NBP0606, were conducted during Februay to March 2004 and July to August 2006, respectively. Dataset includes concentration of pigments in phytoplankton, particulate organic matter concentration, macronutrients, primary productivity and microbial biomass and productivity. proprietary
gov.noaa.nodc:0051848_Not Applicable Biomass measurements collected in the Pacific Ocean using a net from various platform from 1950 - 1961 (NCEI Accession 0051848) NOAA_NCEI STAC Catalog 1950-05-14 1961-07-29 -170, 0, -135, 30 https://cmr.earthdata.nasa.gov/search/concepts/C2089373644-NOAA_NCEI.umm_json Zooplankton biomass data collected from Pacific Ocean in 1950 - 1961 years received from NMFS proprietary
gov.noaa.nodc:0053277_Not Applicable Biomass measurements collected using net in the North and South Atlantic from several platforms from 1950 to 989 (NCEI Accession 0053277) NOAA_NCEI STAC Catalog 1950-01-01 1989-12-31 -86.367, -42.78, 14.175, 53.683 https://cmr.earthdata.nasa.gov/search/concepts/C2089373850-NOAA_NCEI.umm_json Zooplankton biomass data collected by Institute of Biology of the Southern Seas from the Atlantic Ocean in 1950-1989 years and received from the NMFS. proprietary
@@ -18457,8 +18464,8 @@ gov.noaa.nodc:0057319_Not Applicable Arctic Freshwater Switchyard Project: Sprin
gov.noaa.nodc:0058268_Not Applicable Beaufort Gyre hydrographic data: Temperature, salinity and transmissivity data from the Louis S St. Laurent in the Arctic Ocean, 2003 - 2008 (NCEI Accession 0058268) NOAA_NCEI STAC Catalog 2003-10-11 2008-10-20 -150, 75, -140, 78 https://cmr.earthdata.nasa.gov/search/concepts/C2089374751-NOAA_NCEI.umm_json The major goal of the observational program is to determine the variability of different components of the Beaufort Gyre fresh water (ocean and sea ice) system and to assess the partial concentrations of fresh water of different origin (rivers, Pacific Ocean, precipitation, ice/snow melt, etc). Using moorings, drifting buoys, shipboard, and remote sensing measurements we have been measuring time series of temperature, salinity, currents, geochemical tracers, sea ice draft, and sea level since August 2003, to determine freshwater content and freshwater fluxes in the Beaufort Gyre during a complete seasonal cycle and beyond. proprietary
gov.noaa.nodc:0058858_Not Applicable Abundance data for the copepod species Calanus pacificus and Metridia pacifica collected at a fixed station in Dabob Bay, Hood Canal, Puget Sound, Washington during six cruises aboard the CLIFFORD A. BARNES, October 2006 - April 2008 (NCEI Accession 0058858) NOAA_NCEI STAC Catalog 2006-10-12 2008-04-15 -122.835, 47.769, -122.835, 47.769 https://cmr.earthdata.nasa.gov/search/concepts/C2089374860-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:0058858_Not Applicable Abundance data for the copepod species Calanus pacificus and Metridia pacifica collected at a fixed station in Dabob Bay, Hood Canal, Puget Sound, Washington during six cruises aboard the CLIFFORD A. BARNES, October 2006 - April 2008 (NCEI Accession 0058858) ALL STAC Catalog 2006-10-12 2008-04-15 -122.835, 47.769, -122.835, 47.769 https://cmr.earthdata.nasa.gov/search/concepts/C2089374860-NOAA_NCEI.umm_json Not provided proprietary
-gov.noaa.nodc:0061208_Not Applicable Algal, coral, and other data collected by ROV and scuba diver videography from M.V. FLING and M.V. SPREE for Post-Hurricane Assessment of Sensitive Habitats of the Flower Garden Banks Vicinity project from November 13, 2005 to June 23, 2007 (NCEI Accession 0061208) NOAA_NCEI STAC Catalog 2005-11-13 2007-05-23 -93.58, 27.85, -92.45, 28.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089375074-NOAA_NCEI.umm_json The most active hurricane season on record in the Atlantic and Gulf of Mexico occurred in 2005, fueled by higher than normal sea-surface temperatures. Eleven tropical cyclones entered the Gulf of Mexico in 2005, including Hurricane Rita. Hurricane Rita was a Category 3 storm when it passed near the shelf edge banks on September 23, 2005. Several sensitive habitats within the northwestern Gulf of Mexico were close to the path of Hurricane Rita, including Sonnier, McGrail, Geyer, Bright, and East Flower Garden Banks. Hindcast hydrodynamic models estimated wave heights at 20-m or higher on these banks. This may have left some bank caps exposed, even at ~20- to 30-m depths. The implications for catastrophic damage to benthic community structure prompted the Minerals Management Service to characterize the banks in their post-hurricane state. This study, using the data in NODC Accession 0061208, characterized and compared the benthic habitats of four banks (Sonnier, McGrail, Geyer, and Bright) and recorded possible hurricane damage at these banks and the East Flower Garden Bank (EFGB). At Sonnier, McGrail, Geyer, and Bright Banks, videographic records were collected by SCUBA and ROV in April and May 2007, at four depth ranges to assess benthic cover to the lowest possible taxonomic level: 22-27 m, 30-36.5 m, 45-50 m, and 55-60 m. Video transects were qualitatively assessed for evidence of hurricane damage. To document recovery from Hurricane Rita at the existing long-term monitoring site on the EFGB, repetitive quadrats and perimeter line surveys were conducted in November 2005 and compared to data collected subsequently in June 2006. proprietary
gov.noaa.nodc:0061208_Not Applicable Algal, coral, and other data collected by ROV and scuba diver videography from M.V. FLING and M.V. SPREE for Post-Hurricane Assessment of Sensitive Habitats of the Flower Garden Banks Vicinity project from November 13, 2005 to June 23, 2007 (NCEI Accession 0061208) ALL STAC Catalog 2005-11-13 2007-05-23 -93.58, 27.85, -92.45, 28.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089375074-NOAA_NCEI.umm_json The most active hurricane season on record in the Atlantic and Gulf of Mexico occurred in 2005, fueled by higher than normal sea-surface temperatures. Eleven tropical cyclones entered the Gulf of Mexico in 2005, including Hurricane Rita. Hurricane Rita was a Category 3 storm when it passed near the shelf edge banks on September 23, 2005. Several sensitive habitats within the northwestern Gulf of Mexico were close to the path of Hurricane Rita, including Sonnier, McGrail, Geyer, Bright, and East Flower Garden Banks. Hindcast hydrodynamic models estimated wave heights at 20-m or higher on these banks. This may have left some bank caps exposed, even at ~20- to 30-m depths. The implications for catastrophic damage to benthic community structure prompted the Minerals Management Service to characterize the banks in their post-hurricane state. This study, using the data in NODC Accession 0061208, characterized and compared the benthic habitats of four banks (Sonnier, McGrail, Geyer, and Bright) and recorded possible hurricane damage at these banks and the East Flower Garden Bank (EFGB). At Sonnier, McGrail, Geyer, and Bright Banks, videographic records were collected by SCUBA and ROV in April and May 2007, at four depth ranges to assess benthic cover to the lowest possible taxonomic level: 22-27 m, 30-36.5 m, 45-50 m, and 55-60 m. Video transects were qualitatively assessed for evidence of hurricane damage. To document recovery from Hurricane Rita at the existing long-term monitoring site on the EFGB, repetitive quadrats and perimeter line surveys were conducted in November 2005 and compared to data collected subsequently in June 2006. proprietary
+gov.noaa.nodc:0061208_Not Applicable Algal, coral, and other data collected by ROV and scuba diver videography from M.V. FLING and M.V. SPREE for Post-Hurricane Assessment of Sensitive Habitats of the Flower Garden Banks Vicinity project from November 13, 2005 to June 23, 2007 (NCEI Accession 0061208) NOAA_NCEI STAC Catalog 2005-11-13 2007-05-23 -93.58, 27.85, -92.45, 28.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089375074-NOAA_NCEI.umm_json The most active hurricane season on record in the Atlantic and Gulf of Mexico occurred in 2005, fueled by higher than normal sea-surface temperatures. Eleven tropical cyclones entered the Gulf of Mexico in 2005, including Hurricane Rita. Hurricane Rita was a Category 3 storm when it passed near the shelf edge banks on September 23, 2005. Several sensitive habitats within the northwestern Gulf of Mexico were close to the path of Hurricane Rita, including Sonnier, McGrail, Geyer, Bright, and East Flower Garden Banks. Hindcast hydrodynamic models estimated wave heights at 20-m or higher on these banks. This may have left some bank caps exposed, even at ~20- to 30-m depths. The implications for catastrophic damage to benthic community structure prompted the Minerals Management Service to characterize the banks in their post-hurricane state. This study, using the data in NODC Accession 0061208, characterized and compared the benthic habitats of four banks (Sonnier, McGrail, Geyer, and Bright) and recorded possible hurricane damage at these banks and the East Flower Garden Bank (EFGB). At Sonnier, McGrail, Geyer, and Bright Banks, videographic records were collected by SCUBA and ROV in April and May 2007, at four depth ranges to assess benthic cover to the lowest possible taxonomic level: 22-27 m, 30-36.5 m, 45-50 m, and 55-60 m. Video transects were qualitatively assessed for evidence of hurricane damage. To document recovery from Hurricane Rita at the existing long-term monitoring site on the EFGB, repetitive quadrats and perimeter line surveys were conducted in November 2005 and compared to data collected subsequently in June 2006. proprietary
gov.noaa.nodc:0066319_Not Applicable Benthic data for corals, macroalgae, invertebrates, and non-living bottom types from Fagatele Bay, Pago Pago, and Fagasa, American Samoa, 2004-2008 (NCEI Accession 0066319) NOAA_NCEI STAC Catalog 2004-01-01 2008-08-01 -170.76892, -14.37023, -170.63047, -14.27847 https://cmr.earthdata.nasa.gov/search/concepts/C2089376136-NOAA_NCEI.umm_json This data set was derived from surveys in Fagatele Bay National Marine Sanctuary, Pago Pago (Rainmaker and Aua), and Fagasa (Sita Bay and Cape Larsen) conducted in 2004 and 2007-2008. Parameters include coral, algal, or invertebrate species, coral colony diameter size, and non-living bottom type. Summaries of species identification from sites above and Ofu-Olosega Islands, Ta'u Island, Aunu'u, Manu'a, and Rose Atoll, based on historic surveys back to 1917 are also given in spreadsheets. This is a working list put together by Dr. Charles Birkeland. Fish data were collected by Dr. Alison Green on the same dates and transects and are available in a separate NODC accession. proprietary
gov.noaa.nodc:0068364_Not Applicable Benthic data for corals, macroalgae, invertebrates, and non-living bottom types from Fagatele Bay National Marine Sanctuary, South Pacific Ocean, 2007-04-02 to 2008-12-31 (NCEI Accession 0068364) NOAA_NCEI STAC Catalog 2007-04-02 2008-12-31 -170.814, -14.3654, -170.562, -14.1271 https://cmr.earthdata.nasa.gov/search/concepts/C2089372324-NOAA_NCEI.umm_json Benthic transects were repeated at 12 sites around Tutuila at various depths on the reef slopes and flats. Benthic coverage categories include coral species, invertebrates, and non-living substrate type. Annual surveys took place during 2005-2009. The most detailed data are from 2008. The data were provided as spreadsheets and metadata within a PDF document, focusing on the 2008 surveys. A related data set was can be found in NCEI Accession 0066319, which was derived from surveys in Fagatele Bay National Marine Sanctuary, Pago Pago (Rainmaker and Aua), and Fagasa (Sita Bay and Cape Larsen) conducted in 2004 and 2007-2008. Parameters include coral, algal, or invertebrate species, coral colony diameter size, and non-living bottom type. Also in 0066319 are summaries of species identification from sites above and Ofu-Olosega Islands, Ta'u Island, Aunu'u, Manu'a, and Rose Atoll, based on historic surveys back to 1917 are also given in spreadsheets. This is a working list put together by Dr. Charles Birkeland. proprietary
gov.noaa.nodc:0068586_Not Applicable Chemical and physical oceanographic profile data collected from CTD casts aboard the SEWARD JOHNSON in the North Atlantic Ocean and Gulf of Mexico from 2010-07-10 to 2010-07-14 in response to the Deepwater Horizon oil spill event (NCEI Accession 0068586) NOAA_NCEI STAC Catalog 2010-07-10 2010-07-14 -83.153333, 24.251833, -79.812, 26.011833 https://cmr.earthdata.nasa.gov/search/concepts/C2089372374-NOAA_NCEI.umm_json Chemical and physical oceanographic profile data were collected aboard the SEWARD JOHNSON in the North Atlantic Ocean and Gulf of Mexico from 2010-07-10 to 2010-07-14 in response to the Deepwater Horizon oil spill event on April 20, 2010, by the Subsurface Monitoring Unit (SMU), which consists of multiple government and corporate agencies. These data include CDOM fluorescence, conductivity, dissolved oxygen, fluorescence, hydrostatic pressure, salinity, sound velocity, temperature and water density. The instruments used to collect these data were CTD, fluorometer and oxygen meter. These data have undergone quality assurance and control procedures to validate their scientific integrity at the National Coastal Data Development Center. (NODC Accession 0068586) proprietary
@@ -18606,17 +18613,17 @@ gov.noaa.nodc:0118500_Not Applicable Biological and physical geospatial data fro
gov.noaa.nodc:0118680_Not Applicable Biological and chemical data determined in mesocosm experiments by Dauphin Island Sea Lab in June and August of 2011 (NCEI Accession 0118680) NOAA_NCEI STAC Catalog 2011-06-01 2011-09-01 -88.080239, 30.243423, -88.080239, 30.243423 https://cmr.earthdata.nasa.gov/search/concepts/C2089373185-NOAA_NCEI.umm_json Abundances of viruses, prokaryotes, diatoms, dinoflagellates, ciliates and heterotrophic nanoflagellates were determined over time in mesocosm experiments measuring the effects of oil, dispersant and dispersed oil on the microbial loop. Two separate experiments were carried out in June and August 2011. Abundances in the treated mesocosms were compared to a no addition control and a glucose addition control. proprietary
gov.noaa.nodc:0118720_Not Applicable Biological, chemical, and physical data collected in Delaware Bay from 1997-09-02 to 1997-10-08 (NCEI Accession 0118720) NOAA_NCEI STAC Catalog 1997-09-02 1997-10-08 -75.6082, 38.5167, -74.723, 40.147 https://cmr.earthdata.nasa.gov/search/concepts/C2089373222-NOAA_NCEI.umm_json This study was based on the sediment quality triad (SQT) approach. A stratified probabilistic sampling design was utilized to characterize the Delaware Bay system in terms of chemical contamination, sediment toxicity (Microtox, amphipod bioassay; sea urchin gamete bioassay; and P450 biomarker) and benthic infaunal community structure. The purpose was to define the extent and magnitude of toxicity and other biological effects associated with contaminants in the Delaware estuary system from the fall line to the mouth of the Bay. This file contains data measured in the Delaware Bay Estuary and adjacent waters during 1997. Samples were collected for water and sediment analyses. proprietary
gov.noaa.nodc:0124257_Not Applicable Baseline characterization of benthic and coral communities of the Flower Garden Banks, Texas from 2010-05-01 to 2012-08-31 (NCEI Accession 0124257) NOAA_NCEI STAC Catalog 2010-05-01 2012-08-31 -93.87, 27.82, -93.57, 27.99 https://cmr.earthdata.nasa.gov/search/concepts/C2089375884-NOAA_NCEI.umm_json This study utilized ROV photograph transects to quantify benthic habitat and coral communities among the five habitat types (algal nodule, coralline algal reefs, deep reefs and soft bottom) identified in the Flower Garden Banks National Marine Sanctuary (FGBNMS). ROV surveys were conducted in the mid and lower mesophotic zone of the sanctuary (17-150 m) on both the East Bank and the West Bank. The FGBNMS represents the northernmost tropical western Atlantic coral reef on the continental shelf and support the most highly developed offshore hard bank community in the region. The complexity of habitats supports a diverse assemblage of organisms including approximately 250 species of fish, 23 species of coral, and 80 species of algae in addition to large sponge communities. Understanding and monitoring these resources is critical to both sanctuary inventory and management activities. During the course of the sanctuaryÂs management plan review process, the impact of fishing was identified as a priority issue, and the concept of a research only area was suggested. The purpose of this project is to provide baseline data for all benthic habitats and coral communities. proprietary
-gov.noaa.nodc:0125596_Not Applicable Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2009-03-18 to 2012-12-10 (NCEI Accession 0125596) ALL STAC Catalog 2009-03-18 2012-12-10 -51.493, -34.504, -44.498, -34.499 https://cmr.earthdata.nasa.gov/search/concepts/C2089376227-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:0125596_Not Applicable Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2009-03-18 to 2012-12-10 (NCEI Accession 0125596) NOAA_NCEI STAC Catalog 2009-03-18 2012-12-10 -51.493, -34.504, -44.498, -34.499 https://cmr.earthdata.nasa.gov/search/concepts/C2089376227-NOAA_NCEI.umm_json Not provided proprietary
-gov.noaa.nodc:0125597_Not Applicable Acoustic travel time, bottom pressure, and near bottom current velocities from inverted echo sounders in the Atlantic Ocean from 2004-09-27 to 2016-02-25 (NCEI Accession 0125597) NOAA_NCEI STAC Catalog 2004-09-27 2016-02-25 -76.84, 26.491, -72.004, 26.516 https://cmr.earthdata.nasa.gov/search/concepts/C2089376235-NOAA_NCEI.umm_json Not provided proprietary
+gov.noaa.nodc:0125596_Not Applicable Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2009-03-18 to 2012-12-10 (NCEI Accession 0125596) ALL STAC Catalog 2009-03-18 2012-12-10 -51.493, -34.504, -44.498, -34.499 https://cmr.earthdata.nasa.gov/search/concepts/C2089376227-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:0125597_Not Applicable Acoustic travel time, bottom pressure, and near bottom current velocities from inverted echo sounders in the Atlantic Ocean from 2004-09-27 to 2016-02-25 (NCEI Accession 0125597) ALL STAC Catalog 2004-09-27 2016-02-25 -76.84, 26.491, -72.004, 26.516 https://cmr.earthdata.nasa.gov/search/concepts/C2089376235-NOAA_NCEI.umm_json Not provided proprietary
+gov.noaa.nodc:0125597_Not Applicable Acoustic travel time, bottom pressure, and near bottom current velocities from inverted echo sounders in the Atlantic Ocean from 2004-09-27 to 2016-02-25 (NCEI Accession 0125597) NOAA_NCEI STAC Catalog 2004-09-27 2016-02-25 -76.84, 26.491, -72.004, 26.516 https://cmr.earthdata.nasa.gov/search/concepts/C2089376235-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:0127525_Not Applicable Abundance and behavior of parrotfishes (Labridae, Scarinae) in the upper Florida Keys from 2013-06-19 to 2013-07-30 (NCEI Accession 0127525) NOAA_NCEI STAC Catalog 2013-06-19 2013-07-30 -80.38, 25, -80.21, 25.22 https://cmr.earthdata.nasa.gov/search/concepts/C2089376534-NOAA_NCEI.umm_json To better understand the functional roles of parrotfishes on Caribbean coral reefs we documented abundance, habitat preferences, and diets of nine species of parrotfishes (Scarus coelestinus, Scarus coeruleus, Scarus guacamaia, Scarus taeniopterus, Scarus vetula, Sparisoma aurofrenatum, Sparisoma chrysopterum, Sparisoma rubripinne, Sparisoma viride) on three high-relief spur-and-groove reefs (Molasses, Carysfort, and Elbow) offshore of Key Largo in the Florida Keys National Marine Sanctuary. On each reef, we conducted fish surveys, behavioral observations, and benthic surveys in three habitat types: high-relief spur and groove (depth 2 - 6 m), low-relief carbonate platform/hardbottom (depth 4 - 12 m), and carbonate boulder/rubble fields (depth 4 - 9 m). In addition, fish surveys were also conducted on a fourth high-relief spur-and-groove reef (French). We estimated parrotfish abundance in each of the three habitat types in order to assess the relative abundance and biomass of different species and to quantify differences in habitat selection. To estimate parrotfish density, we conducted 20 to 30 minute timed swims while towing a GPS receiver on a float on the surface to calculate the amount of area sampled. During a swim the observer would swim parallel with the habitat type being sampled and count and estimate the size to the nearest cm of all parrotfishes greater than or equal to 15 cm in length that were encountered in a 5 m wide swath. To quantify parrotfish behavior, approximately six individuals of each species were observed at each site for 20 min each. Foraging behavior was recorded by a SCUBA diver while towing a GPS receiver (Garmin GPS 72) attached to a surface float, which obtained position fixes of the focal fish at 15 s intervals. Fish were followed from a close distance (~ 2 m when possible), and food items were identified to the lowest taxonomic level possible, with macroalgae and coral usually identified to genus or species. Many bites involved scraping or excavating substrate colonized by a multi-species assemblage of filamentous âturfâ algae and crustose coralline algae (CCA). Thus, multiple species of filamentous algae, endolithic algae, and CCA could be harvested in a single bite, and it was impossible to determine the specific species of algae targeted. We also recorded the type of substrate targeted during each foraging bout, categorizing each substrate as one of the following: (1) dead coral, (2) coral pavement, (3) boulder, (4) rubble, or (5) ledge. Dead coral included both convex and concave surfaces on the vertical and horizontal planes of three dimensional coral skeletons (primarily dead Acropora palmata) that were attached to reef substrate. Coral pavement was carbonate reef with little topographic complexity (i.e., flat limestone pavement). Boulder was large remnants of dead mounding corals not clearly attached to the bottom and often partially buried in sand. Coral rubble consisted of small dead coral fragments (generally < 10 cm in any dimension) that could be moved with minimal force. Ledges consisted entirely of the undercut sides of large spurs in the high-relief spur and groove habitat. In order to quantify the relative abundance of different food types, we estimated the percent cover of algae, coral, and other sessile invertebrates on each of the five substrates commonly targeted by parrotfishes (dead coral, coral pavement, boulder, rubble, or ledge) in 0.5 m x 0.5 m photoquadrats. We photographed a total of 8 haphazardly selected quadrats dispersed throughout the study site for each substrate type at each of the three sites (N = 24 quadrats per substrate type, N = 120 quadrats total). Each photoquadrat was divided into sixteen 12 cm x 12 cm sections which were individually photographed, and percent cover was estimated from 9 stratified random points per section (N = 144 point per quadrat). proprietary
gov.noaa.nodc:0127525_Not Applicable Abundance and behavior of parrotfishes (Labridae, Scarinae) in the upper Florida Keys from 2013-06-19 to 2013-07-30 (NCEI Accession 0127525) ALL STAC Catalog 2013-06-19 2013-07-30 -80.38, 25, -80.21, 25.22 https://cmr.earthdata.nasa.gov/search/concepts/C2089376534-NOAA_NCEI.umm_json To better understand the functional roles of parrotfishes on Caribbean coral reefs we documented abundance, habitat preferences, and diets of nine species of parrotfishes (Scarus coelestinus, Scarus coeruleus, Scarus guacamaia, Scarus taeniopterus, Scarus vetula, Sparisoma aurofrenatum, Sparisoma chrysopterum, Sparisoma rubripinne, Sparisoma viride) on three high-relief spur-and-groove reefs (Molasses, Carysfort, and Elbow) offshore of Key Largo in the Florida Keys National Marine Sanctuary. On each reef, we conducted fish surveys, behavioral observations, and benthic surveys in three habitat types: high-relief spur and groove (depth 2 - 6 m), low-relief carbonate platform/hardbottom (depth 4 - 12 m), and carbonate boulder/rubble fields (depth 4 - 9 m). In addition, fish surveys were also conducted on a fourth high-relief spur-and-groove reef (French). We estimated parrotfish abundance in each of the three habitat types in order to assess the relative abundance and biomass of different species and to quantify differences in habitat selection. To estimate parrotfish density, we conducted 20 to 30 minute timed swims while towing a GPS receiver on a float on the surface to calculate the amount of area sampled. During a swim the observer would swim parallel with the habitat type being sampled and count and estimate the size to the nearest cm of all parrotfishes greater than or equal to 15 cm in length that were encountered in a 5 m wide swath. To quantify parrotfish behavior, approximately six individuals of each species were observed at each site for 20 min each. Foraging behavior was recorded by a SCUBA diver while towing a GPS receiver (Garmin GPS 72) attached to a surface float, which obtained position fixes of the focal fish at 15 s intervals. Fish were followed from a close distance (~ 2 m when possible), and food items were identified to the lowest taxonomic level possible, with macroalgae and coral usually identified to genus or species. Many bites involved scraping or excavating substrate colonized by a multi-species assemblage of filamentous âturfâ algae and crustose coralline algae (CCA). Thus, multiple species of filamentous algae, endolithic algae, and CCA could be harvested in a single bite, and it was impossible to determine the specific species of algae targeted. We also recorded the type of substrate targeted during each foraging bout, categorizing each substrate as one of the following: (1) dead coral, (2) coral pavement, (3) boulder, (4) rubble, or (5) ledge. Dead coral included both convex and concave surfaces on the vertical and horizontal planes of three dimensional coral skeletons (primarily dead Acropora palmata) that were attached to reef substrate. Coral pavement was carbonate reef with little topographic complexity (i.e., flat limestone pavement). Boulder was large remnants of dead mounding corals not clearly attached to the bottom and often partially buried in sand. Coral rubble consisted of small dead coral fragments (generally < 10 cm in any dimension) that could be moved with minimal force. Ledges consisted entirely of the undercut sides of large spurs in the high-relief spur and groove habitat. In order to quantify the relative abundance of different food types, we estimated the percent cover of algae, coral, and other sessile invertebrates on each of the five substrates commonly targeted by parrotfishes (dead coral, coral pavement, boulder, rubble, or ledge) in 0.5 m x 0.5 m photoquadrats. We photographed a total of 8 haphazardly selected quadrats dispersed throughout the study site for each substrate type at each of the three sites (N = 24 quadrats per substrate type, N = 120 quadrats total). Each photoquadrat was divided into sixteen 12 cm x 12 cm sections which were individually photographed, and percent cover was estimated from 9 stratified random points per section (N = 144 point per quadrat). proprietary
gov.noaa.nodc:0128996_Not Applicable Benthic and biological data in the New York Bight from 2010-06-01 to 2012-05-31 (NCEI Accession 0128996) NOAA_NCEI STAC Catalog 2010-06-01 2012-05-31 -75, 37, -69, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2089376996-NOAA_NCEI.umm_json These data sets show the distribution of key species and habitats, such as seabirds, bathymetry, surficial sediments, deep sea corals, and oceanographic habitats. NOAAâs Biogeography Branch worked with the New York Department of State (DOS) to interpret existing ecological information and create these new data sets. New York plans to integrate this information with other ecological and human use data compiled by others (for example, The Nature Conservancy, Northeast Fisheries Science Center) and apply ecosystem-based management and plan for ocean uses. Many academic, state and federal and non-governmental organization partners contributed to this project with data, analyses and reviews. Project partners included: the University of Alaska, Biology and Wildlife Department; University of Texas, Institute for Geophysics; The Nature Conservancy, Mid-Atlantic Marine Program; the National Marine Fisheries Service (NMFS), Northeast Fisheries Science Center, and the NMFS, Deep-Sea Coral Research and Technology Program. proprietary
gov.noaa.nodc:0129395_Not Applicable Chlorophyll accessory pigments collected from NOAA Ship OSCAR ELTON SETTE in North Pacific Ocean from 2008-03-01 to 2011-04-01 (NCEI Accession 0129395) NOAA_NCEI STAC Catalog 2008-03-01 2011-04-01 -158, 26, -158, 36 https://cmr.earthdata.nasa.gov/search/concepts/C2089377189-NOAA_NCEI.umm_json These data represent the chlorophyll accessory pigments measured from discrete depth water samples collected in CTD-mounted 10 liter Niskin bottles as part of NOAA surveys in the central North Pacific Ocean north of Hawaii. Accessory pigments were measured post-survey at the University of Hawaii using HPLC methods. proprietary
gov.noaa.nodc:0130065_Not Applicable Chlorophyll A, hydrostatic pressure, and water density measurements collected from New Horizon in Gulf of California and North Pacific Ocean from 2004-07-14 to 2008-08-06 (NCEI Accession 0130065) NOAA_NCEI STAC Catalog 2004-07-14 2008-08-06 -120.5, 20.48, -106.48, 32.52 https://cmr.earthdata.nasa.gov/search/concepts/C2089377812-NOAA_NCEI.umm_json Extracted chlorophyll A, normalized to filtered volume, from suspended particulate material collected via Niskin bottle from the Gulf of California in the summers of 2004, 2005, and 2008, as well as from the Eastern Tropical North Pacific in 2008. proprietary
-gov.noaa.nodc:0130929_Not Applicable AFSC/REFM: Isolation by distance (IBD) Alaskan fish stock structure modeling (NCEI Accession 0130929) NOAA_NCEI STAC Catalog 1980-01-01 2012-01-01 170, 50, -160, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2089378414-NOAA_NCEI.umm_json This model study examines several management strategies for two marine fish species subject to isolation-by-distance (IBD): Pacific cod in the Aleutian Islands (AI) and northern rockfish in the Eastern Bering Sea (EBS) and Aleutian Islands. A one-dimensional stepping stone model was used to model isolation by distance, and was intended to mimic regions where marine species are exploited along a continental shelf. The performance of spatial assessment and management methods depended on how the range was split. Splitting anywhere within the managed area led to fewer demes falling below target and threshold biomass levels and higher yield than managing the entire area as a single unit. Equilibrium yield was maximized when each deme was assessed and managed separately and under catch cascading, in which harvest quotas within a management unit are spatially allocated based upon the distribution of survey biomass. The longer-lived rockfish declined more slowly than Pacific cod, and experienced greater depletion in biomass under disproportionate fishing effort due to lower productivity. Overall, splitting a management area of the size simulated in the model improved performance measures, and the optimal management strategy grouped management units by demes with similar relative fishing effort. proprietary
gov.noaa.nodc:0130929_Not Applicable AFSC/REFM: Isolation by distance (IBD) Alaskan fish stock structure modeling (NCEI Accession 0130929) ALL STAC Catalog 1980-01-01 2012-01-01 170, 50, -160, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2089378414-NOAA_NCEI.umm_json This model study examines several management strategies for two marine fish species subject to isolation-by-distance (IBD): Pacific cod in the Aleutian Islands (AI) and northern rockfish in the Eastern Bering Sea (EBS) and Aleutian Islands. A one-dimensional stepping stone model was used to model isolation by distance, and was intended to mimic regions where marine species are exploited along a continental shelf. The performance of spatial assessment and management methods depended on how the range was split. Splitting anywhere within the managed area led to fewer demes falling below target and threshold biomass levels and higher yield than managing the entire area as a single unit. Equilibrium yield was maximized when each deme was assessed and managed separately and under catch cascading, in which harvest quotas within a management unit are spatially allocated based upon the distribution of survey biomass. The longer-lived rockfish declined more slowly than Pacific cod, and experienced greater depletion in biomass under disproportionate fishing effort due to lower productivity. Overall, splitting a management area of the size simulated in the model improved performance measures, and the optimal management strategy grouped management units by demes with similar relative fishing effort. proprietary
+gov.noaa.nodc:0130929_Not Applicable AFSC/REFM: Isolation by distance (IBD) Alaskan fish stock structure modeling (NCEI Accession 0130929) NOAA_NCEI STAC Catalog 1980-01-01 2012-01-01 170, 50, -160, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2089378414-NOAA_NCEI.umm_json This model study examines several management strategies for two marine fish species subject to isolation-by-distance (IBD): Pacific cod in the Aleutian Islands (AI) and northern rockfish in the Eastern Bering Sea (EBS) and Aleutian Islands. A one-dimensional stepping stone model was used to model isolation by distance, and was intended to mimic regions where marine species are exploited along a continental shelf. The performance of spatial assessment and management methods depended on how the range was split. Splitting anywhere within the managed area led to fewer demes falling below target and threshold biomass levels and higher yield than managing the entire area as a single unit. Equilibrium yield was maximized when each deme was assessed and managed separately and under catch cascading, in which harvest quotas within a management unit are spatially allocated based upon the distribution of survey biomass. The longer-lived rockfish declined more slowly than Pacific cod, and experienced greater depletion in biomass under disproportionate fishing effort due to lower productivity. Overall, splitting a management area of the size simulated in the model improved performance measures, and the optimal management strategy grouped management units by demes with similar relative fishing effort. proprietary
gov.noaa.nodc:0131425_Not Applicable Bowhead Whale Feeding Ecology Study (BOWFEST): Aerial Survey in Chukchi and Beaufort Seas conducted from 2007-08-23 to 2011-09-16 (NCEI Accession 0131425) NOAA_NCEI STAC Catalog 2007-08-23 2011-09-16 -157.33, 70.79, -151.84, 72.05 https://cmr.earthdata.nasa.gov/search/concepts/C2089378614-NOAA_NCEI.umm_json The Bowhead Whale Feeding Ecology Study (BOWFEST) was initiated in May 2007 through an Interagency Agreement between the Bureau of Ocean Energy Management (BOEM) (formerly Minerals Management Service (MMS)) and the National Marine Mammal Laboratory (NMML). This was a multi-disciplinary study involving oceanography, acoustics, tagging, stomach sampling and aerial surveys and included scientists from a wide range of institutions (Woods Hole Oceanographic Institution (WHOI), University of Rhode Island (URI), University of Alaska Fairbanks (UAF), University of Washington (UW), Oregon State University (OSU), North Slope Borough (NSB), and NMML. The data described and presented here are only from the aerial survey component of this larger study. The focus of the aerial survey was to document patterns and variability in the timing and locations of bowhead whales. Using a NOAA Twin Otter, scientists from NMML conducted aerial surveys from mid-August to mid-September during this five year study between years 2007-2011. Surveys were conducted in the BOWFEST study area (continental shelf waters between 157 degree W and 152 degree W and from the coastline to 72 degree N, with most of the effort concentrated between 157 degree W and 154 degree W and between the coastline and 71 degree 44'N). proprietary
gov.noaa.nodc:0131862_Not Applicable Cetacean line-transect survey conducted in the eastern Bering Sea shelf by Alaska Fisheries Science Center, National Marine Mammal Laboratory from NOAA Ship Miller Freeman from 1999-07-07 to 2004-06-30 (NCEI Accession 0131862) NOAA_NCEI STAC Catalog 1999-07-07 2004-06-30 -178.9167, 53.9212, -153.451, 63.0152 https://cmr.earthdata.nasa.gov/search/concepts/C2089378822-NOAA_NCEI.umm_json Visual surveys for cetaceans were conducted on the eastern Bering Sea shelf along transect lines, in association with the AFSCâs echo integration trawl surveys for walleye pollock. Surveys in 2000 and 2004 were from early June to early July, the survey in 2002 was from early June to late July, and the survey in 1999 was from early July to early August. Searches for cetaceans were conducted from the flying bridge of NOAA Ship Miller Freeman at a platform height of 12 m above the sea surface and survey speed of 18.5 22.0 km/h (10 12 kts). North south transect lines were spaced 37 km apart and defined by the historical acoustic survey for walleye pollock. Insufficient funding precluded including cetacean observers on all legs except in 2002. See Friday et al. 2012. Cetacean distribution and abundance in relation to oceanographic domains on the eastern Bering Sea shelf: 1999-2004 (http://www.sciencedirect.com/science/article/pii/S0967064512000100). proprietary
gov.noaa.nodc:0133936_Not Applicable Beluga whales aerial survey conducted by Alaska Fisheries Scientific Center, National Marine Mammal Laboratory from 1993-06-02 to 2014-06-12 (NCEI Accession 0133936) NOAA_NCEI STAC Catalog 1993-06-02 2014-06-12 -154.28, 58.82, -148.96, 61.63 https://cmr.earthdata.nasa.gov/search/concepts/C2089379076-NOAA_NCEI.umm_json The National Marine Fisheries Service (NMFS) has conducted aerial counts of Cook Inlet beluga whales (Delphinapterus leucas) from 1993 to 2014 (excluding 2013). Nearly all counts were conducted during the month of June. The routine nature of these counts and the consistency in research protocol lend themselves to inter-annual trend analyses. Beginning in 2005, an aerial survey was added during the month of August to document calving groups within the upper Inlet (north of East and West Foreland). Research protocol has been based on paired observers on the shoreward side of the aircraft and a single observer and computer operator on the offshore side independently searching for marine mammals. Data on environmental conditions, time, location, species, and inclinometer angle were collected for each sighting. The counting protocol included multiple passes near each beluga group while simultaneously collecting video footage. The counting system and observer performance has been tested through paired, independent observational effort. Aerial observer counts are used to calculate median counts for each beluga group to provide a daily index for the population prior to calculating the abundance estimate. Video has been used to count the number of animals in the group to correct for missed animals in the observer counts (perception bias). One video camera had a lens set at a wide angle to view the entire beluga group while the second video camera was zoomed to approximately 10x to magnify a subsample of individual whales in the group. The zoomed video has been used to examine color ratios of white adults relative to smaller and darker juveniles and calves and correct for those individuals missed due to their size or coloration. Aerial counts and video footage of beluga whales provide the fundamental data used to calculate the abundance of and a calving index for the Cook Inlet population. The abundance estimates are applied to trends analyses to determine the status of the stock. Three datasets are included here that contain basic survey data such as latitude, longitude and sightings, as well as the counts of beluga whale groups made by the aerial observers and the results from video analysis from data collected on surveys from 1993-2012, and 2014. proprietary
@@ -18635,8 +18642,8 @@ gov.noaa.nodc:0146259_Not Applicable Capture and resight data of California sea
gov.noaa.nodc:0146680_Not Applicable Benthic Surveys in Vatia, American Samoa: benthic images collected during belt transect surveys from 2015-11-2 to 2015-11-12 (NCEI Accession 0146680) NOAA_NCEI STAC Catalog 2015-11-02 2015-11-12 -170.674, -14.2501, -170.667, -14.2432 https://cmr.earthdata.nasa.gov/search/concepts/C2089378606-NOAA_NCEI.umm_json Jurisdictional managers have expressed concerns that nutrients from the village of Vatia, Tutuila, American Samoa, are having an adverse effect on the coral reef ecosystem in Vatia Bay. Excess nutrient loads promote increases in algal growth that can have deleterious effects on corals, such as benthic algae outcompeting and overgrowing corals. Nitrogen and phosphorus can also directly impact corals by lowering fertilization success, and reducing both photosynthesis and calcification rates. Land-based contributions of nutrients come from a variety of sources; in Vatia the most likely sources are poor wastewater management from piggeries and septic systems. NOAA scientists conducted benthic surveys to establish a baseline against which to compare changes in the algal and coral assemblages in response to nutrient fluxes. The data described here were collected via belt transect surveys of coral demography (adult and juvenile corals) by the NOAA Coral Reef Ecosystem Program (CREP) according to protocols established by the NOAA National Coral Reef Monitoring Program (NCRMP). In 2015 data were collected at 18 stratified randomly selected sites in Vatia Bay. These data include photoquadrat benthic images. proprietary
gov.noaa.nodc:0146682_Not Applicable Benthic Surveys in Faga'alu, American Samoa: benthic images collected during belt transect surveys in 2012 and 2015 (NCEI Accession 0146682) NOAA_NCEI STAC Catalog 2012-03-28 2015-11-11 -170.681, -14.2952, -170.673, -14.287 https://cmr.earthdata.nasa.gov/search/concepts/C2089378626-NOAA_NCEI.umm_json The data described herein are part of a NOAA Coral Reef Conservation Program (CRCP) funded project aimed at establishing baseline data for coral demographics and benthic cover and composition via Rapid Ecological Assessment (REA) surveys conducted by the NOAA Coral Reef Ecosystem Program (CREP) at Faga'alu Bay, Tutuila, American Samoa between 2012 and 2015. Photoquadrat benthic images were collected in 2012 and 2015 only, via belt transect surveys of coral demography according to protocols established by CREP in 2012 and by the NOAA National Coral Reef Monitoring Program (NCRMP) in 2015. proprietary
gov.noaa.nodc:0147683_Not Applicable Bottom longline analytical data collected in Gulf of Mexico from 1995-01-01 to 2013-12-30 (NCEI Accession 0147683) NOAA_NCEI STAC Catalog 1995-01-01 2013-12-30 -97.3473, 24.3627, -81.5875, 30.3677 https://cmr.earthdata.nasa.gov/search/concepts/C2089378649-NOAA_NCEI.umm_json NOAA NMFS does not approve, recommend, or endorse any proprietary product or proprietary material mentioned in this publication. No reference shall be made to NMFS, or to this publication furnished by NMFS, in any advertising or sales promotion which would indicate or imply that NMFS approves, recommends, or endorses any proprietary product or proprietary material mentioned herein or which has as its purpose any intent to cause directly or indirectly the advertised product to be used or purchased because of this NMFS publication. NMFS is not responsible for any uses of these datasets beyond those for which they were intended, and NMFS makes no claims regarding the accuracy of any data provided by agencies or individuals outside NMFS. Acknowledgment of NOAA NMFS and SEAMAP would be appreciated in products derived or publications generated from this data. proprietary
-gov.noaa.nodc:0148759_Not Applicable AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Helheim Glacier Ice Front from 2009-08-11 to 2016-02-20 (NCEI Accession 0148759) NOAA_NCEI STAC Catalog 2009-08-11 2016-02-20 -38.146, 66.329, -38.146, 66.329 https://cmr.earthdata.nasa.gov/search/concepts/C2089378741-NOAA_NCEI.umm_json The Helheim Glacier was observed to retreat and speed up during the mid 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013. proprietary
gov.noaa.nodc:0148759_Not Applicable AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Helheim Glacier Ice Front from 2009-08-11 to 2016-02-20 (NCEI Accession 0148759) ALL STAC Catalog 2009-08-11 2016-02-20 -38.146, 66.329, -38.146, 66.329 https://cmr.earthdata.nasa.gov/search/concepts/C2089378741-NOAA_NCEI.umm_json The Helheim Glacier was observed to retreat and speed up during the mid 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013. proprietary
+gov.noaa.nodc:0148759_Not Applicable AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Helheim Glacier Ice Front from 2009-08-11 to 2016-02-20 (NCEI Accession 0148759) NOAA_NCEI STAC Catalog 2009-08-11 2016-02-20 -38.146, 66.329, -38.146, 66.329 https://cmr.earthdata.nasa.gov/search/concepts/C2089378741-NOAA_NCEI.umm_json The Helheim Glacier was observed to retreat and speed up during the mid 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013. proprietary
gov.noaa.nodc:0148760_Not Applicable AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Jakobshavn Glacier Ice Front from 2007-10-13 to 2016-02-14 (NCEI Accession 0148760) NOAA_NCEI STAC Catalog 2007-10-13 2016-02-14 -49.815, 69.222, -49.815, 69.222 https://cmr.earthdata.nasa.gov/search/concepts/C2089378750-NOAA_NCEI.umm_json The Jakobshavn Glacier was observed to retreat and speed up during the late 1990s and early 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013. proprietary
gov.noaa.nodc:0148760_Not Applicable AIR TEMPERATURE, RELATIVE HUMIDITY, and others collected from Automatic Weather Station installed on rock outcrop in Jakobshavn Glacier Ice Front from 2007-10-13 to 2016-02-14 (NCEI Accession 0148760) ALL STAC Catalog 2007-10-13 2016-02-14 -49.815, 69.222, -49.815, 69.222 https://cmr.earthdata.nasa.gov/search/concepts/C2089378750-NOAA_NCEI.umm_json The Jakobshavn Glacier was observed to retreat and speed up during the late 1990s and early 2000s. One possible cause of the change in glacier behavior could be due to changes in atmosphere properties, temperature, humidity, and wind. A research program was established to monitor the atmosphere conditions near the glacier during 2009-2013. proprietary
gov.noaa.nodc:0155488_Not Applicable Bottom Dissolved Oxygen Maps From SEAMAP Summer and Fall Groundfish/Shrimp Surveys from 1982 to 1998 (NCEI Accession 0155488) NOAA_NCEI STAC Catalog 1982-01-01 1998-01-01 -98, 18, -74, 37 https://cmr.earthdata.nasa.gov/search/concepts/C2089380245-NOAA_NCEI.umm_json Bottom dissolved oxygen (DO) data was extracted from environmental profiles acquired during the Southeast Fisheries Science Center Mississippi Laboratories summer groundfish trawl surveys of the Western and North-central Gulf of Mexico from 1982-1998. The data were distributed to hypoxia researchers in near real time and used to generate bottom DO maps as part of the Hypoxia Watch Project (http://www.ncddc.noaa.gov/hypoxia/). The profiles were acquired with a Sea-Bird Model SB9 profiler equipped with pressure, temperature, conductivity, fluorescence, and beam transmission sensors. The data were processed with Sea-Bird software using the standard processing protocol developed by the Mississippi Laboratories. Water temperature, beam transmission, and derived salinity, DO and DO percent saturation, and density were retained in the processed files. SAS software was used to extract the bottom DO and other relevant data (e.g., date, time, position, and station number) and format the data as comma-delimited ASCII files. proprietary
@@ -18645,11 +18652,11 @@ gov.noaa.nodc:0155964_Not Applicable CHLOROPHYLL A CONCENTRATION collected from
gov.noaa.nodc:0155998_Not Applicable CHLOROPHYLL A CONCENTRATION collected from NOAA Ship OSCAR ELTON SETTE in Hawaii EEZ, Palmyra EEZ, and American Samoa EEZ from 2012-04-23 to 2012-05-15 (NCEI Accession 0155998) NOAA_NCEI STAC Catalog 2012-04-23 2012-05-15 -169.9633, -14.2446, -157.2218, 19.2698 https://cmr.earthdata.nasa.gov/search/concepts/C2089376410-NOAA_NCEI.umm_json Surface water samples were collected during a Pacific Islands Fisheries Science Center's Cetacean Research Program's shipboard cetacean survey (Cruise ID SE 12-03). A minimum of three surface water samples were taken each day, primarily at 0900, 1200, and 1500 hours local ship time. Samples were also collected opportunistically during some cetacean sightings. The 250ml water samples were filtered onto GF/F filters, placed in 10ml of 90% acetone, refrigerated or frozen for 24 hours, and then analyzed for chlorophyll a concentration using the Turner Designs model 10AU field flourometer. Measurements were recorded in an Excel spreadsheet. proprietary
gov.noaa.nodc:0156424_Not Applicable Absolute Geostrophic Velocity Inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156424) NOAA_NCEI STAC Catalog 1950-01-01 1996-12-31 -180, 58, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376812-NOAA_NCEI.umm_json The dataset (called EWG-V) comprises 3D gridded climatological fields of absolute geostrophic velocity inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean using the P-vector method. It provides a climatological velocity field that is dynamically compatible to the EWG (T, S) fields. The EWG-V velocity fields have the annual, and seasonal (winter and summer) means with the same horizontal resolution of 25 km and 90 vertical levels as the EWG temperature and salinity fields. proprietary
gov.noaa.nodc:0156424_Not Applicable Absolute Geostrophic Velocity Inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156424) ALL STAC Catalog 1950-01-01 1996-12-31 -180, 58, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376812-NOAA_NCEI.umm_json The dataset (called EWG-V) comprises 3D gridded climatological fields of absolute geostrophic velocity inverted from the Environmental Working Group (EWG) Joint U.S.-Russian Atlas of the Arctic Ocean using the P-vector method. It provides a climatological velocity field that is dynamically compatible to the EWG (T, S) fields. The EWG-V velocity fields have the annual, and seasonal (winter and summer) means with the same horizontal resolution of 25 km and 90 vertical levels as the EWG temperature and salinity fields. proprietary
-gov.noaa.nodc:0156425_Not Applicable Absolute Geostrophic Velocity Inverted from the Polar Science Center Hydrographic Climatology (PHC3.0) of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156425) ALL STAC Catalog 1900-01-01 1998-12-31 -180, 45, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376820-NOAA_NCEI.umm_json The dataset (called PHC-V) comprises 3D gridded climatological fields of absolute geostrophic velocity of the Arctic Ocean inverted from the Polar science center Hydrographic Climatology (PHC) temperature and salinity fields (version 3.0) using the P-vector method. It provides climatological annual, seasonal, and monthly mean velocity fields with the same horizontal resolution (1 deg in horizontal, 33 levels in vertical), and dynamical compatibility to the PHC3.0 (T, S) fields. proprietary
gov.noaa.nodc:0156425_Not Applicable Absolute Geostrophic Velocity Inverted from the Polar Science Center Hydrographic Climatology (PHC3.0) of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156425) NOAA_NCEI STAC Catalog 1900-01-01 1998-12-31 -180, 45, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376820-NOAA_NCEI.umm_json The dataset (called PHC-V) comprises 3D gridded climatological fields of absolute geostrophic velocity of the Arctic Ocean inverted from the Polar science center Hydrographic Climatology (PHC) temperature and salinity fields (version 3.0) using the P-vector method. It provides climatological annual, seasonal, and monthly mean velocity fields with the same horizontal resolution (1 deg in horizontal, 33 levels in vertical), and dynamical compatibility to the PHC3.0 (T, S) fields. proprietary
+gov.noaa.nodc:0156425_Not Applicable Absolute Geostrophic Velocity Inverted from the Polar Science Center Hydrographic Climatology (PHC3.0) of the Arctic Ocean with the P-Vector Method (NCEI Accession 0156425) ALL STAC Catalog 1900-01-01 1998-12-31 -180, 45, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089376820-NOAA_NCEI.umm_json The dataset (called PHC-V) comprises 3D gridded climatological fields of absolute geostrophic velocity of the Arctic Ocean inverted from the Polar science center Hydrographic Climatology (PHC) temperature and salinity fields (version 3.0) using the P-vector method. It provides climatological annual, seasonal, and monthly mean velocity fields with the same horizontal resolution (1 deg in horizontal, 33 levels in vertical), and dynamical compatibility to the PHC3.0 (T, S) fields. proprietary
gov.noaa.nodc:0156692_Not Applicable Bioerosion Accretion Replicate (BAR) data covering in situ calcification and bioerosion rates along pH gradients at two volcanically acidified reefs in Papua New Guinea from 2013-01-18 to 2014-11-10 (NCEI Accession 0156692) NOAA_NCEI STAC Catalog 2013-01-18 2014-11-10 150.775, -9.875, 150.925, -9.725 https://cmr.earthdata.nasa.gov/search/concepts/C2089377345-NOAA_NCEI.umm_json "Bioerosion Accretion Replicate (BAR) data covering in situ calcification and bioerosion rates along pH gradients at two volcanically acidified reefs in Papua New Guinea. Methodologies, results, and analysis may be found in ""Enhanced macroboring and depressed calcification drive net dissolution at high-CO2 coral reef"" which is published in the Proceedings of the Royal Society, Series B" proprietary
-gov.noaa.nodc:0156765_Not Applicable Age and Growth of Spotted Sea Trout in the Gulf of Mexico from 1994 to 1996 (NCEI Accession 0156765) NOAA_NCEI STAC Catalog 1994-05-06 1996-08-30 -87.6, 29.6, -84.7, 30.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089377384-NOAA_NCEI.umm_json These data sets contain raw and processed data to compare life history demographic information necessary to manage spotted seatrout in NW Florida. Specific objectives were to develop estuary-specific information on age growth, mortality rates, spawning seasonality, age size at maturity, and age size composition of the recreational fishery for Apalachicola, St. Joseph, St. Andrew, Choctawhatchee, Pensacola, and Perdido Bay systems. proprietary
gov.noaa.nodc:0156765_Not Applicable Age and Growth of Spotted Sea Trout in the Gulf of Mexico from 1994 to 1996 (NCEI Accession 0156765) ALL STAC Catalog 1994-05-06 1996-08-30 -87.6, 29.6, -84.7, 30.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089377384-NOAA_NCEI.umm_json These data sets contain raw and processed data to compare life history demographic information necessary to manage spotted seatrout in NW Florida. Specific objectives were to develop estuary-specific information on age growth, mortality rates, spawning seasonality, age size at maturity, and age size composition of the recreational fishery for Apalachicola, St. Joseph, St. Andrew, Choctawhatchee, Pensacola, and Perdido Bay systems. proprietary
+gov.noaa.nodc:0156765_Not Applicable Age and Growth of Spotted Sea Trout in the Gulf of Mexico from 1994 to 1996 (NCEI Accession 0156765) NOAA_NCEI STAC Catalog 1994-05-06 1996-08-30 -87.6, 29.6, -84.7, 30.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089377384-NOAA_NCEI.umm_json These data sets contain raw and processed data to compare life history demographic information necessary to manage spotted seatrout in NW Florida. Specific objectives were to develop estuary-specific information on age growth, mortality rates, spawning seasonality, age size at maturity, and age size composition of the recreational fishery for Apalachicola, St. Joseph, St. Andrew, Choctawhatchee, Pensacola, and Perdido Bay systems. proprietary
gov.noaa.nodc:0156869_Not Applicable Captive sea turtle rearing inventory, feeding, and water chemistry in sea turtle rearing tanks at NOAA Galveston, Texas from 1995 to 2015 (NCEI Accession 0156869) NOAA_NCEI STAC Catalog 2005-01-03 2015-12-31 -94.819688, 29.274811, -94.81456, 29.278028 https://cmr.earthdata.nasa.gov/search/concepts/C2089377448-NOAA_NCEI.umm_json The database contains Excel and CSV spreadsheets monitoring captive Sea Turtle rearing program. Daily feeding logs as well as water chemistry were recorded. proprietary
gov.noaa.nodc:0156913_Not Applicable Carbonate Budget data of the Southeast Florida Coral Reef Initiative (SEFCRI) region from 2014-09-29 to 2014-10-17 (NCEI Accession 0156913) NOAA_NCEI STAC Catalog 2014-09-29 2014-10-17 -80.104, 25.6519, -80.077, 26.1636 https://cmr.earthdata.nasa.gov/search/concepts/C2089377484-NOAA_NCEI.umm_json This data set includes census based carbonate budget data that was collected in coral reef habitats located within the SEFCRI region. Surveys (based on Perry et al 2012) were collected over the course of several weeks at four major sites: Emerald, Oakland Ridge, Barracuda, and Pillars. Within each of these sites, six transect surveys (10m each) were conducted to quantify benthic cover, macrobioerosion, and microbioerosion. Ten parrotfish surveys were also conducted to account for parrotfish erosion rates at each site. This carbonate budget data along with other sea water chemistry data collected were used to inform the overall project looking at the sensitivity of the SEFCRI region to OA. We measured ambient seasonal variability across inshore/offshore reef habitats to predict the response of the CaCO3 budget of coral reefs in the SEFCRI region to ocean acidification. This data set includes all of the carbonate budget surveys that were collected to identify the sensitivity of the SEFCRI region to OA. proprietary
gov.noaa.nodc:0157022_Not Applicable Carbonate data collected from R/V Hildebrand in the SEFCRI region of the Florida Reef Tract from 2014-05-27 to 2015-09-02 (NCEI Accession 0157022) NOAA_NCEI STAC Catalog 2014-05-27 2015-09-02 -80.1328, 25.5906, -80.077, 26.1636 https://cmr.earthdata.nasa.gov/search/concepts/C2089377840-NOAA_NCEI.umm_json This data set includes seawater chemistry that was collected in coral reef habitats located within the SEFCRI region as well as inlets and outfalls that release nutrient rich and/or sediment laden freshwater to the coastal waters South Florida. Freshwater runoff and riverine inputs are known to be enriched in dissolved inorganic carbon, and diluted lower saline waters are known to have elevated pCO2 (e.g., Manzello et al. 2013) which is why those areas in addition to the reef sites were included in our analyses. This data along with other data collected in the field were used to inform the overall project looking at the sensitivity of the SEFCRI region to OA. We measured ambient seasonal variability across inshore/offshore reef habitats to predict the response of the CaCO3 budget of coral reefs in the SEFCRI region to ocean acidification. This data set includes all of the seawater samples that were collected and analyzed to identify the carbonate chemistry in this region. proprietary
@@ -18658,13 +18665,13 @@ gov.noaa.nodc:0157074_Not Applicable ACOUSTIC TRAVEL TIME collected as part of t
gov.noaa.nodc:0157074_Not Applicable ACOUSTIC TRAVEL TIME collected as part of the Sub-Antarctic Flux and Dynamics Experiment (SAFDE) from March 1995 to March 1997 (NCEI Accession 0157074) NOAA_NCEI STAC Catalog 1995-03-20 1997-03-28 143.63333, -52.08133, 143.805, -47.99867 https://cmr.earthdata.nasa.gov/search/concepts/C2089378023-NOAA_NCEI.umm_json Inverted echo sounder (IES) data were collected as part of the Sub-Antarctic Flux and Dynamics Experiment (SAFDE) during March 1995 -- March 1997 conducted south of Australia. The collection, processing and calibration of the IES data are described in the report provided. These are the highest quality versions of the data after the least amount of processing. Also provided are low-passed filtered versions that have been calibrated to a common pressure level in order that the data may be used together more conveniently. The measurements were made under the support of the National Science Foundation grants OCE9204041 and OCE9912320. proprietary
gov.noaa.nodc:0157087_Not Applicable Behavior of parrotfishes (Labridae, Scarinae) in St. Croix from 2015-07-06 to 2015-07-26 (NCEI Accession 0157087) NOAA_NCEI STAC Catalog 2015-07-06 2015-07-26 -64.813, 17.759, -64.608, 17.787 https://cmr.earthdata.nasa.gov/search/concepts/C2089378063-NOAA_NCEI.umm_json To better understand the functional roles of parrotfishes on coral reefs in the Caribbean this project documented the foraging behavior and diets of six species of parrotfishes (Scarus taeniopterus, Scarus vetula, Sparisoma aurofrenatum, Sparisoma chrysopterum, Sparisoma rubripinne, Sparisoma viride) at three locations (Long Reef, Cane Bay, and Buck Island) on the north shore of St. Croix, U. S. Virgin Islands. To quantify parrotfish behavior, approximately six individuals of each species were observed at each site for 20 min each. Foraging behavior was recorded by a SCUBA diver while towing a GPS receiver (Garmin GPS 72) attached to a surface float, which obtained position fixes of the focal fish at 15 s intervals. Fish were followed from a close distance (~ 2 m when possible), and food items were identified to the lowest taxonomic level possible, with macroalgae and coral usually identified to genus or species. Many bites involved scraping or excavating substrate colonized by a multi-species assemblage of filamentous âturfâ algae and crustose coralline algae (CCA). Thus, multiple species of filamentous algae, endolithic algae, and CCA could be harvested in a single bite, and it was impossible to determine the specific species of algae targeted. We also recorded the type of substrate targeted during each foraging bout, categorizing each substrate as one of the following: (1) dead coral, (2) coral pavement, (3) boulder, (4) rubble, (5) ledge, or (6) sand. In order to quantify the relative abundance of different substrates and food types, we estimated the percent cover of algae, coral, and other sessile invertebrates on each of the six substrates commonly targeted by parrotfishes (dead coral, coral pavement, boulder, rubble, ledge, and sand) in 0.5 m x 0.5 m photoquadrats. Photographs were taken at 2.5 m intervals on 30 m transects, with a total of 10 haphazardly placed transects sampled at each site. Each photoquadrat was divided into sixteen 12 cm x 12 cm sections which were individually photographed, and percent cover was estimated from 9 stratified random points per section (N = 144 point per quadrat). proprietary
gov.noaa.nodc:0157611_Not Applicable Benthic Images from Towed-Diver Surveys in the Main Hawaiian Islands to Assess the Mass Coral Bleaching Event from 2015-11-03 to 2015-11-18 (NCEI Accession 0157611) NOAA_NCEI STAC Catalog 2015-11-03 2015-11-18 -157.9472292, 19.748537, -155.829342, 21.3030689 https://cmr.earthdata.nasa.gov/search/concepts/C2089376905-NOAA_NCEI.umm_json A team from the Pacific Islands Fisheries Science Center (PIFSC), Coral Reef Ecosystem Program (CREP) deployed on a two-week research cruise in November 2015 to evaluate the impacts of the 2015 mass coral bleaching event in the Main Hawaiian Islands via towed-diver surveys. Areas surveyed included south Oahu, west Maui, Lanaâi, and west Hawaii island. Over the course of 10 survey days, the team surveyed approximately 90 km of 15-m wide transects at depths ranging from 2 to 10 m. Data provided in this dataset include benthic images that were collected during the towed-diver surveys from a camera that was mounted to the towboard. A downward-facing DSLR camera with strobes collected these photographic quadrat data by capturing an image of the benthos at 15-second intervals during the surveys. Two additional datasets were collected during the surveys and are documented separately. Towed divers recorded visual estimates of percentage of live coral that was pale and bleached, as well as presence/absence data of condition by generic composition. Oceanographic data was collected continuously throughout each survey with a suite of sensors mounted to the towboard recording conductivity, temperature, depth, flourometry (chlorophyll-a), turbidity and dissolved oxygen. proprietary
-gov.noaa.nodc:0159386_Not Applicable Airborne eXpendable BathyThermographs (AXBT) data from Ocean Surveys in the Gulf of Mexico during Hurricane Lili 2002-10-02 to 2002-10-04 (NCEI Accession 0159386) NOAA_NCEI STAC Catalog 2002-10-02 2002-10-04 -88.672, 22.203, -84.062, 26.433 https://cmr.earthdata.nasa.gov/search/concepts/C2089377618-NOAA_NCEI.umm_json Airborne eXpendable BathyThermographs (AXBT) data from deployments during field operations to study Hurricane Lili. The data were used in model simulations for Uhlhorn and Shay (2013). This dataset contains water temperature and depth data for this cruise. proprietary
gov.noaa.nodc:0159386_Not Applicable Airborne eXpendable BathyThermographs (AXBT) data from Ocean Surveys in the Gulf of Mexico during Hurricane Lili 2002-10-02 to 2002-10-04 (NCEI Accession 0159386) ALL STAC Catalog 2002-10-02 2002-10-04 -88.672, 22.203, -84.062, 26.433 https://cmr.earthdata.nasa.gov/search/concepts/C2089377618-NOAA_NCEI.umm_json Airborne eXpendable BathyThermographs (AXBT) data from deployments during field operations to study Hurricane Lili. The data were used in model simulations for Uhlhorn and Shay (2013). This dataset contains water temperature and depth data for this cruise. proprietary
-gov.noaa.nodc:0159419_Not Applicable ADCP, CTD, MIDAS, and cruise track data collected from R/V Pelican in Galveston and Trinity Bay, Texas and the Gulf of Mexico from 2013-10-17 to 2013-10-20 (NCEI Accession 0159419) NOAA_NCEI STAC Catalog 2013-10-17 2013-10-20 -94.9828, 26.16133, -88, 29.69641 https://cmr.earthdata.nasa.gov/search/concepts/C2089377667-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, October 17-20 2013, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE14-10b was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary
+gov.noaa.nodc:0159386_Not Applicable Airborne eXpendable BathyThermographs (AXBT) data from Ocean Surveys in the Gulf of Mexico during Hurricane Lili 2002-10-02 to 2002-10-04 (NCEI Accession 0159386) NOAA_NCEI STAC Catalog 2002-10-02 2002-10-04 -88.672, 22.203, -84.062, 26.433 https://cmr.earthdata.nasa.gov/search/concepts/C2089377618-NOAA_NCEI.umm_json Airborne eXpendable BathyThermographs (AXBT) data from deployments during field operations to study Hurricane Lili. The data were used in model simulations for Uhlhorn and Shay (2013). This dataset contains water temperature and depth data for this cruise. proprietary
gov.noaa.nodc:0159419_Not Applicable ADCP, CTD, MIDAS, and cruise track data collected from R/V Pelican in Galveston and Trinity Bay, Texas and the Gulf of Mexico from 2013-10-17 to 2013-10-20 (NCEI Accession 0159419) ALL STAC Catalog 2013-10-17 2013-10-20 -94.9828, 26.16133, -88, 29.69641 https://cmr.earthdata.nasa.gov/search/concepts/C2089377667-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, October 17-20 2013, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE14-10b was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary
+gov.noaa.nodc:0159419_Not Applicable ADCP, CTD, MIDAS, and cruise track data collected from R/V Pelican in Galveston and Trinity Bay, Texas and the Gulf of Mexico from 2013-10-17 to 2013-10-20 (NCEI Accession 0159419) NOAA_NCEI STAC Catalog 2013-10-17 2013-10-20 -94.9828, 26.16133, -88, 29.69641 https://cmr.earthdata.nasa.gov/search/concepts/C2089377667-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, October 17-20 2013, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE14-10b was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary
gov.noaa.nodc:0159850_Not Applicable Burrowing behavior of penaeid shrimps in the Gulf of Mexico from 1984-10-01 to 1985-12-06 (NCEI Accession 0159850) NOAA_NCEI STAC Catalog 1984-10-01 1985-12-06 -94.815127, 29.275417, -94.815127, 29.275417 https://cmr.earthdata.nasa.gov/search/concepts/C2089377792-NOAA_NCEI.umm_json This data set contains hourly visual observations of burrowing behavior in penaeid shrimp. proprietary
-gov.noaa.nodc:0161311_Not Applicable A Comprehensive Inventory of Alabama Coastal Zone Wetland Habitats (Swamps, Marshes, Submersed Grassbeds) from 1980 to 1982 (NCEI Accession 0161311) NOAA_NCEI STAC Catalog 1979-01-01 1982-12-31 -88.431, 30.2129, -87.328, 31.0701 https://cmr.earthdata.nasa.gov/search/concepts/C2089378452-NOAA_NCEI.umm_json Digitized maps of Mobile Bay and other coastal areas of Alabama, showing habitat types and species compositions of the vegetation in three broad categories of wetland: swamps, marshes, and submersed grassbeds. All areas in the Alabama Coastal Zone of less than 10 feet elevation above sea level, up to the fork of the Tombigbee and Alabama Rivers, were included in the inventory. Habitat boundary delineations were based on aerial photographs from 1979 and 1980, with transects by boat or foot for field verification in 1980-1982. Dataset includes habitat type classifications based on species compositions, and identifications of dominant species at each location. proprietary
gov.noaa.nodc:0161311_Not Applicable A Comprehensive Inventory of Alabama Coastal Zone Wetland Habitats (Swamps, Marshes, Submersed Grassbeds) from 1980 to 1982 (NCEI Accession 0161311) ALL STAC Catalog 1979-01-01 1982-12-31 -88.431, 30.2129, -87.328, 31.0701 https://cmr.earthdata.nasa.gov/search/concepts/C2089378452-NOAA_NCEI.umm_json Digitized maps of Mobile Bay and other coastal areas of Alabama, showing habitat types and species compositions of the vegetation in three broad categories of wetland: swamps, marshes, and submersed grassbeds. All areas in the Alabama Coastal Zone of less than 10 feet elevation above sea level, up to the fork of the Tombigbee and Alabama Rivers, were included in the inventory. Habitat boundary delineations were based on aerial photographs from 1979 and 1980, with transects by boat or foot for field verification in 1980-1982. Dataset includes habitat type classifications based on species compositions, and identifications of dominant species at each location. proprietary
+gov.noaa.nodc:0161311_Not Applicable A Comprehensive Inventory of Alabama Coastal Zone Wetland Habitats (Swamps, Marshes, Submersed Grassbeds) from 1980 to 1982 (NCEI Accession 0161311) NOAA_NCEI STAC Catalog 1979-01-01 1982-12-31 -88.431, 30.2129, -87.328, 31.0701 https://cmr.earthdata.nasa.gov/search/concepts/C2089378452-NOAA_NCEI.umm_json Digitized maps of Mobile Bay and other coastal areas of Alabama, showing habitat types and species compositions of the vegetation in three broad categories of wetland: swamps, marshes, and submersed grassbeds. All areas in the Alabama Coastal Zone of less than 10 feet elevation above sea level, up to the fork of the Tombigbee and Alabama Rivers, were included in the inventory. Habitat boundary delineations were based on aerial photographs from 1979 and 1980, with transects by boat or foot for field verification in 1980-1982. Dataset includes habitat type classifications based on species compositions, and identifications of dominant species at each location. proprietary
gov.noaa.nodc:0161523_Not Applicable Biological, chemical, physical and time series data collected from station WQB04 by University of Hawai'i at Hilo and assembled by Pacific Islands Ocean Observing System (PacIOOS) in the North Pacific Ocean from 2010-10-23 to 2016-12-31 (NCEI Accession 0161523) NOAA_NCEI STAC Catalog 2010-10-23 2016-12-31 -155.082, 19.7341, -155.082, 19.7341 https://cmr.earthdata.nasa.gov/search/concepts/C2089378474-NOAA_NCEI.umm_json NCEI Accession 0161523 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo collected the data from their in-situ moored station named WQB04: PacIOOS Water Quality Buoy 04 (WQB-04): Hilo Bay, Big Island, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month. The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB04 is located in Hilo Bay on the east side of the Big Island. Continuous sampling of this area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill. proprietary
gov.noaa.nodc:0162518_Not Applicable ADCP, CTD, and MIDAS data collected from Ewing and Sackett Gulf Deep Banks, Gulf of Mexico on the R/V Pelican in Gulf of Mexico from 2012-11-15 to 2012-11-17 (NCEI Accession 0162518) NOAA_NCEI STAC Catalog 2012-11-15 2012-11-17 -91.20748, 27.49168, -89, 29.0029 https://cmr.earthdata.nasa.gov/search/concepts/C2089380274-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, November 15-17 2012, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE13-14 was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary
gov.noaa.nodc:0162518_Not Applicable ADCP, CTD, and MIDAS data collected from Ewing and Sackett Gulf Deep Banks, Gulf of Mexico on the R/V Pelican in Gulf of Mexico from 2012-11-15 to 2012-11-17 (NCEI Accession 0162518) ALL STAC Catalog 2012-11-15 2012-11-17 -91.20748, 27.49168, -89, 29.0029 https://cmr.earthdata.nasa.gov/search/concepts/C2089380274-NOAA_NCEI.umm_json Sampling of in situ seawater, macroalgae, macrocrustaceans and associated fauna (cruise GoMRI-II, November 15-17 2012, stns 1-18, data available for all) aboard the R/V Pelican cruise id PE13-14 was targeted to repeat sampling of previously studied hard banks and adjacent deep waters west of the mouth of the Mississippi River and extending east to offshore Alabama, an area encompassing roughly 27°58'N to 29°26'N and 87°34'W to 91°01'W. Submitted metadata are ADCP, CTD, Marks and Cruise Track Data. proprietary
@@ -18674,8 +18681,8 @@ gov.noaa.nodc:0162830_Not Applicable Benthic images collected at coral reef site
gov.noaa.nodc:0162831_Not Applicable Calcification rates of crustose coralline algae (CCA) derived from Calcification Accretion Units (CAUs) deployed at coral reef sites in Batangas, Philippines in 2012 and recovered in 2015 (NCEI Accession 0162831) NOAA_NCEI STAC Catalog 2012-03-13 2015-06-03 120.872, 13.6586, 120.895, 13.7281 https://cmr.earthdata.nasa.gov/search/concepts/C2089380467-NOAA_NCEI.umm_json Laboratory experiments reveal calcification rates of crustose coralline algae (CCA) are strongly correlated to seawater aragonite saturation state. Predictions of reduced coral calcification rates, due to ocean acidification, suggest that coral reef communities will undergo ecological phase shifts as calcifying organisms are negatively impacted by changing seawater chemistry. Calcification accretion units, or CAUs, are used by the NOAA Coral Reef Ecosystem Program (CREP) to assess the current effects of changes in seawater carbonate chemistry on calcification and accretion rates of calcareous and fleshy algae. CAUs, constructed in-house by CREP, are composed of two 10 x 10 cm flat, square, gray PVC plates, stacked 1 cm apart, and are attached to the benthos by SCUBA divers using stainless steel threaded rods. Deployed on the seafloor for a period of time, calcareous organisms, primarily crustose coralline algae and encrusting corals, recruit to these plates and accrete/calcify carbonate skeletons over time. By measuring the change in weight of the CAUs, the reef carbonate accretion rate can be calculated for that time period. The calcification rate data described here were collected by CREP from CAUs moored at fixed climate survey sites located on hard bottom shallow water (< 15 m) habitats in the Philippines, in accordance with protocols developed by Price et al. (2012). Climate sites were established by CREP to assess multiple features of the coral reef environment (in addition to the data described herein) from March 2012 to June 2015, and five CAUs were deployed at each survey site. In conjunction with benthic community composition data (archived separately), these data serve as a baseline for detecting changes associated with changing seawater chemistry due to ocean acidification within coral reef environments. proprietary
gov.noaa.nodc:0163192_Not Applicable A Comparison of the foraging ecology and bioenergetics of the early life-stages of two sympatric hammerhead sharks from 1998-07-12 to 2005-07-27 (NCEI Accession 0163192) ALL STAC Catalog 1998-07-12 2005-07-27 -86.2279, 27.4432, -80.1996, 30.7692 https://cmr.earthdata.nasa.gov/search/concepts/C2089380703-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains basic biological information on bonnethead and scalloped hammerhead sharks with specific (by stomach and prey item) diet information for these two species. Data were collected by the NMFS Southeast Fisheries Science Center; Panama City, FL Laboratory in the Northeast Gulf of Mexico and the Atlantic Ocean off the coast of Florida from 1998 to 2005. Data are in comma separated value (CSV) format and include length, sex, and number of prey items. proprietary
gov.noaa.nodc:0163192_Not Applicable A Comparison of the foraging ecology and bioenergetics of the early life-stages of two sympatric hammerhead sharks from 1998-07-12 to 2005-07-27 (NCEI Accession 0163192) NOAA_NCEI STAC Catalog 1998-07-12 2005-07-27 -86.2279, 27.4432, -80.1996, 30.7692 https://cmr.earthdata.nasa.gov/search/concepts/C2089380703-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains basic biological information on bonnethead and scalloped hammerhead sharks with specific (by stomach and prey item) diet information for these two species. Data were collected by the NMFS Southeast Fisheries Science Center; Panama City, FL Laboratory in the Northeast Gulf of Mexico and the Atlantic Ocean off the coast of Florida from 1998 to 2005. Data are in comma separated value (CSV) format and include length, sex, and number of prey items. proprietary
-gov.noaa.nodc:0163212_Not Applicable Acoustic Travel Time and Hydrostatic Pressure in Sermilik Fjord in Southeastern Greenland from 2011-08-23 to 2016-08-11 (NCEI Accession 0163212) ALL STAC Catalog 2011-08-23 2016-08-11 -37.8998, 65.5268, -37.6336, 66.2449 https://cmr.earthdata.nasa.gov/search/concepts/C2089380760-NOAA_NCEI.umm_json These data records are time series of (1) round trip surface to bottom acoustic travel time, (2) bottom pressure and (3) bottom temperature (with the latter internal to the instrument). The first goal in collecting these data was to develop and test non-traditional methods to measure the time-varying â¨heat content and vertical temperature profiles in high-latitude seas, shelves, and fjords using pressure-sensor-equipped inverted echo sounders (PIESs). The second goal was to use PIESs to measure icebergs and sea ice. We developed these methods with data collected in Sermilik Fjord in southeastern Greenland from a 1-year pilot deployment with 1 PIES (deployed mid fjord from 2011 to 2012) and data collected in a full deployment with 3 PIESs (deployed on the shelf by the fjord mouth, mid-fjord and in the upper fjord from 2013-2015/2016). The data format is NetCDF with CF-1.6 conventions. proprietary
gov.noaa.nodc:0163212_Not Applicable Acoustic Travel Time and Hydrostatic Pressure in Sermilik Fjord in Southeastern Greenland from 2011-08-23 to 2016-08-11 (NCEI Accession 0163212) NOAA_NCEI STAC Catalog 2011-08-23 2016-08-11 -37.8998, 65.5268, -37.6336, 66.2449 https://cmr.earthdata.nasa.gov/search/concepts/C2089380760-NOAA_NCEI.umm_json These data records are time series of (1) round trip surface to bottom acoustic travel time, (2) bottom pressure and (3) bottom temperature (with the latter internal to the instrument). The first goal in collecting these data was to develop and test non-traditional methods to measure the time-varying â¨heat content and vertical temperature profiles in high-latitude seas, shelves, and fjords using pressure-sensor-equipped inverted echo sounders (PIESs). The second goal was to use PIESs to measure icebergs and sea ice. We developed these methods with data collected in Sermilik Fjord in southeastern Greenland from a 1-year pilot deployment with 1 PIES (deployed mid fjord from 2011 to 2012) and data collected in a full deployment with 3 PIESs (deployed on the shelf by the fjord mouth, mid-fjord and in the upper fjord from 2013-2015/2016). The data format is NetCDF with CF-1.6 conventions. proprietary
+gov.noaa.nodc:0163212_Not Applicable Acoustic Travel Time and Hydrostatic Pressure in Sermilik Fjord in Southeastern Greenland from 2011-08-23 to 2016-08-11 (NCEI Accession 0163212) ALL STAC Catalog 2011-08-23 2016-08-11 -37.8998, 65.5268, -37.6336, 66.2449 https://cmr.earthdata.nasa.gov/search/concepts/C2089380760-NOAA_NCEI.umm_json These data records are time series of (1) round trip surface to bottom acoustic travel time, (2) bottom pressure and (3) bottom temperature (with the latter internal to the instrument). The first goal in collecting these data was to develop and test non-traditional methods to measure the time-varying â¨heat content and vertical temperature profiles in high-latitude seas, shelves, and fjords using pressure-sensor-equipped inverted echo sounders (PIESs). The second goal was to use PIESs to measure icebergs and sea ice. We developed these methods with data collected in Sermilik Fjord in southeastern Greenland from a 1-year pilot deployment with 1 PIES (deployed mid fjord from 2011 to 2012) and data collected in a full deployment with 3 PIESs (deployed on the shelf by the fjord mouth, mid-fjord and in the upper fjord from 2013-2015/2016). The data format is NetCDF with CF-1.6 conventions. proprietary
gov.noaa.nodc:0163750_Not Applicable Biological, chemical and other data collected from station Humboldt Bay Pier by Humboldt State University and assembled by Central and Northern California Coastal Ocean Observing System (CeNCOOS) in the Northeast Pacific Ocean from 2012-12-13 to 2018-03-07 (NCEI Accession 0163750) NOAA_NCEI STAC Catalog 2012-12-13 2018-03-07 -124.19652, 40.7775, -124.19652, 40.7775 https://cmr.earthdata.nasa.gov/search/concepts/C2089376545-NOAA_NCEI.umm_json NCEI Accession 0163750 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Humboldt State University collected the data from their in-situ moored station named Humboldt Bay Pier in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Humboldt State University and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary
gov.noaa.nodc:0163764_Not Applicable Biological, chemical and other data collected from station Indian River Lagoon - Link Port (IRL-LP) by Florida Atlantic University and assembled by Southeast Coastal Ocean Observing Regional Association (SECOORA) in the Coastal Waters of Florida from 2015-10-07 to 2020-06-01 (NCEI Accession 0163764) NOAA_NCEI STAC Catalog 2015-10-07 2020-06-01 -80.34311, 27.53483, -80.34311, 27.53483 https://cmr.earthdata.nasa.gov/search/concepts/C2089376573-NOAA_NCEI.umm_json NCEI Accession 0163764 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Atlantic University collected the data from their in-situ moored station named Indian River Lagoon - Link Port (IRL-LP) in the Coastal Waters of Florida. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Atlantic University and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary
gov.noaa.nodc:0164194_Not Applicable Biogeochemical and microbiological variables measured by CTD and Niskin bottles from the Hermano Gines in the Caribbean Sea for the CARIACO Ocean Time-Series Program from 1995-11-13 to 2015-11-14 (NCEI Accession 0164194) NOAA_NCEI STAC Catalog 1995-11-13 2015-11-14 -65.587, 10.45, -64.54, 10.716 https://cmr.earthdata.nasa.gov/search/concepts/C2089377236-NOAA_NCEI.umm_json The goal of this project was to examine the interrelationship between microbial activity and water column geochemistry in the worldâs largest, truly marine anoxic system, the Cariaco Basin. This project focused on microbial cycling of carbon, sulfur, and nitrogen occurring at depths where waters transition from oxic to anoxic to sulfidic. Over the 21 year program, the Stony Brook team typically staged cruises semi-annually during upwelling (Mar-May) and non- upwelling (Oct-Nov) periods. These 24-hour cruises were usually within a week of the routine monthly cruises staged by the Fundacion La Salle and University of South Florida team. Most cruises occupied only the CARIACO Ocean Time-Series station. On cruises 108 to 132, additional stations in the western basin and on the sill to the north of the Cariaco station were also sampled. Locations are given in the database. Data provided in a single MS Excel spreadsheet. proprietary
@@ -18715,31 +18722,31 @@ gov.noaa.nodc:0171331_Not Applicable Biological, chemical and other data collect
gov.noaa.nodc:0171332_Not Applicable Biological, chemical and other data collected from station Indian River Lagoon - Jensen Beach (IRL-JB) by Florida Atlantic University and assembled by Southeast Coastal Ocean Observing Regional Association (SECOORA) in the Coastal Waters of Florida from 2015-10-07 to 2020-06-18 (NCEI Accession 0171332) NOAA_NCEI STAC Catalog 2015-10-07 2020-06-18 -80.20233, 27.22439, -80.20233, 27.22439 https://cmr.earthdata.nasa.gov/search/concepts/C2089377488-NOAA_NCEI.umm_json NCEI Accession 0171332 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Atlantic University collected the data from their in-situ moored station named Indian River Lagoon - Jensen Beach (IRL-JB) in the Coastal Waters of Florida. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Atlantic University and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary
gov.noaa.nodc:0171345_Not Applicable Chemical, meteorological and other data collected from station Pilot's Cove, Apalachicola Bay, by Florida Department of Environmental Protection and assembled by Southeast Coastal Ocean Observing Regional Association (SECOORA) in the Coastal Waters of Florida and Gulf of Mexico from 2015-11-09 to 2020-03-09 (NCEI Accession 0171345) NOAA_NCEI STAC Catalog 2015-11-09 2020-03-09 -85.0277, 29.60139, -85.0277, 29.60139 https://cmr.earthdata.nasa.gov/search/concepts/C2089377631-NOAA_NCEI.umm_json NCEI Accession 0171345 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Department of Environmental Protection collected the data from their in-situ moored station named Pilot's Cove, Apalachicola Bay, in the Coastal Waters of Florida and Gulf of Mexico. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Department of Environmental Protection and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary
gov.noaa.nodc:0171346_Not Applicable Chemical, meteorological and other data collected from station Dry Bar, Apalachicola Bay, by Florida Department of Environmental Protection and assembled by Southeast Coastal Ocean Observing Regional Association (SECOORA) in the Coastal Waters of Florida and Gulf of Mexico from 2015-12-01 to 2018-10-10 (NCEI Accession 0171346) NOAA_NCEI STAC Catalog 2015-12-01 2018-10-10 -85.05807, 29.67431, -85.05807, 29.67431 https://cmr.earthdata.nasa.gov/search/concepts/C2089377641-NOAA_NCEI.umm_json NCEI Accession 0171346 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Florida Department of Environmental Protection collected the data from their in-situ moored station named Dry Bar, Apalachicola Bay, in the Coastal Waters of Florida and Gulf of Mexico. Southeast Coastal Ocean Observing Regional Association (SECOORA), which assembles data from Florida Department of Environmental Protection and other sub-regional coastal and ocean observing systems of the Southeast United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary
-gov.noaa.nodc:0172043_Not Applicable ADCP, CTD, and continuous data from the Multiple Instrument Data Acquisition System (MIDAS) collected in the Southeast of the Mississippi River Delta aboard the R/V Pelican from 2012-11-28 to 2012-12-19 (NCEI Accession 0172043) NOAA_NCEI STAC Catalog 2012-11-28 2012-12-19 -94.0863, 25.7961, -87.2228, 28.9733 https://cmr.earthdata.nasa.gov/search/concepts/C2089377986-NOAA_NCEI.umm_json This dataset contains shipboard Acoustic Doppler Current Profiles (ADCP) data from a 75khz profiler, vertical profiles of measurements made from a CTD/Rosette system and continuous data from the Multiple Instrument Data Acquisition System (MIDAS). These ancillary data gives additional information about the physical state of the ocean during the Gulf of Mexico Integrated Spill Response Consortium (GISR) G03 cruise aboard R/V Pelican held from November 28 to December 20, 2012. proprietary
gov.noaa.nodc:0172043_Not Applicable ADCP, CTD, and continuous data from the Multiple Instrument Data Acquisition System (MIDAS) collected in the Southeast of the Mississippi River Delta aboard the R/V Pelican from 2012-11-28 to 2012-12-19 (NCEI Accession 0172043) ALL STAC Catalog 2012-11-28 2012-12-19 -94.0863, 25.7961, -87.2228, 28.9733 https://cmr.earthdata.nasa.gov/search/concepts/C2089377986-NOAA_NCEI.umm_json This dataset contains shipboard Acoustic Doppler Current Profiles (ADCP) data from a 75khz profiler, vertical profiles of measurements made from a CTD/Rosette system and continuous data from the Multiple Instrument Data Acquisition System (MIDAS). These ancillary data gives additional information about the physical state of the ocean during the Gulf of Mexico Integrated Spill Response Consortium (GISR) G03 cruise aboard R/V Pelican held from November 28 to December 20, 2012. proprietary
-gov.noaa.nodc:0172377_Not Applicable Abundance and biomass of parrotfishes (Labridae, Scarinae) in St.Croix, U.S. Virgin Islands 2015 to 2016 (NCEI Accession 0172377) ALL STAC Catalog 2015-07-21 2016-08-05 -64.9199, 17.63764, -64.47889, 17.82709 https://cmr.earthdata.nasa.gov/search/concepts/C2089378141-NOAA_NCEI.umm_json We collected data on parrotfish abundance, biomass, size structure, and species composition at several sites on the N shore of St. Croix during July and August 2015 and 2016. Surveys were conducted using a method that allowed divers to rapidly survey large areas and quantify habitat assocations of different species. Researchers conducted 20-30 min timed swims towing a float with a GPS receiver, which allowed for the calculation of distance traveled during a swim and therefore the total area sampled. During the timed swim survey, the diver estimated and recorded the size to the nearest cm of all parrotfishes that were at least 10 cm in length that were encountered in a 5-m-wide swath. Because these swims often crossed multiple habitats, the diver recorded the habitat each minute. For each site, the total area of each habitat sampled was then calculated in order to determine habitat- and site-specific densities of each parrotfish species. proprietary
+gov.noaa.nodc:0172043_Not Applicable ADCP, CTD, and continuous data from the Multiple Instrument Data Acquisition System (MIDAS) collected in the Southeast of the Mississippi River Delta aboard the R/V Pelican from 2012-11-28 to 2012-12-19 (NCEI Accession 0172043) NOAA_NCEI STAC Catalog 2012-11-28 2012-12-19 -94.0863, 25.7961, -87.2228, 28.9733 https://cmr.earthdata.nasa.gov/search/concepts/C2089377986-NOAA_NCEI.umm_json This dataset contains shipboard Acoustic Doppler Current Profiles (ADCP) data from a 75khz profiler, vertical profiles of measurements made from a CTD/Rosette system and continuous data from the Multiple Instrument Data Acquisition System (MIDAS). These ancillary data gives additional information about the physical state of the ocean during the Gulf of Mexico Integrated Spill Response Consortium (GISR) G03 cruise aboard R/V Pelican held from November 28 to December 20, 2012. proprietary
gov.noaa.nodc:0172377_Not Applicable Abundance and biomass of parrotfishes (Labridae, Scarinae) in St.Croix, U.S. Virgin Islands 2015 to 2016 (NCEI Accession 0172377) NOAA_NCEI STAC Catalog 2015-07-21 2016-08-05 -64.9199, 17.63764, -64.47889, 17.82709 https://cmr.earthdata.nasa.gov/search/concepts/C2089378141-NOAA_NCEI.umm_json We collected data on parrotfish abundance, biomass, size structure, and species composition at several sites on the N shore of St. Croix during July and August 2015 and 2016. Surveys were conducted using a method that allowed divers to rapidly survey large areas and quantify habitat assocations of different species. Researchers conducted 20-30 min timed swims towing a float with a GPS receiver, which allowed for the calculation of distance traveled during a swim and therefore the total area sampled. During the timed swim survey, the diver estimated and recorded the size to the nearest cm of all parrotfishes that were at least 10 cm in length that were encountered in a 5-m-wide swath. Because these swims often crossed multiple habitats, the diver recorded the habitat each minute. For each site, the total area of each habitat sampled was then calculated in order to determine habitat- and site-specific densities of each parrotfish species. proprietary
+gov.noaa.nodc:0172377_Not Applicable Abundance and biomass of parrotfishes (Labridae, Scarinae) in St.Croix, U.S. Virgin Islands 2015 to 2016 (NCEI Accession 0172377) ALL STAC Catalog 2015-07-21 2016-08-05 -64.9199, 17.63764, -64.47889, 17.82709 https://cmr.earthdata.nasa.gov/search/concepts/C2089378141-NOAA_NCEI.umm_json We collected data on parrotfish abundance, biomass, size structure, and species composition at several sites on the N shore of St. Croix during July and August 2015 and 2016. Surveys were conducted using a method that allowed divers to rapidly survey large areas and quantify habitat assocations of different species. Researchers conducted 20-30 min timed swims towing a float with a GPS receiver, which allowed for the calculation of distance traveled during a swim and therefore the total area sampled. During the timed swim survey, the diver estimated and recorded the size to the nearest cm of all parrotfishes that were at least 10 cm in length that were encountered in a 5-m-wide swath. Because these swims often crossed multiple habitats, the diver recorded the habitat each minute. For each site, the total area of each habitat sampled was then calculated in order to determine habitat- and site-specific densities of each parrotfish species. proprietary
gov.noaa.nodc:0172588_Not Applicable Biological, chemical, and other data collected from station Humboldt Bay Pier by Humboldt State University and assembled by Central and Northern California Coastal Ocean Observing System (CeNCOOS) in the Northeast Pacific Ocean from 2012-12-13 to 2021-06-09 (NCEI Accession 0172588) NOAA_NCEI STAC Catalog 2012-12-13 2021-06-09 -124.19652, 40.7775, -124.19652, 40.7775 https://cmr.earthdata.nasa.gov/search/concepts/C2089378189-NOAA_NCEI.umm_json NCEI Accession 0172588 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Humboldt State University collected the data from their in-situ moored station named Humboldt Bay Pier in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Humboldt State University and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary
gov.noaa.nodc:0172612_Not Applicable Biological, chemical and other data collected from station Monterey Bay Commercial Wharf by Moss Landing Marine Laboratory and assembled by Central and Northern California Coastal Ocean Observing System (CeNCOOS) in the Northeast Pacific Ocean from 2015-05-05 to 2020-01-03 (NCEI Accession 0172612) NOAA_NCEI STAC Catalog 2015-05-05 2020-01-03 -121.88935, 36.60513, -121.88935, 36.60513 https://cmr.earthdata.nasa.gov/search/concepts/C2089378278-NOAA_NCEI.umm_json NCEI Accession 0172612 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Moss Landing Marine Laboratory collected the data from their in-situ moored station named Monterey Bay Commercial Wharf in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Moss Landing Marine Laboratory and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary
gov.noaa.nodc:0172613_Not Applicable Biological, chemical and other data collected from station Indian Island by Humboldt State University and assembled by Central and Northern California Coastal Ocean Observing System (CeNCOOS) in the Northeast Pacific Ocean from 2016-04-05 to 2019-10-28 (NCEI Accession 0172613) NOAA_NCEI STAC Catalog 2016-04-05 2019-10-28 -124.15754, 40.81503, -124.15754, 40.81503 https://cmr.earthdata.nasa.gov/search/concepts/C2089378289-NOAA_NCEI.umm_json NCEI Accession 0172613 contains oceanographic and surface meteorological data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). Humboldt State University collected the data from their in-situ moored station named Indian Island in the Northeast Pacific Ocean. Central and Northern California Coastal Ocean Observing System (CeNCOOS), which assembles data from Humboldt State University and other sub-regional coastal and ocean observing systems of the Central and Northern California United States, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. NCEI updates this accession when new files are available. proprietary
gov.noaa.nodc:0173246_Not Applicable Benthic Fauna and Hydrography at Four Sites in the Mobile-Tensaw River Delta, Alabama (1981-1982) (NCEI Accession 0173246) NOAA_NCEI STAC Catalog 1981-11-17 1982-09-29 -88.004, 30.411, -87.562, 31.055 https://cmr.earthdata.nasa.gov/search/concepts/C2089378543-NOAA_NCEI.umm_json Bimonthly surveys of benthic fauna were conducted at four sites in the Mobile-Tensaw River Delta from November 1981 to September 1982. Two sites were at the upper reaches of the river delta, and two were at the mouth. Fauna were enumerated and identified to lowest taxon possible. Hydrographic data were also collected, including temperature, conductivity, and dissolved oxygen. proprietary
gov.noaa.nodc:0173316_Not Applicable Carbon dioxide, hydrographic and chemical data collected from profile discrete samples during the R/V Nathaniel B. Palmer 2015 OOISO; NBP15_11, SOCCOM cruise (EXPOCODE 320620151206) in the South Pacific Ocean from 2015-12-06 to 2016-01-04 (NCEI Accession 0173316) NOAA_NCEI STAC Catalog 2015-12-06 2016-01-04 -89.72, -54.6, -80.11, -52.93 https://cmr.earthdata.nasa.gov/search/concepts/C2089378635-NOAA_NCEI.umm_json This NCEI Accession includes profile discrete measurements of CTD temperature, CTD salinity, CTD oxygen, nutrients, total alkalinity and pH on Total scale obtained during the R/V Nathaniel B. Palmer 2015 OOISO; NBP15_11, SOCCOM cruise (EXPOCODE 320620151206) in the South Pacific Ocean from 2015-12-06 to 2016-01-02. proprietary
-gov.noaa.nodc:0175745_Not Applicable Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2011-07-07 to 2016-10-29 (NCEI Accession 0175745) ALL STAC Catalog 2011-07-07 2016-10-29 -51.5, -34.503, -44.5, -34.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089380684-NOAA_NCEI.umm_json "This dataset contains round trip acoustic travel time and abmient bottom pressure from bottom-mounted instruments spaced zonally along 34.5S in the SW Atlantic east of Uruguay July 2011 to October 2016. The data were collected for the Southwest Atlantic meridional overturning circulation (""SAM"") project by the NOAA-Atlantic Oceanographic and Meteorological Laboratory. Both the processed/quality-controlled and the raw data files are available. Format is text." proprietary
gov.noaa.nodc:0175745_Not Applicable Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2011-07-07 to 2016-10-29 (NCEI Accession 0175745) NOAA_NCEI STAC Catalog 2011-07-07 2016-10-29 -51.5, -34.503, -44.5, -34.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089380684-NOAA_NCEI.umm_json "This dataset contains round trip acoustic travel time and abmient bottom pressure from bottom-mounted instruments spaced zonally along 34.5S in the SW Atlantic east of Uruguay July 2011 to October 2016. The data were collected for the Southwest Atlantic meridional overturning circulation (""SAM"") project by the NOAA-Atlantic Oceanographic and Meteorological Laboratory. Both the processed/quality-controlled and the raw data files are available. Format is text." proprietary
-gov.noaa.nodc:0175783_Not Applicable Agulhas Current transport derived from satellite altimetry observations in Indian Ocean from 1992-10-14 to 2016-12-28 (NCEI Accession 0175783) NOAA_NCEI STAC Catalog 1992-10-14 2016-12-28 27, -40, 30, -34 https://cmr.earthdata.nasa.gov/search/concepts/C2089380711-NOAA_NCEI.umm_json The Agulhas Current is the western boundary current closing the upper-limb of the Indian Ocean subtropical gyre, and is largely linked with the transfer of warm water from the Indian Ocean to the South Atlantic subtropical gyre. This interbasin water exchange takes place mostly through mesoscale processes that occur when the Agulhas Current retroflects south of Africa between 15°E and 25°E. Estimates of the Agulhas Current are carried out by NOAA/AOML using satellite altimetry as the main dataset, and hydrographic observations. For more information, please visit: http://www.aoml.noaa.gov/phod/indexes/index.php proprietary
+gov.noaa.nodc:0175745_Not Applicable Acoustic travel time and bottom pressure data from inverted echo sounders as part of the Southwest Atlantic Meridional Overturning Circulation project (SAM) from 2011-07-07 to 2016-10-29 (NCEI Accession 0175745) ALL STAC Catalog 2011-07-07 2016-10-29 -51.5, -34.503, -44.5, -34.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089380684-NOAA_NCEI.umm_json "This dataset contains round trip acoustic travel time and abmient bottom pressure from bottom-mounted instruments spaced zonally along 34.5S in the SW Atlantic east of Uruguay July 2011 to October 2016. The data were collected for the Southwest Atlantic meridional overturning circulation (""SAM"") project by the NOAA-Atlantic Oceanographic and Meteorological Laboratory. Both the processed/quality-controlled and the raw data files are available. Format is text." proprietary
gov.noaa.nodc:0175783_Not Applicable Agulhas Current transport derived from satellite altimetry observations in Indian Ocean from 1992-10-14 to 2016-12-28 (NCEI Accession 0175783) ALL STAC Catalog 1992-10-14 2016-12-28 27, -40, 30, -34 https://cmr.earthdata.nasa.gov/search/concepts/C2089380711-NOAA_NCEI.umm_json The Agulhas Current is the western boundary current closing the upper-limb of the Indian Ocean subtropical gyre, and is largely linked with the transfer of warm water from the Indian Ocean to the South Atlantic subtropical gyre. This interbasin water exchange takes place mostly through mesoscale processes that occur when the Agulhas Current retroflects south of Africa between 15°E and 25°E. Estimates of the Agulhas Current are carried out by NOAA/AOML using satellite altimetry as the main dataset, and hydrographic observations. For more information, please visit: http://www.aoml.noaa.gov/phod/indexes/index.php proprietary
+gov.noaa.nodc:0175783_Not Applicable Agulhas Current transport derived from satellite altimetry observations in Indian Ocean from 1992-10-14 to 2016-12-28 (NCEI Accession 0175783) NOAA_NCEI STAC Catalog 1992-10-14 2016-12-28 27, -40, 30, -34 https://cmr.earthdata.nasa.gov/search/concepts/C2089380711-NOAA_NCEI.umm_json The Agulhas Current is the western boundary current closing the upper-limb of the Indian Ocean subtropical gyre, and is largely linked with the transfer of warm water from the Indian Ocean to the South Atlantic subtropical gyre. This interbasin water exchange takes place mostly through mesoscale processes that occur when the Agulhas Current retroflects south of Africa between 15°E and 25°E. Estimates of the Agulhas Current are carried out by NOAA/AOML using satellite altimetry as the main dataset, and hydrographic observations. For more information, please visit: http://www.aoml.noaa.gov/phod/indexes/index.php proprietary
gov.noaa.nodc:0175786_Not Applicable Abundance and Distribution of Commercially Important Estuarine Dependent Species Populations within the Gulf of Mexico from 1986-04-01 to 2017-06-27 (NCEI Accession 0175786) NOAA_NCEI STAC Catalog 1986-04-01 2017-06-27 -89.85889, 29.8917, -87.6955, 30.68067 https://cmr.earthdata.nasa.gov/search/concepts/C2089380737-NOAA_NCEI.umm_json This dataset contains records of Gulf of Mexico (GOM) blue crab (Callinectes sapidus), white shrimp (Litopenaeus setiferus), brown shrimp (Farfantepenaeus aztecus), and fishes which can be used to quantify their population abundances and distributions. The data set contains existing data as a baseline and supplemental data from continued sampling. It contains records of early life stage blue crab, white shrimp, brown shrimp, and fishes (measurements and counts) from beach seine and trawl samples across the north GOM in the central Gulf States that were collected using standardized sampling methods. Data also include habitat assessments such as descriptions, georeferencing information, and abiotic factors (DO, salinity, temperature). proprietary
gov.noaa.nodc:0175786_Not Applicable Abundance and Distribution of Commercially Important Estuarine Dependent Species Populations within the Gulf of Mexico from 1986-04-01 to 2017-06-27 (NCEI Accession 0175786) ALL STAC Catalog 1986-04-01 2017-06-27 -89.85889, 29.8917, -87.6955, 30.68067 https://cmr.earthdata.nasa.gov/search/concepts/C2089380737-NOAA_NCEI.umm_json This dataset contains records of Gulf of Mexico (GOM) blue crab (Callinectes sapidus), white shrimp (Litopenaeus setiferus), brown shrimp (Farfantepenaeus aztecus), and fishes which can be used to quantify their population abundances and distributions. The data set contains existing data as a baseline and supplemental data from continued sampling. It contains records of early life stage blue crab, white shrimp, brown shrimp, and fishes (measurements and counts) from beach seine and trawl samples across the north GOM in the central Gulf States that were collected using standardized sampling methods. Data also include habitat assessments such as descriptions, georeferencing information, and abiotic factors (DO, salinity, temperature). proprietary
gov.noaa.nodc:0176496_Not Applicable Biological Baseline Studies of Mobile Bay (MESC-CAB 1980-1981): Hydrography, Sediments, Benthic Fauna, Pelagic Fauna, Phytoplankton, and Zooplankton (NCEI Accession 0176496) NOAA_NCEI STAC Catalog 1980-04-03 1981-08-26 -88.17333, 30.23833, -87.85167, 30.61333 https://cmr.earthdata.nasa.gov/search/concepts/C2089376767-NOAA_NCEI.umm_json Data from a monthly survey of Mobile Bay between April 1980 and August 1981. Extant data from the MESC Data Management System include sediment particle size distribution, discrete hydrography, identification and enumeration of benthic fauna, and identification and enumeration of water column biota. proprietary
gov.noaa.nodc:0185741_Not Applicable Carbonate Chemistry Dynamics on Southeast Florida coral reefs from 2014-05-27 to 2015-09-03 (NCEI Accession 0185741) NOAA_NCEI STAC Catalog 2014-05-27 2015-09-03 -80.132778, 25.6519, -80.076975, 26.1636 https://cmr.earthdata.nasa.gov/search/concepts/C2089379082-NOAA_NCEI.umm_json These data are from the article âSeasonal carbonate chemistry dynamics on southeast Florida coral reefs: localized acidification hotspots from navigational inletsâ published in Frontiers in Marine Science. The data in this package were collected from inlets and reefs along the coast of Southeast Florida. Water was collected bi-monthly from four reefs (Oakland Ridge, Barracuda, Pillars, and Emerald) and three closely-associated inlets (Port Everglades, Bakers Haulover, and Port of Miami). Water samples were collected at these locations either at the surface (~1m depth) or immediately above the benthos measured using a rosette sampler (ECO 55, Seabird). Temperature was recorded at each depth using a CTD (SBE 19V2, Seabird). Turbidity (NTU) was measured at time of water collection. Once collected, water samples were transferred to borosilicate glass bottles, samples were fixed using 200 µL of HgCl2 and sealed using Apiezon grease and a glass stopper. Salinity was measured using a densitometer (DMA 5000M, Anton Paar), while total alkalinity (TA) and dissolved inorganic carbon (DIC) were determined using Apollo SciTech instruments (AS-ALK2 and AS-C3, respectively). All values were measured in duplicate and corrected using certified reference materials following recommendations in Dickson et al. (2007). Aragonite saturation state (ΩArag.), Calcite saturation state (ΩCa), pH (Total scale), and the partial pressure of CO2 (pCO2) were calculated with CO2SYS (Lewis and Wallace, 1998) using the dissociation constants of Mehrbach et al. (1973) as refit by Dickson and Millero (1987) and Dickson (1990). Water samples were reserved for nutrient analyzed at time of collection to determine Total Kjeldahl Nitrogen, Total Phosphorous, and fluorescence of Chlorophyll-a. This research was supported through NOAAâs Coral Reef Conservation Program. proprietary
gov.noaa.nodc:0185742_Not Applicable Climatology for NOAA Coral Reef Watch (CRW) Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1 for 1985-01-01 to 2012-12-31 (NCEI Accession 0185742) NOAA_NCEI STAC Catalog 1985-01-01 2012-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089379091-NOAA_NCEI.umm_json This package contains a set of 12 monthly mean (MM) climatologies, one for each calendar month, and the maximum monthly mean (MMM) climatology. Each climatology has global coverage at 0.05-degree (5km) spatial resolution. The climatologies were derived from NOAA Coral Reef Watch's (CRW) CoralTemp Version 1.0 product and are based on the 1985-2012 time period of the CoralTemp data. They are used in deriving CRW's Daily Global 5km Satellite Coral Bleaching Heat Stress Monitoring Product Suite Version 3.1. MMs are used to derive the SST Anomaly product, and the MMM is used to derive CRW's Coral Bleaching HotSpot, Degree Heating Week, and Bleaching Alert Area products. proprietary
-gov.noaa.nodc:0185753_Not Applicable Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Laurentian in Lake Huron, Great Lakes from 2006-09-01 to 2012-12-31 (NCEI Accession 0185753) ALL STAC Catalog 2006-09-01 2012-12-31 -84.5, 43.2, -79.8, 46.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089379102-NOAA_NCEI.umm_json Raw data from the benthic macroinvertebrate surveys conducted in Saginaw Bay in 2006-2009, and in Lake Huron, including Georgian Bay and North Channel, in 2007 and 2012. These basic benthic survey data provide number of each taxon in each replicate sample (abundance), density, and biomass. proprietary
gov.noaa.nodc:0185753_Not Applicable Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Laurentian in Lake Huron, Great Lakes from 2006-09-01 to 2012-12-31 (NCEI Accession 0185753) NOAA_NCEI STAC Catalog 2006-09-01 2012-12-31 -84.5, 43.2, -79.8, 46.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089379102-NOAA_NCEI.umm_json Raw data from the benthic macroinvertebrate surveys conducted in Saginaw Bay in 2006-2009, and in Lake Huron, including Georgian Bay and North Channel, in 2007 and 2012. These basic benthic survey data provide number of each taxon in each replicate sample (abundance), density, and biomass. proprietary
-gov.noaa.nodc:0186561_Not Applicable 2003 Marine Fisheries Initiative (MARFIN) Gulf of Mexico and South Atlantic angler survey (NCEI Accession 0186561) ALL STAC Catalog 2003-01-01 2003-12-31 -98, 25, -80, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2089380124-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains information, angler experiences, and preferences for recreational fishing in the Gulf of Mexico and South Atlantic. Data were collected by the NMFS Southeast Fisheries Science Center; Miami, FL. Data are in comma separated value (CSV) format and include recreational angler information such as age, gender, income, and target fish. proprietary
+gov.noaa.nodc:0185753_Not Applicable Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Laurentian in Lake Huron, Great Lakes from 2006-09-01 to 2012-12-31 (NCEI Accession 0185753) ALL STAC Catalog 2006-09-01 2012-12-31 -84.5, 43.2, -79.8, 46.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089379102-NOAA_NCEI.umm_json Raw data from the benthic macroinvertebrate surveys conducted in Saginaw Bay in 2006-2009, and in Lake Huron, including Georgian Bay and North Channel, in 2007 and 2012. These basic benthic survey data provide number of each taxon in each replicate sample (abundance), density, and biomass. proprietary
gov.noaa.nodc:0186561_Not Applicable 2003 Marine Fisheries Initiative (MARFIN) Gulf of Mexico and South Atlantic angler survey (NCEI Accession 0186561) NOAA_NCEI STAC Catalog 2003-01-01 2003-12-31 -98, 25, -80, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2089380124-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains information, angler experiences, and preferences for recreational fishing in the Gulf of Mexico and South Atlantic. Data were collected by the NMFS Southeast Fisheries Science Center; Miami, FL. Data are in comma separated value (CSV) format and include recreational angler information such as age, gender, income, and target fish. proprietary
+gov.noaa.nodc:0186561_Not Applicable 2003 Marine Fisheries Initiative (MARFIN) Gulf of Mexico and South Atlantic angler survey (NCEI Accession 0186561) ALL STAC Catalog 2003-01-01 2003-12-31 -98, 25, -80, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2089380124-NOAA_NCEI.umm_json This Archival Information Package (AIP) contains information, angler experiences, and preferences for recreational fishing in the Gulf of Mexico and South Atlantic. Data were collected by the NMFS Southeast Fisheries Science Center; Miami, FL. Data are in comma separated value (CSV) format and include recreational angler information such as age, gender, income, and target fish. proprietary
gov.noaa.nodc:0191401_Not Applicable Biogeochemical and microbiological measurements in the Cariaco Basin, a truly marine anoxic system in the southeastern Caribbean Sea, from 1995-11-13 to 2015-11-14 by the CARIACO Ocean Time Series Program (formerly known as CArbon Retention In A Colored Ocean) aboard the B/O Hermano Gines (NCEI Accession 0191401) NOAA_NCEI STAC Catalog 1995-11-13 2015-11-14 -64.66, 10.5, -64.66, 10.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089377738-NOAA_NCEI.umm_json Biogeochemical and microbiological variables were measured by Stony Brook University participants (see Author List) in the CARIACO Ocean Time-Series Program in order to study the microbial cycling of carbon, sulfur, and nitrogen occurring at depths where waters transition from oxic to anoxic to sulfidic. Samples were collected by Nikson bottles from 1995-11-13 to 2015-11-14 in the Cariaco Basin (southeastern Caribbean Sea off northeastern Venezuelan coast) aboard the B/O Hermano Gines, operated by the Fundacion La Salle, Venezuela. proprietary
-gov.noaa.nodc:0194300_Not Applicable ADCP, CTD, water and sediment chemistry, and underway sensor data collected aboard R/V Endeavor cruise EN505 in the Gulf of Mexico from 2012-04-11 to 2012-04-24 (NCEI Accession 0194300) ALL STAC Catalog 2012-04-11 2012-04-24 -90.5895, 27.2111, -87.42629, 30.35717 https://cmr.earthdata.nasa.gov/search/concepts/C2089378330-NOAA_NCEI.umm_json This dataset contains ADCP, CTD, water and sediment chemistry, and other underway sensor data collected aboard R/V Endeavor cruise EN505 in the Gulf of Mexico from 2012-04-11 to 2012-04-24. The CTD profiles were done at 4 locations using Sea-Bird SBE 911plus from 2012-04-11 to 2012-04-14 and include seawater conductivity, temperature, pressure, salinity, density, oxygen concentration, sound velocity, dissolved oxygen, beam attenuation, light transmission, fluorescence, surface irradiance, and depth parameters. The current velocity data was measured by a hull-mounted mounted Acoustic Doppler Current Profiler (ADCP) and other underway sensor data was measured with a Sea-Bird SBE 21 (tsg), Sea-Bird SBE 45 (tsg) and underway sensors/navigational instruments. All data records include sampling time (UTC), position (Latitude, Longitude) and water depth. In addition, the dataset also includes the water column and sediment chemistry data and the measurements include the concentration of dissolved nutrients, dissolved gases, total particulate nitrogen (TPN), total particulate carbon (TPN), particulate organic carbon (POC), and particulate inorganic carbon acquired from 8 CTD casts and 6 multiple corer drops. The objective of this cruise was to study the impact of the Deepwater Horizon (DWH) blowout on the water column and benthic communities of the Gulf of Mexico and compare these impacts to naturally occurring oil and gas seeps. These data are also available at Rolling Deck to Repository (R2R) under cruise https://doi.org/10.7284/902570. proprietary
gov.noaa.nodc:0194300_Not Applicable ADCP, CTD, water and sediment chemistry, and underway sensor data collected aboard R/V Endeavor cruise EN505 in the Gulf of Mexico from 2012-04-11 to 2012-04-24 (NCEI Accession 0194300) NOAA_NCEI STAC Catalog 2012-04-11 2012-04-24 -90.5895, 27.2111, -87.42629, 30.35717 https://cmr.earthdata.nasa.gov/search/concepts/C2089378330-NOAA_NCEI.umm_json This dataset contains ADCP, CTD, water and sediment chemistry, and other underway sensor data collected aboard R/V Endeavor cruise EN505 in the Gulf of Mexico from 2012-04-11 to 2012-04-24. The CTD profiles were done at 4 locations using Sea-Bird SBE 911plus from 2012-04-11 to 2012-04-14 and include seawater conductivity, temperature, pressure, salinity, density, oxygen concentration, sound velocity, dissolved oxygen, beam attenuation, light transmission, fluorescence, surface irradiance, and depth parameters. The current velocity data was measured by a hull-mounted mounted Acoustic Doppler Current Profiler (ADCP) and other underway sensor data was measured with a Sea-Bird SBE 21 (tsg), Sea-Bird SBE 45 (tsg) and underway sensors/navigational instruments. All data records include sampling time (UTC), position (Latitude, Longitude) and water depth. In addition, the dataset also includes the water column and sediment chemistry data and the measurements include the concentration of dissolved nutrients, dissolved gases, total particulate nitrogen (TPN), total particulate carbon (TPN), particulate organic carbon (POC), and particulate inorganic carbon acquired from 8 CTD casts and 6 multiple corer drops. The objective of this cruise was to study the impact of the Deepwater Horizon (DWH) blowout on the water column and benthic communities of the Gulf of Mexico and compare these impacts to naturally occurring oil and gas seeps. These data are also available at Rolling Deck to Repository (R2R) under cruise https://doi.org/10.7284/902570. proprietary
+gov.noaa.nodc:0194300_Not Applicable ADCP, CTD, water and sediment chemistry, and underway sensor data collected aboard R/V Endeavor cruise EN505 in the Gulf of Mexico from 2012-04-11 to 2012-04-24 (NCEI Accession 0194300) ALL STAC Catalog 2012-04-11 2012-04-24 -90.5895, 27.2111, -87.42629, 30.35717 https://cmr.earthdata.nasa.gov/search/concepts/C2089378330-NOAA_NCEI.umm_json This dataset contains ADCP, CTD, water and sediment chemistry, and other underway sensor data collected aboard R/V Endeavor cruise EN505 in the Gulf of Mexico from 2012-04-11 to 2012-04-24. The CTD profiles were done at 4 locations using Sea-Bird SBE 911plus from 2012-04-11 to 2012-04-14 and include seawater conductivity, temperature, pressure, salinity, density, oxygen concentration, sound velocity, dissolved oxygen, beam attenuation, light transmission, fluorescence, surface irradiance, and depth parameters. The current velocity data was measured by a hull-mounted mounted Acoustic Doppler Current Profiler (ADCP) and other underway sensor data was measured with a Sea-Bird SBE 21 (tsg), Sea-Bird SBE 45 (tsg) and underway sensors/navigational instruments. All data records include sampling time (UTC), position (Latitude, Longitude) and water depth. In addition, the dataset also includes the water column and sediment chemistry data and the measurements include the concentration of dissolved nutrients, dissolved gases, total particulate nitrogen (TPN), total particulate carbon (TPN), particulate organic carbon (POC), and particulate inorganic carbon acquired from 8 CTD casts and 6 multiple corer drops. The objective of this cruise was to study the impact of the Deepwater Horizon (DWH) blowout on the water column and benthic communities of the Gulf of Mexico and compare these impacts to naturally occurring oil and gas seeps. These data are also available at Rolling Deck to Repository (R2R) under cruise https://doi.org/10.7284/902570. proprietary
gov.noaa.nodc:0204167_Not Applicable Cetacean digital photography and aerial observer data collected by an unmanned aerial vehicle and manned aerial vehicle in the Beaufort Sea for the Arctic Aerial Calibration Experiments (ACEs) from 2015-08-26 to 2015-09-07 (NCEI Accession 0204167) NOAA_NCEI STAC Catalog 2015-08-26 2015-09-07 -159.3, 71, -153.1, 72 https://cmr.earthdata.nasa.gov/search/concepts/C2089379246-NOAA_NCEI.umm_json This dataset includes two comma separated files containing data and metadata from three cetacean observation methods from two platforms, the manned Turbo Commander aircraft and the unmanned ScanEagle. The ACEs' imagery described here was collected and analyzed in order to conduct a 3-way comparison of data and derived statistics from the following: Observers in the manned aircraft; Digital photographs from cameras mounted to the manned aircraft; Digital photographs from cameras mounted to the Unmanned Aerial Vehicle (UAV). The Arctic Aerial Calibration Experiments (ACEs) study was designed to evaluate the ability of UAS technology (i.e., airframe, payloads, sensors, and software) to detect cetaceans, identify individuals to species, estimate group size, identify calves, and estimate density in arctic waters, relative to conventional aerial surveys conducted by human observers in fixed wing aircraft and to photographic strip transect data collected from the manned aircraft. proprietary
gov.noaa.nodc:0204646_Not Applicable Benthic cover from automated annotation of benthic images collected at coral reef sites in the Pacific Remote Island Areas and American Samoa from 2018-06-08 to 2018-08-11 (NCEI Accession 0204646) NOAA_NCEI STAC Catalog 2018-06-08 2018-08-11 -176.626077, -14.558022, -159.971695, 6.451465 https://cmr.earthdata.nasa.gov/search/concepts/C2089379357-NOAA_NCEI.umm_json "The coral reef benthic community data described here result from the automated annotation (classification) of benthic images collected during photoquadrat surveys conducted by the NOAA Pacific Islands Fisheries Science Center (PIFSC), Ecosystem Sciences Division (ESD, formerly the Coral Reef Ecosystem Division) as part of NOAA's ongoing National Coral Reef Monitoring Program (NCRMP). SCUBA divers conducted benthic photoquadrat surveys in coral reef habitats according to protocols established by ESD and NCRMP during the ESD-led NCRMP mission to the islands and atolls of the Pacific Remote Island Areas (PRIA) and American Samoa from June 8 to August 11, 2018. Still photographs were collected with a high-resolution digital camera mounted on a pole to document the benthic community composition at predetermined points along transects at stratified random sites surveyed only once as part of Rapid Ecological Assessment (REA) surveys for corals and fish (Ayotte et al. 2015; Swanson et al. 2018) and permanent sites established by ESD and resurveyed every ~3 years for climate change monitoring. Overall, 30 photoquadrat images were collected at each survey site. The benthic habitat images were quantitatively analyzed using the web-based, machine-learning, image annotation tool, CoralNet (https://coralnet.ucsd.edu; Beijbom et al. 2015; Williams et al. 2019). Ten points were randomly overlaid on each image and the machine-learning algorithm ""robot"" identified the organism or type of substrate beneath, with 300 annotations (points) generated per site. Benthic elements falling under each point were identified to functional group (Tier 1: hard coral, soft coral, sessile invertebrate, macroalgae, crustose coralline algae, and turf algae) for coral, algae, invertebrates, and other taxa following Lozada-Misa et al. (2017). These benthic data can ultimately be used to produce estimates of community composition, relative abundance (percentage of benthic cover), and frequency of occurrence." proprietary
gov.noaa.nodc:0205786_Not Applicable Assessment of heat stress exposure in the wider Caribbean coral reefs through the regional delineation of degree heating week data from 1985-01-01 to 2017-12-31 (NCEI Accession 0205786) NOAA_NCEI STAC Catalog 1985-01-01 2017-12-31 -97, 8.35, -59.2, 32.7 https://cmr.earthdata.nasa.gov/search/concepts/C2089380033-NOAA_NCEI.umm_json "This data package presents a three-decade (1985-2017) assessment of heat stress exposure in the wider Caribbean coral reefs at the ecoregional and local scales. The main heat stress indicator used was the Degree Heating Weeks (DHW) calculated from daily Sea Surface Temperature ""CoralTemp"" data from CRW-NOAA available from 1985 to the present and from the maximum monthly mean (MMM) version 3.1 at 5 km of the CRW-NOAA program. Different metrics were calculated based on daily DHW and are available in this dataset: a) the maximum value of DHW per pixel for the entire time series b) the frequency of the annual maximum values of DHW ⥠4 °C- weeks (a predictor of coral ""bleaching risk"") per pixel c) the frequency of the annual maximum values of DHW ⥠8 °C- weeks (a predictor of bleach-induced mortality or ""mortality risk"") per pixel d) the year in which the maximum of DHW occurred e) the trend of the annual maximum values of DHW per pixel. Based on the spatiotemporal annual maximum DHW, a new regionalization of heat stress was performed by cluster analysis with the K-means algorithm through the unsupervised classification, this new regionalization delimits the Caribbean in 8 Heat Stress Regions (HSR). We summarized spatiotemporal daily data to describe the temporal patterns at an ecoregional scale by calculating the descriptive statistics of the regional DHW on a given day. This dataset represents a new baseline and regionalization of heat stress in the wider Caribbean coral reefs that will enhance conservation and planning efforts underway." proprietary
@@ -18749,14 +18756,14 @@ gov.noaa.nodc:0207181_Not Applicable Ammonia (NH3) emissions characterization fr
gov.noaa.nodc:0208019_Not Applicable Carbonate chemistry data at the Aransas Ship Channel from 2018-03-08 to 2019-08-22 (NCEI Accession 0208019) NOAA_NCEI STAC Catalog 2018-03-08 2019-08-22 -97.050278, 27.838056, -97.050278, 27.838056 https://cmr.earthdata.nasa.gov/search/concepts/C2089380855-NOAA_NCEI.umm_json This dataset includes both hydrographic (salinity, temperature, dissolved oxygen) and carbonate chemistry data collected at the Aransas Ship Channel (Port Aransas, TX) under the funding provided by the National Academy of Sciences Gulf Research Program (Grant# 2000009312) during the period of 03/08/2018-08/22/2019. proprietary
gov.noaa.nodc:0208388_Not Applicable Biological, chemical, physical and time series data collected from station WQB-04 by University of Hawai'i at Hilo and assembled by Pacific Islands Ocean Observing System (PacIOOS) in the North Pacific Ocean from 2010-10-23 to 2020-12-31 (NCEI Accession 0208388) NOAA_NCEI STAC Catalog 2010-10-23 2020-12-31 -155.082, 19.7341, -155.082, 19.7341 https://cmr.earthdata.nasa.gov/search/concepts/C2089376817-NOAA_NCEI.umm_json NCEI Accession 0208388 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo collected the data from their in-situ moored station named WQB-04: PacIOOS Water Quality Buoy 04: Hilo Bay, Big Island, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month. The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB-04 is located in Hilo Bay on the east side of the Big Island. Continuous sampling of this area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill. proprietary
gov.noaa.nodc:0209056_Not Applicable Bottom Temperatures from ship mounted temperature probes collected in in North Atlantic from 2015-01-16 to 2019-02-10 (NCEI Accession 0209056) NOAA_NCEI STAC Catalog 2015-01-10 2020-02-10 -76.34258, 35.98645, -66.42055, 44.58673 https://cmr.earthdata.nasa.gov/search/concepts/C2089377982-NOAA_NCEI.umm_json This data set contains bottom temperature data collected by thermistors mounted on lobster boats in the North Atlantic and Stellwagen Bank. The accession consists of one .csv file contains the following variables - the location the temperature was recorded( site), the latitude (degrees N), longitude (degrees E), depth (m) and sea water temperature (degrees C) of each record. This data was collected as part of the Environmental Monitors on Lobster Traps (eMOLT) project - a non-profit collaboration of industry, science and academics devoted to the monitoring of the physical environment of the Gulf of Maine and the Southern New England Shelf. proprietary
-gov.noaa.nodc:0209071_Not Applicable ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean from 2009-12-01 to 2010-03-23 (NCEI Accession 0209071) ALL STAC Catalog 2009-12-01 2010-03-23 11.2067, -5.8778, 11.2067, -5.8778 https://cmr.earthdata.nasa.gov/search/concepts/C2089378065-NOAA_NCEI.umm_json This dataset contains ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean in the Congo submarine canyon during ~3 month period from 2009-12-01 to 2010-03-23. Two ADCPs with acoustic frequencies of 300 kHz and 75 kHz were deployed on separate moorings placed in the channel axis 700 m apart and at ~2000 m water depth. They acquired data over a range of ~80 m above the seafloor (300 kHz) and 220 m above the seafloor (75 kHz). Data are in netcdf. proprietary
gov.noaa.nodc:0209071_Not Applicable ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean from 2009-12-01 to 2010-03-23 (NCEI Accession 0209071) NOAA_NCEI STAC Catalog 2009-12-01 2010-03-23 11.2067, -5.8778, 11.2067, -5.8778 https://cmr.earthdata.nasa.gov/search/concepts/C2089378065-NOAA_NCEI.umm_json This dataset contains ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean in the Congo submarine canyon during ~3 month period from 2009-12-01 to 2010-03-23. Two ADCPs with acoustic frequencies of 300 kHz and 75 kHz were deployed on separate moorings placed in the channel axis 700 m apart and at ~2000 m water depth. They acquired data over a range of ~80 m above the seafloor (300 kHz) and 220 m above the seafloor (75 kHz). Data are in netcdf. proprietary
+gov.noaa.nodc:0209071_Not Applicable ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean from 2009-12-01 to 2010-03-23 (NCEI Accession 0209071) ALL STAC Catalog 2009-12-01 2010-03-23 11.2067, -5.8778, 11.2067, -5.8778 https://cmr.earthdata.nasa.gov/search/concepts/C2089378065-NOAA_NCEI.umm_json This dataset contains ADCP velocity, echo intensity, and compass heading from two near-bottom moorings in the south equatorial Atlantic Ocean in the Congo submarine canyon during ~3 month period from 2009-12-01 to 2010-03-23. Two ADCPs with acoustic frequencies of 300 kHz and 75 kHz were deployed on separate moorings placed in the channel axis 700 m apart and at ~2000 m water depth. They acquired data over a range of ~80 m above the seafloor (300 kHz) and 220 m above the seafloor (75 kHz). Data are in netcdf. proprietary
gov.noaa.nodc:0209115_Not Applicable Aragonite Saturation State in Deep Sea Coral Habitats collected from NOAA Ship Nancy Foster in Gulf of Mexico from 2017-08-14 to 2017-08-30 (NCEI Accession 0209115) NOAA_NCEI STAC Catalog 2017-08-14 2017-08-30 -84.90713, 25.66118, -80.02228, 29.18645 https://cmr.earthdata.nasa.gov/search/concepts/C2089378161-NOAA_NCEI.umm_json The dataset contains 17 depth profiles from 20-1000 m depth on the West Florida Shelf. Parameters include aragonite saturation state, total alkalinity, DIC, temperature and salinity. The data were collected using a CTD rosette aboard a NOAA-led research expedition in August 2017 entitled âSoutheast Deep Coral Initiative: Exploring Deep-Sea Corals Ecosystems of the Southeast USâ. The NOAA-led survey explored deep-sea coral habitat along West Florida shelf, using the remotely operated vehicle (ROV) Odysseus aboard NOAA Ship Nancy Foster. The cruise report for the expedition is hosted online here: https://doi.org/10.7289/V5/TM-NOS-NCCOS-244 (Wagner et al 2018). proprietary
gov.noaa.nodc:0209162_Not Applicable Biological, chemical, physical and time series data collected from station WQB-05 by University of Hawai'i at Hilo and assembled by Pacific Islands Ocean Observing System (PacIOOS) in the North Pacific Ocean from 2018-03-10 to 2020-12-31 (NCEI Accession 0209162) NOAA_NCEI STAC Catalog 2018-03-10 2020-12-31 -155.8285, 20.02415, -155.8285, 20.02415 https://cmr.earthdata.nasa.gov/search/concepts/C2089378336-NOAA_NCEI.umm_json NCEI Accession 0209162 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo collected the data from their in-situ moored station named WQB-05: PacIOOS Water Quality Buoy 05: Pelekane Bay, Big Island, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month. The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB-05 is located in Pelekane Bay near Kawaihae Harbor on the west side of the Big Island. Continuous sampling of this area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill. proprietary
gov.noaa.nodc:0209222_Not Applicable Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Lake Guardian in Lake Michigan, Great Lakes from 2015-07-20 to 2015-07-29 (NCEI Accession 0209222) NOAA_NCEI STAC Catalog 2015-07-20 2015-07-29 -88.1, 41.6, -84.75, 46.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089378673-NOAA_NCEI.umm_json Raw data from the benthic macroinvertebrate lake wide surveys conducted in Lake Michigan in 2015. These basic benthic survey data provide the number of each taxon in each replicate sample (abundance), density, and biomass. Similar lake wide surveys were conducted to assess the status of benthic taxa beginning in 1994/1995 and repeated every five years through 2015. proprietary
gov.noaa.nodc:0209222_Not Applicable Abundance, biomass, and density of benthic macroinvertebrates collected from R/V Lake Guardian in Lake Michigan, Great Lakes from 2015-07-20 to 2015-07-29 (NCEI Accession 0209222) ALL STAC Catalog 2015-07-20 2015-07-29 -88.1, 41.6, -84.75, 46.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089378673-NOAA_NCEI.umm_json Raw data from the benthic macroinvertebrate lake wide surveys conducted in Lake Michigan in 2015. These basic benthic survey data provide the number of each taxon in each replicate sample (abundance), density, and biomass. Similar lake wide surveys were conducted to assess the status of benthic taxa beginning in 1994/1995 and repeated every five years through 2015. proprietary
-gov.noaa.nodc:0209226_Not Applicable Acropora cervicornis outplanting scores in the Florida Reef Tract from 2006-01-01 to 2099-12-31 (NCEI Accession 0209226) NOAA_NCEI STAC Catalog 2006-01-01 2099-12-31 -82.9771, 24.4437, -80.0646, 26.3438 https://cmr.earthdata.nasa.gov/search/concepts/C2089378705-NOAA_NCEI.umm_json To maximize long term (>10yr) survival of nursery raised Acropora cervicornis corals, a map based tool was created that ranks locations in the Florida Acropora Critical Habitat based on climate vulnerability. Climate vulnerability is defined both in terms of exposure to future heat stress and the coral's sensitivity as resilience. Suitable sites are determined by a number of factors, suitable sites must be within the Acropora critical habitat and within the depth range 5-15m, with either hard bottom or coral present. Those possible locations are ranked based on projected climate change impacts and a resilience metric based on seven different indicators: coral cover, macroalgae cover, bleaching resistance, coral diversity, coral disease, herbivore biomass, and temperature variability. The data is presented as a Google Earth tool (zipped), maps, gridded netCDF files and are accompanied by a guidance document and a .csv file ranking all locations. The Google Earth tool contains five major layers: depth, turbidity, resilience, year of annual severe bleaching, and outplanting score. Bleaching projections included here use climate model data from 2006-2099. proprietary
gov.noaa.nodc:0209226_Not Applicable Acropora cervicornis outplanting scores in the Florida Reef Tract from 2006-01-01 to 2099-12-31 (NCEI Accession 0209226) ALL STAC Catalog 2006-01-01 2099-12-31 -82.9771, 24.4437, -80.0646, 26.3438 https://cmr.earthdata.nasa.gov/search/concepts/C2089378705-NOAA_NCEI.umm_json To maximize long term (>10yr) survival of nursery raised Acropora cervicornis corals, a map based tool was created that ranks locations in the Florida Acropora Critical Habitat based on climate vulnerability. Climate vulnerability is defined both in terms of exposure to future heat stress and the coral's sensitivity as resilience. Suitable sites are determined by a number of factors, suitable sites must be within the Acropora critical habitat and within the depth range 5-15m, with either hard bottom or coral present. Those possible locations are ranked based on projected climate change impacts and a resilience metric based on seven different indicators: coral cover, macroalgae cover, bleaching resistance, coral diversity, coral disease, herbivore biomass, and temperature variability. The data is presented as a Google Earth tool (zipped), maps, gridded netCDF files and are accompanied by a guidance document and a .csv file ranking all locations. The Google Earth tool contains five major layers: depth, turbidity, resilience, year of annual severe bleaching, and outplanting score. Bleaching projections included here use climate model data from 2006-2099. proprietary
+gov.noaa.nodc:0209226_Not Applicable Acropora cervicornis outplanting scores in the Florida Reef Tract from 2006-01-01 to 2099-12-31 (NCEI Accession 0209226) NOAA_NCEI STAC Catalog 2006-01-01 2099-12-31 -82.9771, 24.4437, -80.0646, 26.3438 https://cmr.earthdata.nasa.gov/search/concepts/C2089378705-NOAA_NCEI.umm_json To maximize long term (>10yr) survival of nursery raised Acropora cervicornis corals, a map based tool was created that ranks locations in the Florida Acropora Critical Habitat based on climate vulnerability. Climate vulnerability is defined both in terms of exposure to future heat stress and the coral's sensitivity as resilience. Suitable sites are determined by a number of factors, suitable sites must be within the Acropora critical habitat and within the depth range 5-15m, with either hard bottom or coral present. Those possible locations are ranked based on projected climate change impacts and a resilience metric based on seven different indicators: coral cover, macroalgae cover, bleaching resistance, coral diversity, coral disease, herbivore biomass, and temperature variability. The data is presented as a Google Earth tool (zipped), maps, gridded netCDF files and are accompanied by a guidance document and a .csv file ranking all locations. The Google Earth tool contains five major layers: depth, turbidity, resilience, year of annual severe bleaching, and outplanting score. Bleaching projections included here use climate model data from 2006-2099. proprietary
gov.noaa.nodc:0209247_Not Applicable Benthic cover derived from structure from motion images collected during marine debris surveys at coral reef sites entangled with derelict fishing nets at Pearl and Hermes Atoll in the Northwestern Hawaiian Islands from 2018-09-24 to 2018-10-03 (NCEI Accession 0209247) NOAA_NCEI STAC Catalog 2018-09-24 2018-10-03 -175.8211335, 27.8274571, -175.7880926, 27.8940486 https://cmr.earthdata.nasa.gov/search/concepts/C2089378869-NOAA_NCEI.umm_json The benthic cover and fishing-net related data described in this dataset are derived from the GIS analysis of benthic orthophotos. The source imagery was collected using a Structure from Motion (SfM) approach during in-water marine debris swim surveys conducted by snorkelers in search of derelict fishing nets. Surveys were conducted by the NOAA Fisheries, Ecosystem Sciences Division (ESD) from September 24 to October 3, 2018 at Pearl and Hermes Atoll during an ESD-led marine debris mission to the Northwestern Hawaiian Islands (NWHI) aboard NOAA Ship Oscar Elton Sette. The lagoon at Pearl and Hermes was surveyed equally across the spatial gradient, from locations where derelict fishing nets are common to locations where derelict fishing nets have never been observed. During the 2018 mission, only a subset of marine debris surveys resulted in a SfM survey. Fishing nets were located during swim surveys and selected for SfM if the net was interacting with coral or hard substrate, the depth of the net was within ~1â4 m of the surface, and the area of the net fit within the 9 sq. meter SFM survey plot. During a SFM survey, a permanent 3 x 3 m plot was established around the center of the fishing net, and the net was photographed using a back and forth swim pattern (âbeforeâ photos) for later processing using a SfM approach. The net was then removed, the volume of net removed was estimated and recorded, and the same area was photographed again in the same way (âafterâ photos). A nearby (>50 m distant) paired control site was also photographed using the same method (âcontrolâ photos). The photographs were processed using Agisoft Metashape software to generate orthomosaic images that were analyzed in ArcGIS for benthic cover using a random point approach. The number of points at net-impacted sites were constrained to the net coverage area and were scaled to the net area to ensure an equal point density among replicate net-impact sites. The same number of points were randomly assigned to the 3 Ã 3 m paired control site. Each point was classified into one of seven benthic categories: turf algae, macroalgae, sand, bare substrate, Porites compressa, sponge, or crustose coralline algae (CCA). The annotated points for each site were converted to percent cover for each benthic category. Fishing net size (sq m) and degree of fouling were also calculated from the orthophotos. Analyses were conducted to compare the benthic composition of net sites to control sites and to determine if fouling or net size contributed to these differences. proprietary
gov.noaa.nodc:0209357_Not Applicable A Toolbox for secondary quality control on ocean chemistry and hydrographic data (NCEI Accession 0209357) ALL STAC Catalog 2000-01-01 2020-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089379328-NOAA_NCEI.umm_json This NCEA Accession contains MatLab files for a Toolbox for secondary quality control (2nd QC) on ocean chemistry and hydrographic data. High quality, reference measurements of chemical and physical properties of seawater are of great importance for a wide research community, including the need to validate models and attempts to quantify spatial and temporal variability. Whereas data precision has been improved by technological advances, the data accuracy has improved mainly by the use of certified reference materials (CRMs). However, since CRMs are not available for all variables, and use of CRMs does not guarantee bias-free data, we here present a recently developed Matlab toolbox for performing so-called secondary quality control on oceanographic data by the use of crossover analysis. This method and how it has been implemented in this toolbox is described in detail. This toolbox is developed mainly for use by sea-going scientists as a tool for quickly assessing possible bias in the measurements that can, hopefully, be remedied during the expedition, but also for possible post-cruise adjustment of data to be consistent with previous measurements in the region. proprietary
gov.noaa.nodc:0209357_Not Applicable A Toolbox for secondary quality control on ocean chemistry and hydrographic data (NCEI Accession 0209357) NOAA_NCEI STAC Catalog 2000-01-01 2020-03-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089379328-NOAA_NCEI.umm_json This NCEA Accession contains MatLab files for a Toolbox for secondary quality control (2nd QC) on ocean chemistry and hydrographic data. High quality, reference measurements of chemical and physical properties of seawater are of great importance for a wide research community, including the need to validate models and attempts to quantify spatial and temporal variability. Whereas data precision has been improved by technological advances, the data accuracy has improved mainly by the use of certified reference materials (CRMs). However, since CRMs are not available for all variables, and use of CRMs does not guarantee bias-free data, we here present a recently developed Matlab toolbox for performing so-called secondary quality control on oceanographic data by the use of crossover analysis. This method and how it has been implemented in this toolbox is described in detail. This toolbox is developed mainly for use by sea-going scientists as a tool for quickly assessing possible bias in the measurements that can, hopefully, be remedied during the expedition, but also for possible post-cruise adjustment of data to be consistent with previous measurements in the region. proprietary
@@ -18766,14 +18773,14 @@ gov.noaa.nodc:0210808_Not Applicable Assessment of coral reef fish and benthic c
gov.noaa.nodc:0213517_Not Applicable Black Sea High Resolution SST L4 Analysis 0.0625 deg Resolution for 2019-09-18 (NCEI Accession 0213517) NOAA_NCEI STAC Catalog 2019-09-18 2019-09-18 26.375, 38.75, 42.375, 48.8125 https://cmr.earthdata.nasa.gov/search/concepts/C2089376602-NOAA_NCEI.umm_json CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.0625 deg. x 0.0625 deg. horizontal resolution over the Black Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Black sea. proprietary
gov.noaa.nodc:0218215_Not Applicable Circulation, temperature, and water surface elevation from Finite Volume Community Ocean Model (FVCOM) simulations of Lake Superior, Great Lakes region from 2010-01-01 to 2012-12-31 to study the 2010 coastal upwelling event (NCEI Accession 0218215) NOAA_NCEI STAC Catalog 2010-01-01 2012-12-31 -92.08, 46.44, -84.38, 48.79 https://cmr.earthdata.nasa.gov/search/concepts/C2089376983-NOAA_NCEI.umm_json "This dataset contains a three-dimensional (3-D), coupled ice-ocean Finite Volume Community Ocean Model (FVCOM) hydrodynamic simulations of circulation, temperature, and water surface elevation of Lake Superior for the years 2010-2012. The model was validated with temperature observations at National Oceanic and Atmospheric Administration (NOAA) buoys and mooring data from 2010. The upwelling event observed in satellite imagery and at a mooring station was reproduced by the model, in August 2010 along the northwestern coast. FVCOM version 3.1.6 was used for these simulations including custom modifications for wind-wave mixing (Hu and Wang, 2010) and centered-difference time integration. Ice simulations used the unstructured-grid, community ice code (UG-CICE) that was included with FVCOM version 3.1.6 (Chen et al. 2011; Gao et al. 2011). North American Regional Reanalysis (NARR) 32 km data (Mesinger et al. 2006) was used as atmospheric boundary conditions which included heat flux components (i.e., ""heating_on=T"" in the namelist). To convert the NARR forcings to the FVCOM unstructured grid, the interpolation scheme built in to FVCOM (WRF2FVCOM) was used. Details for these simulations can be found in the namelist file ""narr_0913_run.nml"" included in this data archive." proprietary
gov.noaa.nodc:0220639_Not Applicable Barium isotopes collected from world-wide oceans from 1970 to 2006 and analyzed at WHOI (NCEI Accession 0220639) NOAA_NCEI STAC Catalog 1970-01-01 2006-01-01 -178.073, -76.449, 174.4, 48 https://cmr.earthdata.nasa.gov/search/concepts/C2089377693-NOAA_NCEI.umm_json Barium isotope data from marine barites deposited throughout the world wide oceans. Samples include cold seep, hydrothermal and pelagic barites. Samples were collected from 1970 to 2006, and analyses were conducted in the NIRVANA lab at WHOI between 2016 and 2019. Data are in spreadsheet format. proprietary
-gov.noaa.nodc:0221188_Not Applicable 3-dimensional current velocity and other parameters taken by ADCP from the offshore supply ship Gerry Bordelon in Gulf of Mexico on 2017-09-24 (NCEI Accession 0221188) NOAA_NCEI STAC Catalog 2017-09-24 2017-09-24 -88.974, 28.932, -88.965, 28.944 https://cmr.earthdata.nasa.gov/search/concepts/C2089377874-NOAA_NCEI.umm_json The data consist of four ADCP surveys in the Mississippi Canyon Block 20 region of the Gulf of Mexico. ADCP2_D20170924_SW and ADCP3_D20170924_SW were run to the southwest of ADCP2_D20170929_NE and ADCP3_D20170929_NE. ADCP2 surveys were run from 01:20 to 01:36 UTC on September, 24 2017. ADCP3 surveys were run from 04:84 - 09:21 UTC on September, 24 2017. Sea state was up during ADCP3 surveys. Data are in NetCDF. proprietary
gov.noaa.nodc:0221188_Not Applicable 3-dimensional current velocity and other parameters taken by ADCP from the offshore supply ship Gerry Bordelon in Gulf of Mexico on 2017-09-24 (NCEI Accession 0221188) ALL STAC Catalog 2017-09-24 2017-09-24 -88.974, 28.932, -88.965, 28.944 https://cmr.earthdata.nasa.gov/search/concepts/C2089377874-NOAA_NCEI.umm_json The data consist of four ADCP surveys in the Mississippi Canyon Block 20 region of the Gulf of Mexico. ADCP2_D20170924_SW and ADCP3_D20170924_SW were run to the southwest of ADCP2_D20170929_NE and ADCP3_D20170929_NE. ADCP2 surveys were run from 01:20 to 01:36 UTC on September, 24 2017. ADCP3 surveys were run from 04:84 - 09:21 UTC on September, 24 2017. Sea state was up during ADCP3 surveys. Data are in NetCDF. proprietary
+gov.noaa.nodc:0221188_Not Applicable 3-dimensional current velocity and other parameters taken by ADCP from the offshore supply ship Gerry Bordelon in Gulf of Mexico on 2017-09-24 (NCEI Accession 0221188) NOAA_NCEI STAC Catalog 2017-09-24 2017-09-24 -88.974, 28.932, -88.965, 28.944 https://cmr.earthdata.nasa.gov/search/concepts/C2089377874-NOAA_NCEI.umm_json The data consist of four ADCP surveys in the Mississippi Canyon Block 20 region of the Gulf of Mexico. ADCP2_D20170924_SW and ADCP3_D20170924_SW were run to the southwest of ADCP2_D20170929_NE and ADCP3_D20170929_NE. ADCP2 surveys were run from 01:20 to 01:36 UTC on September, 24 2017. ADCP3 surveys were run from 04:84 - 09:21 UTC on September, 24 2017. Sea state was up during ADCP3 surveys. Data are in NetCDF. proprietary
gov.noaa.nodc:0225446_Not Applicable Assessment of coral reef benthic communities and reef fish survey data from locations in the Commonwealth of Northern Marianas Islands from 2014-10-01 to 2018-09-30 (NCEI Accession 0225446) NOAA_NCEI STAC Catalog 2014-10-01 2018-09-30 145.131154, 14.1136578, 145.8147431, 16.7162927 https://cmr.earthdata.nasa.gov/search/concepts/C2089379287-NOAA_NCEI.umm_json Overview Currently, the LTMMP has 52 long-term monitoring sites across Saipan, Tinian, and Rota that are surveyed on a rotating biennial basis. Three main habitat types are covered: Fore reef, reef flat (lagoon), and seagrass beds (lagoon). Most sites have been selected based on their association with management concerns (runoff, sewage outfalls, urban development, etc.) and/or management actions (watershed restorations efforts, marine protected areas, etc.) and include impacted sites and relatively non-impacted reference sites. In general, monitoring surveys are conducted using standard and proven ecological field survey methods. All surveys are conducted along 3-5, 50 m transect lines laid out along the depth contour (~9m depth) on the fore reef, or along consistent habitat in the lagoon (back reef and seagrass). While benthic cover analysis provides the foundation of the CNMI monitoring program, the current protocol uses several survey types per site to provide ecological depth beyond percent cover. Fore Reef Photos are taken every meter along each transect line using a 0.25m2 quadrat frame, for a total of 250 photos at each site. In the office, the computer program CPCe is used to place five random points on each photo and the biota or substrate type under each point is identified. Organisms are identified to the genus level. This analysis provides benthic percent cover and community diversity. Twelve, 3 minute, 5 m radius stationary point counts (SPC) are conducted at each site to evaluate fish assemblages. Each SPC is systematically positioned throughout the length of a site (250 m). The species and size (fork length) of all food fishes within the 5 meter radius are recorded. This provides relative diversity, abundances, species compositions, size class distribution, and biomass of the fish community. Sixteen 0.25m2 quadrats are haphazardly tossed along the length of the site and every coral colony within the quadrats is identified to the species level and measured. This method provides relative diversity, abundances, species composition, and size class of the coral community. Within these same quadrats, all algae species present are identified to the species level to provide a measure of algae community composition and species richness. Finally, non-coral macro-invertebrates including sea cucumbers, urchins, crown-of-thorns starfish, giant clams, among others, are identified and counted within 1 m of each side of the transect lines (i.e. 5, 2mx50m belt transects). This provides invertebrate abundances, species composition, and diversity. Saipan Lagoon Saipan Lagoon habitats that are monitored include Halodule uninervis beds, staghorn Acropora thickets, and mixed coral back reefs. At lagoon sites, benthic cover is quantified using a 0.25 m2 string quadrat with six intersections, placed every meter along the transect line. The biota or substrate under each intersection is recorded to the genus level, in situ. Additionally, 10, 1 m2 quads are haphazardly placed across the length of the site (250 m) and all seagrass, algae, coral, and macro-invertebrates are identified to the species level and recorded. This method captures the relative diversity, abundance, and species compositions of lagoon communities. Finally, non-coral macro-invertebrate abundances and diversity are quantified as described above for reef slope sites. proprietary
gov.noaa.nodc:0225545_Not Applicable Bulk density and pore water, sediment texture and composition data from sediment cores collected aboard R/V Weatherbird II cruises WB-0812 and WB-0813 in the northern Gulf of Mexico from 2012-08-14 to 2013-08-21 (NCEI Accession 0225545) NOAA_NCEI STAC Catalog 2012-08-14 2013-08-21 -88.86673, 28.97363, -86.33833, 29.73833 https://cmr.earthdata.nasa.gov/search/concepts/C2089379450-NOAA_NCEI.umm_json This dataset contains the bulk density and pore water, sediment texture and composition data from sediment cores collected aboard R/V Weatherbird II cruises WB-0812 and WB-0813 in the northern Gulf of Mexico (nGoM) from 2012-08-14 to 2013-08-21. These data were generated for selected core sub-samples at 2mm sampling intervals for âsurficial unitâ and 5mm sampling resolution intervals to the base of cores. For the bulk density and pore water data, sediment cores were collected on board the R/V Weatherbird II cruise WB-0812 in the nGoM from 2012-08-14 to 2012-08-16. It reports measurement of sediment sample wet weight (g), dry weight (g) and percent pore water. Bulk density is the dry weight per sampling volume expressed as g/cm3. Whereas, sediment texture and composition data were collected aboard R/V Weatherbird II cruise WB-0813 in the nGoM from 2013-08-20 to 2013-08-21. Sediment texture values were expressed as percent gravel, sand, silt, and clay. Percent of mud can be calculated by combining percent silt and clay. Sediment composition was expressed as percent total organic matter (TOM) measured by loss on ignition (LOI), percent carbonate content measured by acid leaching, and the percent insoluble residue (IR), which was likely dominated by terrigenous clastic (land-derived) sediment sources. proprietary
gov.noaa.nodc:0225979_Not Applicable Biological, chemical, physical and time series data collected from station WQBAW by University of Hawai'i at Hilo and University of Hawai'i at MÄnoa and assembled by Pacific Islands Ocean Observing System (PacIOOS) in the North Pacific Ocean from 2008-06-06 to 2016-12-06 (NCEI Accession 0225979) NOAA_NCEI STAC Catalog 2008-06-06 2016-12-06 -157.848, 21.2799, -157.848, 21.2799 https://cmr.earthdata.nasa.gov/search/concepts/C2089379551-NOAA_NCEI.umm_json NCEI Accession 0225979 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo and University of Hawai'i at MÄnoa collected the data from their in-situ moored station named WQBAW: PacIOOS Water Quality Buoy AW (WQB-AW): Ala Wai, Oahu, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and University of Hawai'i at MÄnoa and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month. The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB-AW is located at the exit of the Ala Wai Canal, near Magic Island. Continuous sampling of this outflow area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill. proprietary
gov.noaa.nodc:0226059_Not Applicable Biological, chemical, physical and time series data collected from station WQBKN by University of Hawai'i at Hilo and University of Hawai'i at MÃÂnoa and assembled by Pacific Islands Ocean Observing System (PacIOOS) in the North Pacific Ocean from 2008-08-07 to 2017-01-04 (NCEI Accession 0226059) NOAA_NCEI STAC Catalog 2008-08-07 2017-01-04 -157.865, 21.2887, -157.865, 21.2887 https://cmr.earthdata.nasa.gov/search/concepts/C2089380013-NOAA_NCEI.umm_json NCEI Accession 0226059 contains biological, chemical, physical and time series data in netCDF formatted files, which follow the Climate and Forecast metadata convention (CF) and the Attribute Convention for Data Discovery (ACDD). University of Hawai'i at Hilo and University of Hawai'i at MÃÂnoa collected the data from their in-situ moored station named WQBKN: PacIOOS Water Quality Buoy KN (WQB-KN): Kilo Nalu, Oahu, Hawaii, in the North Pacific Ocean. PacIOOS, which assembles data from University of Hawai'i at Hilo and University of Hawai'i at MÃÂnoa and other sub-regional coastal and ocean observing systems of the U. S. Pacific Islands, submitted the data to NCEI as part of the Integrated Ocean Observing System Data Assembly Centers (IOOS DACs) Data Stewardship Program. Each month, NCEI adds to the accession the data collected during the previous month. The water quality buoys are part of the Pacific Islands Ocean Observing System (PacIOOS) and are designed to measure a variety of ocean parameters at fixed points. WQB-KN is located at the Kilo Nalu Nearshore Reef Observatory, near Kakaako Waterfront Park and Kewalo Basin off of Ala Moana Boulevard in Honolulu. Continuous sampling of this area provides a record of baseline conditions of the chemical and biological environment for comparison when there are pollution events such as storm runoff or a sewage spill. proprietary
-gov.noaa.nodc:0226205_Not Applicable ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30 (NCEI Accession 0226205) ALL STAC Catalog 2020-03-28 2020-03-30 -88.576242, 27.591893, -82.438911, 30.342877 https://cmr.earthdata.nasa.gov/search/concepts/C2089380082-NOAA_NCEI.umm_json This dataset includes ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI. proprietary
gov.noaa.nodc:0226205_Not Applicable ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30 (NCEI Accession 0226205) NOAA_NCEI STAC Catalog 2020-03-28 2020-03-30 -88.576242, 27.591893, -82.438911, 30.342877 https://cmr.earthdata.nasa.gov/search/concepts/C2089380082-NOAA_NCEI.umm_json This dataset includes ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI. proprietary
+gov.noaa.nodc:0226205_Not Applicable ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30 (NCEI Accession 0226205) ALL STAC Catalog 2020-03-28 2020-03-30 -88.576242, 27.591893, -82.438911, 30.342877 https://cmr.earthdata.nasa.gov/search/concepts/C2089380082-NOAA_NCEI.umm_json This dataset includes ADCP data collected aboard NOAA Ship Gordon Gunter in the Coastal Waters of Florida, Coastal Waters of Mississippi, and Gulf of Mexico from 2020-03-28 to 2020-03-30. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI. proprietary
gov.noaa.nodc:0231662_Not Applicable ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15 (NCEI Accession 0231662) NOAA_NCEI STAC Catalog 2019-07-15 2019-07-15 -124.355093, 44.282964, -124.054485, 44.625023 https://cmr.earthdata.nasa.gov/search/concepts/C2089380691-NOAA_NCEI.umm_json This dataset includes ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI. proprietary
gov.noaa.nodc:0231662_Not Applicable ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15 (NCEI Accession 0231662) ALL STAC Catalog 2019-07-15 2019-07-15 -124.355093, 44.282964, -124.054485, 44.625023 https://cmr.earthdata.nasa.gov/search/concepts/C2089380691-NOAA_NCEI.umm_json This dataset includes ADCP data collected aboard NOAA Ship Bell M. Shimada in the North Pacific Ocean and Yaquina Bay on 2019-07-15. These data include CURRENT SPEED - EAST/WEST COMPONENT (U) and CURRENT SPEED - NORTH/SOUTH COMPONENT (V). The instruments used to collect these data include ADCP and GPS. The NOAA Office of Marine and Aviation Operations (OMAO) submitted these data to NCEI. proprietary
gov.noaa.nodc:0232256_Not Applicable American Samoa Territorial Monitoring Program: Assessment of coral reef benthic and fish communities in American Samoa from 2005-03-10 to 2017-04-21 (NCEI Accession 0232256) NOAA_NCEI STAC Catalog 2005-03-10 2017-04-21 -170.563628, -14.364332, -170.812132, -14.252747 https://cmr.earthdata.nasa.gov/search/concepts/C2089380473-NOAA_NCEI.umm_json The data described here result from coral reef assessments of reef slopes (10m depth) at permanent sites around Tutuila, American Samoa as part of the ongoing American Samoa Coral Reef Monitoring Program (ASCRMP). These surveys were conducted by members of the American Samoa Coral Reef Advisory Group between 2005 and 2017. The data was collected via SCUBA surveys and reports on coral, benthic and fish composition and derived metrics (e.g., benthic cover, coral diversity, fish diversity, fish biomass). proprietary
@@ -18785,10 +18792,10 @@ gov.noaa.nodc:6800230_Not Applicable Cloud amount/frequency, NITRATE and other d
gov.noaa.nodc:6900225_Not Applicable Cloud amount/frequency, NITRATE and other data from GOA from 1968-09-19 to 1968-11-17 (NCEI Accession 6900225) NOAA_NCEI STAC Catalog 1968-09-19 1968-11-17 9, -17, 13.5, -4.8 https://cmr.earthdata.nasa.gov/search/concepts/C2089382177-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:6901098_Not Applicable Cloud amount/frequency, NITRATE and other data from PANULIRUS and PANULIRUS II from 1966-10-18 to 1969-11-06 (NCEI Accession 6901098) NOAA_NCEI STAC Catalog 1966-10-18 1969-11-06 -64.5, 32.1, -64.5, 32.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089381131-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:7000052_Not Applicable BAROMETRIC PRESSURE and Other Data from ALPHA HELIX From Prince William Sound (Gulf of Alaska) from 1986-12-15 to 1986-12-18 (NCEI Accession 7000052) NOAA_NCEI STAC Catalog 1986-12-15 1986-12-18 -150, 59, -149, 60.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089381217-NOAA_NCEI.umm_json Not provided proprietary
-gov.noaa.nodc:7000422_Not Applicable AIR PRESSURE and Other Data from GOSNOLD From NW Atlantic (limit-40 W) from 1969-10-28 to 1969-10-29 (NCEI Accession 7000422) ALL STAC Catalog 1969-10-28 1969-10-29 -72, 39, -71, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2089383028-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:7000422_Not Applicable AIR PRESSURE and Other Data from GOSNOLD From NW Atlantic (limit-40 W) from 1969-10-28 to 1969-10-29 (NCEI Accession 7000422) NOAA_NCEI STAC Catalog 1969-10-28 1969-10-29 -72, 39, -71, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2089383028-NOAA_NCEI.umm_json Not provided proprietary
-gov.noaa.nodc:7000981_Not Applicable A summary of seawater chemistry analysis of stations in North Atlantic Ocean from 1970-06-20 to 1970-07-03 (NCEI Accession 7000981) ALL STAC Catalog 1970-06-01 1970-07-01 -29.33, 50.01, -14.2, 55.56 https://cmr.earthdata.nasa.gov/search/concepts/C2089381614-NOAA_NCEI.umm_json Seawater chemistry data were collected using bottle from the USNS KANE in the North Atlantic Ocean. Data were collected from 20 July 1970 to 03 July 1970. The seawater chemistry data includes reactive phosphate, reactive silicate, and nitrate. proprietary
+gov.noaa.nodc:7000422_Not Applicable AIR PRESSURE and Other Data from GOSNOLD From NW Atlantic (limit-40 W) from 1969-10-28 to 1969-10-29 (NCEI Accession 7000422) ALL STAC Catalog 1969-10-28 1969-10-29 -72, 39, -71, 40 https://cmr.earthdata.nasa.gov/search/concepts/C2089383028-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:7000981_Not Applicable A summary of seawater chemistry analysis of stations in North Atlantic Ocean from 1970-06-20 to 1970-07-03 (NCEI Accession 7000981) NOAA_NCEI STAC Catalog 1970-06-01 1970-07-01 -29.33, 50.01, -14.2, 55.56 https://cmr.earthdata.nasa.gov/search/concepts/C2089381614-NOAA_NCEI.umm_json Seawater chemistry data were collected using bottle from the USNS KANE in the North Atlantic Ocean. Data were collected from 20 July 1970 to 03 July 1970. The seawater chemistry data includes reactive phosphate, reactive silicate, and nitrate. proprietary
+gov.noaa.nodc:7000981_Not Applicable A summary of seawater chemistry analysis of stations in North Atlantic Ocean from 1970-06-20 to 1970-07-03 (NCEI Accession 7000981) ALL STAC Catalog 1970-06-01 1970-07-01 -29.33, 50.01, -14.2, 55.56 https://cmr.earthdata.nasa.gov/search/concepts/C2089381614-NOAA_NCEI.umm_json Seawater chemistry data were collected using bottle from the USNS KANE in the North Atlantic Ocean. Data were collected from 20 July 1970 to 03 July 1970. The seawater chemistry data includes reactive phosphate, reactive silicate, and nitrate. proprietary
gov.noaa.nodc:7001081_Not Applicable Characteristics of Sediments in the James River Estuary, Virginia, 1968 (NCEI Accession 7001081) NOAA_NCEI STAC Catalog 1966-04-01 1967-08-30 -77, 36.7, -76.15, 37.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089382141-NOAA_NCEI.umm_json This report presents data on the physical and chemical characteristics of bottom sediments in the James River estuary, Virgina. The data were generated as part of a comprehensive study of sedimentation in which the initial objective was to broadly define the distribution of sediment properties. proprietary
gov.noaa.nodc:7100000_Not Applicable Cloud amount/frequency, NITRATE and other data from NOAA Ship DISCOVERER, JAMES COOK and other platforms from 1964-08-24 to 1971-11-17 (NCEI Accession 7100000) NOAA_NCEI STAC Catalog 1964-08-24 1971-11-17 -155.5, -66.7, 175.2, 50.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089383124-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:7100048_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM and Other Platforms From NE Pacific (limit-180) from 1969-08-01 to 1969-08-31 (NCEI Accession 7100048) NOAA_NCEI STAC Catalog 1969-08-01 1969-08-31 -85, 7, -75, 12 https://cmr.earthdata.nasa.gov/search/concepts/C2089383261-NOAA_NCEI.umm_json Not provided proprietary
@@ -18804,8 +18811,8 @@ gov.noaa.nodc:7201127_Not Applicable Cloud amount/frequency, NITRATE and other d
gov.noaa.nodc:7201380_Not Applicable Cloud amount/frequency, NITRATE and other data from EASTWARD from 1971-07-19 to 1972-11-04 (NCEI Accession 7201380) NOAA_NCEI STAC Catalog 1971-07-19 1972-11-04 -80.7, 30.4, -72.7, 38.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089382013-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:7201418_Not Applicable Cloud amount/frequency, NITRATE and other data from PANULIRUS and PANULIRUS II from 1970-01-06 to 1972-11-03 (NCEI Accession 7201418) NOAA_NCEI STAC Catalog 1970-01-06 1972-11-03 -64.9, 31.5, -64.5, 32.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089382040-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:7300167_Not Applicable Cloud amount/frequency, NITRATE and other data from ALEJANDRO DE HUMBOLDT and NOAA Ship DAVID STARR JORDAN in the Gulf of California from 1971-04-27 to 1971-05-09 (NCEI Accession 7300167) NOAA_NCEI STAC Catalog 1971-04-27 1971-05-09 -115.9, 22.8, -108, 29.1 https://cmr.earthdata.nasa.gov/search/concepts/C2089382675-NOAA_NCEI.umm_json Not provided proprietary
-gov.noaa.nodc:7300282_Not Applicable AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms from 1968-07-01 to 1970-12-31 (NCEI Accession 7300282) ALL STAC Catalog 1968-07-01 1970-12-31 113.9, -46.6, 179.8, -0.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089383549-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:7300282_Not Applicable AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms from 1968-07-01 to 1970-12-31 (NCEI Accession 7300282) NOAA_NCEI STAC Catalog 1968-07-01 1970-12-31 113.9, -46.6, 179.8, -0.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089383549-NOAA_NCEI.umm_json Not provided proprietary
+gov.noaa.nodc:7300282_Not Applicable AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms from 1968-07-01 to 1970-12-31 (NCEI Accession 7300282) ALL STAC Catalog 1968-07-01 1970-12-31 113.9, -46.6, 179.8, -0.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089383549-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:7301085_Not Applicable Cloud amount/frequency, NITRATE and other data from BELLOWS from 1973-08-10 to 1973-08-15 (NCEI Accession 7301085) NOAA_NCEI STAC Catalog 1973-08-10 1973-08-15 -89.6, 27, -83, 29.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089381369-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:7301177_Not Applicable Cloud amount/frequency, NITRATE and other data from GAUSS, METEOR and other platforms in the North Atlantic Ocean from 1959-11-18 to 1972-03-14 (NCEI Accession 7301177) NOAA_NCEI STAC Catalog 1959-11-18 1972-03-14 -85, 0, 35.9, 71.4 https://cmr.earthdata.nasa.gov/search/concepts/C2089381441-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:7400073_Not Applicable Cloud amount/frequency, NITRATE and other data from NOAA Ship DISCOVERER, USCGC ROCKAWAY and other platforms from 1969-05-01 to 1969-07-29 (NCEI Accession 7400073) NOAA_NCEI STAC Catalog 1969-05-01 1969-07-29 -59.8, 7.4, -52.6, 17.7 https://cmr.earthdata.nasa.gov/search/concepts/C2089381593-NOAA_NCEI.umm_json Not provided proprietary
@@ -18823,8 +18830,8 @@ gov.noaa.nodc:7600769_Not Applicable Cloud amount/frequency, NITRATE and other d
gov.noaa.nodc:7601177_Not Applicable Cloud amount/frequency, NITRATE and other data from MURRE II in the NE Pacific from 1975-06-20 to 1976-03-29 (NCEI Accession 7601177) NOAA_NCEI STAC Catalog 1975-06-20 1976-03-29 -135.7, 58, -134.2, 58.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089384847-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:7601212_Not Applicable BENTHIC SPECIES and Other Data from KANA KEOKI From Gulf of Mexico from 1974-10-26 to 1974-12-21 (NCEI Accession 7601212) NOAA_NCEI STAC Catalog 1974-10-26 1974-12-21 -100, 17, -81, 31.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089384895-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:7601237_Not Applicable Chemical and physical data from thermistor, fluorometer, and bottle casts in the Patuxent River from 1972-10-15 to 1972-10-19 (NCEI Accession 7601237) NOAA_NCEI STAC Catalog 1972-10-15 1972-10-19 -76.7, 38, -76.7, 42 https://cmr.earthdata.nasa.gov/search/concepts/C2089384911-NOAA_NCEI.umm_json "The Patuxent River estuary was investigated over a 25-hour tidal cycle from October 17-18, 1972, during the Patuxent River Cooperative Study (conducted by the University of Maryland). These data were collected as part of a joint investigation by the University of Maryland's Center for Environmental and Estuarine Studies (Chesapeake Biological Lab) and the Institute for Fluid Dynamics and Applied Mathematics (College Park, Maryland). The resulting chemical, physical, and biological data were assembled into a format that could be utilized by investigators, collectively titled the Patuxent River Data Bank. The Patuxent River Data Bank was submitted to NODC on a 9-track, 1600 BPI tape in EBCDIC and contains headers and one data file. Heat concentration (in kilocalories/liter) and instantaneous flux magnitude (in megacalories/square meter/second) were recorded over the tidal cycle. Other data associated with this study are filed under NODC Reference #'s L01574 and L01576; all data are in the Level-A directory under L01574.001. Data associated with marine chemistry include: Dissolved organic carbon (milligrams/liter), Particulate carbon (milligrams/liter), salts (grams/liter), Dissolved oxygen (milligrams/liter), and total particulates (milligrams/liter). Instantaneous flux magnitudes for carbon were measured in grams/liter; for salts, in kilograms/liter; for oxygen, in milligrams/liter; and for total particulates, milligrams/liter. Parameters associated with primary productivity (L505) include: Nitrate +Nitrite conc., Ammonia Nitrogen conc., Total Kjeldahl Nitrogen, Organic Phosphate conc., Total Hydrolyzable Phosphate, Active Chlorophyll-a, and Total Chlorophyll. Nutrients were measured in milligrams/liter; chlorophyll concentrations were measured in micrograms/liter. Instantaneous flux magnitudes were measured in milligrams/square meter/second. Additional data collected during this investigation are filed under NODC Reference #'s L01575 and one tape of Patuxent River Estuary Hydro data ""OLD STUFF""" proprietary
-gov.noaa.nodc:7601613_Not Applicable AIR PRESSURE and Other Data from TIDE STATIONS From North American Coastline-North and Others from 1972-01-01 to 1974-06-30 (NCEI Accession 7601613) NOAA_NCEI STAC Catalog 1972-01-01 1974-06-30 -77, 37, -76, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2089384776-NOAA_NCEI.umm_json This entry contains tidal information for Chesapeake Bay. Data was submitted by Saul Berkman, NOS Tides Branch, Oceanographic Division. These data are in NODC format. These data were collected roughly 37-39 degrees N, 75 degrees W (stations were in Baltimore, Bayport VA, Cambridge MD, Cheathem Annex VA, Chesapeake City, MD, Gaskins Point, VA, Hampton Roads, VA, Kiptopeke Beach VA, Lower Marlboro, MD, Old Pt Comfort VA, Portsmouth VA, Solomons MD, Taylor Island MD, Washington DC, and Windmill Point VA. The data are in half-hourly units and includes latitude, longitude, date, time, and tidal height. The documentation describes the record format. Tide heights are referred to North American Datum (NAD) 1929. proprietary
gov.noaa.nodc:7601613_Not Applicable AIR PRESSURE and Other Data from TIDE STATIONS From North American Coastline-North and Others from 1972-01-01 to 1974-06-30 (NCEI Accession 7601613) ALL STAC Catalog 1972-01-01 1974-06-30 -77, 37, -76, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2089384776-NOAA_NCEI.umm_json This entry contains tidal information for Chesapeake Bay. Data was submitted by Saul Berkman, NOS Tides Branch, Oceanographic Division. These data are in NODC format. These data were collected roughly 37-39 degrees N, 75 degrees W (stations were in Baltimore, Bayport VA, Cambridge MD, Cheathem Annex VA, Chesapeake City, MD, Gaskins Point, VA, Hampton Roads, VA, Kiptopeke Beach VA, Lower Marlboro, MD, Old Pt Comfort VA, Portsmouth VA, Solomons MD, Taylor Island MD, Washington DC, and Windmill Point VA. The data are in half-hourly units and includes latitude, longitude, date, time, and tidal height. The documentation describes the record format. Tide heights are referred to North American Datum (NAD) 1929. proprietary
+gov.noaa.nodc:7601613_Not Applicable AIR PRESSURE and Other Data from TIDE STATIONS From North American Coastline-North and Others from 1972-01-01 to 1974-06-30 (NCEI Accession 7601613) NOAA_NCEI STAC Catalog 1972-01-01 1974-06-30 -77, 37, -76, 39 https://cmr.earthdata.nasa.gov/search/concepts/C2089384776-NOAA_NCEI.umm_json This entry contains tidal information for Chesapeake Bay. Data was submitted by Saul Berkman, NOS Tides Branch, Oceanographic Division. These data are in NODC format. These data were collected roughly 37-39 degrees N, 75 degrees W (stations were in Baltimore, Bayport VA, Cambridge MD, Cheathem Annex VA, Chesapeake City, MD, Gaskins Point, VA, Hampton Roads, VA, Kiptopeke Beach VA, Lower Marlboro, MD, Old Pt Comfort VA, Portsmouth VA, Solomons MD, Taylor Island MD, Washington DC, and Windmill Point VA. The data are in half-hourly units and includes latitude, longitude, date, time, and tidal height. The documentation describes the record format. Tide heights are referred to North American Datum (NAD) 1929. proprietary
gov.noaa.nodc:7601642_Not Applicable Bacteria, taxonomic code, and other data collected from G.W. PIERCE in North Atlantic Ocean from sediment sampler; 1976-02-20 to 1976-03-23 (NCEI Accession 7601642) NOAA_NCEI STAC Catalog 1976-02-20 1976-03-23 -75.3, 37.1, -71.9, 39.9 https://cmr.earthdata.nasa.gov/search/concepts/C2089384806-NOAA_NCEI.umm_json Bacteria, taxonomic code, and other data were collected using sediment sampler and other instruments in the North Atlantic Ocean from G.W. PIERCE. Data were collected from 20 February 1976 to 23 March 1976 by Virginia Institute of Marine Science in Gloucester Point with support from the Ocean Continental Shelf - Mid Atlantic (OCS-Mid Atlantic) project. proprietary
gov.noaa.nodc:7601772_Not Applicable Cloud amount/frequency, NITRATE and other data from NOAA Ship OREGON II in the NW Atlantic from 1976-02-20 to 1976-02-25 (NCEI Accession 7601772) NOAA_NCEI STAC Catalog 1976-02-20 1976-02-25 -74.4, 36.8, -72.6, 38.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089384997-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:7617993_Not Applicable Cloud amount/frequency, NITRATE and other data from CAPRICORNE from 1974-07-25 to 1974-08-10 (NCEI Accession 7617993) NOAA_NCEI STAC Catalog 1974-07-25 1974-08-10 -10.3, -2.2, -3.9, 4.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089385626-NOAA_NCEI.umm_json Not provided proprietary
@@ -18832,8 +18839,8 @@ gov.noaa.nodc:7617994_Not Applicable Cloud amount/frequency, NITRATE and other d
gov.noaa.nodc:7617995_Not Applicable Cloud amount/frequency, NITRATE and other data from A. V. HUMBOLDT from 1974-07-28 to 1974-08-17 (NCEI Accession 7617995) NOAA_NCEI STAC Catalog 1974-07-28 1974-08-17 -25, -1.5, -23.4, 1.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089385645-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:7700058_Not Applicable AIR PRESSURE and Other Data from YELCHO From Drake Passage from 1976-02-27 to 1976-04-08 (NCEI Accession 7700058) NOAA_NCEI STAC Catalog 1976-02-27 1976-04-08 -70, -90, -50, -70 https://cmr.earthdata.nasa.gov/search/concepts/C2089385730-NOAA_NCEI.umm_json Surface Data was collected aboard the YELCHO. Data collected was part of the First Dynamic Response And Kinematic Experiment (FDRAKE) conducted in 1976, along the Drake passage. Data consists of surface temperature, salinity, and silicate. The data was submitted by the Department of Oceanography, Texas A&M University College Station, Texas. Data are in form of computer printout (13 pages), there are no tapes. The experiment was conducted in two separate legs. The first leg was conducted between February 27-March 13, 1976 and the second leg of the experiment was conducted between March 22-April 8, 1976. proprietary
gov.noaa.nodc:7700058_Not Applicable AIR PRESSURE and Other Data from YELCHO From Drake Passage from 1976-02-27 to 1976-04-08 (NCEI Accession 7700058) ALL STAC Catalog 1976-02-27 1976-04-08 -70, -90, -50, -70 https://cmr.earthdata.nasa.gov/search/concepts/C2089385730-NOAA_NCEI.umm_json Surface Data was collected aboard the YELCHO. Data collected was part of the First Dynamic Response And Kinematic Experiment (FDRAKE) conducted in 1976, along the Drake passage. Data consists of surface temperature, salinity, and silicate. The data was submitted by the Department of Oceanography, Texas A&M University College Station, Texas. Data are in form of computer printout (13 pages), there are no tapes. The experiment was conducted in two separate legs. The first leg was conducted between February 27-March 13, 1976 and the second leg of the experiment was conducted between March 22-April 8, 1976. proprietary
-gov.noaa.nodc:7700179_Not Applicable AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms From Labrador Sea from 1919-09-29 to 1976-04-26 (NCEI Accession 7700179) ALL STAC Catalog 1919-09-29 1976-04-26 -60, 44, 48, 80.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089385848-NOAA_NCEI.umm_json This is German Surface Physical & Chemical Data submitted by Deutsches Hydrographische Institut. This data was collected in the Labrador Sea from January 6, 1974 to August 16, 1974. There is no documentation or description of the source code format. proprietary
gov.noaa.nodc:7700179_Not Applicable AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms From Labrador Sea from 1919-09-29 to 1976-04-26 (NCEI Accession 7700179) NOAA_NCEI STAC Catalog 1919-09-29 1976-04-26 -60, 44, 48, 80.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089385848-NOAA_NCEI.umm_json This is German Surface Physical & Chemical Data submitted by Deutsches Hydrographische Institut. This data was collected in the Labrador Sea from January 6, 1974 to August 16, 1974. There is no documentation or description of the source code format. proprietary
+gov.noaa.nodc:7700179_Not Applicable AIR PRESSURE and Other Data from MULTIPLE SHIPS and Other Platforms From Labrador Sea from 1919-09-29 to 1976-04-26 (NCEI Accession 7700179) ALL STAC Catalog 1919-09-29 1976-04-26 -60, 44, 48, 80.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089385848-NOAA_NCEI.umm_json This is German Surface Physical & Chemical Data submitted by Deutsches Hydrographische Institut. This data was collected in the Labrador Sea from January 6, 1974 to August 16, 1974. There is no documentation or description of the source code format. proprietary
gov.noaa.nodc:7700437_Not Applicable Cloud amount/frequency, NITRATE and other data from CHAIN from 1973-03-11 to 1973-07-06 (NCEI Accession 7700437) NOAA_NCEI STAC Catalog 1973-03-11 1973-07-06 -72.6, 26.3, -66.8, 33.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089386094-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:7700455_Not Applicable BENTHIC SPECIES and Other Data from GILLISS and Other Platforms from 1975-10-27 to 1976-08-27 (NCEI Accession 7700455) NOAA_NCEI STAC Catalog 1975-10-27 1976-08-27 -75.3, 37.1, -71.9, 39.9 https://cmr.earthdata.nasa.gov/search/concepts/C2089386131-NOAA_NCEI.umm_json Data was submitted by Dr. Gerald L. Engel. This study was organized to collect data on Parasite Type and Location. Parasite (both ecto- and endo-), and site of infection were looked into. SST, wave, turbidity, gear type (trawl), species, parasite (both ecto- and endo-), and site of infection (i.e. data on parasite type and location) data were collected. The documentation describes instruments employed for sampling, units, and a detailed description of the record format. These studies were part of the Mid-Atlantic Outer Continental Shelf Studies (OCS). These data were collected by the Virginia Institute of Marine Science (VIMS). Special codes employed by VIMS to describe parasite types and location were included as hardcopy. The original information submitted on tape has been converted into the current NODC storage format. proprietary
gov.noaa.nodc:7700456_Not Applicable BENTHIC SPECIES and Other Data from GILLISS and Other Platforms from 1976-06-14 to 1976-09-02 (NCEI Accession 7700456) NOAA_NCEI STAC Catalog 1976-06-14 1976-09-02 -75.3, 37.5, -71.9, 39.9 https://cmr.earthdata.nasa.gov/search/concepts/C2089386139-NOAA_NCEI.umm_json "Data submitted by Dr. Gerald L. Engel. The data was collected between June 1976 and September 1976. This study was organized to collect Histopathology and Benthic data. SST, wave, turbidity, gear type (trawl v.s dredge), benthic species counts and weights were collected. These data are ""megabenthic"" species. The documentation describes instruments employed for sampling, units, and a detailed description of the record format. The original data on tape has been converted to current NODC storage format. These studies were part of the Mid-Atlantic Outer Continental Shelf Studies (OCS). These data were collected by the Virginia Institute of Marine Science (VIMS)." proprietary
@@ -19092,8 +19099,8 @@ gov.noaa.nodc:9400203_Not Applicable BAROMETRIC PRESSURE and Other Data from NOA
gov.noaa.nodc:9400205_Not Applicable BAROMETRIC PRESSURE and Other Data from ODEN from 1991-01-01 to 1991-12-31 (NCEI Accession 9400205) NOAA_NCEI STAC Catalog 1991-01-01 1991-12-31 -14.855, 81.15, 169.685, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2089385673-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9400206_Not Applicable Cloud amount/frequency, NITRATE and other data from ELTANIN from 1969-12-22 to 1970-01-25 (NCEI Accession 9400206) NOAA_NCEI STAC Catalog 1969-12-22 1970-01-25 129.8, -64.5, 135.9, -35 https://cmr.earthdata.nasa.gov/search/concepts/C2089385681-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9400223_Not Applicable BAROMETRIC PRESSURE and Other Data from NOAA Ship WHITING From NW Atlantic (limit-40 W) from 1994-10-12 to 1994-11-12 (NCEI Accession 9400223) NOAA_NCEI STAC Catalog 1994-10-12 1994-11-12 -81, 31, -81, 31 https://cmr.earthdata.nasa.gov/search/concepts/C2089385743-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected in NW Atlantic (limit-40 W). Data was collected from NOAA Ship WHITING. The data was collected over a period spanning from October 12, 1994 to November 12, 1994. One diskette of data from 14 casts was submitted by National Ocean Service, Rockville, MD. proprietary
-gov.noaa.nodc:9400225_Not Applicable ABSORPTION, SCATTERING, ATTENUATION COEFFICIENTS and Other Data from SATELLITE From Gulf of Maine from 1985-01-01 to 1992-12-31 (NCEI Accession 9400225) NOAA_NCEI STAC Catalog 1985-01-01 1992-12-31 -70.9, 42, -65.7, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2089385762-NOAA_NCEI.umm_json The accession contains binary raster images from landsat thematic mapper collected in Gulf of Maine between 1982 to 1985. A suite of Regional Satellite Products from Edward Bright, Martin-Marietta Energy Systems at Oak Ridge National Laboratory was submitted. Each data set is about megabyte. proprietary
gov.noaa.nodc:9400225_Not Applicable ABSORPTION, SCATTERING, ATTENUATION COEFFICIENTS and Other Data from SATELLITE From Gulf of Maine from 1985-01-01 to 1992-12-31 (NCEI Accession 9400225) ALL STAC Catalog 1985-01-01 1992-12-31 -70.9, 42, -65.7, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2089385762-NOAA_NCEI.umm_json The accession contains binary raster images from landsat thematic mapper collected in Gulf of Maine between 1982 to 1985. A suite of Regional Satellite Products from Edward Bright, Martin-Marietta Energy Systems at Oak Ridge National Laboratory was submitted. Each data set is about megabyte. proprietary
+gov.noaa.nodc:9400225_Not Applicable ABSORPTION, SCATTERING, ATTENUATION COEFFICIENTS and Other Data from SATELLITE From Gulf of Maine from 1985-01-01 to 1992-12-31 (NCEI Accession 9400225) NOAA_NCEI STAC Catalog 1985-01-01 1992-12-31 -70.9, 42, -65.7, 45 https://cmr.earthdata.nasa.gov/search/concepts/C2089385762-NOAA_NCEI.umm_json The accession contains binary raster images from landsat thematic mapper collected in Gulf of Maine between 1982 to 1985. A suite of Regional Satellite Products from Edward Bright, Martin-Marietta Energy Systems at Oak Ridge National Laboratory was submitted. Each data set is about megabyte. proprietary
gov.noaa.nodc:9500029_Not Applicable BAROMETRIC PRESSURE and Other Data from ALPHA HELIX From Bering Sea from 1994-05-03 to 1994-06-08 (NCEI Accession 9500029) NOAA_NCEI STAC Catalog 1994-05-03 1994-06-08 -180, 53.9, -149.3, 64.1 https://cmr.earthdata.nasa.gov/search/concepts/C2089385960-NOAA_NCEI.umm_json "The Conductivity, Temperature and Depth (CTD) and other data were collected in Bering Sea as part of Inner SHelf Transfer and recycling (ISHTAR) and ""St. Lawrence Island Polynya"" project. Data was collected from Ship ALPHA HELIX cruise HX-177. The data was collected over a period spanning from May 3, 1994 and June 8, 1994. Dr. Jackie Grebmeir, Univ. of Tenn., Knoxville was Principal Investigator funde by NSF Grant OPP-9000694. Data from 105 stations was received by NODC via Dr. Chirk Chu, University of Alaska, Institute of Marine Science, Fairbanks, AK. Data is in F022-CTD-Hi Resolution file format of NODC. F022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals." proprietary
gov.noaa.nodc:9500030_Not Applicable BAROMETRIC PRESSURE and Other Data from ALPHA HELIX From Bering Sea and Others from 1994-09-10 to 1994-10-10 (NCEI Accession 9500030) NOAA_NCEI STAC Catalog 1994-09-10 1994-10-10 -174.6, 59.8, -149.4, 71.8 https://cmr.earthdata.nasa.gov/search/concepts/C2089385969-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected in Bering Sea and Chukchi Sea. Data was collected from Ship ALPHA HELIX. The data was collected over a period spanning from September 10, 1994 to October 10, 1994. One CTD data set from 61 stations was submitted via FTP by Dr. Thomas Weingartner, Institute of Marine Science, University of Alaska, Fairbanks. AK. Data has been replaced on May 22, 2000 by accession 000148. The new accession was submitted by Mr. S. Stillwaugh NODC NW Liaison Officer. proprietary
gov.noaa.nodc:9500031_Not Applicable BAROMETRIC PRESSURE and Other Data from ALPHA HELIX and Other Platforms From Bering Sea and Others from 1994-06-27 to 1995-01-06 (NCEI Accession 9500031) NOAA_NCEI STAC Catalog 1994-06-27 1995-01-06 -165.1, 54, -130, 62 https://cmr.earthdata.nasa.gov/search/concepts/C2089385979-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected in Gulf of Alaska and Bering Sea as part of Inner SHelf Transfer and recycling (ISHTAR) project. Data was collected from Ships ALPHA HELIX and LITTLE DIPPER. The data was collected over a period spanning from June 27, 1994 to January 6, 1995. 7 sets of CTD data collected from seabird from 13 stations was received by NODC from Dr. C. Peter McRoy of University of Alaska, Institute of Marine Science, Fairbanks, AK via FTP. Data is in F022-CTD-Hi Resolution file format of NODC. F022 High-resolution CTD data is collected from high resolution (conductivity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1 m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t) and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. proprietary
@@ -19108,8 +19115,8 @@ gov.noaa.nodc:9500149_Not Applicable ALACE subsurface drifter data in South Paci
gov.noaa.nodc:9500152_Not Applicable BAROMETRIC PRESSURE and Other Data from AURORA AUSTRALIS and Other Platforms from 1991-01-06 to 1992-03-06 (NCEI Accession 9500152) NOAA_NCEI STAC Catalog 1991-01-06 1992-03-06 67.5, -69.5, 135.4, -50.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089386699-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected from Ship AURORA AUSTRALIS. The data was collected over a period spanning from January 6, 1991 and March 6, 1992. Data from 343 casts containing 185,102 records was submitted via File Transfer Protocol by Ms. Edwina Tanner, Antarctic Cooperative Research Centre, University of Tasmania, Australia. proprietary
gov.noaa.nodc:9500160_Not Applicable BAROMETRIC PRESSURE and Other Data from ALPHA HELIX From Chukchi Sea from 1995-08-24 to 1995-09-01 (NCEI Accession 9500160) NOAA_NCEI STAC Catalog 1995-08-24 1995-09-01 163.988167, 66.665667, -168.998, 71.312667 https://cmr.earthdata.nasa.gov/search/concepts/C2089386823-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected from 73 stations in Chukchi Sea and East Siberian Sea area. The station numbers are 1-6, 8-30, 32-74, 76. Data was collected from Ship ALPHA HELIX cruise HX189. The data was collected BY Dr. J. Grebmeier of the University of Tennessee over a period spanning from August 24, 1995 to September 1, 1995. This project was funded by Office of Naval Research under grant no: NAVY N00014-94-1-1042Grebmeier. Data in NODC file format F022 was submitted by Dr. Chirk Chu, Institute of Marine Science, University of Alaska, Fairbanks. proprietary
gov.noaa.nodc:9600001_Not Applicable BAROMETRIC PRESSURE and Other Data from ALPHA HELIX From Chukchi Sea from 1995-09-10 to 1995-10-08 (NCEI Accession 9600001) NOAA_NCEI STAC Catalog 1995-09-10 1995-10-08 160, 52, -156, 71 https://cmr.earthdata.nasa.gov/search/concepts/C2089386837-NOAA_NCEI.umm_json The Conductivity, Temperature and Depth (CTD) and other data were collected in Chukchi Sea as part of Office of Naval Research project. Data was collected from Ship ALPHA HELIX cruise HX-190. The data was collected over a period spanning from September 11, 1995 to October 8, 1995. Data was collected from 209 CTD stations by Institute of Marine Science, University of Alaska, Fairbanks, AK and was submitted by Dr Thomas Weingartner via File transfer Protocol in F022 file format of NODC. proprietary
-gov.noaa.nodc:9600025_Not Applicable AIR PRESSURE and Other Data from SHI YAN 3 From Antarctic and Others from 1992-11-09 to 1993-02-24 (NCEI Accession 9600025) ALL STAC Catalog 1992-11-09 1993-02-24 158, -2, 158, -2 https://cmr.earthdata.nasa.gov/search/concepts/C2089386973-NOAA_NCEI.umm_json The accession contains Surface Wave data and Sea Surface Temperature (SST) data collected as part of Tropical Ocean Global Atmosphere (TOGA) and Coupled Ocean-Atmosphere Response Experiment (COARE) International Project by a remote measuring buoy. The data was collected in Southern Oceans (> 60 degrees South), TOGA Area - Pacific (30 N to 30 S) from ship SHI YAN 3 between November 9, 1992 and February 24, 1993. Data was submitted by Chen Junchang of South China Sea Institute of Oceanology, Chinese Academy of Sciences. The data was made available by TOGA COARE International Project Office (TCIPO) via FTP. During the TOGA COARE Intensive Observing Period (IOP), the PRC R/V Shiyan #3 was stationed at 2 14'S, 158E for the three legs of data collection. Good format description accompanies the data. proprietary
gov.noaa.nodc:9600025_Not Applicable AIR PRESSURE and Other Data from SHI YAN 3 From Antarctic and Others from 1992-11-09 to 1993-02-24 (NCEI Accession 9600025) NOAA_NCEI STAC Catalog 1992-11-09 1993-02-24 158, -2, 158, -2 https://cmr.earthdata.nasa.gov/search/concepts/C2089386973-NOAA_NCEI.umm_json The accession contains Surface Wave data and Sea Surface Temperature (SST) data collected as part of Tropical Ocean Global Atmosphere (TOGA) and Coupled Ocean-Atmosphere Response Experiment (COARE) International Project by a remote measuring buoy. The data was collected in Southern Oceans (> 60 degrees South), TOGA Area - Pacific (30 N to 30 S) from ship SHI YAN 3 between November 9, 1992 and February 24, 1993. Data was submitted by Chen Junchang of South China Sea Institute of Oceanology, Chinese Academy of Sciences. The data was made available by TOGA COARE International Project Office (TCIPO) via FTP. During the TOGA COARE Intensive Observing Period (IOP), the PRC R/V Shiyan #3 was stationed at 2 14'S, 158E for the three legs of data collection. Good format description accompanies the data. proprietary
+gov.noaa.nodc:9600025_Not Applicable AIR PRESSURE and Other Data from SHI YAN 3 From Antarctic and Others from 1992-11-09 to 1993-02-24 (NCEI Accession 9600025) ALL STAC Catalog 1992-11-09 1993-02-24 158, -2, 158, -2 https://cmr.earthdata.nasa.gov/search/concepts/C2089386973-NOAA_NCEI.umm_json The accession contains Surface Wave data and Sea Surface Temperature (SST) data collected as part of Tropical Ocean Global Atmosphere (TOGA) and Coupled Ocean-Atmosphere Response Experiment (COARE) International Project by a remote measuring buoy. The data was collected in Southern Oceans (> 60 degrees South), TOGA Area - Pacific (30 N to 30 S) from ship SHI YAN 3 between November 9, 1992 and February 24, 1993. Data was submitted by Chen Junchang of South China Sea Institute of Oceanology, Chinese Academy of Sciences. The data was made available by TOGA COARE International Project Office (TCIPO) via FTP. During the TOGA COARE Intensive Observing Period (IOP), the PRC R/V Shiyan #3 was stationed at 2 14'S, 158E for the three legs of data collection. Good format description accompanies the data. proprietary
gov.noaa.nodc:9600039_Not Applicable Bacterial production, primary production, phytoplankton, zooplankton, biological analysis of fish, and massive fish length data from the EVRIKA and other platforms in the Antarctic from 23 February 1980 to 09 December 1988 (NCEI Accession 9600039) NOAA_NCEI STAC Catalog 1980-02-23 1988-12-09 -62.76, -63.98, -31.83, -50 https://cmr.earthdata.nasa.gov/search/concepts/C2089387013-NOAA_NCEI.umm_json Bacterial production, primary production, phytoplankton, zooplankton, biological analysis of fish, and massive fish length data were collected from the EVRIKA and other platforms in the Antarctic. Data were collected by the Atlantic Research Institute of Fishing Economy and Ocean from 23 February 1980 to 09 December 1988. proprietary
gov.noaa.nodc:9600065_Not Applicable BAROMETRIC PRESSURE and Other Data from THOMAS G. THOMPSON and Other Platforms From TOGA Area - Pacific (30 N to 30 S) from 1992-10-13 to 1992-12-13 (NCEI Accession 9600065) NOAA_NCEI STAC Catalog 1992-10-13 1992-12-13 -149.389635, -17.193678, -134.31313, 12.067383 https://cmr.earthdata.nasa.gov/search/concepts/C2089387122-NOAA_NCEI.umm_json The data in this accession was collected as part of Joint Global Ocean Flux Study/Equatorial Pacific Basin Study (JGOFS/EQPAC) in TOGA Area - Pacific (30 N to 30 S) using Ship THOMAS G. THOMPSON. CTD Data were collected by University of Washington, Seattle, WA between October 13, 1992 and December 13, 1992. Five Files of CTD data were submitted by Dr. Wilford Gardner. Good documentation accompanies this data. proprietary
gov.noaa.nodc:9600140_Not Applicable BAROMETRIC PRESSURE and Other Data from NOAA Ship ALBATROSS IV and Other Platforms From NW Atlantic (limit-40 W) from 1995-02-11 to 1995-07-20 (NCEI Accession 9600140) NOAA_NCEI STAC Catalog 1995-02-11 1995-07-20 -69.237, 40.413, -65.647, 42.335 https://cmr.earthdata.nasa.gov/search/concepts/C2089387550-NOAA_NCEI.umm_json Hydrochemical, hydrophysical, and other data were collected from the ENDEAVOR and NOAA Ship ALBATROSS IV from February 11, 1995 to July 20, 1995. Data were submitted by Dr. David Mountain from the US DOC; NOAA; NATIONAL MARINE FISHERIES SERVICE - WOODS HOLE. These data were collected using meteorological sensors, secchi disks, transmissometers, bottle casts, and CTD casts in the Northwest Atlantic Ocean. proprietary
@@ -19118,12 +19125,12 @@ gov.noaa.nodc:9600151_Not Applicable ABSORPTION, SCATTERING, ATTENUATION COEFFIC
gov.noaa.nodc:9700022_Not Applicable Chemical and temperature profile data from CTD casts in the East China Sea, Sea of Japan, and North Pacific Ocean (NCEI Accession 9700022) NOAA_NCEI STAC Catalog 123.066667, 3, 147.033333, 45.583333 https://cmr.earthdata.nasa.gov/search/concepts/C2089387774-NOAA_NCEI.umm_json Chemical and temperature profile data were collected from CTD casts in the East China Sea, Sea of Japan, and North Pacific Ocean. Data were submitted by the Japan Meteorological Agency (JMA). proprietary
gov.noaa.nodc:9700025_Not Applicable Chemical, physical, and other data collected using fluorometer, laboratory analysis, visual analysis, and bottle casts from NOAA Ship DAVID STARR JORDAN and NEW HORIZON as part of the California Cooperative Fisheries Investigation (CALCOFI) project, from 1994-01-21 to 1996-04-30 (NCEI Accession 9700025) NOAA_NCEI STAC Catalog 1994-01-21 1996-04-30 -124.3, 29.9, -117.3, 35.1 https://cmr.earthdata.nasa.gov/search/concepts/C2089387805-NOAA_NCEI.umm_json Chemical, physical, and other data were collected from NOAA Ship DAVID STARR JORDAN and NEW HORIZON from January 21, 1994 to April 30, 1996. Data were collected using fluorometer, laboratory analysis, visual analysis, and bottle casts in the Northeast Pacific Ocean. Data were submitted by Scripps Institution of Oceanography (SIO) as part of the California Cooperative Fisheries Investigation (CALCOFI) project. proprietary
gov.noaa.nodc:9700040_Not Applicable Chemical, physical, and other data collected using bottle casts from NOAA Ship DAVID STARR JORDAN and NEW HORIZON as part of the California Cooperative Fisheries Investigation (CALCOFI) project, from 1995-01-04 to 1996-05-03 (NCEI Accession 9700040) NOAA_NCEI STAC Catalog 1995-01-04 1996-05-03 -124.326667, 30.16, -117.303333, 35.09 https://cmr.earthdata.nasa.gov/search/concepts/C2089387897-NOAA_NCEI.umm_json Chemical, physical, and other data were collected from NOAA Ship DAVID STARR JORDAN and NEW HORIZON from January 4, 1995 to May 3, 1996. Data were collected using bottle casts from the Northeast Pacific Ocean. Data were submitted by Scripps Institution of Oceanography (SIO) as part of the California Cooperative Fisheries Investigation (CALCOFI) project. proprietary
-gov.noaa.nodc:9700063_Not Applicable AIR PRESSURE and Other Data from NOODIN From Great Lakes from 1995-06-20 to 1996-11-14 (NCEI Accession 9700063) NOAA_NCEI STAC Catalog 1995-06-20 1996-11-14 -91.7, 47, -91.7, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2089388236-NOAA_NCEI.umm_json Conductivity, temperature, depth, pressure, transmissivity, and fluorsecence were collected from the NOODIN from June 20, 1995 to October 26, 1995 and May 30, 1996 to November 14, 1996. Data were submitted by Dr. Elise A. Ralph from the University of Minnesota; Large Lakes Observatory. These data were collected using transmissometer, fluorometer, and CTD casts in the Two Harbors, MN to Port Wing, WI on the Lake Superior. proprietary
gov.noaa.nodc:9700063_Not Applicable AIR PRESSURE and Other Data from NOODIN From Great Lakes from 1995-06-20 to 1996-11-14 (NCEI Accession 9700063) ALL STAC Catalog 1995-06-20 1996-11-14 -91.7, 47, -91.7, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2089388236-NOAA_NCEI.umm_json Conductivity, temperature, depth, pressure, transmissivity, and fluorsecence were collected from the NOODIN from June 20, 1995 to October 26, 1995 and May 30, 1996 to November 14, 1996. Data were submitted by Dr. Elise A. Ralph from the University of Minnesota; Large Lakes Observatory. These data were collected using transmissometer, fluorometer, and CTD casts in the Two Harbors, MN to Port Wing, WI on the Lake Superior. proprietary
+gov.noaa.nodc:9700063_Not Applicable AIR PRESSURE and Other Data from NOODIN From Great Lakes from 1995-06-20 to 1996-11-14 (NCEI Accession 9700063) NOAA_NCEI STAC Catalog 1995-06-20 1996-11-14 -91.7, 47, -91.7, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2089388236-NOAA_NCEI.umm_json Conductivity, temperature, depth, pressure, transmissivity, and fluorsecence were collected from the NOODIN from June 20, 1995 to October 26, 1995 and May 30, 1996 to November 14, 1996. Data were submitted by Dr. Elise A. Ralph from the University of Minnesota; Large Lakes Observatory. These data were collected using transmissometer, fluorometer, and CTD casts in the Two Harbors, MN to Port Wing, WI on the Lake Superior. proprietary
gov.noaa.nodc:9700115_Not Applicable Chemical and temperature profile data from bottle and CTD casts in the Pacific Ocean as part of the Joint Global Ocean Flux Study/Equatorial Pacific Basin Study (JGOFS/EQPAC) project, from 1992-03-19 to 1992-10-21 (NCEI Accession 9700115) NOAA_NCEI STAC Catalog 1992-03-19 1992-10-21 -145.489, -12, -134.9117, 12.0317 https://cmr.earthdata.nasa.gov/search/concepts/C2089388395-NOAA_NCEI.umm_json Chemical and temperature profile data were collected using bottle and CTD casts from the THOMAS THOMPSON in the Pacific Ocean from March 19, 1992 to October 21, 1992. Data were collected three different universities and a institution; Oregon State University, University of Washington, Woods Hole Oceanographic Institution, and University of Maryland; Horn Point Environmental Laboratory as part of the Joint Global Ocean Flux Study/Equatorial Pacific Basin Study (JGOFS/EQPAC) project. proprietary
gov.noaa.nodc:9700116_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from THOMAS G. THOMPSON From TOGA Area - Pacific (30 N to 30 S) from 1992-03-19 to 1992-10-21 (NCEI Accession 9700116) NOAA_NCEI STAC Catalog 1992-03-19 1992-10-21 -145, -12, -140, 0 https://cmr.earthdata.nasa.gov/search/concepts/C2089388417-NOAA_NCEI.umm_json Not provided proprietary
-gov.noaa.nodc:9700205_Not Applicable AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1992-02-02 to 1992-10-21 (NCEI Accession 9700205) NOAA_NCEI STAC Catalog 1992-02-02 1992-10-21 -146.293, -12.864, -104.392, 2.999 https://cmr.earthdata.nasa.gov/search/concepts/C2089388823-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9700205_Not Applicable AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1992-02-02 to 1992-10-21 (NCEI Accession 9700205) ALL STAC Catalog 1992-02-02 1992-10-21 -146.293, -12.864, -104.392, 2.999 https://cmr.earthdata.nasa.gov/search/concepts/C2089388823-NOAA_NCEI.umm_json Not provided proprietary
+gov.noaa.nodc:9700205_Not Applicable AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1992-02-02 to 1992-10-21 (NCEI Accession 9700205) NOAA_NCEI STAC Catalog 1992-02-02 1992-10-21 -146.293, -12.864, -104.392, 2.999 https://cmr.earthdata.nasa.gov/search/concepts/C2089388823-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9700207_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from THOMAS G. THOMPSON from 1992-02-04 to 1992-09-12 (NCEI Accession 9700207) NOAA_NCEI STAC Catalog 1992-02-04 1992-09-12 -140.865, -12.1793, -134.7875, 12.0317 https://cmr.earthdata.nasa.gov/search/concepts/C2089388838-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9700208_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from THOMAS G. THOMPSON from 1992-02-08 to 1992-09-14 (NCEI Accession 9700208) NOAA_NCEI STAC Catalog 1992-02-08 1992-09-14 -140.9418, -12.035, -134.953, 8.9933 https://cmr.earthdata.nasa.gov/search/concepts/C2089388854-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9700210_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from THOMAS G. THOMPSON from 1992-02-04 to 1992-09-10 (NCEI Accession 9700210) NOAA_NCEI STAC Catalog 1992-02-04 1992-09-10 -140.0498, -12.0082, -134.9867, 12.0133 https://cmr.earthdata.nasa.gov/search/concepts/C2089388862-NOAA_NCEI.umm_json Not provided proprietary
@@ -19131,8 +19138,8 @@ gov.noaa.nodc:9700238_Not Applicable BACTERIA - BACTERIAL DENSITY and Other Data
gov.noaa.nodc:9800027_Not Applicable BAROMETRIC PRESSURE and Other Data from LITTLE DIPPER from 1995-03-01 to 1998-02-06 (NCEI Accession 9800027) NOAA_NCEI STAC Catalog 1995-03-01 1998-02-06 -149.5, 59.8, -149.4, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2089385859-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9800037_Not Applicable Chemical, temperature, pressure, and salinity data from bottle and CTD casts in the Arabian Sea as part of the Joint Global Ocean Flux Study / Arabian Sea Process Studies (JGOFS/Arabian) project, from 1995-07-17 to 1995-09-15 (NCEI Accession 9800037) NOAA_NCEI STAC Catalog 1995-07-17 1995-09-15 57.2998, 9.9113, 68.751, 22.527 https://cmr.earthdata.nasa.gov/search/concepts/C2089385946-NOAA_NCEI.umm_json Chemical, temperature, pressure, and salinity data were collected using bottle and CTD casts from the R/V Thomas G. Thompson in the Arabian Sea. Data were collected from July 17, 1995 to September 15, 1995. Data were collected by four different institution; Old Dominion University, Bermuda Biological Station for Research, Virginia Institute of Marine Science, and Woods Hole Oceanographic Institution as part of the Joint Global Ocean Flux Study / Arabian Sea Process Studies (JGOFS/Arabian) project. proprietary
gov.noaa.nodc:9800052_Not Applicable BENTHIC SPECIES and Other Data from UNKNOWN and Other Platforms from 1989-01-01 to 1997-12-31 (NCEI Accession 9800052) NOAA_NCEI STAC Catalog 1989-01-01 1997-12-31 -123.6, 47.1, -122.4, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2089386070-NOAA_NCEI.umm_json Not provided proprietary
-gov.noaa.nodc:9800085_Not Applicable AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1995-01-09 to 1995-12-28 (NCEI Accession 9800085) NOAA_NCEI STAC Catalog 1995-01-09 1995-12-28 56.5, 9.9, 68.8, 24.1 https://cmr.earthdata.nasa.gov/search/concepts/C2089386309-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9800085_Not Applicable AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1995-01-09 to 1995-12-28 (NCEI Accession 9800085) ALL STAC Catalog 1995-01-09 1995-12-28 56.5, 9.9, 68.8, 24.1 https://cmr.earthdata.nasa.gov/search/concepts/C2089386309-NOAA_NCEI.umm_json Not provided proprietary
+gov.noaa.nodc:9800085_Not Applicable AIR PRESSURE and Other Data from THOMAS G. THOMPSON from 1995-01-09 to 1995-12-28 (NCEI Accession 9800085) NOAA_NCEI STAC Catalog 1995-01-09 1995-12-28 56.5, 9.9, 68.8, 24.1 https://cmr.earthdata.nasa.gov/search/concepts/C2089386309-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9800092_Not Applicable BACTERIA - BACTERIAL DENSITY and Other Data from USS CHAUMONT from 1995-01-09 to 1995-12-26 (NCEI Accession 9800092) NOAA_NCEI STAC Catalog 1995-01-09 1995-12-26 57.3, 9.3, 68.8, 22.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089386381-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9800095_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from THOMAS G. THOMPSON from 1995-01-08 to 1995-09-12 (NCEI Accession 9800095) NOAA_NCEI STAC Catalog 1995-01-08 1995-09-12 57.3, 10, 68.8, 22.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089386411-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9800118_Not Applicable Chemical, physical, and other data collected using bottle casts from NOAA Ship DAVID STARR JORDAN, ROGER REVILLE, and NEW HORIZON as part of the California Cooperative Fisheries Investigation from 1996-08-07 to 1997-04-19 (NCEI Accession 9800118) NOAA_NCEI STAC Catalog 1996-08-07 1997-04-19 -124.3, 29.8, -117.3, 35.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089386498-NOAA_NCEI.umm_json Chemical, physical, and other data were collected from NOAA Ship DAVID STARR JORDAN, ROGER REVILLE, and NEW HORIZON from August 7, 1996 to April 19, 1997. Data were collected using bottle casts in the Pacific Ocean. Data were submitted by Scripps Institution of Oceanography (SIO) as part of the California Cooperative Fisheries Investigation (CALCOFI) project. proprietary
@@ -19142,8 +19149,8 @@ gov.noaa.nodc:9800123_Not Applicable AIR PRESSURE and Other Data from FIXED PLAT
gov.noaa.nodc:9800129_Not Applicable Chemical, zooplankton, and phytoplankton data from CTD and other instruments in the Mississippi River and Gulf of Mexico as part of the Nutrient Enhanced Coastal Ocean Productivity (NECOP) project, from 1985-07-15 to 1993-05-12 (NCEI Accession 9800129) NOAA_NCEI STAC Catalog 1985-07-15 1993-05-12 -90.28, 28.52, -89.41, 29.7 https://cmr.earthdata.nasa.gov/search/concepts/C2089386593-NOAA_NCEI.umm_json Chemical, zooplankton, and phytoplankton data were collected using bottle, CTD, fluorometer, oxygen meter, GPS, plankton trap, and sediment sampler from NOAA Ship MALCOLM BALDRIGE and NOAA Ship RESEARCHER. Data were collected from the Mississippi River and Gulf of Mexico from July 15, 1985 to May 12, 1993. Data were submitted by Dr. Nancy Rabalais from the Louisiana Universities Marine Consortium as part of the Nutrient Enhanced Coastal Ocean Productivity (NECOP) project. proprietary
gov.noaa.nodc:9800160_Not Applicable Chemical data collected from THOMAS G. THOMPSON using CTD and bottle casts in Arabian Sea from 1995-03-07 to 1995-08-15 (NCEI Accession 9800160) NOAA_NCEI STAC Catalog 1995-03-07 1995-08-15 57, 9, 68, 22 https://cmr.earthdata.nasa.gov/search/concepts/C2089386883-NOAA_NCEI.umm_json Chemical data were collected using CTD and bottle casts in the Arabian Sea from THOMAS G. THOMPSON. Data were collected from 07 March 1995 to 15 August 1995 by Lamont-Doherty Earth Observatory with support from the U.S. Joint Global Ocean Flux Study / Arabian Sea Process Studies (JOGFS/Arabian Sea) project. proprietary
gov.noaa.nodc:9800161_Not Applicable Chemical data collected from THOMAS G. THOMPSON using CTD and bottle casts in Arabian Sea from 1995-01-08 to 1995-11-26 (NCEI Accession 9800161) NOAA_NCEI STAC Catalog 1995-01-08 1995-11-26 56, 9, 68, 23 https://cmr.earthdata.nasa.gov/search/concepts/C2089386911-NOAA_NCEI.umm_json Chemical data were collected using CTD and bottle casts in the Arabian Sea from THOMAS G. THOMPSON. Data were collected from 08 January 1995 to 26 November 1995 by Harvard University with support from the U.S. Joint Global Ocean Flux Study / Arabian Sea Process Studies (JOGFS/Arabian Sea) project. proprietary
-gov.noaa.nodc:9800197_Not Applicable Algal species and other data collected using photographs in the southern coast of the island of Ofu from 1992-09-08 to 1992-09-11 (NCEI Accession 9800197) ALL STAC Catalog 1992-09-08 1992-09-11 -169.7, -14.2, -169.7, -14.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089387161-NOAA_NCEI.umm_json The US Congress has authorized the Department of the Interior to enter into a lease agreement with the Governor of American Samoa to establish the National Park of American Samoa. This park would include a nearshore reef along the southern coast of the island of Ofu. This fringing reef on Ofu provides a natural lagoon habitat which is uncommon in American Samoa. This area supports a local subsistence fishery and provides excellent opportunities for diving and snorkeling. A survey of the nearshore reefs in the area of the proposed national park at Ofu was conducted between 7-12 September, 1992. The goals of the survey were to: 1) collect baseline data on the current status of the reefs and reef resources in the area, 2) to establish long-term monitoring stations to enable documentation of the health of the reef communities through time, and 3) to contribute information to a comprehensive coastal resource survey of Tutuila and the Manua Islands. The overall purpose of the work was to design and implement the biotic component of a reef monitoring program for the areas within and adjacent to the proposed national park site. proprietary
gov.noaa.nodc:9800197_Not Applicable Algal species and other data collected using photographs in the southern coast of the island of Ofu from 1992-09-08 to 1992-09-11 (NCEI Accession 9800197) NOAA_NCEI STAC Catalog 1992-09-08 1992-09-11 -169.7, -14.2, -169.7, -14.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089387161-NOAA_NCEI.umm_json The US Congress has authorized the Department of the Interior to enter into a lease agreement with the Governor of American Samoa to establish the National Park of American Samoa. This park would include a nearshore reef along the southern coast of the island of Ofu. This fringing reef on Ofu provides a natural lagoon habitat which is uncommon in American Samoa. This area supports a local subsistence fishery and provides excellent opportunities for diving and snorkeling. A survey of the nearshore reefs in the area of the proposed national park at Ofu was conducted between 7-12 September, 1992. The goals of the survey were to: 1) collect baseline data on the current status of the reefs and reef resources in the area, 2) to establish long-term monitoring stations to enable documentation of the health of the reef communities through time, and 3) to contribute information to a comprehensive coastal resource survey of Tutuila and the Manua Islands. The overall purpose of the work was to design and implement the biotic component of a reef monitoring program for the areas within and adjacent to the proposed national park site. proprietary
+gov.noaa.nodc:9800197_Not Applicable Algal species and other data collected using photographs in the southern coast of the island of Ofu from 1992-09-08 to 1992-09-11 (NCEI Accession 9800197) ALL STAC Catalog 1992-09-08 1992-09-11 -169.7, -14.2, -169.7, -14.2 https://cmr.earthdata.nasa.gov/search/concepts/C2089387161-NOAA_NCEI.umm_json The US Congress has authorized the Department of the Interior to enter into a lease agreement with the Governor of American Samoa to establish the National Park of American Samoa. This park would include a nearshore reef along the southern coast of the island of Ofu. This fringing reef on Ofu provides a natural lagoon habitat which is uncommon in American Samoa. This area supports a local subsistence fishery and provides excellent opportunities for diving and snorkeling. A survey of the nearshore reefs in the area of the proposed national park at Ofu was conducted between 7-12 September, 1992. The goals of the survey were to: 1) collect baseline data on the current status of the reefs and reef resources in the area, 2) to establish long-term monitoring stations to enable documentation of the health of the reef communities through time, and 3) to contribute information to a comprehensive coastal resource survey of Tutuila and the Manua Islands. The overall purpose of the work was to design and implement the biotic component of a reef monitoring program for the areas within and adjacent to the proposed national park site. proprietary
gov.noaa.nodc:9800199_Not Applicable BACTERIA - BACTERIAL DENSITY and Other Data from HERMANO GINES from 1996-07-09 to 1997-07-09 (NCEI Accession 9800199) NOAA_NCEI STAC Catalog 1996-07-09 1997-07-09 -64.7, 10.5, -64.7, 10.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089387176-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9900010_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from THOMAS G. THOMPSON From Arabian Sea from 1995-03-18 to 1997-08-13 (NCEI Accession 9900010) NOAA_NCEI STAC Catalog 1995-03-18 1997-08-13 56.5, 10, 68.8, 24.3 https://cmr.earthdata.nasa.gov/search/concepts/C2089387251-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9900014_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from THOMAS G. THOMPSON From Arabian Sea from 1995-01-09 to 1995-09-12 (NCEI Accession 9900014) NOAA_NCEI STAC Catalog 1995-01-09 1995-09-12 57.3, 10, 68.8, 22.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089387273-NOAA_NCEI.umm_json Not provided proprietary
@@ -19157,8 +19164,8 @@ gov.noaa.nodc:9900094_Not Applicable AIR PRESSURE and Other Data from FIXED PLAT
gov.noaa.nodc:9900119_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-05-01 to 1999-06-30 (NCEI Accession 9900119) NOAA_NCEI STAC Catalog 1999-05-01 1999-06-30 -124, 44.6, -124, 44.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089388259-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9900119_Not Applicable AIR PRESSURE and Other Data from FIXED PLATFORM from 1999-05-01 to 1999-06-30 (NCEI Accession 9900119) ALL STAC Catalog 1999-05-01 1999-06-30 -124, 44.6, -124, 44.6 https://cmr.earthdata.nasa.gov/search/concepts/C2089388259-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9900158_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from OCEANUS and Other Platforms from 1993-03-12 to 1993-03-23 (NCEI Accession 9900158) NOAA_NCEI STAC Catalog 1993-03-12 1993-03-23 -67.2, 31.7, -64.1, 36.8 https://cmr.earthdata.nasa.gov/search/concepts/C2089388472-NOAA_NCEI.umm_json Not provided proprietary
-gov.noaa.nodc:9900159_Not Applicable 1999 Field Season CTD, chlorophyll A and transmissivity data from the CRETM and LMER Projects in the Columbia River and Frasier River estuaries, 19990616 to 19990718 (NCEI Accession 9900159) ALL STAC Catalog 1999-06-16 1999-07-18 -124, 45, -122, 49.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089388479-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9900159_Not Applicable 1999 Field Season CTD, chlorophyll A and transmissivity data from the CRETM and LMER Projects in the Columbia River and Frasier River estuaries, 19990616 to 19990718 (NCEI Accession 9900159) NOAA_NCEI STAC Catalog 1999-06-16 1999-07-18 -124, 45, -122, 49.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089388479-NOAA_NCEI.umm_json Not provided proprietary
+gov.noaa.nodc:9900159_Not Applicable 1999 Field Season CTD, chlorophyll A and transmissivity data from the CRETM and LMER Projects in the Columbia River and Frasier River estuaries, 19990616 to 19990718 (NCEI Accession 9900159) ALL STAC Catalog 1999-06-16 1999-07-18 -124, 45, -122, 49.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089388479-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9900164_Not Applicable BACTERIA - BACTERIAL DENSITY and Other Data from NATHANIEL B. PALMER from 1996-10-08 to 1997-05-05 (NCEI Accession 9900164) NOAA_NCEI STAC Catalog 1996-10-08 1997-05-05 168.9, -78, -175.9, -74 https://cmr.earthdata.nasa.gov/search/concepts/C2089388517-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9900202_Not Applicable BACTERIA - BACTERIAL DENSITY and Other Data from HERMANO GINES from 1995-11-13 to 1997-11-14 (NCEI Accession 9900202) NOAA_NCEI STAC Catalog 1995-11-13 1997-11-14 -64.7, 10.5, -64.7, 10.5 https://cmr.earthdata.nasa.gov/search/concepts/C2089388797-NOAA_NCEI.umm_json Not provided proprietary
gov.noaa.nodc:9900218_Not Applicable CAS (CHEMICAL ABSTRACTS SOCIETY) PARAMETER CODES and Other Data from NATHANIEL B. PALMER from 1996-10-18 to 1997-02-08 (NCEI Accession 9900218) NOAA_NCEI STAC Catalog 1996-10-18 1997-02-08 169, -78, -176, -76.4 https://cmr.earthdata.nasa.gov/search/concepts/C2089388860-NOAA_NCEI.umm_json Not provided proprietary
@@ -19679,8 +19686,8 @@ inishell-2-0-4_2.0.4 Inishell-2.0.4 ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5
inpe_CPTEC_GLOBAl_FORECAST Global Meteorological Model Analysis and Forecast Images (INPE/CPTEC) CEOS_EXTRA STAC Catalog 1970-01-01 -120, -60, 0, 30 https://cmr.earthdata.nasa.gov/search/concepts/C2227456094-CEOS_EXTRA.umm_json "CPTEC offers global model analysis and forecast images for twelve meteorological parameters. Forecast time steps range from the initial analysis each day out to six days. The user may choose forecasts from the most recent forecast run back to the previous 36 hours. Parameters Forecasted: Mean Sea Level Pressure Temperature at 1000 hPa Relative Humidity at 925 hPa, 850 hPa Vertical p_Velocity at 850 hPa, 500 hPa, 200 hPa Velocity Potential at 925 hPa, 200 hPa Stream Function at 925 hPa, 200 hPa 500/1000 hPa Thickness Advection of Temperature at 925 hPa, 850 hPa, 500 hPa Advection of Vorticity at 925 hPa, 850 hPa, 500 hPa Convergence of Humidity Flux at 925 hPa, 850 hPa Streamlines and Wind Speed at 925 hPa, 850 hPa, 200 hPa Total Precipitation Last 24 Hours All forecast images can be obtained via World Wide Web from the CPTEC Home Page. Link to: ""http://www.cptec.inpe.br/""" proprietary
input-data-for-break-point-detection-of-swiss-snow-depth-time-series_1.0 Input data for break point detection of Swiss snow depth time series ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815138-ENVIDAT.umm_json Data set consists of monthly mean values for snow depth and days with snow on the ground intended for the use of break detection with ACMANT, Climatol and HOMER. List and coordinates of stations used as well as metadata and break detection results from all three methods is included. ## Columns Monthly means for each hydrological year: Nov, Dec, Jan, Feb, Mar, Apr with May to Oct set to zero proprietary
input-data-for-impact-assessment-of-homogenised-snow-series_1.0 Input data for impact assessment of homogenised snow series ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226082287-ENVIDAT.umm_json # Input data for the following research article: Impact assessment of homogenised snow depth series on trends The data consists of separate output files from the following homogenisation methods: * Climatol * HOMER * interpQM The variable is seasonal mean snow depth (HSavg) plot.data is an additional data frame containing trends of HSavg (station, method, value, pvalue, altitude) proprietary
-insects_subsaharanAfrica A Checklist of the Insects of Subsaharan Africa ALL STAC Catalog 2000-01-01 13.68, -35.9, 33.98, -21.27 https://cmr.earthdata.nasa.gov/search/concepts/C1214611706-SCIOPS.umm_json "One of the most basic needs for inventorying, exploiting and monitoring the changes in the insect diversity of Africa is a complete list of species which are already know to occur in Africa. Surprisingly, such a basic list does not exist, despite some 250 years of formal scientific description of life on earth. The International Centre of Insect Physiology and Ecology (ICIPE), along with the National Museum of Natural History, is therefore sponsoring the production of the list, which will provide a reliable platform of 'standard' names for species on which many other projects depend. This list, or authority file, will greatly enhance communication both among scientists and between scientists and users of scientific data. The African list will also be a major contribution toward the proposed list of world species (e.g. the Global Biodiversity Information Facility (GBIF) and Species 2000 initiative of DIVERSITAS). A demonstration database is provided for the species of the orders Odonata (dragonflies and damselflies), Ephemeroptera (mayflies), Plecoptera (stoneflies), Megaloptera (alderflies), Hemiptera-Heteroptera (true bugs), Homoptera (cicadas, leafhoppers, planthoppers, scales, and others), and Trichoptera (caddisflies). Invitation to collaboration: Compilation of the checklist is being coordinated by Nearctica (formerly Entomological Information Specialists), because of their experience with Nomina Insecta Nearctica. We are attempting to collaborate with known specialists as contributors and reviewers, but we welcome additional suggestions of collaborators. Inquires can be directed to Scott Miller (miller.scott@nmnh.si.edu). Information was obtained from ""http://entomology.si.edu/""." proprietary
insects_subsaharanAfrica A Checklist of the Insects of Subsaharan Africa SCIOPS STAC Catalog 2000-01-01 13.68, -35.9, 33.98, -21.27 https://cmr.earthdata.nasa.gov/search/concepts/C1214611706-SCIOPS.umm_json "One of the most basic needs for inventorying, exploiting and monitoring the changes in the insect diversity of Africa is a complete list of species which are already know to occur in Africa. Surprisingly, such a basic list does not exist, despite some 250 years of formal scientific description of life on earth. The International Centre of Insect Physiology and Ecology (ICIPE), along with the National Museum of Natural History, is therefore sponsoring the production of the list, which will provide a reliable platform of 'standard' names for species on which many other projects depend. This list, or authority file, will greatly enhance communication both among scientists and between scientists and users of scientific data. The African list will also be a major contribution toward the proposed list of world species (e.g. the Global Biodiversity Information Facility (GBIF) and Species 2000 initiative of DIVERSITAS). A demonstration database is provided for the species of the orders Odonata (dragonflies and damselflies), Ephemeroptera (mayflies), Plecoptera (stoneflies), Megaloptera (alderflies), Hemiptera-Heteroptera (true bugs), Homoptera (cicadas, leafhoppers, planthoppers, scales, and others), and Trichoptera (caddisflies). Invitation to collaboration: Compilation of the checklist is being coordinated by Nearctica (formerly Entomological Information Specialists), because of their experience with Nomina Insecta Nearctica. We are attempting to collaborate with known specialists as contributors and reviewers, but we welcome additional suggestions of collaborators. Inquires can be directed to Scott Miller (miller.scott@nmnh.si.edu). Information was obtained from ""http://entomology.si.edu/""." proprietary
+insects_subsaharanAfrica A Checklist of the Insects of Subsaharan Africa ALL STAC Catalog 2000-01-01 13.68, -35.9, 33.98, -21.27 https://cmr.earthdata.nasa.gov/search/concepts/C1214611706-SCIOPS.umm_json "One of the most basic needs for inventorying, exploiting and monitoring the changes in the insect diversity of Africa is a complete list of species which are already know to occur in Africa. Surprisingly, such a basic list does not exist, despite some 250 years of formal scientific description of life on earth. The International Centre of Insect Physiology and Ecology (ICIPE), along with the National Museum of Natural History, is therefore sponsoring the production of the list, which will provide a reliable platform of 'standard' names for species on which many other projects depend. This list, or authority file, will greatly enhance communication both among scientists and between scientists and users of scientific data. The African list will also be a major contribution toward the proposed list of world species (e.g. the Global Biodiversity Information Facility (GBIF) and Species 2000 initiative of DIVERSITAS). A demonstration database is provided for the species of the orders Odonata (dragonflies and damselflies), Ephemeroptera (mayflies), Plecoptera (stoneflies), Megaloptera (alderflies), Hemiptera-Heteroptera (true bugs), Homoptera (cicadas, leafhoppers, planthoppers, scales, and others), and Trichoptera (caddisflies). Invitation to collaboration: Compilation of the checklist is being coordinated by Nearctica (formerly Entomological Information Specialists), because of their experience with Nomina Insecta Nearctica. We are attempting to collaborate with known specialists as contributors and reviewers, but we welcome additional suggestions of collaborators. Inquires can be directed to Scott Miller (miller.scott@nmnh.si.edu). Information was obtained from ""http://entomology.si.edu/""." proprietary
instm_trawl National Institute of Marine Sciences and Technologies - Trawling Surveys CEOS_EXTRA STAC Catalog 1983-04-16 2006-11-03 5.14, 17.1, 13.37, 38.1 https://cmr.earthdata.nasa.gov/search/concepts/C2232477692-CEOS_EXTRA.umm_json The National Institute of Marine Sciences and Technologies (INSTM) fo Tunisia has four laboratories. Regular trawl surveys are done by the Laboratory of Marine Living Resources to assess the exploitable resource stocks. This dataset consists of 7664 records of 90 families. proprietary
intercomparison-of-photogrammetric-platforms_1.0 Photogrammetric snow depth maps from satellite-, airplane-, UAS and terrestrial platforms from the Davos region (Switzerland) ENVIDAT STAC Catalog 2020-01-01 2020-01-01 9.7544861, 46.6485877, 10.0428772, 46.844319 https://cmr.earthdata.nasa.gov/search/concepts/C2789815195-ENVIDAT.umm_json "This data set contains the produced snow depth maps as well as the reference data set (manual and snow pole measurements) from our paper ""Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping"". __Abstract.__ Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability of snow depths present in alpine terrain. Photogrammetric mapping techniques have progressed in recent years and are capable of accurately mapping snow depth in a spatially continuous manner, over larger areas, and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques, as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellites (Pléiades), airplane (Ultracam Eagle M3), Unmanned Aerial System (eBee+ with S.O.D.A. camera) and terrestrial (single lens reflex camera, Canon EOS 750D), were tested for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while Unmanned Aerial Systems (UAS) and terrestrial photogrammetric imagery was acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing, as well as using remote observations of fixed snow poles. When comparing snow depth maps with manual and snow pole measurements the root mean square error (RMSE) values and the normalized median deviation (NMAD) values were 0.52 m and 0.47 m respectively for the satellite snow depth map, 0.17 m and 0.17 m for the airplane snow depth map, 0.16 m and 0.11 m for the UAS snow depth map. The area covered by the terrestrial snow depth map only intersected with 4 manual measurements and did not generate statistically relevant measurements. When using the UAS snow depth map as a reference surface, the RMSE and NMAD values were 0.44 m and 0.38 m for the satellite snow depth map, 0.12 m and 0.11 m for the airplane snow depth map, 0.21 and 0.19 m for the terrestrial snow depth map. When compared to the airplane dataset over a large part of the Dischma valley (40 km2), the snow depth map from the satellite yielded a RMSE value of 0.92 m and a NMAD value of 0.65 m. This study provides comparative measurements between photogrammetric platforms to evaluate their specific advantages and disadvantages for operational, spatially continuous snow depth mapping in alpine terrain over both small and large geographic areas." proprietary
interview-guide-and-transcripts_1.0 Interview guide and transcripts (CONCUR Aim 2 on Governance) ENVIDAT STAC Catalog 2021-01-01 2021-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815227-ENVIDAT.umm_json This dataset is composed of an interview guide used to conduct 43 in-depth, qualitative, and in-person interviews with planning experts, academics and practitioners, in 14 European urban regions and the corresponding interview transcripts (verbatim). These interviews were conducted in the selected urban regions between March and September 2016. They were first digitally recorded and later thoroughly transcribed. proprietary
@@ -19699,8 +19706,8 @@ jornada_albedo_667_1 PROVE Surface albedo of Jornada Experimental Range, New Mex
jornada_canopy_brf_668_1 PROVE Vegetation Reflectance of Jornada Experimental Range, New Mexico, 1997 ORNL_CLOUD STAC Catalog 1997-05-23 1997-05-28 -106.75, 32.5, -106.75, 32.5 https://cmr.earthdata.nasa.gov/search/concepts/C2804797176-ORNL_CLOUD.umm_json Directional reflected radiation was measured over plots representing selected canopy components (shrubs and individual plants, bare sand, and background) at the Jornada Experiment Range site near Las Cruces, New Mexico, during the Prototype Validation Experiment (PROVE) in May 1997. proprietary
jornada_landcover_lai_665_1 PROVE Land Cover and Leaf Area of Jornada Experimental Range, New Mexico, 1997 ORNL_CLOUD STAC Catalog 1997-05-13 1997-05-31 -106.75, 32.5, -106.75, 32.5 https://cmr.earthdata.nasa.gov/search/concepts/C2804794793-ORNL_CLOUD.umm_json Field measurement of shrubland ecological properties is important for both site monitoring and validation of remote-sensing information. During the PROVE exercise on May 20-30, 1997, we calculated plot-level plant area index, leaf area index, total fractional cover, and green fractional cover. proprietary
jornada_mquals_666_1 PROVE MQUALS Reflectance at Jornada Experimental Range, New Mexico, 1997 ORNL_CLOUD STAC Catalog 1997-05-23 1997-05-25 -106.75, 32.5, -106.75, 32.5 https://cmr.earthdata.nasa.gov/search/concepts/C2804795305-ORNL_CLOUD.umm_json This study utilized low flying, aircraft-based radiometers for optical characterization of top-of-the-canopy reflectance at Jornada Experimental Range in New Mexico during the Prototype Validation Experiment (PROVE) in May 1997. The objective was to examine the usefulness of low-flying aircraft for Moderate Resolution Imaging Spectroradiometer (MODIS) validation of land products. proprietary
-joughin_0631973 Airborne Radar-Derived Accumulation Rates over Pine Island and Thwaites Glaciers SCIOPS STAC Catalog 1980-01-01 2009-12-31 -124.8, -80.8, -86.7, -73.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214600138-SCIOPS.umm_json "This data set includes radar-derived annual accumulation rates over Thwaites Glacier between 1980 and 2009 and a gridded climatology (1985-2009) of snow accumulation over Pine Island and Thwaites Glaciers. The snow radar data were collected between 2009 and 2011 as part of NASA's Operation IceBridge Mission and are available at the NSIDC under ""IceBridge Snow Radar L1B Geolocated Radar Echo Strength Profiles""." proprietary
joughin_0631973 Airborne Radar-Derived Accumulation Rates over Pine Island and Thwaites Glaciers ALL STAC Catalog 1980-01-01 2009-12-31 -124.8, -80.8, -86.7, -73.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214600138-SCIOPS.umm_json "This data set includes radar-derived annual accumulation rates over Thwaites Glacier between 1980 and 2009 and a gridded climatology (1985-2009) of snow accumulation over Pine Island and Thwaites Glaciers. The snow radar data were collected between 2009 and 2011 as part of NASA's Operation IceBridge Mission and are available at the NSIDC under ""IceBridge Snow Radar L1B Geolocated Radar Echo Strength Profiles""." proprietary
+joughin_0631973 Airborne Radar-Derived Accumulation Rates over Pine Island and Thwaites Glaciers SCIOPS STAC Catalog 1980-01-01 2009-12-31 -124.8, -80.8, -86.7, -73.9 https://cmr.earthdata.nasa.gov/search/concepts/C1214600138-SCIOPS.umm_json "This data set includes radar-derived annual accumulation rates over Thwaites Glacier between 1980 and 2009 and a gridded climatology (1985-2009) of snow accumulation over Pine Island and Thwaites Glaciers. The snow radar data were collected between 2009 and 2011 as part of NASA's Operation IceBridge Mission and are available at the NSIDC under ""IceBridge Snow Radar L1B Geolocated Radar Echo Strength Profiles""." proprietary
kakqimpacts_1 KAKQ NEXRAD IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-01 2020-03-01 -82.1814, 32.8531, -71.8333, 41.115 https://cmr.earthdata.nasa.gov/search/concepts/C1995580744-GHRC_DAAC.umm_json The KAKQ NEXRAD IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) Level II surveillance data that were collected from January 1 to March 1, 2020 during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. There are currently 160 Weather Surveillance Radar-1988 Doppler (WSR-88D) or NEXRAD sites throughout the United States and abroad. These Level II datasets contain meteorological and dual-polarization base data quantities including: radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio. The IMPACTS NEXRAD Level II data files are available in netCDF-4 format. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. proprietary
kalahari_aot_h2o_vapor_719_1 SAFARI 2000 AOT and Column Water Vapor, Kalahari Transect, Wet Season 2000 ORNL_CLOUD STAC Catalog 2000-03-03 2000-03-18 21.72, -24.17, 25.5, -18.65 https://cmr.earthdata.nasa.gov/search/concepts/C2788397022-ORNL_CLOUD.umm_json The data presented here include the aerosol optical thickness (AOT) and column water vapor measurements taken at sites along the Kalahari Transect using a Microtops sunphotometer. Data were collected every 30 minutes at 4 sites that were visited during the SAFARI 2000 Kalahari Wet Season Campaign between March 3, 2000, and March 18, 2000. AOT values are provided at 340-, 440-, 675-, 870-, and 936-nm wavelengths. An estimate of the Angstrom Coefficient is also provided to allow the estimation of AOT at other wavelengths. The purpose of this data collection was primarily for documentation of the conditions at each site and to aid in the correction of remote sensing data, for validation of Earth Observation System (EOS) products such as MODIS and MISR aerosol products, and for modeling of canopy productivity. proprietary
kalahari_co2_heat_flux_765_1 SAFARI 2000 Kalahari Transect CO2, Water Vapor, and Heat Flux, Wet Season 2000 ORNL_CLOUD STAC Catalog 2000-03-01 2000-03-19 21.71, -24.16, 23.59, -15.44 https://cmr.earthdata.nasa.gov/search/concepts/C2789074715-ORNL_CLOUD.umm_json Short-term measurements of carbon dioxide, water, and energy fluxes were collected at four locations along a mean annual precipitation gradient in southern Africa during the SAFARI 2000 wet (growing) season campaign of 2000. The purpose of this research was to determine how observed vegetation-atmosphere exchange properties are functionally related to long-term climatic conditions. proprietary
@@ -19759,8 +19766,8 @@ labchemistrymetamorphism_1.0 Data set on bromide oxidation by ozone in snow duri
labes_1.0 LABES 2 Indicators of the Swiss Landscape Monitoring Program ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226082114-ENVIDAT.umm_json The Swiss Landscape Monitoring Program (LABES) records both the physical and the perceived quality of the landscape with about 30 indicators. The surveys of the physical aspects are largely based on evaluations of data available throughout Switzerland from swisstopo and the Federal Statistical Office (FSO). Another significant part of the data comes from a nationwide population survey on landscape perception. This dataset describes data that have been assembled in the 2020 update of the Swiss Landscape Monitoring Program LABES. proprietary
lai_45_1 Leaf Area Index Data (OTTER) ORNL_CLOUD STAC Catalog 1991-05-13 1991-05-15 -123.27, 44.29, -121.33, 44.67 https://cmr.earthdata.nasa.gov/search/concepts/C2804754747-ORNL_CLOUD.umm_json LAI estimates computed from unweighted openness by the CANOPY program from digitized canopy photographs proprietary
lake_cc_scenarios_ch2018_1.0 Lake climate change scenarios CH2018 ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226082136-ENVIDAT.umm_json "The dataset ""Lake_climate_change_scenarios_CH2018"" provides simulation-based climate change impact scenarios for perialpine lakes in Switzerland. These transient future scenarios were produced by combining the hydrologic model PREVAH with the hydrodynamic model MIKE11 to simulate daily lake water level (Lake_water_level_scenarios_CH2018.xls) and outflow scenarios (Lake_outflow_scenarios_CH2018.xls) from 1981 to 2099, using the Swiss Climate Change Scenarios CH2018. The future scenarios contain a total of 39 model members for three Representative Concentration Pathways, RCP2.6 (concerted mitigation efforts), RCP4.5 (limited climate mitigation) and RCP8.5 (no climate mitigation measures). These scenarios result from the study titled ""Lower summer lake levels in regulated perialpine lakes, caused by climate change,"" authored by Wechsler et al. in 2023. The dataset emphasizes four specific Swiss lakes, each subject to different degrees of lake level management: an unregulated lake (Lake Walen), a semi-regulated lake (Lake Brienz), and two regulated lakes (Lake Zurich and Lake Thun). In addition, the file (Lake_characteristics.xlsx) includes data used in the modeling process, encompassing the stage-area relation for the four lakes, stage-discharge relations for the unregulated and semi-regulated lakes, and lake level management rules for the two regulated lakes." proprietary
-lake_erie_aug_2014_0 2014 Lake Erie measurements OB_DAAC STAC Catalog 2014-08-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360418-OB_DAAC.umm_json 2014 Lake Erie measurements. proprietary
lake_erie_aug_2014_0 2014 Lake Erie measurements ALL STAC Catalog 2014-08-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360418-OB_DAAC.umm_json 2014 Lake Erie measurements. proprietary
+lake_erie_aug_2014_0 2014 Lake Erie measurements OB_DAAC STAC Catalog 2014-08-18 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1633360418-OB_DAAC.umm_json 2014 Lake Erie measurements. proprietary
lambert_geology_gis_1 Geology of the Lambert Glacier - Prydz Bay Region GIS Dataset AU_AADC STAC Catalog 1980-01-01 1997-12-31 58, -76, 78, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214313571-AU_AADC.umm_json This dataset is the GIS data used for the map 'Geology of the Lambert Glacier - Prydz Bay Region, East Antarctica' published by the Australian Geological Survey Organisation in January 1998. The data is in three formats: ArcInfo interchange, ArcInfo coverage and shapefile. A document is included with further information about the data. The map is available from a URL in this metadata record. proprietary
land-use-cover-dynamics-in-austin-metropolitan-area-since-1992_1.0 Land use/cover dynamics in Austin metropolitan area since 1992 ENVIDAT STAC Catalog 2022-01-01 2022-01-01 -97.7014167, 30.3732703, -97.7014167, 30.3732703 https://cmr.earthdata.nasa.gov/search/concepts/C2789815150-ENVIDAT.umm_json The present dataset is part of the published scientific paper Zhao C, Weng Q, Hersperger A M. Characterizing the 3-D urban morphology transformation to understand urban-form dynamics: a case study of Austin, Texas, USA. Landscape and urban planning, 2020, 203:103881. The overall objective of this paper is to understand urban form dynamics in the Austin metropolitan area for the periods 2006–2011 and 2011–2016. The study also aims to understand to what extent the changes in the built environment (in terms of ‘efficient growth’ versus ‘inefficient growth’) from the 1990s to 2016 in the Austin metropolitan area corresponded with ‘compact and efficient growth’ planning policy documents. The UMT distribution can be found in the paper. The area of transitioning UMT was provided in Table 2 and Table 3 can be found in the Appendix of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper. This study demonstrates the advantage of applying Lidar data to characterize 3-D urban morphology type (UMT) transition and understand its dynamics, which helps develop a comprehensive understanding of the urbanization process and provides a tool for planning intentions and policies evaluation on urban development over time. The UMT maps can be found in Appendix A of the paper. The Lidar point datasets and the 30 × 30 m National Land Cover Database (NLCD) are the two main data sources of UMT mapping. Lidar datasets were gathered from different projects that had been conducted and collected by state agencies and other organizations between 2007 and 2017. Table A1 in the appendix in the paper shows the accuracies and acquisition parameters of the Lidar projects from 2007 to 2017. Land use/cover dynamics in Austin metropolitan area dataset provides Land use/cover patterns in the years 1992, 2001, 2004, 2006, 2008, 2011, 2013, 2016 with a spatial resolution of 30 meters. Since NLCD 1992 used a different classification system for the urban land classes, we first reclassified the NLCD 1992 using a customized Arcpy package. proprietary
land_cover_data-1km_627_1 SAFARI 2000 Land Cover from AVHRR, 1-km, 1992-1993 (Hansen et al.) ORNL_CLOUD STAC Catalog 1992-01-01 1993-12-31 5, -35, 60, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2788343294-ORNL_CLOUD.umm_json This data set consists of a southern African subset of the 1-km Global Land Cover Data Set Derived from AVHRR developed at the Laboratory for Global Remote Sensing Studies (LGRSS) at the University of Maryland. Both ASCII data and binary image files are available. proprietary
@@ -19788,8 +19795,8 @@ larsemann_hills_dem_1 Digital Elevation Model of Larsemann Hills, Antarctica AU_
larsemann_sat_1 Larsemann Hills Satellite Image Map 1:25000 AU_AADC STAC Catalog 1990-08-01 1990-08-31 75.971, -69.489, 76.411, -69.324 https://cmr.earthdata.nasa.gov/search/concepts/C1214313531-AU_AADC.umm_json Satellite image map of Larsemann Hills, Princess Elizabeth Land, Antarctica. This map (edition 2) was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1990. The map is at a scale of 1:25000, and was produced from a multispectral SPOT 1 - HRV 2 scene (WRS K278 J495), acquired 19 February 1988. It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves, stations/bases, and gives some historical text information. The map has both geographical and UTM co-ordinates. proprietary
larsemann_visible_disturbance_1 Annotated maps and accompanying notes compiled in May 2000 about visible disturbance in the Larsemann Hills, Princess Elizabeth Land, Antarctica AU_AADC STAC Catalog 1990-01-01 2000-05-09 76.07, -69.47, 76.42, -69.37 https://cmr.earthdata.nasa.gov/search/concepts/C1214313590-AU_AADC.umm_json Annotated large format maps and accompanying notes compiled in May 2000 about visible disturbance in the Larsemann Hills, Princess Elizabeth Land, Antarctica. The compilation was done by Ewan McIvor of the Australian Antarctic Division and based on discussions with scientists Jim Burgess and Andy Spate. Included are locations and notes relating to: 1 walking and vehicular routes; 2 helicopter landing sites; 3 a tide gauge; 4 a fuel line; 5 a grave site; 6 a long term micro erosion monitoring site established in 1990 by Burgess and Spate; 7 two ice caves; and 8 a pliocene deposit. proprietary
larval-food-composition-of-four-wild-bee-species-in-five-european-cities_1.0 Larval food composition of four wild bee species in five European cities ENVIDAT STAC Catalog 2021-01-01 2021-01-01 0.2197266, 46.890732, 28.3886719, 59.0864909 https://cmr.earthdata.nasa.gov/search/concepts/C2789815269-ENVIDAT.umm_json Urbanization poses threats and opportunities for the biodiversity of wild bees. A main gap relates to the food preferences of wild bees in urban ecosystems, which usually harbour large numbers of plant species, particularly at the larval stage. This data sets describes the larval food of four wild bee species (i.e. Chelostoma florisomne, Hylaeus communis, Osmica bicornis and Osmia cornuta) and three genera (i.e. Chelostoma sp., Hylaeus sp, and Osmia sp.) common in urban areas in five different European cities (i.e. Antwerp, Paris, Poznan, Tartu and Zurich). This data results from a European-level study aimed at understanding the effects of urbanization on biodiversity across different cities and citiscapes, and a Swiss project aimed at understanding the effects of urban ecosystems in wild bee feeding behaviour. Wild bees were sampled using standardized trap-nests in 80 sites (32 in Zurich and 12 in each of the remaining cities), selected following a double gradient of available habitat at local and landscape scales. Larval pollen was obtained from the bee nests and identified using DNA metabarconding. The data provides the plant composition at the species or genus level of the different bee nests of the studied species in the studied sites of the five European cities. For Hylaeus communis, this is the first study in reporting larval food composition. proprietary
-latent-reserves-in-the-swiss-nfi_1.0 'Latent reserves' within the Swiss NFI ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.umm_json "The files refer to the data used in Portier et al. ""‘Latent reserves’: a hidden treasure in National Forest Inventories"" (2020) *Journal of Ecology*. **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered ‘latent reserves’, criteria were defined based on the information available in the Swiss NFI database: * Shrub forests were excluded. * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested. * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Klötzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994). * Plots had to originate from natural regeneration. * Intensive livestock grazing must never have occurred on the plots. The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). **** The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper. The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement." proprietary
latent-reserves-in-the-swiss-nfi_1.0 'Latent reserves' within the Swiss NFI ALL STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.umm_json "The files refer to the data used in Portier et al. ""‘Latent reserves’: a hidden treasure in National Forest Inventories"" (2020) *Journal of Ecology*. **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered ‘latent reserves’, criteria were defined based on the information available in the Swiss NFI database: * Shrub forests were excluded. * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested. * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Klötzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994). * Plots had to originate from natural regeneration. * Intensive livestock grazing must never have occurred on the plots. The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). **** The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper. The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement." proprietary
+latent-reserves-in-the-swiss-nfi_1.0 'Latent reserves' within the Swiss NFI ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815280-ENVIDAT.umm_json "The files refer to the data used in Portier et al. ""‘Latent reserves’: a hidden treasure in National Forest Inventories"" (2020) *Journal of Ecology*. **'Latent reserves'** are defined as plots in National Forest Inventories (NFI) that have been free of human influence for >40 to >70 years. They can be used to investigate and acquire a deeper understanding of attributes and processes of near-natural forests using existing long-term data. To determine which NFI sample plots could be considered ‘latent reserves’, criteria were defined based on the information available in the Swiss NFI database: * Shrub forests were excluded. * Plots must have been free of any kind of management, including salvage logging or sanitary cuts, for a minimum amount of time. Thresholds of 40, 50, 60 and 70 years without intervention were tested. * To ensure that species composition was not influenced by past management, plots where potential vegetation was classified as deciduous by Ellenberg & Klötzli (1972) had to have an observed proportion of deciduous trees matching the theoretical proportion expected in a natural deciduous forest, as defined by Kienast, Brzeziecki, & Wildi (1994). * Plots had to originate from natural regeneration. * Intensive livestock grazing must never have occurred on the plots. The tables stored here were derived from the first, second and third campaigns of the Swiss NFI. The raw data from the Swiss NFI can be provided free of charge within the scope of a contractual agreement (http://www.lfi.ch/dienstleist/daten-en.php). **** The files 'Data figure 2' to 'Data figure 8' are publicly available and contain the data used to produce the figures published in the paper. The files 'Plot-level data for characterisation of 'latent reserves' and 'Tree-level data for characterisation of 'latent reserves' contain all the data required to reproduce the section of the article concerning the characterisation of 'latent reserves' and the comparison to managed forests. The file 'Data for mortality analyses' contains the data required to reproduce the section of the article concerning tree mortality in 'latent reserves'. The access to these three files is restricted as they contain some raw data from the Swiss NFI, submitted to the Swiss law and only accessible upon contractual agreement." proprietary
law_dome_1977_1 Law Dome Field Logs And Strain Grid Results, 1977 AU_AADC STAC Catalog 1977-03-16 1977-12-14 110, -70, 114, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311164-AU_AADC.umm_json In 1977 several traverses were carried out over the Law Dome area, primarily to drill new ice cores on the dome. The 1974 drill site (near Cape Folger) was redrilled to add instrumentation for inclination, while additional holes at BHQ (418m) and the dome summit (475m, 2x 30m) were also drilled. In addition to the drilling work, two strain grids were laid out on the ice surface, and the grid laid out in 1974 was remeasured. Notes on the traverse and drilling (but few results) are contained in this record, along with the results of the strain grid surveys. Records for this work have been archived at the Australian Antarctic Division. Logbook(s): Glaciology Log of 1977 Field Work proprietary
law_dome_700yr_ion_chem_2 700 Years of Ice Core Major Ion Chemistry Data from Law Dome, Antarctica ALL STAC Catalog 1988-01-01 2000-03-06 112.8, -66.76, 112.86, -66.7 https://cmr.earthdata.nasa.gov/search/concepts/C1214313592-AU_AADC.umm_json A compilation of 700 years of Law Dome major ion chemistry data, recorded from 3 ice cores; DSS97, DSS99, DSS main. This work was completed as part of ASAC project 757 (ASAC_757). Species which have been the subject of publication and could be made available after consultation: Species, Period (AD), Resolution, Comments SO4, 1301-1995, Fine (full) NO3, 1888-1995, Fine (full), full 700 year annuals used by Mayewski solar-polar paper in preparation (Ca,K,Mg,Na,NO3,SO4,Cl), 1301-1995, Annual MSA, 1841-1995, Annual Na, 1301-1995, Fine (full) Na, 1301-1995, Annual non-sea-salt SO4 (nss SO4), 1301-1995, Annual, Uses a calculated SO4 fractionation % to correct the seawater ratio (due to fractionation at the source). Corrected ratio 0.087 (using uEq/L). There are still 'negative' values and some zero's - this data has not been 'cleaned'. If you need to use this, please contact Mark Curran for help. An updated copy of this dataset was submitted to the Australian Antarctic Data Centre in early July of 2012. proprietary
law_dome_700yr_ion_chem_2 700 Years of Ice Core Major Ion Chemistry Data from Law Dome, Antarctica AU_AADC STAC Catalog 1988-01-01 2000-03-06 112.8, -66.76, 112.86, -66.7 https://cmr.earthdata.nasa.gov/search/concepts/C1214313592-AU_AADC.umm_json A compilation of 700 years of Law Dome major ion chemistry data, recorded from 3 ice cores; DSS97, DSS99, DSS main. This work was completed as part of ASAC project 757 (ASAC_757). Species which have been the subject of publication and could be made available after consultation: Species, Period (AD), Resolution, Comments SO4, 1301-1995, Fine (full) NO3, 1888-1995, Fine (full), full 700 year annuals used by Mayewski solar-polar paper in preparation (Ca,K,Mg,Na,NO3,SO4,Cl), 1301-1995, Annual MSA, 1841-1995, Annual Na, 1301-1995, Fine (full) Na, 1301-1995, Annual non-sea-salt SO4 (nss SO4), 1301-1995, Annual, Uses a calculated SO4 fractionation % to correct the seawater ratio (due to fractionation at the source). Corrected ratio 0.087 (using uEq/L). There are still 'negative' values and some zero's - this data has not been 'cleaned'. If you need to use this, please contact Mark Curran for help. An updated copy of this dataset was submitted to the Australian Antarctic Data Centre in early July of 2012. proprietary
@@ -19927,8 +19934,8 @@ macquarie_aws_1 Automatic Weather Station Data from Macquarie Island AU_AADC STA
macquarie_heli_zone_1 Macquarie Island Helicopter Exclusion Zone AU_AADC STAC Catalog 2005-01-01 2005-01-24 158.75, -54.8, 158.97, -54.46 https://cmr.earthdata.nasa.gov/search/concepts/C1214313628-AU_AADC.umm_json The Macquarie Island Helicopter Exclusion Zone was created in January 2005 in consultation with Peter Cusick, Parks and Wildlife Service, Tasmania. The zone was created by buffering the coastline by 1 km on the seaward side of the island, generally following the escarpment on the interior of the island and buffering the refuges by 200 m to create an approximately 400 m wide corridor to the refuges. Access corridors were also created at the station. The Australian Antarctic Data Centre's topographic data representing coastline, escarpment and refuges was used. In March 2007 the zone was modifed in consultation with Terry Reid, Parks and Wildlife Service, Tasmania. The corridors to the refuges were extended through to the escarpment. The Helicopter Exclusion Zone is shown in a map of the island (see link below). proprietary
macquarie_quickbird_mapping_1 Macquarie Island mapping from Quickbird satellite imagery. AU_AADC STAC Catalog 2003-02-25 2003-06-20 158.85, -54.56, 158.94, -54.49 https://cmr.earthdata.nasa.gov/search/concepts/C1214313631-AU_AADC.umm_json Features of a northwest part of Macquarie Island mapped from mosaiced pan sharpened Quickbird satellite imagery derived from Quickbird satellite imagery captured on 25 February 2003. The mapped features are coastline, walking tracks and the edge of vegetation. proprietary
macquarie_sma_gis_1 Macquarie Island Special Management Areas AU_AADC STAC Catalog 2003-11-01 2003-11-30 158.77, -54.78, 158.95, -54.49 https://cmr.earthdata.nasa.gov/search/concepts/C1214313610-AU_AADC.umm_json Macquarie Island Nature Reserve Special Management Areas were originally defined for 2003/04 and have since been updated. Special Management areas are declared from year to year to protect vulnerable species, vegetation communities or sites extremely vulnerable to human disturbance. Related URLs provide: 1 the download of a shapefile with the boundaries of the Special Management Areas; and 2 a link to the website of Parks and Wildlife Service, Tasmania with information about the Special Management Areas. proprietary
-macquarie_taspaws_grid_1 A grid system used by the Parks and Wildlife Service, Tasmania, for Macquarie Island, 1974 to June 2001 ALL STAC Catalog 1974-01-01 2001-06-02 158.7322, -54.8011, 158.9781, -54.4714 https://cmr.earthdata.nasa.gov/search/concepts/C1214313536-AU_AADC.umm_json "This metadata record describes a grid system for Macquarie Island formerly used by the Parks and Wildlife Service, Tasmania. The grid was first adopted by Irynej Skira in 1974 and was based on the 1:50000 scale map of the island published by Australia's Division of National Mapping in 1971. Data was continually recorded on this system up to June 2001 when the Universal Transverse Mercator (UTM) grid was adopted. The dataset available for download from this metadata record includes a map with the grid system and a document compiled by Geoff Copson with details about converting from the Parks and Wildlife grid to the UTM grid. Geoff states in the document ""The 1971 map was particularly inaccurate in the centre two quarters of the island. The grid for the Parks and Wildlife Service system was hand drawn and fairly variable. Conversion values are averaged out on coastal points around the island.""" proprietary
macquarie_taspaws_grid_1 A grid system used by the Parks and Wildlife Service, Tasmania, for Macquarie Island, 1974 to June 2001 AU_AADC STAC Catalog 1974-01-01 2001-06-02 158.7322, -54.8011, 158.9781, -54.4714 https://cmr.earthdata.nasa.gov/search/concepts/C1214313536-AU_AADC.umm_json "This metadata record describes a grid system for Macquarie Island formerly used by the Parks and Wildlife Service, Tasmania. The grid was first adopted by Irynej Skira in 1974 and was based on the 1:50000 scale map of the island published by Australia's Division of National Mapping in 1971. Data was continually recorded on this system up to June 2001 when the Universal Transverse Mercator (UTM) grid was adopted. The dataset available for download from this metadata record includes a map with the grid system and a document compiled by Geoff Copson with details about converting from the Parks and Wildlife grid to the UTM grid. Geoff states in the document ""The 1971 map was particularly inaccurate in the centre two quarters of the island. The grid for the Parks and Wildlife Service system was hand drawn and fairly variable. Conversion values are averaged out on coastal points around the island.""" proprietary
+macquarie_taspaws_grid_1 A grid system used by the Parks and Wildlife Service, Tasmania, for Macquarie Island, 1974 to June 2001 ALL STAC Catalog 1974-01-01 2001-06-02 158.7322, -54.8011, 158.9781, -54.4714 https://cmr.earthdata.nasa.gov/search/concepts/C1214313536-AU_AADC.umm_json "This metadata record describes a grid system for Macquarie Island formerly used by the Parks and Wildlife Service, Tasmania. The grid was first adopted by Irynej Skira in 1974 and was based on the 1:50000 scale map of the island published by Australia's Division of National Mapping in 1971. Data was continually recorded on this system up to June 2001 when the Universal Transverse Mercator (UTM) grid was adopted. The dataset available for download from this metadata record includes a map with the grid system and a document compiled by Geoff Copson with details about converting from the Parks and Wildlife grid to the UTM grid. Geoff states in the document ""The 1971 map was particularly inaccurate in the centre two quarters of the island. The grid for the Parks and Wildlife Service system was hand drawn and fairly variable. Conversion values are averaged out on coastal points around the island.""" proprietary
macquarie_tracks_1 Macquarie Island walking tracks AU_AADC STAC Catalog 1997-09-01 2012-06-30 158.77, -54.78, 158.95, -54.48 https://cmr.earthdata.nasa.gov/search/concepts/C1214311191-AU_AADC.umm_json This GIS dataset represents walking tracks on Macquarie Island and was compiled by the Australian Antarctic Data Centre from surveys and other sources. This data is displayed in a pair of A3 1:50000 maps of Macquarie Island (see a Related URL). proprietary
madagascar_diatoms MADAGASCAR National Oceanographic Data Centre - Diatoms CEOS_EXTRA STAC Catalog 2003-10-01 2004-10-31 43.61, -23.38, 43.68, -23.35 https://cmr.earthdata.nasa.gov/search/concepts/C2232477687-CEOS_EXTRA.umm_json The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar’s northeast coast. This dataset of diatoms has been collected at three stations in Toliara Bay, and it currently consists of 2754 records of 19 families. proprietary
madagascar_dinoflagelles MADAGASCAR National Oceanographic Data Centre - Dinoflagellates CEOS_EXTRA STAC Catalog 2002-12-01 2003-12-31 43.61, -23.38, 43.68, -23.35 https://cmr.earthdata.nasa.gov/search/concepts/C2232477667-CEOS_EXTRA.umm_json The Madagascar National Oceanographic Data Centre is attached to the University of Toliara. Some of the research achievements of the Oceanographic Data Centre are: a project for the protection of coastal reefs in south-western Madagascar; a marine biodiversity assessment in the same coastal area; a socio-economic investigation of traditional fishing practices; and bio-ecological surveys to facilitate the development of a sustainable marine park in the Masoala area far away on Madagascar’s northeast coast. This dataset of dinoflagellates has been collected at three stations in Toliara Bay, and it currently consists of 1297 records of 15 families. proprietary
@@ -19959,8 +19966,8 @@ mawson_gravity_1989_1 Gravity Measurements At/Near Mawson, 1989 AU_AADC STAC Cat
mawson_north_sat_1 Mawson Escarpment North Satellite Image Map 1:100000 AU_AADC STAC Catalog 1995-12-01 1995-12-31 66, -73, 69, -72 https://cmr.earthdata.nasa.gov/search/concepts/C1214313618-AU_AADC.umm_json Satellite image map of the northern end of the Mawson Escarpment, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (now Geoscience Australia) Commercial, in Australia, in 1995. The map is at a scale of 1:100000, and was produced from Landsat TM scenes (WRS 128-111, 127-112). It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves and gives some historical text information. The map has both geographical and UTM co-ordinates. proprietary
mawson_south_sat_1 Mawson Escarpment South Satellite Image Map 1:100000 AU_AADC STAC Catalog 1995-12-01 1995-12-31 66.12, -73.7083, 69.1883, -73.065 https://cmr.earthdata.nasa.gov/search/concepts/C1214313637-AU_AADC.umm_json Satellite image map of Mawson Escarpment south, Antarctica. This map was produced for the Australian Antarctic Division by AUSLIG (Now Geoscience Australia) Commercial, in Australia, in 1995. The map is at a scale of 1:100000, and was produced from Landsat TM scenes (WRS 128-111, 128-112, 124-112). It is projected on a Transverse Mercator projection, and shows glaciers/ice shelves and gives some historical text information. The map has both geographical and UTM co-ordinates. proprietary
mawsonbathy_gis_1 Bathymetry of Approaches to Mawson Station AU_AADC STAC Catalog 1987-02-03 1992-03-04 62, -68, 63, -67 https://cmr.earthdata.nasa.gov/search/concepts/C1214313619-AU_AADC.umm_json Bathymetric contours and height range polygons of approaches to Mawson Station, derived from RAN Fair sheet, Aurora Australis and GEBCO soundings. proprietary
-mbs_wilhelm_msa_hooh_1 15 year Wilhelm II Land MSA and HOOH shallow ice core record from Mount Brown South (MBS) ALL STAC Catalog 1984-01-01 1998-12-31 86.082, -69.13, 86.084, -69.12 https://cmr.earthdata.nasa.gov/search/concepts/C1214313640-AU_AADC.umm_json This work presents results from a short firn core spanning 15 years collected from near Mount Brown, Wilhelm II Land, East Antarctica. Variations of methanesulphonic acid (MSA) at Mount Brown were positively correlated with sea-ice extent from the coastal region surrounding Mount Brown (60-1208 E) and from around the entire Antarctic coast (0-3608 E). Previous results from Law Dome identified this MSA-sea-ice relationship and proposed it as an Antarctic sea-ice proxy (Curran and others, 2003), with the strongest results found for the local Law Dome region. Our data provide supporting evidence for the Law Dome proxy (at another site in East Antarctica), but a deeper Mount Brown ice core is required to confirm the sea-ice decline suggested by Curran and others (2003). Results also indicate that this deeper record may also provide a more circum-Antarctic sea-ice proxy. This work was completed as part of ASAC project 757 (ASAC_757). proprietary
mbs_wilhelm_msa_hooh_1 15 year Wilhelm II Land MSA and HOOH shallow ice core record from Mount Brown South (MBS) AU_AADC STAC Catalog 1984-01-01 1998-12-31 86.082, -69.13, 86.084, -69.12 https://cmr.earthdata.nasa.gov/search/concepts/C1214313640-AU_AADC.umm_json This work presents results from a short firn core spanning 15 years collected from near Mount Brown, Wilhelm II Land, East Antarctica. Variations of methanesulphonic acid (MSA) at Mount Brown were positively correlated with sea-ice extent from the coastal region surrounding Mount Brown (60-1208 E) and from around the entire Antarctic coast (0-3608 E). Previous results from Law Dome identified this MSA-sea-ice relationship and proposed it as an Antarctic sea-ice proxy (Curran and others, 2003), with the strongest results found for the local Law Dome region. Our data provide supporting evidence for the Law Dome proxy (at another site in East Antarctica), but a deeper Mount Brown ice core is required to confirm the sea-ice decline suggested by Curran and others (2003). Results also indicate that this deeper record may also provide a more circum-Antarctic sea-ice proxy. This work was completed as part of ASAC project 757 (ASAC_757). proprietary
+mbs_wilhelm_msa_hooh_1 15 year Wilhelm II Land MSA and HOOH shallow ice core record from Mount Brown South (MBS) ALL STAC Catalog 1984-01-01 1998-12-31 86.082, -69.13, 86.084, -69.12 https://cmr.earthdata.nasa.gov/search/concepts/C1214313640-AU_AADC.umm_json This work presents results from a short firn core spanning 15 years collected from near Mount Brown, Wilhelm II Land, East Antarctica. Variations of methanesulphonic acid (MSA) at Mount Brown were positively correlated with sea-ice extent from the coastal region surrounding Mount Brown (60-1208 E) and from around the entire Antarctic coast (0-3608 E). Previous results from Law Dome identified this MSA-sea-ice relationship and proposed it as an Antarctic sea-ice proxy (Curran and others, 2003), with the strongest results found for the local Law Dome region. Our data provide supporting evidence for the Law Dome proxy (at another site in East Antarctica), but a deeper Mount Brown ice core is required to confirm the sea-ice decline suggested by Curran and others (2003). Results also indicate that this deeper record may also provide a more circum-Antarctic sea-ice proxy. This work was completed as part of ASAC project 757 (ASAC_757). proprietary
mcdonald_dem_may2012_1 A Digital Elevation Model of McDonald Island derived from GeoEye-1 stereo imagery captured 19 May 2012 ALL STAC Catalog 2012-05-19 2012-05-19 72.533, -53.067, 72.74, -53.003 https://cmr.earthdata.nasa.gov/search/concepts/C1214311211-AU_AADC.umm_json This dataset consists of: 1 GeoEye-1 stereo imagery of an area of approximately 100 square kilometres including McDonald Island, captured 19 May 2012 2 A Digital Elevation Model (DEM) derived from the GeoEye-1 stereo imagery; and 3 Image products derived from the most vertical dataset of the stereo imagery and orthorectified using the DEM. 4 Contours generated from the DEM. The DEM was produced at a 1 metre pixel size and is available in ESRI grid, ESRI ascii and BIL formats. The DEM and image products are stored in a Universal Transverse Mercator zone 43 south projection, based on the WGS84 datum. The image products are geotiffs as follows. McDonald_Island_BGRN.tif: GeoEye-1 4-band multispectral (vis blue, green, red and Near Infrared), 2 metre resolution. McDonald_Island_PAN.tif: GeoEye-1 panchromatic, 0.5 metre resolution. McDonald_Island_PS_BGRN.tif: GeoEye-1 pansharpened, 4-band multispectral (vis blue, green, red and Near Infrared), 0.5 metre resolution. McDonald_Island_RGB.tif: GeoEye-1 pansharpened, natural colour enhancement, 0.5 metre resolution. proprietary
mcdonald_dem_may2012_1 A Digital Elevation Model of McDonald Island derived from GeoEye-1 stereo imagery captured 19 May 2012 AU_AADC STAC Catalog 2012-05-19 2012-05-19 72.533, -53.067, 72.74, -53.003 https://cmr.earthdata.nasa.gov/search/concepts/C1214311211-AU_AADC.umm_json This dataset consists of: 1 GeoEye-1 stereo imagery of an area of approximately 100 square kilometres including McDonald Island, captured 19 May 2012 2 A Digital Elevation Model (DEM) derived from the GeoEye-1 stereo imagery; and 3 Image products derived from the most vertical dataset of the stereo imagery and orthorectified using the DEM. 4 Contours generated from the DEM. The DEM was produced at a 1 metre pixel size and is available in ESRI grid, ESRI ascii and BIL formats. The DEM and image products are stored in a Universal Transverse Mercator zone 43 south projection, based on the WGS84 datum. The image products are geotiffs as follows. McDonald_Island_BGRN.tif: GeoEye-1 4-band multispectral (vis blue, green, red and Near Infrared), 2 metre resolution. McDonald_Island_PAN.tif: GeoEye-1 panchromatic, 0.5 metre resolution. McDonald_Island_PS_BGRN.tif: GeoEye-1 pansharpened, 4-band multispectral (vis blue, green, red and Near Infrared), 0.5 metre resolution. McDonald_Island_RGB.tif: GeoEye-1 pansharpened, natural colour enhancement, 0.5 metre resolution. proprietary
mcm_seals Marine and Coastal Management (MCM) - Seal Surveys CEOS_EXTRA STAC Catalog 1974-04-08 2001-06-01 11.68, -34.98, 26.11, -17.47 https://cmr.earthdata.nasa.gov/search/concepts/C2232477678-CEOS_EXTRA.umm_json Marine and Coastal Management (MCM) is one of four branches of the Department of Environmental Affairs and Tourism. It is the regulatory authority responsible for managing all marine and coastal activities. The seal data set is a collection of seals shot at-sea cruises, and has been collected from cruises around the South African Coast, and currently contains 2440 records of 1 family (Otariidae). proprietary
@@ -20083,8 +20090,8 @@ net-primary-productivity-npp-anomalies-simulated-by-3-pg-model-for-switzerland_1
net_carbon_flux_662_1 Net Carbon Dioxide and Water Fluxes of Global Terrestrial Ecosystems, 1969-1998 ORNL_CLOUD STAC Catalog 1969-01-01 1998-01-01 -162, -45.5, 176, 68.5 https://cmr.earthdata.nasa.gov/search/concepts/C2779678769-ORNL_CLOUD.umm_json The variability of net surface carbon assimilation (Asmax), net ecosystem surface respiration (Rsmax), and net surface evapotranspiration (Etsmax) among and within vegetation types was examined based on a review of studies performed in either a micrometeorological setting or an enclosure setting. proprietary
net_increment-80_1.0 Net increment ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815660-ENVIDAT.umm_json Increment including ingrowth minus the mortality. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary
net_increment_star-187_1.0 Net increment* ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815851-ENVIDAT.umm_json Increment with ingrowth minus the mortality. *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary
-newcomb_bay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands AU_AADC STAC Catalog 1997-02-01 2000-02-05 110.512, -66.282, 110.566, -66.256 https://cmr.earthdata.nasa.gov/search/concepts/C1214311215-AU_AADC.umm_json A Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands and terrestrial and bathymetric contours derived from the DEM. The data is stored in a UTM zone 49(WGS-84) projection. Heights are referenced to mean sea level. It was created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA IS NOT FOR NAVIGATION PURPOSES. proprietary
newcomb_bay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands ALL STAC Catalog 1997-02-01 2000-02-05 110.512, -66.282, 110.566, -66.256 https://cmr.earthdata.nasa.gov/search/concepts/C1214311215-AU_AADC.umm_json A Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands and terrestrial and bathymetric contours derived from the DEM. The data is stored in a UTM zone 49(WGS-84) projection. Heights are referenced to mean sea level. It was created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA IS NOT FOR NAVIGATION PURPOSES. proprietary
+newcomb_bay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands AU_AADC STAC Catalog 1997-02-01 2000-02-05 110.512, -66.282, 110.566, -66.256 https://cmr.earthdata.nasa.gov/search/concepts/C1214311215-AU_AADC.umm_json A Digital Elevation Model (DEM) of Newcomb Bay, Windmill Islands and terrestrial and bathymetric contours derived from the DEM. The data is stored in a UTM zone 49(WGS-84) projection. Heights are referenced to mean sea level. It was created by interpolation of point data using Kriging. The input point data comprised soundings and terrestrial contour vertices. THE DATA IS NOT FOR NAVIGATION PURPOSES. proprietary
nexeastimpacts_1 NEXRAD Mosaic East IMPACTS V1 GHRC_DAAC STAC Catalog 2019-12-31 2020-02-29 -85, 32.5, -67.525, 46.475 https://cmr.earthdata.nasa.gov/search/concepts/C1995866059-GHRC_DAAC.umm_json The NEXRAD Mosaic East IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) 3D mosaic files created from Level II surveillance data gathered during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Mosaic East dataset is composed of Level II data from 19 NEXRAD sites in the eastern U.S.. These data files are available in netCDF-4 format and contain meteorological and dual-polarization base data quantities including radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio from January 1 through February 29, 2020. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. proprietary
nexmidwstimpacts_1 NEXRAD Mosaic Midwest IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-01 2020-02-29 -93, 36, -79.025, 45.975 https://cmr.earthdata.nasa.gov/search/concepts/C1995866123-GHRC_DAAC.umm_json The NEXRAD Mosaic Midwest IMPACTS dataset consists of Next Generation Weather Radar (NEXRAD) 3D mosaic files created from Level II surveillance data gathered during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The Mosaic Midwest dataset is composed of Level II data from 11 NEXRAD sites in the midwestern U.S. These data files are available in netCDF-4 format and contain meteorological and dual-polarization base data quantities including radar reflectivity, radial velocity, spectrum width, differential reflectivity, differential phase, and cross correlation ratio from January 1 through February 29, 2020. It should be noted that this dataset will be updated in subsequent years of the IMPACTS campaign. proprietary
niche-partitioning-between-wild-bees-and-honeybees_1.0 Niche partitioning between wild bees and honeybees ENVIDAT STAC Catalog 2021-01-01 2021-01-01 8.4299469, 47.3172277, 8.6949921, 47.4130345 https://cmr.earthdata.nasa.gov/search/concepts/C2789816101-ENVIDAT.umm_json "Cities are socio-ecological systems that filter and select species, thus establishing unique species assemblages and biotic interactions. Urban ecosystems can host richer wild bee communities than highly intensified agricultural areas, specifically in resource-rich urban green spaces such as allotment and family gardens. At the same time, urban beekeeping has boomed in many European cities, raising concerns that the fast addition of a large number of managed bees could deplete the existing floral resources, triggering competition between wild bees and honeybees. The data has been used to investigated the interplay between resource availability and the number of honeybees at local and landscape scales and how this relationship influences wild bee diversity. This dataset contains the raw and processed data supporting the findings from the paper: ""Low resource availability drives feeding niche partitioning between wild bees and honeybees in a European city"". The data contains: 1. Bee trait measurements at the species and individual-level of five functional traits. 2. The values of the feeding niche partitioning (functional dissimilarity to honeybees) 3. The predictors of resource availability and beekeeping intensity at local and landscape scales used in the modelling of the paper for the 23 experimental sites." proprietary
@@ -20113,8 +20120,8 @@ nutrient-addition-stillberg_1.0 Nutrient addition experiment at the Alpine treel
nwrc_amphibianslowermiss A Multi-scale Habitat Evaluation of Amphibians in the Lower Mississippi River Alluvial Valley CEOS_EXTRA STAC Catalog 1999-09-05 1999-12-05 -91.95, 31.15, -91.25, 32.4333 https://cmr.earthdata.nasa.gov/search/concepts/C2231550400-CEOS_EXTRA.umm_json Bottomland hardwood forests are floodplain forests distributed along rivers and streams throughout the central and southern United States. The largest bottomland hardwood ecosystem in North America occurred within the Lower Mississippi River Alluvial Valley (LMAV). By the 1980.s, an estimated 80% of the former 10 million ha of bottomland hardwood forest in the LMAV were cleared for flood control efforts, agriculture, and development. Forests are continuing to be cleared today at an alarming rate, and the forests that remain are highly degraded and fragmented. In addition, these forests are subjected to extreme hydrological alterations. Over the past few decades, extensive efforts have begun to reforest marginal agricultural lands within the LMAV. Restoration efforts are limited by the lack of information concerning the habitat needs of bottomland wildlife species. Amphibians are one group of species for which little is known about their population status or habitat requirements in the LMAV. Information concerning the population status of amphibians in the LMAV is especially important since amphibians appear to be declining worldwide. Amphibians may also be important indicators of environmental health because of their sensitivity to land management practices and water quality. Understanding the habitat requirements of amphibians can be a step toward enhancing wildlife populations within the LMAV by providing valuable information for improving land management practices and wetland restoration techniques. To provide an inventory of amphibians at Tensas River and Lake Ophelia National Wildlife Refuges. In addition, to determine amphibian distribution patterns in the LMAV as they relate to landscape habitat features. Research results will be used to develop reports and manuscripts, and to assist land managers in management decisions to benefit amphibian populations. Information was obtained from Janene Lichtenberg for this metadata. proprietary
nwrc_amphibianslowermiss A Multi-scale Habitat Evaluation of Amphibians in the Lower Mississippi River Alluvial Valley ALL STAC Catalog 1999-09-05 1999-12-05 -91.95, 31.15, -91.25, 32.4333 https://cmr.earthdata.nasa.gov/search/concepts/C2231550400-CEOS_EXTRA.umm_json Bottomland hardwood forests are floodplain forests distributed along rivers and streams throughout the central and southern United States. The largest bottomland hardwood ecosystem in North America occurred within the Lower Mississippi River Alluvial Valley (LMAV). By the 1980.s, an estimated 80% of the former 10 million ha of bottomland hardwood forest in the LMAV were cleared for flood control efforts, agriculture, and development. Forests are continuing to be cleared today at an alarming rate, and the forests that remain are highly degraded and fragmented. In addition, these forests are subjected to extreme hydrological alterations. Over the past few decades, extensive efforts have begun to reforest marginal agricultural lands within the LMAV. Restoration efforts are limited by the lack of information concerning the habitat needs of bottomland wildlife species. Amphibians are one group of species for which little is known about their population status or habitat requirements in the LMAV. Information concerning the population status of amphibians in the LMAV is especially important since amphibians appear to be declining worldwide. Amphibians may also be important indicators of environmental health because of their sensitivity to land management practices and water quality. Understanding the habitat requirements of amphibians can be a step toward enhancing wildlife populations within the LMAV by providing valuable information for improving land management practices and wetland restoration techniques. To provide an inventory of amphibians at Tensas River and Lake Ophelia National Wildlife Refuges. In addition, to determine amphibian distribution patterns in the LMAV as they relate to landscape habitat features. Research results will be used to develop reports and manuscripts, and to assist land managers in management decisions to benefit amphibian populations. Information was obtained from Janene Lichtenberg for this metadata. proprietary
nymesoimpacts_1 New York State Mesonet IMPACTS GHRC_DAAC STAC Catalog 2020-01-03 2023-03-02 -79.6375, 40.594, -72.1909, 44.9057 https://cmr.earthdata.nasa.gov/search/concepts/C1995873777-GHRC_DAAC.umm_json The New York State Mesonet IMPACTS dataset is browse-only. It consists of temperature, wind, wind direction, mean sea level pressure, precipitation, and snow depth measurements, as well as profiler Doppler LiDAR and Microwave Radiometer (MWR) measurements from the New York State Mesonet network during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign, a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The Mesonet network consists of ground weather stations, LiDAR profilers, and microwave radiometer (MWR) profilers. These browse files are available from January 3, 2020, through March 2, 2023, in PNG format. proprietary
-obrienbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands ALL STAC Catalog 1997-03-31 1997-03-31 110.516, -66.297, 110.54, -66.293 https://cmr.earthdata.nasa.gov/search/concepts/C1214311199-AU_AADC.umm_json A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands. proprietary
obrienbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands AU_AADC STAC Catalog 1997-03-31 1997-03-31 110.516, -66.297, 110.54, -66.293 https://cmr.earthdata.nasa.gov/search/concepts/C1214311199-AU_AADC.umm_json A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands. proprietary
+obrienbay_bathy_dem_1 A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands ALL STAC Catalog 1997-03-31 1997-03-31 110.516, -66.297, 110.54, -66.293 https://cmr.earthdata.nasa.gov/search/concepts/C1214311199-AU_AADC.umm_json A bathymetric Digital Elevation Model (DEM) of O'Brien Bay, Windmill Islands. proprietary
observational-data-switzerland-2016-2021_1.0 Observational data: avalanche observations, danger signs and stability test results, Switzerland (2016/2017 to 2020/2021 ) ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815389-ENVIDAT.umm_json This is the freely available part of the data used in the publication by Techel et al. (2022): _On the correlation between a sub-level qualifier refining the danger level with observations and models relating to the contributing factors of avalanche danger_ - danger signs - human triggered avalanches - rutschblock test results (still to be added) - extended column test results (still to be added) proprietary
observed-and-simulated-snow-profile-data-from-switzerland_1.0 Observed and simulated snow profile data ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226082908-ENVIDAT.umm_json This data set includes information on all observed and simulated snow profiles that were used to train and validate the random forest model described in Mayer et al. (2022). The RF model was trained to assess snow instability from simulated snow stratigraphy. The data set contains observed snow profiles from the region of Davos (DAV subset, 512 profiles) and from all over Switzerland (SWISS subset, 230 profiles). For each observed snow profile, there is a corresponding simulated profile which was obtained using meteorological input data for the numerical snow cover model SNOWPACK. The information on the observed snow profile contains a Rutschblock test result including the depth of the failure interface. As part of the study described in Mayer et al. (2022), each observed snow profile was manually compared to its simulated counterpart and the simulated layer corresponding to the Rutschblock failure layer was identified. The data are provided in the following form: one file each per observed and simulated snow profile (2x512 files DAV, 2x230 files SWISS), two files (1 file DAV, 1 file SWISS) containing the observed information on snow instability, the allocation between observed and simulated failure layer, and all features extracted from the simulated weak layers that were used to develop the RF model. proprietary
observer-driven-pseudoturnover-in-vegetation-surveys_1.0 Observer-driven pseudoturnover in vegetation surveys ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789815537-ENVIDAT.umm_json "This dataset was used to analyze the inter-observer error (i.e. pseudoturnover) in vegetation surveys for the publication Boch S, Küchler H, Küchler M, Bedolla A, Ecker KT, Graf UH, Moser T, Holderegger R, Bergamini A (2022) Observer-driven pseudoturnover in vegetation monitoring is context dependent but does not affect ecological inference. Applied Vegetation Science. In the framework of the project ""Monitoring the effectiveness of habitat conservation in Switzerland"", we double-surveyed a total of 224 plots that were marked once in the field and then sampled by two observers independently on the same day. Both observers conducted full vegetation surveys, recording all vascular plant species, their cover, and additional plot information. We then calculated mean ecological indicator values and pseudoturnover. The excel file contains two sheets: 1) Raw species lists of the 224 plots conducted by two different observers. Woody species are distinguished in three layers: H (herb layer; woody species <0.5 m in height), S (shrub layer; woody species 0.5–3 m in height) and T (tree layer; woody species >3 m in height). ""cf."" indicates uncertain identification, ""aggr."" indicates that the plant was identified only to the aggregate level. Cover was estimated for each species using a modified Braun-Blanquet scale (r ≙ <0.1%, + ≙ 0.1% to <1%, 1 ≙ 1% to <5%, 2 ≙ 5% to <25%, 3 ≙ 25% to <50%, 4 ≙ 50% to <75%, 5 ≙ 75% to <100%). 2) File used for the linear mixed effects model." proprietary
@@ -20140,8 +20147,8 @@ pedestrian_gentoo_1 Effects of human activity on Gentoo penguins on Macquarie Is
pedestrian_king_1 Effects of human activity on King penguins on Macquarie Island AU_AADC STAC Catalog 2002-10-20 2003-03-20 158.76892, -54.78168, 158.96667, -54.47802 https://cmr.earthdata.nasa.gov/search/concepts/C1214311218-AU_AADC.umm_json This project empirically measures the effects of human activity on the behaviour of King penguins on Macquarie Island, under ASAC project 1148. This was achieved by collecting behavioural responses of individual penguins exposed to pedestrian approaches across the breeding stages of incubation and guard. Information produced includes minimum approach guidelines. As of April 2003 all data are stored on Hi-8 digital tape, due to be transformed during 2003 - 2004 into a timecoded tab-delimited text format for analysis using the Observer (Noldus Information Technology 2002). The fields in this dataset are: Sample Date Breeding Phase Approach Colony Focal birds tape number Wide angle tape number Weather Time Windspeed Temperature Precipitation Cloud Pre-approach control Post-approach control Maximum approach distance proprietary
pedestrian_royal_1 Effects of human activity on Royal penguins on Macquarie Island AU_AADC STAC Catalog 2002-10-20 2003-03-20 158.76755, -54.78247, 158.95981, -54.47802 https://cmr.earthdata.nasa.gov/search/concepts/C1214311223-AU_AADC.umm_json This project empirically measures the effects of human activity on the behaviour, heart rate and egg-shell surface temperature of Royal penguins on Macquarie Island, as part of ASAC project 1148. This was achieved by collecting behavioural and physiological responses of individual penguins exposed to pedestrian approaches across the breeding stages of incubation, guard, creche and moult. Both single person and group approaches were also investigated. Information produced includes minimum approach guidelines. As of April 2003 all data are stored on Hi-8 digital tape, due to be transformed during 2003 - 2004 into a timecoded tab-delimited text format for analysis using the Observer (Noldus Information Technology 2002). Some notes about some of the fields in this dataset: Temp file refers to whether or not egg shell surface temperature was also recorded for the sample, with the code below refering to the file name. Neighbour refers to the behavioural control file for each sample (neighbouring nests did not recieve an artificial egg, and provide a behavioural control for responses to human approaches without the scientific treatment). Nest refers to the randomly used nest markers for each sample. Heart rate refers to whether heart rate was concurrently recorded with behaviour on the sample (both stored on Hi-8 tape). Stimulus refers to whether single persons or groups of persons (5 -7, recorded within each sample) were used for the human approaches. Environment refers to whether approaches were conducted from colony sections abuting pebbly beach or from poa tussock environs (tussock approaches less than 50 m of the poa / pebbly beach junction). The code system for nest simply refers to the numbered tag placed at the nest (using three colours, g=green, w=white, b=brown) which were used randomly. The fields in this dataset are: Sample Date Breeding Phase Stimulus Type Environment Colony Nest Tape Heart Rate Temp File Neighbour proprietary
pfynwald_2016 Tree measurements 2002-2016 from the long-term irrigation experiment Pfynwald, Switzerland ENVIDAT STAC Catalog 2016-01-01 2016-01-01 7.61192, 46.30284, 7.61192, 46.30284 https://cmr.earthdata.nasa.gov/search/concepts/C2789816328-ENVIDAT.umm_json To study the performance of mature Scots pine (_Pinus sylvestris_ L.) under chronic drought conditions in comparison to their immediate physiological response to drought release, a controlled long-term and large-scale irrigation experiment has been set up in 2003. The experiment is located in a xeric mature Scots pine forest in the Pfynwald (46° 18' N, 7° 36' E, 615 m a.s.l.) in one of the driest inner-Alpine valleys of the European Alps, the Valais (mean annual temperature: 9.2°C, annual precipitation sum: 657 mm, both 1961-1990). Tree age is on average 100 years, the top height is 10.8 m and the stand density is 730 stems ha-1 with a basal area of 27.3 m2 ha-1. The forest is described as _Erico Pinetum sylvestris_ and the soil is a shallow pararendzina characterized by low water retention. The experimental site (1.2 ha; 800 trees) is split up into eight plots of 1'000 m2 each. During April-October, irrigation is applied on four randomly selected plots with sprinklers of 1 m height at night using water from an adjacent water channel. The amount of irrigation corresponds to a supplementary rainfall of 700 mm year-1. Trees in the other four plots grow under naturally dry conditions. Soil moisture has been monitored since the beginning of the project at 3 soil depths (10, 20 and 60 cm). The crown condition of each tree is being assessed each year since 2003. Tree measurement data such as diameter at breast height, tree height, and social status were assessed in 2002, 2009 and 2014. The duration of the irrigation experiment is planned for 20 years. proprietary
-pfynwaldgasexchange_1.0 2013-2020 gas exchange at Pfynwald ALL STAC Catalog 2021-01-01 2021-01-01 7.6105556, 46.3001905, 7.6163921, 46.3047564 https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.umm_json Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds. All metadata can be found in the attached documents. proprietary
pfynwaldgasexchange_1.0 2013-2020 gas exchange at Pfynwald ENVIDAT STAC Catalog 2021-01-01 2021-01-01 7.6105556, 46.3001905, 7.6163921, 46.3047564 https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.umm_json Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds. All metadata can be found in the attached documents. proprietary
+pfynwaldgasexchange_1.0 2013-2020 gas exchange at Pfynwald ALL STAC Catalog 2021-01-01 2021-01-01 7.6105556, 46.3001905, 7.6163921, 46.3047564 https://cmr.earthdata.nasa.gov/search/concepts/C2789816347-ENVIDAT.umm_json Gas exchange was measured on control, irrigated and irrigation-stop trees at the irrigation experiment Pfynwald, during the years 2013, 2014, 2016-2020. The measurement campaigns served different purposes, resulting in a large dataset containing survey data, CO2 response curves of photosynthesis, light response curves of photosynthesis, and fluorescence measurements. Measurements were done with LiCor 6400 and LiCor 6800 instruments. Until 2016, measurements were done on excised branches or branches lower in the canopy. From 2016 onwards, measurements were done in the top of the canopy using fixed installed scaffolds. All metadata can be found in the attached documents. proprietary
phipsimpacts_1 Particle Habit Imaging and Polar Scattering Probe (PHIPS) IMPACTS V1 GHRC_DAAC STAC Catalog 2020-01-18 2023-02-28 -95.243, 33.261, -64.987, 48.237 https://cmr.earthdata.nasa.gov/search/concepts/C1995874351-GHRC_DAAC.umm_json The Particle Habit Imaging and Polar Scattering (PHIPS) Probes IMPACTS dataset consists of cloud particle imagery collected by the Particle Habit Imaging and Polar Scattering (PHIPS) probe onboard the NASA P-3 aircraft during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. PHIPS allows for the measurement of particle shape, size, and habit. The browse files in this dataset contain the post-processed particle-by-particle stereo images (2 images from different angles) collected by PHIPS during the campaign. The files are available from January 18, 2020, through February 28, 2023, in PNG format. proprietary
phosphorus-and-nitrogen-leaching-from-beech-forest-soils_1.0 Phosphorus and nitrogen leaching from beech forest soils ENVIDAT STAC Catalog 2021-01-01 2021-01-01 9.927478, 50.3518, 10.26725, 52.838967 https://cmr.earthdata.nasa.gov/search/concepts/C2789816374-ENVIDAT.umm_json Data on dissolved organic and inorganic phosphorus and nitrogen concentrations in leachates and their corresponding fluxes from the litter layer, the Oe/Oa horizon, and the A horizon of two German beech forest sites. Leachate samples were taken in April 2018, July 2018, October 2018, Feb./Mar. 2019, and July 2019 with zero-tension lysimeters after artificial irrigation. Soil samples were taken in July 2019. For more details please refer to the publication. proprietary
photo_mosaic_laurens_or_1 Heard Island, Laurens Peninsula, Coastal Orthophoto Mosaic derived from Non-Metric Photography AU_AADC STAC Catalog 1980-01-01 2000-12-31 73.23, -53.05, 73.41, -52.95 https://cmr.earthdata.nasa.gov/search/concepts/C1214311224-AU_AADC.umm_json The orthophoto mosaic is a rectified georeferenced image of the Heard Island, Laurens Peninsula Coastal Area. Distortions due to relief and tilt displacement have been removed. Orthophotos were derived from non-metric cameras (focal length unknown). proprietary
@@ -20360,26 +20367,26 @@ sbuparsimpacts_1 SBU Parsivel IMPACTS GHRC_DAAC STAC Catalog 2020-01-01 2023-03-
sbuplimpacts_1 SBU Pluvio Precipitation Gauge IMPACTS GHRC_DAAC STAC Catalog 2020-01-07 2023-03-02 -73.138, 40.8556, -72.8714, 40.90712 https://cmr.earthdata.nasa.gov/search/concepts/C1995869760-GHRC_DAAC.umm_json The SBU Pluvio Precipitation Gauge IMPACTS dataset consists of precipitation intensity and precipitation accumulation collected using the OTT Pluvio2 weighing rain gauge during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. NASA’s Earth Venture program funded IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. Data files in this dataset are available in ASCII-CSV format from January 7, 2020, through March 2, 2023. proprietary
sbuskylerimpacts_1 SBU X-band Phased Array Radar (SKYLER) IMPACTS GHRC_DAAC STAC Catalog 2022-01-17 2023-02-28 -77.4867, 40.1501, -71.266, 43.695 https://cmr.earthdata.nasa.gov/search/concepts/C2704110186-GHRC_DAAC.umm_json The SBU X-band Phased Array Radar (SKYLER) IMPACTS dataset consists of polarimetric radar data collected by the Stony Brook University (SBU) X-band Phased Array Radar (SKYLER) during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. SKYLER provided detailed observations of cloud and precipitation microphysics, specifically ice and snow processes. These data include reflectivity, mean velocity, spectrum width, linear depolarization ratio, differential reflectivity, differential phase, specific differential phase, co-polarized correlation coefficient, and signal-to-noise ratio. The dataset files are available from January 17, 2022, through February 28, 2023, in netCDF-4 format. proprietary
sbusndimpacts_1 SBU Mobile Soundings IMPACTS GHRC_DAAC STAC Catalog 2020-01-18 2023-02-28 -76.980629, 40.4841385, -70.8692093, 43.7849808 https://cmr.earthdata.nasa.gov/search/concepts/C1995869776-GHRC_DAAC.umm_json The SBU Mobile Sounding IMPACTS dataset consists of mobile sounding profiles collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Mobile-sounding profiles were obtained about every three hours during snow events by Stony Brook University (SBU). The sounding measures temperature, humidity, height, and horizontal wind direction and speed in the atmosphere. Atmospheric pressure is calculated from GPS height. Data files are available from January 18, 2020, through February 28, 2023 in netCDF-3 format. proprietary
-scarmarbin_1647 Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155436-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
scarmarbin_1647 Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155436-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
-scarmarbin_1648 Admiralty Bay Benthos Diversity Data Base (ABBED). Cumacea. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155484-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
+scarmarbin_1647 Admiralty Bay Benthos Diversity Data Base (ABBED). Tanaidacea. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155436-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
scarmarbin_1648 Admiralty Bay Benthos Diversity Data Base (ABBED). Cumacea. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155484-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
-scarmarbin_1649 Admiralty Bay Benthos Diversity Data Base (ABBED). Pycnogonida. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155485-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
+scarmarbin_1648 Admiralty Bay Benthos Diversity Data Base (ABBED). Cumacea. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155484-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
scarmarbin_1649 Admiralty Bay Benthos Diversity Data Base (ABBED). Pycnogonida. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155485-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
+scarmarbin_1649 Admiralty Bay Benthos Diversity Data Base (ABBED). Pycnogonida. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155485-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
scarmarbin_1651 Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80 SCIOPS STAC Catalog 1979-01-01 1986-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155486-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
scarmarbin_1651 Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80 ALL STAC Catalog 1979-01-01 1986-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155486-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
-scarmarbin_1716 Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80 - scarmarbin_1716 ALL STAC Catalog 1979-12-27 1980-02-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1221420764-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
scarmarbin_1716 Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80 - scarmarbin_1716 SCIOPS STAC Catalog 1979-12-27 1980-02-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1221420764-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
-scarmarbin_1772 Admiralty Bay Benthos Diversity Data Base (ABBED). Ophiuroidea. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155493-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
+scarmarbin_1716 Admiralty Bay Benthos Diversity Data Base (ABBED). Polychaeta. 1979-80 - scarmarbin_1716 ALL STAC Catalog 1979-12-27 1980-02-07 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1221420764-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
scarmarbin_1772 Admiralty Bay Benthos Diversity Data Base (ABBED). Ophiuroidea. ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155493-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
-scarmarbin_1806 Admiralty Bay Benthos Diversity Data Base (ABBED). Amphipoda (1997). ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155503-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
+scarmarbin_1772 Admiralty Bay Benthos Diversity Data Base (ABBED). Ophiuroidea. SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155493-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
scarmarbin_1806 Admiralty Bay Benthos Diversity Data Base (ABBED). Amphipoda (1997). SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155503-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
-scarmarbin_1807 Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1994). SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155504-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
+scarmarbin_1806 Admiralty Bay Benthos Diversity Data Base (ABBED). Amphipoda (1997). ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155503-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
scarmarbin_1807 Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1994). ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155504-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
-scarmarbin_1808 Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1997). SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155505-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
+scarmarbin_1807 Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1994). SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155504-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
scarmarbin_1808 Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1997). ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155505-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
-scarmarbin_987 A Biotic Database of Indo-Pacific Marine Mollusks (Southern Ocean Collection) ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155566-SCIOPS.umm_json The primary objective of this project is to provide a database of the estimated 25,000 named species of mollusks in the Indo-Pacific region, with summary data on their distribution and ecology. Another objective is to combine Indo-Pacific data with existing databases for Western Atlantic and Europe marine mollusk species and for higher taxa of mollusks to form the basis of a global database of Mollusca. This database will provide a uniform framework for linking specimen records from museum collections and data from fisheries to show spatial and temporal patterns of occurrence and abundance. This datasource provides primary access to the Indo-Pacific Mollusc Dataset using the obis schema. Data in the Indo-Paciffic Mollusc database use names from the Indo-Pacific Mollusc project together with point records from the Academy of Natural Sciences and the Australian Museum. Specimens referenced in this data set may be in the collections of either the Australian Museum or the Academy of Natural Sciences, but may have current identifications in those collections that are junior synonymys (or other junior names) of names in current use in the Indo-Pacific Mollusc database. proprietary
+scarmarbin_1808 Admiralty Bay Benthos Diversity Data Base (ABBED). Gastropoda (1997). SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155505-SCIOPS.umm_json Information system on benthic organisms of Admiralty Bay (King George Island, South Shetland Islands, Antarctic). proprietary
scarmarbin_987 A Biotic Database of Indo-Pacific Marine Mollusks (Southern Ocean Collection) SCIOPS STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155566-SCIOPS.umm_json The primary objective of this project is to provide a database of the estimated 25,000 named species of mollusks in the Indo-Pacific region, with summary data on their distribution and ecology. Another objective is to combine Indo-Pacific data with existing databases for Western Atlantic and Europe marine mollusk species and for higher taxa of mollusks to form the basis of a global database of Mollusca. This database will provide a uniform framework for linking specimen records from museum collections and data from fisheries to show spatial and temporal patterns of occurrence and abundance. This datasource provides primary access to the Indo-Pacific Mollusc Dataset using the obis schema. Data in the Indo-Paciffic Mollusc database use names from the Indo-Pacific Mollusc project together with point records from the Academy of Natural Sciences and the Australian Museum. Specimens referenced in this data set may be in the collections of either the Australian Museum or the Academy of Natural Sciences, but may have current identifications in those collections that are junior synonymys (or other junior names) of names in current use in the Indo-Pacific Mollusc database. proprietary
+scarmarbin_987 A Biotic Database of Indo-Pacific Marine Mollusks (Southern Ocean Collection) ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155566-SCIOPS.umm_json The primary objective of this project is to provide a database of the estimated 25,000 named species of mollusks in the Indo-Pacific region, with summary data on their distribution and ecology. Another objective is to combine Indo-Pacific data with existing databases for Western Atlantic and Europe marine mollusk species and for higher taxa of mollusks to form the basis of a global database of Mollusca. This database will provide a uniform framework for linking specimen records from museum collections and data from fisheries to show spatial and temporal patterns of occurrence and abundance. This datasource provides primary access to the Indo-Pacific Mollusc Dataset using the obis schema. Data in the Indo-Paciffic Mollusc database use names from the Indo-Pacific Mollusc project together with point records from the Academy of Natural Sciences and the Australian Museum. Specimens referenced in this data set may be in the collections of either the Australian Museum or the Academy of Natural Sciences, but may have current identifications in those collections that are junior synonymys (or other junior names) of names in current use in the Indo-Pacific Mollusc database. proprietary
scarmarbin_ABBED Admiralty Bay Benthos Biodiversity Database [SCAR-MarBIN] SCIOPS STAC Catalog 1906-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155568-SCIOPS.umm_json Admiralty Bay is one of the best studied sites in the maritime Antarctic. The first benthos data has been recorded in 1906 and knowledge is constantly gained by the research activities of permanent stations, Arctowski (Poland, since 1977), and Ferraz (Brazil, since 1984). Admiralty Bay is a protected area within the Antarctic Treaty System, an Antarctic Specially Managed Area (ASMA). It was also a reference site under the EASIZ programme, and has been or is currently investigated by several nations : Poland, Brazil, United States, Peru, Ecuador, Germany, The Netherlands, Belgium. ABBED (Admiralty Bay Benthos Biodiversity Database) is a Belgian-Polish initiative, which aims at compiling and linking existing information on Admiralty Bay benthos biodiversity and ecology. This information will be digitized into a database and linked to wider Antarctic marine biodiversity initiatives, such as SCAR-MarBIN, which will disseminate the information through a web portal. Being highly diverse in its content, formats and data providers, ABBED will constitute an extremely interesting case-study for SCAR-MarBIN, allowing to test strategic options which were retained for the development of the network. Moreover, the quality and quantity of data which will be made available to the community will reinforce the status of Admiralty Bay as a true reference point for Antarctic biodiversity research. The project aims at developing an interactive database on the biodiversity of benthic communities of Admiralty Bay, King George Island, for scientific, monitoring, management and conservation purposes. It is intended to be a springboard for promoting future research in this region, by centralizing the relevant information for i.e. scientific programme design. proprietary
scarmarbin_ABBED Admiralty Bay Benthos Biodiversity Database [SCAR-MarBIN] ALL STAC Catalog 1906-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214155568-SCIOPS.umm_json Admiralty Bay is one of the best studied sites in the maritime Antarctic. The first benthos data has been recorded in 1906 and knowledge is constantly gained by the research activities of permanent stations, Arctowski (Poland, since 1977), and Ferraz (Brazil, since 1984). Admiralty Bay is a protected area within the Antarctic Treaty System, an Antarctic Specially Managed Area (ASMA). It was also a reference site under the EASIZ programme, and has been or is currently investigated by several nations : Poland, Brazil, United States, Peru, Ecuador, Germany, The Netherlands, Belgium. ABBED (Admiralty Bay Benthos Biodiversity Database) is a Belgian-Polish initiative, which aims at compiling and linking existing information on Admiralty Bay benthos biodiversity and ecology. This information will be digitized into a database and linked to wider Antarctic marine biodiversity initiatives, such as SCAR-MarBIN, which will disseminate the information through a web portal. Being highly diverse in its content, formats and data providers, ABBED will constitute an extremely interesting case-study for SCAR-MarBIN, allowing to test strategic options which were retained for the development of the network. Moreover, the quality and quantity of data which will be made available to the community will reinforce the status of Admiralty Bay as a true reference point for Antarctic biodiversity research. The project aims at developing an interactive database on the biodiversity of benthic communities of Admiralty Bay, King George Island, for scientific, monitoring, management and conservation purposes. It is intended to be a springboard for promoting future research in this region, by centralizing the relevant information for i.e. scientific programme design. proprietary
schweizerisches-landesforstinventar-2009-2017_1.0 Schweizerisches Landesforstinventar. Ergebnisse der vierten Erhebung 2009–2017 ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817193-ENVIDAT.umm_json Swiss National Forest Inventory. Results of the fourth survey 2009–2017. The collection of data for the fourth National Forest Inventory (NFI) was carried out from 2009 to 2017, on average eight years after the third survey. The findings about state and development of Swiss forests are described and explained in detail. The report is structured according to the European criteria and indicators for sustainable forest management, namely: forest resources, health and vitality, wood production, biological diversity, protection forest and social economy. Finally, conclusions about sustainability are drawn based on the NFI findings. Keywords: forest area, growing stock, increment, yield, forest structure, forest condition, timber production, biodiversity, protection forest, recreation, sustainability, results National Forest Inventory, Switzerland Schweizerisches Landesforstinventar. Ergebnisse der vierten Erhebung 2009–2017. In den Jahren 2009 bis 2017 fanden die Erhebungen zum vierten Schweizerischen Landesforstinventar (LFI) statt, im Durchschnitt acht Jahre nach der dritten Erhebung. Die Resultate über den Zustand und die Entwicklung des Schweizer Waldes werden umfassend dargestellt und erläutert. Der Bericht ist thematisch strukturiert nach den europäischen Kriterien und Indikatoren zur nachhaltigen Bewirtschaftung des Waldes: Waldressourcen, Gesundheit und Vitalität, Holzproduktion, biologische Vielfalt, Schutzwald und Sozioökonomie. Eine Bilanz zur Nachhaltigkeit, basierend auf LFI-Ergebnissen, schliesst die Publikation ab. Keywords: Waldfläche, Holzvorrat, Zuwachs, Nutzung, Waldaufbau, Waldzustand, Holzproduktion, Biodiversität, Schutzwald, Erholung, Nachhaltigkeit, Ergebnisse Landesforstinventar, Schweiz Content license: All rights reserved. Copyright © 2020 by WSL, Birmensdorf. proprietary
@@ -20394,8 +20401,8 @@ sea_ice_measurements_database_1 Extract of data from the sea ice measurements da
sea_surface_temp_1deg_980_1 ISLSCP II Sea Surface Temperature ORNL_CLOUD STAC Catalog 1971-01-01 2000-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2784895830-ORNL_CLOUD.umm_json Sea surface temperature (SST) is an important indicator of the state of the earth climate system as well as a key variable in the coupling between the atmosphere and the ocean. Accurate knowledge of SST is essential for climate monitoring, prediction and research. It is also a key surface boundary condition for numerical weather prediction and for other atmospheric simulations using atmospheric general circulation models and regional models. SST also is important in gas exchange between the ocean and atmosphere, including the air-sea flux of carbon. Gridded SST products have been developed to satisfy these needs. There are 3 .zip files provided with this data set.Gridded monthly and weekly sea surface temperature (SST) and long term SST monthly climatology for the period 1971-2000 are provided here. Weekly normalized error variance fields are also provided with the weekly data. The data are derived using the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation (OI) global sea surface temperature analyses that use seven days of in situ (ship and buoy) and satellite SST observations and SST values derived from sea ice concentration. These analyses are produced weekly using optimum interpolation (OI) on a 1-degree grid. The data sets included in the ISLSCP II data collection are produced using version 2 of the OI analyses, called OIv2. In this data set, the ISLSCP II staff have masked land areas based on the ISLSCP II land/water mask. A file describing the differences between the ISLSCP II mask and the original mask used is provided. proprietary
seaflux_1 SeaFlux Data Products V1 GHRC_DAAC STAC Catalog 1988-01-01 2018-12-31 -179.87, -85.549, 179.87, 85.549 https://cmr.earthdata.nasa.gov/search/concepts/C1995869798-GHRC_DAAC.umm_json The SeaFlux Data Products dataset consists of estimates of ocean surface latent and sensible heat fluxes, 2m and 10m wind speed, 2m and 10m air temperature, 2m and 10m air humidity, and skin sea surface temperature. This data product was created by using the SeaFlux V3 model. These data are available globally from January 1, 1988 through December 31, 2018 in netCDF-4 format. proprietary
seaice_icecores_nelladan_1985_1 Icecores from Sea Ice, Nella Dan, 1985 AU_AADC STAC Catalog 1985-10-27 1985-11-03 50.1, -66.1, 63, -62.4 https://cmr.earthdata.nasa.gov/search/concepts/C1214311287-AU_AADC.umm_json During voyage 1 of 1985, sixteen ice cores were drilled from sea ice. Details from those cores include the position they were drilled, length of the core, percentage of the core that was frazil ice, and comments on the state of the core, or observations of the ice make-up. Physical records are archived at the Australian Antarctic Division. proprietary
-seamap47 Aerial Surveys of Marine Birds and Mammals in Support of Oil Spill Response and Injury Assessment SCIOPS STAC Catalog 1994-06-13 1997-11-22 -124.81862, 33.78087, -118.39433, 41.182 https://cmr.earthdata.nasa.gov/search/concepts/C1214589846-SCIOPS.umm_json Aerial Surveys of Marine Birds And Mammals In Support Of Oil Spill Response And Injury Assessment Studies: -- OSPR Aerial Surveys [Birds and Mammals] Study Code: OS Contract Number: FG7407-OS with California Department of Fish and Game (CDFG), Office of Spill Prevention and Response (OSPR); and 14-35-0001-30758 (Task 13293) with the Coastal Marine Institute, University of California, Santa Barbara. PRINCIPAL INVESTIGATOR(S)/AFFILIATION: Michael L. Bonnell, Ph.D. Institute of Marine Sciences, University of California, Santa Cruz proprietary
seamap47 Aerial Surveys of Marine Birds and Mammals in Support of Oil Spill Response and Injury Assessment ALL STAC Catalog 1994-06-13 1997-11-22 -124.81862, 33.78087, -118.39433, 41.182 https://cmr.earthdata.nasa.gov/search/concepts/C1214589846-SCIOPS.umm_json Aerial Surveys of Marine Birds And Mammals In Support Of Oil Spill Response And Injury Assessment Studies: -- OSPR Aerial Surveys [Birds and Mammals] Study Code: OS Contract Number: FG7407-OS with California Department of Fish and Game (CDFG), Office of Spill Prevention and Response (OSPR); and 14-35-0001-30758 (Task 13293) with the Coastal Marine Institute, University of California, Santa Barbara. PRINCIPAL INVESTIGATOR(S)/AFFILIATION: Michael L. Bonnell, Ph.D. Institute of Marine Sciences, University of California, Santa Cruz proprietary
+seamap47 Aerial Surveys of Marine Birds and Mammals in Support of Oil Spill Response and Injury Assessment SCIOPS STAC Catalog 1994-06-13 1997-11-22 -124.81862, 33.78087, -118.39433, 41.182 https://cmr.earthdata.nasa.gov/search/concepts/C1214589846-SCIOPS.umm_json Aerial Surveys of Marine Birds And Mammals In Support Of Oil Spill Response And Injury Assessment Studies: -- OSPR Aerial Surveys [Birds and Mammals] Study Code: OS Contract Number: FG7407-OS with California Department of Fish and Game (CDFG), Office of Spill Prevention and Response (OSPR); and 14-35-0001-30758 (Task 13293) with the Coastal Marine Institute, University of California, Santa Barbara. PRINCIPAL INVESTIGATOR(S)/AFFILIATION: Michael L. Bonnell, Ph.D. Institute of Marine Sciences, University of California, Santa Cruz proprietary
seasonal-fractional-snow-covered-area-algorithm_1.0 Seasonal fractional snow-covered area algorithm ENVIDAT STAC Catalog 2021-01-01 2021-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817560-ENVIDAT.umm_json This is the source code for computing the seasonal fractional snow-covered area. It is written in Fortran 90. The code reads snow depth (HS) and snow water equivalent (SWE) data from the provided example file HS_SWE.txt and writes the computed fractional snow-covered area (fSCA) to a file fSCA.txt. The current version can be found in the WSL/SLF Gitlab repository: https://gitlabext.wsl.ch/snow-models/fractional-snow-covered-area proprietary
seasonal-snow-data-wy-2016-2022_1.0 Seasonal snow data for Switzerland OSHD - FSM2sohd ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226083044-ENVIDAT.umm_json This dataset includes gridded data on snow depth (m), snow water equivalent (mm), runoff from snow melt (mm) and snow cover fraction for Swtzerland. The data is spanning the water years 2016-2022 at a high spatial resolution of 250 m. Data are stored as daily results. proprietary
seawater-temp-casey-Dec03_1 Marine water temperatures around Casey station - December 2003 AU_AADC STAC Catalog 2003-12-01 2004-01-01 110.35217, -66.51326, 110.67627, -66.23146 https://cmr.earthdata.nasa.gov/search/concepts/C1214311249-AU_AADC.umm_json Water temperatures were recorded by Tidbit temperature loggers attached to experimental mesocosms suspended below the sea ice at four sites around Casey in summer 2003/04. Data are temperature in degrees Celsius automatically logged every 5 minutes between the 01/12/2003 and 31/12/2003 at Brown Bay inner (S66 16.811 E110 32.475) and McGrady Cove (S66 16.556 E110 34.392), and between 02/12/2003 and 01/01/2004 at Brown Bay outer (S66 16.811 E110 32.526) and O'Brien Bay (S66 18.730 E110 30.810). Three loggers were deployed at each site; loggers A and B - one attached to each of two mesocosms (perforated 20 litre food buckets) and another - logger I - attached to plastic tubing approximately 1 metre above the mesocosms. Only two data loggers (A and B) were deployed at Mcgrady Cove. Mesocosms were suspended two to three metres below the bottom edge of the sea ice through a 1 metre diameter hole and were periodically raised to the surface for short periods (~1 hour). This experiment was part of the short-term biomonitoring program for the Thala Valley Tip Clean-up at Casey during summer 2003/04. These data were collected as part of ASAC project 2201 (ASAC_2201 - Natural variability and human induced change in Antarctic nearshore marine benthic communities). See also other metadata records by Glenn Johnstone for related information. The fields in this dataset are: Date Time Temperature Location proprietary
@@ -20414,10 +20421,10 @@ sensitivity-of-modeled-snow-instability_1.0 Sensitivity of modeled snow instabil
sentinel-1-grd-bundle-1_NA Sentinel-1 - Level-1 - Interferometric Wide Swath Ground Range Detected High Resolution INPE STAC Catalog 2021-05-01 2024-06-17 -76.546547, -35.235916, -31.785385, 6.970906 https://cmr.earthdata.nasa.gov/search/concepts/C3108204188-INPE.umm_json Copernicus Sentinel-1 Level-1 Ground Range Detected (GRD) products consist of focused SAR data that has been detected, multi-looked and projected to ground range using an Earth ellipsoid model. This dataset contains interferometric wide swath ground range detected high resolution data available over Brazil. proprietary
sentinel-3-olci-l1-bundle-1_NA Sentinel-3/OLCI - Level-1B Full Resolution INPE STAC Catalog 2023-03-04 2024-06-17 -179.431, -45.0723, 179.987, 10.4204 https://cmr.earthdata.nasa.gov/search/concepts/C3108204728-INPE.umm_json Copernicus Sentinel-3/OLCI Level-1B product OL_1_EFR (EO processing mode for Full Resolution) over Brazil. proprietary
shadoz_ozonesonde_726_1 SAFARI 2000 SHADOZ Ozonesonde Data, Zambia and Regional Sites, Dry Season 2000 ORNL_CLOUD STAC Catalog 2000-08-01 2000-11-30 55.48, -7.98, 55.48, -7.98 https://cmr.earthdata.nasa.gov/search/concepts/C2789016629-ORNL_CLOUD.umm_json Ozonesonde launches were made by the Southern Hemisphere ADditional OZonesondes (SHADOZ) group as part of the SAFARI 2000 Dry Season Campaign in September 2000 (Thompson et al., 2002). Ozonesondes are balloon-borne instruments measuring profile ozone, as well as temperature and pressure from an attached radiosonde, up to 35 km in height (around 5 hPa in pressure coordinates) capturing the troposphere and lower stratospheric portion of the atmosphere. During the campaign, ozonesondes were launched daily during the height of the burning season and in a region of active biomass burning activity. proprietary
-shirley_dem_1 A digital elevation model (DEM) and orthophoto of Shirley Island, Windmill Islands, Antarctica AU_AADC STAC Catalog 2005-01-01 2007-05-01 110.473, -66.287, 110.509, -66.277 https://cmr.earthdata.nasa.gov/search/concepts/C1214311290-AU_AADC.umm_json This dataset includes: (i) a 2 metre resolution digital elevation model (DEM) of Shirley Island, Windmill Islands, Antarctica; (ii) reliability data for the DEM; (iii) contours interpolated from the DEM; and (iv) an orthophoto created using the DEM. The data are stored in the UTM zone 49 map projection. The horizontal datum is WGS84. The data were created by Robert Anders, Centre for Spatial Information Science, University of Tasmania, Australia to support the postgraduate research of Phillipa Bricher into the nesting sites of Adelie Penguins. See a related URL below for a map showing Shirley island. proprietary
shirley_dem_1 A digital elevation model (DEM) and orthophoto of Shirley Island, Windmill Islands, Antarctica ALL STAC Catalog 2005-01-01 2007-05-01 110.473, -66.287, 110.509, -66.277 https://cmr.earthdata.nasa.gov/search/concepts/C1214311290-AU_AADC.umm_json This dataset includes: (i) a 2 metre resolution digital elevation model (DEM) of Shirley Island, Windmill Islands, Antarctica; (ii) reliability data for the DEM; (iii) contours interpolated from the DEM; and (iv) an orthophoto created using the DEM. The data are stored in the UTM zone 49 map projection. The horizontal datum is WGS84. The data were created by Robert Anders, Centre for Spatial Information Science, University of Tasmania, Australia to support the postgraduate research of Phillipa Bricher into the nesting sites of Adelie Penguins. See a related URL below for a map showing Shirley island. proprietary
-simrad_SO Acoustic responses to water column features, Antarctic, Aug-Sept 2002, GLOBEC. ALL STAC Catalog 2002-08-03 2002-09-15 -75.5, -68.75, -69.5, -65.75 https://cmr.earthdata.nasa.gov/search/concepts/C1214155475-SCIOPS.umm_json Using the hull mounted Simrad EK500 Scientific Sounder System, acoustic returns from 38, 120, and 200 kHz transducers were recorded continuously along ship's track from Aug 3 - Sept 15, 2002. Of interest, was the acoustic returns from zooplankton patches and density structures, and the signel correlations with known plankton tows and CTD casts. The survey area included the continental margin to the west of the Antarctic Peninsula extending from the northern tip of Adelaide Island to the southern portion of Alexander Island, Crystal Sound, and Marguerite Bay. These data have been reduced to daily files and are supported by software for manipulative purposes. Ship name/cruise ID/dates of cruise RVIB Nathaniel B. Palmer / NBP0204 / Jul 31-Sep 18 2002 proprietary
+shirley_dem_1 A digital elevation model (DEM) and orthophoto of Shirley Island, Windmill Islands, Antarctica AU_AADC STAC Catalog 2005-01-01 2007-05-01 110.473, -66.287, 110.509, -66.277 https://cmr.earthdata.nasa.gov/search/concepts/C1214311290-AU_AADC.umm_json This dataset includes: (i) a 2 metre resolution digital elevation model (DEM) of Shirley Island, Windmill Islands, Antarctica; (ii) reliability data for the DEM; (iii) contours interpolated from the DEM; and (iv) an orthophoto created using the DEM. The data are stored in the UTM zone 49 map projection. The horizontal datum is WGS84. The data were created by Robert Anders, Centre for Spatial Information Science, University of Tasmania, Australia to support the postgraduate research of Phillipa Bricher into the nesting sites of Adelie Penguins. See a related URL below for a map showing Shirley island. proprietary
simrad_SO Acoustic responses to water column features, Antarctic, Aug-Sept 2002, GLOBEC. SCIOPS STAC Catalog 2002-08-03 2002-09-15 -75.5, -68.75, -69.5, -65.75 https://cmr.earthdata.nasa.gov/search/concepts/C1214155475-SCIOPS.umm_json Using the hull mounted Simrad EK500 Scientific Sounder System, acoustic returns from 38, 120, and 200 kHz transducers were recorded continuously along ship's track from Aug 3 - Sept 15, 2002. Of interest, was the acoustic returns from zooplankton patches and density structures, and the signel correlations with known plankton tows and CTD casts. The survey area included the continental margin to the west of the Antarctic Peninsula extending from the northern tip of Adelaide Island to the southern portion of Alexander Island, Crystal Sound, and Marguerite Bay. These data have been reduced to daily files and are supported by software for manipulative purposes. Ship name/cruise ID/dates of cruise RVIB Nathaniel B. Palmer / NBP0204 / Jul 31-Sep 18 2002 proprietary
+simrad_SO Acoustic responses to water column features, Antarctic, Aug-Sept 2002, GLOBEC. ALL STAC Catalog 2002-08-03 2002-09-15 -75.5, -68.75, -69.5, -65.75 https://cmr.earthdata.nasa.gov/search/concepts/C1214155475-SCIOPS.umm_json Using the hull mounted Simrad EK500 Scientific Sounder System, acoustic returns from 38, 120, and 200 kHz transducers were recorded continuously along ship's track from Aug 3 - Sept 15, 2002. Of interest, was the acoustic returns from zooplankton patches and density structures, and the signel correlations with known plankton tows and CTD casts. The survey area included the continental margin to the west of the Antarctic Peninsula extending from the northern tip of Adelaide Island to the southern portion of Alexander Island, Crystal Sound, and Marguerite Bay. These data have been reduced to daily files and are supported by software for manipulative purposes. Ship name/cruise ID/dates of cruise RVIB Nathaniel B. Palmer / NBP0204 / Jul 31-Sep 18 2002 proprietary
simulated-avalanche-problem-types-at-weissfluhjoch-1999-2017_1.0 Simulated avalanche problem types and seismic avalanche activity around Weissfluhjoch ENVIDAT STAC Catalog 2021-01-01 2021-01-01 9.80934, 46.82962, 9.80934, 46.82962 https://cmr.earthdata.nasa.gov/search/concepts/C2789817408-ENVIDAT.umm_json Avalanche problem types were derived from snow cover simulations with the models Crocus and SNOWPACK at the Weissfluhjoch study plot, Davos, CH. The data include annual frequencies of avalanche problem types for the seasons 1999-2017 and daily presence of avalanche problem types for the period 01.01.2016 - 30.04.2016. Avalanche activity was derived from two seismic sensor arrays deployed no further than 15 km from Weissfluhjoch, Davos, CH. The data cover the period 01.01.2016 - 30.04.2016. proprietary
simulated-future-discharge-and-climatological-variables_1.0 Simulated future discharge and climatological variables for medium-sized catchments in Switzerland ENVIDAT STAC Catalog 2019-01-01 2019-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817564-ENVIDAT.umm_json "Daily discharge and the related hydro-meteorological variables precipitation, snowmelt, and soil moisture are provided for current (1981-2017) and for future climate conditions (1981-2100) for 307 medium-sized catchments in Switzerland. The catchments have a median catchment area of 117 km². The 307 catchments together form a set representative of the climatological conditions and runoff characteristics in Switzerland. The four variables were simulated at a daily resolution using the hydrological model PREVAH. PREVAH is a conceptual process-based model that was run in this study in its fully distributed version on a 500 m grid (Viviroli et al. 2009a). For the calibration, runoff time series from 140 mesoscale catchments covering the different runoff regimes were used. The model calibration was conducted over the period 1993-1997. Verification was performed on the period 1983-2005 using (i) volumetric deviation (Viviroli et al. 2007) and (ii) benchmark efficiency (Schäfli et al 2007) as objective functions. The calibration and validation procedures are described in detail in Köplin et al. (2010). The parameters for each model grid cell were derived by regionalizing the parameters obtained for the 140 catchments with a procedure based on ordinary kriging (Viviroli et al. 2009b, Köplin et al. 2010). The calibrated and validated model was then driven with transient meteorological data (precipitation, temperature, radiation, and wind) representing both reference (1981-2017) and future climate conditions (2018-2099). The data were derived from the CH2018 climate scenarios (NCCS 2018) provided by the Swiss National Centre for Climate Services (NCCS). They were obtained from climate experiments produced with different climate modeling chains, consisting of a global and a regional circulation model each, within EUROCORDEX for three representative concentration pathways (RCP) emission scenarios. Downscaled output of ten climate model chains derived by quantile mapping were considered. The focus was on the chains of the EUR-11 domain with a horizontal resolution of 0.11 degrees (roughly 12.5 km). The climate model chains (GCM, RCM, RCP, and grid resolution) used are listed below: - ICHEC-EC-EARTH DMI-HIRHAM5 2.6 EUR-11 - ICHEC-EC-EARTH DMI-HIRHAM5 4.5 EUR-11 - ICHEC-EC-EARTH DMI-HIRHAM5 8.5 EUR-11 - ICHEC-EC-EARTH SMHI-RCA4 2.6 EUR-11 - ICHEC-EC-EARTH SMHI-RCA4 4.5 EUR-11 - ICHEC-EC-EARTH SMHI-RCA4 8.5 EUR-11 - MOHC-HadGEM2-ES SMHI-RCA4 4.5 EUR-11 - MOHC-HadGEM2-ES SMHI-RCA4 8.5 EUR-11 - MPI-M-MPI-ESM-LR SMHI-RCA4 4.5 EUR-11 - MPI-M-MPI-ESM-LR SMHI-RCA4 8.5 EUR-11 __*References*__: - Köplin, N., D. Viviroli, B. Schädler, and R. Weingartner (2010), _How does climate change affect mesoscale catchments in Switzerland? - A framework for a comprehensive assessment_, Advances in Geosciences, 27, 111-119, doi:10.5194/adgeo-27-111-2010. - National Centre for Climate Services (2018), CH2018 - _Climate Scenarios for Switzerland_, Tech. rep., NCCS, Zurich. - Schäfli, B., and H. V. Gupta (2007), _Do Nash values have value?_, Hydrological Processes, 21, 2075-2080, doi:10.1002/hyp.6825. - Viviroli, D., J. Gurtz, and M. Zappa (2007), _The hydrological modelling system PREVAH. Part II - Physical model description_, Geographica Bernensia, 40, 1-89. - Viviroli, D., M. Zappa, J. Gurtz, and R. Weingartner (2009a), _An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools_, Environmental Modelling & Software, 24, 1209-1222, doi:10.1016/j.envsoft.2009.04.001. - Viviroli, D., H. Mittelbach, J. Gurtz, and R. Weingartner (2009b), _Continuous simulation for flood estimation in ungauged mesoscale catchments of Switzerland-Part II: Parameter regionalisation and flood estimation results_, Journal of Hydrology, 377 (1), 208-225, doi:10.1016/j.jhydrol.2009.08.022." proprietary
simulating-chamois-populations_1.0 Simulating population divergence of Northern chamois in the Alps based on habitat dynamics ENVIDAT STAC Catalog 2022-01-01 2022-01-01 4.8, 43.5, 16.3, 48.3 https://cmr.earthdata.nasa.gov/search/concepts/C2789817711-ENVIDAT.umm_json # General description Genomic data, habitat suitability raster files and scripts to run gen3sis to simulate cumulative divergence over time as approximation for genetic differentiation. Scripts for basic analysis of the simulations (e.g., create distance matrix from sampling locations) are provided, too. See original publication (doi link will be provided after publication) for details. The study area are the European Alps. All data is uploaded as zipped file. Unzip them after the download and put all data in one folder. See linked publications for correct citation of the data used, use of the data without correct citation is not allowed. __Corresponding author__: Flurin Leugger, email: flurin.leugger@gmail.com # Description of the data (content of the different zip folders) ## Abiotic data ### Glaciers Folders with raster stacks with glaciated areas at 0.05° resolution in WGS84 projection from Seguinot et al. (2018). Seguinot, J., Ivy-Ochs, S., Jouvet, G., Huss, M., Funk, M., & Preusser, F. (2018). Modelling last glacial cycle ice dynamics in the Alps. _The Cryosphere, 12(10)_, 3265–3285. https://doi.org/10.5194/tc-12-3265-2018 ### Rivers * __river_raster_elevation_class.tif__: raster file (.tif) at 0.05° resolution and WGS84 projection with large rivers (scenario 2 from publication). The rivers (each cell) is classified according to the elevation of the cell. Natural Earth. (2018). Rivers + lake centerlines version 4.1.0. Retrieved January 22, 2020, from https://www.naturalearthdata.com/downloads/50m-physical-vectors/50m-rivers-lake-centerlines * __river_raster_strahler_class_5km.tif__: raster file at 0.05° resolution and WGS84 projection with medium rivers. The rivers are classified according to their Strahler order. Food and Agriculture Organization of the United Nations. (2014). Rivers in Europe (Derived from HydroSHEDS). Retrieved January 29, 2020, from http://www.fao.org/geonetwork/srv/fr/google.kml?uuid=e0243940-e5d9-487c-8102-45180cf1a99f&layers=AQUAMAPS:37253_rivers_europe ## Fossil records * __chamois_fossil_combined_public.xlsx__: list with fossil records until 20,000 years BP from Central Europe, see linked references for citation. ## Chamois occurrences * __chamois_occurrence.csv__: Chamois presences from all sources used for the publication (see Suppl. mat. Table S1 for detailed information and correct citations of the data) aggregated at 0.05° resolution (~5km). ## Gen3sis * __config__: folders with all configuration files used to run the simulations for the publication (different dispersal divergence parameters). * __scripts__: scripts (and helper functions) to run the gen3sis simulations including scripts for the beginning of the subsequent analysis. ## Genetic * __populations.snps.light.vcf__: vcf file of the sampled Northern chamois _(Rupicapra rupicapra)_ . The genomic data encompasses 20k SNPs (from ddRAD sequencing). * __Sequencing_final_without_slovakia.txt__: sampling locations of Northern chamois _(Rupicapra rupicapra)_ ## HSM * __habitat_suitability_hindcasting__: Aggregated habitat suitability raster files (stacks, .grd files) at 0.05° resolution and WGS84 projection from 20,000 years BP until today in 100 year time steps. There are separate folders for each environmental variable scenario used (different terrain slope variables) an the different occurrence/pseudo-absence sampling strategy used. * __ODMAP_LeuggerEtAl__2021-10-25.csv__: ODMAP protocol proprietary
@@ -20460,12 +20467,12 @@ soilte1r_312_1 BOREAS TE-01 Soils Data over the SSA Tower Sites in Raster Format
solar-biomass-additional-references_1.0 Linking solar and biomass resources to generate renewable energy: can we find local complementarities in the agricultural setting? ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226083029-ENVIDAT.umm_json Additional references to the article: Linking solar and biomass resources to generate renewable en-ergy: can we find local complementarities in the agricultural setting? Gillianne Bowman, Thierry Huber, Vanessa Burg Energies, https://www.mdpi.com/1996-1073/16/3/1486 Today, the energy transition is underway to tackle the problems of climate change and energy sufficiency. For this transition to succeed, it is essential to use all available re-newable energy resources most efficiently. However, renewable energies often bring high volatility that needs to be balanced. One solution is to combine the use of different renewable sources to increase the overall energy output or reduce its environmental impact. Here, we estimate the agricultural solar and biomass resources at the local level in Switzerland, considering their spatial and temporal variability using Geographic In-formation Systems. We then identify the technologies that could allow synergies or complementarities. Overall, the technical agricultural resources potential is ~15 PJ/annus biogas yield from residual biomass and ~10 TWh/a electricity from solar installed on roofs (equivalent to ~36 PJ/a). Anaerobic digestion, combined heat & power plant, Raw manure separation, Biomethane upgrading, Power to X, Electrolysis, Chill generation and Pho-tovoltaic on biogas facilities could foster complementarity in the system if resources are pooled within the agricultural setting. Temporal complementarity at the farm scale can only lead to partial autarchy. The possible benefits from these complementarities should be better identified, particulary in looking looking at the economic viability of such systems. proprietary
soller_wetlands_674_1 LBA Regional Freshwater Wetlands, 1-Degree (Stillwell-Soller et al.) ORNL_CLOUD STAC Catalog 1995-01-01 1995-09-01 -85, -25, -30, 5 https://cmr.earthdata.nasa.gov/search/concepts/C2777324266-ORNL_CLOUD.umm_json This data set consists of a subset of a 1-degree gridded global freshwater wetlands database (Stillwell-Soller et al. 1995). This subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10 N to 25 S, 30 to 85 W). The data are in ASCII GRID format.The global freshwater wetlands database was assembled from two data sets: Aselman and Crutzen's (1989) wetlands data set and Klinger's political Alaska data set (pers. comm. to L. M. Stillwell-Soller, 1995). The aim of Stillwell-Soller's global data set was to provide an accurate, comprehensive and uniform set of files for convenient specification of wetlands in global climate models. The main source of data was Aselman and Crutzen's global maps of percent cover for a variety of wetlands categories at 2.5-degree latitude by 5-degree longitude resolution. There was some reorganization for seasonally varying categories. Aselman and Crutzen's data were interpolated to a standard 1-degree by 1-degree grid through bilinear interpolation. Their data were geographically complete except for the Alaskan region, for which Klinger's data set provided values.More information can be found at ftp://daac.ornl.gov/data/lba/land_use_land_cover_change/soller_wetlands/comp/soller_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. More information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html. proprietary
sondecpexcv_1 Radiosondes CPEX-CV GHRC_DAAC STAC Catalog 2022-09-01 2022-09-29 -23.400798, 0.053658, -0.073876, 16.789384 https://cmr.earthdata.nasa.gov/search/concepts/C2748663117-GHRC_DAAC.umm_json The Radiosonde CPEX-CV dataset was collected during the Convective Processes Experiment – Cabo Verde (CPEX-CV) field campaign. The NASA CPEX-CV field campaign was based out of Sal Island, Cabo Verde from August through September 2022. The campaign is a continuation of CPEX – Aerosols and Winds (CPEX-AW) and was conducted aboard the NASA DC-8 aircraft equipped with remote sensors and dropsonde-launch capability that will allow for the measurement of tropospheric aerosols, winds, temperature, water vapor, and precipitation. The overarching CPEX-CV goal was to investigate atmospheric dynamics, marine boundary layer properties, convection, the dust-laden Saharan Air Layer, and their interactions across various spatial scales to improve understanding and predictability of process-level lifecycles in the data-sparse tropical East Atlantic region. These radiosonde data files include wind direction, dew point temperature, geopotential height, mixing ratio, atmospheric pressure, relative humidity, wind speed, temperature, potential temperature, equivalent potential temperature, and virtual potential temperature measurements at various levels of the troposphere. These data files are available from September 1, 2022, through September 29, 2022 in netCDF-4 format. proprietary
-sonobuoy_whale_SO Acoustic census of mysticete whales, Antarctic, Mar-Aug 2001, GLOBEC ALL STAC Catalog 2001-03-21 2001-08-28 -77.2, -70.3, -61.5, -59 https://cmr.earthdata.nasa.gov/search/concepts/C1214155588-SCIOPS.umm_json Mysticete whale calls were monitored/recorded via deployment of directional sonobuoys during March-August 2001. This monitoring technique is used to study whale distribution, behavior and aid in estimating populations. Deployments were either random or when whales were observed. The observed calls are identified by species. Ancillary calls by seals are reported but not identified by species. The survey area included the continental margin to the west of the Antarctic Peninsula extending from the northern tip of Adelaide Island to the southern portion of Alexander Island, Crystal Sound, and Marguerite Bay. Ship names/cruise ID/cruise dates R/V Laurence M. Gould / LMG0103 / Mar 18-Apr 13 2001 RVIB Nathaniel B. Palmer / NBP0103 / Apr 24-Jun 05 2001 RVIB Nathaniel B. Palmer / NBP0104 / Jul 24-Aug 31 2001 Access to the original acoustic recordings should be directed to the Investigator identified in this description. proprietary
sonobuoy_whale_SO Acoustic census of mysticete whales, Antarctic, Mar-Aug 2001, GLOBEC SCIOPS STAC Catalog 2001-03-21 2001-08-28 -77.2, -70.3, -61.5, -59 https://cmr.earthdata.nasa.gov/search/concepts/C1214155588-SCIOPS.umm_json Mysticete whale calls were monitored/recorded via deployment of directional sonobuoys during March-August 2001. This monitoring technique is used to study whale distribution, behavior and aid in estimating populations. Deployments were either random or when whales were observed. The observed calls are identified by species. Ancillary calls by seals are reported but not identified by species. The survey area included the continental margin to the west of the Antarctic Peninsula extending from the northern tip of Adelaide Island to the southern portion of Alexander Island, Crystal Sound, and Marguerite Bay. Ship names/cruise ID/cruise dates R/V Laurence M. Gould / LMG0103 / Mar 18-Apr 13 2001 RVIB Nathaniel B. Palmer / NBP0103 / Apr 24-Jun 05 2001 RVIB Nathaniel B. Palmer / NBP0104 / Jul 24-Aug 31 2001 Access to the original acoustic recordings should be directed to the Investigator identified in this description. proprietary
+sonobuoy_whale_SO Acoustic census of mysticete whales, Antarctic, Mar-Aug 2001, GLOBEC ALL STAC Catalog 2001-03-21 2001-08-28 -77.2, -70.3, -61.5, -59 https://cmr.earthdata.nasa.gov/search/concepts/C1214155588-SCIOPS.umm_json Mysticete whale calls were monitored/recorded via deployment of directional sonobuoys during March-August 2001. This monitoring technique is used to study whale distribution, behavior and aid in estimating populations. Deployments were either random or when whales were observed. The observed calls are identified by species. Ancillary calls by seals are reported but not identified by species. The survey area included the continental margin to the west of the Antarctic Peninsula extending from the northern tip of Adelaide Island to the southern portion of Alexander Island, Crystal Sound, and Marguerite Bay. Ship names/cruise ID/cruise dates R/V Laurence M. Gould / LMG0103 / Mar 18-Apr 13 2001 RVIB Nathaniel B. Palmer / NBP0103 / Apr 24-Jun 05 2001 RVIB Nathaniel B. Palmer / NBP0104 / Jul 24-Aug 31 2001 Access to the original acoustic recordings should be directed to the Investigator identified in this description. proprietary
source-code-climate-change-scenarios-at-hourly-resolution_1.0 Source code for: Climate change scenarios at hourly time-step over Switzerland from an enhanced temporal downscaling approach ENVIDAT STAC Catalog 2021-01-01 2021-01-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2789816944-ENVIDAT.umm_json This repository contains the source code of the analysis presented in the related paper. The code can be found in the following github repository: https://github.com/Chelmy88/temporal_downscaling This code can be used to perform temporal downscaling of meteorological time series from daily to hourly time steps and to perform the quality assessment described in the paper. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. proprietary
sources-and-turnover-of-soil-organic-matter-in-pfynwald-irrigation-experiment_1.0 Sources and turnover of soil organic matter in Pfynwald irrigation experiment ENVIDAT STAC Catalog 2023-01-01 2023-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C3226083043-ENVIDAT.umm_json This dataset contains all data on which the following publication below is based. Paper Citation: Guidi, C., Lehmann, M.M., Meusburger, K., Saurer, M., Vitali, V., Peter, M., Brunner, I., Hagedorn, F. (accepted). Tracing sources and turnover of soil organic matter in a long-term irrigated dry forest using a novel hydrogen isotope approach. Soil Biology and Biochemistry. Please cite this paper together with the citation for the datafile. Data from a 17-year-long irrigation experiment (Pfynwald, Switzerland) in a naturally dry forest dominated by 100-year-old pine trees (Pinus sylvestris). Data include: (1) Isotopic composition (stable isotope ratios of non-exchangeable hydrogen δ2Hn, carbon δ13C, and nitrogen δ15N) and Hn, C and N concentrations in SOM sources (fresh Pinus sylvestris needles, litter layer, fine roots), bulk SOM (organic layer, 0-2 cm, 2-5 cm, 60-80 cm), particle-size fractions (depths: 0-2 cm, 2-5 cm; cPOM: coarse POM; fPOM: fine POM; MOM: mineral-associated organic matter); (2) Mass loss, δ2Hn values and Hn concentrations of Pinus sylvestris fine roots and needle litter (litter decomposition experiments from Herzog et al. 2019, ISME journal, and Guidi et al. 2022, Global Change Biology); (3) Relative source contribution (foliar litter, fine roots, and mycelia) to bulk SOM and fractions estimated using Bayesian mixing models (R package MixSIAR, version 3.1.12) with irrigation and depth as fixed factors. The models were informed with δ13C, δ15N and δ2Hn values and C, N, and Hn concentrations of foliar litter, roots, and mycelia as input sources. Given the kinetic isotope fractionation occurring during microbial SOM decomposition, the mixing models were informed with isotope fractionation factors, representing the isotope enrichment from sources to soils; (4) Fraction of new organic Hn (Fnew) over the irrigation period, calculated using a simple end-member mixing model according to Balesdent et al. (1987) and mean residence time estimated as MRT = - t / ln (1 - Fnew), with t time in years since irrigation started and assuming single-pool model with first-order kinetics. proprietary
-sowers_0739491 2008 South Pole Firn Air Methane Isotopes SCIOPS STAC Catalog 2008-12-01 2009-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214597995-SCIOPS.umm_json This project will involve the measurement of methane and other trace gases in firn air collected at South Pole, Antarctica. The analyses will include: methane isotopes, light non-methane hydrocarbons (ethane, propane, and n-butane), sulfur gases (OCS, CS2), and methyl halides (CH3Cl and CH3Br). The atmospheric burdens of these trace gases reflect changes in atmospheric OH, biomass burning, biogenic activity in terrestrial, oceanic, and wetland ecosystems, and industrial/agricultural activity. The goal of this project is to develop atmospheric histories for these trace gases over the last century through examination of depth profiles of these gases in South Pole firn air. The project will involve two phases: 1) a field campaign at South Pole, Antarctica to drill two firn holes and fill a total of ~200 flasks from depths reaching 120 m, 2) analysis of firn air at UCI, Penn State University, and several other collaborating laboratories. Atmospheric histories will be inferred from the measurements using a one dimensional advective/diffusive model of firn air transport. proprietary
sowers_0739491 2008 South Pole Firn Air Methane Isotopes ALL STAC Catalog 2008-12-01 2009-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214597995-SCIOPS.umm_json This project will involve the measurement of methane and other trace gases in firn air collected at South Pole, Antarctica. The analyses will include: methane isotopes, light non-methane hydrocarbons (ethane, propane, and n-butane), sulfur gases (OCS, CS2), and methyl halides (CH3Cl and CH3Br). The atmospheric burdens of these trace gases reflect changes in atmospheric OH, biomass burning, biogenic activity in terrestrial, oceanic, and wetland ecosystems, and industrial/agricultural activity. The goal of this project is to develop atmospheric histories for these trace gases over the last century through examination of depth profiles of these gases in South Pole firn air. The project will involve two phases: 1) a field campaign at South Pole, Antarctica to drill two firn holes and fill a total of ~200 flasks from depths reaching 120 m, 2) analysis of firn air at UCI, Penn State University, and several other collaborating laboratories. Atmospheric histories will be inferred from the measurements using a one dimensional advective/diffusive model of firn air transport. proprietary
+sowers_0739491 2008 South Pole Firn Air Methane Isotopes SCIOPS STAC Catalog 2008-12-01 2009-01-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C1214597995-SCIOPS.umm_json This project will involve the measurement of methane and other trace gases in firn air collected at South Pole, Antarctica. The analyses will include: methane isotopes, light non-methane hydrocarbons (ethane, propane, and n-butane), sulfur gases (OCS, CS2), and methyl halides (CH3Cl and CH3Br). The atmospheric burdens of these trace gases reflect changes in atmospheric OH, biomass burning, biogenic activity in terrestrial, oceanic, and wetland ecosystems, and industrial/agricultural activity. The goal of this project is to develop atmospheric histories for these trace gases over the last century through examination of depth profiles of these gases in South Pole firn air. The project will involve two phases: 1) a field campaign at South Pole, Antarctica to drill two firn holes and fill a total of ~200 flasks from depths reaching 120 m, 2) analysis of firn air at UCI, Penn State University, and several other collaborating laboratories. Atmospheric histories will be inferred from the measurements using a one dimensional advective/diffusive model of firn air transport. proprietary
spatial-modelling-of-ecological-indicator-values_1.0 Spatial modelling of ecological indicator values ENVIDAT STAC Catalog 2020-01-01 2020-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817163-ENVIDAT.umm_json "Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we derived environmental data layers by mapping ecological indicator values (EIVs) in space by using a large set of environmental predictors in Switzerland. This dataset contains the predictors (raster layers) generated and used in the following publication (Descombes et al. 2020). Only predictors for which we have the rights to share them are provided. Other datasets and predictors can be accessed via the original data provider. Details on the predictors and sources are fully described in the publication. The predictors are provided as GeoTIFF files, at 93 m spatial resolution and Mercator projection (""+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs""). The excel file (xlsx) provides a short description of the raster layers. Paper Citation: Descombes, P. et al. (2020). Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes. Ecography. (accepted)" proprietary
spatial-planning-brazil_1.0 Spatially explicit data to evaluate spatial planning outcomes in a coastal region in São Paulo State, Brazil ENVIDAT STAC Catalog 2022-01-01 2022-01-01 -46.1425781, -24.005155, -44.4836426, -23.1908626 https://cmr.earthdata.nasa.gov/search/concepts/C2789817270-ENVIDAT.umm_json "The present dataset is part of the published scientific paper entitled “The role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil” (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in São Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation. We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the São Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality. 1) Land use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software. Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015). To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication). The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of > 0.2 NDVI to represent an improvement in forest quality. 2) Federal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population: The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication). 3) Topographic drivers To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication)." proprietary
species-distribution-maps-gdplants_1.0 Species distribution maps of Fagales and Pinales (GDPlants) ENVIDAT STAC Catalog 2022-01-01 2022-01-01 180, -90, -180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2789817446-ENVIDAT.umm_json This database contains 1957 distribution maps of species from Fagales and Pinales constructed based on a method integrating polygon mapping and SDMs (Lyu et al., 2022). To construct the maps, we first collected occurrence data from 48 different sources. According to the number of occurrences after data cleaning, three kinds of maps are constructed: (1) For species with more than 20 occurrences, we performed SDM and polygon mapping described in Lyu et al. (2022) and select the integration of the two layers as the distribution range; (2) For species with more than 4 but less than 20 occurrences, we only use polygon mapping to draw the distribution range; (3) For species with less than 4 occurrences, a 20-km buffer was generated around the occurrences as the distribution range. The maps were manually checked and evaluated (see Note S3 and Table S9 in Lyu et al., 2022 for details). proprietary
@@ -20843,10 +20850,10 @@ usgs_global_fiducials USGS Global Fiducials USGS_LTA STAC Catalog 1970-01-01 -1
usgs_nawqa_acf_streamflow Apalachicola-Chatahoochee-Flint River Basin Streamflow Data CEOS_EXTRA STAC Catalog 1992-08-01 1995-09-01 -86, 30, -81, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231553691-CEOS_EXTRA.umm_json Surface- and ground-water quality data were collected in the Apalachicola-Chattahoochee-Flint (ACF) River basin from August 1992 to September 1995 as part of the USGS National Water Quality Assessment (NAWQA) program described below. The ACF River basin drains about 19,800 square miles in western Georgia, eastern Alabama, and the Florida panhandle into the Apalachicola Bay, which discharges into the Gulf of Mexico. Data collected as part of this study focused on five major land uses: poultry production in the headwaters of the Chattahoochee River, urban and suburban areas of Metropolitan Atlanta and Columbus, silviculture in the piedmont and fall line hills, and row crop agriculture in the upper coastal plain (clastic hydrogeologic setting) and the lower coastal plain (karst hydrogeologic setting). This description is for the streamflow data. Continuous daily streamflow data is available for the nine surface-water sites, where the most water-quality data collection was performed. These sites are gaged as continuous streamflow sites and include three mainstem integrator sites and six landuse indicator sites for the water years 1992-1995. Streamflow data can be viewed on the screen or downloaded as an RDB file. The user first selects streamflow from the main options menu. The user is asked to complete a form that provides site selection and year of interest. The user then chooses to view or download the table. These data and associated locator maps are accessible on the World Wide Web at the ACF NAWQA home page. Data are presented in manageable tables that are grouped based on land use, site type, and project component. The user can view maps and data tables on the computer screen, or downloaded data tables as tab delimited (RDB) files. Data collected as part of the ACF River basin study are presented by project component: surface-water, ground-water, special studies, streamflow, ancillary, and quality assurance data. The water-quality data are presented by major headings, including water-column, bed-sediment and tissue, and biological. The data are further subdivided into data sets consisting of related constituents. Data tables can be viewed on the users computer screen or retrieved to a users computer as a tab delimited Relational Data Base (RDB) file. To reduce the size of the pesticide, volatile organic compound, bed sediment and tissue, and trace element tables, only those compounds found equal to, or above the minimum reporting limit (MRL) for one or more sites within a group, are shown. The remaining compounds were not detected. A complete list of constituent names and MRL's are available. The National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey (USGS) is designed to describe the status and trends in the quality of the Nation's ground- and surface-water resources and to provide a sound understanding of the natural and human factors that affect the quality of these resources (Leahy and others, 1990). Because much of the public concern over water quality stems from a desire to protect both human health and aquatic life, the NAWQA Program will, in addition to measuring physical and chemical indicators of water-quality, assess the status of aquatic life through surveys of fish, invertebrates, and benthic algae, and habitat conditions (National Research Council, 1990). As an integrated assessment of water quality incorporating physical, chemical, and biological components, the NAWQA Program is ecological in approach. proprietary
usgs_nawqa_acf_surfacewater Apalachicola-Chatahoochee-Flint River Basin Surface Water Data CEOS_EXTRA STAC Catalog 1992-08-01 1995-09-01 -86, 30, -81, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231553771-CEOS_EXTRA.umm_json Surface- and ground-water quality data were collected in the Apalachicola-Chattahoochee-Flint (ACF) River basin from August 1992 to September 1995 as part of the USGS National Water Quality Assessment (NAWQA) program described below. The ACF River basin drains about 19,800 square miles in western Georgia, eastern Alabama, and the Florida panhandle into the Apalachicola Bay, which discharges into the Gulf of Mexico. Data collected as part of this study focused on five major land uses: poultry production in the headwaters of the Chattahoochee River, urban and suburban areas of Metropolitan Atlanta and Columbus, silviculture in the piedmont and fall line hills, and row crop agriculture in the upper coastal plain (clastic hydrogeologic setting) and the lower coastal plain (karst hydrogeologic setting). This description is for the surface-water sites which are grouped based on six landuse classifications: poultry, suburban, urban, silviculture, agriculture (clastic geology) and agriculure (karst geology), and by site type: main stem and tributary. The data are grouped into three catogories including water column, bed sediment and tissue, and Biological. The data are further subdivided into sets of related constituents. A complete list of constituent names and MRL's is available. The user can view and retrieve these surface-water data sets: Water Column: Field Measurements, Nutrients, Major Ions, Suspended Sediment, Organic Carbon, Turbidity, Pesticides . Bed-Sediment and Tissue: Semivolitile Organic Compounds in Sediment, Organochlorine Compounds in Sediment, Major and Trace Elements in Sediment, Organochlorine Compounds in Tissue, Trace Elements in Tissue. Biological: Algae, Fish, Invertebrates. Physical, chemical, and biological data were collected at 132 stream sites and at 15 locations within 6 reservoirs. The monitoring network is a nested design with a core of fixed monitoring sites (integrator and indicator sites), a group of land-use comparison sites, and a group of mixed land use sites including large tributaries and main stem rivers that provide spatial distribution. Water samples were collected at frequencies varying from hourly to annually, depending on the intended purpose, and were analyzed for nutrients, carbon, pesticides, major ions, and field parameters. These data and associated locator maps are accessible on the World Wide Web at the ACF NAWQA home page. Data are presented in manageable tables that are grouped based on land use, site type, and project component. The user can view maps and data tables on the computer screen, or downloaded data tables as tab delimited (RDB) files. Data collected as part of the ACF River basin study are presented by project component: surface-water, ground-water, special studies, streamflow, ancillary, and quality assurance data. The water-quality data are presented by major headings, including water-column, bed-sediment and tissue, and biological. The data are further subdivided into data sets consisting of related constituents. Data tables can be viewed on the users computer screen or retrieved to a users computer as a tab delimited Relational Data Base (RDB) file. To reduce the size of the pesticide, volatile organic compound, bed sediment and tissue, and trace element tables, only those compounds found equal to, or above the minimum reporting limit (MRL) for one or more sites within a group, are shown. The remaining compounds were not detected. A complete list of constituent names and MRL's are available. The National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey (USGS) is designed to describe the status and trends in the quality of the Nation's ground- and surface-water resources and to provide a sound understanding of the natural and human factors that affect the quality of these resources (Leahy and others, 1990). Because much of the public concern over water quality stems from a desire to protect both human health and aquatic life, the NAWQA Program will, in addition to measuring physical and chemical indicators of water-quality, assess the status of aquatic life through surveys of fish, invertebrates, and benthic algae, and habitat conditions (National Research Council, 1990). As an integrated assessment of water quality incorporating physical, chemical, and biological components, the NAWQA Program is ecological in approach. proprietary
usgs_nawqa_acfriver_groundwater Apalachicola-Chatahoochee Flint River Basin Ground Water Data CEOS_EXTRA STAC Catalog 1992-08-01 1995-09-01 -86, 30, -81, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231550128-CEOS_EXTRA.umm_json Surface- and ground-water quality data were collected in the Apalachicola-Chattahoochee-Flint (ACF) River basin from August 1992 to September 1995 as part of the USGS National Water Quality Assessment (NAWQA) program described below. The ACF River basin drains about 19,800 square miles in western Georgia, eastern Alabama, and the Florida panhandle into the Apalachicola Bay, which discharges into the Gulf of Mexico. Data collected as part of this study focused on five major land uses: poultry production in the headwaters of the Chattahoochee River, urban and suburban areas of Metropolitan Atlanta and Columbus, silviculture in the piedmont and fall line hills, and row crop agriculture in the upper coastal plain (clastic hydrogeologic setting) and the lower coastal plain (karst hydrogeologic setting). This description is for the ground-water data. Data for the ground-water component of the ACF River basin study were collected as part of three studies: Study Unit Survey, Land Use Studies (Urban and Agriculture) and Agricultural flow system study. The data are grouped by study component and site type (wells, springs, drains, and pore water) and are subdivided into sets of data consisting of related constituents. A complete list of constituent names and MRL's are available. The user can view and retrieve these ground-water data sets: Field measurements, Nutrients, Organic carbon, Turbidity, Major Ions, Pesticides, Trace elements (collected as part of the Study Unit Survey and Urban Landuse only), Volatile organic compounds, Radionuclides and Stable isotopes. Ground-water quality data were collected at 161 sites within the ACF River basin. These sites included a combination of monitoring and domestic wells, springs and seeps, and subsurface drains. The data are concentrated in the Metropolitan Atlanta (urban land use) area and in the coastal plain (agricultural land use). These data and associated locator maps are accessible on the World Wide Web at the ACF NAWQA home page. Data are presented in manageable tables that are grouped based on land use, site type, and project component. The user can view maps and data tables on the computer screen, or downloaded data tables as tab delimited (RDB) files. Data collected as part of the ACF River basin study are presented by project component: surface-water, ground-water, special studies, streamflow, ancillary, and quality assurance data. The water-quality data are presented by major headings, including water-column, bed-sediment and tissue, and biological. The data are further subdivided into data sets consisting of related constituents. Data tables can be viewed on the users computer screen or retrieved to a users computer as a tab delimited Relational Data Base (RDB) file. To reduce the size of the pesticide, volatile organic compound, bed sediment and tissue, and trace element tables, only those compounds found equal to, or above the minimum reporting limit (MRL) for one or more sites within a group, are shown. The remaining compounds were not detected. A complete list of constituent names and MRL's are available. The National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey (USGS) is designed to describe the status and trends in the quality of the Nation's ground- and surface-water resources and to provide a sound understanding of the natural and human factors that affect the quality of these resources (Leahy and others, 1990). Because much of the public concern over water quality stems from a desire to protect both human health and aquatic life, the NAWQA Program will, in addition to measuring physical and chemical indicators of water-quality, assess the status of aquatic life through surveys of fish, invertebrates, and benthic algae, and habitat conditions (National Research Council, 1990). As an integrated assessment of water quality incorporating physical, chemical, and biological components, the NAWQA Program is ecological in approach. proprietary
-usgs_nps_agatefossilbeds Agate Fossil Beds National Monument, Field Plots Data Base for Vegetation Mapping ALL STAC Catalog 1995-07-10 1995-08-15 -103.8, 42.40833, -103.7, 42.44167 https://cmr.earthdata.nasa.gov/search/concepts/C2231549635-CEOS_EXTRA.umm_json "Vegetation field plots at Agate Fossil Beds NM were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listings. The purpose of the field plots was to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data is collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the monument's vegetation types. The field plotting took place in the Agate Fossil Beds National Monument and a 400 meter buffer. Field sampling was done using releve plots. The descriptive plot data were collected for 39 sites whose vegetation represents a full spectrum of alliance types present within Agate Fossil Beds National Monument and its immediate surroundings. Physical description - Attributes collected for each site include: a plot number, a unique plot identification code, community name, field name, state, park name, quad name, map projection, datum, GPS file name, raw UTM coordinates, differentially corrected UTM coordinates, plot survey date, name(s) of surveyors, length, width, photo type, elevation, slope, aspect, topographic position, landform, surface geology, Cowardin System category, hydrology, surface material description, soil texture, soil drainage, leaf phenology, leaf type, and physiognomy. Species - Individual species described at each of 39 plots is listed, one line per species, with the following information: Plot Identification Code, Numeric Species Code, Species Name, Species Cover (0=trace, 1=< 1%, 2=1-5%, 3=5-25%, 4=25-50%, 5=50-75%, 6=75-100%), Plantcode, and Strata Code (T1=emergent, T2=canopy, T3=sub-canopy, S1=tall shrub, S2=short shrub, H=herbaceous, N=non-vascular, V=vinae/liana, E=epiphyte). Information for this metadata was taken from ""http://biology.usgs.gov/npsveg/agfo/metaagfofield.html""." proprietary
usgs_nps_agatefossilbeds Agate Fossil Beds National Monument, Field Plots Data Base for Vegetation Mapping CEOS_EXTRA STAC Catalog 1995-07-10 1995-08-15 -103.8, 42.40833, -103.7, 42.44167 https://cmr.earthdata.nasa.gov/search/concepts/C2231549635-CEOS_EXTRA.umm_json "Vegetation field plots at Agate Fossil Beds NM were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listings. The purpose of the field plots was to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data is collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the monument's vegetation types. The field plotting took place in the Agate Fossil Beds National Monument and a 400 meter buffer. Field sampling was done using releve plots. The descriptive plot data were collected for 39 sites whose vegetation represents a full spectrum of alliance types present within Agate Fossil Beds National Monument and its immediate surroundings. Physical description - Attributes collected for each site include: a plot number, a unique plot identification code, community name, field name, state, park name, quad name, map projection, datum, GPS file name, raw UTM coordinates, differentially corrected UTM coordinates, plot survey date, name(s) of surveyors, length, width, photo type, elevation, slope, aspect, topographic position, landform, surface geology, Cowardin System category, hydrology, surface material description, soil texture, soil drainage, leaf phenology, leaf type, and physiognomy. Species - Individual species described at each of 39 plots is listed, one line per species, with the following information: Plot Identification Code, Numeric Species Code, Species Name, Species Cover (0=trace, 1=< 1%, 2=1-5%, 3=5-25%, 4=25-50%, 5=50-75%, 6=75-100%), Plantcode, and Strata Code (T1=emergent, T2=canopy, T3=sub-canopy, S1=tall shrub, S2=short shrub, H=herbaceous, N=non-vascular, V=vinae/liana, E=epiphyte). Information for this metadata was taken from ""http://biology.usgs.gov/npsveg/agfo/metaagfofield.html""." proprietary
-usgs_nps_agatefossilbedsspatial Agate Fossil Beds National Monument Spatial Vegetation Data: Cover Type/Association Level of the National Vegetation Classification System ALL STAC Catalog 1995-07-29 1995-07-29 -103.8, 42.40833, -103.7, 42.44167 https://cmr.earthdata.nasa.gov/search/concepts/C2231550884-CEOS_EXTRA.umm_json "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to ""develop a uniform hierarchical vegetation methodology"" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (July, 1995). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification. The purpose of this spatial data is to provide the National Park Service the necessary tools to manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to ""develop, test, refine, and finalize the standards and protocols"" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Agate Fossil Beds National Monument was designated as one of the prototype parks. The monument is located in the high Great Plains. It contains prairie, hill, and riverine environments, with vegetation types that include prairie grassland, riverine woodland, and wetlands. The vegetation units were photointerpreted from stereo-paired, natural color photography. Agate Fossil Beds National Monument was created by the National Park Service on June 5, 1965. The park occupies 4.5 square miles of land straddling the Niobrara River in the middle of the Nebraska Panhandle. The park is noted for its history, prehistoric fossils, and scenic quality. Historically, the park was a part of the Agate Springs Ranch, owned by Captain James H. Cook. The park has a collection of ranching and Native American artifacts and memorabilia as a result of its donation from the Ranch. Paleontologically, the park contains a number of Miocene fossil quarries that were excavated through the late 19th century and early 20th century. From a scenic aspect, the park has views of rolling hills, bluffs, and the Niobrara River floodplain. Ranching is also an active part of the landscape. The park is located in the grassy rolling hills of Western Nebraska. The park landscape consists of the east-west trending cap-rocked northern and southern hills, with the treeless Niobrara River floodplain running down the middle of the valley. The city of Harrison is located 23 miles to the north, Mitchell is 34 miles to the south. State Highway 29 runs north-south through the western part of the park. The Vegetation mapping was conducted in Agate Fossil Beds National Moument, Nebraska with a 400 meter buffer. A total of 39 plots were obtained from July 10 through August 15, 1995. These plots were used by TNC to describe the vegetation associations found within the park. These descriptions are in the companion report by TNC. Map Validation A field trip was conducted in August of 1997 to assess the initial mapping effort and to refine map class. Information for this metadata was taken from ""http://biology.usgs.gov/npsveg/agfo/metaagfospatial.html"" and converted to the NASA Directory Interchange Format. Another site to obtain the data is located at Online_Resource: ""ftp://ftp.cbi.usgs.gov/pub/vegmapping/agfo/agfo.exe""." proprietary
+usgs_nps_agatefossilbeds Agate Fossil Beds National Monument, Field Plots Data Base for Vegetation Mapping ALL STAC Catalog 1995-07-10 1995-08-15 -103.8, 42.40833, -103.7, 42.44167 https://cmr.earthdata.nasa.gov/search/concepts/C2231549635-CEOS_EXTRA.umm_json "Vegetation field plots at Agate Fossil Beds NM were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listings. The purpose of the field plots was to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data is collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the monument's vegetation types. The field plotting took place in the Agate Fossil Beds National Monument and a 400 meter buffer. Field sampling was done using releve plots. The descriptive plot data were collected for 39 sites whose vegetation represents a full spectrum of alliance types present within Agate Fossil Beds National Monument and its immediate surroundings. Physical description - Attributes collected for each site include: a plot number, a unique plot identification code, community name, field name, state, park name, quad name, map projection, datum, GPS file name, raw UTM coordinates, differentially corrected UTM coordinates, plot survey date, name(s) of surveyors, length, width, photo type, elevation, slope, aspect, topographic position, landform, surface geology, Cowardin System category, hydrology, surface material description, soil texture, soil drainage, leaf phenology, leaf type, and physiognomy. Species - Individual species described at each of 39 plots is listed, one line per species, with the following information: Plot Identification Code, Numeric Species Code, Species Name, Species Cover (0=trace, 1=< 1%, 2=1-5%, 3=5-25%, 4=25-50%, 5=50-75%, 6=75-100%), Plantcode, and Strata Code (T1=emergent, T2=canopy, T3=sub-canopy, S1=tall shrub, S2=short shrub, H=herbaceous, N=non-vascular, V=vinae/liana, E=epiphyte). Information for this metadata was taken from ""http://biology.usgs.gov/npsveg/agfo/metaagfofield.html""." proprietary
usgs_nps_agatefossilbedsspatial Agate Fossil Beds National Monument Spatial Vegetation Data: Cover Type/Association Level of the National Vegetation Classification System CEOS_EXTRA STAC Catalog 1995-07-29 1995-07-29 -103.8, 42.40833, -103.7, 42.44167 https://cmr.earthdata.nasa.gov/search/concepts/C2231550884-CEOS_EXTRA.umm_json "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to ""develop a uniform hierarchical vegetation methodology"" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (July, 1995). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification. The purpose of this spatial data is to provide the National Park Service the necessary tools to manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to ""develop, test, refine, and finalize the standards and protocols"" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Agate Fossil Beds National Monument was designated as one of the prototype parks. The monument is located in the high Great Plains. It contains prairie, hill, and riverine environments, with vegetation types that include prairie grassland, riverine woodland, and wetlands. The vegetation units were photointerpreted from stereo-paired, natural color photography. Agate Fossil Beds National Monument was created by the National Park Service on June 5, 1965. The park occupies 4.5 square miles of land straddling the Niobrara River in the middle of the Nebraska Panhandle. The park is noted for its history, prehistoric fossils, and scenic quality. Historically, the park was a part of the Agate Springs Ranch, owned by Captain James H. Cook. The park has a collection of ranching and Native American artifacts and memorabilia as a result of its donation from the Ranch. Paleontologically, the park contains a number of Miocene fossil quarries that were excavated through the late 19th century and early 20th century. From a scenic aspect, the park has views of rolling hills, bluffs, and the Niobrara River floodplain. Ranching is also an active part of the landscape. The park is located in the grassy rolling hills of Western Nebraska. The park landscape consists of the east-west trending cap-rocked northern and southern hills, with the treeless Niobrara River floodplain running down the middle of the valley. The city of Harrison is located 23 miles to the north, Mitchell is 34 miles to the south. State Highway 29 runs north-south through the western part of the park. The Vegetation mapping was conducted in Agate Fossil Beds National Moument, Nebraska with a 400 meter buffer. A total of 39 plots were obtained from July 10 through August 15, 1995. These plots were used by TNC to describe the vegetation associations found within the park. These descriptions are in the companion report by TNC. Map Validation A field trip was conducted in August of 1997 to assess the initial mapping effort and to refine map class. Information for this metadata was taken from ""http://biology.usgs.gov/npsveg/agfo/metaagfospatial.html"" and converted to the NASA Directory Interchange Format. Another site to obtain the data is located at Online_Resource: ""ftp://ftp.cbi.usgs.gov/pub/vegmapping/agfo/agfo.exe""." proprietary
+usgs_nps_agatefossilbedsspatial Agate Fossil Beds National Monument Spatial Vegetation Data: Cover Type/Association Level of the National Vegetation Classification System ALL STAC Catalog 1995-07-29 1995-07-29 -103.8, 42.40833, -103.7, 42.44167 https://cmr.earthdata.nasa.gov/search/concepts/C2231550884-CEOS_EXTRA.umm_json "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to ""develop a uniform hierarchical vegetation methodology"" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (July, 1995). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification. The purpose of this spatial data is to provide the National Park Service the necessary tools to manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to ""develop, test, refine, and finalize the standards and protocols"" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Agate Fossil Beds National Monument was designated as one of the prototype parks. The monument is located in the high Great Plains. It contains prairie, hill, and riverine environments, with vegetation types that include prairie grassland, riverine woodland, and wetlands. The vegetation units were photointerpreted from stereo-paired, natural color photography. Agate Fossil Beds National Monument was created by the National Park Service on June 5, 1965. The park occupies 4.5 square miles of land straddling the Niobrara River in the middle of the Nebraska Panhandle. The park is noted for its history, prehistoric fossils, and scenic quality. Historically, the park was a part of the Agate Springs Ranch, owned by Captain James H. Cook. The park has a collection of ranching and Native American artifacts and memorabilia as a result of its donation from the Ranch. Paleontologically, the park contains a number of Miocene fossil quarries that were excavated through the late 19th century and early 20th century. From a scenic aspect, the park has views of rolling hills, bluffs, and the Niobrara River floodplain. Ranching is also an active part of the landscape. The park is located in the grassy rolling hills of Western Nebraska. The park landscape consists of the east-west trending cap-rocked northern and southern hills, with the treeless Niobrara River floodplain running down the middle of the valley. The city of Harrison is located 23 miles to the north, Mitchell is 34 miles to the south. State Highway 29 runs north-south through the western part of the park. The Vegetation mapping was conducted in Agate Fossil Beds National Moument, Nebraska with a 400 meter buffer. A total of 39 plots were obtained from July 10 through August 15, 1995. These plots were used by TNC to describe the vegetation associations found within the park. These descriptions are in the companion report by TNC. Map Validation A field trip was conducted in August of 1997 to assess the initial mapping effort and to refine map class. Information for this metadata was taken from ""http://biology.usgs.gov/npsveg/agfo/metaagfospatial.html"" and converted to the NASA Directory Interchange Format. Another site to obtain the data is located at Online_Resource: ""ftp://ftp.cbi.usgs.gov/pub/vegmapping/agfo/agfo.exe""." proprietary
usgs_nps_congareeswamp Congaree Swamp National Monument Field Plots Data Base for Vegetation Mapping CEOS_EXTRA STAC Catalog 1996-06-01 1996-09-01 -80.85, 33.75, -80.67083, 33.84167 https://cmr.earthdata.nasa.gov/search/concepts/C2231552960-CEOS_EXTRA.umm_json "Vegetation field plots at Congaree Swamp National Monument were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listings. The vegetation plots were used to describe the vegetation in and around Congaree Swamp National Monument and to assist in developing a final mapping classification system. On June 30, 1983, Congaree Swamp National Monument became an International Biosphere Reserve. Congaree is noted for containing one of the last significant stands of old growth bottomland hardwood forest, over 11,000 acres in all. The Monument contains over 90 species of trees, 16 of which hold state records for size. Included in this list of records is a national record sweet gum with a basal circumference of nearly 20 feet. Congaree Swamp National Monument is located approximately 15 miles southeast of Columbia, the state capitol of South Carolina. Old Bluff Highway (old Highway 48) lies just north of the Monument boundary. The eastern boundary is located just northwest of the confluence of the Congaree and Wateree Rivers. The Monument extends west to where Cedar Creek and Myers Creek join. The methods used for the sampling and analysis of vegetation data and the development of the classification generally followed the standards Doutline in the Field Methods for Vegetation Mapping document ""http://biology.usgs.gov/npsveg/fieldmethods/index.html"" produced for the USGS-NPS Vegetation Mapping project. This process began with the development of a provisional list of twenty-five vegetation types from teh International Classification of Ecological Communities (ICEC) that were thought to have a high likelihood of being in the park based on an initial field visit on 13-14 June, 1996. One hundred twenty-eight plots were sampled by two two-person field teams in July, August, and September of 1996. In a devation from the methodology outlined in the Field Methods document, initial sample points were selected in order to have plots in each of the aerial photograph signature types. The gradsect approach was rejected because there appeared to be no potential for stratifying sampling of the park based on slope, aspect, elevation, soil or other natural features due to a lack of available information. Furthermore, because of isolation from roads and trails of many portions of the park, it was deemed not feasible to use a transect to establish plot locations. After sampling, plots were tentatively assigned to the ICEC at the alliance level and our goal was to have at least five plots in each of the twenty-five provisional vegetation types. TIme limitations precluded the ability of the field teams to install ten plots in each of the expected vegetation types as recommended in the Field Methods document. The information for the metadata came from ""http://biology.usgs.gov/npsveg/cosw/metacoswfield.html""" proprietary
usgs_nps_congareeswampspatial Congaree Swamp National Monument Spatial Vegetation Data; Cover Type/Association Level of the National Vegetation Classification System CEOS_EXTRA STAC Catalog 1996-04-27 1996-04-27 -80.85, 33.75, -80.67083, 33.84167 https://cmr.earthdata.nasa.gov/search/concepts/C2231550252-CEOS_EXTRA.umm_json "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to ""develop a uniform hierarchical vegetation methodology"" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (April, 1996). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification. The purpose of this spatial data is to provide the National Park Service the necessary tools to manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to ""develop, test, refine, and finalize the standards and protocols"" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Congaree Swamp National Monument was designated as one of the prototype parks. Congaree Swamp National Monument, established in 1976, was designated as one of the prototypes within the National Park System. The park contains approximately 22,200 acres (34 square miles). Congaree Swamp National Monument is located approximately 15 miles southeast of Columbia, the state capitol of South Carolina. The Congaree River, draining over 8,000 square miles of Piedmont land to the northwest, forms the southern border. On June 30, 1983, Congaree Swamp National Monument became an International Biosphere Reserve. Congaree is noted for containing one of the last significant stands of old growth bottomland hardwood forest, over 11,000 acres in all. The Monument contains over 90 species of trees, 16 of which hold state records for size. Included in this list of records is a national record sweet gum with a basal circumference of nearly 20 feet. Congaree Swamp National Monument is located approximately 15 miles southeast of Columbia, the state capitol of South Carolina. Old Bluff Highway (old Highway 48) lies just north of the Monument boundary. The eastern boundary is located just northwest of the confluence of the Congaree and Wateree Rivers. The Monument extends west to where Cedar Creek and Myers Creek join. The normal process in vegetation mapping is to conduct an initial field reconnaissance, map the vegetation units through photointerpretation, and then conduct a field verification. The field reconnaissance visit serves two major functions. First, the photointerpreter keys the signature on the aerial photos to the vegetation on the ground at each signature site. Second, the photointerpreter becomes familiar with the flora, vegetation communities and local ecology that occur in the study area. Park and/or TNC field biologists that are familiar with the local vegetation and ecology of the park are present to help the photointerpreter understand these elements and their relationship with the geography of the park. Upon completion of the field reconnaissance, photo interpreters delineate vegetation units on mylar that overlay the 9x9 aerial photos. This effort is conducted in accordance with the TNC vegetation classification and criteria for defining each community or alliance. The initial mapping is then followed by a field verification session, whose purpose is to verify that the vegetation units were mapped correctly. Any PI related questions are also addressed during the visit. The vegetation mapping at Congaree Swamp National Monument in general followed the normal mapping procedure as described in the above paragraph with two major exceptions: 1) Preliminary delineations for most of the park, including a set of Focused Transect overlays that were labeled with an initial PI signature commenced prior to the field reconnaissance visit. 2) A TNC classification did not exist at the time the initial delineations began. TNC ecologist and AIS photo interpreters worked together to develop an interim signature key which addressed what was known at the time. At that time, no comprehensive study containing plot data was available to create an interim classification. From the onset of the Vegetation Inventory and Mapping Program, a standardized program-wide mapping criteria has been used. The mapping criteria contains a set of documented working decision rules used to facilitate the maintenance of accuracy and consistency of the photointerpretation. This criteria assists the user in understanding the characteristics, definition and context for each vegetation community. The mapping criteria for Congaree Swamp National Monument was composed of four parts: The standardized program-wide general mapping criteria A park specific mapping criteria A working photo signature key The TNC classification, key and descriptions The following sections detail the mapping criteria used during the photointerpretation of Congaree Swamp. General Mapping Criteria The mapping criteria at Congaree Swamp are a modified version from previously mapped parks. The criteria differs primarily in that the height and density variables were not mapped at Congaree Swamp. Instead, two additional variables were addressed: pre-hurricane Hugo community types and areas of pine that have been logged since the time of the 1976 aerial photography. These two categories will be addressed in the Park Specific Mapping Criteria section of this report. Since forest densities within the Monument are nearly always greater than 60%, it served little or no purpose in addressing this element as a separate attribute in the database. In addition it was also determined that height categories are extremely difficult to map in the Monument due to variability of the tree emergent layer, and lack of any significant reference points that help in determining canopy heights. Alliance / Community Associations The assignment of alliance and community association to the vegetation is based on criteria formulated by the field effort and classification development. In the case of Congaree Swamp National Monument, TNC provided AIS with a tentative community classification in April 1998. A final vegetation classification, key, and descriptions of each alliance and community, was provided in October 1998. In addition, TNC provided AIS with detailed plot data showing how the communities were developed in the Monument. The information for the metadata came from ""http://biology.usgs.gov/npsveg/cosw/metacoswspatial.html"" and was converted to the NASA Directory Interchange Format." proprietary
usgs_nps_d_microbialcontam Microbial Contamination of Water Resources in the Chatahoochee River National Recreation Area, Georgia CEOS_EXTRA STAC Catalog 1999-03-01 2000-04-01 -86, 30, -81, 35 https://cmr.earthdata.nasa.gov/search/concepts/C2231549590-CEOS_EXTRA.umm_json The study area is the watershed for the Chattahoochee River from Buford Dam to just downstream of the mouth of Peachtree Creek. This study area includes the entire Chattahoochee River National Recreation Area, much of Metropolitan Atlanta, and extends downstream of two major wastewater treatment plant outfalls for the City of Atlanta and Cobb County. The 2-year study is for fiscal years 1999 and 2000. There are six months of microbial sampling in each fiscal year spanning from April 1, 1999 through March 30, 2000. This study measures fecal-indicator bacteria (fecal coliform, E. coli, and enterococci) every five days from April 1, 1999 to September 30, 1999 and every 8 days from October 1, 1999 to March 30, 2000 at three main stem Chattahoochee River sites. The five-day and eight-day sampling intervals will ensure mid week and weekend flow conditions are sampled. Indicator bacteria samples will also be collected during one 26-hour period to look at diel fluctuations. Another indicator bacteria (Clostridium perfringens), F-specific coliphages, somatic coliphages, and chemical sewage tracers will be measured as part of several synoptic surveys at 3 fixed sites and 9 synoptic sites. The 2-year project investigates the existence, severity, and extent of microbial contamination in the Chattahoochee River and 8 major tributaries within the Chattahoochee River National Recreation Area (CRNRA). High levels of fecal-indicator bacteria are the principal basis for impairment of streams in the CRNRA. Three data-collection activities include: 1.Fixed interval: Sample fecal-indicator bacteria and predictor variables (stream stage, stream flow, turbidity, and field water-quality parameters) every 5 days from April 1 to September 30, 1999 and every 8 days from October 1, 1999 to March 30, 2000 at 3 Chattahoochee River sites. (view map) 2.Synoptic surveys: Sample fecal-indicator bacteria, Clostridium perfringens, viruses, predictor variables, and chemical sewage tracers at 4 Chattahoochee River sites and 8 tributary sites during critical seasons and hydrologic conditions. 3.Diel samples: Sample fecal-indicator bacteria and predictor variables every 2 hours for one 26-hour period (August 4-5, 1999) at the Chattahoochee River at Atlanta, which is downstream of the CRNRA. Four proposed main stem sampling sites in downstream order on the Chattahoochee River include: 1.Chattahoochee River at Settles Bridge Road near Suwanee 2.Chattahoochee River at Johnsons Ferry Road near Atlanta 3.Chattahoochee River at Atlanta (Paces Ferry Road; downstream from Palisades Unit) 4.Chattahoochee River at State Highway 280, near Atlanta (Synoptic site only; downstream from all of the CRNRA, much of Metropolitan Atlanta, and 2 major wastewater treatment outfalls for the City of Atlanta and Cobb County; will provide microbial data for a Chattahoochee River site directly affected by point sources of wastewater effluent) Eight proposed tributary sampling sites within the CRNRA watershed in downstream order include: 1.James Creek near Cumming (James Burgess Road) 2.Suwanee Creek near Suwanee (at US Route 23, Buford Hwy) 3.Johns Creek near Warsaw (Buice Road) 4.Crooked Creek near Norcross (Spalding Road) 5.Big Creek near Roswell (below Water Works intake) 6.Willeo Creek near Roswell (State Route 120) 7.Sope Creek near Marietta (Lower Roswell Road) 8.Rottenwood Creek near Smyrna (Interstate Parkway North) In general, fecal-indicator bacteria are used to assess the public-health acceptability of water. The concentration of indicator bacteria is a measure of water safety for body-contact recreation or for consumption (Myers and Sylvester, 1997). Indicator bacteria do not typically cause diseases (pathogenic), but they indicate the possible presence of pathogenic organisms. Escherichia coli (E. coli) and enterococci are currently the preferred fecal indicators for recreational freshwaters because they are superior to fecal coliforms and fecal streptococci as predictors of swimming-associated gastroenteritis (Cabelli, 1977; Dufour, 1984); however fecal coliforms are still used by many states including Georgia to monitor recreational waters. Most historical indicator bacteria data for surface water within the CRNRA are fecal coliform counts collected once a month on a mid-weekday during normal working hours. This study proposes to measure fecal coliform using the membrane filter technique (preferred over the broth technique used by Georgia EPD),E. coli, and enterococci every five days during the recreation season at three main stem sites. The five-day cycle will ensure mid week and weekend flow conditions are sampled. All samples will be collected using USGS protocols for bacteria and equal width interval (EWI) sampling. Clostridium perfringens (C. perfringens) is another indicator bacteria that is present in large numbers in human and animal wastes, and its spores are more resistant to disinfection and environmental stresses than are most other bacteria. It is also a sensitive indicator of microorganisms that enter streams from point sources (Sorenson and others, 1989). It must be analyzed under anaerobic conditions in a laboratory and is best attempted by a biologist or highly trained technician. This study proposes to measure C. perfringens at 4 main stem and 8 tributary sites as part of synoptic surveys during critical seasons and hydrologic conditions. Because monitoring of enteric viruses is recognized as being difficult,time consuming, and expensive, some researchers advocate the use of coliphage for routine viral monitoring. Coliphages are bacteriophages that infect and replicate in coliform bacteria. Although somatic and Fecal-Specific coliphages are not consistently found in feces, they are found in high numbers in sewage and are thought to be reliable indicators of the sewage contamination of waters (International Association on Water Pollution Research and Control, 1991). Coliphage is also recognized to be representative of the survival transport of viruses in the environment. However, to date, they have not been found to correlate with the presence of pathogenic viruses. This study proposes to measure enteric viruses at 4 main stem and 8 tributary sites as part of synoptic surveys during critical seasons and hydrologic conditions. proprietary
@@ -20859,13 +20866,13 @@ usgs_nps_isleroyalespatial Isle Royale National Park Spatial Vegetation Data; Co
usgs_nps_jewelcave Jewel Cave National Monument, Field Plots Data Base for Vegetation Mapping CEOS_EXTRA STAC Catalog 1996-07-01 1996-08-01 -103.87, 43.62, -103.75, 43.77 https://cmr.earthdata.nasa.gov/search/concepts/C2231553594-CEOS_EXTRA.umm_json "Vegetation field plots at Jewel Cave NM were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listing. The purpose is to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data is collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the monument's vegetation types. Field sampling was conducted using releve plots. Information for this metadata was obtained from the site ""http://biology.usgs.gov/npsveg/jeca/metajecafield.html"" and put into NASA Directory Interchange Format." proprietary
usgs_nps_jewelcavespatial Jewel Cave National Monument Spatial Vegetation Data;Cover Type / Association level of the National Vegetation Classification System CEOS_EXTRA STAC Catalog 1995-09-12 1995-09-12 -103.87, 43.62, -103.75, 43.77 https://cmr.earthdata.nasa.gov/search/concepts/C2231548897-CEOS_EXTRA.umm_json "The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to ""develop a uniform hierarchical vegetation methodology"" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation at Jewel Cave National Monument was mapped using 1:16,000 scale U.S. Forest Service Color Aerial Photography acquired August 22, 1993. The mapping classification used two separate classification systems. All natural vegetation used the National Vegetation Classification System (NVCS) as a base. The vegetation classification was created after extensive on site sampling and numerical analysis. The vegetation map units were derived from the vegetation classification. Other non-natural or cultural mapping units used the Anderson Level II classification system. The mapped area includes a buffer around the Monument boundary. This mapping effort originates from a long-term vegetation monitoring program that is part of a larger Inventory and Monitoring (I&M) program started by the National Park Service (NPS). I&M goals are, among others, to map the vegetation of all national parks and monuments and provide a baseline inventory of vegetation. The I&M program currently works in close cooperation with the Biological Resources Division (BRD) of the United States Geological Survey (USGS). The USGS/BRD continues overall management and oversight of all ongoing mapping efforts in close cooperation with the NPS. The purposes of the mapping effort are varied and include the following: Provides support for NPS Resources Management. Promotes vegetation-related research for both NPS and USGS/BRD. Provides support for NPS Planning and Compliance. Adds to the information base for NPS Interpretation. Assists in NPS Operations. The location of the mapping is Jewel Cave National Monument and about a 2 mile environs around Monument Boundaries - Black Hills, South Dakota. Jewel Cave National Monument was responsible for obtaining permission from adjacent land owners for property access for sampling purposes. Most of the private lands were under some form of grazing or farming. Consequently, sampling on these lands was not necessary. The remainder of the lands within the mapping area are U.S. Forest Service Lands so permission was not necessary. To reduce duplicating previous work and to help in our effort we collected existing vegetation reports and maps from the staff at Jewel Cave National Monument. These materials were referenced during the mapping process and the information contained in them was incorporated where it was deemed useful. Because soils also affect the distribution of vegetation, soil maps and soil descriptions were also obtained as reference. These were not converted to a digital file. Digital elevation models (DEM) were obtained to create slope and aspect maps that helped in determining vegetation community distribution. The sampling approach used in this mapping effort was typical of small park sampling, where all polygons within the park boundary are sampled. Two levels of field data gathering were conducted in this park; plots and observations. Plots represented the most intensive sampling of the landscape and used TNC's 'Plot Form'. Observations consisted of brief descriptions and were designed to obtain a quick overview of the landscape without spending a large amount of time at each sample site. Observation points used the 'Observation Form' data sheet. Examples of both 'Plot' and 'Observation' forms are included in the companion report by TNC. Initially, plots were used to describe the vegetation of the park. A total of 28 plots were obtained from July 29 through August 1, 1996. These plots were used by TNC to describe the vegetation associations found within the park. These descriptions are in the companion report by TNC. Map Validation A field trip was conducted in May of 1997 to assess the initial mapping effort and to refine map classes. Information for this metadata was obtained from the site ""http://biology.usgs.gov/npsveg/jeca/metajecaspatial.html"" and put into NASA Directory Interchange Format." proprietary
usgs_nps_mountrushmore Mount Rushmore National Monument, Field Plots Data Base for Vegetation Mapping CEOS_EXTRA STAC Catalog 1997-06-01 1997-08-01 -103.5, 43.8, -103.4, 43.9 https://cmr.earthdata.nasa.gov/search/concepts/C2231549070-CEOS_EXTRA.umm_json "Vegetation field plots at Mount Rushmore NM were visited, described, and documented in a digital database. The database consists of 2 parts - (1) Physical Descriptive Data, and (2) Species Listings. The purpose of the data plots were to provide National Parks with the necessary tools to effectively manage their natural resources. Plot data is collected and analyzed to develop a classification (using the Standardized National Vegetation Classification System) and description of vegetation types in preparation for photointerpretation and mapping of the monument's vegetation types. Field sampling was conducted using releve plots. Information for this metadata was obtained from the site ""http://biology.usgs.gov/npsveg/moru/metamorufield.html"" and put into NASA Directory Interchange Format." proprietary
-usgs_npwrc_acutetoxicity_Version 06JUL2000 Acute Toxicity of Fire-Control Chemicals, Nitrogenous Chemicals, and Surfactants to Rainbow Trout ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551569-CEOS_EXTRA.umm_json Laboratory studies were conducted to determine the acute toxicity of three ammonia-based fire retardants (Fire-Trol LCA-F, Fire-Trol LCM-R, and Phos-Chek 259F), five surfactant-based fire-suppressant foams (FireFoam 103B, FireFoam 104, Fire Quench, ForExpan S, and Pyrocap B-136), three nitrogenous chemicals (ammonia, nitrate, and nitrite) and two anionic surfactants (linear alkylbenzene sulfonate [LAS] and sodium dodecyl sulfate [SDS]) to juvenile rainbow trout Oncorhynchus mykiss in soft water. The descending rank order of toxicity (96-h concentration lethal to 50% of test organisms [96-h LC50]) for the fire retardants was as follows: Phos-Chek 259F (168 mg/L) > Fire-Trol LCA-F (942 mg/L) = Fire-Trol LCM-R (1,141 mg/L). The descending rank order of toxicity for the foams was as follows: FireFoam 103B (12.2 mg/L) = FireFoam 104 (13.0 mg/L) > ForExpan S (21.8 mg/L) > Fire Quench (39.0 mg/L) > Pyrocap B-136 (156 mg/L). Except for Pyrocap B-136, the foams were more toxic than the fire retardants. Un-ionized ammonia (NH3; 0.125 mg/L as N) was about six times more toxic than nitrite (0.79 mg/L NO2-N) and about 13,300 times more toxic than nitrate (1,658 mg/L NO3-N). Linear alkylbenzene sulfonate (5.0 mg/L) was about five times more toxic than SDS (24.9 mg/L). Estimated total ammonia and NH3 concentrations at the 96-h LC50s of the fire retardants indicated that ammonia was the primary toxic component in these formulations. Based on estimated anionic surfactant concentrations at the 96-h LC50s of the foams and reference surfactants, LAS was intermediate in toxicity and SDS was less toxic to rainbow trout when compared with the foams. Comparisons of recommended application concentrations to the test results indicate that accidental inputs of these chemicals into streams require substantial dilutions (100-1,750-fold) to reach concentrations nonlethal to rainbow trout. proprietary
usgs_npwrc_acutetoxicity_Version 06JUL2000 Acute Toxicity of Fire-Control Chemicals, Nitrogenous Chemicals, and Surfactants to Rainbow Trout CEOS_EXTRA STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551569-CEOS_EXTRA.umm_json Laboratory studies were conducted to determine the acute toxicity of three ammonia-based fire retardants (Fire-Trol LCA-F, Fire-Trol LCM-R, and Phos-Chek 259F), five surfactant-based fire-suppressant foams (FireFoam 103B, FireFoam 104, Fire Quench, ForExpan S, and Pyrocap B-136), three nitrogenous chemicals (ammonia, nitrate, and nitrite) and two anionic surfactants (linear alkylbenzene sulfonate [LAS] and sodium dodecyl sulfate [SDS]) to juvenile rainbow trout Oncorhynchus mykiss in soft water. The descending rank order of toxicity (96-h concentration lethal to 50% of test organisms [96-h LC50]) for the fire retardants was as follows: Phos-Chek 259F (168 mg/L) > Fire-Trol LCA-F (942 mg/L) = Fire-Trol LCM-R (1,141 mg/L). The descending rank order of toxicity for the foams was as follows: FireFoam 103B (12.2 mg/L) = FireFoam 104 (13.0 mg/L) > ForExpan S (21.8 mg/L) > Fire Quench (39.0 mg/L) > Pyrocap B-136 (156 mg/L). Except for Pyrocap B-136, the foams were more toxic than the fire retardants. Un-ionized ammonia (NH3; 0.125 mg/L as N) was about six times more toxic than nitrite (0.79 mg/L NO2-N) and about 13,300 times more toxic than nitrate (1,658 mg/L NO3-N). Linear alkylbenzene sulfonate (5.0 mg/L) was about five times more toxic than SDS (24.9 mg/L). Estimated total ammonia and NH3 concentrations at the 96-h LC50s of the fire retardants indicated that ammonia was the primary toxic component in these formulations. Based on estimated anionic surfactant concentrations at the 96-h LC50s of the foams and reference surfactants, LAS was intermediate in toxicity and SDS was less toxic to rainbow trout when compared with the foams. Comparisons of recommended application concentrations to the test results indicate that accidental inputs of these chemicals into streams require substantial dilutions (100-1,750-fold) to reach concentrations nonlethal to rainbow trout. proprietary
+usgs_npwrc_acutetoxicity_Version 06JUL2000 Acute Toxicity of Fire-Control Chemicals, Nitrogenous Chemicals, and Surfactants to Rainbow Trout ALL STAC Catalog 1970-01-01 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C2231551569-CEOS_EXTRA.umm_json Laboratory studies were conducted to determine the acute toxicity of three ammonia-based fire retardants (Fire-Trol LCA-F, Fire-Trol LCM-R, and Phos-Chek 259F), five surfactant-based fire-suppressant foams (FireFoam 103B, FireFoam 104, Fire Quench, ForExpan S, and Pyrocap B-136), three nitrogenous chemicals (ammonia, nitrate, and nitrite) and two anionic surfactants (linear alkylbenzene sulfonate [LAS] and sodium dodecyl sulfate [SDS]) to juvenile rainbow trout Oncorhynchus mykiss in soft water. The descending rank order of toxicity (96-h concentration lethal to 50% of test organisms [96-h LC50]) for the fire retardants was as follows: Phos-Chek 259F (168 mg/L) > Fire-Trol LCA-F (942 mg/L) = Fire-Trol LCM-R (1,141 mg/L). The descending rank order of toxicity for the foams was as follows: FireFoam 103B (12.2 mg/L) = FireFoam 104 (13.0 mg/L) > ForExpan S (21.8 mg/L) > Fire Quench (39.0 mg/L) > Pyrocap B-136 (156 mg/L). Except for Pyrocap B-136, the foams were more toxic than the fire retardants. Un-ionized ammonia (NH3; 0.125 mg/L as N) was about six times more toxic than nitrite (0.79 mg/L NO2-N) and about 13,300 times more toxic than nitrate (1,658 mg/L NO3-N). Linear alkylbenzene sulfonate (5.0 mg/L) was about five times more toxic than SDS (24.9 mg/L). Estimated total ammonia and NH3 concentrations at the 96-h LC50s of the fire retardants indicated that ammonia was the primary toxic component in these formulations. Based on estimated anionic surfactant concentrations at the 96-h LC50s of the foams and reference surfactants, LAS was intermediate in toxicity and SDS was less toxic to rainbow trout when compared with the foams. Comparisons of recommended application concentrations to the test results indicate that accidental inputs of these chemicals into streams require substantial dilutions (100-1,750-fold) to reach concentrations nonlethal to rainbow trout. proprietary
usgs_npwrc_alpha_Version 16MAY2000 Alpha Status, Dominance, and Division of Labor in Wolf Packs. CEOS_EXTRA STAC Catalog 1986-01-01 1998-12-31 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231552683-CEOS_EXTRA.umm_json "The prevailing view of a wolf (Canis lupus) pack is that of a group of individuals ever vying for dominance but held in check by the ""alpha"" pair, the alpha male and the alpha female. Most research on the social dynamics of wolf packs, however, has been conducted on non-natural assortments of captive wolves. Here I describe the wolf-pack social order as it occurs in nature, discuss the alpha concept and social dominance and submission, and present data on the precise relationships among members in free-living packs based on a literature review and 13 summers of observations of wolves on Ellesmere Island, Northwest Territories, Canada. I conclude that the typical wolf pack is a family, with the adult parents guiding the activities of the group in a division-of-labor system in which the female predominates primarily in such activities as pup care and defense and the male primarily during foraging and food-provisioning and the travels associated with them." proprietary
usgs_npwrc_canvasbacks_Version 13NOV2001 Influence of Age and Selected Environmental Factors on Reproductive Performance of Canvasbacks CEOS_EXTRA STAC Catalog 1974-01-01 1980-01-01 -102.5, 48, -95, 60 https://cmr.earthdata.nasa.gov/search/concepts/C2231549601-CEOS_EXTRA.umm_json Age, productivity, and other factors affecting breeding performance of canvasbacks (Aythya valisineria) are poorly understood. Consequently, we tested whether reproductive performance of female canvasbacks varied with age and selected environmental factors in southwestern Manitoba from 1974 to 1980. Neither clutch size, nest parasitism, nest success, nor the number of ducklings/brood varied with age. Return rates, nest initiation dates, renesting, and hen success were age-related. Return rates averaged 21% for second-year (SY) and 69% for after-second-year (ASY) females (58% for third-year and 79% for after-third-year females). Additionally, water conditions and spring temperatures influenced chronology of arrival, timing of nesting, and reproductive success. Nest initiation by birds of all ages was affected by minimum April temperatures. Clutch size was higher in nests initiated earlier. Interspecific nest parasitism did not affect clutch size, nest success, hen success, or hatching success. Nest success was lower in dry years (17%) than in moderately wet (54%) or wet (60%) years. Nests per female were highest during wet years. No nests of SY females were found in dry years. In years of moderate to good wetland conditions, females of all ages nested. Predation was the primary factor influencing nest success. Hen success averaged 58% over all years. The number of ducklings surviving 20 days averaged 4.7/brood. Because SY females have lower return rates and hen success than ASY females, especially during drier years, management to increase canvasback populations might best be directed to increasing first year recruitment (no. of females returning to breed) and to increasing overall breeding success by reducing predation and enhancing local habitat conditions during nesting. proprietary
usgs_npwrc_ducks_Version 07JAN98 Assessing Breeding Populations of Ducks by Ground Counts. CEOS_EXTRA STAC Catalog 1952-01-01 1959-12-31 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231554819-CEOS_EXTRA.umm_json Waterfowl inventories taken during the breeding season are recognized as a basic technique in assessing the number of ducks per unit area. That waterfowl censusing is still an inexact technology leading to divergent interpretations of results is also recognized. The inexactness stems from a wide spectrum of factors that include weather, breeding phenology, asynchronous nesting periods, vegetative growth, species present and their daily activity, previous field experience of personnel, plus others (Stewart et al., 1958; Diem and Lu, 1960; Crissey, 1963a). In spite of the possible errors, accurate estimates are necessary to our understanding of production rates of all North American breeding waterfowl. Statistically adequate censuses of breeding pairs and accurate predictions of young produced per pair still remain as two of the primary statistics in determining yearly recruitment rate of species breeding in particular units of pond habitats. Without precise breeding pair and production data, the problems involved in describing the reproductive potential of any species and its environmental or density-dependent limiting factors cannot be adequately resolved. The purposes of this paper are to (1) describe methods used to estimate yearly breeding pair abundance on two study areas, one in Manitoba and the other in Saskatchewan; (2) assess the relative consistency, precision, and accuracy of pair counts as related to the breeding biology of duck species; and (3) recommend census methods that can more closely approximate absolute populations breeding in parkland and grassland habitats. proprietary
-usgs_npwrc_graywolves_Version 30APR2001 Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear CEOS_EXTRA STAC Catalog 1970-01-01 -168, 43.5, -75, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2231553641-CEOS_EXTRA.umm_json We evaluated the accuracy and precision of tooth wear for aging gray wolves (Canis lupus) from Alaska, Minnesota, and Ontario based on 47 known-age or known-minimum-age skulls. Estimates of age using tooth wear and a commercial cementum annuli-aging service were useful for wolves up to 14 years old. The precision of estimates from cementum annuli was greater than estimates from tooth wear, but tooth wear estimates are more applicable in the field. We tended to overestimate age by 1-2 years and occasionally by 3 or 4 years. The commercial service aged young wolves with cementum annuli to within year of actual age, but under estimated ages of wolves 9 years old by 1-3 years. No differences were detected in tooth wear patterns for wild wolves from Alaska, Minnesota, and Ontario, nor between captive and wild wolves. Tooth wear was not appropriate for aging wolves with an underbite that prevented normal wear or severely broken and missing teeth. proprietary
usgs_npwrc_graywolves_Version 30APR2001 Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear ALL STAC Catalog 1970-01-01 -168, 43.5, -75, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2231553641-CEOS_EXTRA.umm_json We evaluated the accuracy and precision of tooth wear for aging gray wolves (Canis lupus) from Alaska, Minnesota, and Ontario based on 47 known-age or known-minimum-age skulls. Estimates of age using tooth wear and a commercial cementum annuli-aging service were useful for wolves up to 14 years old. The precision of estimates from cementum annuli was greater than estimates from tooth wear, but tooth wear estimates are more applicable in the field. We tended to overestimate age by 1-2 years and occasionally by 3 or 4 years. The commercial service aged young wolves with cementum annuli to within year of actual age, but under estimated ages of wolves 9 years old by 1-3 years. No differences were detected in tooth wear patterns for wild wolves from Alaska, Minnesota, and Ontario, nor between captive and wild wolves. Tooth wear was not appropriate for aging wolves with an underbite that prevented normal wear or severely broken and missing teeth. proprietary
+usgs_npwrc_graywolves_Version 30APR2001 Accuracy and Precision of Estimating Age of Gray Wolves by Tooth Wear CEOS_EXTRA STAC Catalog 1970-01-01 -168, 43.5, -75, 55 https://cmr.earthdata.nasa.gov/search/concepts/C2231553641-CEOS_EXTRA.umm_json We evaluated the accuracy and precision of tooth wear for aging gray wolves (Canis lupus) from Alaska, Minnesota, and Ontario based on 47 known-age or known-minimum-age skulls. Estimates of age using tooth wear and a commercial cementum annuli-aging service were useful for wolves up to 14 years old. The precision of estimates from cementum annuli was greater than estimates from tooth wear, but tooth wear estimates are more applicable in the field. We tended to overestimate age by 1-2 years and occasionally by 3 or 4 years. The commercial service aged young wolves with cementum annuli to within year of actual age, but under estimated ages of wolves 9 years old by 1-3 years. No differences were detected in tooth wear patterns for wild wolves from Alaska, Minnesota, and Ontario, nor between captive and wild wolves. Tooth wear was not appropriate for aging wolves with an underbite that prevented normal wear or severely broken and missing teeth. proprietary
usgs_npwrc_incidentalmarinecatc_Version 11APR2001 Incidental Catch of Marine Birds in the North Pacific High Seas Driftnet Fisheries in 1990. CEOS_EXTRA STAC Catalog 1990-01-01 1990-01-01 -140, 20, 140, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231553439-CEOS_EXTRA.umm_json "The incidental take of marine birds was estimated for the following North Pacific driftnet fisheries in 1990: Japanese squid, Japanese large-mesh, Korean squid, and Taiwanese squid and large-mesh combined. The take was estimated by assuming that the data represented a random sample from an unstratified population of all driftnet fisheries in the North Pacific. Estimates for 13 species or species groups are presented, along with some discussion of inadequacies of the design. About 416,000 marine birds were estimated to be taken incidentally during the 1990 season; 80 % of these were in the Japanese squid fishery. Sooty Shearwaters, Short-tailed Shearwaters, and Laysan Albatrosses were the most common species in the bycatch. Regression models were also developed to explore the relations between bycatch rate of three groups Black-footed Albatross, Laysan Albatross, and ""dark"" shearwatersand various explanatory variables, such as latitude, longitude, month, vessel, sea surface temperature, and net soak time (length of time nets were in the water). This was done for only the Japanese squid fishery, for which the most complete information was available. For modeling purposes, fishing operations for each vessel were grouped into 5-degree blocks of latitude and longitude. Results of model building indicated that vessel had a significant influence on bycatch rates of all three groups. This finding emphasizes the importance of the sample of vessels being representative of the entire fleet. In addition, bycatch rates of all three groups varied spatially and temporally. Bycatch rates for Laysan Albatrosses tended to decline during the fishing season, whereas those for Black-footed Albatrosses and dark shearwaters tended to increase as the season progressed. Bycatch rates were positively related to net soak time for Laysan Albatrosses and dark shearwaters. Bycatch rates of dark shearwaters were lower for higher sea surface temperatures." proprietary
usgs_npwrc_manitobaspiders_Version 16JUL97 A Checklist of Manitoba Spiders (Araneae) with Notes on Geographic Relationships ALL STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231553142-CEOS_EXTRA.umm_json An annotated list of spider species is compiled from museum collections and several personal collections. This list includes 483 species in 20 families; 139 species are new provincial records. The spider fauna of Manitoba is compared with that of British Columbia, Quebec, and Newfoundland. Manitoba's spider fauna is composed of northern elements (arctic or subarctic species), boreal elements (holarctic or nearctic), and eastern elements (mainly species of the eastern deciduous forest), and a few that are regarded as introductions from abroad. Forty-three species reach the limits of their ranges here. This relatively small province (6.5% of the total land mass of Canada) contains 59% of the Canadian spider families and 37% of the Canadian species. proprietary
usgs_npwrc_manitobaspiders_Version 16JUL97 A Checklist of Manitoba Spiders (Araneae) with Notes on Geographic Relationships CEOS_EXTRA STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231553142-CEOS_EXTRA.umm_json An annotated list of spider species is compiled from museum collections and several personal collections. This list includes 483 species in 20 families; 139 species are new provincial records. The spider fauna of Manitoba is compared with that of British Columbia, Quebec, and Newfoundland. Manitoba's spider fauna is composed of northern elements (arctic or subarctic species), boreal elements (holarctic or nearctic), and eastern elements (mainly species of the eastern deciduous forest), and a few that are regarded as introductions from abroad. Forty-three species reach the limits of their ranges here. This relatively small province (6.5% of the total land mass of Canada) contains 59% of the Canadian spider families and 37% of the Canadian species. proprietary
@@ -20873,14 +20880,14 @@ usgs_npwrc_muskoxen_Version 31MAY2000 Lack of Reproduction in Muskoxen and Arcti
usgs_npwrc_nestingsuccess_Version 26MAR2001 Importance of Individual Species of Predators on Nesting Success of Ducks in the Canadian Prairie Pothole Region CEOS_EXTRA STAC Catalog 1970-01-01 -145.27, 37.3, -48.11, 87.61 https://cmr.earthdata.nasa.gov/search/concepts/C2231551032-CEOS_EXTRA.umm_json We followed 3094 upland nests of several species of ducks. Clutches in most nests were lost to predation. We related daily nest predation rates to indices of activity of eight egg-eating predators, precipitation during the nesting season, and measures of wetland conditions. Activity indices of red fox (Vulpes vulpes), striped skunk (Mephitis mephitis), and raccoon (Procyon lotor) activity were positively correlated, as were activity indices of coyote (Canis latrans), Franklin's ground squirrel (Spermophilus franklinii), and black-billed magpie (Pica pica). Indices of fox and coyote activity were strongly negatively correlated (r = early-season nests were lower in areas and years in which larger fractions of seasonal wetlands contained water. For late-season nests, a similar relationship held involving semipermanent wetlands. We suspect that the wetland measures, which reflect precipitation during some previous period, also indicate vegetation growth and the abundance of buffer prey, factors that may influence nest predation rates. proprietary
usgs_npwrc_purpleloostrife_Version 04JUN99 Avian Use of Purple Loosestrife Dominated Habitat Relative to Other Vegetation Types in a Lake Huron Wetland Complex CEOS_EXTRA STAC Catalog 1994-01-01 1995-12-31 -84.2, 43.3, -82.5, 44.1 https://cmr.earthdata.nasa.gov/search/concepts/C2231555362-CEOS_EXTRA.umm_json Purple loosestrife (Lythrum salicaria), a native of Eurasia, is an introduced perennial plant in North American wetlands that displaces other wetland plants. Although not well studied, purple loosestrife is widely believed to have little value as habitat for birds. To examine the value of purple loosestrife as avian breeding habitat, we conducted early, mid-, and late season bird surveys during two years (1994 and 1995) at 258 18-m (0.1 ha) fixed-radius plots in coastal wetlands of Saginaw Bay, Lake Huron. We found that loosestrife-dominated habitats had higher avian densities, but lower avian diversities than other vegetation types. The six most commonly observed bird species in all habitats combined were Sedge Wren (Cistothorus platensis), Marsh Wren (C. palustris), Yellow Warbler (Dendroica petechia), Common Yellowthroat (Geothylpis trichas), Swamp Sparrow (Melospiza georgiana), and Red-winged Blackbird (Agelaius phoeniceus). Swamp Sparrow densities were highest and Marsh Wren densities were lowest in loosestrife dominated habitats. We observed ten breeding species in loosestrife dominated habitats. We conclude that avian use of loosestrife warrants further quantitative investigation because avian use may be higher than is commonly believed. Received 27 May 1998, accepted 26 Aug. 1998. proprietary
usgs_npwrc_saltmam Mammal Checklists of the United States - Salton Sea National Wildlife Refuge CEOS_EXTRA STAC Catalog 1970-01-01 -177.1, 13.71, -61.48, 76.63 https://cmr.earthdata.nasa.gov/search/concepts/C2231552573-CEOS_EXTRA.umm_json Wildlife species in this brochure have been grouped into four categories: Birds, Mammals, Reptiles and Amphibians, and Fish. All mammals listed are considered resident species with the exception of the bats which migrate on a seasonal basis like many of the birds. Families follow that of A Field Guide to the Mammals by Burt and Grossenheider. proprietary
-usgsbrdasc00000004 Air quality monitoring protocol - Denali National Park and Preserve SCIOPS STAC Catalog 1992-01-01 1998-01-01 -149, 63, -148, 64 https://cmr.earthdata.nasa.gov/search/concepts/C1214607513-SCIOPS.umm_json Ambient air quality monitoring is important in Denali, to document baseline conditions and to track long term trends. Denali National Park and Preserve is the only National Park in Alaska designated as class I under the Clean Air Act. Geographic Description: Specific coordinates in the Denali National Park and Preserve, Alaska. Denali National Park and Preserve is located in the central Alaska Range, approximately 210 km southwest of Fairbanks, Alaska. Methodology: Denali currently participates in three nationwide air quality monitoring networks: National Atmospheric Deposition Program (NADP), Interagency Monitoring of Protected Visual Environments (IMPROVE), National Park Service Gaseous Pollutant Monitoring Network (ozone monitoring). Air quality monitoring protocols have been written for each network, and approved by the respective network steering committees. Since there is no local control over methodology, the network manuals are the park's guiding documents. This is a compilation of network protocols. proprietary
usgsbrdasc00000004 Air quality monitoring protocol - Denali National Park and Preserve ALL STAC Catalog 1992-01-01 1998-01-01 -149, 63, -148, 64 https://cmr.earthdata.nasa.gov/search/concepts/C1214607513-SCIOPS.umm_json Ambient air quality monitoring is important in Denali, to document baseline conditions and to track long term trends. Denali National Park and Preserve is the only National Park in Alaska designated as class I under the Clean Air Act. Geographic Description: Specific coordinates in the Denali National Park and Preserve, Alaska. Denali National Park and Preserve is located in the central Alaska Range, approximately 210 km southwest of Fairbanks, Alaska. Methodology: Denali currently participates in three nationwide air quality monitoring networks: National Atmospheric Deposition Program (NADP), Interagency Monitoring of Protected Visual Environments (IMPROVE), National Park Service Gaseous Pollutant Monitoring Network (ozone monitoring). Air quality monitoring protocols have been written for each network, and approved by the respective network steering committees. Since there is no local control over methodology, the network manuals are the park's guiding documents. This is a compilation of network protocols. proprietary
+usgsbrdasc00000004 Air quality monitoring protocol - Denali National Park and Preserve SCIOPS STAC Catalog 1992-01-01 1998-01-01 -149, 63, -148, 64 https://cmr.earthdata.nasa.gov/search/concepts/C1214607513-SCIOPS.umm_json Ambient air quality monitoring is important in Denali, to document baseline conditions and to track long term trends. Denali National Park and Preserve is the only National Park in Alaska designated as class I under the Clean Air Act. Geographic Description: Specific coordinates in the Denali National Park and Preserve, Alaska. Denali National Park and Preserve is located in the central Alaska Range, approximately 210 km southwest of Fairbanks, Alaska. Methodology: Denali currently participates in three nationwide air quality monitoring networks: National Atmospheric Deposition Program (NADP), Interagency Monitoring of Protected Visual Environments (IMPROVE), National Park Service Gaseous Pollutant Monitoring Network (ozone monitoring). Air quality monitoring protocols have been written for each network, and approved by the respective network steering committees. Since there is no local control over methodology, the network manuals are the park's guiding documents. This is a compilation of network protocols. proprietary
usgsbrdfcsc_d_seagrass Mapping and Characterizing Seagrass Areas Important to Manatees in Puerto CEOS_EXTRA STAC Catalog 1994-10-01 1995-06-01 -65.75, 18.15, -65.5, 18.3 https://cmr.earthdata.nasa.gov/search/concepts/C2231549769-CEOS_EXTRA.umm_json "The population of manatees in Puerto Rico is the only group of Antillean manatees (Trichechus manatus manatus) managed and protected by the United States. The Manatee Recovery Plan for the Puerto Rico Population of West Indian Manatees includes requirements to identify and manage habitats and develop criteria and biological information important to its recovery. To this end, the Sirenia Project initiated telemetry studies of manatees in Puerto Rico at the U.S. Naval Station Roosevelt Roads (RRNS) in 1992. Concurrently, the Project began gathering information on habitats critical to manatee in eastern Puerto Rico. Computer aided mapping based on the interpretation of aerial photographs and field groundtruthing was used in the current project to define these habitats and map their distribution in the area of high manatee use. Benthic habitats along approximately 32 miles (52 kilometers of RRNS shoreline were mapped. Field assessment and characterization of important seagrass habitats was conducted as a means of identifying seagrass and macroalgae communities, especially in areas with known manatee feeding sites. The purpose of this dataset is to identify and manage manatee habitats and to develop biological information important to the manatees' recovery. Data was obtained during ground truthing in October, 1994 and June, 1995. One hundred and twenty-five sites, many representing questions raised during preliminary habitat delineations were visited, along with sites with characteristic signatures useful for broader interpretations. Transects were made over several areas with rapidly changing benthic communities and confusing signatures. Data recorded at each site included depth (range 0.5-7.1 m), classification, dominant community, subdominant community, and pertinent comments. the locations of all groundtruth sites were plotted onto one Arc Cad layer of mapping information. Groundtruthing was used to field verify and correct the initial delineations made. Improvements were made to the draft classification scheme based on field observations. Sites of questionable draft delineations were located on the water and confirmed or corrected. Known manatee use of the area for resting or feeding was noted. These sites were accurately located on the overlay for inclusion on the maps. Site location (latitude and longitude) was determined with a Garmin 45 GPS and water depth (tape), temperature (hand-held mercury thermometer), and salinity (hand-held temperature compensated refractometer) recorded. In addition, salinity measurements were made at select nearshore locations to assess the influence of drainage creeks and ditches on nearshore water salinity. Underwater video photography and 35 mm photography were used to document observations. A review of vertical images of waters of RRNS was taken on December 17, 1993, for the United States Navy, along with other collateral information, was used to develop a benthic habitat classification system useful for mapping benthic communities in the area. The system developed for this project was similar to that developed for Geographic Information System (GIS) mapping of benthic communities in the Florida Keys National Marine Sanctuary. Clear acetate overlays were placed over the 9"" x 9"" aerial prints and the polygon method of delineation used to outline habitats on the overlays. Computer aided design methods (PC Arc Cad) were used to create a shoreline base map from navigational charts for this region of Puerto Rico. Habitat polygons extending as far from shore as allowed by the resolution of the images were digitized onto the base map. A minimum mapping unit of 0.5 acres was applied based on the scale and quality of the images. Once finalized, maps were printed in both color and black-line. The information for this metadata was partially taken from the document Mapping and Characterizing Seagrass Areas Important to Manatees in Puerto Rico - Benthic Communities Mapping and Assessment. Prepared for the U.S. Department of Interior, National Biological Service, Sirenia Project. Prepared by Curtis Kruer, Senior Biologist, Caribbean Fisheries Consultants, Inc." proprietary
usgsbrdfcsc_d_vieques Mapping and Characterization of Nearshore Benthic Habitats around Vieques Island, Puerto Rico CEOS_EXTRA STAC Catalog 1995-09-01 -65.75, 18.15, -65.5, 18.3 https://cmr.earthdata.nasa.gov/search/concepts/C2231553026-CEOS_EXTRA.umm_json "The Vieques Island Mapping Project was initiated in September 1995 as a cooperative effort between NSRR and the Sirenia Project (Military Interdepartmental Purchase Request no. NOO38995MP00012). Caribbean Fisheries Consultants, Inc. was contracted by the Sirenia Project to help produce the desired information in conjunction with Sirenia Project biologists. Products include maps delineating Vieques' benthic habitat and coastal wetlands, an electronic georeferenced habitat map (UTM coordinate system) in a format compatible with ARC/INFO (Environmental Systems Research Institute, Inc.) and a report describing methods used, the classification scheme, and the relationship of these habitats to manatee use of Vieques Island. These map products complement the Navy's Vieques Land Use Management Plan by identifying marine resources targeted for protection in the plan. Objectives include producing maps of the coastal seagrass beds and other bottom habitat (including coral reefs) surrounding the island of Vieques and characterizing the species composition and density of seagrasses in areas frequented by manatees near Vieques. Ground truthing by boat around Vieques Island was conducted from May 14 to May 19 1996 and from October 4 through October 9 1996. The ground truthing was conducted to verify the interpretation of benthic habitat visible in the images, verify accuracy of the shoreline limits, and refine the habitat classification scheme used for the Vieques maps. Three hundred and thirty-two ground truth stations were established around Vieques Island, located on the aerial image overlays, and digitized. These sites are plotted as a layer on the habitat map. The listing of ground truth sites includes site identifier, latitude and longitude, community classification, depteh, dominant community elements, less dominant elements, and other pertinent information. Latitude and longitude were obtained for each station in the field using a Garmin 45 GPS unit. Water depth for each station was determined from a Hummingbird LCR - 400 Video Fathometer with transom mounted transducer. Underwater Hi-8 video and 35 mm photography were used to document observations at selected sites. The habitat classification scheme used is similar to that used by Kruer and others in southern Forida seagrass beds and other benthic habitats in the Florida Keys National Marine Sanctuary and Biscayne National Park. This scheme, also used for benthic habitat mapping at NSRR in 1994/1995 (Kruer 1995), was refined for the Vieques Island mapping project by adding the category ""sand bottom with rock"". Also, mangroves were mapped in interior areas. The information for this metadata was partially taken from the report - Mapping and characterization of Nearshore Benthic Habitats around Vieques Island, Puerto Rico." proprietary
usgsbrdnpwrc_d_birds_checklists_Version 12MAY03 Birds Checklists of the United States CEOS_EXTRA STAC Catalog 1996-01-01 -125, 25, -67, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231550188-CEOS_EXTRA.umm_json This resource is known as Bird Checklists of the United States. Bird Checklists of the United States. For years, people and groups have developed listings or checklists of birds that occur in a particular region. Information on the distribution or seasonal occurrence of birds in an area, however, can change over time. Bird checklists often are outdated in only a few years after printing, but budget and time constraints prohibit regular updates. The Internet provides new opportunities for the compilation and dissemination of current information on bird distribution. Here we offer bird checklists developed by others that indicate the seasonal occurrence of birds in state, federal, and private management areas, nature preserves, and other areas of special interest in the United States. Bird checklists exist for Great Plains States: Colorado, Iowa, Kansas, Minnesota, Missouri, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, Wyoming; East of Great Plains states: Alabama, Arkansas, Connecticut, Delaware, Florida, Georgia, Illinois, Indiana, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Mississippi, New Hampshire, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Rhode Island, South Carolina, Tennessee, Vermont, Virginia, West Virginia, Wisconsin; and West of Great Plains: Arizona, California, Idaho, Nevada, Oregon, Utah, Washington. It is hoped that these checklists will serve several purposes. First, we hope the checklists will help bird enthusiasts decide where to visit. A visit to these unique areas can be a rewarding experience for both the amateur and expert birdwatcher. Second, we hope that these checklists will provide potential visitors with a guide to birds that might occur in a region during a particular season. The checklists were kept simple to facilitate printing so they can be easily carried into the field. And third, we hope that these checklists will stimulate and encourage visitors to these areas to help improve the accuracy and completeness of the checklists. The information in some checklists already has been updated; these checklists contain more current information than the printed versions. Sightings of birds and other wildlife are an important part of monitoring wildlife use. Visitors are encouraged to share their observations of rare, aberrant, or occasional birds with the staff at these areas. With each checklist, we have included an address for visitors to send information on rare birds so that checklists can be updated. To assist in establishing standards in observation and reporting, we also provide a Record Documentation Form to document supporting details of rare bird observations. The efforts and dedication of the many birders, birding groups, biologists, and resource managers who developed these checklists are acknowledged. The information for this metadata was partially taken from the Northern Prairie Wildlife Research Center website at http://www.npwrc.usgs.gov/resource/birds/chekbird/index.htm proprietary
usgsbrdnpwrc_d_ndfleas_Version 16JUL97 Fleas of North Dakota CEOS_EXTRA STAC Catalog 1970-01-01 -104, 46, -96.5, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231553614-CEOS_EXTRA.umm_json The dataset contains distribution maps for the following species of fleas: Aetheca wagneri, Amaradix euphorbi, Amphipsylla sibirica pollionis, Callistopsyllus terinus campestris, Cediopsylla inaequalis inaequalis, Ceratophyllus (Ceratophyllus) idius, Corrodopsylla curvata curvata, Chaetopsylla lotoris, Ctenocephalides canis, Epitedia faceta, Epitedia wenmanni, Euhoplopsyllus glacialis affinis, Eumolpianus eumolpi eumolpi, Foxella ignota albertensis, Hystrichopsylla dippiei dippiei, Megabothris (Megabothris) asio megacolpus, Megabothris (Amegabothris) lucifer, Meringis jamesoni, Myodopsylla insignis, Nearctopsylla genalis hygini, Neopsylla inopina, Nosopsyllus fasciatus, Oropsylla (Oropsylla) arctomys, Opisodasys (Opisodasys) pseudarctomys, Orchopeas caedens, Orchopeas howardi, Peromyscopsylla hamifer, Pleochaetis exilis, Pules irritans, Rhadinopsylla (Actenophthalmus) fraterna, Thrassis bacchi bacchi. The information for this metadata was partially taken from the Northern Prairie Wildlife Research Center website at http://www.npwrc.usgs.gov/resource/insects/ndfleas/ proprietary
-usgsbrdnpwrcb00000013_Version 30SEP2002 A Bibliography of Fisheries Biology in North and South Dakota CEOS_EXTRA STAC Catalog 1970-01-01 -104, 43, -96, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231550006-CEOS_EXTRA.umm_json This bibliography lists as many fisheries biology and related references as possible from North Dakota and South Dakota waters for use by fishery biologists. Selected references from contiguous states sharing river basins with the Dakotas are included. Studies in the Missouri River downstream from Gavins Point Dam are also included. In addition to published fishery and related aquatic studies, attempts were made to list all dissertations and Masters theses in these fields. proprietary
usgsbrdnpwrcb00000013_Version 30SEP2002 A Bibliography of Fisheries Biology in North and South Dakota ALL STAC Catalog 1970-01-01 -104, 43, -96, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231550006-CEOS_EXTRA.umm_json This bibliography lists as many fisheries biology and related references as possible from North Dakota and South Dakota waters for use by fishery biologists. Selected references from contiguous states sharing river basins with the Dakotas are included. Studies in the Missouri River downstream from Gavins Point Dam are also included. In addition to published fishery and related aquatic studies, attempts were made to list all dissertations and Masters theses in these fields. proprietary
+usgsbrdnpwrcb00000013_Version 30SEP2002 A Bibliography of Fisheries Biology in North and South Dakota CEOS_EXTRA STAC Catalog 1970-01-01 -104, 43, -96, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231550006-CEOS_EXTRA.umm_json This bibliography lists as many fisheries biology and related references as possible from North Dakota and South Dakota waters for use by fishery biologists. Selected references from contiguous states sharing river basins with the Dakotas are included. Studies in the Missouri River downstream from Gavins Point Dam are also included. In addition to published fishery and related aquatic studies, attempts were made to list all dissertations and Masters theses in these fields. proprietary
usgsbrdnpwrcb00000016_Version 16JUL97 American Wildcelery (Vallisneria americana) Ecological Considerations for Restoration CEOS_EXTRA STAC Catalog 1970-01-01 -125, 25, -67, 50 https://cmr.earthdata.nasa.gov/search/concepts/C2231548602-CEOS_EXTRA.umm_json The success of vegetation management programs for waterfowl is dependent on knowing the physical and physiological requirements of the target species. Lakes and riverine impoundments that contain an abundance of the American wildcelery plant (Vallisneria americana) have traditionally been favored by canvasback ducks (Aythya valisineria) and other waterfowl species as feeding areas during migration. Information on the ecology of V. americana is summarized to serve as a guide for potential wetland restoration projects. Because of the geographic diversity and wetland conditions in which V. americana is found, we have avoided making hard-and-fast conclusions about the requirements of the plant. Rather, we present as much general information as possible and provide the sources of more specific information. Vallisneria americana is a submersed aquatic plant that has management potential. Techniques are described for transplanting winter buds from one location to another. Management programs that employ these techniques should define objectives clearly and evaluate the water regime carefully before initiating a major effort. proprietary
usgsbrdnpwrcd00000002_Version 02MAR98 Ecological Effects of Fire Retardant Chemicals and Fire Suppressant Foams CEOS_EXTRA STAC Catalog 1993-01-01 1998-01-01 -98, 47, -98, 47 https://cmr.earthdata.nasa.gov/search/concepts/C2231550534-CEOS_EXTRA.umm_json Laboratory studies with algae, aquatic invertebrates, and fish. Short-term toxicity tests showed that both fire-retardant and foam-suppressant chemicals were very toxic to aquatic organisms including algae, aquatic invertebrates, and fish. Foam-suppressant are more toxic than fire-retardant chemicals. The primary toxicant in fire-retardants is the ammonia component, whereas the nitrite and nitrate components do not seem to contribute much to the toxicity of the formulations. In foam suppressants the primary toxicant is the surfactant component. The most sensitive life-stage for fish is the swim-up stage. Accidental spills of fire-fighting chemicals in streams could cause substantial fish kills depending on the stream size and flow rate. For example, the retardant Fire-Trol GTS-R is prepared for field use by mixing 1.66 pounds per gallon of water to produce 1.1 gallons of slurry, which is equivalent to 198,930 mg/liter. Comparing the concentration of FT GTS-R field mixture to the acute toxicity values for the most sensitive life stage for rainbow trout gives a ratio of 853 in soft water and 1474 in hard water. Applying a safety factor of 100 to this ratio suggests a dilution of 85, 300 in soft water and 147,400 in hard water is needed to lower the chemical concentration in a receiving water to limit adverse effects, i.e., fish kill, in a stream. For rainbow trout, other dilution factors would be 52,100 for Fire-Trol LCG-R, 85,600 for Phos-Chek D75-F, 71,400 for Phos-Chek WD-881, and 50,000 for Silv-ex. Fire-fighting chemicals are very toxic in aquatic environments and fire control managers need to consider protection of aquatic resources, especially if endangered species are present. proprietary
usgsbrdnpwrcd00000012_Version 31JUL97 Changes in Breeding Bird Populations in North Dakota: 1967 to 1992-93. CEOS_EXTRA STAC Catalog 1967-01-01 1993-01-01 -104, 46, -97, 49 https://cmr.earthdata.nasa.gov/search/concepts/C2231551668-CEOS_EXTRA.umm_json Breeding bird populations in North Dakota were compared using surveys conducted in 1967 and 1992-93. In decreasing order, the five most frequently occurring species were Horned Lark (Eremophia alpestris), Brown-headed Cowbird (Molothrus ater), Western Meadowlark (Sturnella neglecta), Red-winged Blackbird (Agelaius phoeniceus), and Eastern Kingbird (Tyrannus tyrannus). The five most abundant species - Horned Lark, Chestnut-collared Longspur (Calcarius ornatus), Red-winged Blackbird, Western Meadowlark, and Brown-headed Cowbird - accounted for 31-41% of the estimated statewide breeding bird population in the three years. Although species composition remained relatively similar among years, between-year patterns in abundance and frequency varied considerably among species. Data from this survey and the North American Breeding Bird Survey indicated that species exhibiting significant declines were primarily grassland- and wetland-breeding birds, whereas species exhibiting significant increases primarily were those associated with human structures and woody vegetation. Population declines and increases for species with similar habitat associations paralleled breeding habitat changes, providing evidence that factors on the breeding grounds are having a detectable effect on breeding birds in the northern Great Plains. proprietary
@@ -20948,8 +20955,8 @@ willmott_673_1 LBA Regional Climate Data, 0.5-Degree Grid, 1960-1990 (Willmott a
wind-topo_model_0.1.0 Wind-Topo_model ENVIDAT STAC Catalog 2022-01-01 2022-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817956-ENVIDAT.umm_json "Wind-Topo is a statistical downscaling model for near surface wind fields especially suited for highly complex terrain. It is based on deep learning and was trained (calibrated) using the hourly wind speed and direction from 261 automatic measurement stations (IMIS and SwissMetNet) located in Switzerland. The periods 1st October 2015 to 1st October 2016 and 1st October 2017 to 1st October 2018 were used for training. The model was validated using 60 other stations on the period 1st October 2016 to 1st October 2017. Wind-Topo was trained using COSMO-1 data and a 53-meter Digital Elevation Model as input. This dataset provides all the necessary code to understand, use and incorporate Wind-Topo in a new downscaling scheme. Specifically, the dataset contains the architecture of Wind-Topo and its optimized parameters, as well as a python script to downscale uniform wind fields with a prescribed vertical profile for any given 53-meter DEM. Accompanies the publication ""Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning"" Dujardin and Lehning, Quarterly Journal of the Royal Meteorological Society, 2022. https://doi.org/10.1002/qj.4265 Please cite this publication if you use Wind-Topo or derive new models from it. The code can be used under the GNU Affero General Public License (AGPL)." proprietary
wind_dem_1 Digital Elevation Model of the Windmill Islands AU_AADC STAC Catalog 1999-07-11 1999-08-23 110, -67, 111, -66 https://cmr.earthdata.nasa.gov/search/concepts/C1214311463-AU_AADC.umm_json This DEM includes all the inshore and offshore islands, all the peninsulas and the lower slopes of the icecap leading up to Law Dome. The DEM has a cell size of 10 m. proprietary
windmill_bathy_surveys_1 Bathymetric surveys of Brown Bay, O'Brien Bay and Newcomb Bay in the Windmill Islands AU_AADC STAC Catalog 1997-02-01 1997-03-31 110.515, -66.297, 110.565, -66.258 https://cmr.earthdata.nasa.gov/search/concepts/C1214311438-AU_AADC.umm_json Bathymetric surveys of Brown Bay, O'Brien Bay and Newcomb Bay in the Windmill Islands. This dataset resulted from bathymetric surveys of Brown Bay, O'Brien Bay and Newcomb Bay in the Windmill Islands, carried out in February and March 1997 as part of ASAC Project 2201. The surveys were carried out by Jonny Stark and Tim Ryan in the workboat the 'Southern Comfort'. proprietary
-winston_bathy_1 A bathymetric survey of Winston Lagoon ALL STAC Catalog 1987-01-09 1987-01-14 73.23557, -53.20274, 73.83911, -52.95006 https://cmr.earthdata.nasa.gov/search/concepts/C1214311480-AU_AADC.umm_json During the 1986-87 Expedition to Heard Island, a 3m inflatable boat was depoted at the shores of Winston Lagoon, on the islands' south-east coast. The boat was to allow access to the important Long Beach Elephant Seal harems for periods when flooding from the lagoon prevented passage across its spit. The availability of the boat together with a 'Furuno' echo sounder, a stabilised, floating, transducer platform (constructed by a crew member from Nella Dan), and field assistance allowed a bathymetric survey of Winston Lagoon to be conducted. Winston Lagoon depth work was done from 9/1/1987-14/1/1987 in the rare calm periods. We (the researchers) lived in the nearby Paddick Valley hut and sheltered there in rough weather. We only ran transects in calm weather. The map used was the largest Heard Island map available in 1986. 30 transects were run across the lake from known points on the map recognisable from the shore. We calibrated the echo sounder (a marine device) for fresh water by checking a range of measured depths against a weighted fibre-glass tape. Water samples were taken from a range of depths to the bottom and the lake was fresh throughout. Lake was very opaque with a secchi depth of 0.46m. proprietary
winston_bathy_1 A bathymetric survey of Winston Lagoon AU_AADC STAC Catalog 1987-01-09 1987-01-14 73.23557, -53.20274, 73.83911, -52.95006 https://cmr.earthdata.nasa.gov/search/concepts/C1214311480-AU_AADC.umm_json During the 1986-87 Expedition to Heard Island, a 3m inflatable boat was depoted at the shores of Winston Lagoon, on the islands' south-east coast. The boat was to allow access to the important Long Beach Elephant Seal harems for periods when flooding from the lagoon prevented passage across its spit. The availability of the boat together with a 'Furuno' echo sounder, a stabilised, floating, transducer platform (constructed by a crew member from Nella Dan), and field assistance allowed a bathymetric survey of Winston Lagoon to be conducted. Winston Lagoon depth work was done from 9/1/1987-14/1/1987 in the rare calm periods. We (the researchers) lived in the nearby Paddick Valley hut and sheltered there in rough weather. We only ran transects in calm weather. The map used was the largest Heard Island map available in 1986. 30 transects were run across the lake from known points on the map recognisable from the shore. We calibrated the echo sounder (a marine device) for fresh water by checking a range of measured depths against a weighted fibre-glass tape. Water samples were taken from a range of depths to the bottom and the lake was fresh throughout. Lake was very opaque with a secchi depth of 0.46m. proprietary
+winston_bathy_1 A bathymetric survey of Winston Lagoon ALL STAC Catalog 1987-01-09 1987-01-14 73.23557, -53.20274, 73.83911, -52.95006 https://cmr.earthdata.nasa.gov/search/concepts/C1214311480-AU_AADC.umm_json During the 1986-87 Expedition to Heard Island, a 3m inflatable boat was depoted at the shores of Winston Lagoon, on the islands' south-east coast. The boat was to allow access to the important Long Beach Elephant Seal harems for periods when flooding from the lagoon prevented passage across its spit. The availability of the boat together with a 'Furuno' echo sounder, a stabilised, floating, transducer platform (constructed by a crew member from Nella Dan), and field assistance allowed a bathymetric survey of Winston Lagoon to be conducted. Winston Lagoon depth work was done from 9/1/1987-14/1/1987 in the rare calm periods. We (the researchers) lived in the nearby Paddick Valley hut and sheltered there in rough weather. We only ran transects in calm weather. The map used was the largest Heard Island map available in 1986. 30 transects were run across the lake from known points on the map recognisable from the shore. We calibrated the echo sounder (a marine device) for fresh water by checking a range of measured depths against a weighted fibre-glass tape. Water samples were taken from a range of depths to the bottom and the lake was fresh throughout. Lake was very opaque with a secchi depth of 0.46m. proprietary
wisperimpacts_1 Water Isotope System for Precipitation and Entrainment Research (WISPER) IMPACTS GHRC_DAAC STAC Catalog 2020-01-18 2023-02-28 -95.2426928, 33.2614038, -67.8781539, 48.2369386 https://cmr.earthdata.nasa.gov/search/concepts/C2175816611-GHRC_DAAC.umm_json The Water Isotope System for Precipitation and Entrainment Research (WISPER) IMPACTS dataset consists of condensed water contents, water vapor measurements, and isotope ratios in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The dataset files are available in ASCII format from January 18, 2020, through February 28, 2023. proprietary
wml_bilderstudie_1.0 Relationship between physical forest characteristics, visual attractiveness and perception of ecosystem services in urban forests ENVIDAT STAC Catalog 2019-01-01 2019-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789818010-ENVIDAT.umm_json "This questionnaire survey was conducted as an online survey and aimed at investigating the relationship between physical forest characteristics, visual attractiveness of forest and the perception of ecological and cultural ecosystem services in urban forests. Each participant was shown 6 photos out of a pool of 50 photos taken from the Swiss National Forest Inventory (NFI) database. Physical forest characteristics were derived from the photos. The study was conducted as part of the ""WaMos meets LFI"" (WML) project." proprietary
wmlganzeschweiz_1.0 WaMos meets LFI, ganze Schweiz ENVIDAT STAC Catalog 2021-01-01 2021-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789818071-ENVIDAT.umm_json The data consists of a forest visitor survey conducted at 50 plots in the whole of Switzerland, once during the winter- and once during the summer season. Physical forest characteristics according to the Swiss National Forest Inventory NFI were collected from the same plots in winter and summer. Visibility was measured using terrestrial laser scanning. At some plots, sound measurements were also conducted. proprietary
@@ -20958,9 +20965,9 @@ woody_biomass_657_1 Woody Biomass for Eastern U.S. Forests, 1983-1996 ORNL_CLOUD
wrfimpacts_1 Weather Research and Forecasting (WRF) Model IMPACTS GHRC_DAAC STAC Catalog 2020-01-12 2023-03-04 -114.2019958, 22.9705658, -53.7980042, 53.5889359 https://cmr.earthdata.nasa.gov/search/concepts/C1995874860-GHRC_DAAC.umm_json The Weather Research and Forecasting (WRF) Model IMPACTS dataset includes model data simulated by the Weather Research and Forecasting (WRF) model for the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The WRF model provided simulations of the precipitation events that were observed during the campaign using initial and boundary conditions from the Global Forecast System (GFS) model and the North American Mesoscale Forecast System (NAM). The WRF IMPACTS dataset files are available from January 12, 2020, through March 4, 2023, in netCDF-3 format. proprietary
wsl-drought-initiative-2018_1.0 Litterfall and pollen data of three LWF beech plots ENVIDAT STAC Catalog 2019-01-01 2019-01-01 6.65804, 46.58377, 9.06707, 47.22516 https://cmr.earthdata.nasa.gov/search/concepts/C2789818298-ENVIDAT.umm_json This dataset contains the parameters used in the statistical analyses for the manuscript SREP-19-40170-T, submitted in Scientific Reports. This study is part of the WSL Drought Initiative 2018 (C3 - Analysis of the beech litterfall of the drought year 2018). Data originate from the Long-term Forest Ecosystem Research Programme LWF (litterfall, soil matric potential, deposition (precipitation) and meteo (temperature)), and from the Swiss Federal Office of Meteorology and Climatology MeteoSwiss (pollen). __Datafile:__ _LWF_beech_plots_litterfall_pollen.xlsx_ 1. Sheet _extreme_weather_: values used for analysis of weather conditions in strongest mast years compared to years with fruit abortion. 2. Sheet _weather_and_resource_allocation_: values used for analysis of weather impacts on mast occurrence and resource allocation models. proprietary
wslintern-article-envidat-supports-open-science_1.0 EnviDat Supports Open Science ENVIDAT STAC Catalog 2020-01-01 2020-01-01 8.4546488, 47.3605728, 8.4546488, 47.3605728 https://cmr.earthdata.nasa.gov/search/concepts/C2789818383-ENVIDAT.umm_json "The article ""EnviDat Supports Open Science"" originally appeared in WSLintern No. 3 (2020), page 14-15 and it is republished here with permission from the WSLintern editorial team. It contains guidelines for WSL scientists about the main issues behind Open Science and how to pragmatically approach the complexities of doing Open Science with EnviDat’s support. License: This article is released by WSL and the EnviDat team to the public domain under a Creative Commons 4.0 CC0 ""No Rights Reserved"" international license. You can reuse the information contained herein in any way you want, for any purposes and without restrictions." proprietary
-wwllnmth_1 World Wide Lightning Location Network (WWLLN) Monthly Thunder Hour Data GHRC_DAAC STAC Catalog 2013-01-01 2023-12-31 -179.975, -89.975, 179.975, 89.975 https://cmr.earthdata.nasa.gov/search/concepts/C3301410475-GHRC_DAAC.umm_json The World Wide Lightning Location Network (WWLLN) has monitored global lightning since late 2004. Since 2013, the number of global WWLLN sensors has remained largely consistent. This WWLLN Monthly Thunder Hour dataset is calculated from lightning detections from 1 January 2013 onward and is an ongoing dataset. A thunder hour is an hour during which thunder can be heard at a given location. Thunder hours represent a historical measure of lightning occurrence and a metric of thunderstorm frequency that is comparatively less sensitive to geographic variations in the detection capabilities of a lightning location system. Thunder hours are the number of hours in a given month during which at least two WWLLN strokes were observed within 15 km of each grid point. Each file includes the monthly accumulated thunder hours for one year. The data are provided at 0.05° latitude and longitude resolution. proprietary
-wygisc_wolphoyo Aerial Photos for Crazy Woman and Clear Creek Watersheds ALL STAC Catalog 1970-01-01 -107, 44, -106.36, 44.75 https://cmr.earthdata.nasa.gov/search/concepts/C1214614362-SCIOPS.umm_json The purpose of this data was to provide a base layer of aerial photos at the watershed scale for two areas used as part of a the Wyoming Open Land pilot area. Digital and registered aerial photos of Crazy Woman and Clear Creek Watersheds, Wyoming. Each photo represents approximatley one-quarter of a U.S.G.S. Topographic map (north-east, north-west, south-each and south-west quarters). TIFF image format. proprietary
+wwllnmth_1 World Wide Lightning Location Network (WWLLN) Monthly Thunder Hour Data GHRC_DAAC STAC Catalog 2013-01-01 2023-12-31 -180, -90, 180, 90 https://cmr.earthdata.nasa.gov/search/concepts/C3301410475-GHRC_DAAC.umm_json The World Wide Lightning Location Network (WWLLN) has monitored global lightning since late 2004. Since 2013, the number of global WWLLN sensors has remained largely consistent. This WWLLN Monthly Thunder Hour dataset is calculated from lightning detections from 1 January 2013 onward and is an ongoing dataset. A thunder hour is an hour during which thunder can be heard at a given location. Thunder hours represent a historical measure of lightning occurrence and a metric of thunderstorm frequency that is comparatively less sensitive to geographic variations in the detection capabilities of a lightning location system. Thunder hours are the number of hours in a given month during which at least two WWLLN strokes were observed within 15 km of each grid point. Each file includes the monthly accumulated thunder hours for one year. The data are provided at 0.05° latitude and longitude resolution. proprietary
wygisc_wolphoyo Aerial Photos for Crazy Woman and Clear Creek Watersheds SCIOPS STAC Catalog 1970-01-01 -107, 44, -106.36, 44.75 https://cmr.earthdata.nasa.gov/search/concepts/C1214614362-SCIOPS.umm_json The purpose of this data was to provide a base layer of aerial photos at the watershed scale for two areas used as part of a the Wyoming Open Land pilot area. Digital and registered aerial photos of Crazy Woman and Clear Creek Watersheds, Wyoming. Each photo represents approximatley one-quarter of a U.S.G.S. Topographic map (north-east, north-west, south-each and south-west quarters). TIFF image format. proprietary
+wygisc_wolphoyo Aerial Photos for Crazy Woman and Clear Creek Watersheds ALL STAC Catalog 1970-01-01 -107, 44, -106.36, 44.75 https://cmr.earthdata.nasa.gov/search/concepts/C1214614362-SCIOPS.umm_json The purpose of this data was to provide a base layer of aerial photos at the watershed scale for two areas used as part of a the Wyoming Open Land pilot area. Digital and registered aerial photos of Crazy Woman and Clear Creek Watersheds, Wyoming. Each photo represents approximatley one-quarter of a U.S.G.S. Topographic map (north-east, north-west, south-each and south-west quarters). TIFF image format. proprietary
yield-15_1.0 Yield ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817175-ENVIDAT.umm_json Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that were felled between two inventories. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary
yield_and_mortality-13_1.0 Yield and mortality ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817288-ENVIDAT.umm_json Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that were felled, died or disappeared between two inventories. The correction for bias with the sample Tarif trees may be so drastic that it results in negative values with small numbers of trees. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary
yield_and_mortality_star-163_1.0 Yield and mortality* ENVIDAT STAC Catalog 2018-01-01 2018-01-01 5.95587, 45.81802, 10.49203, 47.80838 https://cmr.earthdata.nasa.gov/search/concepts/C2789817402-ENVIDAT.umm_json Volume of stemwood with bark of all trees and shrubs starting at 12 cm dbh that were used, died or disappeared between two inventories. *In the calculation no D7/tree height data were used. The values calculated like this have not been corrected for bias, but allow for cantons or forest districts a more robust estimation of changes and could thus be better interpreted. __Citation:__ > _Abegg, M.; Brändli, U.-B.; Cioldi, F.; Fischer, C.; Herold-Bonardi, A.; Huber M.; Keller, M.; Meile, R.; Rösler, E.; Speich, S.; Traub, B.; Vidondo, B. (2014). Fourth national forest inventory - result tables and maps on the Internet for the NFI 2009-2013 (NFI4b). [Published online 06.11.2014] Available from World Wide Web http://www.lfi.ch/resultate/ Birmensdorf, Swiss Federal Research Institute WSL. [doi:10.21258/1057112](https://doi.org/10.21258/1057112)_ proprietary